diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/README.md index 19ef3672c..15817922c 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/README.md +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/README.md @@ -76,3 +76,29 @@ Do not create extra files too early. Start simple, then split only when needed. - Allocation recommendation report (JSON/CSV) - Risk threshold configuration file - Examples showing critical scenarios and responses + +## Validation & Testing + +### Test Scenarios Validated + +The module has been tested with the following scenarios: + +1. **Normal operation** - Matches SCHEMA.md example exactly +2. **Critical density zones** - Density โ‰ฅ 0.85 triggers immediate crowd control alerts +3. **Multiple high-risk zones** - All zones with density โ‰ฅ 0.70 are flagged and monitored +4. **Edge cases** - Empty zones, missing crowd_state handled gracefully +5. **Integration handoff** - Successfully receives data from crowd_behaviour_analytics + +### Risk Thresholds + +| Risk Level | Density Range | Flagged | Action | +|------------|--------------|---------|--------| +| Critical | โ‰ฅ 0.85 | True | Immediate crowd control required | +| High | 0.70 - 0.84 | True | Close monitoring | +| Medium | 0.40 - 0.69 | False | Standard monitoring | +| Low | 0.30 - 0.39 | False | Routine observation | +| Very Low | < 0.30 | False | No action needed | + +### Test Results + +All validation tests passed (5/5). Module is ready for integration. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/fixture_risk_matrix.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/fixture_risk_matrix.py new file mode 100644 index 000000000..cebd72e09 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/fixture_risk_matrix.py @@ -0,0 +1,164 @@ +#!/usr/bin/env python3 +"""Fixture Risk Matrix for Hawthorn Hawks 2026 Season""" + +import json + +# Hawthorn's 2026 fixtures (from R0 to R22) +fixtures = [ + {"round": 0, "opponent": "GWS", "location": "away", "ground": "Giants Stadium"}, + {"round": 1, "opponent": "Essendon", "location": "home", "ground": "MCG"}, + {"round": 2, "opponent": "Sydney", "location": "home", "ground": "MCG"}, + {"round": 3, "opponent": "Geelong", "location": "home", "ground": "MCG"}, + {"round": 4, "opponent": "Bulldogs", "location": "neutral", "ground": "UTAS Stadium"}, + {"round": 5, "opponent": "Port Adelaide", "location": "home", "ground": "MCG"}, + {"round": 6, "opponent": "Gold Coast", "location": "neutral", "ground": "TIO Stadium"}, + {"round": 7, "opponent": "Collingwood", "location": "away", "ground": "MCG"}, + {"round": 8, "opponent": "Fremantle", "location": "away", "ground": "Optus Stadium"}, + {"round": 9, "opponent": "Melbourne", "location": "away", "ground": "MCG"}, + {"round": 10, "opponent": "Adelaide", "location": "home", "ground": "MCG"}, + {"round": 11, "opponent": "St Kilda", "location": "away", "ground": "Marvel Stadium"}, + {"round": 12, "opponent": "Bulldogs", "location": "home", "ground": "MCG"}, + {"round": 13, "opponent": "Gold Coast", "location": "away", "ground": "People First Stadium"}, + {"round": 14, "opponent": "GWS", "location": "home", "ground": "MCG"}, + {"round": 15, "opponent": "Melbourne", "location": "neutral", "ground": "UTAS Stadium"}, + {"round": 16, "opponent": "Carlton", "location": "away", "ground": "MCG"}, + {"round": 17, "opponent": "Richmond", "location": "away", "ground": "MCG"}, + {"round": 18, "opponent": "Essendon", "location": "home", "ground": "MCG"}, + {"round": 19, "opponent": "North Melbourne", "location": "neutral", "ground": "UTAS Stadium"}, + {"round": 20, "opponent": "Brisbane", "location": "away", "ground": "Gabba"}, + {"round": 21, "opponent": "Collingwood", "location": "home", "ground": "MCG"}, + {"round": 22, "opponent": "West Coast", "location": "away", "ground": "Optus Stadium"}, +] + +# Rivalry teams (historical rivals of Hawthorn) +historical_rivals = ["Geelong", "Essendon", "Collingwood"] +melbourne_teams = ["Collingwood", "Essendon", "Carlton", "Richmond", "Melbourne", "Bulldogs", "St Kilda", "North Melbourne", "Geelong"] + +# Opponent fan base risk (1-3 scale) +fan_base_risk = { + "Collingwood": 3, + "Essendon": 2, + "Carlton": 2, + "Richmond": 2, + "Geelong": 2, + "Bulldogs": 1, + "Melbourne": 1, + "Sydney": 1, + "GWS": 1, + "Port Adelaide": 1, + "Adelaide": 1, + "Fremantle": 1, + "West Coast": 1, + "Brisbane": 1, + "Gold Coast": 1, + "St Kilda": 1, + "North Melbourne": 1, +} + +def calculate_risk_score(fixture): + """Calculate risk score from 1-5 for a fixture""" + score = 1 # Base minimum score + + # Rivalry factor (0-3) + if fixture["opponent"] in historical_rivals: + score += 3 + + # Location factor (0-2) + if fixture["location"] == "away": + score += 2 + elif fixture["location"] == "neutral": + score += 0 + + # Melbourne derby factor (0-4) + if fixture["opponent"] in melbourne_teams and fixture["location"] in ["home", "away"]: + score += 2 + + # Finals implication (late season rounds 15-22) + if fixture["round"] >= 15: + score += 1 + + # Fan base factor (0-3) + score += fan_base_risk.get(fixture["opponent"], 1) + + # Normalize to 1-5 scale + normalized = min(5, max(1, round(score / 3))) + + return normalized + +def get_risk_level(score): + """Convert numeric score to risk level and emoji""" + if score >= 4: + return "๐Ÿ”ด HIGH" + elif score == 3: + return "๐ŸŸก MEDIUM" + else: + return "๐ŸŸข LOW" + +def generate_risk_matrix(): + """Generate the full risk matrix for Hawthorn""" + matrix = [] + + for fixture in fixtures: + risk_score = calculate_risk_score(fixture) + + matrix.append({ + "round": fixture["round"], + "opponent": fixture["opponent"], + "location": fixture["location"].upper(), + "ground": fixture["ground"], + "risk_score": risk_score, + "risk_level": get_risk_level(risk_score) + }) + + return matrix + +def print_visual_matrix(matrix): + """Print a beautiful visual matrix to the console""" + print("\n" + "="*80) + print("๐Ÿ‰ HAWTHORN 2026 FIXTURE RISK MATRIX") + print("="*80) + print(f"{'Round':<6} {'Opponent':<15} {'Loc':<4} {'Ground':<20} {'Risk':<8} {'Level'}") + print("-"*80) + + for game in matrix: + print(f"R{game['round']:<3} {game['opponent']:<15} {game['location']:<4} {game['ground']:<20} {game['risk_score']} {game['risk_level']}") + + print("-"*80) + + # Summary statistics + high_risk = [g for g in matrix if g["risk_score"] >= 4] + medium_risk = [g for g in matrix if g["risk_score"] == 3] + low_risk = [g for g in matrix if g["risk_score"] <= 2] + + print(f"\n Hawthorn 2026 Season Fxiture Risk Summary:") + print(f" ๐Ÿ”ด High risk games: {len(high_risk)}") + for game in high_risk: + print(f" - R{game['round']}: vs {game['opponent']} ({game['location']})") + print(f" ๐ŸŸก Medium risk games: {len(medium_risk)}") + print(f" ๐ŸŸข Low risk games: {len(low_risk)}") + print(f"\n Average risk score: {sum(g['risk_score'] for g in matrix)/len(matrix):.1f}/5.0") + +def save_as_json(matrix): + """Save the matrix as JSON for API use""" + output = { + "team": "Hawthorn Hawks", + "season": 2025, + "risk_matrix": matrix, + "summary": { + "high_risk_games": len([g for g in matrix if g["risk_score"] >= 4]), + "medium_risk_games": len([g for g in matrix if g["risk_score"] == 3]), + "low_risk_games": len([g for g in matrix if g["risk_score"] <= 2]), + "average_risk": sum(g["risk_score"] for g in matrix) / len(matrix) + } + } + + with open("hawthorn_risk_matrix.json", "w") as f: + json.dump(output, f, indent=2) + + print("\n Saved to hawthorn_risk_matrix.json") + +if __name__ == "__main__": + # Generate and display the matrix + matrix = generate_risk_matrix() + print_visual_matrix(matrix) + save_as_json(matrix) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/main.py index 4d089fc4f..bf6fabd6f 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/main.py @@ -69,11 +69,17 @@ def get_risk_level(density): recommendations = [] crowd_state = input_data.get("crowd_state", "stable") - # Recommendations based on SCHEMA.md example + # Critical zone recommendations (highest priority) + for zone in assessed_zones: + if zone["risk_level"] == "critical": + recommendations.append(f"๐Ÿšจ CRITICAL: Zone {zone['zone_id']} at critical density - immediate crowd control required") + + # High risk zone recommendations for zone in assessed_zones: if zone["risk_level"] == "high" and zone["flagged"]: recommendations.append(f"Monitor zone {zone['zone_id']} closely") + # Crowd state recommendations if crowd_state == "increasing_density": recommendations.append("Prepare crowd redirection if density increases further") diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_integration.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_integration.py new file mode 100644 index 000000000..4b296171d --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_integration.py @@ -0,0 +1,51 @@ +"""Integration test to verify handoff from crowd_behaviour_analytics""" + +import sys +sys.path.insert(0, '/Users/xiwan2020/redback-orion/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1') + +# Import both tasks +from crowd_allocation_risk_zone.main import assess_risk + +# Mock the behavior analytics output (since it's not fully implemented yet) +def mock_analyze_behaviour(input_data): + """Simulates what crowd_behaviour_analytics would return""" + return { + "video_id": input_data.get("video_id", "test"), + "crowd_state": input_data.get("crowd_state", "stable"), + "zones": input_data.get("zones", []) + } + +# Test data that would come from the shared service +test_pipeline_data = { + "video_id": "integration_test_01", + "crowd_state": "increasing_density", + "zones": [ + {"zone_id": "Z1", "person_count": 12, "density": 0.88}, + {"zone_id": "Z2", "person_count": 7, "density": 0.65}, + {"zone_id": "Z3", "person_count": 2, "density": 0.20} + ] +} + +print("="*60) +print("INTEGRATION TEST: Handoff from crowd_behaviour_analytics") +print("="*60) + +# Simulate the pipeline +print("\n1. Behaviour Analytics processes input...") +behaviour_result = mock_analyze_behaviour(test_pipeline_data) +print(f" โ†’ Returns: video_id={behaviour_result['video_id']}, crowd_state={behaviour_result['crowd_state']}, zones={len(behaviour_result['zones'])} zones") + +print("\n2. Risk Zone task receives behaviour_result...") +risk_result = assess_risk(behaviour_result) +print(f" โ†’ Returns: video_id={risk_result['video_id']}, zones assessed={len(risk_result['zones'])}") + +print("\n3. Final output from pipeline:") +print(f" Video ID: {risk_result['video_id']}") +print(f" Zones assessed:") +for zone in risk_result['zones']: + print(f" - {zone['zone_id']}: {zone['risk_level']} (flagged: {zone['flagged']})") +print(f" Recommendations:") +for rec in risk_result['recommendations']: + print(f" โ€ข {rec}") + +print("\nโœ… Integration handoff verified successfully!") \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_scenarios.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_scenarios.py new file mode 100644 index 000000000..cd53f2481 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/test_scenarios.py @@ -0,0 +1,241 @@ +"""Test scenarios for crowd allocation risk zone validation""" + +import json +from main import assess_risk + +def run_test(test_name, input_data, expected_output): + """Run a single test and report results""" + print(f"\n{'='*60}") + print(f"Test: {test_name}") + print(f"{'='*60}") + + try: + result = assess_risk(input_data) + + # Compare results + matches = True + issues = [] + + # Check video_id + if result.get("video_id") != expected_output.get("video_id"): + matches = False + issues.append(f"video_id mismatch: got {result.get('video_id')}, expected {expected_output.get('video_id')}") + + # Check zones length + if len(result.get("zones", [])) != len(expected_output.get("zones", [])): + matches = False + issues.append(f"Zone count mismatch: got {len(result.get('zones', []))}, expected {len(expected_output.get('zones', []))}") + + # Check each zone + for i, (result_zone, expected_zone) in enumerate(zip(result.get("zones", []), expected_output.get("zones", []))): + if result_zone.get("risk_level") != expected_zone.get("risk_level"): + matches = False + issues.append(f"Zone {i} risk_level: got {result_zone.get('risk_level')}, expected {expected_zone.get('risk_level')}") + if result_zone.get("flagged") != expected_zone.get("flagged"): + matches = False + issues.append(f"Zone {i} flagged: got {result_zone.get('flagged')}, expected {expected_zone.get('flagged')}") + + # Check recommendations + if result.get("recommendations") != expected_output.get("recommendations"): + matches = False + issues.append(f"Recommendations mismatch") + + if matches: + print("โœ… PASSED") + print(f"Output: {json.dumps(result, indent=2)}") + else: + print("โŒ FAILED") + for issue in issues: + print(f" โ€ข {issue}") + print(f"\nGot: {json.dumps(result, indent=2)}") + print(f"\nExpected: {json.dumps(expected_output, indent=2)}") + + return matches + + except Exception as e: + print(f"โŒ ERROR: {str(e)}") + return False + +# Test Scenario 1: Normal operation (from SCHEMA.md) +def scenario_1(): + input_data = { + "video_id": "match_01", + "crowd_state": "increasing_density", + "zones": [ + {"zone_id": "A1", "person_count": 8, "density": 0.72}, + {"zone_id": "A2", "person_count": 5, "density": 0.45} + ] + } + expected = { + "video_id": "match_01", + "zones": [ + {"zone_id": "A1", "risk_level": "high", "flagged": True}, + {"zone_id": "A2", "risk_level": "medium", "flagged": False} + ], + "recommendations": [ + "Monitor zone A1 closely", + "Prepare crowd redirection if density increases further" + ] + } + return run_test("Normal operation (SCHEMA.md example)", input_data, expected) + +# Test Scenario 2: Critical density zone +def scenario_2(): + input_data = { + "video_id": "match_02", + "crowd_state": "stable", + "zones": [ + {"zone_id": "B1", "person_count": 15, "density": 0.92}, + {"zone_id": "B2", "person_count": 3, "density": 0.25} + ] + } + result = assess_risk(input_data) + print(f"\n{'='*60}") + print(f"Test: Critical density zone") + print(f"{'='*60}") + + # Manual checks + issues = [] + zones = result.get("zones", []) + + if zones[0].get("risk_level") != "critical": + issues.append(f"Zone B1 risk_level: got {zones[0].get('risk_level')}, expected critical") + if not zones[0].get("flagged"): + issues.append(f"Zone B1 flagged: expected True") + if zones[1].get("risk_level") != "very_low": + issues.append(f"Zone B2 risk_level: got {zones[1].get('risk_level')}, expected very_low") + + # Check for critical zone recommendation + has_critical_rec = any("critical" in rec.lower() for rec in result.get("recommendations", [])) + if not has_critical_rec: + issues.append("Missing recommendation for critical zone") + + # Also check that critical recommendation includes the zone ID + critical_rec_for_b1 = any("B1" in rec and "critical" in rec.lower() for rec in result.get("recommendations", [])) + if not critical_rec_for_b1: + issues.append("Critical recommendation should mention zone B1") + + if not issues: + print("โœ… PASSED") + print(f"Output: {json.dumps(result, indent=2)}") + return True + else: + print("โŒ FAILED") + for issue in issues: + print(f" โ€ข {issue}") + return False + +# Test Scenario 3: Multiple high-risk zones +def scenario_3(): + input_data = { + "video_id": "match_03", + "crowd_state": "increasing_density", + "zones": [ + {"zone_id": "C1", "person_count": 10, "density": 0.75}, + {"zone_id": "C2", "person_count": 9, "density": 0.72}, + {"zone_id": "C3", "person_count": 4, "density": 0.38} + ] + } + result = assess_risk(input_data) + print(f"\n{'='*60}") + print(f"Test: Multiple high-risk zones") + print(f"{'='*60}") + + issues = [] + zones = result.get("zones", []) + + # Check risk levels + if zones[0].get("risk_level") != "high": + issues.append(f"Zone C1: expected high, got {zones[0].get('risk_level')}") + if zones[1].get("risk_level") != "high": + issues.append(f"Zone C2: expected high, got {zones[1].get('risk_level')}") + if zones[2].get("risk_level") != "low": + issues.append(f"Zone C3: expected low, got {zones[2].get('risk_level')}") + + # Check flagged status + if not zones[0].get("flagged"): + issues.append("Zone C1 should be flagged") + if not zones[1].get("flagged"): + issues.append("Zone C2 should be flagged") + + # Check recommendations include high-risk zones + recommendations = result.get("recommendations", []) + if not any("C1" in rec for rec in recommendations): + issues.append("Recommendation missing for C1") + if not any("C2" in rec for rec in recommendations): + issues.append("Recommendation missing for C2") + + if not issues: + print("โœ… PASSED") + print(f"Output: {json.dumps(result, indent=2)}") + return True + else: + print("โŒ FAILED") + for issue in issues: + print(f" โ€ข {issue}") + return False + +# Test Scenario 4: Empty zones list +def scenario_4(): + input_data = { + "video_id": "match_04", + "crowd_state": "stable", + "zones": [] + } + expected = { + "video_id": "match_04", + "zones": [], + "recommendations": ["All zones within safe thresholds - continue monitoring"] + } + return run_test("Empty zones list", input_data, expected) + +# Test Scenario 5: Missing crowd_state (should default to stable) +def scenario_5(): + input_data = { + "video_id": "match_05", + "zones": [ + {"zone_id": "D1", "person_count": 3, "density": 0.25} + ] + } + result = assess_risk(input_data) + print(f"\n{'='*60}") + print(f"Test: Missing crowd_state (should default to stable)") + print(f"{'='*60}") + + # Should not throw error and should work + if result.get("video_id") == "match_05" and result.get("zones"): + print("โœ… PASSED - Handles missing crowd_state gracefully") + print(f"Output: {json.dumps(result, indent=2)}") + return True + else: + print("โŒ FAILED") + return False + +# Run all tests +def run_all_tests(): + print("\n" + "="*60) + print("RUNNING CROWD ALLOCATION RISK ZONE VALIDATION TESTS") + print("="*60) + + results = [] + results.append(scenario_1()) + results.append(scenario_2()) + results.append(scenario_3()) + results.append(scenario_4()) + results.append(scenario_5()) + + print("\n" + "="*60) + print("TEST SUMMARY") + print("="*60) + passed = sum(results) + total = len(results) + print(f"Passed: {passed}/{total}") + print(f"Failed: {total - passed}/{total}") + + if passed == total: + print("\n๐ŸŽ‰ ALL TESTS PASSED! Module is ready for integration.") + else: + print(f"\nโš ๏ธ {total - passed} test(s) failed. Please review and fix.") + +if __name__ == "__main__": + run_all_tests() \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/SCHEMA.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/SCHEMA.md index 8babd8a51..8b397fe83 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/SCHEMA.md +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/SCHEMA.md @@ -27,55 +27,24 @@ This task receives analytics output and determines overall crowd behaviour trend } ``` -``` -{ - "video_id": "match_02", - "zones": [ - { - "zone_id": "A1", - "person_count": 16, - "density": 0.88 - }, - { - "zone_id": "A2", - "person_count": 14, - "density": 0.79 - }, - { - "zone_id": "B1", - "person_count": 11, - "density": 0.68 - } - ], - "heatmap": { - "image_path": "output/heatmap_match_02.png" - } -} -``` +Optional additional input for AI-vision processing: -``` +```json { - "video_id": "match_03", - "zones": [ + "frames": [ { - "zone_id": "A1", - "person_count": 2, - "density": 0.12 - }, - { - "zone_id": "A2", - "person_count": 3, - "density": 0.18 - }, - { - "zone_id": "B1", - "person_count": 1, - "density": 0.10 + "frame_id": 1, + "timestamp": 0.04, + "people_annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", + "people_detections": [ + { + "bbox": [100, 50, 160, 180], + "confidence": 0.93 + } + ], + "face_detections": [] } - ], - "heatmap": { - "image_path": "output/heatmap_match_03.png" - } + ] } ``` @@ -91,7 +60,76 @@ This task receives analytics output and determines overall crowd behaviour trend "person_count": 8, "density": 0.72 } - ] + ], + "event_flags": [ + "running_detection", + "crowd_surge", + "motion_anomaly" + ], + "artifact_paths": [ + "output/heatmap_match_01.png", + "crowd_behaviour_analytics/output/running_frames/motion_frame_0008.jpg" + ], + "vision_metrics": { + "vision_enabled": true, + "avg_motion_magnitude": 0.84, + "peak_motion_magnitude": 1.27, + "reverse_flow_ratio": 0.18, + "motion_intensity": 1.05, + "tracking": { + "track_count": 3, + "walking_track_count": 1, + "walking_track_ids": [2], + "running_track_count": 1, + "running_track_ids": [1], + "tracks": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "max_speed": 12.6, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "normalized_displacement": 1.24, + "height_variation": 0.08, + "is_walking": false, + "is_running": true, + "movement_state": "running" + }, + { + "track_id": 2, + "history_length": 4, + "avg_speed": 5.2, + "max_speed": 6.4, + "avg_normalized_speed": 0.22, + "max_normalized_speed": 0.36, + "normalized_displacement": 0.72, + "height_variation": 0.05, + "is_walking": true, + "is_running": false, + "movement_state": "walking" + } + ] + }, + "anomaly_model": { + "model_enabled": true, + "anomaly_track_ids": [1], + "running_track_ids": [1], + "anomaly_count": 1, + "track_scores": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "normalized_displacement": 1.24, + "anomaly_score": 0.2174, + "is_anomaly": true + } + ] + } + } } ``` @@ -99,5 +137,10 @@ This task receives analytics output and determines overall crowd behaviour trend - output of this task is used by `crowd_allocation_risk_zone` - keep `crowd_state` aligned with the intelligence service schema -- behaviour analysis can use zone density patterns and heatmap availability as input features -- this task can evolve from rule-based scoring to a trained ML model later without changing the output schema +- behaviour analysis can use zone density patterns, heatmap availability, and sequential annotated frames as input features +- `event_flags` and `artifact_paths` are optional extended outputs for demo and frontend visibility +- optional `frames` should use people bbox-annotated frame paths from `crowd_detection` for downstream visual analysis and motion analysis +- `people_detections` is the input used for person tracking +- `tracking` contains per-person movement-state outputs derived from lightweight tracking +- `anomaly_model` contains IsolationForest-based motion anomaly outputs +- current movement states are `stationary`, `walking`, and `running` diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/anomaly_model.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/anomaly_model.py new file mode 100644 index 000000000..ebff332be --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/anomaly_model.py @@ -0,0 +1,129 @@ +"""IsolationForest-based anomaly scoring for tracked crowd behaviour.""" + +from math import sqrt + +import numpy as np +from sklearn.ensemble import IsolationForest + + +def _track_feature_vector(track_id, history): + speeds = [entry.get("speed", 0.0) for entry in history] + normalized_speeds = [entry.get("normalized_speed", 0.0) for entry in history] + first_centroid = history[0].get("centroid", [0.0, 0.0]) + last_centroid = history[-1].get("centroid", [0.0, 0.0]) + dx = float(last_centroid[0]) - float(first_centroid[0]) + dy = float(last_centroid[1]) - float(first_centroid[1]) + displacement = sqrt(dx * dx + dy * dy) + avg_height = max( + sum(entry.get("bbox_height", 1.0) for entry in history) / max(len(history), 1), + 1.0, + ) + normalized_displacement = displacement / avg_height + + return { + "track_id": track_id, + "vector": [ + float(len(history)), + float(sum(normalized_speeds) / len(normalized_speeds)) if normalized_speeds else 0.0, + float(max(normalized_speeds, default=0.0)), + float(normalized_displacement), + float(sum(speeds) / len(speeds)) if speeds else 0.0, + ], + } + + +def detect_track_anomalies(track_histories): + """Score tracked people using IsolationForest and return anomalous tracks.""" + if not track_histories: + return { + "model_enabled": False, + "anomaly_track_ids": [], + "running_track_ids": [], + "anomaly_count": 0, + "track_scores": [], + } + + track_vectors = [ + _track_feature_vector(track_id, history) + for track_id, history in track_histories.items() + if history + ] + + if not track_vectors: + return { + "model_enabled": False, + "anomaly_track_ids": [], + "running_track_ids": [], + "anomaly_count": 0, + "track_scores": [], + } + + # Synthetic reference samples represent relatively normal crowd motion. + reference_vectors = np.array( + [ + [2.0, 0.05, 0.10, 0.12, 1.5], + [3.0, 0.08, 0.14, 0.20, 2.0], + [4.0, 0.12, 0.22, 0.30, 2.7], + [5.0, 0.18, 0.30, 0.45, 3.5], + [4.0, 0.10, 0.18, 0.25, 2.3], + [5.0, 0.16, 0.28, 0.38, 3.2], + [6.0, 0.22, 0.36, 0.55, 4.0], + [7.0, 0.28, 0.42, 0.72, 5.0], + ], + dtype=float, + ) + observed_vectors = np.array([entry["vector"] for entry in track_vectors], dtype=float) + training_vectors = np.vstack([reference_vectors, observed_vectors]) + + model = IsolationForest( + n_estimators=100, + contamination=0.15, + random_state=42, + ) + model.fit(training_vectors) + + predictions = model.predict(observed_vectors) + scores = model.decision_function(observed_vectors) + + anomaly_track_ids = [] + running_track_ids = [] + track_scores = [] + + for track_entry, prediction, score in zip(track_vectors, predictions, scores): + track_id = track_entry["track_id"] + history_length, avg_normalized_speed, max_normalized_speed, normalized_displacement, avg_speed = track_entry["vector"] + is_anomaly = int(prediction) == -1 + anomaly_score = round(float(-score), 4) + + if is_anomaly: + anomaly_track_ids.append(track_id) + + if ( + is_anomaly + and history_length >= 3 + and avg_normalized_speed >= 0.55 + and max_normalized_speed >= 0.9 + and normalized_displacement >= 1.0 + ): + running_track_ids.append(track_id) + + track_scores.append( + { + "track_id": track_id, + "history_length": int(history_length), + "avg_speed": round(avg_speed, 2), + "avg_normalized_speed": round(avg_normalized_speed, 4), + "max_normalized_speed": round(max_normalized_speed, 4), + "normalized_displacement": round(normalized_displacement, 4), + "anomaly_score": anomaly_score, + "is_anomaly": is_anomaly, + } + ) + + return { + "model_enabled": True, + "anomaly_track_ids": anomaly_track_ids, + "running_track_ids": running_track_ids, + "anomaly_count": len(anomaly_track_ids), + "track_scores": track_scores, + } diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/event_detection.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/event_detection.py new file mode 100644 index 000000000..7588439f0 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/event_detection.py @@ -0,0 +1,49 @@ +"""Event-detection helpers for crowd behaviour analytics.""" + + +def detect_behaviour_events( + features, + vision_features, + zones, + tracking_summary=None, + anomaly_summary=None, +): + """Generate event flags using density patterns and motion cues.""" + event_flags = [] + tracking_summary = tracking_summary or {} + anomaly_summary = anomaly_summary or {} + + if features["max_density"] >= 0.80 or features["hotspot_count"] >= 2: + event_flags.append("overcrowding_spike") + + if features["density_variation"] >= 0.35: + event_flags.append("sudden_gathering") + + if features["avg_density"] <= 0.20 and zones: + event_flags.append("crowd_dispersing") + + if anomaly_summary.get("running_track_ids"): + event_flags.append("running_detection") + elif tracking_summary.get("running_track_count", 0) > 0: + event_flags.append("running_detection") + + if tracking_summary.get("walking_track_count", 0) > 0: + event_flags.append("walking_detection") + + if tracking_summary.get("stationary_track_count", 0) > 0: + event_flags.append("stationary_detection") + + if vision_features["vision_enabled"] and vision_features["reverse_flow_ratio"] >= 0.30: + event_flags.append("reverse_flow") + + if ( + features["avg_density"] >= 0.60 + and vision_features["vision_enabled"] + and vision_features["avg_motion_magnitude"] >= 0.80 + ): + event_flags.append("crowd_surge") + + if anomaly_summary.get("anomaly_count", 0) > 0: + event_flags.append("motion_anomaly") + + return event_flags diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/feature_extraction.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/feature_extraction.py new file mode 100644 index 000000000..d9e87afeb --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/feature_extraction.py @@ -0,0 +1,50 @@ +"""Feature extraction helpers for crowd behaviour analytics.""" + + +def extract_density_features(zones, heatmap): + """Build behaviour features from zone density and heatmap availability.""" + if not zones: + return { + "avg_density": 0.0, + "max_density": 0.0, + "density_variation": 0.0, + "total_people": 0, + "hotspot_count": 0, + "heatmap_available": False, + } + + densities = [zone.get("density", 0.0) for zone in zones] + avg_density = sum(densities) / len(densities) + max_density = max(densities) + min_density = min(densities) + total_people = sum(zone.get("person_count", 0) for zone in zones) + hotspot_count = sum(1 for density in densities if density >= 0.6) + + return { + "avg_density": avg_density, + "max_density": max_density, + "density_variation": max_density - min_density, + "total_people": total_people, + "hotspot_count": hotspot_count, + "heatmap_available": bool(heatmap and heatmap.get("image_path")), + } + + +def classify_crowd_state(features): + """ML-style scoring scaffold for overall crowd-state classification.""" + score = 0.0 + + score += features["avg_density"] * 0.35 + score += features["max_density"] * 0.35 + score += features["density_variation"] * 0.15 + score += min(features["hotspot_count"] / 3, 1.0) * 0.10 + score += min(features["total_people"] / 30, 1.0) * 0.05 + + if not features["heatmap_available"]: + score -= 0.05 + + if score >= 0.60: + return "increasing_density" + if score <= 0.20: + return "dispersing" + return "stable" diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/main.py index 0ec164fda..3ed49ba78 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/main.py @@ -1,53 +1,23 @@ -"""Minimal entry point for the crowd behaviour analytics task.""" +"""Crowd behaviour analytics task orchestration.""" - -def _extract_features(zones, heatmap): - """Build simple behaviour features from zone density and heatmap availability.""" - if not zones: - return { - "avg_density": 0.0, - "max_density": 0.0, - "density_variation": 0.0, - "total_people": 0, - "hotspot_count": 0, - "heatmap_available": False, - } - - densities = [zone.get("density", 0.0) for zone in zones] - avg_density = sum(densities) / len(densities) - max_density = max(densities) - min_density = min(densities) - total_people = sum(zone.get("person_count", 0) for zone in zones) - hotspot_count = sum(1 for density in densities if density >= 0.6) - - return { - "avg_density": avg_density, - "max_density": max_density, - "density_variation": max_density - min_density, - "total_people": total_people, - "hotspot_count": hotspot_count, - "heatmap_available": bool(heatmap and heatmap.get("image_path")), - } - - -def _classify_crowd_state(features): - """AI-style scoring scaffold that can later be replaced with a trained model.""" - score = 0.0 - - score += features["avg_density"] * 0.35 - score += features["max_density"] * 0.35 - score += features["density_variation"] * 0.15 - score += min(features["hotspot_count"] / 3, 1.0) * 0.10 - score += min(features["total_people"] / 30, 1.0) * 0.05 - - if not features["heatmap_available"]: - score -= 0.05 - - if score >= 0.60: - return "increasing_density" - if score <= 0.20: - return "dispersing" - return "stable" +from crowd_behaviour_analytics.anomaly_model import detect_track_anomalies +from crowd_behaviour_analytics.event_detection import detect_behaviour_events +from crowd_behaviour_analytics.feature_extraction import ( + classify_crowd_state, + extract_density_features, +) +from crowd_behaviour_analytics.pose_analysis import refine_tracking_summary_with_pose +from crowd_behaviour_analytics.tracking import ( + build_frame_activity_series, + save_motion_annotations, + summarise_tracks, + track_people, +) +from crowd_behaviour_analytics.vision_analysis import ( + extract_motion_features, + load_grayscale_frames, + resolve_frame_paths, +) def analyze_behaviour(input_data): @@ -55,17 +25,50 @@ def analyze_behaviour(input_data): zones = input_data.get("zones", []) heatmap = input_data.get("heatmap", {}) video_id = input_data.get("video_id") + frames = input_data.get("frames", []) + frame_paths = resolve_frame_paths(input_data) + + features = extract_density_features(zones, heatmap) + vision_features = extract_motion_features(load_grayscale_frames(frame_paths)) + frame_tracks, track_histories = track_people(frames) + tracking_summary = summarise_tracks(track_histories) + tracking_summary = refine_tracking_summary_with_pose(frames, frame_tracks, tracking_summary) + anomaly_summary = detect_track_anomalies(track_histories) + crowd_state = classify_crowd_state(features) + event_flags = detect_behaviour_events( + features, + vision_features, + zones, + tracking_summary, + anomaly_summary, + ) + + artifact_paths = [] + if heatmap and heatmap.get("image_path"): + artifact_paths.append(heatmap["image_path"]) + merged_tracking_summary = dict(tracking_summary) + merged_running_ids = set(tracking_summary.get("running_track_ids", [])) + merged_running_ids.update(anomaly_summary.get("running_track_ids", [])) + merged_tracking_summary["running_track_ids"] = sorted(merged_running_ids) + merged_tracking_summary["running_track_count"] = len(merged_running_ids) + frame_activity_series = build_frame_activity_series(frame_tracks, merged_tracking_summary) + artifact_paths.extend(save_motion_annotations(frame_tracks, merged_tracking_summary, video_id)) - features = _extract_features(zones, heatmap) - crowd_state = _classify_crowd_state(features) + vision_metrics = dict(vision_features) + vision_metrics["tracking"] = tracking_summary + vision_metrics["anomaly_model"] = anomaly_summary return { "video_id": video_id, "crowd_state": crowd_state, "zones": zones, + "event_flags": event_flags, + "artifact_paths": artifact_paths, + "frame_movement_summary": frame_activity_series, + "frame_activity_series": frame_activity_series, + "vision_metrics": vision_metrics, } if __name__ == "__main__": - # Add a simple local test call here when implementation starts. pass diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/pose_analysis.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/pose_analysis.py new file mode 100644 index 000000000..8a97b20b2 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/pose_analysis.py @@ -0,0 +1,202 @@ +"""Optional pose-based validation for crowd movement states.""" + +from __future__ import annotations + +from pathlib import Path + +import cv2 +from ultralytics import YOLO + + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +POSE_MODEL_CANDIDATES = [ + PROJECT_ROOT / "crowd_behaviour_analytics" / "yolov8n-pose.pt", + PROJECT_ROOT / "crowd_behaviour_analytics" / "yolov8s-pose.pt", + PROJECT_ROOT / "yolov8n-pose.pt", + PROJECT_ROOT / "yolov8s-pose.pt", +] +LEG_KEYPOINT_IDS = (13, 14, 15, 16) + + +def _resolve_frame_path(frame_path: str | None) -> Path | None: + if not frame_path: + return None + candidate = Path(frame_path) + return candidate if candidate.is_absolute() else PROJECT_ROOT / candidate + + +def _load_pose_model(): + for model_path in POSE_MODEL_CANDIDATES: + if model_path.exists(): + return YOLO(str(model_path)) + try: + return YOLO("yolov8n-pose.pt") + except Exception: + return None + + +def _extract_leg_keypoints(model, image, bbox, min_pose_confidence): + x1, y1, x2, y2 = bbox + crop = image[max(y1, 0):max(y2, 0), max(x1, 0):max(x2, 0)] + if crop.size == 0: + return None + + result = model(crop, verbose=False)[0] + if result.keypoints is None or len(result.keypoints.data) == 0: + return None + + keypoint_tensor = result.keypoints.data[0] + if keypoint_tensor is None: + return None + + keypoints = keypoint_tensor.tolist() + visible_keypoints = {} + for idx in LEG_KEYPOINT_IDS: + if idx >= len(keypoints): + continue + point = keypoints[idx] + if len(point) < 3 or float(point[2]) < min_pose_confidence: + continue + visible_keypoints[idx] = (float(point[0]), float(point[1])) + + if len(visible_keypoints) < 2: + return None + + return visible_keypoints + + +def _build_track_pose_sequences(frames, frame_tracks, tracking_summary, pose_model, min_bbox_height, min_pose_confidence): + frame_entries = { + frame.get("frame_id"): frame + for frame in frames or [] + } + walking_track_ids = set(tracking_summary.get("walking_track_ids", [])) + pose_sequences = {track_id: [] for track_id in walking_track_ids} + + for frame_track in frame_tracks: + frame_id = frame_track.get("frame_id") + frame_entry = frame_entries.get(frame_id, {}) + resolved_frame_path = _resolve_frame_path(frame_entry.get("frame_path") or frame_track.get("frame_path")) + if resolved_frame_path is None: + resolved_frame_path = _resolve_frame_path(frame_track.get("people_annotated_frame_path")) + if resolved_frame_path is None or not resolved_frame_path.exists(): + continue + + image = cv2.imread(str(resolved_frame_path)) + if image is None: + continue + + for tracked in frame_track.get("tracked_detections", []): + track_id = tracked.get("track_id") + if track_id not in pose_sequences: + continue + + bbox = tracked.get("bbox", []) + if len(bbox) != 4: + continue + + bbox_height = float(tracked.get("bbox_height", 0.0)) + if bbox_height < min_bbox_height: + continue + + leg_keypoints = _extract_leg_keypoints( + pose_model, + image, + bbox, + min_pose_confidence, + ) + if leg_keypoints is None: + continue + + pose_sequences[track_id].append( + { + "frame_id": frame_id, + "bbox_height": max(bbox_height, 1.0), + "leg_keypoints": leg_keypoints, + } + ) + + return pose_sequences + + +def _pose_leg_motion_score(sequence): + if len(sequence) < 3: + return 0.0 + + normalized_steps = [] + for previous, current in zip(sequence, sequence[1:]): + shared_ids = set(previous["leg_keypoints"]).intersection(current["leg_keypoints"]) + if len(shared_ids) < 2: + continue + + step_magnitudes = [] + for keypoint_id in shared_ids: + prev_x, prev_y = previous["leg_keypoints"][keypoint_id] + curr_x, curr_y = current["leg_keypoints"][keypoint_id] + dx = curr_x - prev_x + dy = curr_y - prev_y + step_magnitudes.append((dx * dx + dy * dy) ** 0.5) + + if not step_magnitudes: + continue + + avg_height = max((previous["bbox_height"] + current["bbox_height"]) / 2.0, 1.0) + normalized_steps.append((sum(step_magnitudes) / len(step_magnitudes)) / avg_height) + + if not normalized_steps: + return 0.0 + + return sum(normalized_steps) / len(normalized_steps) + + +def refine_tracking_summary_with_pose( + frames, + frame_tracks, + tracking_summary, + min_bbox_height=60.0, + min_pose_confidence=0.25, + min_pose_motion_score=0.03, +): + """Validate walking tracks with leg-keypoint motion when a local pose model is available.""" + pose_model = _load_pose_model() + if pose_model is None: + return tracking_summary + + refined_summary = dict(tracking_summary) + tracks = [dict(track) for track in tracking_summary.get("tracks", [])] + pose_sequences = _build_track_pose_sequences( + frames, + frame_tracks, + tracking_summary, + pose_model, + min_bbox_height, + min_pose_confidence, + ) + + updated_walking_track_ids = [] + updated_stationary_track_ids = set(tracking_summary.get("stationary_track_ids", [])) + + for track in tracks: + track_id = track.get("track_id") + if not track.get("is_walking"): + continue + + pose_motion_score = _pose_leg_motion_score(pose_sequences.get(track_id, [])) + track["pose_used"] = bool(pose_sequences.get(track_id)) + track["pose_motion_score"] = round(pose_motion_score, 4) + + if track["pose_used"] and pose_motion_score < min_pose_motion_score: + track["is_walking"] = False + track["is_stationary"] = True + track["movement_state"] = "stationary" + updated_stationary_track_ids.add(track_id) + continue + + updated_walking_track_ids.append(track_id) + + refined_summary["tracks"] = tracks + refined_summary["walking_track_ids"] = sorted(updated_walking_track_ids) + refined_summary["walking_track_count"] = len(updated_walking_track_ids) + refined_summary["stationary_track_ids"] = sorted(updated_stationary_track_ids) + refined_summary["stationary_track_count"] = len(updated_stationary_track_ids) + return refined_summary diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/tracking.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/tracking.py new file mode 100644 index 000000000..4567a7ee5 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/tracking.py @@ -0,0 +1,425 @@ +"""Lightweight person tracking for crowd behaviour analytics.""" + +from math import sqrt +from pathlib import Path +import shutil + +import cv2 + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +OUTPUT_ROOT = PROJECT_ROOT / "crowd_behaviour_analytics" / "output" + + +def _centroid(bbox): + x1, y1, x2, y2 = bbox + return ((x1 + x2) / 2.0, (y1 + y2) / 2.0) + + +def _bbox_size(bbox): + x1, y1, x2, y2 = bbox + return max(x2 - x1, 1), max(y2 - y1, 1) + + +def _bbox_iou(box_a, box_b): + ax1, ay1, ax2, ay2 = box_a + bx1, by1, bx2, by2 = box_b + + inter_x1 = max(ax1, bx1) + inter_y1 = max(ay1, by1) + inter_x2 = min(ax2, bx2) + inter_y2 = min(ay2, by2) + + inter_w = max(0, inter_x2 - inter_x1) + inter_h = max(0, inter_y2 - inter_y1) + inter_area = inter_w * inter_h + + area_a = max(ax2 - ax1, 0) * max(ay2 - ay1, 0) + area_b = max(bx2 - bx1, 0) * max(by2 - by1, 0) + union_area = max(area_a + area_b - inter_area, 1) + + return inter_area / union_area + + +def _distance(point_a, point_b): + return sqrt((point_a[0] - point_b[0]) ** 2 + (point_a[1] - point_b[1]) ** 2) + + +def _direction_consistency(history): + """Measure how consistently a track moves in one direction across updates.""" + if len(history) < 3: + return 0.0 + + step_vectors = [] + for previous, current in zip(history, history[1:]): + prev_centroid = previous.get("centroid", [0.0, 0.0]) + curr_centroid = current.get("centroid", [0.0, 0.0]) + dx = float(curr_centroid[0]) - float(prev_centroid[0]) + dy = float(curr_centroid[1]) - float(prev_centroid[1]) + magnitude = sqrt(dx * dx + dy * dy) + if magnitude < 1e-6: + continue + step_vectors.append((dx / magnitude, dy / magnitude)) + + if len(step_vectors) < 2: + return 0.0 + + alignment_scores = [] + for previous, current in zip(step_vectors, step_vectors[1:]): + alignment_scores.append((previous[0] * current[0]) + (previous[1] * current[1])) + + positive_alignment = [score for score in alignment_scores if score > 0] + if not positive_alignment: + return 0.0 + + return sum(positive_alignment) / len(alignment_scores) + + +def track_people(frames, max_distance=80.0, min_iou=0.1, max_missed_time=3.0): + """Associate detections across frames using IoU and centroid distance.""" + active_tracks = {} + track_histories = {} + frame_tracks = [] + next_track_id = 1 + + sorted_frames = sorted(frames or [], key=lambda frame: frame.get("frame_id", 0)) + + for frame in sorted_frames: + timestamp = float(frame.get("timestamp", 0.0)) + detections = frame.get("people_detections", []) + used_track_ids = set() + tracked_detections = [] + + for detection in detections: + bbox = detection.get("bbox", []) + if len(bbox) != 4: + continue + + centroid = _centroid(bbox) + best_match = None + best_score = None + + for track_id, track in active_tracks.items(): + if track_id in used_track_ids: + continue + + time_gap = max(timestamp - track["timestamp"], 0.0001) + if time_gap > max_missed_time: + continue + + centroid_distance = _distance(centroid, track["centroid"]) + iou = _bbox_iou(bbox, track["bbox"]) + + # Prefer stronger IoU matches; fall back to centroid distance when IoU is weak. + if iou >= min_iou: + score = (2.0 * iou) - (centroid_distance / max(max_distance, 1.0)) + elif centroid_distance <= max_distance: + score = 0.05 - (centroid_distance / max(max_distance, 1.0)) + else: + continue + + if best_score is None or score > best_score: + best_score = score + best_match = track_id + + if best_match is None: + track_id = next_track_id + next_track_id += 1 + speed = 0.0 + normalized_speed = 0.0 + direction = (0.0, 0.0) + history = [] + else: + previous = active_tracks[best_match] + delta_t = max(timestamp - previous["timestamp"], 0.0001) + dx = centroid[0] - previous["centroid"][0] + dy = centroid[1] - previous["centroid"][1] + pixel_distance = _distance(centroid, previous["centroid"]) + speed = pixel_distance / delta_t + _, bbox_height = _bbox_size(bbox) + previous_height = previous["bbox_height"] + avg_height = max((bbox_height + previous_height) / 2.0, 1.0) + normalized_speed = pixel_distance / avg_height + direction = (round(dx, 2), round(dy, 2)) + track_id = best_match + history = track_histories.get(track_id, []) + + bbox_width, bbox_height = _bbox_size(bbox) + track_entry = { + "track_id": track_id, + "bbox": bbox, + "centroid": [round(centroid[0], 2), round(centroid[1], 2)], + "speed": round(speed, 2), + "normalized_speed": round(normalized_speed, 4), + "direction": [direction[0], direction[1]], + "bbox_width": bbox_width, + "bbox_height": bbox_height, + "confidence": detection.get("confidence", 0.0), + } + tracked_detections.append(track_entry) + + history = history + [ + { + "frame_id": frame.get("frame_id"), + "timestamp": timestamp, + "centroid": track_entry["centroid"], + "speed": track_entry["speed"], + "normalized_speed": track_entry["normalized_speed"], + "bbox_height": bbox_height, + } + ] + + track_histories[track_id] = history + active_tracks[track_id] = { + "centroid": centroid, + "timestamp": timestamp, + "bbox_height": bbox_height, + "bbox": bbox, + } + used_track_ids.add(track_id) + + frame_tracks.append( + { + "frame_id": frame.get("frame_id"), + "timestamp": timestamp, + "frame_path": frame.get("frame_path"), + "people_annotated_frame_path": frame.get("people_annotated_frame_path"), + "tracked_detections": tracked_detections, + } + ) + + return frame_tracks, track_histories + + +def summarise_tracks( + track_histories, + stationary_motion_threshold=0.06, + walking_motion_threshold=0.12, + running_motion_threshold=0.9, + min_history_for_motion=3, +): + """Build tracking summary for anomaly/event logic.""" + track_summaries = [] + stationary_track_ids = [] + walking_track_ids = [] + running_track_ids = [] + + for track_id, history in track_histories.items(): + speeds = [entry["speed"] for entry in history] + normalized_speeds = [entry.get("normalized_speed", 0.0) for entry in history] + max_speed = max(speeds, default=0.0) + avg_speed = sum(speeds) / len(speeds) if speeds else 0.0 + max_normalized_speed = max(normalized_speeds, default=0.0) + avg_normalized_speed = sum(normalized_speeds) / len(normalized_speeds) if normalized_speeds else 0.0 + heights = [entry.get("bbox_height", 1.0) for entry in history] + avg_height_history = max(sum(heights) / max(len(heights), 1), 1.0) + height_variation = ( + max(abs(height - avg_height_history) for height in heights) / avg_height_history + if heights + else 0.0 + ) + first_centroid = history[0].get("centroid", [0.0, 0.0]) + last_centroid = history[-1].get("centroid", [0.0, 0.0]) + displacement = _distance(first_centroid, last_centroid) + avg_height = max( + sum(entry.get("bbox_height", 1.0) for entry in history) / max(len(history), 1), + 1.0, + ) + normalized_displacement = displacement / avg_height + history_length = len(history) + has_motion_history = history_length >= min_history_for_motion + moving_steps = sum(1 for speed in normalized_speeds if speed >= 0.05) + sustained_motion_steps = sum(1 for speed in normalized_speeds if speed >= 0.08) + direction_consistency = _direction_consistency(history) + is_running = ( + has_motion_history + and avg_normalized_speed >= 0.55 + and max_normalized_speed >= running_motion_threshold + and normalized_displacement >= 0.9 + ) + sustained_walking_motion = ( + history_length >= 6 + and avg_normalized_speed >= 0.06 + and max_normalized_speed >= 0.14 + and normalized_displacement >= 0.45 + and height_variation <= 0.5 + ) + clear_walking_motion = ( + avg_normalized_speed >= walking_motion_threshold + and max_normalized_speed >= 0.16 + and normalized_displacement >= 0.22 + and height_variation <= 0.45 + ) + is_walking = ( + has_motion_history + and not is_running + and (clear_walking_motion or sustained_walking_motion) + and moving_steps >= 3 + and sustained_motion_steps >= 2 + and direction_consistency >= 0.35 + and max_normalized_speed < running_motion_threshold + 0.55 + ) + is_stationary = ( + (not has_motion_history) + or ( + avg_normalized_speed <= stationary_motion_threshold + and max_normalized_speed <= 0.12 + and normalized_displacement <= 0.18 + ) + ) + + if is_running: + running_track_ids.append(track_id) + movement_state = "running" + elif is_walking: + walking_track_ids.append(track_id) + movement_state = "walking" + elif is_stationary: + stationary_track_ids.append(track_id) + movement_state = "stationary" + else: + movement_state = "stationary" + stationary_track_ids.append(track_id) + + track_summaries.append( + { + "track_id": track_id, + "history_length": history_length, + "avg_speed": round(avg_speed, 2), + "max_speed": round(max_speed, 2), + "avg_normalized_speed": round(avg_normalized_speed, 4), + "max_normalized_speed": round(max_normalized_speed, 4), + "normalized_displacement": round(normalized_displacement, 4), + "height_variation": round(height_variation, 4), + "direction_consistency": round(direction_consistency, 4), + "is_stationary": is_stationary, + "is_walking": is_walking, + "is_running": is_running, + "movement_state": movement_state, + } + ) + + return { + "track_count": len(track_summaries), + "stationary_track_count": len(stationary_track_ids), + "stationary_track_ids": stationary_track_ids, + "walking_track_count": len(walking_track_ids), + "walking_track_ids": walking_track_ids, + "running_track_count": len(running_track_ids), + "running_track_ids": running_track_ids, + "tracks": track_summaries, + } + + +def build_frame_activity_series(frame_tracks, tracking_summary): + """Return per-frame movement counts for stationary, walking, and running tracks.""" + walking_track_ids = set(tracking_summary.get("walking_track_ids", [])) + running_track_ids = set(tracking_summary.get("running_track_ids", [])) + stationary_track_ids = set(tracking_summary.get("stationary_track_ids", [])) + + activity_series = [] + for frame in frame_tracks: + walking_count = 0 + running_count = 0 + stationary_count = 0 + + for tracked in frame.get("tracked_detections", []): + track_id = tracked.get("track_id") + if track_id in running_track_ids: + running_count += 1 + elif track_id in walking_track_ids: + walking_count += 1 + elif track_id in stationary_track_ids: + stationary_count += 1 + + activity_series.append( + { + "frame_id": frame.get("frame_id"), + "timestamp": frame.get("timestamp", 0.0), + "walking_count": walking_count, + "running_count": running_count, + "stationary_count": stationary_count, + "active_count": walking_count + running_count, + "people_annotated_frame_path": frame.get("people_annotated_frame_path"), + } + ) + + return activity_series + + +def save_motion_annotations(frame_tracks, tracking_summary, video_id=None): + """Save annotated frames highlighting useful movement states for frontend visuals.""" + if not frame_tracks or not tracking_summary: + return [] + + safe_video_id = video_id or "unknown_video" + output_dir = OUTPUT_ROOT / safe_video_id + try: + if output_dir.exists(): + shutil.rmtree(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + except PermissionError: + output_dir = OUTPUT_ROOT / f"{safe_video_id}_artifacts" + if output_dir.exists(): + shutil.rmtree(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + artifact_paths = [] + stationary_track_ids = set(tracking_summary.get("stationary_track_ids", [])) + walking_track_ids = set(tracking_summary.get("walking_track_ids", [])) + running_track_ids = set(tracking_summary.get("running_track_ids", [])) + if not stationary_track_ids and not walking_track_ids and not running_track_ids: + return [] + + highlight_dynamic_only = bool(walking_track_ids or running_track_ids) + + for frame in frame_tracks: + source_path = frame.get("people_annotated_frame_path") + if not source_path: + continue + + resolved_path = Path(source_path) + if not resolved_path.is_absolute(): + resolved_path = PROJECT_ROOT / resolved_path + + image = cv2.imread(str(resolved_path)) + if image is None: + continue + + wrote_annotation = False + + for tracked in frame.get("tracked_detections", []): + if tracked["track_id"] in running_track_ids: + label = f"RUNNING T{tracked['track_id']}" + color = (0, 0, 255) + elif tracked["track_id"] in walking_track_ids: + label = f"WALKING T{tracked['track_id']}" + color = (0, 165, 255) + elif tracked["track_id"] in stationary_track_ids: + if highlight_dynamic_only: + continue + label = f"STATIONARY T{tracked['track_id']}" + color = (0, 255, 0) + else: + continue + + x1, y1, x2, y2 = tracked["bbox"] + cv2.rectangle(image, (x1, y1), (x2, y2), color, 2) + label_y = min(y2 + 22, image.shape[0] - 10) + cv2.putText( + image, + label, + (x1, label_y), + cv2.FONT_HERSHEY_SIMPLEX, + 0.5, + color, + 2, + ) + wrote_annotation = True + + if not wrote_annotation: + continue + + output_path = output_dir / f"motion_frame_{int(frame.get('frame_id', 0)):04d}.jpg" + if cv2.imwrite(str(output_path), image): + artifact_paths.append(str(output_path.relative_to(PROJECT_ROOT)).replace("\\", "/")) + + return artifact_paths diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/vision_analysis.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/vision_analysis.py new file mode 100644 index 000000000..a9c35ac5d --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/vision_analysis.py @@ -0,0 +1,96 @@ +"""Vision-analysis helpers for crowd behaviour analytics.""" + +from pathlib import Path + +import cv2 +import numpy as np + +PROJECT_ROOT = Path(__file__).resolve().parent.parent + + +def resolve_frame_paths(input_data): + """Collect annotated frame paths for behaviour analysis.""" + legacy_frame_paths = input_data.get("frame_paths", []) + if legacy_frame_paths: + return legacy_frame_paths + + frame_entries = input_data.get("frames", []) + resolved_paths = [] + for frame in frame_entries: + annotated_path = frame.get("people_annotated_frame_path") + if annotated_path: + resolved_paths.append(annotated_path) + return resolved_paths + + +def load_grayscale_frames(frame_paths): + """Load a limited sequence of grayscale frames for motion analysis.""" + if not frame_paths: + return [] + + loaded_frames = [] + + for path in frame_paths[:8]: + resolved_path = Path(path) + if not resolved_path.is_absolute(): + resolved_path = PROJECT_ROOT / resolved_path + + frame = cv2.imread(str(resolved_path), cv2.IMREAD_GRAYSCALE) + if frame is not None: + loaded_frames.append(frame) + + return loaded_frames + + +def extract_motion_features(frames): + """Estimate motion-related features from consecutive frames using optical flow.""" + if len(frames) < 2: + return { + "vision_enabled": False, + "avg_motion_magnitude": 0.0, + "peak_motion_magnitude": 0.0, + "reverse_flow_ratio": 0.0, + "motion_intensity": 0.0, + } + + magnitudes = [] + reverse_flow_ratios = [] + + for idx in range(len(frames) - 1): + prev_frame = frames[idx] + next_frame = frames[idx + 1] + + flow = cv2.calcOpticalFlowFarneback( + prev_frame, + next_frame, + None, + 0.5, + 3, + 15, + 3, + 5, + 1.2, + 0, + ) + + mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1], angleInDegrees=True) + magnitudes.append(float(np.mean(mag))) + + angle_bins = ((ang % 360) // 90).astype(int) + bin_counts = np.bincount(angle_bins.ravel(), minlength=4) + dominant_bin = int(np.argmax(bin_counts)) + opposite_bin = (dominant_bin + 2) % 4 + reverse_ratio = float(bin_counts[opposite_bin] / max(bin_counts.sum(), 1)) + reverse_flow_ratios.append(reverse_ratio) + + avg_motion = float(np.mean(magnitudes)) + peak_motion = float(np.max(magnitudes)) + reverse_ratio = float(np.mean(reverse_flow_ratios)) + + return { + "vision_enabled": True, + "avg_motion_magnitude": round(avg_motion, 4), + "peak_motion_magnitude": round(peak_motion, 4), + "reverse_flow_ratio": round(reverse_ratio, 4), + "motion_intensity": round((avg_motion + peak_motion) / 2, 4), + } diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/config.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/config.py index f8af00479..1ec3a61f1 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/config.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/config.py @@ -2,15 +2,17 @@ from pathlib import Path CURRENT_DIR=os.path.dirname(os.path.abspath(__file__)) -MODEL_NAME = os.path.join(CURRENT_DIR, "model.pt") # Model downloaded from https://huggingface.co/arnabdhar/YOLOv8-Face-Detection +MODEL_NAME = os.path.join(CURRENT_DIR, "face_model.pt") # Model downloaded from https://huggingface.co/arnabdhar/YOLOv8-Face-Detection +PEOPLE_MODEL_NAME = os.path.join(CURRENT_DIR, "yolov8n_crowdhuman.pt") +ANNOTATED_DIR = Path("crowd_detection_output") / "face_detection_results" PERSON_CLASS = None +PEOPLE_ANNOTATED_DIR = Path("crowd_detection_output") / "people_detection_results" - -DEFAULT_CONF = 0.45 -DEFAULT_IOU = 0.40 +DEFAULT_CONF = 0.35 +DEFAULT_IOU = 0.30 ALLOWED_IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"} -OUTPUT_DIR = Path("detection_output") \ No newline at end of file +OUTPUT_DIR = Path("detection_output") diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/model.pt b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/face_model.pt similarity index 100% rename from 26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/model.pt rename to 26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/face_model.pt diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/main.py index 399dff9f9..748ff1590 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/main.py @@ -3,15 +3,23 @@ import cv2 from ultralytics import YOLO +from pathlib import Path -from .config import DEFAULT_CONF, DEFAULT_IOU, MODEL_NAME +from .config import DEFAULT_CONF, DEFAULT_IOU, MODEL_NAME, PEOPLE_ANNOTATED_DIR, PEOPLE_MODEL_NAME, ANNOTATED_DIR +PROJECT_ROOT = Path(__file__).resolve().parent.parent +FACE_OUTPUT_DIR = ANNOTATED_DIR if ANNOTATED_DIR.is_absolute() else PROJECT_ROOT / ANNOTATED_DIR +PEOPLE_OUTPUT_DIR = PEOPLE_ANNOTATED_DIR if PEOPLE_ANNOTATED_DIR.is_absolute() else PROJECT_ROOT / PEOPLE_ANNOTATED_DIR -def load_model(): - print(f"[INFO] Loading model: {MODEL_NAME}") - model = YOLO(MODEL_NAME) - print("[INFO] Model ready โœ“\n") - return model +def load_models(): + print(f"[INFO] Loading face model: {MODEL_NAME}") + face_model = YOLO(MODEL_NAME) + + print(f"[INFO] Loading people model: {PEOPLE_MODEL_NAME}") + people_model = YOLO(PEOPLE_MODEL_NAME) + + print("[INFO] Models ready โœ“\n") + return face_model, people_model @@ -27,34 +35,114 @@ def detect_faces(model, frame, conf, iou): }) return detections +def detect_people(model, frame, conf, iou): + results = model(frame, conf=conf, iou=iou, verbose=False)[0] + detections = [] + + for box in results.boxes: + cls = int(box.cls[0]) + + # COCO class 0 = person + if cls != 0: + continue + + x1, y1, x2, y2 = map(int, box.xyxy[0].tolist()) + + detections.append({ + "bbox": [x1, y1, x2, y2], + "confidence": round(float(box.conf[0]), 4), + }) + + return detections + +def draw_people_boxes(frame, detections): + output = frame.copy() + + for d in detections: + x1, y1, x2, y2 = d["bbox"] + + # Blue boxes for people + cv2.rectangle(output, (x1, y1), (x2, y2), (255, 100, 0), 2) + + label = f"{d['confidence']:.2f}" + cv2.putText(output, label, (x1, y1 - 5), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, + (255, 100, 0), 1) + + cv2.putText(output, f"People: {len(detections)}", (10, 25),cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 100, 0), 2) + + return output + +def draw_boxes(frame, detections): + output = frame.copy() + + for d in detections: + x1, y1, x2, y2 = d["bbox"] + + # Draw bounding box around face + cv2.rectangle(output, (x1, y1), (x2, y2), (0, 200, 80), 2) + + # Draw confidence score above the box + label = f"{d['confidence']:.2f}" + cv2.putText(output, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 200, 80), 1) + + # Draw total face count in top left corner + cv2.putText(output, f"Faces: {len(detections)}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 220, 100), 2) + + return output def detect_crowd(processed_video: dict) -> dict: - - model = load_model() + face_model, people_model = load_models() all_results = [] + + # create output folder + FACE_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + PEOPLE_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) for frame_data in processed_video["frames"]: - - frame = cv2.imread(frame_data["frame_path"]) + frame_path = frame_data["frame_path"] + resolved_frame_path = Path(frame_path) + if not resolved_frame_path.is_absolute(): + resolved_frame_path = PROJECT_ROOT / resolved_frame_path + + frame = cv2.imread(str(resolved_frame_path)) if frame is None: print(f"[WARN] Could not read frame {frame_data['frame_id']} โ€” skipping") continue - detections = detect_faces(model, frame, DEFAULT_CONF, DEFAULT_IOU) + face_detections = detect_faces(face_model, frame, DEFAULT_CONF, DEFAULT_IOU) + people_detections = detect_people(people_model, frame, DEFAULT_CONF, DEFAULT_IOU) + + # save annotated frame + annotated = draw_boxes(frame, face_detections) + face_output_path = FACE_OUTPUT_DIR / f"frame_{frame_data['frame_id']:04d}.jpg" + cv2.imwrite(str(face_output_path), annotated) + + # save annotated frame for people + people_annotated = draw_people_boxes(frame, people_detections) + people_output_path = PEOPLE_OUTPUT_DIR / f"frame_{frame_data['frame_id']:04d}.jpg" + cv2.imwrite(str(people_output_path), people_annotated) + face_annotated_frame_path = str(face_output_path.relative_to(PROJECT_ROOT)).replace("\\", "/") + people_annotated_frame_path = str(people_output_path.relative_to(PROJECT_ROOT)).replace("\\", "/") all_results.append({ - "frame_id": frame_data["frame_id"], - "timestamp": frame_data["timestamp"], - "person_count": len(detections), - "detections": detections + "frame_id": frame_data["frame_id"], + "timestamp": frame_data["timestamp"], + "frame_path": frame_path, + "face_annotated_frame_path": face_annotated_frame_path, + "people_annotated_frame_path": people_annotated_frame_path, + "person_count": len(people_detections), + "face_count": len(face_detections), + "face_detections": face_detections, + "people_detections": people_detections, }) return { "video_id": processed_video["video_id"], - "frames": all_results + "frames": all_results, } - \ No newline at end of file + diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/yolov8n_crowdhuman.pt b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/yolov8n_crowdhuman.pt new file mode 100644 index 000000000..a30579ad4 Binary files /dev/null and b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/yolov8n_crowdhuman.pt differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/README.md new file mode 100644 index 000000000..21270c276 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/README.md @@ -0,0 +1,24 @@ +# Crowd Region Preprocessing + +## Objective + +Prepare extracted stadium frames so downstream crowd detection focuses on visible spectator regions instead of the playing field. + +## Approach + +- accept extracted frames from `video_processing` +- generate a field exclusion mask +- preserve only the crowd-visible region in a new frame copy +- return the same frame metadata structure expected by `crowd_detection` + +## Current Mask Strategy + +- use manual polygons when configured +- otherwise detect the green playing field in HSV space +- black out the field region before YOLO person detection runs + +## Output + +- focused frames saved in `output/focused_frames/` +- updated `frame_path` values for downstream services +- per-frame metadata for field and crowd visibility diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/main.py new file mode 100644 index 000000000..3595c118c --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_region_preprocessing/main.py @@ -0,0 +1,130 @@ +"""Prepare crowd-focused frames before crowd detection runs.""" + +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path + +import cv2 +import numpy as np + + +PROJECT_ROOT = Path(__file__).resolve().parent.parent +CONFIG_PATH = PROJECT_ROOT / "shared" / "config" / "crowd_region_preprocessing_config.json" + + +def load_config() -> dict: + with CONFIG_PATH.open("r", encoding="utf-8") as config_file: + return json.load(config_file) + + +def _resolve_frame_path(frame_path: str) -> Path: + candidate = Path(frame_path) + return candidate if candidate.is_absolute() else PROJECT_ROOT / candidate + + +def _build_polygon_mask(frame_shape: tuple[int, int, int], points: list[list[float]]) -> np.ndarray: + height, width = frame_shape[:2] + polygon = np.array( + [[int(point[0] * width), int(point[1] * height)] for point in points], + dtype=np.int32, + ) + mask = np.zeros((height, width), dtype=np.uint8) + cv2.fillPoly(mask, [polygon], 255) + return mask + + +def _detect_field_mask(frame: np.ndarray, config: dict) -> np.ndarray: + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + lower = np.array(config["green_hsv_lower"], dtype=np.uint8) + upper = np.array(config["green_hsv_upper"], dtype=np.uint8) + + mask = cv2.inRange(hsv, lower, upper) + kernel_size = max(1, int(config["morph_kernel_size"])) + kernel = np.ones((kernel_size, kernel_size), dtype=np.uint8) + mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) + + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + return np.zeros_like(mask) + + min_ratio = float(config["min_field_area_ratio"]) + min_area = frame.shape[0] * frame.shape[1] * min_ratio + + filtered = np.zeros_like(mask) + for contour in contours: + area = cv2.contourArea(contour) + if area >= min_area: + cv2.drawContours(filtered, [contour], -1, 255, thickness=cv2.FILLED) + + if not np.any(filtered): + return np.zeros_like(mask) + + dilation_size = max(1, int(config["field_mask_dilation_kernel"])) + dilation_kernel = np.ones((dilation_size, dilation_size), dtype=np.uint8) + return cv2.dilate(filtered, dilation_kernel, iterations=1) + + +def _prepare_frame(frame: np.ndarray, config: dict) -> tuple[np.ndarray, dict]: + field_polygon = config.get("field_polygon_normalized", []) + crowd_polygon = config.get("crowd_polygon_normalized", []) + + if field_polygon: + field_mask = _build_polygon_mask(frame.shape, field_polygon) + mask_source = "manual_field_polygon" + else: + field_mask = _detect_field_mask(frame, config) + mask_source = "auto_green_field_mask" + + if crowd_polygon: + crowd_mask = _build_polygon_mask(frame.shape, crowd_polygon) + mask_source = f"{mask_source}+manual_crowd_polygon" + elif np.any(field_mask): + crowd_mask = cv2.bitwise_not(field_mask) + else: + crowd_mask = np.full(frame.shape[:2], 255, dtype=np.uint8) + mask_source = "no_field_mask_detected" + + focused = cv2.bitwise_and(frame, frame, mask=crowd_mask) + field_ratio = round(float(np.count_nonzero(field_mask)) / float(field_mask.size), 4) if np.any(field_mask) else 0.0 + crowd_ratio = round(float(np.count_nonzero(crowd_mask)) / float(crowd_mask.size), 4) + + metadata = { + "mask_source": mask_source, + "field_visible_ratio": field_ratio, + "crowd_visible_ratio": crowd_ratio, + } + return focused, metadata + + +def prepare_crowd_frames(processed_video: dict) -> dict: + config = load_config() + output_dir = PROJECT_ROOT / config["focused_frames_dir"] + output_dir.mkdir(parents=True, exist_ok=True) + + focused_video = deepcopy(processed_video) + focused_frames = [] + + for frame_data in processed_video.get("frames", []): + source_path = _resolve_frame_path(frame_data["frame_path"]) + frame = cv2.imread(str(source_path)) + + if frame is None: + focused_frames.append(frame_data) + continue + + focused_frame, metadata = _prepare_frame(frame, config) + output_name = f"frame_{frame_data['frame_id']:04d}.jpg" + output_path = output_dir / output_name + cv2.imwrite(str(output_path), focused_frame) + + updated_frame = dict(frame_data) + updated_frame["source_frame_path"] = frame_data["frame_path"] + updated_frame["frame_path"] = str(output_path.relative_to(PROJECT_ROOT)).replace("\\", "/") + updated_frame["crowd_focus_metadata"] = metadata + focused_frames.append(updated_frame) + + focused_video["frames"] = focused_frames + return focused_video diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/density_zoning/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/density_zoning/main.py index bcc7e20ce..a1d81c9c7 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/density_zoning/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/density_zoning/main.py @@ -1,4 +1,4 @@ -"""Minimal entry point for the density and zoning task.""" + from __future__ import annotations @@ -7,25 +7,58 @@ from typing import Any -def get_zone_definitions(frame_width: int, frame_height: int) -> list[dict[str, Any]]: +def get_zone_definitions( + frame_width: int, + frame_height: int, + rows: int = 2, + cols: int = 2, +) -> list[dict[str, Any]]: """ - Create a simple 2x2 grid of zones. + Create a configurable grid of zones. - Zone layout: - A1 = top-left - A2 = top-right - B1 = bottom-left - B2 = bottom-right + Example for 2x2: + A1 A2 + B1 B2 """ - half_width = frame_width / 2 - half_height = frame_height / 2 + if frame_width <= 0 or frame_height <= 0: + raise ValueError("Frame width and height must be positive integers.") + if rows <= 0 or cols <= 0: + raise ValueError("Rows and cols must be positive integers.") + + zone_width = frame_width / cols + zone_height = frame_height / rows + zones: list[dict[str, Any]] = [] + + for row in range(rows): + for col in range(cols): + row_label = chr(ord("A") + row) + zone_id = f"{row_label}{col + 1}" + + x_min = col * zone_width + y_min = row * zone_height + x_max = (col + 1) * zone_width + y_max = (row + 1) * zone_height + + zones.append( + { + "zone_id": zone_id, + "x_min": x_min, + "y_min": y_min, + "x_max": x_max, + "y_max": y_max, + } + ) + + return zones + + +def is_valid_bbox(bbox: list[float] | None) -> bool: + """Check whether a bounding box is valid.""" + if bbox is None or len(bbox) != 4: + return False - return [ - {"zone_id": "A1", "x_min": 0, "y_min": 0, "x_max": half_width, "y_max": half_height}, - {"zone_id": "A2", "x_min": half_width, "y_min": 0, "x_max": frame_width, "y_max": half_height}, - {"zone_id": "B1", "x_min": 0, "y_min": half_height, "x_max": half_width, "y_max": frame_height}, - {"zone_id": "B2", "x_min": half_width, "y_min": half_height, "x_max": frame_width, "y_max": frame_height}, - ] + x1, y1, x2, y2 = bbox + return x2 > x1 and y2 > y1 def bbox_center(bbox: list[float]) -> tuple[float, float]: @@ -34,8 +67,8 @@ def bbox_center(bbox: list[float]) -> tuple[float, float]: bbox format: [x1, y1, x2, y2] """ - if len(bbox) != 4: - raise ValueError("Bounding box must contain exactly 4 values: [x1, y1, x2, y2].") + if not is_valid_bbox(bbox): + raise ValueError("Invalid bounding box. Expected [x1, y1, x2, y2] with x2 > x1 and y2 > y1.") x1, y1, x2, y2 = bbox center_x = (x1 + x2) / 2 @@ -43,14 +76,33 @@ def bbox_center(bbox: list[float]) -> tuple[float, float]: return center_x, center_y -def find_zone(center_x: float, center_y: float, zones: list[dict[str, Any]]) -> str | None: +def clamp_point(x: float, y: float, frame_width: int, frame_height: int) -> tuple[float, float]: + """ + Clamp a point so it stays inside the frame. + Helps handle edge cases near frame boundaries. + """ + x = min(max(x, 0), frame_width - 1e-6) + y = min(max(y, 0), frame_height - 1e-6) + return x, y + + +def find_zone( + center_x: float, + center_y: float, + zones: list[dict[str, Any]], + frame_width: int, + frame_height: int, +) -> str | None: """Return the zone_id for a center point.""" + center_x, center_y = clamp_point(center_x, center_y, frame_width, frame_height) + for zone in zones: if ( zone["x_min"] <= center_x < zone["x_max"] and zone["y_min"] <= center_y < zone["y_max"] ): return zone["zone_id"] + return None @@ -70,35 +122,50 @@ def normalize_counts(zone_counts: dict[str, int]) -> dict[str, float]: } +def classify_density(density: float) -> str: + """Convert normalized density into a label.""" + if density == 0: + return "Low" + if density < 0.67: + return "Medium" + return "High" + + def analyze_density(input_data: dict[str, Any]) -> dict[str, Any]: """Calculate zone counts and density values from detection results.""" video_id = input_data.get("video_id", "unknown_video") frames = input_data.get("frames", []) - # Configurable frame size for simple first version frame_width = input_data.get("frame_width", 500) frame_height = input_data.get("frame_height", 500) - zones = get_zone_definitions(frame_width, frame_height) + grid_rows = input_data.get("grid_rows", 2) + grid_cols = input_data.get("grid_cols", 2) + confidence_threshold = input_data.get("confidence_threshold", 0.50) + + zones = get_zone_definitions(frame_width, frame_height, grid_rows, grid_cols) - # Initialize counts for all zones zone_counts = {zone["zone_id"]: 0 for zone in zones} + skipped_invalid_bbox = 0 + skipped_low_confidence = 0 - # Process detections frame by frame for frame in frames: - detections = frame.get("detections", []) + detections = frame.get("people_detections", []) for detection in detections: bbox = detection.get("bbox") - if not bbox: + confidence = detection.get("confidence", 1.0) + + if confidence < confidence_threshold: + skipped_low_confidence += 1 continue - try: - center_x, center_y = bbox_center(bbox) - except ValueError: + if not is_valid_bbox(bbox): + skipped_invalid_bbox += 1 continue - zone_id = find_zone(center_x, center_y, zones) + center_x, center_y = bbox_center(bbox) + zone_id = find_zone(center_x, center_y, zones, frame_width, frame_height) if zone_id is not None: zone_counts[zone_id] += 1 @@ -107,11 +174,22 @@ def analyze_density(input_data: dict[str, Any]) -> dict[str, Any]: return { "video_id": video_id, + "frame_width": frame_width, + "frame_height": frame_height, + "grid_rows": grid_rows, + "grid_cols": grid_cols, + "confidence_threshold": confidence_threshold, + "summary": { + "total_frames": len(frames), + "skipped_invalid_bbox": skipped_invalid_bbox, + "skipped_low_confidence": skipped_low_confidence, + }, "zones": [ { "zone_id": zone["zone_id"], "person_count": zone_counts[zone["zone_id"]], "density": densities[zone["zone_id"]], + "density_level": classify_density(densities[zone["zone_id"]]), } for zone in zones ], @@ -119,21 +197,24 @@ def analyze_density(input_data: dict[str, Any]) -> dict[str, Any]: if __name__ == "__main__": - sample_input = { - "video_id": "match_01", - "frame_width": 500, - "frame_height": 500, + "video_id": "crowd_video_test", + "frame_width": 1280, + "frame_height": 720, + "grid_rows": 2, + "grid_cols": 2, + "confidence_threshold": 0.50, "frames": [ { "frame_id": 1, - "timestamp": 0.04, - "person_count": 4, + "timestamp": 0.03, "detections": [ - {"bbox": [80, 80, 120, 120], "confidence": 0.95}, # A1 - {"bbox": [140, 100, 180, 140], "confidence": 0.92}, # A1 - {"bbox": [280, 150, 320, 190], "confidence": 0.90}, # A2 - {"bbox": [400, 350, 440, 390], "confidence": 0.88}, # B2 + {"bbox": [100, 200, 180, 350], "confidence": 0.92}, + {"bbox": [300, 220, 380, 370], "confidence": 0.89}, + {"bbox": [700, 250, 780, 400], "confidence": 0.95}, + {"bbox": [900, 300, 980, 450], "confidence": 0.87}, + {"bbox": [600, 200, 600, 260], "confidence": 0.91}, + {"bbox": [500, 300, 560, 390], "confidence": 0.20}, ], } ], @@ -142,10 +223,10 @@ def analyze_density(input_data: dict[str, Any]) -> dict[str, Any]: result = analyze_density(sample_input) os.makedirs("output", exist_ok=True) - output_path = os.path.join("output", "density_summary.json") + output_path = os.path.join("output", "density_summary_sprint2.json") with open(output_path, "w", encoding="utf-8") as file: json.dump(result, file, indent=2) print(json.dumps(result, indent=2)) - print(f"\nSaved output to: {output_path}") \ No newline at end of file + print(f"\nSaved output to: {output_path}") diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/generate_average_heatmap_data.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/generate_average_heatmap_data.py new file mode 100644 index 000000000..9ac6eebe2 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/generate_average_heatmap_data.py @@ -0,0 +1,131 @@ +import json +import math +import random +from typing import Dict, List + + +def zone_name(row_index: int, col_index: int) -> str: + """Convert row/col index into zone names like A1, B3, AA10.""" + letters = "" + n = row_index + while True: + letters = chr(ord("A") + (n % 26)) + letters + n = n // 26 - 1 + if n < 0: + break + return f"{letters}{col_index + 1}" + + +def clamp(value: float, low: float = 0.0, high: float = 1.0) -> float: + return max(low, min(high, value)) + + +def gaussian_hotspot(row: int, col: int, center_row: float, center_col: float, spread: float, amplitude: float) -> float: + """Create a hotspot effect for density.""" + distance_sq = (row - center_row) ** 2 + (col - center_col) ** 2 + return amplitude * math.exp(-distance_sq / (2 * spread ** 2)) + + +def generate_average_heatmap_data( + video_id: str = "match_avg_01", + rows: int = 10, + cols: int = 10, + num_frames: int = 30, + base_density: float = 0.10, + noise_level: float = 0.05, + hotspots: List[Dict] = None, + seed: int = 42 +) -> Dict: + """ + Generate realistic zone data where person_count is the average count per zone across frames. + """ + random.seed(seed) + + if hotspots is None: + hotspots = [ + { + "center_row": rows / 2, + "center_col": cols / 2, + "spread": max(rows, cols) / 5, + "amplitude": 0.75 + } + ] + + zones = [] + + for r in range(rows): + for c in range(cols): + base_zone_density = base_density + + for hotspot in hotspots: + base_zone_density += gaussian_hotspot( + r, + c, + hotspot["center_row"], + hotspot["center_col"], + hotspot["spread"], + hotspot["amplitude"] + ) + + base_zone_density = clamp(base_zone_density) + + frame_counts = [] + for _ in range(num_frames): + noisy_density = clamp(base_zone_density + random.uniform(-noise_level, noise_level)) + frame_count = max(0, round(noisy_density * 20 + random.uniform(-2, 2))) + frame_counts.append(frame_count) + + avg_count = round(sum(frame_counts) / len(frame_counts)) + final_density = round(sum(frame_counts) / (len(frame_counts) * 20), 2) + final_density = clamp(final_density) + + zones.append( + { + "zone_id": zone_name(r, c), + "person_count": avg_count, + "density": round(final_density, 2) + } + ) + + return { + "video_id": video_id, + "zones": zones + } + + +def save_json(data: Dict, filename: str) -> None: + with open(filename, "w", encoding="utf-8") as f: + json.dump(data, f, indent=2) + print(f"Saved: {filename}") + + +if __name__ == "__main__": + data_10x10 = generate_average_heatmap_data( + video_id="match_avg_10x10", + rows=10, + cols=10, + num_frames=40, + base_density=0.08, + noise_level=0.04, + hotspots=[ + {"center_row": 4, "center_col": 4, "spread": 2.0, "amplitude": 0.65}, + {"center_row": 7, "center_col": 7, "spread": 2.5, "amplitude": 0.55}, + ], + seed=42 + ) + save_json(data_10x10, "heatmap_avg_10x10.json") + + data_20x20 = generate_average_heatmap_data( + video_id="match_avg_20x20", + rows=20, + cols=20, + num_frames=50, + base_density=0.05, + noise_level=0.03, + hotspots=[ + {"center_row": 6, "center_col": 8, "spread": 3.5, "amplitude": 0.60}, + {"center_row": 14, "center_col": 12, "spread": 4.0, "amplitude": 0.75}, + ], + seed=123 + ) + save_json(data_20x20, "heatmap_avg_20x20.json") \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/main.py index 3fa64d509..58e9ba8fb 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/main.py @@ -1,132 +1,289 @@ -"""Minimal entry point for the heatmap task.""" +"""Heatmap task implementation with validation and stadium-style visualization.""" import json +import math import os -from typing import Dict, List +from typing import Dict, List, Tuple +import matplotlib + +matplotlib.use("Agg") import matplotlib.pyplot as plt +import matplotlib.patches as patches import numpy as np def validate_input(input_data: Dict) -> None: - """Validate incoming heatmap input data.""" + """Validate the input JSON structure.""" if not isinstance(input_data, dict): - raise ValueError("Input data must be a dictionary.") + raise ValueError("Input must be a dictionary.") - if "video_id" not in input_data or not input_data["video_id"]: + video_id = input_data.get("video_id") + if not video_id or not isinstance(video_id, str): raise ValueError("Missing or empty 'video_id'.") - if "zones" not in input_data: - raise ValueError("Missing 'zones' field.") + zones = input_data.get("zones") + if not isinstance(zones, list) or len(zones) == 0: + raise ValueError("Missing or empty 'zones' list.") - if not isinstance(input_data["zones"], list): - raise ValueError("'zones' must be a list.") + for zone in zones: + if not isinstance(zone, dict): + raise ValueError("Each zone must be a dictionary.") - if len(input_data["zones"]) == 0: - raise ValueError("'zones' list cannot be empty.") + zone_id = zone.get("zone_id") + if not zone_id or not isinstance(zone_id, str): + raise ValueError(f"Each zone must have a valid 'zone_id'.") - required_zone_fields = {"zone_id", "person_count", "density"} + person_count = zone.get("person_count") + if not isinstance(person_count, (int, float)): + raise ValueError(f"Person count for zone '{zone_id}' must be numeric.") - for index, zone in enumerate(input_data["zones"]): - if not isinstance(zone, dict): - raise ValueError(f"Zone at index {index} must be a dictionary.") + density = zone.get("density") + if not isinstance(density, (int, float)): + raise ValueError(f"Density for zone '{zone_id}' must be numeric.") + + +def zone_name(row_index: int, col_index: int) -> str: + """Convert row/col index into zone names like A1, B3, AA10.""" + letters = "" + n = row_index + while True: + letters = chr(ord("A") + (n % 26)) + letters + n = n // 26 - 1 + if n < 0: + break + return f"{letters}{col_index + 1}" + + +def build_video_style_input(video_id: str = "match_02") -> Dict: + """ + Create a higher-grid stadium-style layout that roughly matches the shared frame. + """ + rows, cols = 8, 12 + zones = [] + + for r in range(rows): + for c in range(cols): + density = 0.06 + + if r <= 1: + density += 0.02 + 0.01 * c / cols - missing_fields = required_zone_fields - zone.keys() - if missing_fields: - raise ValueError( - f"Zone at index {index} is missing fields: {', '.join(sorted(missing_fields))}" + if 2 <= r <= 4: + density += 0.10 + 0.08 * (c / cols) + + if r >= 5: + density += 0.14 + 0.10 * (c / cols) + + hotspot1 = math.exp(-(((r - 5.6) ** 2) / 2.8 + ((c - 7.0) ** 2) / 5.0)) + density += 0.42 * hotspot1 + + hotspot2 = math.exp(-(((r - 3.8) ** 2) / 2.0 + ((c - 3.2) ** 2) / 3.5)) + density += 0.18 * hotspot2 + + density = max(0.0, min(1.0, density)) + person_count = int(round(density * 20)) + + zones.append( + { + "zone_id": zone_name(r, c), + "person_count": person_count, + "density": round(density, 2), + } ) + return {"video_id": video_id, "zones": zones} + + +def compute_grid_shape(num_zones: int) -> Tuple[int, int]: + cols = max(8, int(math.ceil(math.sqrt(num_zones * 1.6)))) + rows = int(math.ceil(num_zones / cols)) + return rows, cols + def generate_heatmap(input_data: Dict) -> Dict: - """Generate a validated and schema-compliant heatmap image from zone density data.""" + """Generate a stadium-style heatmap image from zone density data.""" validate_input(input_data) - video_id = str(input_data["video_id"]) + video_id = input_data["video_id"] zones: List[Dict] = input_data["zones"] output_dir = "output" os.makedirs(output_dir, exist_ok=True) num_zones = len(zones) - cols = int(np.ceil(np.sqrt(num_zones))) - rows = int(np.ceil(num_zones / cols)) - - heatmap_array = np.full((rows, cols), np.nan) - labels = [["" for _ in range(cols)] for _ in range(rows)] + rows, cols = compute_grid_shape(num_zones) + + fig = plt.figure(figsize=(14, 10), facecolor="#0b1220") + ax = plt.axes([0.03, 0.10, 0.94, 0.80]) + ax.set_facecolor("#071224") + ax.set_xlim(-1.15, 1.15) + ax.set_ylim(-0.92, 0.92) + ax.axis("off") + + outer = patches.Ellipse( + (0, 0), + width=1.85, + height=1.42, + facecolor="#0a1b34", + edgecolor="#183b69", + linewidth=2.5, + zorder=1, + ) + ax.add_patch(outer) + + pitch = patches.FancyBboxPatch( + (-0.42, -0.23), + 0.84, + 0.46, + boxstyle="round,pad=0.01,rounding_size=0.02", + facecolor="#1f8f43", + edgecolor="#2c3f66", + linewidth=6, + zorder=5, + ) + ax.add_patch(pitch) + + ax.plot([0, 0], [-0.21, 0.21], color="white", alpha=0.55, lw=1.6, zorder=6) + center_circle = patches.Circle((0, 0), 0.07, fill=False, ec="white", alpha=0.5, lw=1.3, zorder=6) + ax.add_patch(center_circle) + ax.plot(0, 0, "wo", ms=4, alpha=0.5, zorder=6) + + for x0, sign in [(-0.42, 1), (0.33, -1)]: + ax.add_patch( + patches.Rectangle( + (x0, -0.09), + 0.09, + 0.18, + fill=False, + ec="white", + alpha=0.45, + lw=1.2, + zorder=6, + ) + ) + ax.add_patch( + patches.Rectangle( + (x0, -0.14), + 0.16 * sign, + 0.28, + fill=False, + ec="white", + alpha=0.45, + lw=1.2, + zorder=6, + ) + ) - for index, zone in enumerate(zones): - row = index // cols - col = index % cols + ax.text(0, 0.80, "NORTH STAND", ha="center", va="center", fontsize=24, color="#7f94bd", alpha=0.95) + ax.text(0, -0.82, "SOUTH STAND", ha="center", va="center", fontsize=24, color="#7f94bd", alpha=0.95) + ax.text(-0.98, 0.00, "WEST", ha="center", va="center", fontsize=20, color="#7f94bd", alpha=0.95) + ax.text(0.98, 0.00, "EAST", ha="center", va="center", fontsize=20, color="#7f94bd", alpha=0.95) - zone_id = str(zone["zone_id"]) + a_outer, b_outer = 0.92, 0.70 + a_inner, b_inner = 0.50, 0.33 + cmap = plt.cm.turbo - try: - density = float(zone["density"]) - except (TypeError, ValueError): - raise ValueError(f"Density for zone '{zone_id}' must be numeric.") + for i, zone in enumerate(zones): + r_idx = i // cols + c_idx = i % cols + density = float(zone["density"]) density = max(0.0, min(1.0, density)) - try: - person_count = int(zone["person_count"]) - except (TypeError, ValueError): - raise ValueError(f"Person count for zone '{zone_id}' must be an integer.") + t_r0 = r_idx / rows + t_r1 = (r_idx + 1) / rows - heatmap_array[row, col] = density - labels[row][col] = ( - f"{zone_id}\n" - f"Count: {person_count}\n" - f"Density: {density:.2f}" - ) + theta0 = math.pi - (c_idx / cols) * 2 * math.pi + theta1 = math.pi - ((c_idx + 1) / cols) * 2 * math.pi + + n_theta = 18 + n_rad = 3 + + xs = [] + ys = [] - fig, ax = plt.subplots(figsize=(8, 6)) + for rr in np.linspace(t_r0, t_r1, n_rad): + a = a_inner + rr * (a_outer - a_inner) + b = b_inner + rr * (b_outer - b_inner) - cmap = plt.cm.YlOrRd.copy() - cmap.set_bad(color="lightgrey") + for th in np.linspace(theta0, theta1, n_theta): + x = a * math.cos(th) + y = b * math.sin(th) - im = ax.imshow( - heatmap_array, - cmap=cmap, - interpolation="nearest", - vmin=0, - vmax=1, + if abs(x) < 0.47 and abs(y) < 0.26: + continue + + xs.append(x) + ys.append(y) + + if xs: + ax.scatter( + xs, + ys, + s=420, + c=[density] * len(xs), + cmap=cmap, + vmin=0, + vmax=1, + alpha=0.62, + linewidths=0, + zorder=2, + ) + + for rr in np.linspace(0.18, 1.0, 5): + a = a_inner + rr * (a_outer - a_inner) + b = b_inner + rr * (b_outer - b_inner) + t = np.linspace(0, 2 * np.pi, 400) + x = a * np.cos(t) + y = b * np.sin(t) + mask = ~((np.abs(x) < 0.47) & (np.abs(y) < 0.26)) + ax.plot(x[mask], y[mask], color="#2c4b76", lw=1.0, alpha=0.7, zorder=3) + + for cc in range(cols): + th = math.pi - (cc / cols) * 2 * math.pi + x1 = a_inner * math.cos(th) + y1 = b_inner * math.sin(th) + x2 = a_outer * math.cos(th) + y2 = b_outer * math.sin(th) + if not (abs(x1) < 0.47 and abs(y1) < 0.26): + ax.plot([x1, x2], [y1, y2], color="#29476f", lw=0.9, alpha=0.7, zorder=3) + + ax.text( + 0, + 0.96, + f"CROWD HEATMAP โ€ข {video_id.upper()}", + ha="center", + va="top", + fontsize=30, + color="#f8fafc", + fontweight="bold", + zorder=10, ) - for row in range(rows): - for col in range(cols): - if labels[row][col]: - cell_value = heatmap_array[row, col] - - if np.isnan(cell_value): - text_color = "black" - else: - text_color = "black" if cell_value <= 0.35 or cell_value >= 0.65 else "white" - - ax.text( - col, - row, - labels[row][col], - ha="center", - va="center", - color=text_color, - fontsize=10, - fontweight="bold", - ) - - ax.set_title(f"Heatmap for {video_id}", fontsize=16, fontweight="bold") - ax.set_xticks(np.arange(-0.5, cols, 1), minor=True) - ax.set_yticks(np.arange(-0.5, rows, 1), minor=True) - ax.grid(which="minor", color="black", linestyle="-", linewidth=1.5) - ax.tick_params(which="both", bottom=False, left=False, labelbottom=False, labelleft=False) - - cbar = plt.colorbar(im, ax=ax) - cbar.set_label("Density", fontsize=12) + fig.text( + 0.055, + 0.935, + "Analytics View โ€ข Redback Orion Crowd Monitoring", + color="#8aa0c8", + fontsize=11, + ha="left", + ) + + gradient = np.linspace(0, 1, 256).reshape(1, -1) + cax = fig.add_axes([0.10, 0.04, 0.80, 0.02]) + cax.imshow(gradient, aspect="auto", cmap=cmap, extent=[0, 1, 0, 1]) + cax.set_xticks([]) + cax.set_yticks([]) + for spine in cax.spines.values(): + spine.set_visible(False) + + fig.text(0.055, 0.05, "Low", color="#f8fafc", fontsize=20, va="center") + fig.text(0.915, 0.05, "High", color="#f8fafc", fontsize=20, va="center") image_path = os.path.join(output_dir, f"heatmap_{video_id}.png") - plt.tight_layout() - plt.savefig(image_path, dpi=200, bbox_inches="tight") - plt.close() + plt.savefig(image_path, dpi=240, bbox_inches="tight", facecolor=fig.get_facecolor()) + plt.close(fig) return { "video_id": video_id, @@ -137,15 +294,6 @@ def generate_heatmap(input_data: Dict) -> Dict: if __name__ == "__main__": - sample_input = { - "video_id": "match_02", - "zones": [ - {"zone_id": "A1", "person_count": 2, "density": 0.10}, - {"zone_id": "A2", "person_count": 6, "density": 0.55}, - {"zone_id": "A3", "person_count": 12, "density": 0.95}, - {"zone_id": "A4", "person_count": 4, "density": 0.30}, - ], - } - - result = generate_heatmap(sample_input) + video_based_input = build_video_style_input("match_02") + result = generate_heatmap(video_based_input) print(json.dumps(result, indent=2)) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/crowd_region_preprocessing_config.json b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/crowd_region_preprocessing_config.json new file mode 100644 index 000000000..cbcabc8c5 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/crowd_region_preprocessing_config.json @@ -0,0 +1,10 @@ +{ + "focused_frames_dir": "crowd_region_preprocessing/output/focused_frames", + "green_hsv_lower": [25, 30, 30], + "green_hsv_upper": [95, 255, 255], + "morph_kernel_size": 9, + "field_mask_dilation_kernel": 21, + "min_field_area_ratio": 0.01, + "field_polygon_normalized": [], + "crowd_polygon_normalized": [] +} diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/video_processing_config.json b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/video_processing_config.json index 240526069..c769302bb 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/video_processing_config.json +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/config/video_processing_config.json @@ -1,5 +1,5 @@ { - "sample_rate": 30, + "sample_rate": 5, "output_resolution": [640, 640], "extracted_frames_dir": "video_processing/data/extracted_frames" -} \ No newline at end of file +} diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/crowd_pipeline_schema.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/crowd_pipeline_schema.md new file mode 100644 index 000000000..28cea2f5a --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/crowd_pipeline_schema.md @@ -0,0 +1,217 @@ +# Crowd Pipeline Service Schema + +## Endpoint + +`POST /process-crowd-detection` + +## Purpose + +This frontend-facing service runs the full crowd monitoring flow in one request. + +It combines: + +- video processing +- crowd detection +- density zoning +- heatmap generation +- crowd behaviour analytics +- crowd allocation risk zone + +The frontend should use this endpoint instead of calling the individual module endpoints. + +## Input JSON + +```json +{ + "video_id": "match_01", + "video_path": "data/raw/match_01.mp4" +} +``` + +## Input Fields + +- `video_id` - string - unique identifier for the video +- `video_path` - string - path to the source video file + +## Output JSON + +```json +{ + "video_id": "match_01", + "crowd_detection": { + "video_id": "match_01", + "frames": [ + { + "frame_id": 1, + "timestamp": 0.04, + "frame_path": "video_processing/data/extracted_frames/frame_0001.jpg", + "annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", + "face_annotated_frame_path": "crowd_detection_output/face_detection_results/frame_0001.jpg", + "people_annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", + "person_count": 2, + "face_count": 1, + "face_detections": [ + { + "bbox": [120, 60, 155, 100], + "confidence": 0.91 + } + ], + "people_detections": [ + { + "bbox": [100, 50, 160, 180], + "confidence": 0.93 + } + ] + } + ] + }, + "density_zoning": [ + { + "zone_id": "A1", + "person_count": 8, + "density": 0.72 + } + ], + "heatmap": { + "image_path": "output/heatmap_match_01.png" + }, + "crowd_behaviour_analytics": { + "video_id": "match_01", + "crowd_state": "dispersing", + "zones": [ + { + "zone_id": "A1", + "person_count": 8, + "density": 0.72 + } + ], + "event_flags": [ + "walking_detection", + "stationary_detection" + ], + "artifact_paths": [ + "output/heatmap_match_01.png", + "crowd_behaviour_analytics/output/match_01/motion_frame_0001.jpg" + ], + "vision_metrics": { + "vision_enabled": true, + "avg_motion_magnitude": 0.4599, + "peak_motion_magnitude": 0.5651, + "reverse_flow_ratio": 0.0883, + "motion_intensity": 0.5125, + "tracking": { + "track_count": 3, + "stationary_track_count": 1, + "stationary_track_ids": [1], + "walking_track_count": 1, + "walking_track_ids": [2], + "running_track_count": 1, + "running_track_ids": [3], + "tracks": [ + { + "track_id": 2, + "history_length": 23, + "avg_speed": 12.81, + "max_speed": 32.17, + "avg_normalized_speed": 0.064, + "max_normalized_speed": 0.1429, + "normalized_displacement": 0.8137, + "height_variation": 0.3806, + "is_stationary": false, + "is_walking": true, + "is_running": false, + "movement_state": "walking" + } + ] + }, + "anomaly_model": { + "model_enabled": true, + "anomaly_track_ids": [3], + "running_track_ids": [3], + "anomaly_count": 1, + "track_scores": [ + { + "track_id": 3, + "history_length": 18, + "avg_speed": 19.1, + "avg_normalized_speed": 0.2079, + "max_normalized_speed": 1.6892, + "normalized_displacement": 0.1438, + "anomaly_score": 0.0158, + "is_anomaly": true + } + ] + } + } + }, + "crowd_allocation_risk_zone": { + "video_id": "match_01", + "zones": [ + { + "zone_id": "A1", + "risk_level": "very_low", + "flagged": false + } + ], + "recommendations": [ + "All zones within safe thresholds - continue monitoring" + ] + } +} +``` + +## Top-Level Output Fields + +- `video_id` - string - same video identifier from the request +- `crowd_detection` - object - people and face detection output for each processed frame +- `density_zoning` - list - zone-level person counts and density values +- `heatmap` - object - generated heatmap image path +- `crowd_behaviour_analytics` - object - crowd state, movement analytics, event flags, and artifact paths +- `crowd_allocation_risk_zone` - object - zone risk levels and recommendations + +## Crowd Detection Fields + +- `frames` - list - processed frame results +- `frame_id` - integer - frame number +- `timestamp` - number - time in seconds for the frame +- `frame_path` - string or null - extracted frame image path +- `people_annotated_frame_path` - string or null - people detection annotated image path used by the demo peak-frame preview +- `face_annotated_frame_path` - string or null - face detection annotated image path +- `person_count` - integer - number of detected people +- `face_count` - integer or null - number of detected faces +- `face_detections` - list - detected face bounding boxes +- `people_detections` - list - detected person bounding boxes +- `bbox` - list of 4 integers - bounding box as `[x1, y1, x2, y2]` +- `confidence` - number - detection confidence score + +## Density And Heatmap Fields + +- `density_zoning` - list - density result per zone +- `zone_id` - string - zone identifier such as `A1`, `A2`, `B1`, `B2` +- `person_count` - integer - people counted in the zone +- `density` - number - calculated density value for the zone +- `heatmap.image_path` - string - saved heatmap image path + +## Behaviour Analytics Fields + +- `crowd_state` - string - high-level crowd state such as `stable`, `dispersing`, or `increasing_density` +- `zones` - list - zone density data used for behaviour analysis +- `event_flags` - list - detected event labels such as `walking_detection`, `stationary_detection`, or `motion_anomaly` +- `artifact_paths` - list - generated image artifacts, including heatmap and motion annotated frames +- `vision_metrics` - object or null - motion and tracking metrics when annotated frames are available +- `tracking` - object - track counts and per-track movement state +- `anomaly_model` - object - anomaly scores and anomaly track identifiers + +## Risk Zone Fields + +- `zones` - list - risk assessment per zone +- `risk_level` - string - zone risk label such as `very_low`, `low`, `medium`, or `high` +- `flagged` - boolean - whether the zone needs attention +- `recommendations` - list - operational recommendations based on risk + +## Notes + +- This schema is the frontend contract for the combined route. +- The individual service schemas remain useful for testing each module separately. +- `crowd_allocation_risk_zone` is generated from the behaviour analytics result. +- `artifact_paths` includes motion frame images only when behaviour analytics receives valid annotated frame paths. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/detection_schema.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/detection_schema.md index f8bea7a50..7249c676c 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/detection_schema.md +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/detection_schema.md @@ -6,7 +6,7 @@ ## Purpose -This service receives a video reference, runs video processing and crowd detection, and returns detection results per frame. +This service receives a video reference, runs video processing and crowd detection, and returns face and people detection results per processed frame. ## Input JSON @@ -31,8 +31,19 @@ This service receives a video reference, runs video processing and crowd detecti { "frame_id": 1, "timestamp": 0.04, + "frame_path": "video_processing/data/extracted_frames/frame_0001.jpg", + "annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", + "face_annotated_frame_path": "crowd_detection_output/face_detection_results/frame_0001.jpg", + "people_annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", "person_count": 2, - "detections": [ + "face_count": 1, + "face_detections": [ + { + "bbox": [110, 60, 145, 100], + "confidence": 0.88 + } + ], + "people_detections": [ { "bbox": [100, 50, 160, 180], "confidence": 0.93 @@ -53,13 +64,19 @@ This service receives a video reference, runs video processing and crowd detecti - `frames` - list - detection result for each processed frame - `frame_id` - integer - frame number - `timestamp` - number - time in seconds for the frame +- `frame_path` - string - original extracted frame path from video processing +- `annotated_frame_path` - string - default annotated frame path for downstream use; currently same as `people_annotated_frame_path` +- `face_annotated_frame_path` - string - saved frame with face boxes +- `people_annotated_frame_path` - string - saved frame with people boxes - `person_count` - integer - number of detected people in the frame -- `detections` - list - detected people in the frame +- `face_count` - integer - number of detected faces in the frame +- `face_detections` - list - detected faces in the frame +- `people_detections` - list - detected people in the frame - `bbox` - list of 4 integers - bounding box as `[x1, y1, x2, y2]` - `confidence` - number - model confidence score ## Notes -- keep field names stable -- use the same `video_id` through all services +- use `people_detections` for explicit people-detection output +- use `face_detections` for explicit face-detection output - `frames` from this output become the input for the analytics service diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/intelligence_schema.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/intelligence_schema.md index b7347e272..bd77a06b9 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/intelligence_schema.md +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/schemas/intelligence_schema.md @@ -27,7 +27,33 @@ This service receives analytics output, analyses crowd behaviour, and returns ri ], "heatmap": { "image_path": "output/heatmap_match_01.png" - } + }, + "frames": [ + { + "frame_id": 1, + "timestamp": 0.04, + "people_annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0001.jpg", + "people_detections": [ + { + "bbox": [100, 50, 160, 180], + "confidence": 0.93 + } + ], + "face_detections": [] + }, + { + "frame_id": 2, + "timestamp": 0.08, + "people_annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0002.jpg", + "people_detections": [ + { + "bbox": [104, 52, 164, 182], + "confidence": 0.91 + } + ], + "face_detections": [] + } + ] } ``` @@ -40,6 +66,12 @@ This service receives analytics output, analyses crowd behaviour, and returns ri - `density` - number - calculated density value - `heatmap` - object - generated heatmap result - `image_path` - string - saved output path for the heatmap image +- `frames` - optional list of sequential detection-aware frame records for motion-based analysis +- `frame_id` - integer - frame number in the sequence +- `timestamp` - number - timestamp of the frame in seconds +- `people_annotated_frame_path` - string - people bbox-annotated frame path from `crowd_detection` +- `people_detections` - list - people detection records used for behaviour tracking +- `face_detections` - list - optional face detection records from `crowd_detection` ## Output JSON @@ -62,7 +94,76 @@ This service receives analytics output, analyses crowd behaviour, and returns ri "recommendations": [ "Monitor zone A1 closely", "Prepare crowd redirection if density increases further" - ] + ], + "event_flags": [ + "running_detection", + "crowd_surge", + "motion_anomaly" + ], + "artifact_paths": [ + "output/heatmap_match_01.png", + "crowd_behaviour_analytics/output/running_frames/motion_frame_0008.jpg" + ], + "vision_metrics": { + "vision_enabled": true, + "avg_motion_magnitude": 0.84, + "peak_motion_magnitude": 1.27, + "reverse_flow_ratio": 0.18, + "motion_intensity": 1.05, + "tracking": { + "track_count": 3, + "walking_track_count": 1, + "walking_track_ids": [2], + "running_track_count": 1, + "running_track_ids": [1], + "tracks": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "max_speed": 12.6, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "normalized_displacement": 1.24, + "height_variation": 0.08, + "is_walking": false, + "is_running": true, + "movement_state": "running" + }, + { + "track_id": 2, + "history_length": 4, + "avg_speed": 5.2, + "max_speed": 6.4, + "avg_normalized_speed": 0.22, + "max_normalized_speed": 0.36, + "normalized_displacement": 0.72, + "height_variation": 0.05, + "is_walking": true, + "is_running": false, + "movement_state": "walking" + } + ] + }, + "anomaly_model": { + "model_enabled": true, + "anomaly_track_ids": [1], + "running_track_ids": [1], + "anomaly_count": 1, + "track_scores": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "normalized_displacement": 1.24, + "anomaly_score": 0.2174, + "is_anomaly": true + } + ] + } + } } ``` @@ -75,8 +176,20 @@ This service receives analytics output, analyses crowd behaviour, and returns ri - `risk_level` - string - risk classification such as `low`, `medium`, `high` - `flagged` - boolean - whether the zone requires attention - `recommendations` - list of strings - suggested actions or notes +- `event_flags` - optional list of behaviour or anomaly labels from the behaviour analysis module +- `artifact_paths` - optional list of saved output paths for demo or frontend visualisation +- `vision_metrics` - optional summary of motion-analysis outputs from the behaviour-analysis module +- `tracking` - tracking summary generated inside `crowd_behaviour_analytics` +- `walking_track_ids` - tracked people classified as walking-like motion +- `running_track_ids` - tracked people classified as running-like motion +- `movement_state` - per-track label such as `stationary`, `walking`, or `running` +- `anomaly_model` - IsolationForest-based anomaly summary for tracked motion +- `anomaly_track_ids` - tracked people flagged as anomalous motion +- `track_scores` - per-track anomaly details including speed, normalized displacement, and anomaly score ## Notes - this service combines behaviour analysis and risk assessment - keep risk labels stable for backend and dashboard use +- the required response contract is unchanged; `event_flags`, `artifact_paths`, and `vision_metrics` are backward-compatible extensions +- walking/running labels are current movement-state outputs derived from detections, tracking, motion features, and anomaly scoring diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_detection_service.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_detection_service.py index 54972320f..94c19b8b3 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_detection_service.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_detection_service.py @@ -1,6 +1,7 @@ """Service flow for video processing and crowd detection.""" from video_processing.main import process_video +from crowd_region_preprocessing.main import prepare_crowd_frames from crowd_detection.main import detect_crowd @@ -10,7 +11,17 @@ def process_detection(data: dict): video_path = data.get("video_path") processed_video = process_video(video_id, video_path) - detection_result = detect_crowd(processed_video) + if not isinstance(processed_video, dict): + raise RuntimeError("Video processing did not return a valid response") + + if processed_video.get("error"): + raise FileNotFoundError(processed_video["error"]) + + if "video_id" not in processed_video or "frames" not in processed_video: + raise RuntimeError("Video processing returned incomplete output") + + focused_video = prepare_crowd_frames(processed_video) + detection_result = detect_crowd(focused_video) if isinstance(detection_result, dict) and "video_id" not in detection_result: detection_result["video_id"] = video_id diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_intelligence_service.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_intelligence_service.py index 65113788c..c84c97621 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_intelligence_service.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_intelligence_service.py @@ -14,4 +14,7 @@ def process_intelligence(data: dict): "crowd_state": behaviour_result.get("crowd_state"), "zones": risk_result.get("zones", []), "recommendations": risk_result.get("recommendations", []), + "event_flags": behaviour_result.get("event_flags", []), + "artifact_paths": behaviour_result.get("artifact_paths", []), + "vision_metrics": behaviour_result.get("vision_metrics"), } diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_pipeline_service.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_pipeline_service.py new file mode 100644 index 000000000..d8cb471ae --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/crowd_pipeline_service.py @@ -0,0 +1,183 @@ +"""End-to-end service flow for the full crowd monitoring pipeline.""" + +from pathlib import Path + +import matplotlib.pyplot as plt + +from .crowd_analytics_service import process_analytics +from .crowd_detection_service import process_detection +from crowd_allocation_risk_zone.main import assess_risk +from crowd_behaviour_analytics.main import analyze_behaviour + +PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent + + +def _safe_round(value, digits=2): + if value is None: + return None + return round(float(value), digits) + + +def _build_summary(detection_result: dict, behaviour_result: dict, risk_result: dict, analytics_result: dict) -> dict: + frames = detection_result.get("frames", []) + counts = [frame.get("person_count", 0) for frame in frames] + zone_densities = analytics_result.get("zones", []) + flagged_zones = [zone for zone in risk_result.get("zones", []) if zone.get("flagged")] + + highest_density_zone = max(zone_densities, key=lambda zone: zone.get("density", 0), default=None) + highest_risk_zone = flagged_zones[0] if flagged_zones else None + + return { + "total_frames_processed": len(frames), + "peak_person_count": max(counts, default=0), + "crowd_state": behaviour_result.get("crowd_state", "unknown"), + "highest_density_zone": highest_density_zone.get("zone_id") if highest_density_zone else None, + "highest_risk_zone": highest_risk_zone.get("zone_id") if highest_risk_zone else None, + } + + +def _build_peak_crowd_frame(detection_result: dict) -> dict: + frames = detection_result.get("frames", []) + peak_frame = max(frames, key=lambda frame: frame.get("person_count", 0), default=None) + if not peak_frame: + return {} + + return { + "frame_id": peak_frame.get("frame_id"), + "timestamp": peak_frame.get("timestamp"), + "person_count": peak_frame.get("person_count", 0), + "people_annotated_frame_path": peak_frame.get("people_annotated_frame_path"), + } + + +def _build_anomaly_visual(behaviour_result: dict) -> dict: + artifact_paths = behaviour_result.get("artifact_paths") or [] + event_flags = behaviour_result.get("event_flags") or [] + activity_series = behaviour_result.get("frame_movement_summary") or behaviour_result.get("frame_activity_series", []) + motion_artifacts = [ + path for path in artifact_paths + if "motion_frame_" in path.replace("\\", "/") + ] + artifact_by_frame = {} + for path in motion_artifacts: + normalized_path = path.replace("\\", "/") + frame_name = normalized_path.rsplit("/", 1)[-1] + frame_token = frame_name.replace("motion_frame_", "").replace(".jpg", "") + try: + artifact_by_frame[int(frame_token)] = path + except ValueError: + continue + + preferred_frame = max( + activity_series, + key=lambda entry: ( + entry.get("walking_count", 0), + entry.get("running_count", 0), + entry.get("active_count", 0), + ), + default=None, + ) + selected_path = None + if preferred_frame: + selected_path = artifact_by_frame.get(preferred_frame.get("frame_id")) + if not selected_path: + selected_path = motion_artifacts[0] if motion_artifacts else (artifact_paths[-1] if artifact_paths else None) + if not selected_path: + return {} + + if preferred_frame and preferred_frame.get("running_count", 0) > 0: + event_type = "running_activity" + elif preferred_frame and preferred_frame.get("walking_count", 0) > 0: + event_type = "walking_or_running_activity" + else: + event_type = event_flags[0] if event_flags else "movement_alert" + + return { + "event_type": event_type, + "image_path": selected_path, + } + + +def _build_time_series_chart(detection_result: dict, behaviour_result: dict, video_id: str | None) -> dict: + frames = detection_result.get("frames", []) + if not frames: + return {} + + person_timestamps = [frame.get("timestamp", 0.0) for frame in frames] + person_counts = [frame.get("person_count", 0) for frame in frames] + + output_dir = PROJECT_ROOT / "analytics_output" / "charts" + output_dir.mkdir(parents=True, exist_ok=True) + safe_video_id = video_id or detection_result.get("video_id") or "unknown_video" + output_path = output_dir / f"{safe_video_id}_crowd_activity_chart.png" + + figure, axis = plt.subplots(figsize=(10, 4.5)) + axis.plot(person_timestamps, person_counts, color="#1f77b4", linewidth=2.4) + axis.set_xlabel("Time (s)") + axis.set_ylabel("Person count") + axis.grid(True, linestyle="--", alpha=0.35) + + figure.suptitle("Person Count Over Time") + figure.tight_layout() + figure.savefig(output_path, dpi=180, bbox_inches="tight") + plt.close(figure) + + return { + "image_path": str(output_path.relative_to(PROJECT_ROOT)).replace("\\", "/") + } + + +def _build_density_extremes(analytics_result: dict, risk_result: dict) -> dict: + risk_by_zone = { + zone.get("zone_id"): zone + for zone in risk_result.get("zones", []) + } + + zone_insights = [] + for zone in analytics_result.get("zones", []): + risk = risk_by_zone.get(zone.get("zone_id"), {}) + zone_insights.append({ + "zone_id": zone.get("zone_id"), + "person_count": zone.get("person_count", 0), + "density": _safe_round(zone.get("density", 0.0), 4), + "risk_level": risk.get("risk_level", "unknown"), + "flagged": risk.get("flagged", False), + }) + + if not zone_insights: + return { + "highest_density_zone": {}, + "lowest_density_zone": {}, + } + + highest_density_zone = max(zone_insights, key=lambda zone: zone.get("density", 0.0)) + lowest_density_zone = min(zone_insights, key=lambda zone: zone.get("density", 0.0)) + return { + "highest_density_zone": highest_density_zone, + "lowest_density_zone": lowest_density_zone, + } + + +def process_crowd_detection(data: dict): + """Run detection, analytics, and intelligence as one frontend-facing flow.""" + detection_result = process_detection(data) + analytics_result = process_analytics(detection_result) + + intelligence_input = { + "video_id": data.get("video_id"), + "zones": analytics_result.get("zones", []), + "heatmap": analytics_result.get("heatmap", {}), + "frames": detection_result.get("frames", []), + } + behaviour_result = analyze_behaviour(intelligence_input) + risk_result = assess_risk(behaviour_result) + + return { + "video_id": data.get("video_id"), + "summary": _build_summary(detection_result, behaviour_result, risk_result, analytics_result), + "peak_crowd_frame": _build_peak_crowd_frame(detection_result), + "anomaly_visual": _build_anomaly_visual(behaviour_result), + "heatmap": analytics_result.get("heatmap", {}), + "time_series_chart": _build_time_series_chart(detection_result, behaviour_result, data.get("video_id")), + "density_extremes": _build_density_extremes(analytics_result, risk_result), + } diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/main.py index a1468eb3f..c0be1aab6 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/main.py @@ -1,13 +1,354 @@ """FastAPI entry point for the shared service layer.""" +from pathlib import Path + from fastapi import FastAPI +from fastapi import Request +from fastapi.responses import HTMLResponse, JSONResponse +from fastapi.staticfiles import StaticFiles from .routes import router +PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent + app = FastAPI( title="Crowd Monitoring Services", + description="Crowd monitoring APIs and demo UI.\n\n[Open Demo Page](/demo)", docs_url="/", redoc_url="/redoc", ) -app.include_router(router) \ No newline at end of file + +@app.exception_handler(Exception) +async def unhandled_exception_handler(request: Request, exc: Exception): + """Return actual runtime errors as JSON instead of generic 500 pages.""" + return JSONResponse( + status_code=500, + content={ + "detail": str(exc), + "path": request.url.path, + }, + ) + + +app.mount("/artifacts", StaticFiles(directory=PROJECT_ROOT), name="artifacts") + + +@app.get("/demo", response_class=HTMLResponse) +def demo_page(): + """Simple demo UI for the crowd monitoring pipeline.""" + return """ + + + + + + Crowd Monitoring Demo + + + +
+
+

Crowd Monitoring Demo

+

Run the full crowd pipeline and preview the key outputs without working through Swagger. Swagger remains available at /.

+
+ +
+
+ + + +
+
+
+ + +
+ + + + +""" + + +app.include_router(router) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/models.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/models.py index 6e9e87d0f..2cd26eab7 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/models.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/models.py @@ -1,6 +1,7 @@ """Pydantic models for FastAPI request and response schemas.""" from pydantic import BaseModel, Field +from typing import Optional class BoundingBoxDetection(BaseModel): @@ -11,8 +12,34 @@ class BoundingBoxDetection(BaseModel): class DetectionFrame(BaseModel): frame_id: int = Field(..., examples=[1]) timestamp: float = Field(..., examples=[0.04]) + frame_path: Optional[str] = Field(default=None, examples=["video_processing/output/frames/frame_0001.jpg"]) + annotated_frame_path: Optional[str] = Field( + default=None, + examples=["crowd_detection_output/people_detection_results/frame_0001.jpg"], + ) + face_annotated_frame_path: Optional[str] = Field( + default=None, + examples=["crowd_detection_output/face_detection_results/frame_0001.jpg"], + ) + people_annotated_frame_path: Optional[str] = Field( + default=None, + examples=["crowd_detection_output/people_detection_results/frame_0001.jpg"], + ) person_count: int = Field(..., examples=[2]) - detections: list[BoundingBoxDetection] + face_count: Optional[int] = Field(default=None, examples=[1]) + face_detections: list[BoundingBoxDetection] = Field(default_factory=list) + people_detections: list[BoundingBoxDetection] = Field(default_factory=list) + + +class BehaviourFrameInput(BaseModel): + frame_id: int = Field(..., examples=[1]) + timestamp: float = Field(..., examples=[0.04]) + people_annotated_frame_path: str = Field( + ..., + examples=["crowd_detection_output/people_detection_results/frame_0001.jpg"], + ) + face_detections: list[BoundingBoxDetection] = Field(default_factory=list) + people_detections: list[BoundingBoxDetection] = Field(default_factory=list) class ZoneDensity(BaseModel): @@ -25,17 +52,118 @@ class HeatmapResult(BaseModel): image_path: str = Field(..., examples=["output/heatmap_match_01.png"]) +class ChartAsset(BaseModel): + image_path: str = Field(..., examples=["analytics_output/charts/match_01_crowd_activity_chart.png"]) + + +class SummaryMetrics(BaseModel): + total_frames_processed: int = Field(..., examples=[65]) + peak_person_count: int = Field(..., examples=[68]) + crowd_state: str = Field(..., examples=["increasing_density"]) + highest_density_zone: Optional[str] = Field(default=None, examples=["A1"]) + highest_risk_zone: Optional[str] = Field(default=None, examples=["A1"]) + + +class PeakCrowdFrame(BaseModel): + frame_id: int = Field(..., examples=[18]) + timestamp: float = Field(..., examples=[12.4]) + person_count: int = Field(..., examples=[68]) + people_annotated_frame_path: Optional[str] = Field( + default=None, + examples=["crowd_detection_output/people_detection_results/frame_0018.jpg"], + ) + + +class AnomalyVisual(BaseModel): + event_type: str = Field(..., examples=["sudden_movement"]) + image_path: str = Field(..., examples=["crowd_behaviour_analytics/output/running_frames/motion_frame_0008.jpg"]) + + +class ZoneInsight(BaseModel): + zone_id: str = Field(..., examples=["A1"]) + person_count: int = Field(..., examples=[20]) + density: float = Field(..., examples=[0.82]) + risk_level: str = Field(..., examples=["high"]) + flagged: bool = Field(..., examples=[True]) + + +class DensityExtremes(BaseModel): + highest_density_zone: dict | ZoneInsight = Field(default_factory=dict) + lowest_density_zone: dict | ZoneInsight = Field(default_factory=dict) + + class RiskZone(BaseModel): zone_id: str = Field(..., examples=["A1"]) risk_level: str = Field(..., examples=["high"]) flagged: bool = Field(..., examples=[True]) +class TrackSummary(BaseModel): + track_id: int = Field(..., examples=[1]) + history_length: int = Field(..., examples=[4]) + avg_speed: float = Field(..., examples=[8.4]) + max_speed: float = Field(..., examples=[12.6]) + avg_normalized_speed: float = Field(..., examples=[0.42]) + max_normalized_speed: float = Field(..., examples=[0.88]) + normalized_displacement: float = Field(default=0.0, examples=[1.24]) + height_variation: float = Field(default=0.0, examples=[0.08]) + is_stationary: bool = Field(..., examples=[False]) + is_walking: bool = Field(..., examples=[True]) + is_running: bool = Field(..., examples=[False]) + movement_state: str = Field(..., examples=["walking"]) + + +class TrackingSummary(BaseModel): + track_count: int = Field(..., examples=[3]) + stationary_track_count: int = Field(..., examples=[1]) + stationary_track_ids: list[int] = Field(default_factory=list, examples=[[3]]) + walking_track_count: int = Field(..., examples=[1]) + walking_track_ids: list[int] = Field(default_factory=list, examples=[[2]]) + running_track_count: int = Field(..., examples=[1]) + running_track_ids: list[int] = Field(default_factory=list, examples=[[1]]) + tracks: list[TrackSummary] = Field(default_factory=list) + + +class AnomalyTrackScore(BaseModel): + track_id: int = Field(..., examples=[1]) + history_length: int = Field(..., examples=[4]) + avg_speed: float = Field(..., examples=[8.4]) + avg_normalized_speed: float = Field(..., examples=[0.42]) + max_normalized_speed: float = Field(..., examples=[0.88]) + normalized_displacement: float = Field(..., examples=[1.24]) + anomaly_score: float = Field(..., examples=[0.2174]) + is_anomaly: bool = Field(..., examples=[True]) + + +class AnomalyModelSummary(BaseModel): + model_enabled: bool = Field(..., examples=[True]) + anomaly_track_ids: list[int] = Field(default_factory=list, examples=[[1]]) + running_track_ids: list[int] = Field(default_factory=list, examples=[[1]]) + anomaly_count: int = Field(..., examples=[1]) + track_scores: list[AnomalyTrackScore] = Field(default_factory=list) + + +class VisionMetrics(BaseModel): + vision_enabled: bool = Field(..., examples=[True]) + avg_motion_magnitude: float = Field(..., examples=[0.84]) + peak_motion_magnitude: float = Field(..., examples=[1.27]) + reverse_flow_ratio: float = Field(..., examples=[0.18]) + motion_intensity: float = Field(..., examples=[1.05]) + tracking: TrackingSummary + anomaly_model: AnomalyModelSummary + + class DetectionRequest(BaseModel): video_id: str = Field(..., examples=["match_01"]) video_path: str = Field(..., examples=["data/raw/match_01.mp4"]) +class ProcessingErrorResponse(BaseModel): + detail: str = Field(..., examples=["Internal processing error while running crowd detection pipeline"]) + video_id: Optional[str] = Field(default=None, examples=["match_01"]) + stage: Optional[str] = Field(default=None, examples=["crowd_pipeline"]) + + class DetectionResponse(BaseModel): video_id: str = Field(..., examples=["match_01"]) frames: list[DetectionFrame] @@ -56,6 +184,7 @@ class IntelligenceRequest(BaseModel): video_id: str = Field(..., examples=["match_01"]) zones: list[ZoneDensity] heatmap: HeatmapResult + frames: Optional[list[BehaviourFrameInput]] = None class IntelligenceResponse(BaseModel): @@ -66,3 +195,87 @@ class IntelligenceResponse(BaseModel): ..., examples=[["Monitor zone A1 closely", "Prepare crowd redirection if density increases further"]], ) + event_flags: Optional[list[str]] = Field( + default=None, + examples=[["overcrowding_spike", "sudden_gathering"]], + ) + artifact_paths: Optional[list[str]] = Field( + default=None, + examples=[["output/heatmap_match_01.png", "crowd_behaviour_analytics/output/running_frames/motion_frame_0008.jpg"]], + ) + vision_metrics: Optional[VisionMetrics] = Field( + default=None, + examples=[{ + "vision_enabled": True, + "avg_motion_magnitude": 0.84, + "peak_motion_magnitude": 1.27, + "reverse_flow_ratio": 0.18, + "motion_intensity": 1.05, + "tracking": { + "track_count": 3, + "stationary_track_count": 1, + "stationary_track_ids": [3], + "walking_track_count": 1, + "walking_track_ids": [2], + "running_track_count": 1, + "running_track_ids": [1], + "tracks": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "max_speed": 12.6, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "is_stationary": False, + "is_walking": False, + "is_running": True, + "movement_state": "running" + } + ] + }, + "anomaly_model": { + "model_enabled": True, + "anomaly_track_ids": [1], + "running_track_ids": [1], + "anomaly_count": 1, + "track_scores": [ + { + "track_id": 1, + "history_length": 4, + "avg_speed": 8.4, + "avg_normalized_speed": 0.42, + "max_normalized_speed": 0.88, + "normalized_displacement": 1.24, + "anomaly_score": 0.2174, + "is_anomaly": True + } + ] + } + }], + ) + + +class BehaviourAnalyticsResponse(BaseModel): + video_id: str = Field(..., examples=["match_01"]) + crowd_state: str = Field(..., examples=["increasing_density"]) + zones: list[ZoneDensity] + event_flags: Optional[list[str]] = Field(default=None) + artifact_paths: Optional[list[str]] = Field(default=None) + vision_metrics: Optional[VisionMetrics] = None + + +class RiskZoneResponse(BaseModel): + video_id: str = Field(..., examples=["match_01"]) + zones: list[RiskZone] + recommendations: list[str] + + +class CrowdPipelineResponse(BaseModel): + video_id: str = Field(..., examples=["match_01"]) + summary: SummaryMetrics + peak_crowd_frame: dict | PeakCrowdFrame = Field(default_factory=dict) + anomaly_visual: dict | AnomalyVisual = Field(default_factory=dict) + heatmap: HeatmapResult + time_series_chart: dict | ChartAsset = Field(default_factory=dict) + density_extremes: DensityExtremes diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/routes.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/routes.py index 1f19fd33d..df10646fe 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/routes.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/routes.py @@ -1,17 +1,21 @@ """API routes for the shared service layer.""" from fastapi import APIRouter +from fastapi.responses import JSONResponse from .crowd_analytics_service import process_analytics from .crowd_detection_service import process_detection from .crowd_intelligence_service import process_intelligence +from .crowd_pipeline_service import process_crowd_detection from .models import ( AnalyticsRequest, AnalyticsResponse, + CrowdPipelineResponse, DetectionRequest, DetectionResponse, IntelligenceRequest, IntelligenceResponse, + ProcessingErrorResponse, ) router = APIRouter() @@ -32,3 +36,28 @@ def process_analytics_route(data: AnalyticsRequest): def process_intelligence_route(data: IntelligenceRequest): """Run the crowd intelligence service flow.""" return process_intelligence(data.model_dump()) + + +@router.post( + "/process-crowd-detection", + response_model=CrowdPipelineResponse, + responses={ + 500: { + "model": ProcessingErrorResponse, + "description": "Internal processing error while running the crowd monitoring pipeline", + } + }, +) +def process_crowd_detection_route(data: DetectionRequest): + """Run the full crowd monitoring pipeline for frontend use.""" + try: + return process_crowd_detection(data.model_dump()) + except Exception as exc: + return JSONResponse( + status_code=500, + content={ + "detail": str(exc), + "video_id": data.video_id, + "stage": "crowd_pipeline", + }, + ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/main.py index 7d5c55089..598d41d0e 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/main.py @@ -1,6 +1,10 @@ import cv2 import os import json +import numpy as np # Used for creating the "canvas" for letterboxing +from concurrent.futures import ThreadPoolExecutor # For background file saving +# Import utils +from video_processing.utils import get_video_stats, check_blur, apply_preprocessing, save_frame_worker #Logic to find the config relative to the Project Root #By doing this, the code works on any computer because it doesn't care about the folders above the project(our project is at 2026_T1 folder level) @@ -8,6 +12,9 @@ #join() is used to build a path to json config file CONFIG_PATH = os.path.join(BASE_DIR, "shared", "config", "video_processing_config.json") +#max_workers 4 reserved for writing extracted frame to folder where we want to save +executor = ThreadPoolExecutor(max_workers=4) + #Important paths and parameters are stored in this config file which can be updated if needed #We load and read that config file def load_config(): @@ -24,9 +31,15 @@ def process_video(video_id: str, video_path: str): #contains path where input video is present full_input_path = os.path.normpath(os.path.join(BASE_DIR, video_path)) + # Establish the 'Sharpness Floor' for this specific crowd footage + print(f"Analyzing crowd video quality for {video_id}...") + # If variance is less then threshold blurry image else sharp image + dynamic_threshold = get_video_stats(full_input_path, config["sample_rate"]) + print(f"Calculated Crowd Quality Threshold: {dynamic_threshold:.2f}") + #contains path where output frames will be stored output_dir = os.path.join(BASE_DIR, config["extracted_frames_dir"]) - #it creates folder where output frames will be stored if only folder is already not created + #It creates folder where output frames will be stored if only folder is already not created os.makedirs(output_dir, exist_ok=True) #opens video stream @@ -41,6 +54,7 @@ def process_video(video_id: str, video_path: str): res_w, res_h = config["output_resolution"] frames_metadata = [] + save_futures = [] count = 0 extracted_count = 1 @@ -55,15 +69,33 @@ def process_video(video_id: str, video_path: str): #Frame Sampling (We take snapshot every 30 frames, instead of taking snapshot of all frames) if count % config["sample_rate"] == 0: - #Resize for the Detection model - resized = cv2.resize(frame, (res_w, res_h)) + score, is_sharp = check_blur(frame, dynamic_threshold) + + # If the camera is panning or shaking(blurry frame), check the next few frames. + # Crowd faces are unrecognizable in motion blur. + search_count = 0 + while not is_sharp and search_count < 8: # Slightly longer window for crowd stabilization + ret, frame = cap.read() + if not ret: break + count += 1 + search_count += 1 + score, is_sharp = check_blur(frame, dynamic_threshold) + + + # Process the sharp (or best available) frame using LetterBoxing(if it fails accuracy, switch to Tiling) + processed = apply_preprocessing(frame, (res_h, res_w)) + + # #Resize for the Detection model + # resized = cv2.resize(frame, (res_w, res_h)) #frame naming for maintaining frame order fname = f"frame_{extracted_count:04d}.jpg" save_path = os.path.join(output_dir, fname) - #saving frame to output directory - cv2.imwrite(save_path, resized) + # #saving frame to output directory + # cv2.imwrite(save_path, resized) + save_futures.append(executor.submit(save_frame_worker, save_path, processed)) + #Match the 'DetectionFrame' schema in shared/models.py frames_metadata.append({ "frame_id": extracted_count, @@ -77,6 +109,9 @@ def process_video(video_id: str, video_path: str): #This "closes" the video file. If we don't do this, the computer might keep the file "locked," and we won't be able to delete or move it until we restart the PC cap.release() + for future in save_futures: + future.result() + #Return the dictionary for the Service Layer to use return { "video_id": video_id, @@ -101,4 +136,4 @@ def process_video(video_id: str, video_path: str): #2. Motion Blur. The Fix: Ensure you are extracting Keyframes (I-frames) where possible, as these contain the most complete visual data. #3. Overcompression The Fix: Always save frames with high JPEG quality (90-95). Code: cv2.imwrite(path, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 95]) #4. Poor Normalization (Lighting/Contrast) The Fix: In the future, you can add Histogram Equalization to your processing flow to balance the lighting before the AI sees it. -#5. For person far away. The Fix: If the team needs to detect people in the far distance, you might need to implement Tiling (chopping the 4K frame into four 640x640 blocks) instead of shrinking the whole thing. \ No newline at end of file +#5. For person far away. The Fix: If the team needs to detect people in the far distance, you might need to implement Tiling (chopping the 4K frame into four 640x640 blocks) instead of shrinking the whole thing. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/utils.py new file mode 100644 index 000000000..366a2e34c --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/utils.py @@ -0,0 +1,85 @@ +import cv2 +import numpy as np + +def save_frame_worker(path, image): + """ + Background worker to save images. + Note: We save with high JPEG quality (95) to preserve crowd details. + """ + # If your main loop uses BGR (OpenCV default), no need to convert. + # If your main loop converts to RGB for AI models, uncomment the line below: + # image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + cv2.imwrite(path, image, [int(cv2.IMWRITE_JPEG_QUALITY), 95]) + +def get_video_stats(full_input_path, sample_rate): + """ + Scans the video to find the range of sharpness (min/max Laplacian variance). + Returns a calculated baseline threshold. + """ + cap = cv2.VideoCapture(full_input_path) + variances = [] + + # Sample every decided frame_rate to get a fast but accurate representation + count = 0 + while True: + ret, frame = cap.read() + if not ret: break + + if count % sample_rate == 0: + gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) + var = cv2.Laplacian(gray, cv2.CV_64F).var() + if var > 10: # Ignore pitch black/empty frames + variances.append(var) + count += 1 + + cap.release() + + if not variances: + return 100.0 # Fallback + + v_min = min(variances) + v_max = max(variances) + v_avg = sum(variances) / len(variances) + + # DECISION LOGIC: + # We want a threshold that is higher than the minimum, + # but not so high that we reject everything. + # A good 'dynamic' threshold is 80% of the average. + dynamic_threshold = v_avg * 0.8 + + print(f"Stats - Min: {v_min:.2f}, Max: {v_max:.2f}, Avg: {v_avg:.2f}") + print(f"Calculated Threshold: {dynamic_threshold:.2f}") + + return dynamic_threshold + +def check_blur(image, threshold): + """ + Computes the Laplacian variance to measure focus. + Higher value = Sharper image. Lower value = Blurrier image. + 100.0 is a good starting point for 1080p footage + """ + #Converting from BGR to Grayscale because computers dont need full color image to detect sharpness, they only need intensity (brightness changes), and processing one channel(gray) is faster than 3 channels + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + #kernel (a small matrix) applied over image to find edges (a place where a light pixel is right next to dark pixel) + #var() variance between these contrasting pixel values. + variance = cv2.Laplacian(gray, cv2.CV_64F).var() + return variance, variance >= threshold + +def apply_preprocessing(img, target_size=(640, 640)): + #Letterboxing (Proportional Scaling) + h, w = img.shape[:2] + th, tw = target_size + ratio = min(tw / w, th / h) + new_w, new_h = int(w * ratio), int(h * ratio) + + # INTER_AREA averages pixel blocks rather than picking single points. + resized = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA) + + # Create black canvas and center the image over it + canvas = np.zeros((th, tw, 3), dtype=np.uint8) + dx, dy = (tw - new_w) // 2, (th - new_h) // 2 + canvas[dy:dy+new_h, dx:dx+new_w] = resized + + # RGB Conversion for YOLO model + return cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/verify_processing.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/verify_processing.py new file mode 100644 index 000000000..c3f1644db --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/verify_processing.py @@ -0,0 +1,69 @@ +import cv2 +import os +import time +import numpy as np +from main import process_video, executor + +def run_verification(video_id, video_path): + print("=== STARTING VERIFICATION SYSTEM ===") + + # 1. Performance Measurement + start_time = time.time() + result = process_video(video_id, video_path) + # Ensure all background threads finish before we measure time + executor.shutdown(wait=True) + end_time = time.time() + + total_time = end_time - start_time + num_frames = len(result.get("frames", [])) + + print(f"\n[1] Performance Results:") + print(f"- Total Time: {total_time:.2f} seconds") + print(f"- Frames Processed: {num_frames}") + if num_frames > 0: + print(f"- Speed: {total_time/num_frames:.4f} seconds per frame") + + # 2. Visual & Structural Check + if num_frames > 0: + # Get the first saved frame path + first_frame_rel_path = result["frames"][0]["frame_path"] + # Convert relative path to absolute + first_frame_path = os.path.join(os.getcwd(), first_frame_rel_path) + + # Load the image + # Note: We use cv2.imread which loads in BGR. + # If your script saved it correctly as RGB, it will look 'wrong' in imread + # but 'right' in your AI model. + img = cv2.imread(first_frame_path) + + if img is not None: + h, w, c = img.shape + print(f"\n[2] Image Structure Check:") + print(f"- Resolution: {w}x{h} (Target should be 640x640)") + + # Check for Letterboxing (Top and Bottom bars) + # Sample a few pixels from the very top center + top_strip = img[0:5, w//2] + is_letterboxed = np.mean(top_strip) < 10 + print(f"- Letterboxing Detected: {is_letterboxed}") + + # Check Color (Green Grass Check) + # In a normal BGR image, Green is [0, 255, 0]. + # If you saved it as RGB, it's [0, 255, 0] but imread sees it as BGR. + # We just want to ensure the image isn't grayscale or corrupted. + has_color = not (np.allclose(img[:,:,0], img[:,:,1]) and np.allclose(img[:,:,1], img[:,:,2])) + print(f"- Color Data Present: {has_color}") + + # Visual Display + print("\n[3] Visual confirmation: Close the window to finish.") + cv2.imshow("Verification - Press any key", img) + cv2.waitKey(0) + cv2.destroyAllWindows() + else: + print(f"\n[!] Error: Could not load the saved frame at {first_frame_path}") + else: + print("\n[!] Error: No frames were extracted. Check your blur threshold or sample rate.") + +if __name__ == "__main__": + # Ensure paths match your project structure + run_verification("match_01", "data/raw/match_01.mp4") \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/README.md deleted file mode 100644 index ddb2ec35a..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/README.md +++ /dev/null @@ -1,116 +0,0 @@ -# Forecasting Model Documentation - -## Overview -This document provides a detailed overview of the forecasting model developed for predicting the number of visitors to a place. The model uses the **Prophet algorithm** and incorporates features such as the **day of the week** and **user-specified forecasting periods**. The following sections cover installation, usage, and output details, including placeholders for screenshots. ---- -## 1. Installation - -### 1.1 Requirements -To run the forecasting model, ensure the following packages are installed: -- pandas -- prophet -- matplotlib -- openpyxl (for saving output to Excel) - -### 1.2 Install the Required Packages -Before running the visitor forecasting model, ensure you have the following Python packages installed in your environment. These libraries are essential for data handling, forecasting, plotting graphs, and saving results. - -#### Step-by-Step Installation: -You can install these packages using `pip` by running the following commands in your terminal or command prompt: - -1. Install pandas: - ```bash - pip install pandas - ``` - -2. Install Prophet (this will also install cmdstanpy, which Prophet depends on): - ```bash - pip install prophet - ``` - -3. Install matplotlib: - ```bash - pip install matplotlib - ``` - -4. Install openpyxl (for exporting results to Excel): - ```bash - pip install openpyxl - ``` - -Alternatively, you can install all the required packages at once by running: - ```bash - pip install pandas prophet matplotlib openpyxl - ``` - -#### Verifying the Installation: -Once the packages are installed, you can verify them by importing them in Python. Open a Python environment (either a Jupyter Notebook or Python shell) and run: - -``` -import pandas as pd -from prophet import Prophet -import matplotlib.pyplot as plt -import openpyxl -``` -If there are no errors, the packages are installed correctly, and you are ready to run the forecasting model. - ---- -## 2. How to Use the Forecasting Model - -### 2.1 Load the Data -- Prepare a CSV file containing historical visitor data, with columns for date and visitor count. -- For demonstration, website visitor data was used to build the model. - -### 2.2 Execute the Script -- Run the provided script to start the forecasting process. -- You will be prompted to input the number of days to forecast. - ---- - -## 3. User Interaction - -### 3.1 Input Prompt for Number of Days -- Once the script is executed, it will prompt you to input a number representing how many days into the future you want to predict. -- Enter a valid numerical value. -``` -How many days do you want to forecast? [Input Box] -``` -![Input Prompt Example](./images/input.png) - ---- - -## 4. Output Details - -### 4.1 Forecast Graph -- After execution, the model will display a forecast graph: - - **X-axis**: Date - - **Y-axis**: Predicted number of visitors -- The graph will include both historical data and the forecast for easy comparison. - -![Forecast Graph Example](./images/forecast_graph.png) - ---- - -## 5. Saving the Results - -### 5.1 Exporting to Excel -- The forecasted visitor data (with lower and upper confidence intervals) is automatically saved in an Excel file named `visitor_forecast_output.xlsx`. -- This file can be used for further analysis or sharing. - -![User Input Example](./images/user_input_example.png) - ---- - -## 6. Technical Details - -### 6.1 Data Preprocessing -- The historical visitor data is processed by: - - Converting the **Date** column into a `datetime` object. - - Cleaning the **Visitor.Count** column by removing any non-numerical characters to ensure accurate numerical operations. - -### 6.2 Model Features -- **Days of the Week**: The model accounts for variations in visitor patterns on weekdays vs. weekends. -- **Holidays (Optional)**: For greater accuracy, the model can optionally incorporate holiday data, which is particularly useful during special events or holiday seasons. - ---- - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/Visitor_Forecasting_Model_Documentation.pdf b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/Visitor_Forecasting_Model_Documentation.pdf deleted file mode 100644 index 2774c9268..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/Visitor_Forecasting_Model_Documentation.pdf and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/daily-website-visitors.csv b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/daily-website-visitors.csv deleted file mode 100644 index 037133652..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/daily-website-visitors.csv +++ /dev/null @@ -1,2168 +0,0 @@ -Row,Day,Day.Of.Week,Date,Page.Loads,Unique.Visits,First.Time.Visits,Returning.Visits -1,Sunday,1,9/14/2014,"2,146","1,582","1,430",152 -2,Monday,2,9/15/2014,"3,621","2,528","2,297",231 -3,Tuesday,3,9/16/2014,"3,698","2,630","2,352",278 -4,Wednesday,4,9/17/2014,"3,667","2,614","2,327",287 -5,Thursday,5,9/18/2014,"3,316","2,366","2,130",236 -6,Friday,6,9/19/2014,"2,815","1,863","1,622",241 -7,Saturday,7,9/20/2014,"1,658","1,118",985,133 -8,Sunday,1,9/21/2014,"2,288","1,656","1,481",175 -9,Monday,2,9/22/2014,"3,638","2,586","2,312",274 -10,Tuesday,3,9/23/2014,"4,462","3,257","2,989",268 -11,Wednesday,4,9/24/2014,"4,414","3,175","2,891",284 -12,Thursday,5,9/25/2014,"4,315","3,029","2,743",286 -13,Friday,6,9/26/2014,"3,323","2,249","2,033",216 -14,Saturday,7,9/27/2014,"1,656","1,180","1,040",140 -15,Sunday,1,9/28/2014,"2,465","1,806","1,613",193 -16,Monday,2,9/29/2014,"4,096","2,873","2,577",296 -17,Tuesday,3,9/30/2014,"4,474","3,032","2,720",312 -18,Wednesday,4,10/1/2014,"4,124","2,849","2,541",308 -19,Thursday,5,10/2/2014,"3,514","2,489","2,239",250 -20,Friday,6,10/3/2014,"3,005","2,097","1,856",241 -21,Saturday,7,10/4/2014,"2,054","1,436","1,274",162 -22,Sunday,1,10/5/2014,"2,847","1,913","1,713",200 -23,Monday,2,10/6/2014,"4,501","3,181","2,853",328 -24,Tuesday,3,10/7/2014,"4,603","3,163","2,804",359 -25,Wednesday,4,10/8/2014,"4,187","3,014","2,663",351 -26,Thursday,5,10/9/2014,"4,343","2,864","2,545",319 -27,Friday,6,10/10/2014,"3,565","2,382","2,100",282 -28,Saturday,7,10/11/2014,"2,080","1,457","1,280",177 -29,Sunday,1,10/12/2014,"3,031","2,089","1,856",233 -30,Monday,2,10/13/2014,"4,814","3,339","2,973",366 -31,Tuesday,3,10/14/2014,"5,040","3,604","3,217",387 -32,Wednesday,4,10/15/2014,"5,028","3,515","3,094",421 -33,Thursday,5,10/16/2014,"4,658","3,331","2,955",376 -34,Friday,6,10/17/2014,"3,624","2,477","2,148",329 -35,Saturday,7,10/18/2014,"2,285","1,619","1,416",203 -36,Sunday,1,10/19/2014,"3,454","2,346","2,060",286 -37,Monday,2,10/20/2014,"5,307","3,717","3,308",409 -38,Tuesday,3,10/21/2014,"5,135","3,701","3,280",421 -39,Wednesday,4,10/22/2014,"5,084","3,611","3,177",434 -40,Thursday,5,10/23/2014,"4,650","3,316","2,940",376 -41,Friday,6,10/24/2014,"3,571","2,498","2,170",328 -42,Saturday,7,10/25/2014,"2,354","1,661","1,417",244 -43,Sunday,1,10/26/2014,"3,497","2,508","2,218",290 -44,Monday,2,10/27/2014,"5,294","3,737","3,286",451 -45,Tuesday,3,10/28/2014,"4,643","3,328","2,905",423 -46,Wednesday,4,10/29/2014,"4,596","3,279","2,863",416 -47,Thursday,5,10/30/2014,"4,162","3,041","2,657",384 -48,Friday,6,10/31/2014,"2,933","2,007","1,728",279 -49,Saturday,7,11/1/2014,"2,202","1,496","1,270",226 -50,Sunday,1,11/2/2014,"3,083","2,204","1,940",264 -51,Monday,2,11/3/2014,"4,376","3,057","2,629",428 -52,Tuesday,3,11/4/2014,"4,704","3,141","2,739",402 -53,Wednesday,4,11/5/2014,"4,306","3,200","2,797",403 -54,Thursday,5,11/6/2014,"4,178","3,034","2,627",407 -55,Friday,6,11/7/2014,"3,236","2,318","1,975",343 -56,Saturday,7,11/8/2014,"2,010","1,536","1,345",191 -57,Sunday,1,11/9/2014,"2,901","2,141","1,849",292 -58,Monday,2,11/10/2014,"4,754","3,325","2,885",440 -59,Tuesday,3,11/11/2014,"4,417","3,203","2,793",410 -60,Wednesday,4,11/12/2014,"4,535","3,351","2,897",454 -61,Thursday,5,11/13/2014,"4,274","3,194","2,758",436 -62,Friday,6,11/14/2014,"3,382","2,423","2,061",362 -63,Saturday,7,11/15/2014,"2,085","1,488","1,280",208 -64,Sunday,1,11/16/2014,"3,083","2,215","1,926",289 -65,Monday,2,11/17/2014,"4,860","3,559","3,069",490 -66,Tuesday,3,11/18/2014,"4,893","3,570","3,133",437 -67,Wednesday,4,11/19/2014,"4,866","3,544","3,056",488 -68,Thursday,5,11/20/2014,"4,584","3,357","2,899",458 -69,Friday,6,11/21/2014,"3,914","2,750","2,361",389 -70,Saturday,7,11/22/2014,"2,431","1,716","1,463",253 -71,Sunday,1,11/23/2014,"3,157","2,369","2,082",287 -72,Monday,2,11/24/2014,"5,045","3,615","3,146",469 -73,Tuesday,3,11/25/2014,"4,746","3,236","2,821",415 -74,Wednesday,4,11/26/2014,"4,314","2,919","2,513",406 -75,Thursday,5,11/27/2014,"3,663","2,476","2,162",314 -76,Friday,6,11/28/2014,"3,270","2,268","1,931",337 -77,Saturday,7,11/29/2014,"2,905","1,976","1,695",281 -78,Sunday,1,11/30/2014,"4,016","2,832","2,489",343 -79,Monday,2,12/1/2014,"5,266","3,877","3,317",560 -80,Tuesday,3,12/2/2014,"5,795","4,274","3,700",574 -81,Wednesday,4,12/3/2014,"5,728","4,217","3,656",561 -82,Thursday,5,12/4/2014,"5,293","3,830","3,295",535 -83,Friday,6,12/5/2014,"3,934","2,856","2,479",377 -84,Saturday,7,12/6/2014,"3,071","2,165","1,855",310 -85,Sunday,1,12/7/2014,"3,876","2,984","2,582",402 -86,Monday,2,12/8/2014,"5,878","4,375","3,829",546 -87,Tuesday,3,12/9/2014,"5,712","4,187","3,635",552 -88,Wednesday,4,12/10/2014,"5,491","4,074","3,480",594 -89,Thursday,5,12/11/2014,"5,144","3,801","3,247",554 -90,Friday,6,12/12/2014,"3,880","2,860","2,418",442 -91,Saturday,7,12/13/2014,"2,605","1,838","1,538",300 -92,Sunday,1,12/14/2014,"3,625","2,650","2,259",391 -93,Monday,2,12/15/2014,"4,685","3,478","3,000",478 -94,Tuesday,3,12/16/2014,"4,891","3,338","2,861",477 -95,Wednesday,4,12/17/2014,"4,222","3,053","2,606",447 -96,Thursday,5,12/18/2014,"3,691","2,690","2,275",415 -97,Friday,6,12/19/2014,"2,748","1,879","1,559",320 -98,Saturday,7,12/20/2014,"1,380","1,011",836,175 -99,Sunday,1,12/21/2014,"1,474","1,054",876,178 -100,Monday,2,12/22/2014,"2,452","1,715","1,394",321 -101,Tuesday,3,12/23/2014,"2,298","1,440","1,159",281 -102,Wednesday,4,12/24/2014,"1,430",947,772,175 -103,Thursday,5,12/25/2014,"1,002",667,522,145 -104,Friday,6,12/26/2014,"1,486","1,005",808,197 -105,Saturday,7,12/27/2014,"1,345",941,781,160 -106,Sunday,1,12/28/2014,"1,453",948,765,183 -107,Monday,2,12/29/2014,"2,173","1,472","1,228",244 -108,Tuesday,3,12/30/2014,"2,208","1,424","1,172",252 -109,Wednesday,4,12/31/2014,"1,381",955,773,182 -110,Thursday,5,1/1/2015,"1,265",876,715,161 -111,Friday,6,1/2/2015,"1,948","1,288","1,030",258 -112,Saturday,7,1/3/2015,"1,742","1,096",946,150 -113,Sunday,1,1/4/2015,"1,896","1,409","1,186",223 -114,Monday,2,1/5/2015,"3,033","2,062","1,706",356 -115,Tuesday,3,1/6/2015,"3,445","2,327","1,942",385 -116,Wednesday,4,1/7/2015,"3,423","2,384","1,991",393 -117,Thursday,5,1/8/2015,"3,319","2,272","1,922",350 -118,Friday,6,1/9/2015,"2,783","1,941","1,663",278 -119,Saturday,7,1/10/2015,"1,952","1,240","1,066",174 -120,Sunday,1,1/11/2015,"2,245","1,611","1,391",220 -121,Monday,2,1/12/2015,"3,928","2,518","2,125",393 -122,Tuesday,3,1/13/2015,"3,810","2,621","2,239",382 -123,Wednesday,4,1/14/2015,"3,931","2,656","2,229",427 -124,Thursday,5,1/15/2015,"3,523","2,457","2,069",388 -125,Friday,6,1/16/2015,"2,943","1,928","1,640",288 -126,Saturday,7,1/17/2015,"1,751","1,237","1,052",185 -127,Sunday,1,1/18/2015,"2,491","1,762","1,541",221 -128,Monday,2,1/19/2015,"3,766","2,665","2,271",394 -129,Tuesday,3,1/20/2015,"4,194","2,852","2,423",429 -130,Wednesday,4,1/21/2015,"4,038","2,916","2,482",434 -131,Thursday,5,1/22/2015,"3,891","2,702","2,334",368 -132,Friday,6,1/23/2015,"3,111","2,181","1,897",284 -133,Saturday,7,1/24/2015,"1,946","1,414","1,239",175 -134,Sunday,1,1/25/2015,"2,802","2,010","1,740",270 -135,Monday,2,1/26/2015,"3,892","2,841","2,466",375 -136,Tuesday,3,1/27/2015,"4,172","3,003","2,592",411 -137,Wednesday,4,1/28/2015,"4,464","3,280","2,862",418 -138,Thursday,5,1/29/2015,"4,377","3,143","2,679",464 -139,Friday,6,1/30/2015,"3,734","2,514","2,129",385 -140,Saturday,7,1/31/2015,"2,262","1,657","1,443",214 -141,Sunday,1,2/1/2015,"3,050","2,122","1,825",297 -142,Monday,2,2/2/2015,"4,794","3,366","2,867",499 -143,Tuesday,3,2/3/2015,"4,936","3,423","2,922",501 -144,Wednesday,4,2/4/2015,"5,016","3,500","3,023",477 -145,Thursday,5,2/5/2015,"4,489","3,170","2,753",417 -146,Friday,6,2/6/2015,"3,505","2,468","2,075",393 -147,Saturday,7,2/7/2015,"2,264","1,679","1,437",242 -148,Sunday,1,2/8/2015,"3,314","2,435","2,100",335 -149,Monday,2,2/9/2015,"5,095","3,619","3,125",494 -150,Tuesday,3,2/10/2015,"5,525","3,793","3,272",521 -151,Wednesday,4,2/11/2015,"5,074","3,689","3,190",499 -152,Thursday,5,2/12/2015,"4,571","3,319","2,810",509 -153,Friday,6,2/13/2015,"3,570","2,664","2,253",411 -154,Saturday,7,2/14/2015,"2,182","1,565","1,323",242 -155,Sunday,1,2/15/2015,"3,265","2,277","1,935",342 -156,Monday,2,2/16/2015,"4,670","3,297","2,826",471 -157,Tuesday,3,2/17/2015,"4,764","3,452","2,957",495 -158,Wednesday,4,2/18/2015,"4,943","3,526","3,026",500 -159,Thursday,5,2/19/2015,"4,790","3,307","2,811",496 -160,Friday,6,2/20/2015,"3,604","2,621","2,241",380 -161,Saturday,7,2/21/2015,"2,455","1,823","1,569",254 -162,Sunday,1,2/22/2015,"3,650","2,690","2,329",361 -163,Monday,2,2/23/2015,"5,463","3,975","3,433",542 -164,Tuesday,3,2/24/2015,"5,884","4,112","3,542",570 -165,Wednesday,4,2/25/2015,"5,615","4,019","3,432",587 -166,Thursday,5,2/26/2015,"5,289","3,747","3,186",561 -167,Friday,6,2/27/2015,"4,434","2,969","2,486",483 -168,Saturday,7,2/28/2015,"2,665","1,977","1,694",283 -169,Sunday,1,3/1/2015,"3,907","2,906","2,519",387 -170,Monday,2,3/2/2015,"5,308","3,778","3,222",556 -171,Tuesday,3,3/3/2015,"5,773","4,025","3,490",535 -172,Wednesday,4,3/4/2015,"5,585","4,060","3,457",603 -173,Thursday,5,3/5/2015,"4,783","3,582","3,049",533 -174,Friday,6,3/6/2015,"3,893","2,707","2,259",448 -175,Saturday,7,3/7/2015,"2,917","2,048","1,723",325 -176,Sunday,1,3/8/2015,"3,353","2,468","2,104",364 -177,Monday,2,3/9/2015,"5,400","3,824","3,246",578 -178,Tuesday,3,3/10/2015,"5,395","3,926","3,350",576 -179,Wednesday,4,3/11/2015,"5,229","3,859","3,263",596 -180,Thursday,5,3/12/2015,"5,371","3,784","3,204",580 -181,Friday,6,3/13/2015,"4,185","3,045","2,559",486 -182,Saturday,7,3/14/2015,"2,724","1,988","1,675",313 -183,Sunday,1,3/15/2015,"3,590","2,620","2,196",424 -184,Monday,2,3/16/2015,"5,367","3,771","3,192",579 -185,Tuesday,3,3/17/2015,"5,440","3,968","3,387",581 -186,Wednesday,4,3/18/2015,"5,465","4,043","3,458",585 -187,Thursday,5,3/19/2015,"5,318","3,813","3,262",551 -188,Friday,6,3/20/2015,"4,225","2,916","2,462",454 -189,Saturday,7,3/21/2015,"2,687","1,975","1,667",308 -190,Sunday,1,3/22/2015,"3,791","2,786","2,388",398 -191,Monday,2,3/23/2015,"5,661","3,949","3,331",618 -192,Tuesday,3,3/24/2015,"5,759","4,081","3,487",594 -193,Wednesday,4,3/25/2015,"5,599","3,983","3,378",605 -194,Thursday,5,3/26/2015,"5,103","3,755","3,184",571 -195,Friday,6,3/27/2015,"4,313","2,946","2,446",500 -196,Saturday,7,3/28/2015,"2,971","2,011","1,651",360 -197,Sunday,1,3/29/2015,"3,662","2,693","2,285",408 -198,Monday,2,3/30/2015,"5,635","3,985","3,386",599 -199,Tuesday,3,3/31/2015,"5,581","3,963","3,359",604 -200,Wednesday,4,4/1/2015,"5,329","3,761","3,170",591 -201,Thursday,5,4/2/2015,"4,553","3,214","2,710",504 -202,Friday,6,4/3/2015,"3,423","2,404","1,988",416 -203,Saturday,7,4/4/2015,"2,708","1,807","1,512",295 -204,Sunday,1,4/5/2015,"3,306","2,266","1,895",371 -205,Monday,2,4/6/2015,"5,203","3,692","3,133",559 -206,Tuesday,3,4/7/2015,"5,712","4,104","3,456",648 -207,Wednesday,4,4/8/2015,"5,601","4,015","3,401",614 -208,Thursday,5,4/9/2015,"5,486","4,063","3,430",633 -209,Friday,6,4/10/2015,"4,178","3,213","2,697",516 -210,Saturday,7,4/11/2015,"2,878","2,153","1,835",318 -211,Sunday,1,4/12/2015,"4,169","2,903","2,447",456 -212,Monday,2,4/13/2015,"6,372","4,485","3,787",698 -213,Tuesday,3,4/14/2015,"6,240","4,391","3,698",693 -214,Wednesday,4,4/15/2015,"6,170","4,409","3,723",686 -215,Thursday,5,4/16/2015,"6,071","4,275","3,586",689 -216,Friday,6,4/17/2015,"4,682","3,307","2,741",566 -217,Saturday,7,4/18/2015,"3,208","2,291","1,894",397 -218,Sunday,1,4/19/2015,"4,310","3,144","2,669",475 -219,Monday,2,4/20/2015,"6,435","4,694","3,929",765 -220,Tuesday,3,4/21/2015,"6,299","4,455","3,769",686 -221,Wednesday,4,4/22/2015,"6,360","4,512","3,797",715 -222,Thursday,5,4/23/2015,"6,349","4,360","3,664",696 -223,Friday,6,4/24/2015,"4,660","3,387","2,845",542 -224,Saturday,7,4/25/2015,"3,354","2,420","2,035",385 -225,Sunday,1,4/26/2015,"4,371","3,189","2,688",501 -226,Monday,2,4/27/2015,"6,534","4,731","3,977",754 -227,Tuesday,3,4/28/2015,"6,832","4,941","4,161",780 -228,Wednesday,4,4/29/2015,"6,689","4,870","4,110",760 -229,Thursday,5,4/30/2015,"5,849","4,086","3,450",636 -230,Friday,6,5/1/2015,"4,247","3,064","2,562",502 -231,Saturday,7,5/2/2015,"3,219","2,360","1,958",402 -232,Sunday,1,5/3/2015,"4,211","3,210","2,765",445 -233,Monday,2,5/4/2015,"6,424","4,752","4,043",709 -234,Tuesday,3,5/5/2015,"6,223","4,520","3,818",702 -235,Wednesday,4,5/6/2015,"6,551","4,768","4,059",709 -236,Thursday,5,5/7/2015,"6,343","4,449","3,770",679 -237,Friday,6,5/8/2015,"4,696","3,366","2,824",542 -238,Saturday,7,5/9/2015,"3,134","2,239","1,846",393 -239,Sunday,1,5/10/2015,"3,868","2,844","2,365",479 -240,Monday,2,5/11/2015,"6,020","4,482","3,787",695 -241,Tuesday,3,5/12/2015,"5,705","4,160","3,490",670 -242,Wednesday,4,5/13/2015,"5,752","4,016","3,348",668 -243,Thursday,5,5/14/2015,"5,181","3,605","3,008",597 -244,Friday,6,5/15/2015,"4,269","2,913","2,404",509 -245,Saturday,7,5/16/2015,"2,633","1,818","1,473",345 -246,Sunday,1,5/17/2015,"3,336","2,376","1,988",388 -247,Monday,2,5/18/2015,"5,127","3,587","2,955",632 -248,Tuesday,3,5/19/2015,"5,449","3,767","3,149",618 -249,Wednesday,4,5/20/2015,"5,746","3,956","3,371",585 -250,Thursday,5,5/21/2015,"5,115","3,642","3,046",596 -251,Friday,6,5/22/2015,"3,995","2,749","2,243",506 -252,Saturday,7,5/23/2015,"2,618","1,884","1,585",299 -253,Sunday,1,5/24/2015,"2,961","2,117","1,763",354 -254,Monday,2,5/25/2015,"4,090","2,913","2,427",486 -255,Tuesday,3,5/26/2015,"5,201","3,637","3,073",564 -256,Wednesday,4,5/27/2015,"5,321","3,797","3,146",651 -257,Thursday,5,5/28/2015,"5,219","3,509","2,887",622 -258,Friday,6,5/29/2015,"4,017","2,804","2,338",466 -259,Saturday,7,5/30/2015,"2,297","1,629","1,346",283 -260,Sunday,1,5/31/2015,"3,145","2,226","1,860",366 -261,Monday,2,6/1/2015,"4,905","3,419","2,797",622 -262,Tuesday,3,6/2/2015,"4,747","3,284","2,701",583 -263,Wednesday,4,6/3/2015,"4,591","3,317","2,713",604 -264,Thursday,5,6/4/2015,"4,431","3,173","2,652",521 -265,Friday,6,6/5/2015,"3,608","2,523","2,068",455 -266,Saturday,7,6/6/2015,"2,422","1,673","1,336",337 -267,Sunday,1,6/7/2015,"3,132","2,145","1,790",355 -268,Monday,2,6/8/2015,"4,962","3,377","2,788",589 -269,Tuesday,3,6/9/2015,"4,881","3,469","2,862",607 -270,Wednesday,4,6/10/2015,"5,001","3,425","2,786",639 -271,Thursday,5,6/11/2015,"4,635","3,242","2,614",628 -272,Friday,6,6/12/2015,"3,545","2,494","2,066",428 -273,Saturday,7,6/13/2015,"2,262","1,490","1,235",255 -274,Sunday,1,6/14/2015,"2,866","2,034","1,692",342 -275,Monday,2,6/15/2015,"4,692","3,261","2,682",579 -276,Tuesday,3,6/16/2015,"4,602","3,204","2,660",544 -277,Wednesday,4,6/17/2015,"4,620","3,101","2,556",545 -278,Thursday,5,6/18/2015,"4,475","2,955","2,386",569 -279,Friday,6,6/19/2015,"3,564","2,422","1,954",468 -280,Saturday,7,6/20/2015,"2,071","1,370","1,129",241 -281,Sunday,1,6/21/2015,"2,659","1,840","1,523",317 -282,Monday,2,6/22/2015,"4,338","2,963","2,410",553 -283,Tuesday,3,6/23/2015,"4,531","3,139","2,566",573 -284,Wednesday,4,6/24/2015,"4,498","3,022","2,481",541 -285,Thursday,5,6/25/2015,"4,128","2,730","2,210",520 -286,Friday,6,6/26/2015,"3,132","2,153","1,721",432 -287,Saturday,7,6/27/2015,"1,829","1,264","1,067",197 -288,Sunday,1,6/28/2015,"2,149","1,517","1,224",293 -289,Monday,2,6/29/2015,"3,975","2,691","2,169",522 -290,Tuesday,3,6/30/2015,"4,046","2,718","2,180",538 -291,Wednesday,4,7/1/2015,"3,897","2,745","2,266",479 -292,Thursday,5,7/2/2015,"3,871","2,540","2,072",468 -293,Friday,6,7/3/2015,"2,630","1,716","1,353",363 -294,Saturday,7,7/4/2015,"1,602","1,104",900,204 -295,Sunday,1,7/5/2015,"1,960","1,370","1,141",229 -296,Monday,2,7/6/2015,"4,143","2,655","2,171",484 -297,Tuesday,3,7/7/2015,"4,205","2,789","2,266",523 -298,Wednesday,4,7/8/2015,"4,193","2,783","2,255",528 -299,Thursday,5,7/9/2015,"3,710","2,536","2,069",467 -300,Friday,6,7/10/2015,"3,137","2,203","1,767",436 -301,Saturday,7,7/11/2015,"1,834","1,145",944,201 -302,Sunday,1,7/12/2015,"2,070","1,473","1,197",276 -303,Monday,2,7/13/2015,"4,180","2,852","2,310",542 -304,Tuesday,3,7/14/2015,"4,038","2,731","2,226",505 -305,Wednesday,4,7/15/2015,"3,906","2,667","2,119",548 -306,Thursday,5,7/16/2015,"3,580","2,424","1,940",484 -307,Friday,6,7/17/2015,"2,948","2,070","1,708",362 -308,Saturday,7,7/18/2015,"1,622","1,121",924,197 -309,Sunday,1,7/19/2015,"2,179","1,480","1,214",266 -310,Monday,2,7/20/2015,"3,906","2,615","2,146",469 -311,Tuesday,3,7/21/2015,"3,745","2,596","2,071",525 -312,Wednesday,4,7/22/2015,"4,212","2,761","2,205",556 -313,Thursday,5,7/23/2015,"3,901","2,595","2,095",500 -314,Friday,6,7/24/2015,"3,292","2,207","1,785",422 -315,Saturday,7,7/25/2015,"1,819","1,229","1,016",213 -316,Sunday,1,7/26/2015,"2,068","1,562","1,286",276 -317,Monday,2,7/27/2015,"3,991","2,805","2,243",562 -318,Tuesday,3,7/28/2015,"4,073","2,862","2,361",501 -319,Wednesday,4,7/29/2015,"4,069","2,825","2,324",501 -320,Thursday,5,7/30/2015,"3,716","2,629","2,126",503 -321,Friday,6,7/31/2015,"3,380","2,120","1,709",411 -322,Saturday,7,8/1/2015,"1,737","1,197",958,239 -323,Sunday,1,8/2/2015,"2,181","1,567","1,282",285 -324,Monday,2,8/3/2015,"3,973","2,794","2,263",531 -325,Tuesday,3,8/4/2015,"3,997","2,794","2,269",525 -326,Wednesday,4,8/5/2015,"4,132","2,798","2,287",511 -327,Thursday,5,8/6/2015,"3,791","2,693","2,204",489 -328,Friday,6,8/7/2015,"2,968","2,139","1,738",401 -329,Saturday,7,8/8/2015,"1,752","1,266","1,021",245 -330,Sunday,1,8/9/2015,"2,070","1,497","1,231",266 -331,Monday,2,8/10/2015,"3,920","2,684","2,139",545 -332,Tuesday,3,8/11/2015,"4,106","2,791","2,266",525 -333,Wednesday,4,8/12/2015,"4,006","2,726","2,166",560 -334,Thursday,5,8/13/2015,"3,827","2,639","2,133",506 -335,Friday,6,8/14/2015,"2,944","1,973","1,564",409 -336,Saturday,7,8/15/2015,"1,827","1,193",954,239 -337,Sunday,1,8/16/2015,"2,373","1,658","1,369",289 -338,Monday,2,8/17/2015,"4,031","2,763","2,290",473 -339,Tuesday,3,8/18/2015,"4,027","2,780","2,248",532 -340,Wednesday,4,8/19/2015,"3,912","2,795","2,291",504 -341,Thursday,5,8/20/2015,"3,867","2,715","2,191",524 -342,Friday,6,8/21/2015,"3,304","2,238","1,806",432 -343,Saturday,7,8/22/2015,"1,685","1,208","1,011",197 -344,Sunday,1,8/23/2015,"2,130","1,542","1,282",260 -345,Monday,2,8/24/2015,"3,936","2,697","2,182",515 -346,Tuesday,3,8/25/2015,"4,029","2,744","2,203",541 -347,Wednesday,4,8/26/2015,"4,010","2,789","2,237",552 -348,Thursday,5,8/27/2015,"4,027","2,890","2,392",498 -349,Friday,6,8/28/2015,"2,829","2,000","1,585",415 -350,Saturday,7,8/29/2015,"1,736","1,227","1,023",204 -351,Sunday,1,8/30/2015,"2,517","1,686","1,416",270 -352,Monday,2,8/31/2015,"4,029","2,769","2,299",470 -353,Tuesday,3,9/1/2015,"4,261","2,890","2,358",532 -354,Wednesday,4,9/2/2015,"4,058","2,923","2,442",481 -355,Thursday,5,9/3/2015,"4,392","3,007","2,466",541 -356,Friday,6,9/4/2015,"3,072","2,221","1,846",375 -357,Saturday,7,9/5/2015,"1,799","1,284","1,068",216 -358,Sunday,1,9/6/2015,"2,341","1,611","1,345",266 -359,Monday,2,9/7/2015,"3,330","2,354","1,963",391 -360,Tuesday,3,9/8/2015,"4,734","3,381","2,833",548 -361,Wednesday,4,9/9/2015,"4,801","3,381","2,845",536 -362,Thursday,5,9/10/2015,"4,399","3,079","2,575",504 -363,Friday,6,9/11/2015,"3,275","2,375","1,958",417 -364,Saturday,7,9/12/2015,"2,042","1,429","1,223",206 -365,Sunday,1,9/13/2015,"2,827","2,103","1,844",259 -366,Monday,2,9/14/2015,"4,836","3,437","2,954",483 -367,Tuesday,3,9/15/2015,"4,840","3,465","2,944",521 -368,Wednesday,4,9/16/2015,"4,504","3,356","2,902",454 -369,Thursday,5,9/17/2015,"4,379","3,240","2,736",504 -370,Friday,6,9/18/2015,"3,391","2,428","2,024",404 -371,Saturday,7,9/19/2015,"2,045","1,513","1,278",235 -372,Sunday,1,9/20/2015,"2,721","2,081","1,817",264 -373,Monday,2,9/21/2015,"4,630","3,535","2,998",537 -374,Tuesday,3,9/22/2015,"4,748","3,540","3,004",536 -375,Wednesday,4,9/23/2015,"4,702","3,377","2,893",484 -376,Thursday,5,9/24/2015,"4,440","3,309","2,827",482 -377,Friday,6,9/25/2015,"3,513","2,518","2,116",402 -378,Saturday,7,9/26/2015,"2,253","1,633","1,395",238 -379,Sunday,1,9/27/2015,"3,141","2,291","1,980",311 -380,Monday,2,9/28/2015,"5,122","3,808","3,289",519 -381,Tuesday,3,9/29/2015,"5,151","3,868","3,305",563 -382,Wednesday,4,9/30/2015,"4,969","3,703","3,182",521 -383,Thursday,5,10/1/2015,"4,619","3,365","2,876",489 -384,Friday,6,10/2/2015,"3,443","2,598","2,196",402 -385,Saturday,7,10/3/2015,"2,302","1,754","1,503",251 -386,Sunday,1,10/4/2015,"3,291","2,506","2,173",333 -387,Monday,2,10/5/2015,"5,206","3,737","3,170",567 -388,Tuesday,3,10/6/2015,"5,474","3,940","3,337",603 -389,Wednesday,4,10/7/2015,"5,400","3,934","3,350",584 -390,Thursday,5,10/8/2015,"5,334","3,821","3,244",577 -391,Friday,6,10/9/2015,"4,174","3,022","2,547",475 -392,Saturday,7,10/10/2015,"2,571","1,853","1,597",256 -393,Sunday,1,10/11/2015,"3,572","2,636","2,283",353 -394,Monday,2,10/12/2015,"5,018","3,788","3,241",547 -395,Tuesday,3,10/13/2015,"5,565","4,159","3,536",623 -396,Wednesday,4,10/14/2015,"5,420","4,048","3,403",645 -397,Thursday,5,10/15/2015,"5,366","3,908","3,331",577 -398,Friday,6,10/16/2015,"4,091","3,002","2,507",495 -399,Saturday,7,10/17/2015,"2,824","2,087","1,770",317 -400,Sunday,1,10/18/2015,"3,843","2,825","2,441",384 -401,Monday,2,10/19/2015,"5,516","4,140","3,491",649 -402,Tuesday,3,10/20/2015,"5,572","4,121","3,460",661 -403,Wednesday,4,10/21/2015,"5,243","3,882","3,298",584 -404,Thursday,5,10/22/2015,"5,133","3,720","3,104",616 -405,Friday,6,10/23/2015,"4,084","2,947","2,441",506 -406,Saturday,7,10/24/2015,"2,739","1,978","1,647",331 -407,Sunday,1,10/25/2015,"3,781","2,857","2,435",422 -408,Monday,2,10/26/2015,"5,684","4,073","3,417",656 -409,Tuesday,3,10/27/2015,"5,687","4,179","3,534",645 -410,Wednesday,4,10/28/2015,"5,906","4,266","3,614",652 -411,Thursday,5,10/29/2015,"5,541","4,020","3,368",652 -412,Friday,6,10/30/2015,"4,171","3,043","2,501",542 -413,Saturday,7,10/31/2015,"2,586","1,820","1,496",324 -414,Sunday,1,11/1/2015,"3,838","2,922","2,456",466 -415,Monday,2,11/2/2015,"5,675","4,076","3,419",657 -416,Tuesday,3,11/3/2015,"5,624","4,178","3,504",674 -417,Wednesday,4,11/4/2015,"5,794","4,316","3,670",646 -418,Thursday,5,11/5/2015,"5,389","4,031","3,379",652 -419,Friday,6,11/6/2015,"4,305","3,047","2,522",525 -420,Saturday,7,11/7/2015,"3,281","2,202","1,854",348 -421,Sunday,1,11/8/2015,"3,790","2,837","2,418",419 -422,Monday,2,11/9/2015,"5,894","4,203","3,487",716 -423,Tuesday,3,11/10/2015,"5,461","4,048","3,388",660 -424,Wednesday,4,11/11/2015,"5,206","3,869","3,219",650 -425,Thursday,5,11/12/2015,"5,228","3,929","3,281",648 -426,Friday,6,11/13/2015,"4,097","2,971","2,436",535 -427,Saturday,7,11/14/2015,"2,694","1,964","1,616",348 -428,Sunday,1,11/15/2015,"3,893","2,930","2,483",447 -429,Monday,2,11/16/2015,"6,185","4,550","3,872",678 -430,Tuesday,3,11/17/2015,"6,178","4,503","3,795",708 -431,Wednesday,4,11/18/2015,"6,067","4,397","3,693",704 -432,Thursday,5,11/19/2015,"5,656","4,166","3,487",679 -433,Friday,6,11/20/2015,"4,398","3,173","2,645",528 -434,Saturday,7,11/21/2015,"2,836","2,086","1,741",345 -435,Sunday,1,11/22/2015,"3,896","2,927","2,424",503 -436,Monday,2,11/23/2015,"5,814","4,236","3,543",693 -437,Tuesday,3,11/24/2015,"5,879","4,140","3,475",665 -438,Wednesday,4,11/25/2015,"4,800","3,457","2,836",621 -439,Thursday,5,11/26/2015,"3,838","2,825","2,334",491 -440,Friday,6,11/27/2015,"3,576","2,579","2,118",461 -441,Saturday,7,11/28/2015,"2,799","1,974","1,652",322 -442,Sunday,1,11/29/2015,"4,228","3,127","2,605",522 -443,Monday,2,11/30/2015,"6,331","4,570","3,817",753 -444,Tuesday,3,12/1/2015,"6,380","4,743","4,027",716 -445,Wednesday,4,12/2/2015,"6,314","4,829","4,039",790 -446,Thursday,5,12/3/2015,"6,160","4,584","3,835",749 -447,Friday,6,12/4/2015,"4,859","3,615","2,995",620 -448,Saturday,7,12/5/2015,"3,317","2,468","2,040",428 -449,Sunday,1,12/6/2015,"4,715","3,550","2,972",578 -450,Monday,2,12/7/2015,"6,940","5,136","4,303",833 -451,Tuesday,3,12/8/2015,"6,551","4,866","4,068",798 -452,Wednesday,4,12/9/2015,"6,327","4,761","3,961",800 -453,Thursday,5,12/10/2015,"6,212","4,578","3,830",748 -454,Friday,6,12/11/2015,"4,687","3,388","2,771",617 -455,Saturday,7,12/12/2015,"3,014","2,283","1,856",427 -456,Sunday,1,12/13/2015,"4,132","3,064","2,548",516 -457,Monday,2,12/14/2015,"5,989","4,330","3,606",724 -458,Tuesday,3,12/15/2015,"5,331","3,967","3,309",658 -459,Wednesday,4,12/16/2015,"5,183","3,665","3,025",640 -460,Thursday,5,12/17/2015,"4,769","3,515","2,917",598 -461,Friday,6,12/18/2015,"3,627","2,518","2,071",447 -462,Saturday,7,12/19/2015,"1,857","1,319","1,087",232 -463,Sunday,1,12/20/2015,"2,221","1,489","1,204",285 -464,Monday,2,12/21/2015,"3,335","2,335","1,906",429 -465,Tuesday,3,12/22/2015,"2,883","2,047","1,639",408 -466,Wednesday,4,12/23/2015,"2,353","1,621","1,312",309 -467,Thursday,5,12/24/2015,"1,578","1,099",886,213 -468,Friday,6,12/25/2015,"1,017",724,586,138 -469,Saturday,7,12/26/2015,"1,334",936,760,176 -470,Sunday,1,12/27/2015,"1,690","1,155",947,208 -471,Monday,2,12/28/2015,"2,838","1,829","1,474",355 -472,Tuesday,3,12/29/2015,"2,815","1,786","1,419",367 -473,Wednesday,4,12/30/2015,"2,382","1,618","1,292",326 -474,Thursday,5,12/31/2015,"1,587","1,035",809,226 -475,Friday,6,1/1/2016,"1,411",960,786,174 -476,Saturday,7,1/2/2016,"1,838","1,197",969,228 -477,Sunday,1,1/3/2016,"2,180","1,562","1,279",283 -478,Monday,2,1/4/2016,"3,587","2,494","2,037",457 -479,Tuesday,3,1/5/2016,"3,855","2,742","2,230",512 -480,Wednesday,4,1/6/2016,"3,840","2,628","2,127",501 -481,Thursday,5,1/7/2016,"3,735","2,746","2,258",488 -482,Friday,6,1/8/2016,"3,223","2,286","1,860",426 -483,Saturday,7,1/9/2016,"2,207","1,531","1,280",251 -484,Sunday,1,1/10/2016,"2,814","2,000","1,661",339 -485,Monday,2,1/11/2016,"4,007","2,976","2,444",532 -486,Tuesday,3,1/12/2016,"4,119","2,961","2,385",576 -487,Wednesday,4,1/13/2016,"4,338","3,164","2,631",533 -488,Thursday,5,1/14/2016,"4,432","2,962","2,418",544 -489,Friday,6,1/15/2016,"3,306","2,349","1,866",483 -490,Saturday,7,1/16/2016,"2,223","1,557","1,262",295 -491,Sunday,1,1/17/2016,"2,757","2,002","1,635",367 -492,Monday,2,1/18/2016,"4,047","2,898","2,373",525 -493,Tuesday,3,1/19/2016,"4,489","3,190","2,566",624 -494,Wednesday,4,1/20/2016,"4,588","3,232","2,656",576 -495,Thursday,5,1/21/2016,"4,398","3,106","2,561",545 -496,Friday,6,1/22/2016,"3,340","2,455","1,990",465 -497,Saturday,7,1/23/2016,"2,344","1,639","1,338",301 -498,Sunday,1,1/24/2016,"2,973","2,094","1,736",358 -499,Monday,2,1/25/2016,"4,638","3,205","2,649",556 -500,Tuesday,3,1/26/2016,"4,791","3,409","2,834",575 -501,Wednesday,4,1/27/2016,"5,104","3,768","3,197",571 -502,Thursday,5,1/28/2016,"4,621","3,439","2,876",563 -503,Friday,6,1/29/2016,"3,831","2,715","2,232",483 -504,Saturday,7,1/30/2016,"2,295","1,690","1,400",290 -505,Sunday,1,1/31/2016,"3,235","2,462","2,050",412 -506,Monday,2,2/1/2016,"4,918","3,640","3,060",580 -507,Tuesday,3,2/2/2016,"5,127","3,783","3,195",588 -508,Wednesday,4,2/3/2016,"4,794","3,575","2,999",576 -509,Thursday,5,2/4/2016,"4,788","3,420","2,848",572 -510,Friday,6,2/5/2016,"3,948","2,727","2,256",471 -511,Saturday,7,2/6/2016,"2,383","1,765","1,477",288 -512,Sunday,1,2/7/2016,"2,950","2,231","1,839",392 -513,Monday,2,2/8/2016,"4,905","3,592","3,024",568 -514,Tuesday,3,2/9/2016,"5,353","3,931","3,299",632 -515,Wednesday,4,2/10/2016,"5,492","4,007","3,358",649 -516,Thursday,5,2/11/2016,"5,052","3,628","3,019",609 -517,Friday,6,2/12/2016,"3,764","2,807","2,300",507 -518,Saturday,7,2/13/2016,"2,458","1,788","1,459",329 -519,Sunday,1,2/14/2016,"3,098","2,277","1,866",411 -520,Monday,2,2/15/2016,"4,951","3,652","3,039",613 -521,Tuesday,3,2/16/2016,"5,495","4,000","3,304",696 -522,Wednesday,4,2/17/2016,"5,505","4,062","3,375",687 -523,Thursday,5,2/18/2016,"5,188","3,812","3,156",656 -524,Friday,6,2/19/2016,"4,002","2,932","2,377",555 -525,Saturday,7,2/20/2016,"2,607","1,888","1,537",351 -526,Sunday,1,2/21/2016,"3,906","2,838","2,355",483 -527,Monday,2,2/22/2016,"6,360","4,432","3,684",748 -528,Tuesday,3,2/23/2016,"6,046","4,460","3,714",746 -529,Wednesday,4,2/24/2016,"5,938","4,309","3,499",810 -530,Thursday,5,2/25/2016,"5,325","3,892","3,199",693 -531,Friday,6,2/26/2016,"4,165","3,048","2,490",558 -532,Saturday,7,2/27/2016,"2,829","2,096","1,726",370 -533,Sunday,1,2/28/2016,"4,090","2,979","2,462",517 -534,Monday,2,2/29/2016,"5,734","4,224","3,493",731 -535,Tuesday,3,3/1/2016,"6,214","4,474","3,731",743 -536,Wednesday,4,3/2/2016,"5,906","4,333","3,597",736 -537,Thursday,5,3/3/2016,"5,814","4,191","3,419",772 -538,Friday,6,3/4/2016,"4,507","3,188","2,624",564 -539,Saturday,7,3/5/2016,"2,859","2,064","1,718",346 -540,Sunday,1,3/6/2016,"3,897","2,806","2,326",480 -541,Monday,2,3/7/2016,"5,849","4,240","3,503",737 -542,Tuesday,3,3/8/2016,"6,264","4,526","3,743",783 -543,Wednesday,4,3/9/2016,"6,062","4,404","3,624",780 -544,Thursday,5,3/10/2016,"5,685","4,159","3,415",744 -545,Friday,6,3/11/2016,"4,391","3,241","2,627",614 -546,Saturday,7,3/12/2016,"2,880","2,046","1,638",408 -547,Sunday,1,3/13/2016,"3,740","2,673","2,163",510 -548,Monday,2,3/14/2016,"5,710","4,261","3,469",792 -549,Tuesday,3,3/15/2016,"5,168","3,778","3,047",731 -550,Wednesday,4,3/16/2016,"5,577","4,062","3,287",775 -551,Thursday,5,3/17/2016,"5,238","3,827","3,134",693 -552,Friday,6,3/18/2016,"3,931","2,871","2,320",551 -553,Saturday,7,3/19/2016,"2,606","1,892","1,568",324 -554,Sunday,1,3/20/2016,"3,149","2,410","2,028",382 -555,Monday,2,3/21/2016,"5,565","3,968","3,274",694 -556,Tuesday,3,3/22/2016,"5,281","3,770","3,035",735 -557,Wednesday,4,3/23/2016,"5,202","3,780","3,146",634 -558,Thursday,5,3/24/2016,"4,581","3,203","2,649",554 -559,Friday,6,3/25/2016,"3,264","2,281","1,847",434 -560,Saturday,7,3/26/2016,"2,344","1,759","1,393",366 -561,Sunday,1,3/27/2016,"3,112","2,311","1,890",421 -562,Monday,2,3/28/2016,"5,051","3,655","2,985",670 -563,Tuesday,3,3/29/2016,"5,333","3,944","3,217",727 -564,Wednesday,4,3/30/2016,"5,671","4,078","3,369",709 -565,Thursday,5,3/31/2016,"5,097","3,707","3,026",681 -566,Friday,6,4/1/2016,"4,555","2,971","2,408",563 -567,Saturday,7,4/2/2016,"2,987","2,129","1,737",392 -568,Sunday,1,4/3/2016,"4,257","2,964","2,421",543 -569,Monday,2,4/4/2016,"5,804","4,216","3,426",790 -570,Tuesday,3,4/5/2016,"5,885","4,379","3,597",782 -571,Wednesday,4,4/6/2016,"6,054","4,293","3,522",771 -572,Thursday,5,4/7/2016,"5,791","4,147","3,422",725 -573,Friday,6,4/8/2016,"4,741","3,430","2,820",610 -574,Saturday,7,4/9/2016,"2,946","2,140","1,740",400 -575,Sunday,1,4/10/2016,"4,071","2,864","2,330",534 -576,Monday,2,4/11/2016,"6,265","4,503","3,667",836 -577,Tuesday,3,4/12/2016,"6,298","4,626","3,769",857 -578,Wednesday,4,4/13/2016,"6,495","4,628","3,791",837 -579,Thursday,5,4/14/2016,"5,536","4,189","3,431",758 -580,Friday,6,4/15/2016,"4,737","3,447","2,785",662 -581,Saturday,7,4/16/2016,"3,232","2,333","1,862",471 -582,Sunday,1,4/17/2016,"4,201","3,093","2,504",589 -583,Monday,2,4/18/2016,"6,806","4,769","3,920",849 -584,Tuesday,3,4/19/2016,"6,769","4,817","3,940",877 -585,Wednesday,4,4/20/2016,"6,581","4,824","3,926",898 -586,Thursday,5,4/21/2016,"6,254","4,537","3,707",830 -587,Friday,6,4/22/2016,"5,163","3,685","3,009",676 -588,Saturday,7,4/23/2016,"3,851","2,595","2,113",482 -589,Sunday,1,4/24/2016,"4,695","3,386","2,761",625 -590,Monday,2,4/25/2016,"7,098","5,224","4,237",987 -591,Tuesday,3,4/26/2016,"7,156","5,392","4,468",924 -592,Wednesday,4,4/27/2016,"6,836","5,056","4,130",926 -593,Thursday,5,4/28/2016,"6,329","4,724","3,892",832 -594,Friday,6,4/29/2016,"4,990","3,608","2,922",686 -595,Saturday,7,4/30/2016,"3,536","2,489","1,993",496 -596,Sunday,1,5/1/2016,"4,366","3,236","2,605",631 -597,Monday,2,5/2/2016,"6,510","4,786","3,909",877 -598,Tuesday,3,5/3/2016,"6,855","4,962","4,085",877 -599,Wednesday,4,5/4/2016,"6,599","4,651","3,744",907 -600,Thursday,5,5/5/2016,"5,510","4,105","3,375",730 -601,Friday,6,5/6/2016,"4,549","3,245","2,639",606 -602,Saturday,7,5/7/2016,"3,064","2,167","1,760",407 -603,Sunday,1,5/8/2016,"3,633","2,663","2,180",483 -604,Monday,2,5/9/2016,"5,838","4,283","3,489",794 -605,Tuesday,3,5/10/2016,"6,026","4,336","3,581",755 -606,Wednesday,4,5/11/2016,"5,780","4,226","3,479",747 -607,Thursday,5,5/12/2016,"5,131","3,781","3,101",680 -608,Friday,6,5/13/2016,"3,928","2,845","2,294",551 -609,Saturday,7,5/14/2016,"2,636","1,930","1,552",378 -610,Sunday,1,5/15/2016,"3,278","2,366","1,922",444 -611,Monday,2,5/16/2016,"4,785","3,560","2,882",678 -612,Tuesday,3,5/17/2016,"4,886","3,601","2,914",687 -613,Wednesday,4,5/18/2016,"5,145","3,672","2,987",685 -614,Thursday,5,5/19/2016,"5,849","4,211","3,484",727 -615,Friday,6,5/20/2016,"4,456","3,324","2,725",599 -616,Saturday,7,5/21/2016,"2,960","2,138","1,716",422 -617,Sunday,1,5/22/2016,"3,885","2,860","2,338",522 -618,Monday,2,5/23/2016,"5,857","4,155","3,368",787 -619,Tuesday,3,5/24/2016,"6,162","4,345","3,526",819 -620,Wednesday,4,5/25/2016,"6,014","4,254","3,398",856 -621,Thursday,5,5/26/2016,"5,652","4,119","3,385",734 -622,Friday,6,5/27/2016,"4,793","3,340","2,692",648 -623,Saturday,7,5/28/2016,"2,819","1,995","1,611",384 -624,Sunday,1,5/29/2016,"3,687","2,546","2,067",479 -625,Monday,2,5/30/2016,"4,851","3,399","2,701",698 -626,Tuesday,3,5/31/2016,"5,775","4,216","3,413",803 -627,Wednesday,4,6/1/2016,"5,443","3,962","3,193",769 -628,Thursday,5,6/2/2016,"5,590","3,959","3,176",783 -629,Friday,6,6/3/2016,"4,482","3,164","2,489",675 -630,Saturday,7,6/4/2016,"2,792","1,947","1,533",414 -631,Sunday,1,6/5/2016,"3,371","2,449","1,979",470 -632,Monday,2,6/6/2016,"5,547","3,832","3,036",796 -633,Tuesday,3,6/7/2016,"5,468","3,954","3,226",728 -634,Wednesday,4,6/8/2016,"5,104","3,751","3,064",687 -635,Thursday,5,6/9/2016,"4,728","3,485","2,812",673 -636,Friday,6,6/10/2016,"4,024","2,855","2,290",565 -637,Saturday,7,6/11/2016,"2,350","1,677","1,348",329 -638,Sunday,1,6/12/2016,"3,234","2,196","1,779",417 -639,Monday,2,6/13/2016,"4,948","3,550","2,790",760 -640,Tuesday,3,6/14/2016,"5,247","3,760","3,052",708 -641,Wednesday,4,6/15/2016,"5,003","3,557","2,810",747 -642,Thursday,5,6/16/2016,"4,816","3,482","2,775",707 -643,Friday,6,6/17/2016,"4,208","2,818","2,267",551 -644,Saturday,7,6/18/2016,"2,454","1,668","1,357",311 -645,Sunday,1,6/19/2016,"2,763","1,919","1,506",413 -646,Monday,2,6/20/2016,"4,954","3,522","2,829",693 -647,Tuesday,3,6/21/2016,"4,903","3,470","2,793",677 -648,Wednesday,4,6/22/2016,"4,907","3,464","2,772",692 -649,Thursday,5,6/23/2016,"4,625","3,336","2,669",667 -650,Friday,6,6/24/2016,"3,497","2,482","1,947",535 -651,Saturday,7,6/25/2016,"1,826","1,358","1,101",257 -652,Sunday,1,6/26/2016,"2,307","1,721","1,356",365 -653,Monday,2,6/27/2016,"4,368","3,078","2,433",645 -654,Tuesday,3,6/28/2016,"4,233","3,091","2,466",625 -655,Wednesday,4,6/29/2016,"4,187","2,948","2,377",571 -656,Thursday,5,6/30/2016,"3,811","2,726","2,135",591 -657,Friday,6,7/1/2016,"3,179","2,226","1,730",496 -658,Saturday,7,7/2/2016,"1,615","1,039",814,225 -659,Sunday,1,7/3/2016,"1,788","1,307","1,028",279 -660,Monday,2,7/4/2016,"2,959","2,144","1,653",491 -661,Tuesday,3,7/5/2016,"3,947","2,840","2,235",605 -662,Wednesday,4,7/6/2016,"3,841","2,821","2,248",573 -663,Thursday,5,7/7/2016,"3,729","2,700","2,148",552 -664,Friday,6,7/8/2016,"3,324","2,381","1,891",490 -665,Saturday,7,7/9/2016,"1,795","1,305","1,036",269 -666,Sunday,1,7/10/2016,"2,373","1,655","1,321",334 -667,Monday,2,7/11/2016,"4,101","2,855","2,208",647 -668,Tuesday,3,7/12/2016,"4,241","2,960","2,352",608 -669,Wednesday,4,7/13/2016,"4,478","3,189","2,578",611 -670,Thursday,5,7/14/2016,"3,961","2,814","2,214",600 -671,Friday,6,7/15/2016,"3,367","2,325","1,796",529 -672,Saturday,7,7/16/2016,"1,828","1,222",951,271 -673,Sunday,1,7/17/2016,"2,344","1,636","1,319",317 -674,Monday,2,7/18/2016,"4,415","3,007","2,382",625 -675,Tuesday,3,7/19/2016,"4,403","3,151","2,502",649 -676,Wednesday,4,7/20/2016,"4,244","3,082","2,450",632 -677,Thursday,5,7/21/2016,"4,322","3,077","2,404",673 -678,Friday,6,7/22/2016,"3,456","2,431","1,936",495 -679,Saturday,7,7/23/2016,"2,068","1,407","1,099",308 -680,Sunday,1,7/24/2016,"2,568","1,755","1,374",381 -681,Monday,2,7/25/2016,"4,770","3,320","2,628",692 -682,Tuesday,3,7/26/2016,"4,621","3,277","2,595",682 -683,Wednesday,4,7/27/2016,"4,470","3,164","2,513",651 -684,Thursday,5,7/28/2016,"4,307","3,077","2,459",618 -685,Friday,6,7/29/2016,"3,370","2,366","1,818",548 -686,Saturday,7,7/30/2016,"1,960","1,341","1,017",324 -687,Sunday,1,7/31/2016,"2,412","1,765","1,423",342 -688,Monday,2,8/1/2016,"4,504","3,095","2,489",606 -689,Tuesday,3,8/2/2016,"4,382","3,138","2,499",639 -690,Wednesday,4,8/3/2016,"4,710","3,369","2,697",672 -691,Thursday,5,8/4/2016,"4,324","3,106","2,479",627 -692,Friday,6,8/5/2016,"3,472","2,497","1,980",517 -693,Saturday,7,8/6/2016,"1,909","1,339","1,081",258 -694,Sunday,1,8/7/2016,"2,365","1,688","1,334",354 -695,Monday,2,8/8/2016,"4,460","3,056","2,438",618 -696,Tuesday,3,8/9/2016,"4,479","3,135","2,482",653 -697,Wednesday,4,8/10/2016,"4,510","3,207","2,550",657 -698,Thursday,5,8/11/2016,"4,340","3,111","2,491",620 -699,Friday,6,8/12/2016,"3,508","2,436","1,933",503 -700,Saturday,7,8/13/2016,"2,030","1,420","1,122",298 -701,Sunday,1,8/14/2016,"2,586","1,749","1,399",350 -702,Monday,2,8/15/2016,"4,476","2,970","2,346",624 -703,Tuesday,3,8/16/2016,"3,232","2,299","1,823",476 -704,Wednesday,4,8/17/2016,"4,291","3,106","2,498",608 -705,Thursday,5,8/18/2016,"4,247","2,926","2,327",599 -706,Friday,6,8/19/2016,"3,477","2,539","2,045",494 -707,Saturday,7,8/20/2016,"2,169","1,470","1,169",301 -708,Sunday,1,8/21/2016,"2,430","1,808","1,453",355 -709,Monday,2,8/22/2016,"4,358","3,082","2,439",643 -710,Tuesday,3,8/23/2016,"4,465","3,249","2,550",699 -711,Wednesday,4,8/24/2016,"4,550","3,210","2,615",595 -712,Thursday,5,8/25/2016,"4,147","3,011","2,380",631 -713,Friday,6,8/26/2016,"3,634","2,532","2,000",532 -714,Saturday,7,8/27/2016,"2,014","1,430","1,141",289 -715,Sunday,1,8/28/2016,"2,687","1,910","1,555",355 -716,Monday,2,8/29/2016,"4,376","3,115","2,521",594 -717,Tuesday,3,8/30/2016,"4,318","3,226","2,622",604 -718,Wednesday,4,8/31/2016,"4,441","3,219","2,620",599 -719,Thursday,5,9/1/2016,"4,234","3,116","2,556",560 -720,Friday,6,9/2/2016,"3,027","2,230","1,762",468 -721,Saturday,7,9/3/2016,"1,966","1,376","1,101",275 -722,Sunday,1,9/4/2016,"2,446","1,761","1,452",309 -723,Monday,2,9/5/2016,"3,779","2,643","2,145",498 -724,Tuesday,3,9/6/2016,"4,619","3,341","2,727",614 -725,Wednesday,4,9/7/2016,"4,983","3,584","2,925",659 -726,Thursday,5,9/8/2016,"4,645","3,319","2,716",603 -727,Friday,6,9/9/2016,"3,612","2,630","2,139",491 -728,Saturday,7,9/10/2016,"2,153","1,577","1,323",254 -729,Sunday,1,9/11/2016,"3,089","2,307","1,978",329 -730,Monday,2,9/12/2016,"4,580","3,446","2,882",564 -731,Tuesday,3,9/13/2016,"4,788","3,625","2,992",633 -732,Wednesday,4,9/14/2016,"5,095","3,759","3,113",646 -733,Thursday,5,9/15/2016,"4,668","3,454","2,860",594 -734,Friday,6,9/16/2016,"3,711","2,785","2,272",513 -735,Saturday,7,9/17/2016,"2,523","1,861","1,577",284 -736,Sunday,1,9/18/2016,"3,364","2,547","2,122",425 -737,Monday,2,9/19/2016,"5,513","4,050","3,321",729 -738,Tuesday,3,9/20/2016,"5,362","3,762","3,095",667 -739,Wednesday,4,9/21/2016,"5,402","3,987","3,293",694 -740,Thursday,5,9/22/2016,"5,398","3,996","3,299",697 -741,Friday,6,9/23/2016,"3,783","2,663","2,134",529 -742,Saturday,7,9/24/2016,"2,395","1,797","1,503",294 -743,Sunday,1,9/25/2016,"3,679","2,719","2,289",430 -744,Monday,2,9/26/2016,"5,688","4,325","3,570",755 -745,Tuesday,3,9/27/2016,"5,842","4,382","3,645",737 -746,Wednesday,4,9/28/2016,"5,923","4,458","3,682",776 -747,Thursday,5,9/29/2016,"5,717","4,222","3,487",735 -748,Friday,6,9/30/2016,"4,250","3,176","2,565",611 -749,Saturday,7,10/1/2016,"2,525","1,986","1,656",330 -750,Sunday,1,10/2/2016,"3,923","3,023","2,559",464 -751,Monday,2,10/3/2016,"5,785","4,281","3,545",736 -752,Tuesday,3,10/4/2016,"6,052","4,522","3,780",742 -753,Wednesday,4,10/5/2016,"6,150","4,588","3,814",774 -754,Thursday,5,10/6/2016,"5,591","4,134","3,419",715 -755,Friday,6,10/7/2016,"4,279","3,196","2,616",580 -756,Saturday,7,10/8/2016,"2,897","2,066","1,717",349 -757,Sunday,1,10/9/2016,"3,904","2,873","2,374",499 -758,Monday,2,10/10/2016,"5,458","4,145","3,442",703 -759,Tuesday,3,10/11/2016,"5,909","4,399","3,685",714 -760,Wednesday,4,10/12/2016,"6,175","4,620","3,823",797 -761,Thursday,5,10/13/2016,"5,856","4,422","3,581",841 -762,Friday,6,10/14/2016,"4,610","3,505","2,837",668 -763,Saturday,7,10/15/2016,"3,081","2,269","1,864",405 -764,Sunday,1,10/16/2016,"4,390","3,265","2,718",547 -765,Monday,2,10/17/2016,"6,507","4,917","4,018",899 -766,Tuesday,3,10/18/2016,"6,941","5,103","4,189",914 -767,Wednesday,4,10/19/2016,"6,576","4,776","3,961",815 -768,Thursday,5,10/20/2016,"5,985","4,462","3,701",761 -769,Friday,6,10/21/2016,"4,789","3,497","2,840",657 -770,Saturday,7,10/22/2016,"3,263","2,292","1,881",411 -771,Sunday,1,10/23/2016,"4,180","3,135","2,561",574 -772,Monday,2,10/24/2016,"6,371","4,865","3,993",872 -773,Tuesday,3,10/25/2016,"6,648","4,960","4,070",890 -774,Wednesday,4,10/26/2016,"6,501","4,777","3,880",897 -775,Thursday,5,10/27/2016,"6,088","4,431","3,574",857 -776,Friday,6,10/28/2016,"4,376","3,179","2,510",669 -777,Saturday,7,10/29/2016,"2,733","1,955","1,549",406 -778,Sunday,1,10/30/2016,"3,953","2,921","2,344",577 -779,Monday,2,10/31/2016,"6,108","4,416","3,563",853 -780,Tuesday,3,11/1/2016,"5,668","4,185","3,360",825 -781,Wednesday,4,11/2/2016,"6,159","4,508","3,663",845 -782,Thursday,5,11/3/2016,"5,810","4,241","3,426",815 -783,Friday,6,11/4/2016,"4,491","3,394","2,670",724 -784,Saturday,7,11/5/2016,"3,110","2,263","1,842",421 -785,Sunday,1,11/6/2016,"4,418","3,281","2,713",568 -786,Monday,2,11/7/2016,"6,529","4,488","3,573",915 -787,Tuesday,3,11/8/2016,"5,756","4,251","3,437",814 -788,Wednesday,4,11/9/2016,"5,716","4,102","3,315",787 -789,Thursday,5,11/10/2016,"5,323","4,022","3,246",776 -790,Friday,6,11/11/2016,"4,280","3,085","2,441",644 -791,Saturday,7,11/12/2016,"2,982","2,169","1,777",392 -792,Sunday,1,11/13/2016,"4,365","3,172","2,595",577 -793,Monday,2,11/14/2016,"6,627","4,888","3,942",946 -794,Tuesday,3,11/15/2016,"6,467","4,808","3,905",903 -795,Wednesday,4,11/16/2016,"6,631","4,903","3,984",919 -796,Thursday,5,11/17/2016,"6,378","4,681","3,779",902 -797,Friday,6,11/18/2016,"4,767","3,424","2,734",690 -798,Saturday,7,11/19/2016,"3,501","2,580","2,136",444 -799,Sunday,1,11/20/2016,"4,647","3,426","2,777",649 -800,Monday,2,11/21/2016,"6,336","4,693","3,788",905 -801,Tuesday,3,11/22/2016,"6,222","4,564","3,646",918 -802,Wednesday,4,11/23/2016,"5,527","3,904","3,082",822 -803,Thursday,5,11/24/2016,"4,267","3,197","2,561",636 -804,Friday,6,11/25/2016,"3,733","2,678","2,135",543 -805,Saturday,7,11/26/2016,"3,299","2,394","1,907",487 -806,Sunday,1,11/27/2016,"4,530","3,396","2,722",674 -807,Monday,2,11/28/2016,"6,908","5,047","4,167",880 -808,Tuesday,3,11/29/2016,"7,714","5,338","4,369",969 -809,Wednesday,4,11/30/2016,"7,490","5,425","4,389","1,036" -810,Thursday,5,12/1/2016,"7,190","5,081","4,120",961 -811,Friday,6,12/2/2016,"5,391","3,977","3,171",806 -812,Saturday,7,12/3/2016,"3,575","2,667","2,178",489 -813,Sunday,1,12/4/2016,"5,285","4,031","3,247",784 -814,Monday,2,12/5/2016,"7,330","5,439","4,452",987 -815,Tuesday,3,12/6/2016,"7,489","5,479","4,500",979 -816,Wednesday,4,12/7/2016,"7,345","5,467","4,469",998 -817,Thursday,5,12/8/2016,"6,679","4,991","3,998",993 -818,Friday,6,12/9/2016,"5,403","3,970","3,190",780 -819,Saturday,7,12/10/2016,"3,573","2,712","2,203",509 -820,Sunday,1,12/11/2016,"4,834","3,515","2,838",677 -821,Monday,2,12/12/2016,"6,905","4,945","3,988",957 -822,Tuesday,3,12/13/2016,"6,743","4,927","3,942",985 -823,Wednesday,4,12/14/2016,"6,201","4,488","3,593",895 -824,Thursday,5,12/15/2016,"5,571","4,071","3,229",842 -825,Friday,6,12/16/2016,"3,872","2,780","2,191",589 -826,Saturday,7,12/17/2016,"2,613","1,884","1,480",404 -827,Sunday,1,12/18/2016,"3,109","2,293","1,853",440 -828,Monday,2,12/19/2016,"4,324","3,145","2,477",668 -829,Tuesday,3,12/20/2016,"4,226","3,064","2,339",725 -830,Wednesday,4,12/21/2016,"3,991","2,914","2,278",636 -831,Thursday,5,12/22/2016,"3,160","2,211","1,728",483 -832,Friday,6,12/23/2016,"2,386","1,626","1,257",369 -833,Saturday,7,12/24/2016,"1,115",825,634,191 -834,Sunday,1,12/25/2016,"1,387",930,740,190 -835,Monday,2,12/26/2016,"2,147","1,418","1,142",276 -836,Tuesday,3,12/27/2016,"2,686","1,777","1,349",428 -837,Wednesday,4,12/28/2016,"2,885","1,915","1,476",439 -838,Thursday,5,12/29/2016,"2,694","1,837","1,400",437 -839,Friday,6,12/30/2016,"2,273","1,541","1,164",377 -840,Saturday,7,12/31/2016,"1,188",836,643,193 -841,Sunday,1,1/1/2017,"1,447","1,039",832,207 -842,Monday,2,1/2/2017,"2,568","1,844","1,448",396 -843,Tuesday,3,1/3/2017,"3,566","2,527","1,970",557 -844,Wednesday,4,1/4/2017,"3,941","2,816","2,226",590 -845,Thursday,5,1/5/2017,"3,841","2,625","2,058",567 -846,Friday,6,1/6/2017,"3,261","2,338","1,812",526 -847,Saturday,7,1/7/2017,"2,241","1,523","1,204",319 -848,Sunday,1,1/8/2017,"2,679","1,892","1,519",373 -849,Monday,2,1/9/2017,"4,182","2,841","2,191",650 -850,Tuesday,3,1/10/2017,"4,236","3,039","2,391",648 -851,Wednesday,4,1/11/2017,"4,628","3,206","2,536",670 -852,Thursday,5,1/12/2017,"4,205","3,025","2,403",622 -853,Friday,6,1/13/2017,"3,459","2,508","2,033",475 -854,Saturday,7,1/14/2017,"2,448","1,744","1,390",354 -855,Sunday,1,1/15/2017,"2,853","2,015","1,586",429 -856,Monday,2,1/16/2017,"4,638","3,178","2,543",635 -857,Tuesday,3,1/17/2017,"5,325","3,669","2,942",727 -858,Wednesday,4,1/18/2017,"4,866","3,364","2,670",694 -859,Thursday,5,1/19/2017,"4,715","3,375","2,689",686 -860,Friday,6,1/20/2017,"3,753","2,713","2,136",577 -861,Saturday,7,1/21/2017,"2,481","1,778","1,437",341 -862,Sunday,1,1/22/2017,"3,233","2,401","1,981",420 -863,Monday,2,1/23/2017,"5,034","3,660","2,957",703 -864,Tuesday,3,1/24/2017,"4,787","3,593","2,909",684 -865,Wednesday,4,1/25/2017,"4,901","3,602","2,923",679 -866,Thursday,5,1/26/2017,"4,398","3,266","2,653",613 -867,Friday,6,1/27/2017,"3,823","2,832","2,267",565 -868,Saturday,7,1/28/2017,"2,619","1,901","1,534",367 -869,Sunday,1,1/29/2017,"3,518","2,612","2,205",407 -870,Monday,2,1/30/2017,"5,178","3,826","3,127",699 -871,Tuesday,3,1/31/2017,"5,290","3,876","3,127",749 -872,Wednesday,4,2/1/2017,"5,469","4,116","3,402",714 -873,Thursday,5,2/2/2017,"5,399","4,028","3,303",725 -874,Friday,6,2/3/2017,"4,232","3,012","2,342",670 -875,Saturday,7,2/4/2017,"2,828","2,084","1,697",387 -876,Sunday,1,2/5/2017,"3,724","2,783","2,281",502 -877,Monday,2,2/6/2017,"5,733","4,249","3,448",801 -878,Tuesday,3,2/7/2017,"5,814","4,284","3,521",763 -879,Wednesday,4,2/8/2017,"5,349","4,006","3,248",758 -880,Thursday,5,2/9/2017,"5,025","3,681","3,010",671 -881,Friday,6,2/10/2017,"4,044","2,922","2,335",587 -882,Saturday,7,2/11/2017,"2,323","1,731","1,377",354 -883,Sunday,1,2/12/2017,"3,147","2,469","2,005",464 -884,Monday,2,2/13/2017,"4,972","3,719","3,014",705 -885,Tuesday,3,2/14/2017,"4,878","3,582","2,897",685 -886,Wednesday,4,2/15/2017,"5,288","3,786","3,039",747 -887,Thursday,5,2/16/2017,"4,968","3,668","2,934",734 -888,Friday,6,2/17/2017,"3,788","2,748","2,158",590 -889,Saturday,7,2/18/2017,"2,514","1,839","1,496",343 -890,Sunday,1,2/19/2017,"3,463","2,602","2,083",519 -891,Monday,2,2/20/2017,"4,931","3,700","3,009",691 -892,Tuesday,3,2/21/2017,"5,101","3,873","3,079",794 -893,Wednesday,4,2/22/2017,"5,144","3,727","2,998",729 -894,Thursday,5,2/23/2017,"4,433","3,372","2,657",715 -895,Friday,6,2/24/2017,"3,597","2,648","2,111",537 -896,Saturday,7,2/25/2017,"2,621","1,826","1,482",344 -897,Sunday,1,2/26/2017,"3,397","2,494","2,021",473 -898,Monday,2,2/27/2017,"5,062","3,812","3,009",803 -899,Tuesday,3,2/28/2017,"4,926","3,678","2,933",745 -900,Wednesday,4,3/1/2017,"4,936","3,558","2,822",736 -901,Thursday,5,3/2/2017,"4,688","3,441","2,747",694 -902,Friday,6,3/3/2017,"3,827","2,699","2,140",559 -903,Saturday,7,3/4/2017,"2,452","1,754","1,408",346 -904,Sunday,1,3/5/2017,"3,283","2,394","1,958",436 -905,Monday,2,3/6/2017,"4,823","3,481","2,764",717 -906,Tuesday,3,3/7/2017,"4,821","3,662","2,942",720 -907,Wednesday,4,3/8/2017,"5,059","3,613","2,923",690 -908,Thursday,5,3/9/2017,"4,585","3,444","2,744",700 -909,Friday,6,3/10/2017,"3,682","2,733","2,178",555 -910,Saturday,7,3/11/2017,"2,350","1,714","1,351",363 -911,Sunday,1,3/12/2017,"3,056","2,189","1,755",434 -912,Monday,2,3/13/2017,"4,746","3,367","2,721",646 -913,Tuesday,3,3/14/2017,"4,613","3,465","2,808",657 -914,Wednesday,4,3/15/2017,"4,560","3,441","2,694",747 -915,Thursday,5,3/16/2017,"4,520","3,304","2,629",675 -916,Friday,6,3/17/2017,"3,346","2,433","1,930",503 -917,Saturday,7,3/18/2017,"2,253","1,649","1,325",324 -918,Sunday,1,3/19/2017,"2,927","2,267","1,807",460 -919,Monday,2,3/20/2017,"4,878","3,463","2,799",664 -920,Tuesday,3,3/21/2017,"4,852","3,481","2,802",679 -921,Wednesday,4,3/22/2017,"4,585","3,430","2,757",673 -922,Thursday,5,3/23/2017,"4,477","3,260","2,600",660 -923,Friday,6,3/24/2017,"3,654","2,566","1,993",573 -924,Saturday,7,3/25/2017,"2,411","1,760","1,420",340 -925,Sunday,1,3/26/2017,"3,054","2,336","1,882",454 -926,Monday,2,3/27/2017,"4,696","3,479","2,765",714 -927,Tuesday,3,3/28/2017,"5,033","3,633","2,875",758 -928,Wednesday,4,3/29/2017,"4,948","3,530","2,809",721 -929,Thursday,5,3/30/2017,"4,636","3,390","2,704",686 -930,Friday,6,3/31/2017,"3,539","2,636","2,060",576 -931,Saturday,7,4/1/2017,"2,371","1,678","1,338",340 -932,Sunday,1,4/2/2017,"2,816","2,159","1,715",444 -933,Monday,2,4/3/2017,"4,493","3,356","2,672",684 -934,Tuesday,3,4/4/2017,"4,678","3,486","2,739",747 -935,Wednesday,4,4/5/2017,"4,737","3,484","2,762",722 -936,Thursday,5,4/6/2017,"4,775","3,514","2,806",708 -937,Friday,6,4/7/2017,"3,658","2,725","2,181",544 -938,Saturday,7,4/8/2017,"2,260","1,664","1,324",340 -939,Sunday,1,4/9/2017,"3,343","2,433","1,933",500 -940,Monday,2,4/10/2017,"4,640","3,440","2,756",684 -941,Tuesday,3,4/11/2017,"4,694","3,532","2,867",665 -942,Wednesday,4,4/12/2017,"4,771","3,517","2,800",717 -943,Thursday,5,4/13/2017,"4,173","3,017","2,395",622 -944,Friday,6,4/14/2017,"3,046","2,238","1,752",486 -945,Saturday,7,4/15/2017,"2,260","1,701","1,333",368 -946,Sunday,1,4/16/2017,"2,876","2,143","1,700",443 -947,Monday,2,4/17/2017,"4,774","3,402","2,719",683 -948,Tuesday,3,4/18/2017,"5,009","3,702","2,906",796 -949,Wednesday,4,4/19/2017,"5,369","3,852","3,133",719 -950,Thursday,5,4/20/2017,"5,020","3,669","2,969",700 -951,Friday,6,4/21/2017,"3,935","2,802","2,203",599 -952,Saturday,7,4/22/2017,"2,795","2,049","1,630",419 -953,Sunday,1,4/23/2017,"3,795","2,827","2,305",522 -954,Monday,2,4/24/2017,"5,489","4,083","3,252",831 -955,Tuesday,3,4/25/2017,"5,680","4,108","3,336",772 -956,Wednesday,4,4/26/2017,"5,383","3,940","3,205",735 -957,Thursday,5,4/27/2017,"4,890","3,695","2,985",710 -958,Friday,6,4/28/2017,"3,872","2,887","2,310",577 -959,Saturday,7,4/29/2017,"2,790","1,987","1,602",385 -960,Sunday,1,4/30/2017,"3,524","2,616","2,118",498 -961,Monday,2,5/1/2017,"4,903","3,551","2,875",676 -962,Tuesday,3,5/2/2017,"5,160","3,802","3,079",723 -963,Wednesday,4,5/3/2017,"5,194","3,766","2,995",771 -964,Thursday,5,5/4/2017,"5,142","3,755","3,013",742 -965,Friday,6,5/5/2017,"3,836","2,925","2,323",602 -966,Saturday,7,5/6/2017,"2,737","1,981","1,572",409 -967,Sunday,1,5/7/2017,"3,386","2,534","2,025",509 -968,Monday,2,5/8/2017,"4,816","3,585","2,900",685 -969,Tuesday,3,5/9/2017,"4,734","3,458","2,763",695 -970,Wednesday,4,5/10/2017,"4,761","3,475","2,754",721 -971,Thursday,5,5/11/2017,"4,277","3,224","2,570",654 -972,Friday,6,5/12/2017,"3,563","2,620","2,056",564 -973,Saturday,7,5/13/2017,"2,401","1,749","1,379",370 -974,Sunday,1,5/14/2017,"2,877","2,126","1,674",452 -975,Monday,2,5/15/2017,"4,448","3,191","2,494",697 -976,Tuesday,3,5/16/2017,"4,525","3,226","2,593",633 -977,Wednesday,4,5/17/2017,"4,533","3,318","2,676",642 -978,Thursday,5,5/18/2017,"4,431","3,307","2,686",621 -979,Friday,6,5/19/2017,"3,456","2,470","1,938",532 -980,Saturday,7,5/20/2017,"2,162","1,651","1,332",319 -981,Sunday,1,5/21/2017,"2,786","1,971","1,597",374 -982,Monday,2,5/22/2017,"4,172","3,041","2,403",638 -983,Tuesday,3,5/23/2017,"4,255","3,077","2,457",620 -984,Wednesday,4,5/24/2017,"4,154","2,996","2,400",596 -985,Thursday,5,5/25/2017,"3,803","2,839","2,263",576 -986,Friday,6,5/26/2017,"2,950","2,183","1,720",463 -987,Saturday,7,5/27/2017,"1,909","1,444","1,155",289 -988,Sunday,1,5/28/2017,"2,506","1,781","1,399",382 -989,Monday,2,5/29/2017,"3,325","2,457","1,913",544 -990,Tuesday,3,5/30/2017,"4,086","2,909","2,298",611 -991,Wednesday,4,5/31/2017,"3,716","2,667","2,069",598 -992,Thursday,5,6/1/2017,"3,702","2,639","2,103",536 -993,Friday,6,6/2/2017,"2,959","2,171","1,680",491 -994,Saturday,7,6/3/2017,"2,006","1,471","1,161",310 -995,Sunday,1,6/4/2017,"2,479","1,795","1,419",376 -996,Monday,2,6/5/2017,"3,501","2,610","2,072",538 -997,Tuesday,3,6/6/2017,"3,985","2,922","2,361",561 -998,Wednesday,4,6/7/2017,"3,920","2,913","2,314",599 -999,Thursday,5,6/8/2017,"3,868","2,796","2,209",587 -1000,Friday,6,6/9/2017,"2,921","2,101","1,660",441 -1001,Saturday,7,6/10/2017,"1,655","1,244",975,269 -1002,Sunday,1,6/11/2017,"2,229","1,622","1,299",323 -1003,Monday,2,6/12/2017,"3,660","2,598","2,020",578 -1004,Tuesday,3,6/13/2017,"3,898","2,809","2,202",607 -1005,Wednesday,4,6/14/2017,"3,912","2,797","2,200",597 -1006,Thursday,5,6/15/2017,"3,604","2,624","2,029",595 -1007,Friday,6,6/16/2017,"2,970","2,105","1,621",484 -1008,Saturday,7,6/17/2017,"1,546","1,187",957,230 -1009,Sunday,1,6/18/2017,"1,944","1,465","1,146",319 -1010,Monday,2,6/19/2017,"3,608","2,493","1,959",534 -1011,Tuesday,3,6/20/2017,"3,514","2,472","1,905",567 -1012,Wednesday,4,6/21/2017,"3,344","2,475","1,915",560 -1013,Thursday,5,6/22/2017,"3,292","2,351","1,824",527 -1014,Friday,6,6/23/2017,"2,469","1,850","1,445",405 -1015,Saturday,7,6/24/2017,"1,296",925,702,223 -1016,Sunday,1,6/25/2017,"1,546","1,071",811,260 -1017,Monday,2,6/26/2017,"2,948","1,998","1,528",470 -1018,Tuesday,3,6/27/2017,"3,077","2,190","1,690",500 -1019,Wednesday,4,6/28/2017,"3,028","2,145","1,649",496 -1020,Thursday,5,6/29/2017,"2,738","1,976","1,474",502 -1021,Friday,6,6/30/2017,"2,147","1,478","1,087",391 -1022,Saturday,7,7/1/2017,"1,277",903,675,228 -1023,Sunday,1,7/2/2017,"1,606","1,142",879,263 -1024,Monday,2,7/3/2017,"2,504","1,726","1,296",430 -1025,Tuesday,3,7/4/2017,"2,218","1,566","1,217",349 -1026,Wednesday,4,7/5/2017,"3,024","2,149","1,681",468 -1027,Thursday,5,7/6/2017,"2,951","2,047","1,555",492 -1028,Friday,6,7/7/2017,"2,401","1,693","1,310",383 -1029,Saturday,7,7/8/2017,"1,242",891,700,191 -1030,Sunday,1,7/9/2017,"1,511","1,172",938,234 -1031,Monday,2,7/10/2017,"2,934","2,062","1,619",443 -1032,Tuesday,3,7/11/2017,"3,003","2,140","1,663",477 -1033,Wednesday,4,7/12/2017,"3,073","2,192","1,710",482 -1034,Thursday,5,7/13/2017,"2,933","2,043","1,550",493 -1035,Friday,6,7/14/2017,"2,468","1,710","1,287",423 -1036,Saturday,7,7/15/2017,"1,355",892,689,203 -1037,Sunday,1,7/16/2017,"1,553","1,122",891,231 -1038,Monday,2,7/17/2017,"2,902","1,954","1,479",475 -1039,Tuesday,3,7/18/2017,"2,899","2,109","1,591",518 -1040,Wednesday,4,7/19/2017,"2,986","2,032","1,584",448 -1041,Thursday,5,7/20/2017,"2,964","2,031","1,571",460 -1042,Friday,6,7/21/2017,"2,433","1,609","1,214",395 -1043,Saturday,7,7/22/2017,"1,280",923,725,198 -1044,Sunday,1,7/23/2017,"1,603","1,170",909,261 -1045,Monday,2,7/24/2017,"3,154","2,214","1,726",488 -1046,Tuesday,3,7/25/2017,"3,084","2,148","1,666",482 -1047,Wednesday,4,7/26/2017,"2,983","1,925","1,490",435 -1048,Thursday,5,7/27/2017,"2,849","1,908","1,440",468 -1049,Friday,6,7/28/2017,"2,420","1,538","1,150",388 -1050,Saturday,7,7/29/2017,"1,300",895,712,183 -1051,Sunday,1,7/30/2017,"1,526","1,112",863,249 -1052,Monday,2,7/31/2017,"2,872","1,978","1,481",497 -1053,Tuesday,3,8/1/2017,"2,914","1,957","1,463",494 -1054,Wednesday,4,8/2/2017,"2,843","1,994","1,536",458 -1055,Thursday,5,8/3/2017,"2,649","1,894","1,447",447 -1056,Friday,6,8/4/2017,"2,042","1,478","1,150",328 -1057,Saturday,7,8/5/2017,"1,225",901,681,220 -1058,Sunday,1,8/6/2017,"1,562","1,045",792,253 -1059,Monday,2,8/7/2017,"2,751","1,823","1,398",425 -1060,Tuesday,3,8/8/2017,"2,634","1,868","1,415",453 -1061,Wednesday,4,8/9/2017,"2,704","1,961","1,511",450 -1062,Thursday,5,8/10/2017,"2,670","1,864","1,463",401 -1063,Friday,6,8/11/2017,"2,073","1,498","1,121",377 -1064,Saturday,7,8/12/2017,"1,240",888,669,219 -1065,Sunday,1,8/13/2017,"1,530","1,076",811,265 -1066,Monday,2,8/14/2017,"2,392","1,704","1,346",358 -1067,Tuesday,3,8/15/2017,"2,675","1,846","1,471",375 -1068,Wednesday,4,8/16/2017,"2,755","1,911","1,436",475 -1069,Thursday,5,8/17/2017,"2,608","1,824","1,421",403 -1070,Friday,6,8/18/2017,"2,163","1,521","1,163",358 -1071,Saturday,7,8/19/2017,"1,304",951,743,208 -1072,Sunday,1,8/20/2017,"1,969","1,419","1,156",263 -1073,Monday,2,8/21/2017,"2,985","2,069","1,575",494 -1074,Tuesday,3,8/22/2017,"3,087","2,259","1,772",487 -1075,Wednesday,4,8/23/2017,"3,047","2,147","1,704",443 -1076,Thursday,5,8/24/2017,"2,961","2,026","1,600",426 -1077,Friday,6,8/25/2017,"2,293","1,589","1,257",332 -1078,Saturday,7,8/26/2017,"1,261",934,751,183 -1079,Sunday,1,8/27/2017,"1,676","1,211",983,228 -1080,Monday,2,8/28/2017,"1,786","1,287","1,005",282 -1081,Tuesday,3,8/29/2017,"3,252","2,310","1,845",465 -1082,Wednesday,4,8/30/2017,"3,082","2,179","1,741",438 -1083,Thursday,5,8/31/2017,"2,721","1,963","1,579",384 -1084,Friday,6,9/1/2017,"2,217","1,583","1,235",348 -1085,Saturday,7,9/2/2017,"1,271",908,730,178 -1086,Sunday,1,9/3/2017,"1,742","1,197",958,239 -1087,Monday,2,9/4/2017,"2,293","1,704","1,380",324 -1088,Tuesday,3,9/5/2017,"3,155","2,289","1,818",471 -1089,Wednesday,4,9/6/2017,"3,159","2,320","1,882",438 -1090,Thursday,5,9/7/2017,"3,054","2,243","1,777",466 -1091,Friday,6,9/8/2017,"2,440","1,848","1,488",360 -1092,Saturday,7,9/9/2017,"1,532","1,108",924,184 -1093,Sunday,1,9/10/2017,"2,247","1,657","1,366",291 -1094,Monday,2,9/11/2017,"3,570","2,496","2,033",463 -1095,Tuesday,3,9/12/2017,"3,524","2,540","2,075",465 -1096,Wednesday,4,9/13/2017,"3,471","2,473","2,040",433 -1097,Thursday,5,9/14/2017,"3,303","2,415","1,974",441 -1098,Friday,6,9/15/2017,"2,560","1,867","1,519",348 -1099,Saturday,7,9/16/2017,"1,470","1,090",912,178 -1100,Sunday,1,9/17/2017,"2,234","1,715","1,446",269 -1101,Monday,2,9/18/2017,"3,504","2,596","2,123",473 -1102,Tuesday,3,9/19/2017,"3,984","2,840","2,382",458 -1103,Wednesday,4,9/20/2017,"3,746","2,660","2,188",472 -1104,Thursday,5,9/21/2017,"3,524","2,667","2,143",524 -1105,Friday,6,9/22/2017,"2,639","1,944","1,591",353 -1106,Saturday,7,9/23/2017,"1,523","1,178",975,203 -1107,Sunday,1,9/24/2017,"2,425","1,809","1,521",288 -1108,Monday,2,9/25/2017,"3,671","2,705","2,202",503 -1109,Tuesday,3,9/26/2017,"3,669","2,780","2,306",474 -1110,Wednesday,4,9/27/2017,"3,537","2,657","2,211",446 -1111,Thursday,5,9/28/2017,"3,413","2,507","2,048",459 -1112,Friday,6,9/29/2017,"2,699","1,902","1,554",348 -1113,Saturday,7,9/30/2017,"1,829","1,289","1,058",231 -1114,Sunday,1,10/1/2017,"2,379","1,812","1,507",305 -1115,Monday,2,10/2/2017,"3,637","2,669","2,221",448 -1116,Tuesday,3,10/3/2017,"3,775","2,787","2,253",534 -1117,Wednesday,4,10/4/2017,"3,572","2,731","2,245",486 -1118,Thursday,5,10/5/2017,"3,605","2,695","2,230",465 -1119,Friday,6,10/6/2017,"2,774","2,078","1,670",408 -1120,Saturday,7,10/7/2017,"1,780","1,354","1,106",248 -1121,Sunday,1,10/8/2017,"2,469","1,884","1,571",313 -1122,Monday,2,10/9/2017,"3,881","2,869","2,348",521 -1123,Tuesday,3,10/10/2017,"3,767","2,785","2,296",489 -1124,Wednesday,4,10/11/2017,"4,082","2,924","2,335",589 -1125,Thursday,5,10/12/2017,"4,047","2,768","2,240",528 -1126,Friday,6,10/13/2017,"2,877","2,114","1,706",408 -1127,Saturday,7,10/14/2017,"2,073","1,559","1,298",261 -1128,Sunday,1,10/15/2017,"2,759","2,066","1,739",327 -1129,Monday,2,10/16/2017,"3,817","2,880","2,344",536 -1130,Tuesday,3,10/17/2017,"3,949","2,930","2,402",528 -1131,Wednesday,4,10/18/2017,"3,867","2,896","2,395",501 -1132,Thursday,5,10/19/2017,"3,621","2,582","2,119",463 -1133,Friday,6,10/20/2017,"2,978","2,220","1,772",448 -1134,Saturday,7,10/21/2017,"1,992","1,434","1,158",276 -1135,Sunday,1,10/22/2017,"2,851","2,142","1,779",363 -1136,Monday,2,10/23/2017,"4,390","3,177","2,651",526 -1137,Tuesday,3,10/24/2017,"3,886","2,938","2,406",532 -1138,Wednesday,4,10/25/2017,"3,424","2,477","2,017",460 -1139,Thursday,5,10/26/2017,"4,207","3,087","2,561",526 -1140,Friday,6,10/27/2017,"3,345","2,478","1,984",494 -1141,Saturday,7,10/28/2017,"2,128","1,567","1,277",290 -1142,Sunday,1,10/29/2017,"2,205","1,637","1,319",318 -1143,Monday,2,10/30/2017,"4,091","2,999","2,453",546 -1144,Tuesday,3,10/31/2017,"4,223","3,143","2,555",588 -1145,Wednesday,4,11/1/2017,"4,361","3,147","2,554",593 -1146,Thursday,5,11/2/2017,"4,263","3,103","2,513",590 -1147,Friday,6,11/3/2017,"3,341","2,397","1,903",494 -1148,Saturday,7,11/4/2017,"2,278","1,634","1,307",327 -1149,Sunday,1,11/5/2017,"3,097","2,299","1,888",411 -1150,Monday,2,11/6/2017,"4,185","3,113","2,538",575 -1151,Tuesday,3,11/7/2017,"4,477","3,341","2,689",652 -1152,Wednesday,4,11/8/2017,"4,354","3,227","2,579",648 -1153,Thursday,5,11/9/2017,"4,194","3,005","2,401",604 -1154,Friday,6,11/10/2017,"3,240","2,319","1,833",486 -1155,Saturday,7,11/11/2017,"2,424","1,613","1,283",330 -1156,Sunday,1,11/12/2017,"3,168","2,309","1,853",456 -1157,Monday,2,11/13/2017,"4,709","3,433","2,784",649 -1158,Tuesday,3,11/14/2017,"4,634","3,373","2,727",646 -1159,Wednesday,4,11/15/2017,"4,545","3,345","2,681",664 -1160,Thursday,5,11/16/2017,"4,532","3,332","2,693",639 -1161,Friday,6,11/17/2017,"3,317","2,508","2,018",490 -1162,Saturday,7,11/18/2017,"2,449","1,741","1,386",355 -1163,Sunday,1,11/19/2017,"2,893","2,231","1,780",451 -1164,Monday,2,11/20/2017,"4,170","3,135","2,555",580 -1165,Tuesday,3,11/21/2017,"4,277","3,026","2,419",607 -1166,Wednesday,4,11/22/2017,"3,588","2,625","2,078",547 -1167,Thursday,5,11/23/2017,"3,141","2,237","1,760",477 -1168,Friday,6,11/24/2017,"2,658","1,843","1,429",414 -1169,Saturday,7,11/25/2017,"2,391","1,652","1,327",325 -1170,Sunday,1,11/26/2017,"3,202","2,354","1,901",453 -1171,Monday,2,11/27/2017,"4,349","3,251","2,601",650 -1172,Tuesday,3,11/28/2017,"4,886","3,660","2,968",692 -1173,Wednesday,4,11/29/2017,"4,555","3,484","2,824",660 -1174,Thursday,5,11/30/2017,"4,477","3,134","2,530",604 -1175,Friday,6,12/1/2017,"3,577","2,603","2,056",547 -1176,Saturday,7,12/2/2017,"2,430","1,827","1,461",366 -1177,Sunday,1,12/3/2017,"3,449","2,504","2,006",498 -1178,Monday,2,12/4/2017,"4,955","3,673","3,019",654 -1179,Tuesday,3,12/5/2017,"4,475","3,435","2,775",660 -1180,Wednesday,4,12/6/2017,"4,595","3,452","2,766",686 -1181,Thursday,5,12/7/2017,"4,451","3,199","2,563",636 -1182,Friday,6,12/8/2017,"3,418","2,549","2,037",512 -1183,Saturday,7,12/9/2017,"2,605","1,944","1,572",372 -1184,Sunday,1,12/10/2017,"3,483","2,607","2,102",505 -1185,Monday,2,12/11/2017,"4,651","3,350","2,656",694 -1186,Tuesday,3,12/12/2017,"4,029","3,011","2,351",660 -1187,Wednesday,4,12/13/2017,"4,282","3,205","2,609",596 -1188,Thursday,5,12/14/2017,"4,138","2,930","2,315",615 -1189,Friday,6,12/15/2017,"4,130","2,803","2,211",592 -1190,Saturday,7,12/16/2017,"2,581","1,761","1,429",332 -1191,Sunday,1,12/17/2017,"3,355","2,300","1,876",424 -1192,Monday,2,12/18/2017,"4,482","3,109","2,491",618 -1193,Tuesday,3,12/19/2017,"4,584","3,021","2,456",565 -1194,Wednesday,4,12/20/2017,"4,106","2,661","2,133",528 -1195,Thursday,5,12/21/2017,"3,378","2,243","1,798",445 -1196,Friday,6,12/22/2017,"2,446","1,544","1,211",333 -1197,Saturday,7,12/23/2017,"1,447",958,753,205 -1198,Sunday,1,12/24/2017,"1,326",895,696,199 -1199,Monday,2,12/25/2017,"1,609","1,015",809,206 -1200,Tuesday,3,12/26/2017,"2,481","1,438","1,105",333 -1201,Wednesday,4,12/27/2017,"2,798","1,740","1,350",390 -1202,Thursday,5,12/28/2017,"2,809","1,797","1,425",372 -1203,Friday,6,12/29/2017,"2,216","1,410","1,083",327 -1204,Saturday,7,12/30/2017,"1,546","1,071",839,232 -1205,Sunday,1,12/31/2017,"1,339",923,724,199 -1206,Monday,2,1/1/2018,"1,709","1,120",878,242 -1207,Tuesday,3,1/2/2018,"3,389","2,208","1,755",453 -1208,Wednesday,4,1/3/2018,"3,391","2,282","1,827",455 -1209,Thursday,5,1/4/2018,"3,698","2,310","1,878",432 -1210,Friday,6,1/5/2018,"3,511","2,272","1,801",471 -1211,Saturday,7,1/6/2018,"2,279","1,498","1,196",302 -1212,Sunday,1,1/7/2018,"2,822","1,869","1,495",374 -1213,Monday,2,1/8/2018,"4,240","2,784","2,186",598 -1214,Tuesday,3,1/9/2018,"4,319","2,844","2,302",542 -1215,Wednesday,4,1/10/2018,"4,224","2,866","2,304",562 -1216,Thursday,5,1/11/2018,"3,797","2,704","2,203",501 -1217,Friday,6,1/12/2018,"3,190","2,127","1,692",435 -1218,Saturday,7,1/13/2018,"1,998","1,373","1,128",245 -1219,Sunday,1,1/14/2018,"2,515","1,711","1,393",318 -1220,Monday,2,1/15/2018,"3,950","2,612","2,065",547 -1221,Tuesday,3,1/16/2018,"4,033","2,631","2,133",498 -1222,Wednesday,4,1/17/2018,"4,269","2,812","2,285",527 -1223,Thursday,5,1/18/2018,"4,124","2,677","2,157",520 -1224,Friday,6,1/19/2018,"3,228","2,198","1,763",435 -1225,Saturday,7,1/20/2018,"2,287","1,423","1,160",263 -1226,Sunday,1,1/21/2018,"2,946","1,996","1,628",368 -1227,Monday,2,1/22/2018,"4,608","3,035","2,534",501 -1228,Tuesday,3,1/23/2018,"4,786","3,154","2,566",588 -1229,Wednesday,4,1/24/2018,"4,724","3,244","2,680",564 -1230,Thursday,5,1/25/2018,"4,417","2,875","2,348",527 -1231,Friday,6,1/26/2018,"3,580","2,314","1,825",489 -1232,Saturday,7,1/27/2018,"2,345","1,544","1,229",315 -1233,Sunday,1,1/28/2018,"3,170","2,140","1,776",364 -1234,Monday,2,1/29/2018,"4,862","3,194","2,573",621 -1235,Tuesday,3,1/30/2018,"4,600","3,106","2,504",602 -1236,Wednesday,4,1/31/2018,"4,705","3,249","2,704",545 -1237,Thursday,5,2/1/2018,"4,723","3,164","2,571",593 -1238,Friday,6,2/2/2018,"3,969","2,572","2,104",468 -1239,Saturday,7,2/3/2018,"2,994","2,034","1,702",332 -1240,Sunday,1,2/4/2018,"3,162","2,186","1,811",375 -1241,Monday,2,2/5/2018,"4,949","3,287","2,735",552 -1242,Tuesday,3,2/6/2018,"5,092","3,465","2,838",627 -1243,Wednesday,4,2/7/2018,"4,767","3,319","2,729",590 -1244,Thursday,5,2/8/2018,"4,625","3,179","2,603",576 -1245,Friday,6,2/9/2018,"3,738","2,477","2,015",462 -1246,Saturday,7,2/10/2018,"2,739","1,793","1,477",316 -1247,Sunday,1,2/11/2018,"3,923","2,476","2,077",399 -1248,Monday,2,2/12/2018,"5,341","3,687","3,045",642 -1249,Tuesday,3,2/13/2018,"5,114","3,460","2,836",624 -1250,Wednesday,4,2/14/2018,"4,896","3,214","2,568",646 -1251,Thursday,5,2/15/2018,"4,502","3,050","2,507",543 -1252,Friday,6,2/16/2018,"3,729","2,518","2,012",506 -1253,Saturday,7,2/17/2018,"2,388","1,618","1,319",299 -1254,Sunday,1,2/18/2018,"3,405","2,314","1,928",386 -1255,Monday,2,2/19/2018,"5,176","3,620","2,971",649 -1256,Tuesday,3,2/20/2018,"5,429","3,597","2,978",619 -1257,Wednesday,4,2/21/2018,"5,120","3,448","2,869",579 -1258,Thursday,5,2/22/2018,"4,993","3,379","2,774",605 -1259,Friday,6,2/23/2018,"4,148","2,751","2,221",530 -1260,Saturday,7,2/24/2018,"2,763","1,981","1,648",333 -1261,Sunday,1,2/25/2018,"3,599","2,446","2,011",435 -1262,Monday,2,2/26/2018,"5,542","3,728","3,052",676 -1263,Tuesday,3,2/27/2018,"5,378","3,764","3,123",641 -1264,Wednesday,4,2/28/2018,"5,536","3,734","3,095",639 -1265,Thursday,5,3/1/2018,"4,984","3,315","2,713",602 -1266,Friday,6,3/2/2018,"4,130","2,754","2,231",523 -1267,Saturday,7,3/3/2018,"2,818","1,922","1,579",343 -1268,Sunday,1,3/4/2018,"3,978","2,699","2,249",450 -1269,Monday,2,3/5/2018,"5,502","3,727","3,019",708 -1270,Tuesday,3,3/6/2018,"5,740","3,842","3,160",682 -1271,Wednesday,4,3/7/2018,"5,635","3,734","3,037",697 -1272,Thursday,5,3/8/2018,"5,375","3,704","3,044",660 -1273,Friday,6,3/9/2018,"4,273","3,038","2,490",548 -1274,Saturday,7,3/10/2018,"3,223","2,305","1,918",387 -1275,Sunday,1,3/11/2018,"3,956","2,727","2,288",439 -1276,Monday,2,3/12/2018,"5,895","4,141","3,447",694 -1277,Tuesday,3,3/13/2018,"6,152","4,207","3,398",809 -1278,Wednesday,4,3/14/2018,"5,906","4,021","3,305",716 -1279,Thursday,5,3/15/2018,"6,300","4,061","3,279",782 -1280,Friday,6,3/16/2018,"4,915","3,259","2,673",586 -1281,Saturday,7,3/17/2018,"3,218","2,161","1,818",343 -1282,Sunday,1,3/18/2018,"4,429","2,919","2,435",484 -1283,Monday,2,3/19/2018,"5,739","4,038","3,316",722 -1284,Tuesday,3,3/20/2018,"6,220","4,292","3,553",739 -1285,Wednesday,4,3/21/2018,"6,238","4,163","3,396",767 -1286,Thursday,5,3/22/2018,"6,226","4,251","3,595",656 -1287,Friday,6,3/23/2018,"4,364","2,994","2,457",537 -1288,Saturday,7,3/24/2018,"2,882","2,021","1,682",339 -1289,Sunday,1,3/25/2018,"4,010","2,748","2,263",485 -1290,Monday,2,3/26/2018,"6,084","3,995","3,289",706 -1291,Tuesday,3,3/27/2018,"5,874","4,147","3,446",701 -1292,Wednesday,4,3/28/2018,"5,719","3,917","3,288",629 -1293,Thursday,5,3/29/2018,"5,003","3,396","2,812",584 -1294,Friday,6,3/30/2018,"3,605","2,541","2,062",479 -1295,Saturday,7,3/31/2018,"2,811","1,901","1,512",389 -1296,Sunday,1,4/1/2018,"3,567","2,438","1,956",482 -1297,Monday,2,4/2/2018,"5,237","3,542","2,875",667 -1298,Tuesday,3,4/3/2018,"6,154","4,126","3,438",688 -1299,Wednesday,4,4/4/2018,"5,895","4,112","3,418",694 -1300,Thursday,5,4/5/2018,"5,858","3,943","3,232",711 -1301,Friday,6,4/6/2018,"5,034","3,348","2,749",599 -1302,Saturday,7,4/7/2018,"3,613","2,291","1,840",451 -1303,Sunday,1,4/8/2018,"4,389","3,059","2,530",529 -1304,Monday,2,4/9/2018,"6,278","4,290","3,546",744 -1305,Tuesday,3,4/10/2018,"6,836","4,514","3,747",767 -1306,Wednesday,4,4/11/2018,"6,731","4,596","3,759",837 -1307,Thursday,5,4/12/2018,"6,287","4,332","3,563",769 -1308,Friday,6,4/13/2018,"5,089","3,454","2,850",604 -1309,Saturday,7,4/14/2018,"3,812","2,542","2,094",448 -1310,Sunday,1,4/15/2018,"5,290","3,538","2,914",624 -1311,Monday,2,4/16/2018,"7,161","4,993","4,188",805 -1312,Tuesday,3,4/17/2018,"7,352","4,939","4,058",881 -1313,Wednesday,4,4/18/2018,"7,218","4,842","4,001",841 -1314,Thursday,5,4/19/2018,"7,040","4,672","3,895",777 -1315,Friday,6,4/20/2018,"5,367","3,728","3,029",699 -1316,Saturday,7,4/21/2018,"4,021","2,749","2,253",496 -1317,Sunday,1,4/22/2018,"5,397","3,722","3,059",663 -1318,Monday,2,4/23/2018,"7,605","5,124","4,216",908 -1319,Tuesday,3,4/24/2018,"7,614","5,094","4,181",913 -1320,Wednesday,4,4/25/2018,"7,984","5,541","4,616",925 -1321,Thursday,5,4/26/2018,"7,040","4,823","3,957",866 -1322,Friday,6,4/27/2018,"5,628","3,749","3,095",654 -1323,Saturday,7,4/28/2018,"3,926","2,618","2,141",477 -1324,Sunday,1,4/29/2018,"5,479","3,635","3,057",578 -1325,Monday,2,4/30/2018,"6,670","4,584","3,784",800 -1326,Tuesday,3,5/1/2018,"6,783","4,542","3,742",800 -1327,Wednesday,4,5/2/2018,"7,105","4,865","4,013",852 -1328,Thursday,5,5/3/2018,"6,877","4,702","3,854",848 -1329,Friday,6,5/4/2018,"5,460","3,711","3,039",672 -1330,Saturday,7,5/5/2018,"3,648","2,568","2,058",510 -1331,Sunday,1,5/6/2018,"4,917","3,311","2,737",574 -1332,Monday,2,5/7/2018,"6,980","4,654","3,881",773 -1333,Tuesday,3,5/8/2018,"6,702","4,577","3,700",877 -1334,Wednesday,4,5/9/2018,"6,699","4,488","3,581",907 -1335,Thursday,5,5/10/2018,"6,001","4,059","3,265",794 -1336,Friday,6,5/11/2018,"5,094","3,454","2,766",688 -1337,Saturday,7,5/12/2018,"3,179","2,237","1,821",416 -1338,Sunday,1,5/13/2018,"4,169","2,912","2,413",499 -1339,Monday,2,5/14/2018,"6,392","4,388","3,637",751 -1340,Tuesday,3,5/15/2018,"5,878","4,094","3,384",710 -1341,Wednesday,4,5/16/2018,"5,800","3,976","3,266",710 -1342,Thursday,5,5/17/2018,"5,690","4,023","3,254",769 -1343,Friday,6,5/18/2018,"4,314","2,986","2,400",586 -1344,Saturday,7,5/19/2018,"2,948","2,117","1,735",382 -1345,Sunday,1,5/20/2018,"3,864","2,674","2,155",519 -1346,Monday,2,5/21/2018,"5,499","3,708","3,005",703 -1347,Tuesday,3,5/22/2018,"5,790","4,040","3,269",771 -1348,Wednesday,4,5/23/2018,"5,720","3,971","3,206",765 -1349,Thursday,5,5/24/2018,"5,692","3,675","2,965",710 -1350,Friday,6,5/25/2018,"4,082","2,803","2,188",615 -1351,Saturday,7,5/26/2018,"2,716","1,965","1,597",368 -1352,Sunday,1,5/27/2018,"3,430","2,374","1,887",487 -1353,Monday,2,5/28/2018,"4,353","2,994","2,392",602 -1354,Tuesday,3,5/29/2018,"5,525","3,777","3,112",665 -1355,Wednesday,4,5/30/2018,"5,407","3,574","2,908",666 -1356,Thursday,5,5/31/2018,"4,994","3,485","2,792",693 -1357,Friday,6,6/1/2018,"3,835","2,681","2,172",509 -1358,Saturday,7,6/2/2018,"2,511","1,750","1,423",327 -1359,Sunday,1,6/3/2018,"3,371","2,308","1,891",417 -1360,Monday,2,6/4/2018,"5,226","3,399","2,741",658 -1361,Tuesday,3,6/5/2018,"5,139","3,453","2,811",642 -1362,Wednesday,4,6/6/2018,"5,051","3,474","2,828",646 -1363,Thursday,5,6/7/2018,"4,787","3,283","2,685",598 -1364,Friday,6,6/8/2018,"3,956","2,670","2,107",563 -1365,Saturday,7,6/9/2018,"2,303","1,589","1,278",311 -1366,Sunday,1,6/10/2018,"3,011","2,091","1,717",374 -1367,Monday,2,6/11/2018,"4,896","3,326","2,714",612 -1368,Tuesday,3,6/12/2018,"4,952","3,309","2,624",685 -1369,Wednesday,4,6/13/2018,"4,570","3,099","2,439",660 -1370,Thursday,5,6/14/2018,"4,542","2,962","2,377",585 -1371,Friday,6,6/15/2018,"3,383","2,320","1,847",473 -1372,Saturday,7,6/16/2018,"1,922","1,316","1,061",255 -1373,Sunday,1,6/17/2018,"2,404","1,677","1,361",316 -1374,Monday,2,6/18/2018,"4,576","2,964","2,313",651 -1375,Tuesday,3,6/19/2018,"4,727","3,144","2,519",625 -1376,Wednesday,4,6/20/2018,"4,639","2,989","2,370",619 -1377,Thursday,5,6/21/2018,"4,108","2,762","2,154",608 -1378,Friday,6,6/22/2018,"3,372","2,207","1,744",463 -1379,Saturday,7,6/23/2018,"1,996","1,277",989,288 -1380,Sunday,1,6/24/2018,"2,415","1,619","1,266",353 -1381,Monday,2,6/25/2018,"4,457","2,881","2,272",609 -1382,Tuesday,3,6/26/2018,"4,736","3,039","2,410",629 -1383,Wednesday,4,6/27/2018,"4,245","2,818","2,244",574 -1384,Thursday,5,6/28/2018,"4,081","2,785","2,160",625 -1385,Friday,6,6/29/2018,"3,053","2,008","1,568",440 -1386,Saturday,7,6/30/2018,"1,741","1,147",927,220 -1387,Sunday,1,7/1/2018,"2,186","1,447","1,140",307 -1388,Monday,2,7/2/2018,"4,026","2,533","1,952",581 -1389,Tuesday,3,7/3/2018,"3,961","2,515","1,966",549 -1390,Wednesday,4,7/4/2018,"3,019","2,032","1,595",437 -1391,Thursday,5,7/5/2018,"3,743","2,496","1,997",499 -1392,Friday,6,7/6/2018,"3,050","1,995","1,575",420 -1393,Saturday,7,7/7/2018,"1,688","1,080",857,223 -1394,Sunday,1,7/8/2018,"2,185","1,440","1,145",295 -1395,Monday,2,7/9/2018,"4,104","2,576","2,016",560 -1396,Tuesday,3,7/10/2018,"4,097","2,694","2,059",635 -1397,Wednesday,4,7/11/2018,"3,844","2,506","1,895",611 -1398,Thursday,5,7/12/2018,"3,743","2,530","2,001",529 -1399,Friday,6,7/13/2018,"3,073","2,068","1,581",487 -1400,Saturday,7,7/14/2018,"1,852","1,249",992,257 -1401,Sunday,1,7/15/2018,"2,105","1,474","1,173",301 -1402,Monday,2,7/16/2018,"4,078","2,657","2,095",562 -1403,Tuesday,3,7/17/2018,"4,515","2,856","2,255",601 -1404,Wednesday,4,7/18/2018,"4,276","2,779","2,173",606 -1405,Thursday,5,7/19/2018,"4,043","2,705","2,114",591 -1406,Friday,6,7/20/2018,"3,004","2,028","1,576",452 -1407,Saturday,7,7/21/2018,"1,664","1,177",942,235 -1408,Sunday,1,7/22/2018,"2,248","1,561","1,246",315 -1409,Monday,2,7/23/2018,"4,062","2,658","2,081",577 -1410,Tuesday,3,7/24/2018,"4,053","2,680","2,091",589 -1411,Wednesday,4,7/25/2018,"3,628","2,431","1,879",552 -1412,Thursday,5,7/26/2018,"3,596","2,415","1,925",490 -1413,Friday,6,7/27/2018,"3,140","2,002","1,539",463 -1414,Saturday,7,7/28/2018,"1,938","1,304","1,054",250 -1415,Sunday,1,7/29/2018,"2,460","1,699","1,364",335 -1416,Monday,2,7/30/2018,"4,175","2,771","2,166",605 -1417,Tuesday,3,7/31/2018,"4,113","2,881","2,310",571 -1418,Wednesday,4,8/1/2018,"4,273","2,901","2,310",591 -1419,Thursday,5,8/2/2018,"4,194","2,754","2,202",552 -1420,Friday,6,8/3/2018,"3,405","2,345","1,833",512 -1421,Saturday,7,8/4/2018,"1,800","1,251","1,025",226 -1422,Sunday,1,8/5/2018,"2,378","1,694","1,388",306 -1423,Monday,2,8/6/2018,"4,319","2,856","2,233",623 -1424,Tuesday,3,8/7/2018,"4,388","2,971","2,375",596 -1425,Wednesday,4,8/8/2018,"4,205","2,724","2,118",606 -1426,Thursday,5,8/9/2018,"3,973","2,555","1,998",557 -1427,Friday,6,8/10/2018,"3,510","2,224","1,777",447 -1428,Saturday,7,8/11/2018,"1,913","1,281","1,012",269 -1429,Sunday,1,8/12/2018,"2,397","1,644","1,284",360 -1430,Monday,2,8/13/2018,"4,348","2,789","2,224",565 -1431,Tuesday,3,8/14/2018,"3,966","2,688","2,127",561 -1432,Wednesday,4,8/15/2018,"3,805","2,544","2,035",509 -1433,Thursday,5,8/16/2018,"3,866","2,604","2,074",530 -1434,Friday,6,8/17/2018,"3,489","2,219","1,737",482 -1435,Saturday,7,8/18/2018,"2,074","1,394","1,108",286 -1436,Sunday,1,8/19/2018,"2,519","1,687","1,318",369 -1437,Monday,2,8/20/2018,"4,202","2,734","2,128",606 -1438,Tuesday,3,8/21/2018,"4,023","2,711","2,147",564 -1439,Wednesday,4,8/22/2018,"4,048","2,685","2,121",564 -1440,Thursday,5,8/23/2018,"4,150","2,860","2,249",611 -1441,Friday,6,8/24/2018,"3,154","2,199","1,742",457 -1442,Saturday,7,8/25/2018,"2,143","1,317","1,012",305 -1443,Sunday,1,8/26/2018,"2,576","1,705","1,372",333 -1444,Monday,2,8/27/2018,"4,317","2,954","2,367",587 -1445,Tuesday,3,8/28/2018,"4,421","3,023","2,427",596 -1446,Wednesday,4,8/29/2018,"4,563","3,019","2,395",624 -1447,Thursday,5,8/30/2018,"4,055","2,798","2,265",533 -1448,Friday,6,8/31/2018,"3,427","2,305","1,815",490 -1449,Saturday,7,9/1/2018,"1,731","1,241",989,252 -1450,Sunday,1,9/2/2018,"2,258","1,641","1,293",348 -1451,Monday,2,9/3/2018,"3,655","2,499","2,032",467 -1452,Tuesday,3,9/4/2018,"4,456","2,995","2,435",560 -1453,Wednesday,4,9/5/2018,"4,566","3,042","2,456",586 -1454,Thursday,5,9/6/2018,"4,314","3,049","2,508",541 -1455,Friday,6,9/7/2018,"3,557","2,349","1,890",459 -1456,Saturday,7,9/8/2018,"2,281","1,548","1,255",293 -1457,Sunday,1,9/9/2018,"3,346","2,255","1,906",349 -1458,Monday,2,9/10/2018,"4,711","3,200","2,600",600 -1459,Tuesday,3,9/11/2018,"4,709","3,134","2,595",539 -1460,Wednesday,4,9/12/2018,"4,379","3,032","2,464",568 -1461,Thursday,5,9/13/2018,"4,357","2,870","2,325",545 -1462,Friday,6,9/14/2018,"3,560","2,396","1,907",489 -1463,Saturday,7,9/15/2018,"2,049","1,488","1,244",244 -1464,Sunday,1,9/16/2018,"2,813","1,934","1,614",320 -1465,Monday,2,9/17/2018,"5,064","3,241","2,621",620 -1466,Tuesday,3,9/18/2018,"5,006","3,221","2,595",626 -1467,Wednesday,4,9/19/2018,"4,949","3,282","2,708",574 -1468,Thursday,5,9/20/2018,"4,695","3,165","2,573",592 -1469,Friday,6,9/21/2018,"3,516","2,383","1,933",450 -1470,Saturday,7,9/22/2018,"2,460","1,704","1,374",330 -1471,Sunday,1,9/23/2018,"2,994","2,127","1,733",394 -1472,Monday,2,9/24/2018,"4,688","3,273","2,679",594 -1473,Tuesday,3,9/25/2018,"4,924","3,481","2,885",596 -1474,Wednesday,4,9/26/2018,"5,209","3,546","2,936",610 -1475,Thursday,5,9/27/2018,"5,966","4,029","3,385",644 -1476,Friday,6,9/28/2018,"4,740","3,171","2,643",528 -1477,Saturday,7,9/29/2018,"2,715","1,870","1,618",252 -1478,Sunday,1,9/30/2018,"3,739","2,573","2,194",379 -1479,Monday,2,10/1/2018,"5,841","3,982","3,331",651 -1480,Tuesday,3,10/2/2018,"5,881","4,096","3,514",582 -1481,Wednesday,4,10/3/2018,"5,540","3,863","3,249",614 -1482,Thursday,5,10/4/2018,"5,636","3,894","3,306",588 -1483,Friday,6,10/5/2018,"4,479","3,120","2,638",482 -1484,Saturday,7,10/6/2018,"3,076","2,094","1,750",344 -1485,Sunday,1,10/7/2018,"3,973","2,865","2,457",408 -1486,Monday,2,10/8/2018,"6,045","4,310","3,659",651 -1487,Tuesday,3,10/9/2018,"6,126","4,173","3,522",651 -1488,Wednesday,4,10/10/2018,"6,084","4,319","3,616",703 -1489,Thursday,5,10/11/2018,"6,154","4,254","3,632",622 -1490,Friday,6,10/12/2018,"4,868","3,385","2,784",601 -1491,Saturday,7,10/13/2018,"3,409","2,379","2,006",373 -1492,Sunday,1,10/14/2018,"4,722","3,357","2,859",498 -1493,Monday,2,10/15/2018,"6,555","4,550","3,766",784 -1494,Tuesday,3,10/16/2018,"7,079","4,652","3,893",759 -1495,Wednesday,4,10/17/2018,"6,691","4,673","3,893",780 -1496,Thursday,5,10/18/2018,"6,522","4,398","3,666",732 -1497,Friday,6,10/19/2018,"4,922","3,390","2,857",533 -1498,Saturday,7,10/20/2018,"3,409","2,399","1,990",409 -1499,Sunday,1,10/21/2018,"4,483","3,092","2,623",469 -1500,Monday,2,10/22/2018,"6,666","4,499","3,769",730 -1501,Tuesday,3,10/23/2018,"6,991","4,677","3,916",761 -1502,Wednesday,4,10/24/2018,"6,871","4,619","3,808",811 -1503,Thursday,5,10/25/2018,"6,357","4,331","3,579",752 -1504,Friday,6,10/26/2018,"5,114","3,468","2,827",641 -1505,Saturday,7,10/27/2018,"3,456","2,400","2,006",394 -1506,Sunday,1,10/28/2018,"4,924","3,251","2,735",516 -1507,Monday,2,10/29/2018,"6,837","4,704","3,890",814 -1508,Tuesday,3,10/30/2018,"6,481","4,547","3,798",749 -1509,Wednesday,4,10/31/2018,"6,296","4,316","3,545",771 -1510,Thursday,5,11/1/2018,"6,044","3,988","3,299",689 -1511,Friday,6,11/2/2018,"4,860","3,326","2,738",588 -1512,Saturday,7,11/3/2018,"3,430","2,289","1,946",343 -1513,Sunday,1,11/4/2018,"4,638","3,236","2,744",492 -1514,Monday,2,11/5/2018,"6,365","4,329","3,609",720 -1515,Tuesday,3,11/6/2018,"6,330","4,291","3,556",735 -1516,Wednesday,4,11/7/2018,"5,865","3,937","3,267",670 -1517,Thursday,5,11/8/2018,"5,692","3,938","3,298",640 -1518,Friday,6,11/9/2018,"5,026","3,533","2,917",616 -1519,Saturday,7,11/10/2018,"3,506","2,428","2,018",410 -1520,Sunday,1,11/11/2018,"4,778","3,265","2,709",556 -1521,Monday,2,11/12/2018,"6,684","4,592","3,795",797 -1522,Tuesday,3,11/13/2018,"6,766","4,720","3,918",802 -1523,Wednesday,4,11/14/2018,"6,916","4,674","3,872",802 -1524,Thursday,5,11/15/2018,"6,440","4,379","3,634",745 -1525,Friday,6,11/16/2018,"5,066","3,497","2,863",634 -1526,Saturday,7,11/17/2018,"3,503","2,489","2,043",446 -1527,Sunday,1,11/18/2018,"4,855","3,321","2,691",630 -1528,Monday,2,11/19/2018,"6,818","4,601","3,795",806 -1529,Tuesday,3,11/20/2018,"6,239","4,424","3,640",784 -1530,Wednesday,4,11/21/2018,"5,471","3,904","3,194",710 -1531,Thursday,5,11/22/2018,"4,453","3,186","2,592",594 -1532,Friday,6,11/23/2018,"4,062","2,892","2,347",545 -1533,Saturday,7,11/24/2018,"3,485","2,445","1,987",458 -1534,Sunday,1,11/25/2018,"4,906","3,371","2,815",556 -1535,Monday,2,11/26/2018,"7,099","4,871","4,012",859 -1536,Tuesday,3,11/27/2018,"7,503","5,181","4,267",914 -1537,Wednesday,4,11/28/2018,"7,554","5,227","4,330",897 -1538,Thursday,5,11/29/2018,"7,169","4,944","4,106",838 -1539,Friday,6,11/30/2018,"5,608","3,895","3,211",684 -1540,Saturday,7,12/1/2018,"4,055","2,800","2,306",494 -1541,Sunday,1,12/2/2018,"5,611","3,906","3,259",647 -1542,Monday,2,12/3/2018,"7,650","5,378","4,431",947 -1543,Tuesday,3,12/4/2018,"7,372","5,225","4,328",897 -1544,Wednesday,4,12/5/2018,"7,369","5,166","4,316",850 -1545,Thursday,5,12/6/2018,"7,250","5,093","4,213",880 -1546,Friday,6,12/7/2018,"5,735","4,032","3,296",736 -1547,Saturday,7,12/8/2018,"4,614","3,240","2,699",541 -1548,Sunday,1,12/9/2018,"5,979","4,062","3,352",710 -1549,Monday,2,12/10/2018,"7,840","5,421","4,465",956 -1550,Tuesday,3,12/11/2018,"7,659","5,267","4,330",937 -1551,Wednesday,4,12/12/2018,"7,319","4,983","4,010",973 -1552,Thursday,5,12/13/2018,"6,373","4,452","3,620",832 -1553,Friday,6,12/14/2018,"4,897","3,404","2,776",628 -1554,Saturday,7,12/15/2018,"3,325","2,209","1,833",376 -1555,Sunday,1,12/16/2018,"3,874","2,671","2,206",465 -1556,Monday,2,12/17/2018,"5,436","3,791","3,062",729 -1557,Tuesday,3,12/18/2018,"5,174","3,562","2,945",617 -1558,Wednesday,4,12/19/2018,"4,873","3,255","2,632",623 -1559,Thursday,5,12/20/2018,"3,913","2,716","2,164",552 -1560,Friday,6,12/21/2018,"2,934","1,979","1,543",436 -1561,Saturday,7,12/22/2018,"1,523","1,074",846,228 -1562,Sunday,1,12/23/2018,"1,859","1,223",997,226 -1563,Monday,2,12/24/2018,"1,835","1,310","1,019",291 -1564,Tuesday,3,12/25/2018,"1,852","1,214",955,259 -1565,Wednesday,4,12/26/2018,"2,357","1,594","1,243",351 -1566,Thursday,5,12/27/2018,"2,583","1,783","1,418",365 -1567,Friday,6,12/28/2018,"2,477","1,613","1,279",334 -1568,Saturday,7,12/29/2018,"1,694","1,197",990,207 -1569,Sunday,1,12/30/2018,"1,985","1,340","1,097",243 -1570,Monday,2,12/31/2018,"1,723","1,159",874,285 -1571,Tuesday,3,1/1/2019,"1,715","1,168",891,277 -1572,Wednesday,4,1/2/2019,"3,431","2,361","1,899",462 -1573,Thursday,5,1/3/2019,"4,118","2,793","2,259",534 -1574,Friday,6,1/4/2019,"3,504","2,373","1,936",437 -1575,Saturday,7,1/5/2019,"2,308","1,609","1,348",261 -1576,Sunday,1,1/6/2019,"2,925","2,046","1,648",398 -1577,Monday,2,1/7/2019,"4,587","3,100","2,550",550 -1578,Tuesday,3,1/8/2019,"4,731","3,182","2,585",597 -1579,Wednesday,4,1/9/2019,"4,831","3,245","2,660",585 -1580,Thursday,5,1/10/2019,"4,162","2,836","2,298",538 -1581,Friday,6,1/11/2019,"3,726","2,476","1,999",477 -1582,Saturday,7,1/12/2019,"2,458","1,722","1,413",309 -1583,Sunday,1,1/13/2019,"3,266","2,264","1,878",386 -1584,Monday,2,1/14/2019,"5,016","3,348","2,760",588 -1585,Tuesday,3,1/15/2019,"4,732","3,320","2,738",582 -1586,Wednesday,4,1/16/2019,"4,938","3,447","2,845",602 -1587,Thursday,5,1/17/2019,"5,033","3,516","2,967",549 -1588,Friday,6,1/18/2019,"4,114","2,731","2,236",495 -1589,Saturday,7,1/19/2019,"2,780","1,929","1,599",330 -1590,Sunday,1,1/20/2019,"4,205","2,367","1,974",393 -1591,Monday,2,1/21/2019,"4,983","3,391","2,783",608 -1592,Tuesday,3,1/22/2019,"5,182","3,674","3,052",622 -1593,Wednesday,4,1/23/2019,"5,316","3,856","3,199",657 -1594,Thursday,5,1/24/2019,"5,021","3,462","2,853",609 -1595,Friday,6,1/25/2019,"4,230","2,882","2,382",500 -1596,Saturday,7,1/26/2019,"2,743","1,882","1,541",341 -1597,Sunday,1,1/27/2019,"3,890","2,622","2,232",390 -1598,Monday,2,1/28/2019,"5,575","3,925","3,232",693 -1599,Tuesday,3,1/29/2019,"5,732","3,866","3,260",606 -1600,Wednesday,4,1/30/2019,"5,402","3,759","3,134",625 -1601,Thursday,5,1/31/2019,"5,069","3,597","2,984",613 -1602,Friday,6,2/1/2019,"4,222","2,923","2,364",559 -1603,Saturday,7,2/2/2019,"2,864","1,902","1,596",306 -1604,Sunday,1,2/3/2019,"3,598","2,447","2,044",403 -1605,Monday,2,2/4/2019,"5,366","3,767","3,146",621 -1606,Tuesday,3,2/5/2019,"5,427","3,757","3,124",633 -1607,Wednesday,4,2/6/2019,"5,667","3,759","3,130",629 -1608,Thursday,5,2/7/2019,"5,517","3,763","3,069",694 -1609,Friday,6,2/8/2019,"4,420","2,943","2,429",514 -1610,Saturday,7,2/9/2019,"3,150","2,274","1,911",363 -1611,Sunday,1,2/10/2019,"4,264","2,962","2,470",492 -1612,Monday,2,2/11/2019,"6,569","4,276","3,566",710 -1613,Tuesday,3,2/12/2019,"6,145","4,092","3,391",701 -1614,Wednesday,4,2/13/2019,"5,743","3,932","3,236",696 -1615,Thursday,5,2/14/2019,"5,512","3,762","3,075",687 -1616,Friday,6,2/15/2019,"4,127","2,952","2,377",575 -1617,Saturday,7,2/16/2019,"3,309","2,195","1,809",386 -1618,Sunday,1,2/17/2019,"3,761","2,669","2,217",452 -1619,Monday,2,2/18/2019,"5,592","3,912","3,226",686 -1620,Tuesday,3,2/19/2019,"6,100","4,322","3,575",747 -1621,Wednesday,4,2/20/2019,"6,208","4,265","3,543",722 -1622,Thursday,5,2/21/2019,"5,730","4,071","3,352",719 -1623,Friday,6,2/22/2019,"4,372","3,073","2,508",565 -1624,Saturday,7,2/23/2019,"3,378","2,259","1,871",388 -1625,Sunday,1,2/24/2019,"4,367","3,012","2,524",488 -1626,Monday,2,2/25/2019,"6,318","4,424","3,678",746 -1627,Tuesday,3,2/26/2019,"6,431","4,304","3,530",774 -1628,Wednesday,4,2/27/2019,"5,919","4,205","3,442",763 -1629,Thursday,5,2/28/2019,"5,668","3,966","3,266",700 -1630,Friday,6,3/1/2019,"4,581","3,178","2,594",584 -1631,Saturday,7,3/2/2019,"3,383","2,263","1,878",385 -1632,Sunday,1,3/3/2019,"4,311","3,005","2,476",529 -1633,Monday,2,3/4/2019,"6,357","4,214","3,443",771 -1634,Tuesday,3,3/5/2019,"6,092","4,092","3,343",749 -1635,Wednesday,4,3/6/2019,"5,772","4,070","3,340",730 -1636,Thursday,5,3/7/2019,"5,861","4,064","3,330",734 -1637,Friday,6,3/8/2019,"4,600","3,147","2,590",557 -1638,Saturday,7,3/9/2019,"3,390","2,218","1,849",369 -1639,Sunday,1,3/10/2019,"4,099","2,898","2,397",501 -1640,Monday,2,3/11/2019,"6,240","4,320","3,599",721 -1641,Tuesday,3,3/12/2019,"5,871","4,127","3,375",752 -1642,Wednesday,4,3/13/2019,"6,381","4,506","3,726",780 -1643,Thursday,5,3/14/2019,"5,853","4,043","3,328",715 -1644,Friday,6,3/15/2019,"4,425","3,116","2,499",617 -1645,Saturday,7,3/16/2019,"2,961","2,099","1,685",414 -1646,Sunday,1,3/17/2019,"4,008","2,744","2,258",486 -1647,Monday,2,3/18/2019,"5,830","4,040","3,319",721 -1648,Tuesday,3,3/19/2019,"6,089","4,164","3,448",716 -1649,Wednesday,4,3/20/2019,"5,800","3,946","3,192",754 -1650,Thursday,5,3/21/2019,"5,431","3,761","3,100",661 -1651,Friday,6,3/22/2019,"4,560","3,071","2,543",528 -1652,Saturday,7,3/23/2019,"3,256","2,254","1,859",395 -1653,Sunday,1,3/24/2019,"4,156","2,858","2,386",472 -1654,Monday,2,3/25/2019,"6,060","4,232","3,469",763 -1655,Tuesday,3,3/26/2019,"6,191","4,344","3,578",766 -1656,Wednesday,4,3/27/2019,"6,678","4,612","3,842",770 -1657,Thursday,5,3/28/2019,"6,167","4,086","3,361",725 -1658,Friday,6,3/29/2019,"4,441","3,147","2,601",546 -1659,Saturday,7,3/30/2019,"3,243","2,325","1,949",376 -1660,Sunday,1,3/31/2019,"4,260","3,039","2,543",496 -1661,Monday,2,4/1/2019,"6,078","4,238","3,550",688 -1662,Tuesday,3,4/2/2019,"6,824","4,656","3,833",823 -1663,Wednesday,4,4/3/2019,"6,621","4,727","3,942",785 -1664,Thursday,5,4/4/2019,"6,231","4,343","3,647",696 -1665,Friday,6,4/5/2019,"4,776","3,346","2,719",627 -1666,Saturday,7,4/6/2019,"3,148","2,319","1,924",395 -1667,Sunday,1,4/7/2019,"4,357","3,050","2,511",539 -1668,Monday,2,4/8/2019,"6,794","4,738","3,960",778 -1669,Tuesday,3,4/9/2019,"6,688","4,505","3,736",769 -1670,Wednesday,4,4/10/2019,"6,570","4,448","3,646",802 -1671,Thursday,5,4/11/2019,"6,113","4,264","3,504",760 -1672,Friday,6,4/12/2019,"4,856","3,336","2,726",610 -1673,Saturday,7,4/13/2019,"3,401","2,409","2,011",398 -1674,Sunday,1,4/14/2019,"4,551","3,197","2,600",597 -1675,Monday,2,4/15/2019,"6,614","4,773","3,967",806 -1676,Tuesday,3,4/16/2019,"7,019","4,837","4,037",800 -1677,Wednesday,4,4/17/2019,"6,643","4,470","3,681",789 -1678,Thursday,5,4/18/2019,"5,398","3,825","3,133",692 -1679,Friday,6,4/19/2019,"4,323","2,981","2,482",499 -1680,Saturday,7,4/20/2019,"3,546","2,442","2,020",422 -1681,Sunday,1,4/21/2019,"4,312","3,045","2,534",511 -1682,Monday,2,4/22/2019,"6,363","4,520","3,729",791 -1683,Tuesday,3,4/23/2019,"6,797","4,968","4,147",821 -1684,Wednesday,4,4/24/2019,"7,144","5,002","4,136",866 -1685,Thursday,5,4/25/2019,"6,555","4,621","3,804",817 -1686,Friday,6,4/26/2019,"5,400","3,780","3,089",691 -1687,Saturday,7,4/27/2019,"3,710","2,465","2,038",427 -1688,Sunday,1,4/28/2019,"4,788","3,294","2,728",566 -1689,Monday,2,4/29/2019,"6,232","4,340","3,578",762 -1690,Tuesday,3,4/30/2019,"6,093","4,264","3,440",824 -1691,Wednesday,4,5/1/2019,"6,117","4,275","3,556",719 -1692,Thursday,5,5/2/2019,"6,274","4,153","3,433",720 -1693,Friday,6,5/3/2019,"4,774","3,370","2,763",607 -1694,Saturday,7,5/4/2019,"3,437","2,462","1,999",463 -1695,Sunday,1,5/5/2019,"4,330","3,009","2,457",552 -1696,Monday,2,5/6/2019,"5,902","4,164","3,402",762 -1697,Tuesday,3,5/7/2019,"6,064","4,258","3,469",789 -1698,Wednesday,4,5/8/2019,"5,709","4,049","3,310",739 -1699,Thursday,5,5/9/2019,"5,596","3,944","3,275",669 -1700,Friday,6,5/10/2019,"4,498","3,144","2,494",650 -1701,Saturday,7,5/11/2019,"3,258","2,119","1,702",417 -1702,Sunday,1,5/12/2019,"3,571","2,565","2,070",495 -1703,Monday,2,5/13/2019,"5,677","3,908","3,136",772 -1704,Tuesday,3,5/14/2019,"5,534","3,669","2,977",692 -1705,Wednesday,4,5/15/2019,"5,391","3,667","2,989",678 -1706,Thursday,5,5/16/2019,"5,043","3,577","2,953",624 -1707,Friday,6,5/17/2019,"3,993","2,795","2,254",541 -1708,Saturday,7,5/18/2019,"2,565","1,876","1,545",331 -1709,Sunday,1,5/19/2019,"3,369","2,364","1,954",410 -1710,Monday,2,5/20/2019,"4,993","3,442","2,806",636 -1711,Tuesday,3,5/21/2019,"4,728","3,406","2,813",593 -1712,Wednesday,4,5/22/2019,"4,963","3,431","2,772",659 -1713,Thursday,5,5/23/2019,"4,637","3,243","2,698",545 -1714,Friday,6,5/24/2019,"3,694","2,559","2,070",489 -1715,Saturday,7,5/25/2019,"2,460","1,658","1,347",311 -1716,Sunday,1,5/26/2019,"3,006","2,064","1,693",371 -1717,Monday,2,5/27/2019,"3,992","2,780","2,271",509 -1718,Tuesday,3,5/28/2019,"4,960","3,403","2,767",636 -1719,Wednesday,4,5/29/2019,"4,376","3,115","2,562",553 -1720,Thursday,5,5/30/2019,"4,143","2,896","2,353",543 -1721,Friday,6,5/31/2019,"3,359","2,363","1,909",454 -1722,Saturday,7,6/1/2019,"2,218","1,474","1,221",253 -1723,Sunday,1,6/2/2019,"2,768","1,845","1,516",329 -1724,Monday,2,6/3/2019,"3,967","2,831","2,295",536 -1725,Tuesday,3,6/4/2019,"4,501","3,008","2,426",582 -1726,Wednesday,4,6/5/2019,"3,973","2,727","2,233",494 -1727,Thursday,5,6/6/2019,"4,259","2,871","2,312",559 -1728,Friday,6,6/7/2019,"3,181","2,313","1,869",444 -1729,Saturday,7,6/8/2019,"2,170","1,583","1,324",259 -1730,Sunday,1,6/9/2019,"2,810","1,930","1,586",344 -1731,Monday,2,6/10/2019,"4,420","3,030","2,463",567 -1732,Tuesday,3,6/11/2019,"4,793","3,258","2,664",594 -1733,Wednesday,4,6/12/2019,"4,410","3,064","2,479",585 -1734,Thursday,5,6/13/2019,"4,076","2,844","2,280",564 -1735,Friday,6,6/14/2019,"3,287","2,265","1,789",476 -1736,Saturday,7,6/15/2019,"2,176","1,467","1,201",266 -1737,Sunday,1,6/16/2019,"2,516","1,747","1,419",328 -1738,Monday,2,6/17/2019,"4,142","2,925","2,398",527 -1739,Tuesday,3,6/18/2019,"4,225","3,013","2,461",552 -1740,Wednesday,4,6/19/2019,"4,162","2,882","2,351",531 -1741,Thursday,5,6/20/2019,"3,716","2,615","2,137",478 -1742,Friday,6,6/21/2019,"3,333","2,275","1,853",422 -1743,Saturday,7,6/22/2019,"1,918","1,360","1,118",242 -1744,Sunday,1,6/23/2019,"2,344","1,621","1,317",304 -1745,Monday,2,6/24/2019,"3,691","2,591","2,149",442 -1746,Tuesday,3,6/25/2019,"4,035","2,770","2,247",523 -1747,Wednesday,4,6/26/2019,"3,939","2,675","2,186",489 -1748,Thursday,5,6/27/2019,"3,738","2,671","2,197",474 -1749,Friday,6,6/28/2019,"2,846","2,021","1,657",364 -1750,Saturday,7,6/29/2019,"1,733","1,199",983,216 -1751,Sunday,1,6/30/2019,"2,184","1,494","1,240",254 -1752,Monday,2,7/1/2019,"3,512","2,485","2,001",484 -1753,Tuesday,3,7/2/2019,"3,675","2,636","2,163",473 -1754,Wednesday,4,7/3/2019,"3,831","2,579","2,144",435 -1755,Thursday,5,7/4/2019,"2,801","1,971","1,648",323 -1756,Friday,6,7/5/2019,"2,552","1,790","1,446",344 -1757,Saturday,7,7/6/2019,"1,950","1,291","1,110",181 -1758,Sunday,1,7/7/2019,"2,110","1,477","1,209",268 -1759,Monday,2,7/8/2019,"4,303","2,903","2,383",520 -1760,Tuesday,3,7/9/2019,"3,883","2,799","2,335",464 -1761,Wednesday,4,7/10/2019,"4,244","2,907","2,347",560 -1762,Thursday,5,7/11/2019,"3,765","2,669","2,150",519 -1763,Friday,6,7/12/2019,"3,286","2,276","1,816",460 -1764,Saturday,7,7/13/2019,"1,851","1,305","1,063",242 -1765,Sunday,1,7/14/2019,"2,580","1,754","1,433",321 -1766,Monday,2,7/15/2019,"4,140","2,911","2,382",529 -1767,Tuesday,3,7/16/2019,"4,076","2,785","2,285",500 -1768,Wednesday,4,7/17/2019,"4,141","2,900","2,360",540 -1769,Thursday,5,7/18/2019,"3,898","2,774","2,249",525 -1770,Friday,6,7/19/2019,"3,027","2,078","1,641",437 -1771,Saturday,7,7/20/2019,"1,831","1,271","1,033",238 -1772,Sunday,1,7/21/2019,"2,282","1,617","1,356",261 -1773,Monday,2,7/22/2019,"4,138","2,890","2,376",514 -1774,Tuesday,3,7/23/2019,"4,049","2,868","2,344",524 -1775,Wednesday,4,7/24/2019,"4,272","2,963","2,359",604 -1776,Thursday,5,7/25/2019,"4,089","2,755","2,210",545 -1777,Friday,6,7/26/2019,"3,277","2,264","1,842",422 -1778,Saturday,7,7/27/2019,"1,954","1,308","1,069",239 -1779,Sunday,1,7/28/2019,"2,111","1,509","1,239",270 -1780,Monday,2,7/29/2019,"3,625","2,539","2,053",486 -1781,Tuesday,3,7/30/2019,"4,415","2,906","2,386",520 -1782,Wednesday,4,7/31/2019,"3,854","2,743","2,260",483 -1783,Thursday,5,8/1/2019,"4,114","2,771","2,262",509 -1784,Friday,6,8/2/2019,"3,492","2,280","1,874",406 -1785,Saturday,7,8/3/2019,"1,849","1,312","1,096",216 -1786,Sunday,1,8/4/2019,"2,239","1,630","1,347",283 -1787,Monday,2,8/5/2019,"3,976","2,664","2,191",473 -1788,Tuesday,3,8/6/2019,"4,061","2,833","2,284",549 -1789,Wednesday,4,8/7/2019,"3,702","2,675","2,215",460 -1790,Thursday,5,8/8/2019,"3,622","2,553","2,112",441 -1791,Friday,6,8/9/2019,"3,039","2,033","1,660",373 -1792,Saturday,7,8/10/2019,"1,848","1,265","1,049",216 -1793,Sunday,1,8/11/2019,"2,082","1,502","1,242",260 -1794,Monday,2,8/12/2019,"3,807","2,574","2,121",453 -1795,Tuesday,3,8/13/2019,"3,993","2,667","2,215",452 -1796,Wednesday,4,8/14/2019,"3,567","2,517","2,087",430 -1797,Thursday,5,8/15/2019,"3,484","2,450","2,046",404 -1798,Friday,6,8/16/2019,"2,994","2,022","1,663",359 -1799,Saturday,7,8/17/2019,"1,793","1,276","1,074",202 -1800,Sunday,1,8/18/2019,"2,422","1,721","1,477",244 -1801,Monday,2,8/19/2019,"4,070","2,847","2,371",476 -1802,Tuesday,3,8/20/2019,"3,987","2,800","2,293",507 -1803,Wednesday,4,8/21/2019,"3,355","2,407","1,963",444 -1804,Thursday,5,8/22/2019,"2,925","1,959","1,627",332 -1805,Friday,6,8/23/2019,"3,010","2,143","1,769",374 -1806,Saturday,7,8/24/2019,"1,896","1,380","1,151",229 -1807,Sunday,1,8/25/2019,"2,501","1,791","1,497",294 -1808,Monday,2,8/26/2019,"3,851","2,714","2,257",457 -1809,Tuesday,3,8/27/2019,"4,403","3,075","2,622",453 -1810,Wednesday,4,8/28/2019,"4,627","3,125","2,623",502 -1811,Thursday,5,8/29/2019,"4,221","2,982","2,477",505 -1812,Friday,6,8/30/2019,"3,025","2,102","1,727",375 -1813,Saturday,7,8/31/2019,"1,998","1,386","1,179",207 -1814,Sunday,1,9/1/2019,"2,297","1,645","1,414",231 -1815,Monday,2,9/2/2019,"3,192","2,272","1,910",362 -1816,Tuesday,3,9/3/2019,"4,087","2,921","2,434",487 -1817,Wednesday,4,9/4/2019,"4,787","3,348","2,842",506 -1818,Thursday,5,9/5/2019,"4,483","3,078","2,570",508 -1819,Friday,6,9/6/2019,"3,571","2,483","2,095",388 -1820,Saturday,7,9/7/2019,"2,263","1,566","1,343",223 -1821,Sunday,1,9/8/2019,"2,948","2,118","1,827",291 -1822,Monday,2,9/9/2019,"4,521","3,287","2,785",502 -1823,Tuesday,3,9/10/2019,"4,563","3,251","2,758",493 -1824,Wednesday,4,9/11/2019,"4,598","3,365","2,901",464 -1825,Thursday,5,9/12/2019,"4,603","3,356","2,868",488 -1826,Friday,6,9/13/2019,"3,349","2,508","2,121",387 -1827,Saturday,7,9/14/2019,"2,437","1,716","1,451",265 -1828,Sunday,1,9/15/2019,"3,188","2,367","2,082",285 -1829,Monday,2,9/16/2019,"5,291","3,839","3,280",559 -1830,Tuesday,3,9/17/2019,"5,477","3,815","3,273",542 -1831,Wednesday,4,9/18/2019,"5,257","3,726","3,222",504 -1832,Thursday,5,9/19/2019,"5,000","3,500","3,028",472 -1833,Friday,6,9/20/2019,"3,890","2,822","2,407",415 -1834,Saturday,7,9/21/2019,"2,654","1,994","1,739",255 -1835,Sunday,1,9/22/2019,"3,656","2,640","2,328",312 -1836,Monday,2,9/23/2019,"5,223","3,804","3,291",513 -1837,Tuesday,3,9/24/2019,"5,276","3,826","3,287",539 -1838,Wednesday,4,9/25/2019,"5,476","3,838","3,306",532 -1839,Thursday,5,9/26/2019,"5,196","3,718","3,208",510 -1840,Friday,6,9/27/2019,"4,052","2,863","2,448",415 -1841,Saturday,7,9/28/2019,"2,635","1,945","1,696",249 -1842,Sunday,1,9/29/2019,"4,076","2,895","2,541",354 -1843,Monday,2,9/30/2019,"5,167","3,754","3,216",538 -1844,Tuesday,3,10/1/2019,"4,972","3,634","3,082",552 -1845,Wednesday,4,10/2/2019,"5,143","3,683","3,209",474 -1846,Thursday,5,10/3/2019,"4,753","3,454","2,941",513 -1847,Friday,6,10/4/2019,"3,795","2,854","2,440",414 -1848,Saturday,7,10/5/2019,"2,920","2,118","1,827",291 -1849,Sunday,1,10/6/2019,"3,950","2,850","2,471",379 -1850,Monday,2,10/7/2019,"5,493","3,929","3,432",497 -1851,Tuesday,3,10/8/2019,"5,652","4,186","3,705",481 -1852,Wednesday,4,10/9/2019,"5,617","3,936","3,388",548 -1853,Thursday,5,10/10/2019,"5,234","3,802","3,261",541 -1854,Friday,6,10/11/2019,"4,118","3,147","2,674",473 -1855,Saturday,7,10/12/2019,"2,863","2,111","1,803",308 -1856,Sunday,1,10/13/2019,"4,032","3,013","2,645",368 -1857,Monday,2,10/14/2019,"5,476","4,022","3,466",556 -1858,Tuesday,3,10/15/2019,"5,542","4,117","3,572",545 -1859,Wednesday,4,10/16/2019,"5,340","3,966","3,403",563 -1860,Thursday,5,10/17/2019,"5,388","3,905","3,333",572 -1861,Friday,6,10/18/2019,"4,309","3,097","2,675",422 -1862,Saturday,7,10/19/2019,"3,117","2,223","1,897",326 -1863,Sunday,1,10/20/2019,"4,234","3,081","2,679",402 -1864,Monday,2,10/21/2019,"5,698","4,144","3,507",637 -1865,Tuesday,3,10/22/2019,"5,721","4,132","3,536",596 -1866,Wednesday,4,10/23/2019,"6,018","4,446","3,823",623 -1867,Thursday,5,10/24/2019,"5,563","3,995","3,397",598 -1868,Friday,6,10/25/2019,"4,159","2,961","2,461",500 -1869,Saturday,7,10/26/2019,"2,732","2,057","1,753",304 -1870,Sunday,1,10/27/2019,"3,901","2,854","2,460",394 -1871,Monday,2,10/28/2019,"5,749","4,120","3,529",591 -1872,Tuesday,3,10/29/2019,"5,821","4,140","3,576",564 -1873,Wednesday,4,10/30/2019,"5,647","4,059","3,452",607 -1874,Thursday,5,10/31/2019,"5,219","3,689","3,180",509 -1875,Friday,6,11/1/2019,"4,054","3,010","2,604",406 -1876,Saturday,7,11/2/2019,"2,815","2,170","1,889",281 -1877,Sunday,1,11/3/2019,"4,253","3,063","2,603",460 -1878,Monday,2,11/4/2019,"5,816","4,184","3,640",544 -1879,Tuesday,3,11/5/2019,"5,843","4,176","3,549",627 -1880,Wednesday,4,11/6/2019,"6,058","4,351","3,672",679 -1881,Thursday,5,11/7/2019,"6,291","4,489","3,810",679 -1882,Friday,6,11/8/2019,"4,604","3,357","2,839",518 -1883,Saturday,7,11/9/2019,"3,354","2,337","2,000",337 -1884,Sunday,1,11/10/2019,"4,190","3,075","2,634",441 -1885,Monday,2,11/11/2019,"5,931","4,194","3,573",621 -1886,Tuesday,3,11/12/2019,"6,443","4,589","3,877",712 -1887,Wednesday,4,11/13/2019,"5,805","4,275","3,572",703 -1888,Thursday,5,11/14/2019,"5,753","4,172","3,569",603 -1889,Friday,6,11/15/2019,"4,709","3,452","2,927",525 -1890,Saturday,7,11/16/2019,"3,418","2,506","2,136",370 -1891,Sunday,1,11/17/2019,"4,821","3,387","2,872",515 -1892,Monday,2,11/18/2019,"6,444","4,699","3,985",714 -1893,Tuesday,3,11/19/2019,"6,607","4,772","4,119",653 -1894,Wednesday,4,11/20/2019,"6,726","4,781","4,115",666 -1895,Thursday,5,11/21/2019,"6,334","4,462","3,840",622 -1896,Friday,6,11/22/2019,"4,575","3,214","2,724",490 -1897,Saturday,7,11/23/2019,"3,414","2,474","2,118",356 -1898,Sunday,1,11/24/2019,"4,535","3,242","2,787",455 -1899,Monday,2,11/25/2019,"5,970","4,467","3,836",631 -1900,Tuesday,3,11/26/2019,"5,630","4,115","3,522",593 -1901,Wednesday,4,11/27/2019,"5,384","3,823","3,225",598 -1902,Thursday,5,11/28/2019,"4,201","3,223","2,747",476 -1903,Friday,6,11/29/2019,"3,799","2,947","2,504",443 -1904,Saturday,7,11/30/2019,"3,301","2,443","2,053",390 -1905,Sunday,1,12/1/2019,"4,420","3,308","2,849",459 -1906,Monday,2,12/2/2019,"6,384","4,674","4,031",643 -1907,Tuesday,3,12/3/2019,"6,964","4,975","4,285",690 -1908,Wednesday,4,12/4/2019,"6,957","4,959","4,252",707 -1909,Thursday,5,12/5/2019,"6,350","4,671","3,989",682 -1910,Friday,6,12/6/2019,"5,324","3,796","3,258",538 -1911,Saturday,7,12/7/2019,"3,795","2,760","2,362",398 -1912,Sunday,1,12/8/2019,"5,013","3,636","3,159",477 -1913,Monday,2,12/9/2019,"7,005","5,250","4,569",681 -1914,Tuesday,3,12/10/2019,"6,606","4,791","4,131",660 -1915,Wednesday,4,12/11/2019,"6,686","4,902","4,240",662 -1916,Thursday,5,12/12/2019,"6,163","4,533","3,871",662 -1917,Friday,6,12/13/2019,"4,581","3,272","2,801",471 -1918,Saturday,7,12/14/2019,"2,930","2,240","1,958",282 -1919,Sunday,1,12/15/2019,"3,585","2,672","2,280",392 -1920,Monday,2,12/16/2019,"4,925","3,653","3,128",525 -1921,Tuesday,3,12/17/2019,"4,743","3,441","2,895",546 -1922,Wednesday,4,12/18/2019,"4,716","3,390","2,918",472 -1923,Thursday,5,12/19/2019,"3,793","2,821","2,381",440 -1924,Friday,6,12/20/2019,"2,688","1,906","1,583",323 -1925,Saturday,7,12/21/2019,"1,682","1,177","1,020",157 -1926,Sunday,1,12/22/2019,"1,880","1,411","1,190",221 -1927,Monday,2,12/23/2019,"2,451","1,738","1,442",296 -1928,Tuesday,3,12/24/2019,"1,644","1,197",977,220 -1929,Wednesday,4,12/25/2019,"1,483","1,027",851,176 -1930,Thursday,5,12/26/2019,"1,864","1,355","1,102",253 -1931,Friday,6,12/27/2019,"1,927","1,319","1,109",210 -1932,Saturday,7,12/28/2019,"1,679","1,219","1,019",200 -1933,Sunday,1,12/29/2019,"1,962","1,299","1,111",188 -1934,Monday,2,12/30/2019,"2,469","1,762","1,479",283 -1935,Tuesday,3,12/31/2019,"1,750","1,192","1,005",187 -1936,Wednesday,4,1/1/2020,"1,554","1,105",870,235 -1937,Thursday,5,1/2/2020,"2,820","2,083","1,754",329 -1938,Friday,6,1/3/2020,"2,970","2,180","1,859",321 -1939,Saturday,7,1/4/2020,"2,111","1,526","1,300",226 -1940,Sunday,1,1/5/2020,"2,393","1,788","1,514",274 -1941,Monday,2,1/6/2020,"3,704","2,770","2,341",429 -1942,Tuesday,3,1/7/2020,"3,760","2,915","2,492",423 -1943,Wednesday,4,1/8/2020,"3,698","2,844","2,402",442 -1944,Thursday,5,1/9/2020,"4,243","3,374","2,910",464 -1945,Friday,6,1/10/2020,"3,177","2,415","2,043",372 -1946,Saturday,7,1/11/2020,"2,361","1,834","1,581",253 -1947,Sunday,1,1/12/2020,"2,762","2,238","1,961",277 -1948,Monday,2,1/13/2020,"4,298","3,242","2,727",515 -1949,Tuesday,3,1/14/2020,"3,838","2,884","2,450",434 -1950,Wednesday,4,1/15/2020,"3,754","2,864","2,470",394 -1951,Thursday,5,1/16/2020,"3,817","2,951","2,510",441 -1952,Friday,6,1/17/2020,"3,175","2,419","2,006",413 -1953,Saturday,7,1/18/2020,"2,336","1,927","1,681",246 -1954,Sunday,1,1/19/2020,"2,597","2,031","1,717",314 -1955,Monday,2,1/20/2020,"3,715","2,948","2,505",443 -1956,Tuesday,3,1/21/2020,"4,459","3,501","3,017",484 -1957,Wednesday,4,1/22/2020,"4,483","3,552","3,079",473 -1958,Thursday,5,1/23/2020,"4,225","3,295","2,805",490 -1959,Friday,6,1/24/2020,"3,111","2,484","2,131",353 -1960,Saturday,7,1/25/2020,"2,353","1,784","1,535",249 -1961,Sunday,1,1/26/2020,"3,198","2,520","2,213",307 -1962,Monday,2,1/27/2020,"4,432","3,539","3,058",481 -1963,Tuesday,3,1/28/2020,"4,664","3,654","3,148",506 -1964,Wednesday,4,1/29/2020,"4,471","3,544","3,025",519 -1965,Thursday,5,1/30/2020,"4,486","3,499","3,031",468 -1966,Friday,6,1/31/2020,"3,527","2,704","2,304",400 -1967,Saturday,7,2/1/2020,"2,514","1,995","1,728",267 -1968,Sunday,1,2/2/2020,"2,993","2,409","2,106",303 -1969,Monday,2,2/3/2020,"4,728","3,694","3,174",520 -1970,Tuesday,3,2/4/2020,"4,883","3,982","3,472",510 -1971,Wednesday,4,2/5/2020,"5,071","4,061","3,528",533 -1972,Thursday,5,2/6/2020,"4,638","3,715","3,217",498 -1973,Friday,6,2/7/2020,"3,757","2,942","2,512",430 -1974,Saturday,7,2/8/2020,"2,761","2,193","1,890",303 -1975,Sunday,1,2/9/2020,"3,423","2,849","2,498",351 -1976,Monday,2,2/10/2020,"5,066","4,016","3,464",552 -1977,Tuesday,3,2/11/2020,"4,928","4,045","3,499",546 -1978,Wednesday,4,2/12/2020,"4,996","4,019","3,497",522 -1979,Thursday,5,2/13/2020,"5,004","3,823","3,188",635 -1980,Friday,6,2/14/2020,"3,675","2,949","2,529",420 -1981,Saturday,7,2/15/2020,"2,556","2,024","1,757",267 -1982,Sunday,1,2/16/2020,"3,732","2,808","2,361",447 -1983,Monday,2,2/17/2020,"4,958","3,951","3,382",569 -1984,Tuesday,3,2/18/2020,"4,869","3,806","3,273",533 -1985,Wednesday,4,2/19/2020,"5,224","4,042","3,495",547 -1986,Thursday,5,2/20/2020,"4,890","3,748","3,212",536 -1987,Friday,6,2/21/2020,"3,678","2,914","2,528",386 -1988,Saturday,7,2/22/2020,"2,456","2,025","1,759",266 -1989,Sunday,1,2/23/2020,"3,444","2,708","2,387",321 -1990,Monday,2,2/24/2020,"5,000","3,974","3,390",584 -1991,Tuesday,3,2/25/2020,"5,496","4,210","3,601",609 -1992,Wednesday,4,2/26/2020,"5,059","4,014","3,468",546 -1993,Thursday,5,2/27/2020,"4,813","3,759","3,247",512 -1994,Friday,6,2/28/2020,"3,810","3,052","2,569",483 -1995,Saturday,7,2/29/2020,"2,682","2,169","1,858",311 -1996,Sunday,1,3/1/2020,"3,332","2,754","2,379",375 -1997,Monday,2,3/2/2020,"4,872","3,801","3,264",537 -1998,Tuesday,3,3/3/2020,"5,097","3,956","3,452",504 -1999,Wednesday,4,3/4/2020,"4,714","3,591","3,118",473 -2000,Thursday,5,3/5/2020,"5,048","3,994","3,428",566 -2001,Friday,6,3/6/2020,"3,698","2,843","2,386",457 -2002,Saturday,7,3/7/2020,"2,670","2,095","1,798",297 -2003,Sunday,1,3/8/2020,"3,416","2,743","2,362",381 -2004,Monday,2,3/9/2020,"4,731","3,830","3,269",561 -2005,Tuesday,3,3/10/2020,"4,820","3,718","3,169",549 -2006,Wednesday,4,3/11/2020,"4,813","3,711","3,182",529 -2007,Thursday,5,3/12/2020,"4,393","3,508","2,952",556 -2008,Friday,6,3/13/2020,"3,316","2,613","2,172",441 -2009,Saturday,7,3/14/2020,"2,416","1,888","1,569",319 -2010,Sunday,1,3/15/2020,"2,934","2,251","1,937",314 -2011,Monday,2,3/16/2020,"2,732","2,157","1,809",348 -2012,Tuesday,3,3/17/2020,"3,805","3,007","2,525",482 -2013,Wednesday,4,3/18/2020,"3,748","2,976","2,508",468 -2014,Thursday,5,3/19/2020,"3,678","2,883","2,471",412 -2015,Friday,6,3/20/2020,"3,322","2,647","2,239",408 -2016,Saturday,7,3/21/2020,"2,701","2,049","1,749",300 -2017,Sunday,1,3/22/2020,"3,263","2,469","2,120",349 -2018,Monday,2,3/23/2020,"4,105","3,166","2,690",476 -2019,Tuesday,3,3/24/2020,"4,156","3,247","2,766",481 -2020,Wednesday,4,3/25/2020,"4,153","3,367","2,878",489 -2021,Thursday,5,3/26/2020,"4,256","3,394","2,944",450 -2022,Friday,6,3/27/2020,"3,818","3,002","2,588",414 -2023,Saturday,7,3/28/2020,"3,045","2,438","2,103",335 -2024,Sunday,1,3/29/2020,"4,079","3,047","2,603",444 -2025,Monday,2,3/30/2020,"4,899","3,852","3,280",572 -2026,Tuesday,3,3/31/2020,"4,943","3,822","3,310",512 -2027,Wednesday,4,4/1/2020,"4,907","3,799","3,317",482 -2028,Thursday,5,4/2/2020,"4,933","3,838","3,305",533 -2029,Friday,6,4/3/2020,"4,665","3,498","2,975",523 -2030,Saturday,7,4/4/2020,"3,366","2,536","2,175",361 -2031,Sunday,1,4/5/2020,"3,976","3,045","2,644",401 -2032,Monday,2,4/6/2020,"4,981","3,921","3,351",570 -2033,Tuesday,3,4/7/2020,"5,331","4,144","3,552",592 -2034,Wednesday,4,4/8/2020,"5,331","4,203","3,656",547 -2035,Thursday,5,4/9/2020,"5,063","3,982","3,426",556 -2036,Friday,6,4/10/2020,"4,252","3,354","2,869",485 -2037,Saturday,7,4/11/2020,"3,656","2,712","2,279",433 -2038,Sunday,1,4/12/2020,"4,152","3,125","2,666",459 -2039,Monday,2,4/13/2020,"5,089","3,922","3,379",543 -2040,Tuesday,3,4/14/2020,"5,491","4,314","3,721",593 -2041,Wednesday,4,4/15/2020,"5,665","4,424","3,754",670 -2042,Thursday,5,4/16/2020,"5,423","4,203","3,592",611 -2043,Friday,6,4/17/2020,"4,688","3,682","3,146",536 -2044,Saturday,7,4/18/2020,"4,055","3,007","2,521",486 -2045,Sunday,1,4/19/2020,"4,830","3,671","3,057",614 -2046,Monday,2,4/20/2020,"5,908","4,517","3,869",648 -2047,Tuesday,3,4/21/2020,"5,633","4,377","3,722",655 -2048,Wednesday,4,4/22/2020,"5,786","4,520","3,856",664 -2049,Thursday,5,4/23/2020,"5,967","4,758","4,074",684 -2050,Friday,6,4/24/2020,"4,937","3,879","3,319",560 -2051,Saturday,7,4/25/2020,"4,327","3,284","2,814",470 -2052,Sunday,1,4/26/2020,"5,086","3,964","3,393",571 -2053,Monday,2,4/27/2020,"6,038","4,771","4,054",717 -2054,Tuesday,3,4/28/2020,"6,324","4,920","4,210",710 -2055,Wednesday,4,4/29/2020,"6,062","4,735","4,006",729 -2056,Thursday,5,4/30/2020,"5,520","4,375","3,776",599 -2057,Friday,6,5/1/2020,"4,670","3,621","3,068",553 -2058,Saturday,7,5/2/2020,"4,171","3,145","2,683",462 -2059,Sunday,1,5/3/2020,"4,716","3,648","3,067",581 -2060,Monday,2,5/4/2020,"5,939","4,567","3,882",685 -2061,Tuesday,3,5/5/2020,"6,361","4,900","4,169",731 -2062,Wednesday,4,5/6/2020,"6,659","5,322","4,580",742 -2063,Thursday,5,5/7/2020,"6,264","4,857","4,067",790 -2064,Friday,6,5/8/2020,"5,359","4,202","3,592",610 -2065,Saturday,7,5/9/2020,"4,593","3,452","2,932",520 -2066,Sunday,1,5/10/2020,"5,092","3,917","3,338",579 -2067,Monday,2,5/11/2020,"6,563","5,112","4,278",834 -2068,Tuesday,3,5/12/2020,"6,124","4,775","4,028",747 -2069,Wednesday,4,5/13/2020,"5,817","4,596","3,886",710 -2070,Thursday,5,5/14/2020,"6,057","4,583","3,937",646 -2071,Friday,6,5/15/2020,"4,958","3,829","3,183",646 -2072,Saturday,7,5/16/2020,"3,651","2,827","2,384",443 -2073,Sunday,1,5/17/2020,"4,248","3,291","2,737",554 -2074,Monday,2,5/18/2020,"5,624","4,231","3,588",643 -2075,Tuesday,3,5/19/2020,"5,595","4,300","3,666",634 -2076,Wednesday,4,5/20/2020,"5,213","4,106","3,466",640 -2077,Thursday,5,5/21/2020,"5,156","3,990","3,372",618 -2078,Friday,6,5/22/2020,"4,350","3,343","2,816",527 -2079,Saturday,7,5/23/2020,"3,446","2,696","2,291",405 -2080,Sunday,1,5/24/2020,"3,945","2,969","2,479",490 -2081,Monday,2,5/25/2020,"4,639","3,610","3,054",556 -2082,Tuesday,3,5/26/2020,"5,224","4,069","3,416",653 -2083,Wednesday,4,5/27/2020,"5,242","4,105","3,474",631 -2084,Thursday,5,5/28/2020,"4,939","3,966","3,371",595 -2085,Friday,6,5/29/2020,"4,387","3,486","2,949",537 -2086,Saturday,7,5/30/2020,"3,327","2,583","2,189",394 -2087,Sunday,1,5/31/2020,"3,692","2,919","2,482",437 -2088,Monday,2,6/1/2020,"4,734","3,707","3,133",574 -2089,Tuesday,3,6/2/2020,"4,727","3,690","3,057",633 -2090,Wednesday,4,6/3/2020,"4,994","3,869","3,239",630 -2091,Thursday,5,6/4/2020,"4,767","3,733","3,156",577 -2092,Friday,6,6/5/2020,"4,124","3,247","2,778",469 -2093,Saturday,7,6/6/2020,"3,230","2,425","2,045",380 -2094,Sunday,1,6/7/2020,"3,906","3,049","2,574",475 -2095,Monday,2,6/8/2020,"4,895","3,787","3,188",599 -2096,Tuesday,3,6/9/2020,"4,871","3,644","3,050",594 -2097,Wednesday,4,6/10/2020,"4,989","3,857","3,269",588 -2098,Thursday,5,6/11/2020,"4,633","3,560","3,015",545 -2099,Friday,6,6/12/2020,"4,475","3,292","2,757",535 -2100,Saturday,7,6/13/2020,"2,967","2,304","1,942",362 -2101,Sunday,1,6/14/2020,"3,507","2,684","2,282",402 -2102,Monday,2,6/15/2020,"4,337","3,348","2,794",554 -2103,Tuesday,3,6/16/2020,"4,470","3,461","2,964",497 -2104,Wednesday,4,6/17/2020,"4,439","3,450","2,898",552 -2105,Thursday,5,6/18/2020,"4,895","3,677","3,132",545 -2106,Friday,6,6/19/2020,"3,924","2,911","2,459",452 -2107,Saturday,7,6/20/2020,"2,827","2,190","1,851",339 -2108,Sunday,1,6/21/2020,"3,481","2,668","2,256",412 -2109,Monday,2,6/22/2020,"4,627","3,493","2,935",558 -2110,Tuesday,3,6/23/2020,"4,561","3,493","2,950",543 -2111,Wednesday,4,6/24/2020,"4,626","3,521","2,982",539 -2112,Thursday,5,6/25/2020,"4,137","3,215","2,668",547 -2113,Friday,6,6/26/2020,"3,555","2,675","2,248",427 -2114,Saturday,7,6/27/2020,"2,671","1,999","1,680",319 -2115,Sunday,1,6/28/2020,"3,486","2,635","2,233",402 -2116,Monday,2,6/29/2020,"4,000","3,119","2,616",503 -2117,Tuesday,3,6/30/2020,"3,828","3,013","2,553",460 -2118,Wednesday,4,7/1/2020,"3,935","3,049","2,599",450 -2119,Thursday,5,7/2/2020,"3,633","2,881","2,397",484 -2120,Friday,6,7/3/2020,"2,997","2,286","1,932",354 -2121,Saturday,7,7/4/2020,"2,194","1,704","1,420",284 -2122,Sunday,1,7/5/2020,"2,948","2,198","1,836",362 -2123,Monday,2,7/6/2020,"4,049","3,215","2,724",491 -2124,Tuesday,3,7/7/2020,"3,792","2,998","2,485",513 -2125,Wednesday,4,7/8/2020,"4,029","3,096","2,613",483 -2126,Thursday,5,7/9/2020,"3,945","3,039","2,587",452 -2127,Friday,6,7/10/2020,"3,523","2,620","2,173",447 -2128,Saturday,7,7/11/2020,"2,646","1,981","1,665",316 -2129,Sunday,1,7/12/2020,"3,454","2,611","2,213",398 -2130,Monday,2,7/13/2020,"3,703","2,916","2,459",457 -2131,Tuesday,3,7/14/2020,"3,680","2,853","2,397",456 -2132,Wednesday,4,7/15/2020,"3,479","2,627","2,196",431 -2133,Thursday,5,7/16/2020,"3,756","2,916","2,463",453 -2134,Friday,6,7/17/2020,"3,289","2,557","2,122",435 -2135,Saturday,7,7/18/2020,"2,416","1,892","1,606",286 -2136,Sunday,1,7/19/2020,"2,793","2,197","1,892",305 -2137,Monday,2,7/20/2020,"3,749","2,881","2,428",453 -2138,Tuesday,3,7/21/2020,"3,786","2,889","2,450",439 -2139,Wednesday,4,7/22/2020,"4,002","3,039","2,566",473 -2140,Thursday,5,7/23/2020,"3,823","3,033","2,525",508 -2141,Friday,6,7/24/2020,"3,430","2,623","2,262",361 -2142,Saturday,7,7/25/2020,"2,400","1,767","1,487",280 -2143,Sunday,1,7/26/2020,"2,835","2,207","1,869",338 -2144,Monday,2,7/27/2020,"4,001","3,031","2,523",508 -2145,Tuesday,3,7/28/2020,"3,795","2,969","2,525",444 -2146,Wednesday,4,7/29/2020,"3,903","3,010","2,591",419 -2147,Thursday,5,7/30/2020,"3,397","2,687","2,265",422 -2148,Friday,6,7/31/2020,"3,005","2,317","1,887",430 -2149,Saturday,7,8/1/2020,"2,305","1,750","1,479",271 -2150,Sunday,1,8/2/2020,"2,696","1,993","1,701",292 -2151,Monday,2,8/3/2020,"3,866","2,814","2,368",446 -2152,Tuesday,3,8/4/2020,"3,826","2,845","2,400",445 -2153,Wednesday,4,8/5/2020,"3,673","2,774","2,334",440 -2154,Thursday,5,8/6/2020,"3,660","2,625","2,189",436 -2155,Friday,6,8/7/2020,"3,136","2,364","1,941",423 -2156,Saturday,7,8/8/2020,"2,223","1,628","1,354",274 -2157,Sunday,1,8/9/2020,"2,623","1,941","1,644",297 -2158,Monday,2,8/10/2020,"3,638","2,745","2,325",420 -2159,Tuesday,3,8/11/2020,"3,740","2,742","2,258",484 -2160,Wednesday,4,8/12/2020,"3,767","2,904","2,477",427 -2161,Thursday,5,8/13/2020,"3,621","2,780","2,322",458 -2162,Friday,6,8/14/2020,"2,971","2,308","1,922",386 -2163,Saturday,7,8/15/2020,"2,221","1,696","1,373",323 -2164,Sunday,1,8/16/2020,"2,724","2,037","1,686",351 -2165,Monday,2,8/17/2020,"3,456","2,638","2,181",457 -2166,Tuesday,3,8/18/2020,"3,581","2,683","2,184",499 -2167,Wednesday,4,8/19/2020,"2,064","1,564","1,297",267 diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/forecast_graph.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/forecast_graph.png deleted file mode 100644 index 1e4c9f02a..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/forecast_graph.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/input.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/input.png deleted file mode 100644 index dba33c819..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/input.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/user_input_example.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/user_input_example.png deleted file mode 100644 index fbde23f91..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Forecasting_Model/images/user_input_example.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/README.md deleted file mode 100644 index 0edc82e43..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/README.md +++ /dev/null @@ -1,162 +0,0 @@ - -# Automated Setup for Crowd Monitoring Project - -This guide explains how to set up the project environment using the provided automated setup scripts for both Linux/Mac and Windows systems. The setup process includes the installation of necessary dependencies such as YOLOv8, Prophet for forecasting, Flask, OpenCV, and others. - -The scripts automate the creation of a virtual environment, installation of dependencies from the `requirements.txt`, and setup for MongoDB connection via a `.env` file. - -## Project Structure - -``` -โ”œโ”€โ”€ README.md # This file (setup guide) -โ”œโ”€โ”€ setup.sh # Automated setup script for Linux/Mac -โ”œโ”€โ”€ setup.bat # Automated setup script for Windows -โ”œโ”€โ”€ requirements.txt # List of all dependencies for the project -``` - -## Prerequisites - -Before running the setup scripts, make sure you have the following installed on your system: - -- **Python 3.7+** -- **pip** (Python package manager) -- **curl** (for Linux/Mac) to download the YOLOv8 model weights -- **MongoDB** (optional, if using MongoDB for data storage) - -## Setup Instructions - -### For Linux/Mac Users - -1. **Clone the repository**: - ```bash - git clone https://github.com/Redback-Operations/redback-orion.git - cd Crowd_Monitoring - ``` - -2. **Run the automated setup script**: - ```bash - ./setup.sh - ``` - - This script will: - - Create a Python virtual environment. - - Install all the dependencies listed in `requirements.txt`. - - Download the YOLOv8 pre-trained model weights. - - Create a `.env` file for MongoDB connection. - -3. **Activate the virtual environment**: - ```bash - source venv/bin/activate - ``` - -4. **Run the application** (after the setup is complete): - ```bash - python app.py # Or the entry point for your project - ``` - ---- - -### For Windows Users - -1. **Clone the repository**: - ```batch - git clone https://github.com/Redback-Operations/redback-orion.git - cd Crowd_Monitoring - ``` - -2. **Run the automated setup script**: - ```batch - setup.bat - ``` - - This script will: - - Create a Python virtual environment. - - Install all the dependencies listed in `requirements.txt`. - - Download the YOLOv8 pre-trained model weights. - - Create a `.env` file for MongoDB connection. - -3. **Activate the virtual environment**: - ```batch - call venv\Scripts\activate - ``` - -4. **Run the application** (after the setup is complete): - ```batch - python app.py # Or the entry point for your project - ``` - ---- - -## Manual Setup (Optional) - -If you want to set up the project manually without using the automated scripts, follow these steps: - -1. **Create a Python virtual environment**: - ```bash - python -m venv venv - ``` - -2. **Activate the virtual environment**: - - For Linux/Mac: - ```bash - source venv/bin/activate - ``` - - For Windows: - ```batch - call venv\Scripts\activate - ``` - -3. **Install dependencies from `requirements.txt`**: - ```bash - pip install -r requirements.txt - ``` - -4. **Download YOLOv8 pre-trained weights (optional)**: - ```bash - curl -L -o yolov8n.pt https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt - ``` - -5. **Create a `.env` file** for MongoDB connection (optional): - ```bash - echo MONGO_URI=mongodb://localhost:27017 > .env - ``` - -6. **Run the application**: - ```bash - python app.py # Or the entry point for your project - ``` - ---- - -## Troubleshooting - -- **Permission Issues (Linux/Mac)**: If you encounter a "Permission Denied" error when running the script, use this command to make the script executable: - ```bash - chmod +x setup.sh - ``` - -- **Virtual Environment Activation Issues**: If you have trouble activating the virtual environment, ensure that Python is installed correctly and the virtual environment folder (`venv`) exists. - -- **Missing Dependencies**: Make sure you have installed all prerequisites like Python 3.7+ and pip. If any package installation fails, check the error logs to manually install missing packages. - ---- - -## Frequently Asked Questions (FAQ) - -### 1. What does the setup script do? -The setup script automates the creation of a virtual environment, installs all necessary Python dependencies, downloads the YOLOv8 model weights, and sets up environment variables using a `.env` file. - -### 2. What is the `.env` file for? -The `.env` file is used to securely store environment variables, such as MongoDB connection details. You can update this file with your specific connection strings or secrets. - -### 3. How do I manually install dependencies without the script? -You can manually install the dependencies by activating your virtual environment and running: -```bash -pip install -r requirements.txt -``` - ---- - -## Conclusion - -By following these instructions, you will be able to quickly set up your environment and get the project running without manually installing each component. If you encounter any issues or need further assistance, feel free to reach out for help. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/Documentation_for_Warning_Message_Feature.pdf b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/Documentation_for_Warning_Message_Feature.pdf deleted file mode 100644 index 7074e8ec5..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/Documentation_for_Warning_Message_Feature.pdf and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/README.md deleted file mode 100644 index f5559186f..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# Documentation for Warning Message Feature - -## 1. Introduction -This feature enhances real-time crowd detection by providing warnings when specific conditions related to crowd movement or density are met. The warning message is integrated within the final code and is designed to ensure timely alerts in situations requiring immediate attention. - ---- - -## 2. Purpose -The purpose of this feature is to alert the system operator when unusual or rapid movement occurs in the crowd, enabling them to take necessary actions to ensure safety and manage the situation effectively. - ---- - -## 3. Implementation Details -The warning message is triggered based on predefined thresholds for crowd behavior, such as: -- Rapid crowd movement -- Sudden increase in the number of people detected in a short time - -The code block responsible for generating the warning is embedded within the real-time detection loop. - ---- - -## 4. User Interface -The warning message is displayed on the video feed in real-time. When the specified condition is met, a red alert box appears on the screen with the message: - -### Warning: Rapid Movement in Crowd - -The message is positioned near the detected individuals. - -![Warning Message Example](./images/warning_message_example1.png) -[Click here to watch the video](./videos/video1.mp4) - -![Warning Message Example](./images/warning_message_example2.png) -[Click here to watch the video](./videos/video1.mp4) - -![Warning Message Example](./images/warning_message_example3.png) -[Click here to watch the video](./videos/video3.mp4) - ---- \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video1.mp4 b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video1.mp4 deleted file mode 100644 index a6ee63e7f..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video1.mp4 and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video2.mp4 b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video2.mp4 deleted file mode 100644 index cec20e2b9..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video2.mp4 and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video3.mp4 b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video3.mp4 deleted file mode 100644 index 2eb62351f..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/video3.mp4 and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example1.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example1.png deleted file mode 100644 index cfccbefd8..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example1.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example2.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example2.png deleted file mode 100644 index 511971664..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example2.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example3.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example3.png deleted file mode 100644 index 03f885570..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/Warning_Message_Feature/images/warning_message_example3.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/requirements.txt b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/requirements.txt deleted file mode 100644 index 8e0c3a7f6..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/requirements.txt +++ /dev/null @@ -1,38 +0,0 @@ -# YOLOv8 for object detection -ultralytics==8.0.0 - -# Forecasting model -prophet==1.1.0 - -# Web framework for potential integration -flask==2.1.1 - -# MongoDB integration for storing data -pymongo==3.12.0 - -# OpenCV for image and video processing -opencv-python==4.5.3.56 - -# For managing environment variables securely -python-dotenv==0.21.0 - -# NumPy for numerical operations -numpy==1.23.3 - -# Matplotlib for visualizing graphs and forecasts -matplotlib==3.5.1 - -# Requests library for making HTTP requests -requests==2.26.0 - -# Scikit-learn for machine learning models and preprocessing -scikit-learn==1.0.2 - -# Supervision for object slicing (if using it for small object detection) -supervision==0.2.0 - -# TensorFlow for machine learning (optional, if needed) -tensorflow==2.9.1 - -# PyTorch for machine learning (optional, if YOLOv8 uses PyTorch backend) -torch==2.0.1 diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.bat b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.bat deleted file mode 100644 index b43e36bb7..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.bat +++ /dev/null @@ -1,59 +0,0 @@ -@echo off -SETLOCAL ENABLEDELAYEDEXPANSION - -:: Step 1: Create a Python virtual environment -echo Creating a Python virtual environment... -python -m venv venv - -:: Check if the virtual environment was created successfully -if exist venv ( - echo Virtual environment created successfully. -) else ( - echo Failed to create virtual environment. - exit /b 1 -) - -:: Step 2: Activate the virtual environment -echo Activating the virtual environment... -call venv\Scripts\activate - -:: Step 3: Upgrade pip to the latest version -echo Upgrading pip to the latest version... -python -m pip install --upgrade pip - -:: Step 4: Install the required Python packages from requirements.txt -echo Installing dependencies from requirements.txt... -if exist requirements.txt ( - pip install -r requirements.txt - echo Dependencies installed successfully. -) else ( - echo requirements.txt file not found! Please make sure the file exists. - exit /b 1 -) - -:: Step 5: Download YOLOv8 pre-trained model weights (optional) -set YOLO_WEIGHTS_URL=https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt -echo Downloading YOLOv8 model weights... -curl -L -o yolov8n.pt %YOLO_WEIGHTS_URL% - -if exist yolov8n.pt ( - echo YOLOv8 model weights downloaded successfully. -) else ( - echo Failed to download YOLOv8 model weights. Please check the URL or your internet connection. -) - -:: Step 6: Create .env file for environment variables if it doesn't exist -if exist .env ( - echo .env file already exists. -) else ( - echo Creating .env file for environment variables... - echo MONGO_URI=mongodb://localhost:27017 > .env - echo .env file created with default MongoDB URI. -) - -:: Final Step: Display success message and instructions for activation -echo Setup completed successfully! -echo To activate the virtual environment, run the following command: -echo call venv\Scripts\activate - -pause diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.sh b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.sh deleted file mode 100644 index ab3d2bd72..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Crowd_Alert_and_Forecasting/setup.sh +++ /dev/null @@ -1,65 +0,0 @@ -#!/bin/bash - -# Exit immediately if any command exits with a non-zero status -set -e - -# Displaying a message for starting the setup process -echo "Starting setup process..." - -# Step 1: Create and activate a Python virtual environment -echo "Creating a Python virtual environment..." -python3 -m venv venv - -# Check if virtualenv creation was successful -if [ -d "venv" ]; then - echo "Virtual environment created successfully." -else - echo "Failed to create virtual environment." - exit 1 -fi - -# Activate the virtual environment -echo "Activating the virtual environment..." -source venv/bin/activate - -# Step 2: Upgrade pip to the latest version -echo "Upgrading pip to the latest version..." -pip install --upgrade pip - -# Step 3: Install the required Python packages from requirements.txt -echo "Installing dependencies from requirements.txt..." -if [ -f "requirements.txt" ]; then - pip install -r requirements.txt - echo "Dependencies installed successfully." -else - echo "requirements.txt file not found! Please make sure the file exists." - exit 1 -fi - -# Step 4: Download YOLOv8 pre-trained model weights (optional) -YOLO_WEIGHTS_URL="https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt" -echo "Downloading YOLOv8 model weights..." -if curl --output /dev/null --silent --head --fail "$YOLO_WEIGHTS_URL"; then - curl -L -o yolov8n.pt $YOLO_WEIGHTS_URL - echo "YOLOv8 model weights downloaded successfully." -else - echo "Failed to download YOLOv8 model weights. Please check the URL." -fi - -# Step 5: Create .env file for environment variables if it doesn't exist -if [ -f ".env" ]; then - echo ".env file already exists." -else - echo "Creating .env file for environment variables..." - touch .env - echo "MONGO_URI=mongodb://localhost:27017" >> .env - echo ".env file created with default MongoDB URI." -fi - -# Final Step: Display success message and instructions for activation -echo "Setup completed successfully!" -echo "To activate the virtual environment, run the following command:" -echo "source venv/bin/activate" - -# Exit the script -exit 0 diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/API_usage.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/API_usage.md deleted file mode 100644 index 7a21243ce..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/API_usage.md +++ /dev/null @@ -1,66 +0,0 @@ - ---- -# Crowd Density API โ€“ Usage Guide - -This API processes uploaded video frames (images) and returns: -- **Estimated people count** in the stands (via CSRNet + LISASegmentor). -- **Heatmap image** (PNG format) showing density distribution. - ---- - -## Base URL -http://localhost:8000 - -## Endpoint - -### `POST /analyze_frame/` - -Analyzes a single uploaded image frame. - ---- - -## Headers - -- **Response headers** - - `People-Count`: integer value (estimated number of people in the frame). - ---- - -## Input Requirements - -- **Method:** `POST` -- **Content-Type:** `multipart/form-data` -- **Form field:** `file` (the uploaded image). -- **Supported formats:** `.jpg`, `.jpeg`, `.png` -- **Image type:** RGB (will be auto-converted if not) - ---- - -## Request Body - -Form-data with a single image file: - -| Key | Type | Description | -|-------|------|------------------------------------| -| file | file | Image frame (PNG/JPG) to analyze. | - ---- - -## Response - -- **200 OK** - - **Content-Type:** `image/png` - - **Body:** Heatmap image (PNG) - - **Headers:** `People-Count: ` ---- - -## Example Usage - -### cURL - -```bash -curl -X POST "http://localhost:8000/analyze_frame/" \ - -H "accept: image/png" \ - -F "file=@frame_10.jpg" \ - -o output_heatmap.png \ - -D headers.txt diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/LISA_gradio_interface.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/LISA_gradio_interface.py deleted file mode 100644 index f7f10712e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/LISA_gradio_interface.py +++ /dev/null @@ -1,416 +0,0 @@ -import gradio as gr -import argparse -import os -import sys -import tempfile -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor -from PIL import Image -import threading -import time - -from model.LISA import LISAForCausalLM -from model.llava import conversation as conversation_lib -from model.llava.mm_utils import tokenizer_image_token -from model.segment_anything.utils.transforms import ResizeLongestSide -from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX) - -class LISASegmentor: - def __init__(self): - self.model = None - self.tokenizer = None - self.clip_image_processor = None - self.transform = None - self.args = None - - def initialize_model(self, - version="xinlai/LISA-13B-llama2-v1", - precision="fp16", - image_size=1024, - model_max_length=512, - vision_tower="openai/clip-vit-large-patch14", - conv_type="llava_v1", - load_in_4bit=False, - load_in_8bit=True): - """Initialize the LISA model""" - - # Create args object - self.args = argparse.Namespace() - self.args.version = version - self.args.precision = precision - self.args.image_size = image_size - self.args.model_max_length = model_max_length - self.args.vision_tower = vision_tower - self.args.conv_type = conv_type - self.args.load_in_4bit = load_in_4bit - self.args.load_in_8bit = load_in_8bit - self.args.use_mm_start_end = True - self.args.local_rank = 0 - - # Create tokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - self.args.version, - cache_dir=None, - model_max_length=self.args.model_max_length, - padding_side="right", - use_fast=False, - ) - self.tokenizer.pad_token = self.tokenizer.unk_token - self.args.seg_token_idx = self.tokenizer("[SEG]", add_special_tokens=False).input_ids[0] - - # Set torch dtype - torch_dtype = torch.float32 - if self.args.precision == "bf16": - torch_dtype = torch.bfloat16 - elif self.args.precision == "fp16": - torch_dtype = torch.half - - # Set up quantization - kwargs = {"torch_dtype": torch_dtype} - if self.args.load_in_4bit: - kwargs.update({ - "torch_dtype": torch.half, - "load_in_4bit": True, - "quantization_config": BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - llm_int8_skip_modules=["visual_model"], - ), - }) - elif self.args.load_in_8bit: - kwargs.update({ - "torch_dtype": torch.half, - "quantization_config": BitsAndBytesConfig( - llm_int8_skip_modules=["visual_model"], - load_in_8bit=True, - ), - }) - - # Load model - self.model = LISAForCausalLM.from_pretrained( - self.args.version, - low_cpu_mem_usage=True, - vision_tower=self.args.vision_tower, - seg_token_idx=self.args.seg_token_idx, - **kwargs - ) - - self.model.config.eos_token_id = self.tokenizer.eos_token_id - self.model.config.bos_token_id = self.tokenizer.bos_token_id - self.model.config.pad_token_id = self.tokenizer.pad_token_id - - self.model.get_model().initialize_vision_modules(self.model.get_model().config) - vision_tower = self.model.get_model().get_vision_tower() - vision_tower.to(dtype=torch_dtype) - - # Move model to appropriate precision and device - if self.args.precision == "bf16": - self.model = self.model.bfloat16().cuda() - elif self.args.precision == "fp16" and (not self.args.load_in_4bit) and (not self.args.load_in_8bit): - vision_tower = self.model.get_model().get_vision_tower() - self.model.model.vision_tower = None - try: - import deepspeed - model_engine = deepspeed.init_inference( - model=self.model, - dtype=torch.half, - replace_with_kernel_inject=True, - replace_method="auto", - ) - self.model = model_engine.module - self.model.model.vision_tower = vision_tower.half().cuda() - except ImportError: - print("DeepSpeed not available, using regular half precision") - self.model = self.model.half().cuda() - self.model.model.vision_tower = vision_tower.half().cuda() - elif self.args.precision == "fp32": - self.model = self.model.float().cuda() - - vision_tower = self.model.get_model().get_vision_tower() - vision_tower.to(device=self.args.local_rank) - - # Initialize processors - self.clip_image_processor = CLIPImageProcessor.from_pretrained(self.model.config.vision_tower) - self.transform = ResizeLongestSide(self.args.image_size) - - self.model.eval() - print("Model initialized successfully!") - - def preprocess_image(self, x, img_size=1024): - """Normalize pixel values and pad to a square input.""" - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - - # Normalize colors - x = (x - pixel_mean) / pixel_std - # Pad - h, w = x.shape[-2:] - padh = img_size - h - padw = img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def segment_image(self, image, prompt): - """Perform segmentation on the input image with the given prompt""" - if self.model is None: - return None, "Model not initialized. Please wait for initialization to complete." - - if image is None: - return None, "Please upload an image." - - if not prompt.strip(): - prompt = "Where is the object in the image? Please output the segmentation mask." - - try: - # Convert PIL image to numpy array - image_np = np.array(image) - if len(image_np.shape) == 3 and image_np.shape[2] == 3: - # Already RGB - pass - else: - return None, "Invalid image format. Please upload an RGB image." - - original_size_list = [image_np.shape[:2]] - - # Process image for CLIP - image_clip = ( - self.clip_image_processor.preprocess(image_np, return_tensors="pt")[ - "pixel_values" - ][0] - .unsqueeze(0) - .cuda() - ) - - if self.args.precision == "bf16": - image_clip = image_clip.bfloat16() - elif self.args.precision == "fp16": - image_clip = image_clip.half() - else: - image_clip = image_clip.float() - - # Transform image for segmentation - image_transformed = self.transform.apply_image(image_np) - resize_list = [image_transformed.shape[:2]] - - image_tensor = ( - self.preprocess_image(torch.from_numpy(image_transformed).permute(2, 0, 1).contiguous()) - .unsqueeze(0) - .cuda() - ) - - if self.args.precision == "bf16": - image_tensor = image_tensor.bfloat16() - elif self.args.precision == "fp16": - image_tensor = image_tensor.half() - else: - image_tensor = image_tensor.float() - - # Prepare conversation - conv = conversation_lib.conv_templates[self.args.conv_type].copy() - conv.messages = [] - - # Process prompt - prompt_processed = DEFAULT_IMAGE_TOKEN + "\n" + prompt - if self.args.use_mm_start_end: - replace_token = ( - DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN - ) - prompt_processed = prompt_processed.replace(DEFAULT_IMAGE_TOKEN, replace_token) - - conv.append_message(conv.roles[0], prompt_processed) - conv.append_message(conv.roles[1], "") - prompt_final = conv.get_prompt() - - # Tokenize - input_ids = tokenizer_image_token(prompt_final, self.tokenizer, return_tensors="pt") - input_ids = input_ids.unsqueeze(0).cuda() - - # Generate segmentation - with torch.no_grad(): - output_ids, pred_masks = self.model.evaluate( - image_clip, - image_tensor, - input_ids, - resize_list, - original_size_list, - max_new_tokens=512, - tokenizer=self.tokenizer, - ) - - # Process output - output_ids = output_ids[0][output_ids[0] != IMAGE_TOKEN_INDEX] - text_output = self.tokenizer.decode(output_ids, skip_special_tokens=False) - text_output = text_output.replace("\n", "").replace(" ", " ") - - # Create visualization - if len(pred_masks) > 0 and pred_masks[0].shape[0] > 0: - pred_mask = pred_masks[0].detach().cpu().numpy()[0] - pred_mask = pred_mask > 0 - - # Create masked image - save_img = image_np.copy() - if pred_mask.any(): - # Apply red overlay to segmented areas - save_img[pred_mask] = ( - image_np * 0.5 - + pred_mask[:, :, None].astype(np.uint8) * np.array([255, 0, 0]) * 0.5 - )[pred_mask] - - result_image = Image.fromarray(save_img.astype(np.uint8)) - return result_image, f"Segmentation completed. Model output: {text_output}" - else: - return image, f"No segmentation mask generated. Model output: {text_output}" - - except Exception as e: - return None, f"Error during segmentation: {str(e)}" - -# Initialize the segmentor -segmentor = LISASegmentor() -model_initialized = False -initialization_lock = threading.Lock() - -def initialize_model_at_startup(): - """Initialize the model at startup""" - global model_initialized - print("๐Ÿš€ Initializing LISA model at startup...") - try: - segmentor.initialize_model() - model_initialized = True - print("โœ… Model initialized successfully!") - return True - except Exception as e: - print(f"โŒ Error initializing model: {str(e)}") - model_initialized = False - return False - -def get_model_status(): - """Get current model status""" - if model_initialized: - return "โœ… Model is ready! Upload an image and start segmenting." - else: - return "โณ Model is initializing... Please wait." - -def segment_interface(image, prompt): - """Interface function for Gradio""" - if not model_initialized: - return None, "โŒ Model is still initializing. Please wait and try again." - - result_image, message = segmentor.segment_image(image, prompt) - return result_image, message - -# Initialize model at startup (in background thread) -def initialize_in_background(): - """Initialize model in background thread""" - initialize_model_at_startup() - -# Start initialization in background -initialization_thread = threading.Thread(target=initialize_in_background, daemon=True) -initialization_thread.start() - -# Create Gradio interface -with gr.Blocks(title="LISA Image Segmentation", theme=gr.themes.Soft()) as demo: - gr.Markdown(""" - # ๐ŸŽฏ LISA Image Segmentation Interface - - Upload an image and provide a text prompt to get semantic segmentation results. - The segmented areas will be highlighted in red overlay. - - **Note:** The model is automatically initializing in the background when you start the server. - Please wait for the initialization to complete before using the interface. - """) - - with gr.Row(): - with gr.Column(): - # Model status (no manual initialization needed) - model_status = gr.Textbox( - label="Model Status", - value="โณ Model is initializing... Please wait.", - interactive=False - ) - - # Input components - image_input = gr.Image( - label="Upload Image", - type="pil", - height=400 - ) - - prompt_input = gr.Textbox( - label="Segmentation Prompt", - placeholder="Enter your prompt here (e.g., 'segment the dog', 'where is the car?')", - value="Where is the object in the image? Please output the segmentation mask.", - lines=3 - ) - - segment_button = gr.Button("๐ŸŽฏ Segment Image", variant="primary", size="lg") - refresh_status_btn = gr.Button("๐Ÿ”„ Refresh Status", variant="secondary") - - with gr.Column(): - # Output components - image_output = gr.Image( - label="Segmentation Result", - height=400 - ) - - result_text = gr.Textbox( - label="Status & Model Output", - lines=5, - interactive=False - ) - - # Examples - gr.Markdown("### ๐Ÿ“ Example Prompts:") - gr.Examples( - examples=[ - "Where is the person in the image?", - "Segment the dog", - "Where is the car?", - "Please segment the cat", - "Find and segment the building", - "Where are the trees?", - "Segment the face of the person" - ], - inputs=prompt_input - ) - - # Event handlers - refresh_status_btn.click( - fn=get_model_status, - outputs=model_status - ) - - segment_button.click( - fn=segment_interface, - inputs=[image_input, prompt_input], - outputs=[image_output, result_text] - ) - - # Allow Enter key to trigger segmentation - prompt_input.submit( - fn=segment_interface, - inputs=[image_input, prompt_input], - outputs=[image_output, result_text] - ) - - # Load initial status when page loads - demo.load( - fn=get_model_status, - outputs=model_status - ) - -if __name__ == "__main__": - # Enable queue for better handling of concurrent requests - demo.queue(max_size=20) # Allow up to 20 requests in queue - - demo.launch( - server_name="0.0.0.0", - server_port=7860, - share=True, - debug=True - ) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/README.md deleted file mode 100755 index 259ad5816..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# LISA: Reasoning Segmentation via Large Language Model and Heatmap generate by CSRNet - - -## Introduction -This repository leverages **LISA** for audience segmentation and **CSRNet** for heatmap generation. -- Use `app.py` to run the API. -- Use `lisa_gradio_interface.py` to launch a Gradio-based interface. - -If you have any questions about this project, feel free to contact me at buisontung2310@gmail.com. - -## Installation -``` -git clone https://github.com/sontung2310/LISA.git -cd LISA -conda create -n lisa python=3.9 -pip install -r requirements.txt -pip install flash-attn --no-build-isolation -``` - -Download the weight from [this link](https://drive.google.com/file/d/1Ti_DQ0lYXCLqhH9nGwtMMWH5Zt5xiJA2/view?pli=1) and save it into the `csrnet/` folder. - - -## Inference - -``` -CUDA_VISIBLE_DEVICES=0 python chat.py --version='xinlai/LISA-13B-llama2-v1' -CUDA_VISIBLE_DEVICES=0 python chat.py --version='xinlai/LISA-13B-llama2-v1-explanatory' -``` -To use `bf16` or `fp16` data type for inference: -``` -CUDA_VISIBLE_DEVICES=0 python chat.py --version='xinlai/LISA-13B-llama2-v1' --precision='bf16' -``` -To use `8bit` or `4bit` data type for inference (this enables running 13B model on a single 24G or 12G GPU at some cost of generation quality): -``` -CUDA_VISIBLE_DEVICES=0 python chat.py --version='xinlai/LISA-13B-llama2-v1' --precision='fp16' --load_in_8bit -CUDA_VISIBLE_DEVICES=0 python chat.py --version='xinlai/LISA-13B-llama2-v1' --precision='fp16' --load_in_4bit -``` -Hint: for 13B model, 16-bit inference consumes 30G VRAM with a single GPU, 8-bit inference consumes 16G, and 4-bit inference consumes 9G. - -After that, input the text prompt and then the image path. For example๏ผŒ -``` -- Please input your prompt: Where can the driver see the car speed in this image? Please output segmentation mask. -- Please input the image path: imgs/example1.jpg - -- Please input your prompt: Can you segment the food that tastes spicy and hot? -- Please input the image path: imgs/example2.jpg -``` -The results should be like: -

- -To run the gradio interface (load in 8bit and fp16): -``` -python lisa_gradio_interface.py -``` - -Run the API -``` -uvicorn app:app --host 0.0.0.0 --port 8000 -``` - -## Acknowledgement -- This work is built upon the [LLaVA](https://github.com/haotian-liu/LLaVA) and [SAM](https://github.com/facebookresearch/segment-anything). diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/app.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/app.py deleted file mode 100644 index 393366486..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/app.py +++ /dev/null @@ -1,77 +0,0 @@ -import io -import numpy as np -from fastapi import FastAPI, UploadFile, File -from fastapi.responses import JSONResponse, StreamingResponse -from PIL import Image -from matplotlib import cm as mpl_cm - -from lisa_segmentor import LISASegmentor -from csrnet_service import CSRNetService - -app = FastAPI(title="Crowd Density API", description="Video frame โ†’ Heatmap + People Count") - -# Initialize models once (reused for all requests) -seg = LISASegmentor() -seg.initialize_model(precision="fp16", load_in_4bit=True) -print("โœ… LISASegmentor initialized") - -csr = CSRNetService(device="cuda") # Change to "cuda" if GPU available -print("โœ… CSRNetService initialized") - -print("Available routes:") -for route in app.routes: - print(route.path, route.methods) - -def generate_heatmap_image(heatmap: np.ndarray, cmap: str = "jet") -> Image.Image: - """Convert density map to colored heatmap PIL image in memory.""" - arr = heatmap.astype(np.float32) - if not np.isfinite(arr).any(): - arr = np.zeros_like(arr, dtype=np.float32) - else: - arr = arr - np.nanmin(arr) - maxv = np.nanmax(arr) - if maxv > 0: - arr = arr / maxv - colormap = mpl_cm.get_cmap(cmap) - colored = (colormap(arr)[..., :3] * 255.0).astype(np.uint8) # drop alpha - return Image.fromarray(colored) - - -@app.post("/analyze_frame/") -async def analyze_frame(file: UploadFile = File(...)): - """ - Upload a video frame (image). - Returns JSON with: - - count: estimated number of people - - heatmap: heatmap image (PNG bytes) - """ - # Load uploaded image - contents = await file.read() - image = Image.open(io.BytesIO(contents)).convert("RGB") - - # Run segmentation with LISA - prompt = "Where is the audience in the image? Output the segmentation mask." - mask = seg.segment(image, prompt) - - # Run CSRNet on masked image - density_map, count = csr.infer(image, mask) - - if count < 50: - return StreamingResponse( - content=Image.open(io.BytesIO(contents)).convert("RGB"), - media_type="image/jpeg", - headers={"People-Count": 0} - ) - - # Generate heatmap image in memory - heatmap_img = generate_heatmap_image(density_map) - img_byte_arr = io.BytesIO() - heatmap_img.save(img_byte_arr, format="PNG") - img_byte_arr.seek(0) - - # Return StreamingResponse with heatmap + JSON - return StreamingResponse( - content=img_byte_arr, - media_type="image/png", - headers={"People-Count": str(round(float(count), 2))} - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/chat.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/chat.py deleted file mode 100755 index 7fbe550bc..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/chat.py +++ /dev/null @@ -1,253 +0,0 @@ -import argparse -import os -import sys - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor - -from model.LISA import LISAForCausalLM -from model.llava import conversation as conversation_lib -from model.llava.mm_utils import tokenizer_image_token -from model.segment_anything.utils.transforms import ResizeLongestSide -from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX) - - -def parse_args(args): - parser = argparse.ArgumentParser(description="LISA chat") - parser.add_argument("--version", default="xinlai/LISA-13B-llama2-v1") - parser.add_argument("--vis_save_path", default="./vis_output", type=str) - parser.add_argument( - "--precision", - default="bf16", - type=str, - choices=["fp32", "bf16", "fp16"], - help="precision for inference", - ) - parser.add_argument("--image_size", default=1024, type=int, help="image size") - parser.add_argument("--model_max_length", default=512, type=int) - parser.add_argument("--lora_r", default=8, type=int) - parser.add_argument( - "--vision-tower", default="openai/clip-vit-large-patch14", type=str - ) - parser.add_argument("--local-rank", default=0, type=int, help="node rank") - parser.add_argument("--load_in_8bit", action="store_true", default=False) - parser.add_argument("--load_in_4bit", action="store_true", default=False) - parser.add_argument("--use_mm_start_end", action="store_true", default=True) - parser.add_argument( - "--conv_type", - default="llava_v1", - type=str, - choices=["llava_v1", "llava_llama_2"], - ) - return parser.parse_args(args) - - -def preprocess( - x, - pixel_mean=torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1), - pixel_std=torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1), - img_size=1024, -) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - pixel_mean) / pixel_std - # Pad - h, w = x.shape[-2:] - padh = img_size - h - padw = img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - -def main(args): - args = parse_args(args) - os.makedirs(args.vis_save_path, exist_ok=True) - - # Create model - tokenizer = AutoTokenizer.from_pretrained( - args.version, - cache_dir=None, - model_max_length=args.model_max_length, - padding_side="right", - use_fast=False, - ) - tokenizer.pad_token = tokenizer.unk_token - args.seg_token_idx = tokenizer("[SEG]", add_special_tokens=False).input_ids[0] - - - torch_dtype = torch.float32 - if args.precision == "bf16": - torch_dtype = torch.bfloat16 - elif args.precision == "fp16": - torch_dtype = torch.half - - kwargs = {"torch_dtype": torch_dtype} - if args.load_in_4bit: - kwargs.update( - { - "torch_dtype": torch.half, - "load_in_4bit": True, - "quantization_config": BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - llm_int8_skip_modules=["visual_model"], - ), - } - ) - elif args.load_in_8bit: - kwargs.update( - { - "torch_dtype": torch.half, - "quantization_config": BitsAndBytesConfig( - llm_int8_skip_modules=["visual_model"], - load_in_8bit=True, - ), - } - ) - - model = LISAForCausalLM.from_pretrained( - args.version, low_cpu_mem_usage=True, vision_tower=args.vision_tower, seg_token_idx=args.seg_token_idx, **kwargs - ) - - model.config.eos_token_id = tokenizer.eos_token_id - model.config.bos_token_id = tokenizer.bos_token_id - model.config.pad_token_id = tokenizer.pad_token_id - - model.get_model().initialize_vision_modules(model.get_model().config) - vision_tower = model.get_model().get_vision_tower() - vision_tower.to(dtype=torch_dtype) - - if args.precision == "bf16": - model = model.bfloat16().cuda() - elif ( - args.precision == "fp16" and (not args.load_in_4bit) and (not args.load_in_8bit) - ): - vision_tower = model.get_model().get_vision_tower() - model.model.vision_tower = None - import deepspeed - - model_engine = deepspeed.init_inference( - model=model, - dtype=torch.half, - replace_with_kernel_inject=True, - replace_method="auto", - ) - model = model_engine.module - model.model.vision_tower = vision_tower.half().cuda() - elif args.precision == "fp32": - model = model.float().cuda() - - vision_tower = model.get_model().get_vision_tower() - vision_tower.to(device=args.local_rank) - - clip_image_processor = CLIPImageProcessor.from_pretrained(model.config.vision_tower) - transform = ResizeLongestSide(args.image_size) - - model.eval() - - while True: - conv = conversation_lib.conv_templates[args.conv_type].copy() - conv.messages = [] - - prompt = input("Please input your prompt: ") - prompt = DEFAULT_IMAGE_TOKEN + "\n" + prompt - if args.use_mm_start_end: - replace_token = ( - DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN - ) - prompt = prompt.replace(DEFAULT_IMAGE_TOKEN, replace_token) - - conv.append_message(conv.roles[0], prompt) - conv.append_message(conv.roles[1], "") - prompt = conv.get_prompt() - - image_path = input("Please input the image path: ") - if not os.path.exists(image_path): - print("File not found in {}".format(image_path)) - continue - - image_np = cv2.imread(image_path) - image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) - original_size_list = [image_np.shape[:2]] - - image_clip = ( - clip_image_processor.preprocess(image_np, return_tensors="pt")[ - "pixel_values" - ][0] - .unsqueeze(0) - .cuda() - ) - if args.precision == "bf16": - image_clip = image_clip.bfloat16() - elif args.precision == "fp16": - image_clip = image_clip.half() - else: - image_clip = image_clip.float() - - image = transform.apply_image(image_np) - resize_list = [image.shape[:2]] - - image = ( - preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - .unsqueeze(0) - .cuda() - ) - if args.precision == "bf16": - image = image.bfloat16() - elif args.precision == "fp16": - image = image.half() - else: - image = image.float() - - input_ids = tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - input_ids = input_ids.unsqueeze(0).cuda() - - output_ids, pred_masks = model.evaluate( - image_clip, - image, - input_ids, - resize_list, - original_size_list, - max_new_tokens=512, - tokenizer=tokenizer, - ) - output_ids = output_ids[0][output_ids[0] != IMAGE_TOKEN_INDEX] - - text_output = tokenizer.decode(output_ids, skip_special_tokens=False) - text_output = text_output.replace("\n", "").replace(" ", " ") - print("text_output: ", text_output) - - for i, pred_mask in enumerate(pred_masks): - if pred_mask.shape[0] == 0: - continue - - pred_mask = pred_mask.detach().cpu().numpy()[0] - pred_mask = pred_mask > 0 - - save_path = "{}/{}_mask_{}.jpg".format( - args.vis_save_path, image_path.split("/")[-1].split(".")[0], i - ) - cv2.imwrite(save_path, pred_mask * 100) - print("{} has been saved.".format(save_path)) - - save_path = "{}/{}_masked_img_{}.jpg".format( - args.vis_save_path, image_path.split("/")[-1].split(".")[0], i - ) - save_img = image_np.copy() - save_img[pred_mask] = ( - image_np * 0.5 - + pred_mask[:, :, None].astype(np.uint8) * np.array([255, 0, 0]) * 0.5 - )[pred_mask] - save_img = cv2.cvtColor(save_img, cv2.COLOR_RGB2BGR) - cv2.imwrite(save_path, save_img) - print("{} has been saved.".format(save_path)) - - -if __name__ == "__main__": - main(sys.argv[1:]) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/model.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/model.py deleted file mode 100644 index 7f9ca4761..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/model.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch.nn as nn -from torchvision import models - -class CSRNet(nn.Module): - def __init__(self, load_weights=False): - super(CSRNet, self).__init__() - self.seen = 0 - self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512] - self.backend_feat = [512, 512, 512,256,128,64] - self.frontend = make_layers(self.frontend_feat) - self.backend = make_layers(self.backend_feat,in_channels = 512,dilation = True) - self.output_layer = nn.Conv2d(64, 1, kernel_size=1) - if not load_weights: - mod = models.vgg16(pretrained = True) - self._initialize_weights() - for i in range(len(self.frontend.state_dict().items())): - list(self.frontend.state_dict().items())[i][1].data[:] = list(mod.state_dict().items())[i][1].data[:] - def forward(self,x): - x = self.frontend(x) - x = self.backend(x) - x = self.output_layer(x) - return x - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.normal_(m.weight, std=0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - -def make_layers(cfg, in_channels = 3,batch_norm=False,dilation = False): - if dilation: - d_rate = 2 - else: - d_rate = 1 - layers = [] - for v in cfg: - if v == 'M': - layers += [nn.MaxPool2d(kernel_size=2, stride=2)] - else: - conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=d_rate,dilation = d_rate) - if batch_norm: - layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] - else: - layers += [conv2d, nn.ReLU(inplace=True)] - in_channels = v - return nn.Sequential(*layers) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/run.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/run.py deleted file mode 100644 index e4fc1d457..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet/run.py +++ /dev/null @@ -1,55 +0,0 @@ -import h5py -import scipy.io as io -import PIL.Image as Image -import numpy as np -from matplotlib import pyplot as plt, cm as c -from scipy.ndimage.filters import gaussian_filter -import scipy -import torchvision.transforms.functional as F -from model import CSRNet -import torch -from torchvision import transforms -import time - - - -transform=transforms.Compose([ - transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]), - ]) - -model = CSRNet() - -checkpoint = torch.load('weights.pth', map_location="cpu") -model.load_state_dict(checkpoint) - -img_path = "/home/s223915978/LISA/afl_test_frame.png" - -print("Original Image") -plt.imshow(plt.imread(img_path)) -plt.show() - -start_time = time.time() -img = transform(Image.open(img_path).convert('RGB')) -output = model(img.unsqueeze(0)) -end_time = time.time() -print(f"Time taken: {end_time - start_time} seconds") -print("Predicted Count : ",int(output.detach().cpu().sum().numpy())) -temp = np.asarray(output.detach().cpu().reshape(output.detach().cpu().shape[2],output.detach().cpu().shape[3])) -# plt.imshow(temp,cmap = c.jet) -# Add text annotation for predicted count - - -# Save heatmap visualization -plt.figure(figsize=(10,10)) -plt.imshow(temp, cmap=c.jet) -plt.colorbar() -plt.title("Density Map") - -save_path = img_path.replace(".jpg", "_heatmap.jpg") -plt.text(0, 0, f'Count: {int(output.detach().cpu().sum().numpy())}', - color='white', fontsize=12, bbox=dict(facecolor='black', alpha=0.7)) - -plt.savefig(save_path) -plt.close() -print(f"Heatmap saved to: {save_path}") diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet_service.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet_service.py deleted file mode 100644 index 25b4c8817..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/csrnet_service.py +++ /dev/null @@ -1,54 +0,0 @@ -import io -from typing import Optional, Tuple - -import numpy as np -import torch -from PIL import Image -from torchvision import transforms - -from csrnet.model import CSRNet - - -class CSRNetService: - """CSRNet inference wrapper with optional mask application. - - If a boolean mask is provided (H, W), the input image will be multiplied by the mask - before running through the network, focusing the density on the masked area. - """ - - def __init__(self, weights_path: str = "/home/s223915978/LISA/csrnet/weights.pth", device: str = "cpu"): - self.device = torch.device(device) - self.model = CSRNet().to(self.device) - checkpoint = torch.load(weights_path, map_location=self.device) - self.model.load_state_dict(checkpoint) - self.model.eval() - - self.transform = transforms.Compose( - [ - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), - ] - ) - - @torch.no_grad() - def infer(self, image: Image.Image, mask: Optional[np.ndarray] = None) -> Tuple[np.ndarray, float]: - """ - Returns density map (H', W') as numpy float32 and estimated count as float. - If mask is provided, it should be a boolean array matching the input image size (H, W). - """ - img_np = np.array(image).astype(np.float32) - if mask is not None: - if mask.shape[:2] != img_np.shape[:2]: - mask = np.array(Image.fromarray(mask.astype(np.uint8) * 255).resize((img_np.shape[1], img_np.shape[0]), Image.NEAREST)) > 0 - # Apply mask by zeroing non-masked pixels - img_np = img_np * mask[..., None].astype(np.float32) - - img_tensor = self.transform(Image.fromarray(img_np.astype(np.uint8)).convert("RGB")) - img_tensor = img_tensor.unsqueeze(0).to(self.device) - - output = self.model(img_tensor) - density_map = output.detach().cpu().squeeze(0).squeeze(0).numpy().astype(np.float32) - count = float(output.detach().cpu().sum().item()) - return density_map, count - - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/heatmap.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/heatmap.png deleted file mode 100644 index 3adb74301..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/heatmap.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/input.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/input.png deleted file mode 100644 index a24ad737b..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/input.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/segment.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/segment.png deleted file mode 100644 index a487886e6..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/images/segment.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/lisa_segmentor.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/lisa_segmentor.py deleted file mode 100644 index 90449afe1..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/lisa_segmentor.py +++ /dev/null @@ -1,244 +0,0 @@ -import argparse -import numpy as np -import torch -import torch.nn.functional as F -from PIL import Image -from transformers import AutoTokenizer, BitsAndBytesConfig, CLIPImageProcessor - -from model.LISA import LISAForCausalLM -from model.llava import conversation as conversation_lib -from model.llava.mm_utils import tokenizer_image_token -from model.segment_anything.utils.transforms import ResizeLongestSide -from utils.utils import ( - DEFAULT_IM_END_TOKEN, - DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_TOKEN, - IMAGE_TOKEN_INDEX, -) - - -class LISASegmentor: - """Headless LISA segmentor for programmatic use (no Gradio side effects).""" - - def __init__(self): - self.model = None - self.tokenizer = None - self.clip_image_processor = None - self.transform = None - self.args = None - - def initialize_model( - self, - version="xinlai/LISA-13B-llama2-v1", - precision="fp16", - image_size=1024, - model_max_length=512, - vision_tower="openai/clip-vit-large-patch14", - conv_type="llava_v1", - load_in_4bit=True, - load_in_8bit=False, - device: str = "cuda", - ) -> None: - self.args = argparse.Namespace() - self.args.version = version - self.args.precision = precision - self.args.image_size = image_size - self.args.model_max_length = model_max_length - self.args.vision_tower = vision_tower - self.args.conv_type = conv_type - self.args.load_in_4bit = load_in_4bit - self.args.load_in_8bit = load_in_8bit - self.args.use_mm_start_end = True - self.args.local_rank = 0 - - # Tokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - self.args.version, - cache_dir=None, - model_max_length=self.args.model_max_length, - padding_side="right", - use_fast=False, - ) - self.tokenizer.pad_token = self.tokenizer.unk_token - self.args.seg_token_idx = self.tokenizer("[SEG]", add_special_tokens=False).input_ids[0] - - # Dtype - torch_dtype = torch.float32 - if self.args.precision == "bf16": - torch_dtype = torch.bfloat16 - elif self.args.precision == "fp16": - torch_dtype = torch.half - - # Quantization - kwargs = {"torch_dtype": torch_dtype} - if self.args.load_in_4bit: - kwargs.update( - { - "torch_dtype": torch.half, - "load_in_4bit": True, - "quantization_config": BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - llm_int8_skip_modules=["visual_model"], - ), - } - ) - elif self.args.load_in_8bit: - kwargs.update( - { - "torch_dtype": torch.half, - "quantization_config": BitsAndBytesConfig( - llm_int8_skip_modules=["visual_model"], - load_in_8bit=True, - ), - } - ) - - # Model - self.model = LISAForCausalLM.from_pretrained( - self.args.version, - low_cpu_mem_usage=True, - vision_tower=self.args.vision_tower, - seg_token_idx=self.args.seg_token_idx, - **kwargs, - ) - - self.model.config.eos_token_id = self.tokenizer.eos_token_id - self.model.config.bos_token_id = self.tokenizer.bos_token_id - self.model.config.pad_token_id = self.tokenizer.pad_token_id - - self.model.get_model().initialize_vision_modules(self.model.get_model().config) - vision_tower = self.model.get_model().get_vision_tower() - vision_tower.to(dtype=torch_dtype) - - if device == "cuda": - if self.args.precision == "bf16": - self.model = self.model.bfloat16().cuda() - elif self.args.precision == "fp16" and (not self.args.load_in_4bit) and (not self.args.load_in_8bit): - vision_tower = self.model.get_model().get_vision_tower() - self.model.model.vision_tower = None - try: - import deepspeed # type: ignore - - model_engine = deepspeed.init_inference( - model=self.model, - dtype=torch.half, - replace_with_kernel_inject=True, - replace_method="auto", - ) - self.model = model_engine.module - self.model.model.vision_tower = vision_tower.half().cuda() - except Exception: - self.model = self.model.half().cuda() - self.model.model.vision_tower = vision_tower.half().cuda() - elif self.args.precision == "fp32": - self.model = self.model.float().cuda() - - vision_tower = self.model.get_model().get_vision_tower() - vision_tower.to(device=self.args.local_rank) - else: - # CPU fallback (very slow, but functional) - if self.args.precision == "fp16": - self.model = self.model.float() - self.model = self.model.to("cpu") - - self.clip_image_processor = CLIPImageProcessor.from_pretrained(self.model.config.vision_tower) - self.transform = ResizeLongestSide(self.args.image_size) - self.model.eval() - - @staticmethod - def _preprocess_image_for_segmentation(x: torch.Tensor, img_size: int = 1024) -> torch.Tensor: - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - x = (x - pixel_mean) / pixel_std - h, w = x.shape[-2:] - padh = img_size - h - padw = img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def segment(self, image: Image.Image, prompt: str) -> np.ndarray: - """ - Returns a boolean numpy mask (H, W) matching the input image size. - If no mask is generated, returns an all-false mask. - """ - if self.model is None: - raise RuntimeError("LISA model not initialized. Call initialize_model() first.") - - image_np = np.array(image) - if image_np.ndim != 3 or image_np.shape[2] != 3: - raise ValueError("Expected an RGB image.") - - original_h, original_w = image_np.shape[:2] - - # CLIP preprocess - image_clip = ( - self.clip_image_processor.preprocess(image_np, return_tensors="pt")["pixel_values"][0] - .unsqueeze(0) - ) - image_clip = image_clip.cuda() if next(self.model.parameters()).is_cuda else image_clip - - if self.args.precision == "bf16": - image_clip = image_clip.bfloat16() - elif self.args.precision == "fp16": - image_clip = image_clip.half() - else: - image_clip = image_clip.float() - - # Segmentation transform - image_transformed = self.transform.apply_image(image_np) - image_tensor = torch.from_numpy(image_transformed).permute(2, 0, 1).contiguous() - image_tensor = self._preprocess_image_for_segmentation(image_tensor, self.args.image_size).unsqueeze(0) - image_tensor = image_tensor.cuda() if next(self.model.parameters()).is_cuda else image_tensor - - if self.args.precision == "bf16": - image_tensor = image_tensor.bfloat16() - elif self.args.precision == "fp16": - image_tensor = image_tensor.half() - else: - image_tensor = image_tensor.float() - - # Conversation prompt - if not prompt or not prompt.strip(): - prompt = "Where is the audience in the image? Output the segmentation mask." - - conv = conversation_lib.conv_templates[self.args.conv_type].copy() - conv.messages = [] - prompt_processed = DEFAULT_IMAGE_TOKEN + "\n" + prompt - if self.args.use_mm_start_end: - replace_token = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN - prompt_processed = prompt_processed.replace(DEFAULT_IMAGE_TOKEN, replace_token) - conv.append_message(conv.roles[0], prompt_processed) - conv.append_message(conv.roles[1], "") - prompt_final = conv.get_prompt() - - input_ids = tokenizer_image_token(prompt_final, self.tokenizer, return_tensors="pt").unsqueeze(0) - input_ids = input_ids.cuda() if next(self.model.parameters()).is_cuda else input_ids - - resize_list = [image_transformed.shape[:2]] - original_size_list = [image_np.shape[:2]] - - with torch.no_grad(): - _, pred_masks = self.model.evaluate( - image_clip, - image_tensor, - input_ids, - resize_list, - original_size_list, - max_new_tokens=512, - tokenizer=self.tokenizer, - ) - - if len(pred_masks) == 0 or pred_masks[0].shape[0] == 0: - return np.zeros((original_h, original_w), dtype=bool) - - pred_mask = pred_masks[0].detach().cpu().numpy()[0] > 0 - # Ensure shape to original - if pred_mask.shape[0] != original_h or pred_mask.shape[1] != original_w: - pred_mask = np.array(Image.fromarray(pred_mask.astype(np.uint8) * 255).resize((original_w, original_h), Image.NEAREST)) > 0 - - return pred_mask - - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/LISA.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/LISA.py deleted file mode 100755 index 09a5e1a21..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/LISA.py +++ /dev/null @@ -1,427 +0,0 @@ -from typing import List - -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers import BitsAndBytesConfig, CLIPVisionModel - -from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_PATCH_TOKEN) - -from .llava.model.language_model.llava_llama import (LlavaLlamaForCausalLM, - LlavaLlamaModel) -from .segment_anything import build_sam_vit_h - - -def dice_loss( - inputs: torch.Tensor, - targets: torch.Tensor, - num_masks: float, - scale=1000, # 100000.0, - eps=1e-6, -): - """ - Compute the DICE loss, similar to generalized IOU for masks - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - """ - inputs = inputs.sigmoid() - inputs = inputs.flatten(1, 2) - targets = targets.flatten(1, 2) - numerator = 2 * (inputs / scale * targets).sum(-1) - denominator = (inputs / scale).sum(-1) + (targets / scale).sum(-1) - loss = 1 - (numerator + eps) / (denominator + eps) - loss = loss.sum() / (num_masks + 1e-8) - return loss - - -def sigmoid_ce_loss( - inputs: torch.Tensor, - targets: torch.Tensor, - num_masks: float, -): - """ - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - Returns: - Loss tensor - """ - loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") - loss = loss.flatten(1, 2).mean(1).sum() / (num_masks + 1e-8) - return loss - - -class LisaMetaModel: - def __init__( - self, - config, - **kwargs, - ): - super(LisaMetaModel, self).__init__(config) - - self.config = config - if not hasattr(self.config, "train_mask_decoder"): - self.config.train_mask_decoder = kwargs["train_mask_decoder"] - self.config.out_dim = kwargs["out_dim"] - self.vision_pretrained = kwargs.get("vision_pretrained", None) - else: - self.vision_pretrained = kwargs.get("vision_pretrained", None) - self.initialize_lisa_modules(self.config) - - def initialize_lisa_modules(self, config): - # SAM - self.visual_model = build_sam_vit_h(self.vision_pretrained) - for param in self.visual_model.parameters(): - param.requires_grad = False - if config.train_mask_decoder: - self.visual_model.mask_decoder.train() - for param in self.visual_model.mask_decoder.parameters(): - param.requires_grad = True - - # Projection layer - in_dim = config.hidden_size - out_dim = config.out_dim - text_fc = [ - nn.Linear(in_dim, in_dim), - nn.ReLU(inplace=True), - nn.Linear(in_dim, out_dim), - nn.Dropout(0.0), - ] - self.text_hidden_fcs = nn.ModuleList([nn.Sequential(*text_fc)]) - self.text_hidden_fcs.train() - for param in self.text_hidden_fcs.parameters(): - param.requires_grad = True - - -class LisaModel(LisaMetaModel, LlavaLlamaModel): - def __init__( - self, - config, - **kwargs, - ): - super(LisaModel, self).__init__(config, **kwargs) - - self.config.use_cache = False - self.config.vision_tower = self.config.mm_vision_tower - self.config.mm_vision_select_feature = "patch" - self.config.image_aspect_ratio = "square" - self.config.image_grid_pinpoints = None - self.config.tune_mm_mlp_adapter = False - self.config.freeze_mm_mlp_adapter = True - self.config.pretrain_mm_mlp_adapter = None - self.config.mm_use_im_patch_token = False - - -class LISAForCausalLM(LlavaLlamaForCausalLM): - def __init__( - self, - config, - **kwargs, - ): - if not hasattr(config, "train_mask_decoder"): - config.mm_use_im_start_end = kwargs.pop("use_mm_start_end", True) - config.mm_vision_tower = kwargs.get( - "vision_tower", "openai/clip-vit-large-patch14" - ) - self.ce_loss_weight = kwargs.pop("ce_loss_weight", None) - self.dice_loss_weight = kwargs.pop("dice_loss_weight", None) - self.bce_loss_weight = kwargs.pop("bce_loss_weight", None) - else: - config.mm_vision_tower = config.vision_tower - - self.seg_token_idx = kwargs.pop("seg_token_idx") - - super().__init__(config) - - self.model = LisaModel(config, **kwargs) - - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_visual_embs(self, pixel_values: torch.FloatTensor): - with torch.no_grad(): - image_embeddings_list = [] - for i in range(pixel_values.shape[0]): - torch.cuda.empty_cache() - image_embeddings = self.model.visual_model.image_encoder( - pixel_values[i].unsqueeze(0) - ) - image_embeddings_list.append(image_embeddings) - torch.cuda.empty_cache() - image_embeddings = torch.cat(image_embeddings_list, 0) - return image_embeddings - - def forward(self, **kwargs): - if "past_key_values" in kwargs: - return super().forward(**kwargs) - return self.model_forward(**kwargs) - - def model_forward( - self, - images: torch.FloatTensor, - images_clip: torch.FloatTensor, - input_ids: torch.LongTensor, - labels: torch.LongTensor, - attention_masks: torch.LongTensor, - offset: torch.LongTensor, - masks_list: List[torch.FloatTensor], - label_list: List[torch.Tensor], - resize_list: List[tuple], - inference: bool = False, - **kwargs, - ): - image_embeddings = self.get_visual_embs(images) - batch_size = image_embeddings.shape[0] - assert batch_size == len(offset) - 1 - - seg_token_mask = input_ids[:, 1:] == self.seg_token_idx - seg_token_mask = torch.cat( - [ - seg_token_mask, - torch.zeros((seg_token_mask.shape[0], 1)).bool().cuda(), - ], - dim=1, - ) - # hack for IMAGE_TOKEN_INDEX (we suppose that there is only one image, and it is in the front) - seg_token_mask = torch.cat( - [torch.zeros((seg_token_mask.shape[0], 255)).bool().cuda(), seg_token_mask], - dim=1, - ) - - if inference: - n_batch = 1 - length = input_ids.shape[0] - assert images_clip.shape[0] == 1 - images_clip_extend = images_clip.expand(length, -1, -1, -1).contiguous() - - output_hidden_states = [] - for i in range(n_batch): - start_i, end_i = i * length, min((i + 1) * length, input_ids.shape[0]) - output_i = super().forward( - images=images_clip_extend[: end_i - start_i], - attention_mask=attention_masks[start_i:end_i], - input_ids=input_ids[start_i:end_i], - output_hidden_states=True, - ) - output_hidden_states.append(output_i.hidden_states) - torch.cuda.empty_cache() - - output_hidden_states_list = [] - output_hidden_states_level = torch.cat(output_hidden_states, dim=0) - output_hidden_states_list.append(output_hidden_states_level) - output_hidden_states = output_hidden_states_list - output = None - - else: - images_clip_list = [] - for i in range(len(offset) - 1): - start_i, end_i = offset[i], offset[i + 1] - images_clip_i = ( - images_clip[i] - .unsqueeze(0) - .expand(end_i - start_i, -1, -1, -1) - .contiguous() - ) - images_clip_list.append(images_clip_i) - images_clip = torch.cat(images_clip_list, dim=0) - - output = super().forward( - images=images_clip, - attention_mask=attention_masks, - input_ids=input_ids, - labels=labels, - output_hidden_states=True, - ) - output_hidden_states = output.hidden_states - - hidden_states = [] - - assert len(self.model.text_hidden_fcs) == 1 - hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states[-1])) - - last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1) - pred_embeddings = last_hidden_state[seg_token_mask] - seg_token_counts = seg_token_mask.int().sum(-1) # [bs, ] - - seg_token_offset = seg_token_counts.cumsum(-1) - seg_token_offset = torch.cat( - [torch.zeros(1).long().cuda(), seg_token_offset], dim=0 - ) - - seg_token_offset = seg_token_offset[offset] - - pred_embeddings_ = [] - for i in range(len(seg_token_offset) - 1): - start_i, end_i = seg_token_offset[i], seg_token_offset[i + 1] - pred_embeddings_.append(pred_embeddings[start_i:end_i]) - pred_embeddings = pred_embeddings_ - - multimask_output = False - pred_masks = [] - for i in range(len(pred_embeddings)): - ( - sparse_embeddings, - dense_embeddings, - ) = self.model.visual_model.prompt_encoder( - points=None, - boxes=None, - masks=None, - text_embeds=pred_embeddings[i].unsqueeze(1), - ) - sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype) - low_res_masks, iou_predictions = self.model.visual_model.mask_decoder( - image_embeddings=image_embeddings[i].unsqueeze(0), - image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings, - dense_prompt_embeddings=dense_embeddings, - multimask_output=multimask_output, - ) - pred_mask = self.model.visual_model.postprocess_masks( - low_res_masks, - input_size=resize_list[i], - original_size=label_list[i].shape, - ) - pred_masks.append(pred_mask[:, 0]) - - model_output = output - gt_masks = masks_list - - if inference: - return { - "pred_masks": pred_masks, - "gt_masks": gt_masks, - } - - output = model_output.logits - - ce_loss = model_output.loss - ce_loss = ce_loss * self.ce_loss_weight - mask_bce_loss = 0 - mask_dice_loss = 0 - num_masks = 0 - for batch_idx in range(len(pred_masks)): - gt_mask = gt_masks[batch_idx] - pred_mask = pred_masks[batch_idx] - - assert ( - gt_mask.shape[0] == pred_mask.shape[0] - ), "gt_mask.shape: {}, pred_mask.shape: {}".format( - gt_mask.shape, pred_mask.shape - ) - mask_bce_loss += ( - sigmoid_ce_loss(pred_mask, gt_mask, num_masks=gt_mask.shape[0]) - * gt_mask.shape[0] - ) - mask_dice_loss += ( - dice_loss(pred_mask, gt_mask, num_masks=gt_mask.shape[0]) - * gt_mask.shape[0] - ) - num_masks += gt_mask.shape[0] - - mask_bce_loss = self.bce_loss_weight * mask_bce_loss / (num_masks + 1e-8) - mask_dice_loss = self.dice_loss_weight * mask_dice_loss / (num_masks + 1e-8) - mask_loss = mask_bce_loss + mask_dice_loss - - loss = ce_loss + mask_loss - - return { - "loss": loss, - "ce_loss": ce_loss, - "mask_bce_loss": mask_bce_loss, - "mask_dice_loss": mask_dice_loss, - "mask_loss": mask_loss, - } - - def evaluate( - self, - images_clip, - images, - input_ids, - resize_list, - original_size_list, - max_new_tokens=32, - tokenizer=None, - ): - with torch.no_grad(): - outputs = self.generate( - images=images_clip, - input_ids=input_ids, - max_new_tokens=max_new_tokens, - num_beams=1, - output_hidden_states=True, - return_dict_in_generate=True, - ) - output_hidden_states = outputs.hidden_states[-1] - output_ids = outputs.sequences - - seg_token_mask = output_ids[:, 1:] == self.seg_token_idx - # hack for IMAGE_TOKEN_INDEX (we suppose that there is only one image, and it is in the front) - seg_token_mask = torch.cat( - [ - torch.zeros((seg_token_mask.shape[0], 255)).bool().cuda(), - seg_token_mask, - ], - dim=1, - ) - - hidden_states = [] - - assert len(self.model.text_hidden_fcs) == 1 - hidden_states.append(self.model.text_hidden_fcs[0](output_hidden_states)) - - last_hidden_state = torch.stack(hidden_states, dim=-1).sum(dim=-1) - pred_embeddings = last_hidden_state[seg_token_mask] - - seg_token_counts = seg_token_mask.int().sum(-1) # [bs, ] - seg_token_offset = seg_token_counts.cumsum(-1) - seg_token_offset = torch.cat( - [torch.zeros(1).long().cuda(), seg_token_offset], dim=0 - ) - - pred_embeddings_ = [] - for i in range(len(seg_token_offset) - 1): - start_i, end_i = seg_token_offset[i], seg_token_offset[i + 1] - pred_embeddings_.append(pred_embeddings[start_i:end_i]) - pred_embeddings = pred_embeddings_ - - image_embeddings = self.get_visual_embs(images) - - multimask_output = False - pred_masks = [] - for i in range(len(pred_embeddings)): - ( - sparse_embeddings, - dense_embeddings, - ) = self.model.visual_model.prompt_encoder( - points=None, - boxes=None, - masks=None, - text_embeds=pred_embeddings[i].unsqueeze(1), - ) - - sparse_embeddings = sparse_embeddings.to(pred_embeddings[i].dtype) - low_res_masks, iou_predictions = self.model.visual_model.mask_decoder( - image_embeddings=image_embeddings[i].unsqueeze(0), - image_pe=self.model.visual_model.prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings, - dense_prompt_embeddings=dense_embeddings, - multimask_output=multimask_output, - ) - pred_mask = self.model.visual_model.postprocess_masks( - low_res_masks, - input_size=resize_list[i], - original_size=original_size_list[i], - ) - pred_masks.append(pred_mask[:, 0]) - - return output_ids, pred_masks diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/__init__.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/__init__.py deleted file mode 100755 index 4d1f016db..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .model import LlavaLlamaForCausalLM diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/constants.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/constants.py deleted file mode 100755 index be8cf0204..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/constants.py +++ /dev/null @@ -1,12 +0,0 @@ -CONTROLLER_HEART_BEAT_EXPIRATION = 30 -WORKER_HEART_BEAT_INTERVAL = 15 - -LOGDIR = "." - -# Model Constants -IGNORE_INDEX = -100 -IMAGE_TOKEN_INDEX = -200 -DEFAULT_IMAGE_TOKEN = "" -DEFAULT_IMAGE_PATCH_TOKEN = "" -DEFAULT_IM_START_TOKEN = "" -DEFAULT_IM_END_TOKEN = "" diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/conversation.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/conversation.py deleted file mode 100755 index 11fe82f0c..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/conversation.py +++ /dev/null @@ -1,399 +0,0 @@ -import dataclasses -from enum import Enum, auto -from typing import List, Tuple - - -class SeparatorStyle(Enum): - """Different separator style.""" - - SINGLE = auto() - TWO = auto() - MPT = auto() - PLAIN = auto() - LLAMA_2 = auto() - - -@dataclasses.dataclass -class Conversation: - """A class that keeps all conversation history.""" - - system: str - roles: List[str] - messages: List[List[str]] - offset: int - sep_style: SeparatorStyle = SeparatorStyle.SINGLE - sep: str = "###" - sep2: str = None - version: str = "Unknown" - - skip_next: bool = False - - def get_prompt(self): - messages = self.messages - if len(messages) > 0 and type(messages[0][1]) is tuple: - messages = self.messages.copy() - init_role, init_msg = messages[0].copy() - init_msg = init_msg[0].replace("", "").strip() - if "mmtag" in self.version: - messages[0] = (init_role, init_msg) - messages.insert(0, (self.roles[0], "")) - messages.insert(1, (self.roles[1], "Received.")) - else: - messages[0] = (init_role, "\n" + init_msg) - - if self.sep_style == SeparatorStyle.SINGLE: - ret = self.system + self.sep - for role, message in messages: - if message: - if type(message) is tuple: - message, _, _ = message - ret += role + ": " + message + self.sep - else: - ret += role + ":" - elif self.sep_style == SeparatorStyle.TWO: - seps = [self.sep, self.sep2] - ret = self.system + seps[0] - for i, (role, message) in enumerate(messages): - if message: - if type(message) is tuple: - message, _, _ = message - ret += role + ": " + message + seps[i % 2] - else: - ret += role + ":" - elif self.sep_style == SeparatorStyle.MPT: - ret = self.system + self.sep - for role, message in messages: - if message: - if type(message) is tuple: - message, _, _ = message - ret += role + message + self.sep - else: - ret += role - elif self.sep_style == SeparatorStyle.LLAMA_2: - wrap_sys = lambda msg: f"<>\n{msg}\n<>\n\n" - wrap_inst = lambda msg: f"[INST] {msg} [/INST]" - ret = "" - - for i, (role, message) in enumerate(messages): - if i == 0: - assert message, "first message should not be none" - assert role == self.roles[0], "first message should come from user" - if message: - if type(message) is tuple: - message, _, _ = message - if i == 0: - message = wrap_sys(self.system) + message - if i % 2 == 0: - message = wrap_inst(message) - ret += self.sep + message - else: - ret += " " + message + " " + self.sep2 - else: - ret += "" - ret = ret.lstrip(self.sep) - elif self.sep_style == SeparatorStyle.PLAIN: - seps = [self.sep, self.sep2] - ret = self.system - for i, (role, message) in enumerate(messages): - if message: - if type(message) is tuple: - message, _, _ = message - ret += message + seps[i % 2] - else: - ret += "" - else: - raise ValueError(f"Invalid style: {self.sep_style}") - - return ret - - def append_message(self, role, message): - self.messages.append([role, message]) - - def get_images(self, return_pil=False): - images = [] - for i, (role, msg) in enumerate(self.messages[self.offset :]): - if i % 2 == 0: - if type(msg) is tuple: - import base64 - from io import BytesIO - - from PIL import Image - - msg, image, image_process_mode = msg - if image_process_mode == "Pad": - - def expand2square(pil_img, background_color=(122, 116, 104)): - width, height = pil_img.size - if width == height: - return pil_img - elif width > height: - result = Image.new( - pil_img.mode, (width, width), background_color - ) - result.paste(pil_img, (0, (width - height) // 2)) - return result - else: - result = Image.new( - pil_img.mode, (height, height), background_color - ) - result.paste(pil_img, ((height - width) // 2, 0)) - return result - - image = expand2square(image) - elif image_process_mode == "Crop": - pass - elif image_process_mode == "Resize": - image = image.resize((336, 336)) - else: - raise ValueError( - f"Invalid image_process_mode: {image_process_mode}" - ) - max_hw, min_hw = max(image.size), min(image.size) - aspect_ratio = max_hw / min_hw - max_len, min_len = 800, 400 - shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) - longest_edge = int(shortest_edge * aspect_ratio) - W, H = image.size - if H > W: - H, W = longest_edge, shortest_edge - else: - H, W = shortest_edge, longest_edge - image = image.resize((W, H)) - if return_pil: - images.append(image) - else: - buffered = BytesIO() - image.save(buffered, format="PNG") - img_b64_str = base64.b64encode(buffered.getvalue()).decode() - images.append(img_b64_str) - return images - - def to_gradio_chatbot(self): - ret = [] - for i, (role, msg) in enumerate(self.messages[self.offset :]): - if i % 2 == 0: - if type(msg) is tuple: - import base64 - from io import BytesIO - - msg, image, image_process_mode = msg - max_hw, min_hw = max(image.size), min(image.size) - aspect_ratio = max_hw / min_hw - max_len, min_len = 800, 400 - shortest_edge = int(min(max_len / aspect_ratio, min_len, min_hw)) - longest_edge = int(shortest_edge * aspect_ratio) - W, H = image.size - if H > W: - H, W = longest_edge, shortest_edge - else: - H, W = shortest_edge, longest_edge - image = image.resize((W, H)) - buffered = BytesIO() - image.save(buffered, format="JPEG") - img_b64_str = base64.b64encode(buffered.getvalue()).decode() - img_str = f'user upload image' - ret.append([img_str, None]) - msg = msg.replace("", "").strip() - if len(msg) > 0: - ret.append([msg, None]) - else: - ret.append([msg, None]) - else: - ret[-1][-1] = msg - return ret - - def copy(self): - return Conversation( - system=self.system, - roles=self.roles, - messages=[[x, y] for x, y in self.messages], - offset=self.offset, - sep_style=self.sep_style, - sep=self.sep, - sep2=self.sep2, - version=self.version, - ) - - def dict(self): - if len(self.get_images()) > 0: - return { - "system": self.system, - "roles": self.roles, - "messages": [ - [x, y[0] if type(y) is tuple else y] for x, y in self.messages - ], - "offset": self.offset, - "sep": self.sep, - "sep2": self.sep2, - } - return { - "system": self.system, - "roles": self.roles, - "messages": self.messages, - "offset": self.offset, - "sep": self.sep, - "sep2": self.sep2, - } - - -conv_vicuna_v0 = Conversation( - system="A chat between a curious human and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the human's questions.", - roles=("Human", "Assistant"), - messages=( - ( - "Human", - "What are the key differences between renewable and non-renewable energy sources?", - ), - ( - "Assistant", - "Renewable energy sources are those that can be replenished naturally in a relatively " - "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " - "Non-renewable energy sources, on the other hand, are finite and will eventually be " - "depleted, such as coal, oil, and natural gas. Here are some key differences between " - "renewable and non-renewable energy sources:\n" - "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " - "energy sources are finite and will eventually run out.\n" - "2. Environmental impact: Renewable energy sources have a much lower environmental impact " - "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " - "and other negative effects.\n" - "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " - "have lower operational costs than non-renewable sources.\n" - "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " - "locations than non-renewable sources.\n" - "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " - "situations and needs, while non-renewable sources are more rigid and inflexible.\n" - "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " - "non-renewable sources are not, and their depletion can lead to economic and social instability.\n", - ), - ), - offset=2, - sep_style=SeparatorStyle.SINGLE, - sep="###", -) - -conv_vicuna_v1 = Conversation( - system="A chat between a curious user and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the user's questions.", - roles=("USER", "ASSISTANT"), - version="v1", - messages=(), - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_llama_2 = Conversation( - system="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. - -If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""", - roles=("USER", "ASSISTANT"), - version="llama_v2", - messages=(), - offset=0, - sep_style=SeparatorStyle.LLAMA_2, - sep="", - sep2="", -) - -conv_llava_llama_2 = Conversation( - system="You are a helpful language and vision assistant. " - "You are able to understand the visual content that the user provides, " - "and assist the user with a variety of tasks using natural language.", - roles=("USER", "ASSISTANT"), - version="llama_v2", - messages=(), - offset=0, - sep_style=SeparatorStyle.LLAMA_2, - sep="", - sep2="", -) - -conv_mpt = Conversation( - system="""<|im_start|>system -A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.""", - roles=("<|im_start|>user\n", "<|im_start|>assistant\n"), - version="mpt", - messages=(), - offset=0, - sep_style=SeparatorStyle.MPT, - sep="<|im_end|>", -) - -conv_llava_plain = Conversation( - system="", - roles=("", ""), - messages=(), - offset=0, - sep_style=SeparatorStyle.PLAIN, - sep="\n", -) - -conv_llava_v0 = Conversation( - system="A chat between a curious human and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the human's questions.", - roles=("Human", "Assistant"), - messages=(("Human", "Hi!"), ("Assistant", "Hi there! How can I help you today?")), - offset=2, - sep_style=SeparatorStyle.SINGLE, - sep="###", -) - -conv_llava_v0_mmtag = Conversation( - system="A chat between a curious user and an artificial intelligence assistant. " - "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." - "The visual content will be provided with the following format: visual content.", - roles=("Human", "Assistant"), - messages=(), - offset=0, - sep_style=SeparatorStyle.SINGLE, - sep="###", - version="v0_mmtag", -) - -conv_llava_v1 = Conversation( - system="A chat between a curious human and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the human's questions.", - roles=("USER", "ASSISTANT"), - version="v1", - messages=(), - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", -) - -conv_llava_v1_mmtag = Conversation( - system="A chat between a curious user and an artificial intelligence assistant. " - "The assistant is able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language." - "The visual content will be provided with the following format: visual content.", - roles=("USER", "ASSISTANT"), - messages=(), - offset=0, - sep_style=SeparatorStyle.TWO, - sep=" ", - sep2="", - version="v1_mmtag", -) - -default_conversation = conv_vicuna_v0 -conv_templates = { - "default": conv_vicuna_v0, - "v0": conv_vicuna_v0, - "v1": conv_vicuna_v1, - "vicuna_v1": conv_vicuna_v1, - "llama_2": conv_llama_2, - "plain": conv_llava_plain, - "v0_plain": conv_llava_plain, - "llava_v0": conv_llava_v0, - "v0_mmtag": conv_llava_v0_mmtag, - "llava_v1": conv_llava_v1, - "v1_mmtag": conv_llava_v1_mmtag, - "llava_llama_2": conv_llava_llama_2, - "mpt": conv_mpt, -} - - -if __name__ == "__main__": - print(default_conversation.get_prompt()) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/mm_utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/mm_utils.py deleted file mode 100755 index 92b1f5a6e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/mm_utils.py +++ /dev/null @@ -1,88 +0,0 @@ -import base64 -from io import BytesIO - -import torch -from PIL import Image -from transformers import StoppingCriteria - -from .constants import IMAGE_TOKEN_INDEX - - -def load_image_from_base64(image): - return Image.open(BytesIO(base64.b64decode(image))) - - -def process_images(images, image_processor, model_cfg): - return image_processor(images, return_tensors="pt")["pixel_values"] - - -def tokenizer_image_token( - prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None -): - prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split("")] - - def insert_separator(X, sep): - return [ele for sublist in zip(X, [sep] * len(X)) for ele in sublist][:-1] - - input_ids = [] - offset = 0 - if ( - len(prompt_chunks) > 0 - and len(prompt_chunks[0]) > 0 - and prompt_chunks[0][0] == tokenizer.bos_token_id - ): - offset = 1 - input_ids.append(prompt_chunks[0][0]) - - for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): - input_ids.extend(x[offset:]) - - if return_tensors is not None: - if return_tensors == "pt": - return torch.tensor(input_ids, dtype=torch.long) - raise ValueError(f"Unsupported tensor type: {return_tensors}") - return input_ids - - -def get_model_name_from_path(model_path): - model_path = model_path.strip("/") - model_paths = model_path.split("/") - if model_paths[-1].startswith("checkpoint-"): - return model_paths[-2] + "_" + model_paths[-1] - else: - return model_paths[-1] - - -class KeywordsStoppingCriteria(StoppingCriteria): - def __init__(self, keywords, tokenizer, input_ids): - self.keywords = keywords - self.keyword_ids = [] - for keyword in keywords: - cur_keyword_ids = tokenizer(keyword).input_ids - if ( - len(cur_keyword_ids) > 1 - and cur_keyword_ids[0] == tokenizer.bos_token_id - ): - cur_keyword_ids = cur_keyword_ids[1:] - self.keyword_ids.append(torch.tensor(cur_keyword_ids)) - self.tokenizer = tokenizer - self.start_len = input_ids.shape[1] - - def __call__( - self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs - ) -> bool: - assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO - offset = min(output_ids.shape[1] - self.start_len, 3) - self.keyword_ids = [ - keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids - ] - for keyword_id in self.keyword_ids: - if output_ids[0, -keyword_id.shape[0] :] == keyword_id: - return True - outputs = self.tokenizer.batch_decode( - output_ids[:, -offset:], skip_special_tokens=True - )[0] - for keyword in self.keywords: - if keyword in outputs: - return True - return False diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/__init__.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/__init__.py deleted file mode 100755 index 59e87786a..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .language_model.llava_llama import LlavaConfig, LlavaLlamaForCausalLM -from .language_model.llava_mpt import LlavaMPTConfig, LlavaMPTForCausalLM diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/apply_delta.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/apply_delta.py deleted file mode 100755 index 2f7380926..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/apply_delta.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Usage: -python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta -""" -import argparse - -import torch -from llava import LlavaLlamaForCausalLM -from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def apply_delta(base_model_path, target_model_path, delta_path): - print("Loading base model") - base = AutoModelForCausalLM.from_pretrained( - base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True - ) - - print("Loading delta") - delta = LlavaLlamaForCausalLM.from_pretrained( - delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True - ) - delta_tokenizer = AutoTokenizer.from_pretrained(delta_path) - - print("Applying delta") - for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"): - if name not in base.state_dict(): - assert name in [ - "model.mm_projector.weight", - "model.mm_projector.bias", - ], f"{name} not in base model" - continue - if param.data.shape == base.state_dict()[name].shape: - param.data += base.state_dict()[name] - else: - assert name in [ - "model.embed_tokens.weight", - "lm_head.weight", - ], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}" - bparam = base.state_dict()[name] - param.data[: bparam.shape[0], : bparam.shape[1]] += bparam - - print("Saving target model") - delta.save_pretrained(target_model_path) - delta_tokenizer.save_pretrained(target_model_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--base-model-path", type=str, required=True) - parser.add_argument("--target-model-path", type=str, required=True) - parser.add_argument("--delta-path", type=str, required=True) - - args = parser.parse_args() - - apply_delta(args.base_model_path, args.target_model_path, args.delta_path) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/builder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/builder.py deleted file mode 100755 index 0c841ab48..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/builder.py +++ /dev/null @@ -1,206 +0,0 @@ -# Copyright 2023 Haotian Liu -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import os -import shutil - -import torch -from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_PATCH_TOKEN) -from llava.model import * -from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer, - BitsAndBytesConfig) - - -def load_pretrained_model( - model_path, - model_base, - model_name, - load_8bit=False, - load_4bit=False, - device_map="auto", -): - kwargs = {"device_map": device_map} - - if load_8bit: - kwargs["load_in_8bit"] = True - elif load_4bit: - kwargs["load_in_4bit"] = True - kwargs["quantization_config"] = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_compute_dtype=torch.float16, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - ) - else: - kwargs["torch_dtype"] = torch.float16 - - if "llava" in model_name.lower(): - # Load LLaVA model - if "lora" in model_name.lower() and model_base is not None: - lora_cfg_pretrained = AutoConfig.from_pretrained(model_path) - tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) - print("Loading LLaVA from base model...") - model = LlavaLlamaForCausalLM.from_pretrained( - model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs - ) - token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features - if model.lm_head.weight.shape[0] != token_num: - model.lm_head.weight = torch.nn.Parameter( - torch.empty( - token_num, tokem_dim, device=model.device, dtype=model.dtype - ) - ) - model.model.embed_tokens.weight = torch.nn.Parameter( - torch.empty( - token_num, tokem_dim, device=model.device, dtype=model.dtype - ) - ) - - print("Loading additional LLaVA weights...") - if os.path.exists(os.path.join(model_path, "non_lora_trainables.bin")): - non_lora_trainables = torch.load( - os.path.join(model_path, "non_lora_trainables.bin"), - map_location="cpu", - ) - else: - # this is probably from HF Hub - from huggingface_hub import hf_hub_download - - def load_from_hf(repo_id, filename, subfolder=None): - cache_file = hf_hub_download( - repo_id=repo_id, filename=filename, subfolder=subfolder - ) - return torch.load(cache_file, map_location="cpu") - - non_lora_trainables = load_from_hf( - model_path, "non_lora_trainables.bin" - ) - non_lora_trainables = { - (k[11:] if k.startswith("base_model.") else k): v - for k, v in non_lora_trainables.items() - } - if any(k.startswith("model.model.") for k in non_lora_trainables): - non_lora_trainables = { - (k[6:] if k.startswith("model.") else k): v - for k, v in non_lora_trainables.items() - } - model.load_state_dict(non_lora_trainables, strict=False) - - from peft import PeftModel - - print("Loading LoRA weights...") - model = PeftModel.from_pretrained(model, model_path) - print("Merging LoRA weights...") - model = model.merge_and_unload() - print("Model is loaded...") - elif model_base is not None: - # this may be mm projector only - print("Loading LLaVA from base model...") - if "mpt" in model_name.lower(): - if not os.path.isfile(os.path.join(model_path, "configuration_mpt.py")): - shutil.copyfile( - os.path.join(model_base, "configuration_mpt.py"), - os.path.join(model_path, "configuration_mpt.py"), - ) - tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True) - cfg_pretrained = AutoConfig.from_pretrained( - model_path, trust_remote_code=True - ) - model = LlavaMPTForCausalLM.from_pretrained( - model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs - ) - else: - tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) - cfg_pretrained = AutoConfig.from_pretrained(model_path) - model = LlavaLlamaForCausalLM.from_pretrained( - model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs - ) - - mm_projector_weights = torch.load( - os.path.join(model_path, "mm_projector.bin"), map_location="cpu" - ) - mm_projector_weights = { - k: v.to(torch.float16) for k, v in mm_projector_weights.items() - } - model.load_state_dict(mm_projector_weights, strict=False) - else: - if "mpt" in model_name.lower(): - tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) - model = LlavaMPTForCausalLM.from_pretrained( - model_path, low_cpu_mem_usage=True, **kwargs - ) - else: - tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) - model = LlavaLlamaForCausalLM.from_pretrained( - model_path, low_cpu_mem_usage=True, **kwargs - ) - else: - # Load language model - if model_base is not None: - # PEFT model - from peft import PeftModel - - tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) - model = AutoModelForCausalLM.from_pretrained( - model_base, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - device_map="auto", - ) - print(f"Loading LoRA weights from {model_path}") - model = PeftModel.from_pretrained(model, model_path) - print(f"Merging weights") - model = model.merge_and_unload() - print("Convert to FP16...") - model.to(torch.float16) - else: - use_fast = False - if "mpt" in model_name.lower(): - tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True) - model = AutoModelForCausalLM.from_pretrained( - model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs - ) - else: - tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) - model = AutoModelForCausalLM.from_pretrained( - model_path, low_cpu_mem_usage=True, **kwargs - ) - - image_processor = None - - if "llava" in model_name.lower(): - mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False) - mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True) - if mm_use_im_patch_token: - tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) - if mm_use_im_start_end: - tokenizer.add_tokens( - [DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True - ) - model.resize_token_embeddings(len(tokenizer)) - - vision_tower = model.get_vision_tower() - if not vision_tower.is_loaded: - vision_tower.load_model() - vision_tower.to(device="cuda", dtype=torch.float16) - image_processor = vision_tower.image_processor - - if hasattr(model.config, "max_sequence_length"): - context_len = model.config.max_sequence_length - else: - context_len = 2048 - - return tokenizer, model, image_processor, context_len diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/consolidate.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/consolidate.py deleted file mode 100755 index f1fd9b722..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/consolidate.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -Usage: -python3 -m llava.model.consolidate --src ~/model_weights/llava-7b --dst ~/model_weights/llava-7b_consolidate -""" -import argparse - -import torch -from llava.model import * -from llava.model.utils import auto_upgrade -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def consolidate_ckpt(src_path, dst_path): - print("Loading model") - auto_upgrade(src_path) - src_model = AutoModelForCausalLM.from_pretrained( - src_path, torch_dtype=torch.float16, low_cpu_mem_usage=True - ) - src_tokenizer = AutoTokenizer.from_pretrained(src_path, use_fast=False) - src_model.save_pretrained(dst_path) - src_tokenizer.save_pretrained(dst_path) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--src", type=str, required=True) - parser.add_argument("--dst", type=str, required=True) - - args = parser.parse_args() - - consolidate_ckpt(args.src, args.dst) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_llama.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_llama.py deleted file mode 100755 index 460c00199..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_llama.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2023 Haotian Liu -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from typing import List, Optional, Tuple, Union - -import torch -import torch.nn as nn -from torch.nn import CrossEntropyLoss -from transformers import (AutoConfig, AutoModelForCausalLM, LlamaConfig, - LlamaForCausalLM, LlamaModel) -from transformers.modeling_outputs import CausalLMOutputWithPast - -from ..llava_arch import LlavaMetaForCausalLM, LlavaMetaModel - - -class LlavaConfig(LlamaConfig): - model_type = "llava" - - -class LlavaLlamaModel(LlavaMetaModel, LlamaModel): - config_class = LlavaConfig - - def __init__(self, config: LlamaConfig): - super(LlavaLlamaModel, self).__init__(config) - - -class LlavaLlamaForCausalLM(LlamaForCausalLM, LlavaMetaForCausalLM): - config_class = LlavaConfig - - def __init__(self, config): - super(LlamaForCausalLM, self).__init__(config) - - self.model = LlavaLlamaModel(config) - - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_model(self): - return self.model - - def forward( - self, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - images: Optional[torch.FloatTensor] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, CausalLMOutputWithPast]: - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - ( - input_ids, - attention_mask, - past_key_values, - inputs_embeds, - labels, - ) = self.prepare_inputs_labels_for_multimodal( - input_ids, attention_mask, past_key_values, labels, images - ) - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - - outputs = self.model( - input_ids=input_ids, - attention_mask=attention_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - hidden_states = outputs[0] - logits = self.lm_head(hidden_states) - - loss = None - if labels is not None: - # Shift so that tokens < n predict n - shift_logits = logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - # Flatten the tokens - loss_fct = CrossEntropyLoss() - shift_logits = shift_logits.view(-1, self.config.vocab_size) - shift_labels = shift_labels.view(-1) - # Enable model/pipeline parallelism - shift_labels = shift_labels.to(shift_logits.device) - loss = loss_fct(shift_logits, shift_labels) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - if self.training: - output_hidden_states = outputs.hidden_states - else: - output_hidden_states = hidden_states - - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=output_hidden_states, # outputs.hidden_states, - attentions=outputs.attentions, - ) - - def prepare_inputs_for_generation( - self, - input_ids, - past_key_values=None, - attention_mask=None, - inputs_embeds=None, - images=None, - **kwargs - ): - if past_key_values: - input_ids = input_ids[:, -1:] - - # if `inputs_embeds` are passed, we only want to use them in the 1st generation step - if inputs_embeds is not None and past_key_values is None: - model_inputs = {"inputs_embeds": inputs_embeds} - else: - model_inputs = {"input_ids": input_ids} - - model_inputs.update( - { - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), - "attention_mask": attention_mask, - "images": images, - } - ) - return model_inputs - - -AutoConfig.register("llava", LlavaConfig) -AutoModelForCausalLM.register(LlavaConfig, LlavaLlamaForCausalLM) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_mpt.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_mpt.py deleted file mode 100755 index 1549fb501..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/llava_mpt.py +++ /dev/null @@ -1,174 +0,0 @@ -# Copyright 2023 Haotian Liu -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import math -import warnings -from typing import List, Optional, Tuple - -import torch -import torch.nn.functional as F -from transformers import AutoConfig, AutoModelForCausalLM -from transformers.modeling_outputs import CausalLMOutputWithPast - -from ..llava_arch import LlavaMetaForCausalLM, LlavaMetaModel -from .mpt.modeling_mpt import MPTConfig, MPTForCausalLM, MPTModel - - -class LlavaMPTConfig(MPTConfig): - model_type = "llava_mpt" - - -class LlavaMPTModel(LlavaMetaModel, MPTModel): - config_class = LlavaMPTConfig - - def __init__(self, config: MPTConfig): - config.hidden_size = config.d_model - super(LlavaMPTModel, self).__init__(config) - - def embed_tokens(self, x): - return self.wte(x) - - -class LlavaMPTForCausalLM(MPTForCausalLM, LlavaMetaForCausalLM): - config_class = LlavaMPTConfig - supports_gradient_checkpointing = True - - def __init__(self, config): - super(MPTForCausalLM, self).__init__(config) - - if not config.tie_word_embeddings: - raise ValueError("MPTForCausalLM only supports tied word embeddings") - self.transformer = LlavaMPTModel(config) - self.logit_scale = None - if config.logit_scale is not None: - logit_scale = config.logit_scale - if isinstance(logit_scale, str): - if logit_scale == "inv_sqrt_d_model": - logit_scale = 1 / math.sqrt(config.d_model) - else: - raise ValueError( - f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." - ) - self.logit_scale = logit_scale - - def get_model(self): - return self.transformer - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, LlavaMPTModel): - module.gradient_checkpointing = value - - def forward( - self, - input_ids: torch.LongTensor, - past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None, - attention_mask: Optional[torch.ByteTensor] = None, - prefix_mask: Optional[torch.ByteTensor] = None, - sequence_id: Optional[torch.LongTensor] = None, - labels: Optional[torch.LongTensor] = None, - return_dict: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - use_cache: Optional[bool] = None, - images=None, - ): - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - - ( - input_ids, - attention_mask, - past_key_values, - inputs_embeds, - labels, - ) = self.prepare_inputs_labels_for_multimodal( - input_ids, attention_mask, past_key_values, labels, images - ) - outputs = self.transformer( - input_ids=input_ids, - inputs_embeds=inputs_embeds, - past_key_values=past_key_values, - attention_mask=attention_mask, - prefix_mask=prefix_mask, - sequence_id=sequence_id, - return_dict=return_dict, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - use_cache=use_cache, - ) - # FIXME: this is a hack to fix the multiple gpu inference issue in https://github.com/haotian-liu/LLaVA/issues/338 - logits = F.linear( - outputs.last_hidden_state.to(self.transformer.wte.weight.device), - self.transformer.wte.weight, - ) - if self.logit_scale is not None: - if self.logit_scale == 0: - warnings.warn( - f"Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs." - ) - logits *= self.logit_scale - loss = None - if labels is not None: - labels = torch.roll(labels, shifts=-1) - labels[:, -1] = -100 - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1) - ) - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - ) - - def prepare_inputs_for_generation( - self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs - ): - if inputs_embeds is not None: - raise NotImplementedError("inputs_embeds is not implemented for MPT yet") - attention_mask = kwargs["attention_mask"].bool() - if attention_mask[:, -1].sum() != attention_mask.shape[0]: - raise NotImplementedError( - "MPT does not support generation with right padding." - ) - if self.transformer.attn_uses_sequence_id and self.training: - sequence_id = torch.zeros_like(input_ids[:1]) - else: - sequence_id = None - if past_key_values is not None: - input_ids = input_ids[:, -1].unsqueeze(-1) - if self.transformer.prefix_lm: - prefix_mask = torch.ones_like(attention_mask) - if kwargs.get("use_cache") == False: - raise NotImplementedError( - "MPT with prefix_lm=True does not support use_cache=False." - ) - else: - prefix_mask = None - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "prefix_mask": prefix_mask, - "sequence_id": sequence_id, - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache", True), - "images": kwargs.get("images", None), - } - - -AutoConfig.register("llava_mpt", LlavaMPTConfig) -AutoModelForCausalLM.register(LlavaMPTConfig, LlavaMPTForCausalLM) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/adapt_tokenizer.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/adapt_tokenizer.py deleted file mode 100755 index b6c2acaca..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/adapt_tokenizer.py +++ /dev/null @@ -1,46 +0,0 @@ -from typing import Union - -from transformers import (AutoTokenizer, PreTrainedTokenizer, - PreTrainedTokenizerFast) - -Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] -NUM_SENTINEL_TOKENS: int = 100 - - -def adapt_tokenizer_for_denoising(tokenizer: Tokenizer): - """Adds sentinel tokens and padding token (if missing). - - Expands the tokenizer vocabulary to include sentinel tokens - used in mixture-of-denoiser tasks as well as a padding token. - - All added tokens are added as special tokens. No tokens are - added if sentinel tokens and padding token already exist. - """ - sentinels_to_add = [f"" for i in range(NUM_SENTINEL_TOKENS)] - tokenizer.add_tokens(sentinels_to_add, special_tokens=True) - if tokenizer.pad_token is None: - tokenizer.add_tokens("", special_tokens=True) - tokenizer.pad_token = "" - assert tokenizer.pad_token_id is not None - sentinels = "".join([f"" for i in range(NUM_SENTINEL_TOKENS)]) - _sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids - tokenizer.sentinel_token_ids = _sentinel_token_ids - - -class AutoTokenizerForMOD(AutoTokenizer): - """AutoTokenizer + Adaptation for MOD. - - A simple wrapper around AutoTokenizer to make instantiating - an MOD-adapted tokenizer a bit easier. - - MOD-adapted tokenizers have sentinel tokens (e.g., ), - a padding token, and a property to get the token ids of the - sentinel tokens. - """ - - @classmethod - def from_pretrained(cls, *args, **kwargs): - """See `AutoTokenizer.from_pretrained` docstring.""" - tokenizer = super().from_pretrained(*args, **kwargs) - adapt_tokenizer_for_denoising(tokenizer) - return tokenizer diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/attention.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/attention.py deleted file mode 100755 index 24fcd8fb1..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/attention.py +++ /dev/null @@ -1,526 +0,0 @@ -"""Attention layers.""" -import math -import warnings -from typing import Optional - -import torch -import torch.nn as nn -from einops import rearrange -from packaging import version -from torch import nn - -from .norm import LPLayerNorm - - -def _reset_is_causal( - num_query_tokens: int, num_key_tokens: int, original_is_causal: bool -): - if original_is_causal and num_query_tokens != num_key_tokens: - if num_query_tokens != 1: - raise NotImplementedError( - "MPT does not support query and key with different number of tokens, unless number of query tokens is 1." - ) - else: - return False - return original_is_causal - - -def scaled_multihead_dot_product_attention( - query, - key, - value, - n_heads, - past_key_value=None, - softmax_scale=None, - attn_bias=None, - key_padding_mask=None, - is_causal=False, - dropout_p=0.0, - training=False, - needs_weights=False, - multiquery=False, -): - q = rearrange(query, "b s (h d) -> b h s d", h=n_heads) - kv_n_heads = 1 if multiquery else n_heads - k = rearrange(key, "b s (h d) -> b h d s", h=kv_n_heads) - v = rearrange(value, "b s (h d) -> b h s d", h=kv_n_heads) - if past_key_value is not None: - if len(past_key_value) != 0: - k = torch.cat([past_key_value[0], k], dim=3) - v = torch.cat([past_key_value[1], v], dim=2) - past_key_value = (k, v) - (b, _, s_q, d) = q.shape - s_k = k.size(-1) - if softmax_scale is None: - softmax_scale = 1 / math.sqrt(d) - attn_weight = q.matmul(k) * softmax_scale - if attn_bias is not None: - _s_q = max(0, attn_bias.size(2) - s_q) - _s_k = max(0, attn_bias.size(3) - s_k) - attn_bias = attn_bias[:, :, _s_q:, _s_k:] - if ( - attn_bias.size(-1) != 1 - and attn_bias.size(-1) != s_k - or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q) - ): - raise RuntimeError( - f"attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}." - ) - attn_weight = attn_weight + attn_bias - min_val = torch.finfo(q.dtype).min - if key_padding_mask is not None: - if attn_bias is not None: - warnings.warn( - "Propogating key_padding_mask to the attention module " - + "and applying it within the attention module can cause " - + "unneccessary computation/memory usage. Consider integrating " - + "into attn_bias once and passing that to each attention " - + "module instead." - ) - attn_weight = attn_weight.masked_fill( - ~key_padding_mask.view((b, 1, 1, s_k)), min_val - ) - if is_causal and (not q.size(2) == 1): - s = max(s_q, s_k) - causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16) - causal_mask = causal_mask.tril() - causal_mask = causal_mask.to(torch.bool) - causal_mask = ~causal_mask - causal_mask = causal_mask[-s_q:, -s_k:] - attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val) - attn_weight = torch.softmax(attn_weight, dim=-1) - if dropout_p: - attn_weight = torch.nn.functional.dropout( - attn_weight, p=dropout_p, training=training, inplace=True - ) - out = attn_weight.to(v.dtype).matmul(v) - out = rearrange(out, "b h s d -> b s (h d)") - if needs_weights: - return (out, attn_weight, past_key_value) - return (out, None, past_key_value) - - -def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]): - for tensor in tensors: - if tensor.dtype not in valid_dtypes: - raise TypeError( - f"tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}." - ) - if not tensor.is_cuda: - raise TypeError( - f"Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r})." - ) - - -def flash_attn_fn( - query, - key, - value, - n_heads, - past_key_value=None, - softmax_scale=None, - attn_bias=None, - key_padding_mask=None, - is_causal=False, - dropout_p=0.0, - training=False, - needs_weights=False, - multiquery=False, -): - try: - from flash_attn import bert_padding, flash_attn_interface - except: - raise RuntimeError("Please install flash-attn==1.0.3.post0") - check_valid_inputs(query, key, value) - if past_key_value is not None: - if len(past_key_value) != 0: - key = torch.cat([past_key_value[0], key], dim=1) - value = torch.cat([past_key_value[1], value], dim=1) - past_key_value = (key, value) - if attn_bias is not None: - _s_q = max(0, attn_bias.size(2) - query.size(1)) - _s_k = max(0, attn_bias.size(3) - key.size(1)) - attn_bias = attn_bias[:, :, _s_q:, _s_k:] - if attn_bias is not None: - raise NotImplementedError(f"attn_bias not implemented for flash attn.") - (batch_size, seqlen) = query.shape[:2] - if key_padding_mask is None: - key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool) - query_padding_mask = key_padding_mask[:, -query.size(1) :] - (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input( - query, query_padding_mask - ) - query_unpad = rearrange(query_unpad, "nnz (h d) -> nnz h d", h=n_heads) - (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input( - key, key_padding_mask - ) - key_unpad = rearrange( - key_unpad, "nnz (h d) -> nnz h d", h=1 if multiquery else n_heads - ) - (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask) - value_unpad = rearrange( - value_unpad, "nnz (h d) -> nnz h d", h=1 if multiquery else n_heads - ) - if multiquery: - key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1)) - value_unpad = value_unpad.expand( - value_unpad.size(0), n_heads, value_unpad.size(-1) - ) - dropout_p = dropout_p if training else 0.0 - reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) - output_unpad = flash_attn_interface.flash_attn_unpadded_func( - query_unpad, - key_unpad, - value_unpad, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - dropout_p, - softmax_scale=softmax_scale, - causal=reset_is_causal, - return_attn_probs=needs_weights, - ) - output = bert_padding.pad_input( - rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices_q, batch_size, seqlen - ) - return (output, None, past_key_value) - - -def triton_flash_attn_fn( - query, - key, - value, - n_heads, - past_key_value=None, - softmax_scale=None, - attn_bias=None, - key_padding_mask=None, - is_causal=False, - dropout_p=0.0, - training=False, - needs_weights=False, - multiquery=False, -): - try: - from .flash_attn_triton import flash_attn_func - except: - _installed = False - if version.parse(torch.__version__) < version.parse("2.0.0"): - _installed = True - try: - from flash_attn.flash_attn_triton import flash_attn_func - except: - _installed = False - if not _installed: - raise RuntimeError( - "Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed." - ) - check_valid_inputs(query, key, value) - if past_key_value is not None: - if len(past_key_value) != 0: - key = torch.cat([past_key_value[0], key], dim=1) - value = torch.cat([past_key_value[1], value], dim=1) - past_key_value = (key, value) - if attn_bias is not None: - _s_q = max(0, attn_bias.size(2) - query.size(1)) - _s_k = max(0, attn_bias.size(3) - key.size(1)) - attn_bias = attn_bias[:, :, _s_q:, _s_k:] - if dropout_p: - raise NotImplementedError(f"Dropout not implemented for attn_impl: triton.") - if needs_weights: - raise NotImplementedError(f"attn_impl: triton cannot return attn weights.") - if key_padding_mask is not None: - warnings.warn( - "Propagating key_padding_mask to the attention module " - + "and applying it within the attention module can cause " - + "unnecessary computation/memory usage. Consider integrating " - + "into attn_bias once and passing that to each attention " - + "module instead." - ) - (b_size, s_k) = key_padding_mask.shape[:2] - if attn_bias is None: - attn_bias = query.new_zeros(b_size, 1, 1, s_k) - attn_bias = attn_bias.masked_fill( - ~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min - ) - query = rearrange(query, "b s (h d) -> b s h d", h=n_heads) - key = rearrange(key, "b s (h d) -> b s h d", h=1 if multiquery else n_heads) - value = rearrange(value, "b s (h d) -> b s h d", h=1 if multiquery else n_heads) - if multiquery: - key = key.expand(*key.shape[:2], n_heads, key.size(-1)) - value = value.expand(*value.shape[:2], n_heads, value.size(-1)) - reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal) - attn_output = flash_attn_func( - query, key, value, attn_bias, reset_is_causal, softmax_scale - ) - output = attn_output.view(*attn_output.shape[:2], -1) - return (output, None, past_key_value) - - -class MultiheadAttention(nn.Module): - """Multi-head self attention. - - Using torch or triton attention implemetation enables user to also use - additive bias. - """ - - def __init__( - self, - d_model: int, - n_heads: int, - attn_impl: str = "triton", - clip_qkv: Optional[float] = None, - qk_ln: bool = False, - softmax_scale: Optional[float] = None, - attn_pdrop: float = 0.0, - low_precision_layernorm: bool = False, - verbose: int = 0, - device: Optional[str] = None, - ): - super().__init__() - self.attn_impl = attn_impl - self.clip_qkv = clip_qkv - self.qk_ln = qk_ln - self.d_model = d_model - self.n_heads = n_heads - self.softmax_scale = softmax_scale - if self.softmax_scale is None: - self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads) - self.attn_dropout_p = attn_pdrop - self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device) - fuse_splits = (d_model, 2 * d_model) - self.Wqkv._fused = (0, fuse_splits) - if self.qk_ln: - layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm - self.q_ln = layernorm_class(self.d_model, device=device) - self.k_ln = layernorm_class(self.d_model, device=device) - if self.attn_impl == "flash": - self.attn_fn = flash_attn_fn - elif self.attn_impl == "triton": - self.attn_fn = triton_flash_attn_fn - if verbose: - warnings.warn( - "While `attn_impl: triton` can be faster than `attn_impl: flash` " - + "it uses more memory. When training larger models this can trigger " - + "alloc retries which hurts performance. If encountered, we recommend " - + "using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`." - ) - elif self.attn_impl == "torch": - self.attn_fn = scaled_multihead_dot_product_attention - if torch.cuda.is_available() and verbose: - warnings.warn( - "Using `attn_impl: torch`. If your model does not use `alibi` or " - + "`prefix_lm` we recommend using `attn_impl: flash` otherwise " - + "we recommend using `attn_impl: triton`." - ) - else: - raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.") - self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) - self.out_proj._is_residual = True - - def forward( - self, - x, - past_key_value=None, - attn_bias=None, - attention_mask=None, - is_causal=True, - needs_weights=False, - ): - qkv = self.Wqkv(x) - if self.clip_qkv: - qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) - (query, key, value) = qkv.chunk(3, dim=2) - key_padding_mask = attention_mask - if self.qk_ln: - dtype = query.dtype - query = self.q_ln(query).to(dtype) - key = self.k_ln(key).to(dtype) - (context, attn_weights, past_key_value) = self.attn_fn( - query, - key, - value, - self.n_heads, - past_key_value=past_key_value, - softmax_scale=self.softmax_scale, - attn_bias=attn_bias, - key_padding_mask=key_padding_mask, - is_causal=is_causal, - dropout_p=self.attn_dropout_p, - training=self.training, - needs_weights=needs_weights, - ) - return (self.out_proj(context), attn_weights, past_key_value) - - -class MultiQueryAttention(nn.Module): - """Multi-Query self attention. - - Using torch or triton attention implemetation enables user to also use - additive bias. - """ - - def __init__( - self, - d_model: int, - n_heads: int, - attn_impl: str = "triton", - clip_qkv: Optional[float] = None, - qk_ln: bool = False, - softmax_scale: Optional[float] = None, - attn_pdrop: float = 0.0, - low_precision_layernorm: bool = False, - verbose: int = 0, - device: Optional[str] = None, - ): - super().__init__() - self.attn_impl = attn_impl - self.clip_qkv = clip_qkv - self.qk_ln = qk_ln - self.d_model = d_model - self.n_heads = n_heads - self.head_dim = d_model // n_heads - self.softmax_scale = softmax_scale - if self.softmax_scale is None: - self.softmax_scale = 1 / math.sqrt(self.head_dim) - self.attn_dropout_p = attn_pdrop - self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device) - fuse_splits = (d_model, d_model + self.head_dim) - self.Wqkv._fused = (0, fuse_splits) - if self.qk_ln: - layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm - self.q_ln = layernorm_class(d_model, device=device) - self.k_ln = layernorm_class(self.head_dim, device=device) - if self.attn_impl == "flash": - self.attn_fn = flash_attn_fn - elif self.attn_impl == "triton": - self.attn_fn = triton_flash_attn_fn - if verbose: - warnings.warn( - "While `attn_impl: triton` can be faster than `attn_impl: flash` " - + "it uses more memory. When training larger models this can trigger " - + "alloc retries which hurts performance. If encountered, we recommend " - + "using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`." - ) - elif self.attn_impl == "torch": - self.attn_fn = scaled_multihead_dot_product_attention - if torch.cuda.is_available() and verbose: - warnings.warn( - "Using `attn_impl: torch`. If your model does not use `alibi` or " - + "`prefix_lm` we recommend using `attn_impl: flash` otherwise " - + "we recommend using `attn_impl: triton`." - ) - else: - raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.") - self.out_proj = nn.Linear(self.d_model, self.d_model, device=device) - self.out_proj._is_residual = True - - def forward( - self, - x, - past_key_value=None, - attn_bias=None, - attention_mask=None, - is_causal=True, - needs_weights=False, - ): - qkv = self.Wqkv(x) - if self.clip_qkv: - qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) - (query, key, value) = qkv.split( - [self.d_model, self.head_dim, self.head_dim], dim=2 - ) - key_padding_mask = attention_mask - if self.qk_ln: - dtype = query.dtype - query = self.q_ln(query).to(dtype) - key = self.k_ln(key).to(dtype) - (context, attn_weights, past_key_value) = self.attn_fn( - query, - key, - value, - self.n_heads, - past_key_value=past_key_value, - softmax_scale=self.softmax_scale, - attn_bias=attn_bias, - key_padding_mask=key_padding_mask, - is_causal=is_causal, - dropout_p=self.attn_dropout_p, - training=self.training, - needs_weights=needs_weights, - multiquery=True, - ) - return (self.out_proj(context), attn_weights, past_key_value) - - -def attn_bias_shape( - attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id -): - if attn_impl == "flash": - return None - elif attn_impl in ["torch", "triton"]: - if alibi: - if (prefix_lm or not causal) or use_sequence_id: - return (1, n_heads, seq_len, seq_len) - return (1, n_heads, 1, seq_len) - elif prefix_lm or use_sequence_id: - return (1, 1, seq_len, seq_len) - return None - else: - raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.") - - -def build_attn_bias( - attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8 -): - if attn_impl == "flash": - return None - elif attn_impl in ["torch", "triton"]: - if alibi: - (device, dtype) = (attn_bias.device, attn_bias.dtype) - attn_bias = attn_bias.add( - build_alibi_bias( - n_heads, - seq_len, - full=not causal, - alibi_bias_max=alibi_bias_max, - device=device, - dtype=dtype, - ) - ) - return attn_bias - else: - raise ValueError(f"attn_impl={attn_impl!r} is an invalid setting.") - - -def gen_slopes(n_heads, alibi_bias_max=8, device=None): - _n_heads = 2 ** math.ceil(math.log2(n_heads)) - m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device) - m = m.mul(alibi_bias_max / _n_heads) - slopes = 1.0 / torch.pow(2, m) - if _n_heads != n_heads: - slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads] - return slopes.view(1, n_heads, 1, 1) - - -def build_alibi_bias( - n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None -): - alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view( - 1, 1, 1, seq_len - ) - if full: - alibi_bias = alibi_bias - torch.arange( - 1 - seq_len, 1, dtype=torch.int32, device=device - ).view(1, 1, seq_len, 1) - alibi_bias = alibi_bias.abs().mul(-1) - slopes = gen_slopes(n_heads, alibi_bias_max, device=device) - alibi_bias = alibi_bias * slopes - return alibi_bias.to(dtype=dtype) - - -ATTN_CLASS_REGISTRY = { - "multihead_attention": MultiheadAttention, - "multiquery_attention": MultiQueryAttention, -} diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/blocks.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/blocks.py deleted file mode 100755 index 2f036432e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/blocks.py +++ /dev/null @@ -1,92 +0,0 @@ -"""GPT Blocks used for the GPT Model.""" -from typing import Dict, Optional, Tuple - -import torch -import torch.nn as nn - -from .attention import ATTN_CLASS_REGISTRY -from .norm import NORM_CLASS_REGISTRY - - -class MPTMLP(nn.Module): - def __init__( - self, d_model: int, expansion_ratio: int, device: Optional[str] = None - ): - super().__init__() - self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device) - self.act = nn.GELU(approximate="none") - self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device) - self.down_proj._is_residual = True - - def forward(self, x): - return self.down_proj(self.act(self.up_proj(x))) - - -class MPTBlock(nn.Module): - def __init__( - self, - d_model: int, - n_heads: int, - expansion_ratio: int, - attn_config: Dict = { - "attn_type": "multihead_attention", - "attn_pdrop": 0.0, - "attn_impl": "triton", - "qk_ln": False, - "clip_qkv": None, - "softmax_scale": None, - "prefix_lm": False, - "attn_uses_sequence_id": False, - "alibi": False, - "alibi_bias_max": 8, - }, - resid_pdrop: float = 0.0, - norm_type: str = "low_precision_layernorm", - verbose: int = 0, - device: Optional[str] = None, - **kwargs - ): - del kwargs - super().__init__() - norm_class = NORM_CLASS_REGISTRY[norm_type.lower()] - attn_class = ATTN_CLASS_REGISTRY[attn_config["attn_type"]] - self.norm_1 = norm_class(d_model, device=device) - self.attn = attn_class( - attn_impl=attn_config["attn_impl"], - clip_qkv=attn_config["clip_qkv"], - qk_ln=attn_config["qk_ln"], - softmax_scale=attn_config["softmax_scale"], - attn_pdrop=attn_config["attn_pdrop"], - d_model=d_model, - n_heads=n_heads, - verbose=verbose, - device=device, - ) - self.norm_2 = norm_class(d_model, device=device) - self.ffn = MPTMLP( - d_model=d_model, expansion_ratio=expansion_ratio, device=device - ) - self.resid_attn_dropout = nn.Dropout(resid_pdrop) - self.resid_ffn_dropout = nn.Dropout(resid_pdrop) - - def forward( - self, - x: torch.Tensor, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - attn_bias: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.ByteTensor] = None, - is_causal: bool = True, - ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]: - a = self.norm_1(x) - (b, attn_weights, past_key_value) = self.attn( - a, - past_key_value=past_key_value, - attn_bias=attn_bias, - attention_mask=attention_mask, - is_causal=is_causal, - ) - x = x + self.resid_attn_dropout(b) - m = self.norm_2(x) - n = self.ffn(m) - x = x + self.resid_ffn_dropout(n) - return (x, attn_weights, past_key_value) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/configuration_mpt.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/configuration_mpt.py deleted file mode 100755 index 06da3b8f0..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/configuration_mpt.py +++ /dev/null @@ -1,199 +0,0 @@ -"""A HuggingFace-style model configuration.""" -from typing import Dict, Optional, Union - -from transformers import PretrainedConfig - -attn_config_defaults: Dict = { - "attn_type": "multihead_attention", - "attn_pdrop": 0.0, - "attn_impl": "triton", - "qk_ln": False, - "clip_qkv": None, - "softmax_scale": None, - "prefix_lm": False, - "attn_uses_sequence_id": False, - "alibi": False, - "alibi_bias_max": 8, -} -init_config_defaults: Dict = { - "name": "kaiming_normal_", - "fan_mode": "fan_in", - "init_nonlinearity": "relu", - "init_div_is_residual": True, - "emb_init_std": None, - "emb_init_uniform_lim": None, - "init_std": None, - "init_gain": 0.0, -} - - -class MPTConfig(PretrainedConfig): - model_type = "mpt" - - def __init__( - self, - d_model: int = 2048, - n_heads: int = 16, - n_layers: int = 24, - expansion_ratio: int = 4, - max_seq_len: int = 2048, - vocab_size: int = 50368, - resid_pdrop: float = 0.0, - emb_pdrop: float = 0.0, - learned_pos_emb: bool = True, - attn_config: Dict = attn_config_defaults, - init_device: str = "cpu", - logit_scale: Optional[Union[float, str]] = None, - no_bias: bool = False, - verbose: int = 0, - embedding_fraction: float = 1.0, - norm_type: str = "low_precision_layernorm", - use_cache: bool = False, - init_config: Dict = init_config_defaults, - **kwargs, - ): - """The MPT configuration class. - - Args: - d_model (int): The size of the embedding dimension of the model. - n_heads (int): The number of attention heads. - n_layers (int): The number of layers in the model. - expansion_ratio (int): The ratio of the up/down scale in the MLP. - max_seq_len (int): The maximum sequence length of the model. - vocab_size (int): The size of the vocabulary. - resid_pdrop (float): The dropout probability applied to the attention output before combining with residual. - emb_pdrop (float): The dropout probability for the embedding layer. - learned_pos_emb (bool): Whether to use learned positional embeddings - attn_config (Dict): A dictionary used to configure the model's attention module: - attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention - attn_pdrop (float): The dropout probability for the attention layers. - attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'. - qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer. - clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to - this value. - softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None, - use the default scale of ``1/sqrt(d_keys)``. - prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an - extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix - can attend to one another bi-directionally. Tokens outside the prefix use causal attention. - attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id. - When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates - which sub-sequence each token belongs to. - Defaults to ``False`` meaning any provided `sequence_id` will be ignored. - alibi (bool): Whether to use the alibi bias instead of position embeddings. - alibi_bias_max (int): The maximum value of the alibi bias. - init_device (str): The device to use for parameter initialization. - logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value. - no_bias (bool): Whether to use bias in all layers. - verbose (int): The verbosity level. 0 is silent. - embedding_fraction (float): The fraction to scale the gradients of the embedding layer by. - norm_type (str): choose type of norm to use - multiquery_attention (bool): Whether to use multiquery attention implementation. - use_cache (bool): Whether or not the model should return the last key/values attentions - init_config (Dict): A dictionary used to configure the model initialization: - init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_', - 'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or - 'xavier_normal_'. These mimic the parameter initialization methods in PyTorch. - init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True. - emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer. - emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution - used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``. - init_std (float): The standard deviation of the normal distribution used to initialize the model, - if using the baseline_ parameter initialization scheme. - init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes. - fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes. - init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes. - --- - See llmfoundry.models.utils.param_init_fns.py for info on other param init config options - """ - self.d_model = d_model - self.n_heads = n_heads - self.n_layers = n_layers - self.expansion_ratio = expansion_ratio - self.max_seq_len = max_seq_len - self.vocab_size = vocab_size - self.resid_pdrop = resid_pdrop - self.emb_pdrop = emb_pdrop - self.learned_pos_emb = learned_pos_emb - self.attn_config = attn_config - self.init_device = init_device - self.logit_scale = logit_scale - self.no_bias = no_bias - self.verbose = verbose - self.embedding_fraction = embedding_fraction - self.norm_type = norm_type - self.use_cache = use_cache - self.init_config = init_config - if "name" in kwargs: - del kwargs["name"] - if "loss_fn" in kwargs: - del kwargs["loss_fn"] - super().__init__(**kwargs) - self._validate_config() - - def _set_config_defaults(self, config, config_defaults): - for k, v in config_defaults.items(): - if k not in config: - config[k] = v - return config - - def _validate_config(self): - self.attn_config = self._set_config_defaults( - self.attn_config, attn_config_defaults - ) - self.init_config = self._set_config_defaults( - self.init_config, init_config_defaults - ) - if self.d_model % self.n_heads != 0: - raise ValueError("d_model must be divisible by n_heads") - if any( - ( - prob < 0 or prob > 1 - for prob in [ - self.attn_config["attn_pdrop"], - self.resid_pdrop, - self.emb_pdrop, - ] - ) - ): - raise ValueError( - "self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1" - ) - if self.attn_config["attn_impl"] not in ["torch", "flash", "triton"]: - raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}") - if self.attn_config["prefix_lm"] and self.attn_config["attn_impl"] not in [ - "torch", - "triton", - ]: - raise NotImplementedError( - "prefix_lm only implemented with torch and triton attention." - ) - if self.attn_config["alibi"] and self.attn_config["attn_impl"] not in [ - "torch", - "triton", - ]: - raise NotImplementedError( - "alibi only implemented with torch and triton attention." - ) - if self.attn_config["attn_uses_sequence_id"] and self.attn_config[ - "attn_impl" - ] not in ["torch", "triton"]: - raise NotImplementedError( - "attn_uses_sequence_id only implemented with torch and triton attention." - ) - if self.embedding_fraction > 1 or self.embedding_fraction <= 0: - raise ValueError( - "model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!" - ) - if isinstance(self.logit_scale, str) and self.logit_scale != "inv_sqrt_d_model": - raise ValueError( - f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." - ) - if self.init_config.get("name", None) is None: - raise ValueError( - f"self.init_config={self.init_config!r} 'name' needs to be set." - ) - if not self.learned_pos_emb and (not self.attn_config["alibi"]): - raise ValueError( - f"Positional information must be provided to the model using either learned_pos_emb or alibi." - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/custom_embedding.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/custom_embedding.py deleted file mode 100755 index 83979e7e7..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/custom_embedding.py +++ /dev/null @@ -1,11 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch import Tensor - - -class SharedEmbedding(nn.Embedding): - def forward(self, input: Tensor, unembed: bool = False) -> Tensor: - if unembed: - return F.linear(input, self.weight) - return super().forward(input) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/flash_attn_triton.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/flash_attn_triton.py deleted file mode 100755 index 1247b53cd..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/flash_attn_triton.py +++ /dev/null @@ -1,1087 +0,0 @@ -""" -Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py -update imports to use 'triton_pre_mlir' - -*Experimental* implementation of FlashAttention in Triton. -Tested with triton==2.0.0.dev20221202. -Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions -other than 64: -https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207 -We'll update this implementation with the new Triton backend once this is fixed. - -We use the FlashAttention implementation from Phil Tillet a starting point. -https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py - -Changes: -- Implement both causal and non-causal attention. -- Implement both self-attention and cross-attention. -- Support arbitrary seqlens (not just multiples of 128), for both forward and backward. -- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. -- Support attention bias. -- Speed up the forward pass a bit, and only store the LSE instead of m and l. -- Make the backward for d=128 much faster by reducing register spilling. -- Optionally parallelize the backward pass across seqlen_k, to deal with the case of -small batch size * nheads. - -Caution: -- This is an *experimental* implementation. The forward pass should be quite robust but -I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler). -- This implementation has only been tested on A100. -- If you plan to use headdim other than 64 and 128, you should test for race conditions -(due to the Triton compiler), as done in tests/test_flash_attn.py -"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions -for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident -that there are none left for other head dimensions. - -Differences between this Triton version and the CUDA version: -- Triton version doesn't support dropout. -- Triton forward is generally faster than CUDA forward, while Triton backward is -generally slower than CUDA backward. Overall Triton forward + backward is slightly slower -than CUDA forward + backward. -- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). -- Triton version supports attention bias, while CUDA version doesn't. -""" -import math - -import torch -import triton_pre_mlir as triton -import triton_pre_mlir.language as tl - - -@triton.heuristics( - { - "EVEN_M": lambda args: args["seqlen_q"] % args["BLOCK_M"] == 0, - "EVEN_N": lambda args: args["seqlen_k"] % args["BLOCK_N"] == 0, - "EVEN_HEADDIM": lambda args: args["headdim"] == args["BLOCK_HEADDIM"], - } -) -@triton.jit -def _fwd_kernel( - Q, - K, - V, - Bias, - Out, - Lse, - TMP, - softmax_scale, - stride_qb, - stride_qh, - stride_qm, - stride_kb, - stride_kh, - stride_kn, - stride_vb, - stride_vh, - stride_vn, - stride_bb, - stride_bh, - stride_bm, - stride_ob, - stride_oh, - stride_om, - nheads, - seqlen_q, - seqlen_k, - seqlen_q_rounded, - headdim, - CACHE_KEY_SEQLEN_Q, - CACHE_KEY_SEQLEN_K, - BIAS_TYPE: tl.constexpr, - IS_CAUSAL: tl.constexpr, - BLOCK_HEADDIM: tl.constexpr, - EVEN_M: tl.constexpr, - EVEN_N: tl.constexpr, - EVEN_HEADDIM: tl.constexpr, - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, -): - start_m = tl.program_id(0) - off_hb = tl.program_id(1) - off_b = off_hb // nheads - off_h = off_hb % nheads - offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) - offs_n = tl.arange(0, BLOCK_N) - offs_d = tl.arange(0, BLOCK_HEADDIM) - q_ptrs = ( - Q - + off_b * stride_qb - + off_h * stride_qh - + (offs_m[:, None] * stride_qm + offs_d[None, :]) - ) - k_ptrs = ( - K - + off_b * stride_kb - + off_h * stride_kh - + (offs_n[:, None] * stride_kn + offs_d[None, :]) - ) - v_ptrs = ( - V - + off_b * stride_vb - + off_h * stride_vh - + (offs_n[:, None] * stride_vn + offs_d[None, :]) - ) - if BIAS_TYPE == "vector": - b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n - elif BIAS_TYPE == "matrix": - b_ptrs = ( - Bias - + off_b * stride_bb - + off_h * stride_bh - + (offs_m[:, None] * stride_bm + offs_n[None, :]) - ) - t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m - lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") - m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") - acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32) - if EVEN_M & EVEN_N: - if EVEN_HEADDIM: - q = tl.load(q_ptrs) - else: - q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0) - elif EVEN_HEADDIM: - q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0) - else: - q = tl.load( - q_ptrs, - mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - other=0.0, - ) - end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k) - for start_n in range(0, end_n, BLOCK_N): - start_n = tl.multiple_of(start_n, BLOCK_N) - if EVEN_N & EVEN_M: - if EVEN_HEADDIM: - k = tl.load(k_ptrs + start_n * stride_kn) - else: - k = tl.load( - k_ptrs + start_n * stride_kn, - mask=offs_d[None, :] < headdim, - other=0.0, - ) - elif EVEN_HEADDIM: - k = tl.load( - k_ptrs + start_n * stride_kn, - mask=(start_n + offs_n)[:, None] < seqlen_k, - other=0.0, - ) - else: - k = tl.load( - k_ptrs + start_n * stride_kn, - mask=((start_n + offs_n)[:, None] < seqlen_k) - & (offs_d[None, :] < headdim), - other=0.0, - ) - qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - qk += tl.dot(q, k, trans_b=True) - if not EVEN_N: - qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float("-inf")) - if IS_CAUSAL: - qk += tl.where( - offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float("-inf") - ) - if BIAS_TYPE != "none": - if BIAS_TYPE == "vector": - if EVEN_N: - bias = tl.load(b_ptrs + start_n).to(tl.float32) - else: - bias = tl.load( - b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0 - ).to(tl.float32) - bias = bias[None, :] - elif BIAS_TYPE == "matrix": - if EVEN_M & EVEN_N: - bias = tl.load(b_ptrs + start_n).to(tl.float32) - else: - bias = tl.load( - b_ptrs + start_n, - mask=(offs_m[:, None] < seqlen_q) - & ((start_n + offs_n)[None, :] < seqlen_k), - other=0.0, - ).to(tl.float32) - qk = qk * softmax_scale + bias - m_ij = tl.maximum(tl.max(qk, 1), lse_i) - p = tl.exp(qk - m_ij[:, None]) - else: - m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i) - p = tl.exp(qk * softmax_scale - m_ij[:, None]) - l_ij = tl.sum(p, 1) - acc_o_scale = tl.exp(m_i - m_ij) - tl.store(t_ptrs, acc_o_scale) - acc_o_scale = tl.load(t_ptrs) - acc_o = acc_o * acc_o_scale[:, None] - if EVEN_N & EVEN_M: - if EVEN_HEADDIM: - v = tl.load(v_ptrs + start_n * stride_vn) - else: - v = tl.load( - v_ptrs + start_n * stride_vn, - mask=offs_d[None, :] < headdim, - other=0.0, - ) - elif EVEN_HEADDIM: - v = tl.load( - v_ptrs + start_n * stride_vn, - mask=(start_n + offs_n)[:, None] < seqlen_k, - other=0.0, - ) - else: - v = tl.load( - v_ptrs + start_n * stride_vn, - mask=((start_n + offs_n)[:, None] < seqlen_k) - & (offs_d[None, :] < headdim), - other=0.0, - ) - p = p.to(v.dtype) - acc_o += tl.dot(p, v) - m_i = m_ij - l_i_new = tl.exp(lse_i - m_ij) + l_ij - lse_i = m_ij + tl.log(l_i_new) - o_scale = tl.exp(m_i - lse_i) - tl.store(t_ptrs, o_scale) - o_scale = tl.load(t_ptrs) - acc_o = acc_o * o_scale[:, None] - start_m = tl.program_id(0) - offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) - lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m - tl.store(lse_ptrs, lse_i) - offs_d = tl.arange(0, BLOCK_HEADDIM) - out_ptrs = ( - Out - + off_b * stride_ob - + off_h * stride_oh - + (offs_m[:, None] * stride_om + offs_d[None, :]) - ) - if EVEN_M: - if EVEN_HEADDIM: - tl.store(out_ptrs, acc_o) - else: - tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim) - elif EVEN_HEADDIM: - tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q) - else: - tl.store( - out_ptrs, - acc_o, - mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - ) - - -@triton.jit -def _bwd_preprocess_do_o_dot( - Out, - DO, - Delta, - stride_ob, - stride_oh, - stride_om, - stride_dob, - stride_doh, - stride_dom, - nheads, - seqlen_q, - seqlen_q_rounded, - headdim, - BLOCK_M: tl.constexpr, - BLOCK_HEADDIM: tl.constexpr, -): - start_m = tl.program_id(0) - off_hb = tl.program_id(1) - off_b = off_hb // nheads - off_h = off_hb % nheads - offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) - offs_d = tl.arange(0, BLOCK_HEADDIM) - o = tl.load( - Out - + off_b * stride_ob - + off_h * stride_oh - + offs_m[:, None] * stride_om - + offs_d[None, :], - mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - other=0.0, - ).to(tl.float32) - do = tl.load( - DO - + off_b * stride_dob - + off_h * stride_doh - + offs_m[:, None] * stride_dom - + offs_d[None, :], - mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - other=0.0, - ).to(tl.float32) - delta = tl.sum(o * do, axis=1) - tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta) - - -@triton.jit -def _bwd_store_dk_dv( - dk_ptrs, - dv_ptrs, - dk, - dv, - offs_n, - offs_d, - seqlen_k, - headdim, - EVEN_M: tl.constexpr, - EVEN_N: tl.constexpr, - EVEN_HEADDIM: tl.constexpr, -): - if EVEN_N & EVEN_M: - if EVEN_HEADDIM: - tl.store(dv_ptrs, dv) - tl.store(dk_ptrs, dk) - else: - tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim) - tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim) - elif EVEN_HEADDIM: - tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k) - tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k) - else: - tl.store( - dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim) - ) - tl.store( - dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim) - ) - - -@triton.jit -def _bwd_kernel_one_col_block( - start_n, - Q, - K, - V, - Bias, - DO, - DQ, - DK, - DV, - LSE, - D, - softmax_scale, - stride_qm, - stride_kn, - stride_vn, - stride_bm, - stride_dom, - stride_dqm, - stride_dkn, - stride_dvn, - seqlen_q, - seqlen_k, - headdim, - ATOMIC_ADD: tl.constexpr, - BIAS_TYPE: tl.constexpr, - IS_CAUSAL: tl.constexpr, - BLOCK_HEADDIM: tl.constexpr, - EVEN_M: tl.constexpr, - EVEN_N: tl.constexpr, - EVEN_HEADDIM: tl.constexpr, - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, -): - begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M - offs_qm = begin_m + tl.arange(0, BLOCK_M) - offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N) - offs_m = tl.arange(0, BLOCK_M) - offs_d = tl.arange(0, BLOCK_HEADDIM) - q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :]) - k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :]) - v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :]) - do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :]) - dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :]) - if BIAS_TYPE == "vector": - b_ptrs = Bias + offs_n - elif BIAS_TYPE == "matrix": - b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :]) - dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) - dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32) - if begin_m >= seqlen_q: - dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) - dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) - _bwd_store_dk_dv( - dk_ptrs, - dv_ptrs, - dk, - dv, - offs_n, - offs_d, - seqlen_k, - headdim, - EVEN_M=EVEN_M, - EVEN_N=EVEN_N, - EVEN_HEADDIM=EVEN_HEADDIM, - ) - return - if EVEN_N & EVEN_M: - if EVEN_HEADDIM: - k = tl.load(k_ptrs) - v = tl.load(v_ptrs) - else: - k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0) - v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0) - elif EVEN_HEADDIM: - k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) - v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0) - else: - k = tl.load( - k_ptrs, - mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), - other=0.0, - ) - v = tl.load( - v_ptrs, - mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), - other=0.0, - ) - num_block_m = tl.cdiv(seqlen_q, BLOCK_M) - for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M): - start_m = tl.multiple_of(start_m, BLOCK_M) - offs_m_curr = start_m + offs_m - if EVEN_M & EVEN_HEADDIM: - q = tl.load(q_ptrs) - elif EVEN_HEADDIM: - q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0) - else: - q = tl.load( - q_ptrs, - mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - other=0.0, - ) - qk = tl.dot(q, k, trans_b=True) - if not EVEN_N: - qk = tl.where(offs_n[None, :] < seqlen_k, qk, float("-inf")) - if IS_CAUSAL: - qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float("-inf")) - if BIAS_TYPE != "none": - tl.debug_barrier() - if BIAS_TYPE == "vector": - if EVEN_N: - bias = tl.load(b_ptrs).to(tl.float32) - else: - bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to( - tl.float32 - ) - bias = bias[None, :] - elif BIAS_TYPE == "matrix": - if EVEN_M & EVEN_N: - bias = tl.load(b_ptrs).to(tl.float32) - else: - bias = tl.load( - b_ptrs, - mask=(offs_m_curr[:, None] < seqlen_q) - & (offs_n[None, :] < seqlen_k), - other=0.0, - ).to(tl.float32) - qk = qk * softmax_scale + bias - if not EVEN_M & EVEN_HEADDIM: - tl.debug_barrier() - lse_i = tl.load(LSE + offs_m_curr) - if BIAS_TYPE == "none": - p = tl.exp(qk * softmax_scale - lse_i[:, None]) - else: - p = tl.exp(qk - lse_i[:, None]) - if EVEN_M & EVEN_HEADDIM: - do = tl.load(do_ptrs) - else: - do = tl.load( - do_ptrs, - mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), - other=0.0, - ) - dv += tl.dot(p.to(do.dtype), do, trans_a=True) - if not EVEN_M & EVEN_HEADDIM: - tl.debug_barrier() - dp = tl.dot(do, v, trans_b=True) - if not EVEN_HEADDIM: - tl.debug_barrier() - Di = tl.load(D + offs_m_curr) - ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype) - dk += tl.dot(ds, q, trans_a=True) - if not EVEN_M & EVEN_HEADDIM: - tl.debug_barrier() - if not ATOMIC_ADD: - if EVEN_M & EVEN_HEADDIM: - dq = tl.load(dq_ptrs, eviction_policy="evict_last") - dq += tl.dot(ds, k) - tl.store(dq_ptrs, dq, eviction_policy="evict_last") - elif EVEN_HEADDIM: - dq = tl.load( - dq_ptrs, - mask=offs_m_curr[:, None] < seqlen_q, - other=0.0, - eviction_policy="evict_last", - ) - dq += tl.dot(ds, k) - tl.store( - dq_ptrs, - dq, - mask=offs_m_curr[:, None] < seqlen_q, - eviction_policy="evict_last", - ) - else: - dq = tl.load( - dq_ptrs, - mask=(offs_m_curr[:, None] < seqlen_q) - & (offs_d[None, :] < headdim), - other=0.0, - eviction_policy="evict_last", - ) - dq += tl.dot(ds, k) - tl.store( - dq_ptrs, - dq, - mask=(offs_m_curr[:, None] < seqlen_q) - & (offs_d[None, :] < headdim), - eviction_policy="evict_last", - ) - else: - dq = tl.dot(ds, k) - if EVEN_M & EVEN_HEADDIM: - tl.atomic_add(dq_ptrs, dq) - elif EVEN_HEADDIM: - tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q) - else: - tl.atomic_add( - dq_ptrs, - dq, - mask=(offs_m_curr[:, None] < seqlen_q) - & (offs_d[None, :] < headdim), - ) - dq_ptrs += BLOCK_M * stride_dqm - q_ptrs += BLOCK_M * stride_qm - do_ptrs += BLOCK_M * stride_dom - if BIAS_TYPE == "matrix": - b_ptrs += BLOCK_M * stride_bm - dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :]) - dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :]) - _bwd_store_dk_dv( - dk_ptrs, - dv_ptrs, - dk, - dv, - offs_n, - offs_d, - seqlen_k, - headdim, - EVEN_M=EVEN_M, - EVEN_N=EVEN_N, - EVEN_HEADDIM=EVEN_HEADDIM, - ) - - -def init_to_zero(name): - return lambda nargs: nargs[name].zero_() - - -@triton.autotune( - configs=[ - triton.Config( - {"BLOCK_M": 128, "BLOCK_N": 128, "SEQUENCE_PARALLEL": False}, - num_warps=8, - num_stages=1, - pre_hook=init_to_zero("DQ"), - ), - triton.Config( - {"BLOCK_M": 128, "BLOCK_N": 128, "SEQUENCE_PARALLEL": True}, - num_warps=8, - num_stages=1, - pre_hook=init_to_zero("DQ"), - ), - ], - key=[ - "CACHE_KEY_SEQLEN_Q", - "CACHE_KEY_SEQLEN_K", - "BIAS_TYPE", - "IS_CAUSAL", - "BLOCK_HEADDIM", - ], -) -@triton.heuristics( - { - "EVEN_M": lambda args: args["seqlen_q"] % args["BLOCK_M"] == 0, - "EVEN_N": lambda args: args["seqlen_k"] % args["BLOCK_N"] == 0, - "EVEN_HEADDIM": lambda args: args["headdim"] == args["BLOCK_HEADDIM"], - } -) -@triton.jit -def _bwd_kernel( - Q, - K, - V, - Bias, - DO, - DQ, - DK, - DV, - LSE, - D, - softmax_scale, - stride_qb, - stride_qh, - stride_qm, - stride_kb, - stride_kh, - stride_kn, - stride_vb, - stride_vh, - stride_vn, - stride_bb, - stride_bh, - stride_bm, - stride_dob, - stride_doh, - stride_dom, - stride_dqb, - stride_dqh, - stride_dqm, - stride_dkb, - stride_dkh, - stride_dkn, - stride_dvb, - stride_dvh, - stride_dvn, - nheads, - seqlen_q, - seqlen_k, - seqlen_q_rounded, - headdim, - CACHE_KEY_SEQLEN_Q, - CACHE_KEY_SEQLEN_K, - BIAS_TYPE: tl.constexpr, - IS_CAUSAL: tl.constexpr, - BLOCK_HEADDIM: tl.constexpr, - SEQUENCE_PARALLEL: tl.constexpr, - EVEN_M: tl.constexpr, - EVEN_N: tl.constexpr, - EVEN_HEADDIM: tl.constexpr, - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, -): - off_hb = tl.program_id(1) - off_b = off_hb // nheads - off_h = off_hb % nheads - Q += off_b * stride_qb + off_h * stride_qh - K += off_b * stride_kb + off_h * stride_kh - V += off_b * stride_vb + off_h * stride_vh - DO += off_b * stride_dob + off_h * stride_doh - DQ += off_b * stride_dqb + off_h * stride_dqh - DK += off_b * stride_dkb + off_h * stride_dkh - DV += off_b * stride_dvb + off_h * stride_dvh - if BIAS_TYPE != "none": - Bias += off_b * stride_bb + off_h * stride_bh - D += off_hb * seqlen_q_rounded - LSE += off_hb * seqlen_q_rounded - if not SEQUENCE_PARALLEL: - num_block_n = tl.cdiv(seqlen_k, BLOCK_N) - for start_n in range(0, num_block_n): - _bwd_kernel_one_col_block( - start_n, - Q, - K, - V, - Bias, - DO, - DQ, - DK, - DV, - LSE, - D, - softmax_scale, - stride_qm, - stride_kn, - stride_vn, - stride_bm, - stride_dom, - stride_dqm, - stride_dkn, - stride_dvn, - seqlen_q, - seqlen_k, - headdim, - ATOMIC_ADD=False, - BIAS_TYPE=BIAS_TYPE, - IS_CAUSAL=IS_CAUSAL, - BLOCK_HEADDIM=BLOCK_HEADDIM, - EVEN_M=EVEN_M, - EVEN_N=EVEN_N, - EVEN_HEADDIM=EVEN_HEADDIM, - BLOCK_M=BLOCK_M, - BLOCK_N=BLOCK_N, - ) - else: - start_n = tl.program_id(0) - _bwd_kernel_one_col_block( - start_n, - Q, - K, - V, - Bias, - DO, - DQ, - DK, - DV, - LSE, - D, - softmax_scale, - stride_qm, - stride_kn, - stride_vn, - stride_bm, - stride_dom, - stride_dqm, - stride_dkn, - stride_dvn, - seqlen_q, - seqlen_k, - headdim, - ATOMIC_ADD=True, - BIAS_TYPE=BIAS_TYPE, - IS_CAUSAL=IS_CAUSAL, - BLOCK_HEADDIM=BLOCK_HEADDIM, - EVEN_M=EVEN_M, - EVEN_N=EVEN_N, - EVEN_HEADDIM=EVEN_HEADDIM, - BLOCK_M=BLOCK_M, - BLOCK_N=BLOCK_N, - ) - - -def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None): - (batch, seqlen_q, nheads, d) = q.shape - (_, seqlen_k, _, _) = k.shape - assert k.shape == (batch, seqlen_k, nheads, d) - assert v.shape == (batch, seqlen_k, nheads, d) - assert d <= 128, "FlashAttention only support head dimensions up to 128" - assert q.dtype == k.dtype == v.dtype, "All tensors must have the same type" - assert q.dtype in [torch.float16, torch.bfloat16], "Only support fp16 and bf16" - assert q.is_cuda and k.is_cuda and v.is_cuda - softmax_scale = softmax_scale or 1.0 / math.sqrt(d) - has_bias = bias is not None - bias_type = "none" - if has_bias: - assert bias.dtype in [q.dtype, torch.float] - assert bias.is_cuda - assert bias.dim() == 4 - if bias.stride(-1) != 1: - bias = bias.contiguous() - if bias.shape[2:] == (1, seqlen_k): - bias_type = "vector" - elif bias.shape[2:] == (seqlen_q, seqlen_k): - bias_type = "matrix" - else: - raise RuntimeError( - "Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)" - ) - bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) - bias_strides = ( - (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) - ) - seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 - lse = torch.empty( - (batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32 - ) - tmp = torch.empty( - (batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32 - ) - o = torch.empty_like(q) - BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) - BLOCK = 128 - num_warps = 4 if d <= 64 else 8 - grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads) - _fwd_kernel[grid]( - q, - k, - v, - bias, - o, - lse, - tmp, - softmax_scale, - q.stride(0), - q.stride(2), - q.stride(1), - k.stride(0), - k.stride(2), - k.stride(1), - v.stride(0), - v.stride(2), - v.stride(1), - *bias_strides, - o.stride(0), - o.stride(2), - o.stride(1), - nheads, - seqlen_q, - seqlen_k, - seqlen_q_rounded, - d, - seqlen_q // 32, - seqlen_k // 32, - bias_type, - causal, - BLOCK_HEADDIM, - BLOCK_M=BLOCK, - BLOCK_N=BLOCK, - num_warps=num_warps, - num_stages=1 - ) - return (o, lse, softmax_scale) - - -def _flash_attn_backward( - do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None -): - if do.stride(-1) != 1: - do = do.contiguous() - (batch, seqlen_q, nheads, d) = q.shape - (_, seqlen_k, _, _) = k.shape - assert d <= 128 - seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128 - assert lse.shape == (batch, nheads, seqlen_q_rounded) - assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1 - assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1 - softmax_scale = softmax_scale or 1.0 / math.sqrt(d) - dq_accum = torch.empty_like(q, dtype=torch.float32) - delta = torch.empty_like(lse) - BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16) - grid = lambda META: (triton.cdiv(seqlen_q, META["BLOCK_M"]), batch * nheads) - _bwd_preprocess_do_o_dot[grid]( - o, - do, - delta, - o.stride(0), - o.stride(2), - o.stride(1), - do.stride(0), - do.stride(2), - do.stride(1), - nheads, - seqlen_q, - seqlen_q_rounded, - d, - BLOCK_M=128, - BLOCK_HEADDIM=BLOCK_HEADDIM, - ) - has_bias = bias is not None - bias_type = "none" - if has_bias: - assert bias.dtype in [q.dtype, torch.float] - assert bias.is_cuda - assert bias.dim() == 4 - assert bias.stride(-1) == 1 - if bias.shape[2:] == (1, seqlen_k): - bias_type = "vector" - elif bias.shape[2:] == (seqlen_q, seqlen_k): - bias_type = "matrix" - else: - raise RuntimeError( - "Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)" - ) - bias = bias.expand(batch, nheads, seqlen_q, seqlen_k) - bias_strides = ( - (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0) - ) - grid = lambda META: ( - triton.cdiv(seqlen_k, META["BLOCK_N"]) if META["SEQUENCE_PARALLEL"] else 1, - batch * nheads, - ) - _bwd_kernel[grid]( - q, - k, - v, - bias, - do, - dq_accum, - dk, - dv, - lse, - delta, - softmax_scale, - q.stride(0), - q.stride(2), - q.stride(1), - k.stride(0), - k.stride(2), - k.stride(1), - v.stride(0), - v.stride(2), - v.stride(1), - *bias_strides, - do.stride(0), - do.stride(2), - do.stride(1), - dq_accum.stride(0), - dq_accum.stride(2), - dq_accum.stride(1), - dk.stride(0), - dk.stride(2), - dk.stride(1), - dv.stride(0), - dv.stride(2), - dv.stride(1), - nheads, - seqlen_q, - seqlen_k, - seqlen_q_rounded, - d, - seqlen_q // 32, - seqlen_k // 32, - bias_type, - causal, - BLOCK_HEADDIM - ) - dq.copy_(dq_accum) - - -class FlashAttnQKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None): - """ - qkv: (batch, seqlen, 3, nheads, headdim) - bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen). - For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). - ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen) - """ - if qkv.stride(-1) != 1: - qkv = qkv.contiguous() - (o, lse, ctx.softmax_scale) = _flash_attn_forward( - qkv[:, :, 0], - qkv[:, :, 1], - qkv[:, :, 2], - bias=bias, - causal=causal, - softmax_scale=softmax_scale, - ) - ctx.save_for_backward(qkv, o, lse, bias) - ctx.causal = causal - return o - - @staticmethod - def backward(ctx, do): - (qkv, o, lse, bias) = ctx.saved_tensors - assert not ctx.needs_input_grad[ - 1 - ], "FlashAttention does not support bias gradient yet" - with torch.inference_mode(): - dqkv = torch.empty_like(qkv) - _flash_attn_backward( - do, - qkv[:, :, 0], - qkv[:, :, 1], - qkv[:, :, 2], - o, - lse, - dqkv[:, :, 0], - dqkv[:, :, 1], - dqkv[:, :, 2], - bias=bias, - causal=ctx.causal, - softmax_scale=ctx.softmax_scale, - ) - return (dqkv, None, None, None) - - -flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply - - -class FlashAttnKVPackedFunc(torch.autograd.Function): - @staticmethod - def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None): - """ - q: (batch, seqlen_q, nheads, headdim) - kv: (batch, seqlen_k, 2, nheads, headdim) - bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). - For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). - ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) - """ - (q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]] - (o, lse, ctx.softmax_scale) = _flash_attn_forward( - q, - kv[:, :, 0], - kv[:, :, 1], - bias=bias, - causal=causal, - softmax_scale=softmax_scale, - ) - ctx.save_for_backward(q, kv, o, lse, bias) - ctx.causal = causal - return o - - @staticmethod - def backward(ctx, do): - (q, kv, o, lse, bias) = ctx.saved_tensors - if len(ctx.needs_input_grad) >= 3: - assert not ctx.needs_input_grad[ - 2 - ], "FlashAttention does not support bias gradient yet" - with torch.inference_mode(): - dq = torch.empty_like(q) - dkv = torch.empty_like(kv) - _flash_attn_backward( - do, - q, - kv[:, :, 0], - kv[:, :, 1], - o, - lse, - dq, - dkv[:, :, 0], - dkv[:, :, 1], - bias=bias, - causal=ctx.causal, - softmax_scale=ctx.softmax_scale, - ) - return (dq, dkv, None, None, None) - - -flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply - - -class FlashAttnFunc(torch.autograd.Function): - @staticmethod - def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None): - """ - q: (batch_size, seqlen_q, nheads, headdim) - k, v: (batch_size, seqlen_k, nheads, headdim) - bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). - For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). - ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) - """ - (q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]] - (o, lse, ctx.softmax_scale) = _flash_attn_forward( - q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale - ) - ctx.save_for_backward(q, k, v, o, lse, bias) - ctx.causal = causal - return o - - @staticmethod - def backward(ctx, do): - (q, k, v, o, lse, bias) = ctx.saved_tensors - assert not ctx.needs_input_grad[ - 3 - ], "FlashAttention does not support bias gradient yet" - with torch.inference_mode(): - dq = torch.empty_like(q) - dk = torch.empty_like(k) - dv = torch.empty_like(v) - _flash_attn_backward( - do, - q, - k, - v, - o, - lse, - dq, - dk, - dv, - bias=bias, - causal=ctx.causal, - softmax_scale=ctx.softmax_scale, - ) - return (dq, dk, dv, None, None, None) - - -flash_attn_func = FlashAttnFunc.apply diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/hf_prefixlm_converter.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/hf_prefixlm_converter.py deleted file mode 100755 index 427d38781..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/hf_prefixlm_converter.py +++ /dev/null @@ -1,750 +0,0 @@ -"""Converts Huggingface Causal LM to Prefix LM. - -Conversion does lightweight surgery on a HuggingFace -Causal LM to convert it to a Prefix LM. - -Prefix LMs accepts a `bidirectional_mask` input in `forward` -and treat the input prompt as the prefix in `generate`. -""" -import math -import warnings -from types import MethodType -from typing import Any, Dict, List, Optional, Tuple, Union - -import torch -from transformers.models.bloom.modeling_bloom import ( - BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, - CausalLMOutputWithCrossAttentions, CrossEntropyLoss) -from transformers.models.bloom.modeling_bloom import \ - _expand_mask as _expand_mask_bloom -from transformers.models.bloom.modeling_bloom import \ - _make_causal_mask as _make_causal_mask_bloom -from transformers.models.bloom.modeling_bloom import logging -from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel -from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM -from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM -from transformers.models.gptj.modeling_gptj import GPTJForCausalLM -from transformers.models.opt.modeling_opt import OPTForCausalLM -from transformers.models.opt.modeling_opt import \ - _expand_mask as _expand_mask_opt -from transformers.models.opt.modeling_opt import \ - _make_causal_mask as _make_causal_mask_opt - -logger = logging.get_logger(__name__) -_SUPPORTED_GPT_MODELS = ( - GPT2LMHeadModel, - GPTJForCausalLM, - GPTNeoForCausalLM, - GPTNeoXForCausalLM, -) -CAUSAL_GPT_TYPES = Union[ - GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM -] - - -def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES: - """Converts a GPT-style Causal LM to a Prefix LM. - - Supported HuggingFace model classes: - - `GPT2LMHeadModel` - - `GPTNeoForCausalLM` - - `GPTNeoXForCausalLM` - - `GPTJForCausalLM` - - See `convert_hf_causal_lm_to_prefix_lm` for more details. - """ - if hasattr(model, "_prefix_lm_converted"): - return model - assert isinstance(model, _SUPPORTED_GPT_MODELS) - assert ( - model.config.add_cross_attention == False - ), "Only supports GPT-style decoder-only models" - - def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]: - """Helper that gets a list of the model's attention modules. - - Each module has a `bias` buffer used for causal masking. The Prefix LM - conversion adds logic to dynamically manipulate these biases to support - Prefix LM attention masking. - """ - attn_modules = [] - if isinstance(model, GPTNeoXForCausalLM): - blocks = model.gpt_neox.layers - else: - blocks = model.transformer.h - for block in blocks: - if isinstance(model, GPTNeoForCausalLM): - if block.attn.attention_type != "global": - continue - attn_module = block.attn.attention - elif isinstance(model, GPTNeoXForCausalLM): - attn_module = block.attention - else: - attn_module = block.attn - attn_modules.append(attn_module) - return attn_modules - - setattr(model, "_original_forward", getattr(model, "forward")) - setattr(model, "_original_generate", getattr(model, "generate")) - - def forward( - self: CAUSAL_GPT_TYPES, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, - attention_mask: Optional[torch.FloatTensor] = None, - bidirectional_mask: Optional[torch.Tensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ): - """Wraps original forward to enable PrefixLM attention.""" - - def call_og_forward(): - if isinstance(self, GPTNeoXForCausalLM): - return self._original_forward( - input_ids=input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - labels=labels, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - else: - return self._original_forward( - input_ids=input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - labels=labels, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - if bidirectional_mask is None: - return call_og_forward() - assert isinstance(bidirectional_mask, torch.Tensor) - attn_modules = _get_attn_modules(model) - (b, s) = bidirectional_mask.shape - max_length = attn_modules[0].bias.shape[-1] - if s > max_length: - raise ValueError( - f"bidirectional_mask sequence length (={s}) exceeds the " - + f"max length allowed by the model ({max_length})." - ) - assert s <= max_length - if s < max_length: - pad = torch.zeros( - (int(b), int(max_length - s)), - dtype=bidirectional_mask.dtype, - device=bidirectional_mask.device, - ) - bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1) - bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1) - for attn_module in attn_modules: - attn_module.bias.data = torch.logical_or( - attn_module.bias.data, bidirectional - ) - output = call_og_forward() - for attn_module in attn_modules: - attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] - return output - - def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]): - """Wraps original generate to enable PrefixLM attention.""" - attn_modules = _get_attn_modules(model) - for attn_module in attn_modules: - attn_module.bias.data[:] = 1 - output = self._original_generate(*args, **kwargs) - for attn_module in attn_modules: - attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None] - return output - - setattr(model, "forward", MethodType(forward, model)) - setattr(model, "generate", MethodType(generate, model)) - setattr(model, "_prefix_lm_converted", True) - return model - - -def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM: - """Converts a BLOOM Causal LM to a Prefix LM. - - Supported HuggingFace model classes: - - `BloomForCausalLM` - - See `convert_hf_causal_lm_to_prefix_lm` for more details. - """ - if hasattr(model, "_prefix_lm_converted"): - return model - assert isinstance(model, BloomForCausalLM) - assert ( - model.config.add_cross_attention == False - ), "Only supports BLOOM decoder-only models" - - def _prepare_attn_mask( - self: BloomModel, - attention_mask: torch.Tensor, - bidirectional_mask: Optional[torch.Tensor], - input_shape: Tuple[int, int], - past_key_values_length: int, - ) -> torch.BoolTensor: - combined_attention_mask = None - device = attention_mask.device - (_, src_length) = input_shape - if src_length > 1: - combined_attention_mask = _make_causal_mask_bloom( - input_shape, - device=device, - past_key_values_length=past_key_values_length, - ) - if bidirectional_mask is not None: - assert attention_mask.shape == bidirectional_mask.shape - expanded_bidirectional_mask = _expand_mask_bloom( - bidirectional_mask, tgt_length=src_length - ) - combined_attention_mask = torch.logical_and( - combined_attention_mask, expanded_bidirectional_mask - ) - expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length) - combined_attention_mask = ( - expanded_attn_mask - if combined_attention_mask is None - else expanded_attn_mask | combined_attention_mask - ) - return combined_attention_mask - - def _build_alibi_tensor( - self: BloomModel, - batch_size: int, - query_length: int, - key_length: int, - dtype: torch.dtype, - device: torch.device, - ) -> torch.Tensor: - num_heads = self.config.n_head - closest_power_of_2 = 2 ** math.floor(math.log2(num_heads)) - base = torch.tensor( - 2 ** (-(2 ** (-(math.log2(closest_power_of_2) - 3)))), - device=device, - dtype=torch.float32, - ) - powers = torch.arange( - 1, 1 + closest_power_of_2, device=device, dtype=torch.int32 - ) - slopes = torch.pow(base, powers) - if closest_power_of_2 != num_heads: - extra_base = torch.tensor( - 2 ** (-(2 ** (-(math.log2(2 * closest_power_of_2) - 3)))), - device=device, - dtype=torch.float32, - ) - num_remaining_heads = min( - closest_power_of_2, num_heads - closest_power_of_2 - ) - extra_powers = torch.arange( - 1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32 - ) - slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0) - qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1) - ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1) - diffs = qa - ka + key_length - query_length - diffs = -diffs.abs() - alibi = slopes.view(1, num_heads, 1, 1) * diffs.view( - 1, 1, query_length, key_length - ) - alibi = alibi.expand(batch_size, -1, -1, -1).reshape( - -1, query_length, key_length - ) - return alibi.to(dtype) - - KeyValueT = Tuple[torch.Tensor, torch.Tensor] - - def forward( - self: BloomModel, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[KeyValueT, ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - bidirectional_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.LongTensor] = None, - inputs_embeds: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - **deprecated_arguments, - ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]: - if deprecated_arguments.pop("position_ids", False) is not False: - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. " - + "You can safely ignore passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - if input_ids is not None and inputs_embeds is not None: - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time" - ) - elif input_ids is not None: - (batch_size, seq_length) = input_ids.shape - elif inputs_embeds is not None: - (batch_size, seq_length, _) = inputs_embeds.shape - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - if past_key_values is None: - past_key_values = tuple([None] * len(self.h)) - head_mask = self.get_head_mask(head_mask, self.config.n_layer) - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - hidden_states = self.word_embeddings_layernorm(inputs_embeds) - presents = () if use_cache else None - all_self_attentions = () if output_attentions else None - all_hidden_states = () if output_hidden_states else None - seq_length_with_past = seq_length - past_key_values_length = 0 - if past_key_values[0] is not None: - tmp = past_key_values[0][0] - past_key_values_length = tmp.shape[2] - seq_length_with_past = seq_length_with_past + past_key_values_length - if attention_mask is None: - attention_mask = torch.ones( - (batch_size, seq_length_with_past), device=hidden_states.device - ) - else: - attention_mask = attention_mask.to(hidden_states.device) - alibi = self._build_alibi_tensor( - batch_size=batch_size, - query_length=seq_length, - key_length=seq_length_with_past, - dtype=hidden_states.dtype, - device=hidden_states.device, - ) - causal_mask = self._prepare_attn_mask( - attention_mask, - bidirectional_mask, - input_shape=(batch_size, seq_length), - past_key_values_length=past_key_values_length, - ) - for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): - if output_hidden_states: - hst = (hidden_states,) - all_hidden_states = all_hidden_states + hst - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module( - *inputs, - use_cache=use_cache, - output_attentions=output_attentions, - ) - - return custom_forward - - outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(block), - hidden_states, - alibi, - causal_mask, - head_mask[i], - ) - else: - outputs = block( - hidden_states, - layer_past=layer_past, - attention_mask=causal_mask, - head_mask=head_mask[i], - use_cache=use_cache, - output_attentions=output_attentions, - alibi=alibi, - ) - hidden_states = outputs[0] - if use_cache is True: - presents = presents + (outputs[1],) - if output_attentions: - oa = (outputs[2 if use_cache else 1],) - all_self_attentions = all_self_attentions + oa - hidden_states = self.ln_f(hidden_states) - if output_hidden_states: - hst = (hidden_states,) - all_hidden_states = all_hidden_states + hst - if not return_dict: - return tuple( - ( - v - for v in [ - hidden_states, - presents, - all_hidden_states, - all_self_attentions, - ] - if v is not None - ) - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - ) - - setattr( - model.transformer, - "_prepare_attn_mask", - MethodType(_prepare_attn_mask, model.transformer), - ) - setattr( - model.transformer, - "_build_alibi_tensor", - MethodType(_build_alibi_tensor, model.transformer), - ) - setattr(model.transformer, "forward", MethodType(forward, model.transformer)) - KeyValueT = Tuple[torch.Tensor, torch.Tensor] - - def forward( - self: BloomForCausalLM, - input_ids: Optional[torch.LongTensor] = None, - past_key_values: Optional[Tuple[KeyValueT, ...]] = None, - attention_mask: Optional[torch.Tensor] = None, - bidirectional_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - **deprecated_arguments, - ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: - """Replacement forward method for BloomCausalLM.""" - if deprecated_arguments.pop("position_ids", False) is not False: - warnings.warn( - "`position_ids` have no functionality in BLOOM and will be removed " - + "in v5.0.0. You can safely ignore passing `position_ids`.", - FutureWarning, - ) - if len(deprecated_arguments) > 0: - raise ValueError(f"Got unexpected arguments: {deprecated_arguments}") - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - transformer_outputs = self.transformer( - input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - bidirectional_mask=bidirectional_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - hidden_states = transformer_outputs[0] - lm_logits = self.lm_head(hidden_states) - loss = None - if labels is not None: - shift_logits = lm_logits[..., :-1, :].contiguous() - shift_labels = labels[..., 1:].contiguous() - (batch_size, seq_length, vocab_size) = shift_logits.shape - loss_fct = CrossEntropyLoss() - loss = loss_fct( - shift_logits.view(batch_size * seq_length, vocab_size), - shift_labels.view(batch_size * seq_length), - ) - if not return_dict: - output = (lm_logits,) + transformer_outputs[1:] - return (loss,) + output if loss is not None else output - return CausalLMOutputWithCrossAttentions( - loss=loss, - logits=lm_logits, - past_key_values=transformer_outputs.past_key_values, - hidden_states=transformer_outputs.hidden_states, - attentions=transformer_outputs.attentions, - ) - - def prepare_inputs_for_generation( - self: BloomForCausalLM, - input_ids: torch.LongTensor, - past: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - **kwargs, - ) -> dict: - if past: - input_ids = input_ids[:, -1].unsqueeze(-1) - bidirectional_mask = None - if past[0][0].shape[0] == input_ids.shape[0]: - past = self._convert_to_bloom_cache(past) - else: - bidirectional_mask = torch.ones_like(input_ids) - return { - "input_ids": input_ids, - "past_key_values": past, - "use_cache": True, - "attention_mask": attention_mask, - "bidirectional_mask": bidirectional_mask, - } - - setattr(model, "forward", MethodType(forward, model)) - setattr( - model, - "prepare_inputs_for_generation", - MethodType(prepare_inputs_for_generation, model), - ) - setattr(model, "_prefix_lm_converted", True) - return model - - -def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM: - """Converts an OPT Causal LM to a Prefix LM. - - Supported HuggingFace model classes: - - `OPTForCausalLM` - - See `convert_hf_causal_lm_to_prefix_lm` for more details. - """ - if hasattr(model, "_prefix_lm_converted"): - return model - assert isinstance(model, OPTForCausalLM) - assert ( - model.config.add_cross_attention == False - ), "Only supports OPT decoder-only models" - setattr(model, "_original_forward", getattr(model, "forward")) - setattr(model, "_original_generate", getattr(model, "generate")) - model.model.decoder.bidirectional_mask = None - - def _prepare_decoder_attention_mask( - self, attention_mask, input_shape, inputs_embeds, past_key_values_length - ): - combined_attention_mask = None - if input_shape[-1] > 1: - if self.bidirectional_mask == "g": - (bsz, src_length) = input_shape - combined_attention_mask = torch.zeros( - (bsz, 1, src_length, src_length + past_key_values_length), - dtype=inputs_embeds.dtype, - device=inputs_embeds.device, - ) - else: - combined_attention_mask = _make_causal_mask_opt( - input_shape, - inputs_embeds.dtype, - past_key_values_length=past_key_values_length, - ).to(inputs_embeds.device) - if self.bidirectional_mask is not None: - assert attention_mask.shape == self.bidirectional_mask.shape - expanded_bidirectional_mask = _expand_mask_opt( - self.bidirectional_mask, - inputs_embeds.dtype, - tgt_len=input_shape[-1], - ).to(inputs_embeds.device) - combined_attention_mask = torch.maximum( - expanded_bidirectional_mask, combined_attention_mask - ) - if attention_mask is not None: - expanded_attn_mask = _expand_mask_opt( - attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] - ).to(inputs_embeds.device) - combined_attention_mask = ( - expanded_attn_mask - if combined_attention_mask is None - else expanded_attn_mask + combined_attention_mask - ) - return combined_attention_mask - - setattr( - model.model.decoder, - "_prepare_decoder_attention_mask", - MethodType(_prepare_decoder_attention_mask, model.model.decoder), - ) - - def forward( - self: OPTForCausalLM, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.Tensor] = None, - bidirectional_mask: Optional[torch.ByteTensor] = None, - head_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ): - def call_og_forward(): - return self._original_forward( - input_ids=input_ids, - attention_mask=attention_mask, - head_mask=head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - labels=labels, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - if bidirectional_mask is None: - return call_og_forward() - self.model.decoder.bidirectional_mask = bidirectional_mask - try: - outputs = call_og_forward() - except: - self.model.decoder.bidirectional_mask = None - raise - self.model.decoder.bidirectional_mask = None - return outputs - - def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]): - """Wraps original generate to enable PrefixLM-style attention.""" - self.model.decoder.bidirectional_mask = "g" - try: - output = self._original_generate(*args, **kwargs) - except: - self.model.decoder.bidirectional_mask = None - raise - self.model.decoder.bidirectional_mask = None - return output - - setattr(model, "forward", MethodType(forward, model)) - setattr(model, "generate", MethodType(generate, model)) - setattr(model, "_prefix_lm_converted", True) - return model - - -_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM) -CAUSAL_LM_TYPES = Union[ - GPT2LMHeadModel, - GPTJForCausalLM, - GPTNeoForCausalLM, - GPTNeoXForCausalLM, - BloomForCausalLM, - OPTForCausalLM, -] - - -def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES: - """Converts a HuggingFace Causal LM to a Prefix LM. - - Supported HuggingFace model classes: - - `GPT2LMHeadModel` - - `GPTNeoForCausalLM` - - `GPTNeoXForCausalLM` - - `GPTJForCausalLM` - - `BloomForCausalLM` - - `OPTForCausalLM` - - Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the - `generate` method and/or select underlying methods depending on the model class. - - These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask". - - Notes on training: - To actually train the converted model as a Prefix LM, training batches will need to indicate - the prefix/target structure by including `bidirectional_mask` as part of the batch inputs. - - **This is not a standard input and requires custom layers either within or after your dataloader.** - - In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels` - such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`. - That is, the prefix portion of the sequence should not generate any loss. Loss should only be - generated by the target portion of the sequence. - - Notes on `GPTNeoForCausalLM`: - To simplify the implementation, "global" and "local" attention layers are handled differently. - For "global" layers, we handle conversion as described above. For "local" layers, which use a - causal attention mask within a restricted local window, we do not alter the masking. - - Notes on `forward` method conversion: - After conversion, the `forward` method will handle a new input, `bidirectional_mask`, - which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions - belonging to the prefix (prefix tokens can attend to one another bidirectionally), and - 0 indicates token positions belonging to the target. - - The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing - causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset - the causal masks before returning the result. - - Notes on `generate` method conversion: - After conversion, the `generate` method will have the same signature but will internally - convert all causal masks to be purely bidirectional, call the original `generate` method, and - (where appropriate) reset the causal masks before returning the result. - - This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token - "prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates - each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one - another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and - previously-generated tokens (also as expected in a Prefix LM). - - To preserve the API, the original methods are renamed to `_original_forward` and - `_original_generate`, and replaced with new `forward` and `generate` methods that wrap - them, respectively. Although implementation details vary by model class. - """ - if isinstance(model, _SUPPORTED_GPT_MODELS): - return _convert_gpt_causal_lm_to_prefix_lm(model) - elif isinstance(model, BloomForCausalLM): - return _convert_bloom_causal_lm_to_prefix_lm(model) - elif isinstance(model, OPTForCausalLM): - return _convert_opt_causal_lm_to_prefix_lm(model) - else: - raise TypeError( - f"Cannot convert model to Prefix LM. " - + f"Model does not belong to set of supported HF models:" - + f"\n{_SUPPORTED_HF_MODELS}" - ) - - -def add_bidirectional_mask_if_missing(batch: Dict[str, Any]): - """Attempts to add bidirectional_mask to batch if missing. - - Raises: - KeyError if bidirectional_mask is missing and can't be inferred - """ - if "bidirectional_mask" not in batch: - if batch.get("mode", None) == "icl_task": - batch["bidirectional_mask"] = batch["attention_mask"].clone() - for i, continuation_indices in enumerate(batch["continuation_indices"]): - batch["bidirectional_mask"][i, continuation_indices] = 0 - elif "labels" in batch and "attention_mask" in batch: - batch["bidirectional_mask"] = torch.logical_and( - torch.eq(batch["attention_mask"], 1), torch.eq(batch["labels"], -100) - ).type_as(batch["attention_mask"]) - else: - raise KeyError( - "No bidirectional_mask in batch and not sure how to construct one." - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/meta_init_context.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/meta_init_context.py deleted file mode 100755 index 208ab255c..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/meta_init_context.py +++ /dev/null @@ -1,111 +0,0 @@ -from contextlib import contextmanager - -import torch -import torch.nn as nn - - -@contextmanager -def init_empty_weights(include_buffers: bool = False): - """Meta initialization context manager. - - A context manager under which models are initialized with all parameters - on the meta device, therefore creating an empty model. Useful when just - initializing the model would blow the available RAM. - - Args: - include_buffers (`bool`, *optional*, defaults to `False`): Whether or - not to also put all buffers on the meta device while initializing. - - Example: - ```python - import torch.nn as nn - - # Initialize a model with 100 billions parameters in no time and without using any RAM. - with init_empty_weights(): - tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)]) - ``` - - - - Any model created under this context manager has no weights. As such you can't do something like - `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`]. - - - """ - with init_on_device(torch.device("meta"), include_buffers=include_buffers) as f: - yield f - - -@contextmanager -def init_on_device(device: torch.device, include_buffers: bool = False): - """Device initialization context manager. - - A context manager under which models are initialized with all parameters - on the specified device. - - Args: - device (`torch.device`): Device to initialize all parameters on. - include_buffers (`bool`, *optional*, defaults to `False`): Whether or - not to also put all buffers on the meta device while initializing. - - Example: - ```python - import torch.nn as nn - - with init_on_device(device=torch.device("cuda")): - tst = nn.Liner(100, 100) # on `cuda` device - ``` - """ - old_register_parameter = nn.Module.register_parameter - if include_buffers: - old_register_buffer = nn.Module.register_buffer - - def register_empty_parameter(module, name, param): - old_register_parameter(module, name, param) - if param is not None: - param_cls = type(module._parameters[name]) - kwargs = module._parameters[name].__dict__ - module._parameters[name] = param_cls( - module._parameters[name].to(device), **kwargs - ) - - def register_empty_buffer(module, name, buffer): - old_register_buffer(module, name, buffer) - if buffer is not None: - module._buffers[name] = module._buffers[name].to(device) - - if include_buffers: - tensor_constructors_to_patch = { - torch_function_name: getattr(torch, torch_function_name) - for torch_function_name in ["empty", "zeros", "ones", "full"] - } - else: - tensor_constructors_to_patch = {} - - def patch_tensor_constructor(fn): - def wrapper(*args, **kwargs): - kwargs["device"] = device - return fn(*args, **kwargs) - - return wrapper - - try: - nn.Module.register_parameter = register_empty_parameter - if include_buffers: - nn.Module.register_buffer = register_empty_buffer - for torch_function_name in tensor_constructors_to_patch.keys(): - setattr( - torch, - torch_function_name, - patch_tensor_constructor(getattr(torch, torch_function_name)), - ) - yield - finally: - nn.Module.register_parameter = old_register_parameter - if include_buffers: - nn.Module.register_buffer = old_register_buffer - for ( - torch_function_name, - old_torch_function, - ) in tensor_constructors_to_patch.items(): - setattr(torch, torch_function_name, old_torch_function) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/modeling_mpt.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/modeling_mpt.py deleted file mode 100755 index 98ae82229..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/modeling_mpt.py +++ /dev/null @@ -1,538 +0,0 @@ -"""A simple, flexible implementation of a GPT model. - -Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py -""" -import math -import warnings -from typing import List, Optional, Tuple, Union - -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers import (PreTrainedModel, PreTrainedTokenizer, - PreTrainedTokenizerFast) -from transformers.modeling_outputs import (BaseModelOutputWithPast, - CausalLMOutputWithPast) - -from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising -from .attention import attn_bias_shape, build_attn_bias -from .blocks import MPTBlock -from .configuration_mpt import MPTConfig -from .custom_embedding import SharedEmbedding -from .hf_prefixlm_converter import (add_bidirectional_mask_if_missing, - convert_hf_causal_lm_to_prefix_lm) -from .meta_init_context import init_empty_weights -from .norm import NORM_CLASS_REGISTRY -from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_ - -try: - from .flash_attn_triton import flash_attn_func -except: - pass -Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast] - - -class MPTPreTrainedModel(PreTrainedModel): - config_class = MPTConfig - base_model_prefix = "model" - _no_split_modules = ["MPTBlock"] - - -class MPTModel(MPTPreTrainedModel): - def __init__(self, config: MPTConfig): - config._validate_config() - super().__init__(config) - self.attn_impl = config.attn_config["attn_impl"] - self.prefix_lm = config.attn_config["prefix_lm"] - self.attn_uses_sequence_id = config.attn_config["attn_uses_sequence_id"] - self.alibi = config.attn_config["alibi"] - self.alibi_bias_max = config.attn_config["alibi_bias_max"] - if config.init_device == "mixed": - if dist.get_local_rank() == 0: - config.init_device = "cpu" - else: - config.init_device = "meta" - if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys(): - norm_options = " | ".join(NORM_CLASS_REGISTRY.keys()) - raise NotImplementedError( - f"Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options})." - ) - norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()] - self.embedding_fraction = config.embedding_fraction - self.wte = SharedEmbedding( - config.vocab_size, config.d_model, device=config.init_device - ) - if not self.alibi: - self.wpe = torch.nn.Embedding( - config.max_seq_len, config.d_model, device=config.init_device - ) - self.emb_drop = nn.Dropout(config.emb_pdrop) - self.blocks = nn.ModuleList( - [ - MPTBlock(device=config.init_device, **config.to_dict()) - for _ in range(config.n_layers) - ] - ) - self.norm_f = norm_class(config.d_model, device=config.init_device) - if config.init_device != "meta": - print( - f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.' - ) - self.apply(self.param_init_fn) - self.is_causal = not self.prefix_lm - self._attn_bias_initialized = False - self.attn_bias = None - self.attn_bias_shape = attn_bias_shape( - self.attn_impl, - config.n_heads, - config.max_seq_len, - self.alibi, - prefix_lm=self.prefix_lm, - causal=self.is_causal, - use_sequence_id=self.attn_uses_sequence_id, - ) - if config.no_bias: - for module in self.modules(): - if hasattr(module, "bias") and isinstance(module.bias, nn.Parameter): - if config.verbose: - warnings.warn(f"Removing bias ({module.bias}) from {module}.") - module.register_parameter("bias", None) - if config.verbose and config.verbose > 2: - print(self) - if "verbose" not in self.config.init_config: - self.config.init_config["verbose"] = self.config.verbose - if self.config.init_config["verbose"] > 1: - init_fn_name = self.config.init_config["name"] - warnings.warn(f"Using {init_fn_name} initialization.") - self.gradient_checkpointing = False - - def get_input_embeddings(self): - return self.wte - - def set_input_embeddings(self, value): - self.wte = value - - @torch.no_grad() - def _attn_bias( - self, - device, - dtype, - attention_mask: Optional[torch.ByteTensor] = None, - prefix_mask: Optional[torch.ByteTensor] = None, - sequence_id: Optional[torch.LongTensor] = None, - ): - if not self._attn_bias_initialized: - if self.attn_bias_shape: - self.attn_bias = torch.zeros( - self.attn_bias_shape, device=device, dtype=dtype - ) - self.attn_bias = build_attn_bias( - self.attn_impl, - self.attn_bias, - self.config.n_heads, - self.config.max_seq_len, - causal=self.is_causal, - alibi=self.alibi, - alibi_bias_max=self.alibi_bias_max, - ) - self._attn_bias_initialized = True - if self.attn_impl == "flash": - return (self.attn_bias, attention_mask) - if self.attn_bias is not None: - self.attn_bias = self.attn_bias.to(dtype=dtype, device=device) - attn_bias = self.attn_bias - if self.prefix_lm: - assert isinstance(attn_bias, torch.Tensor) - assert isinstance(prefix_mask, torch.Tensor) - attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask) - if self.attn_uses_sequence_id and sequence_id is not None: - assert isinstance(attn_bias, torch.Tensor) - attn_bias = self._apply_sequence_id(attn_bias, sequence_id) - if attention_mask is not None: - s_k = attention_mask.shape[-1] - if attn_bias is None: - attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype) - else: - _s_k = max(0, attn_bias.size(-1) - s_k) - attn_bias = attn_bias[:, :, :, _s_k:] - if prefix_mask is not None and attention_mask.shape != prefix_mask.shape: - raise ValueError( - f"attention_mask shape={attention_mask.shape} " - + f"and prefix_mask shape={prefix_mask.shape} are not equal." - ) - min_val = torch.finfo(attn_bias.dtype).min - attn_bias = attn_bias.masked_fill( - ~attention_mask.view(-1, 1, 1, s_k), min_val - ) - return (attn_bias, None) - - def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor): - (s_k, s_q) = attn_bias.shape[-2:] - if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len: - raise ValueError( - "attn_bias does not match the expected shape. " - + f"The last two dimensions should both be {self.config.max_length} " - + f"but are {s_k} and {s_q}." - ) - seq_len = prefix_mask.shape[-1] - if seq_len > self.config.max_seq_len: - raise ValueError( - f"prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}" - ) - attn_bias = attn_bias[..., :seq_len, :seq_len] - causal = torch.tril( - torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device) - ).view(1, 1, seq_len, seq_len) - prefix = prefix_mask.view(-1, 1, 1, seq_len) - cannot_attend = ~torch.logical_or(causal, prefix.bool()) - min_val = torch.finfo(attn_bias.dtype).min - attn_bias = attn_bias.masked_fill(cannot_attend, min_val) - return attn_bias - - def _apply_sequence_id( - self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor - ): - seq_len = sequence_id.shape[-1] - if seq_len > self.config.max_seq_len: - raise ValueError( - f"sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}" - ) - attn_bias = attn_bias[..., :seq_len, :seq_len] - cannot_attend = torch.logical_not( - torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len)) - ).unsqueeze(1) - min_val = torch.finfo(attn_bias.dtype).min - attn_bias = attn_bias.masked_fill(cannot_attend, min_val) - return attn_bias - - def forward( - self, - input_ids: torch.LongTensor, - past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None, - attention_mask: Optional[torch.ByteTensor] = None, - prefix_mask: Optional[torch.ByteTensor] = None, - sequence_id: Optional[torch.LongTensor] = None, - return_dict: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - use_cache: Optional[bool] = None, - inputs_embeds: Optional[torch.Tensor] = None, - ): - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - if attention_mask is not None: - attention_mask = attention_mask.bool() - if prefix_mask is not None: - prefix_mask = prefix_mask.bool() - if not return_dict: - raise NotImplementedError( - "return_dict False is not implemented yet for MPT" - ) - if output_attentions: - if self.attn_impl != "torch": - raise NotImplementedError( - "output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`." - ) - if ( - attention_mask is not None - and attention_mask[:, 0].sum() != attention_mask.shape[0] - and self.training - ): - raise NotImplementedError( - "MPT does not support training with left padding." - ) - if self.prefix_lm and prefix_mask is None: - raise ValueError( - "prefix_mask is a required argument when MPT is configured with prefix_lm=True." - ) - if self.training: - if self.attn_uses_sequence_id and sequence_id is None: - raise ValueError( - "sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True " - + "and the model is in train mode." - ) - elif self.attn_uses_sequence_id is False and sequence_id is not None: - warnings.warn( - "MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. " - + "This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True." - ) - if input_ids is not None: - S = input_ids.size(1) - assert ( - S <= self.config.max_seq_len - ), f"Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}" - tok_emb = self.wte(input_ids) - else: - assert inputs_embeds is not None - assert ( - self.alibi - ), "inputs_embeds is not implemented for MPT unless for alibi." - S = inputs_embeds.size(1) - tok_emb = inputs_embeds - if self.alibi: - x = tok_emb - else: - past_position = 0 - if past_key_values is not None: - if len(past_key_values) != self.config.n_layers: - raise ValueError( - f"past_key_values must provide a past_key_value for each attention " - + f"layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r})." - ) - past_position = past_key_values[0][0].size(1) - if self.attn_impl == "torch": - past_position = past_key_values[0][0].size(3) - if S + past_position > self.config.max_seq_len: - raise ValueError( - f"Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}." - ) - pos = torch.arange( - past_position, - S + past_position, - dtype=torch.long, - device=input_ids.device, - ).unsqueeze(0) - if attention_mask is not None: - pos = torch.clamp( - pos - - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[ - :, past_position: - ], - min=0, - ) - pos_emb = self.wpe(pos) - x = tok_emb + pos_emb - if self.embedding_fraction == 1: - x = self.emb_drop(x) - else: - x_shrunk = x * self.embedding_fraction + x.detach() * ( - 1 - self.embedding_fraction - ) - assert isinstance(self.emb_drop, nn.Module) - x = self.emb_drop(x_shrunk) - (attn_bias, attention_mask) = self._attn_bias( - device=x.device, - dtype=torch.float32, - attention_mask=attention_mask, - prefix_mask=prefix_mask, - sequence_id=sequence_id, - ) - if use_cache and past_key_values is None: - past_key_values = [() for _ in range(self.config.n_layers)] - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - for b_idx, block in enumerate(self.blocks): - if output_hidden_states: - assert all_hidden_states is not None - all_hidden_states = all_hidden_states + (x,) - past_key_value = ( - past_key_values[b_idx] if past_key_values is not None else None - ) - if self.gradient_checkpointing and self.training: - (x, attn_weights, past_key_value) = torch.utils.checkpoint.checkpoint( - block, x, past_key_value, attn_bias, attention_mask, self.is_causal - ) - else: - (x, attn_weights, past_key_value) = block( - x, - past_key_value=past_key_value, - attn_bias=attn_bias, - attention_mask=attention_mask, - is_causal=self.is_causal, - ) - if past_key_values is not None: - past_key_values[b_idx] = past_key_value - if output_attentions: - assert all_self_attns is not None - all_self_attns = all_self_attns + (attn_weights,) - x = self.norm_f(x) - if output_hidden_states: - assert all_hidden_states is not None - all_hidden_states = all_hidden_states + (x,) - return BaseModelOutputWithPast( - last_hidden_state=x, - past_key_values=past_key_values, - hidden_states=all_hidden_states, - attentions=all_self_attns, - ) - - def param_init_fn(self, module): - init_fn_name = self.config.init_config["name"] - MODEL_INIT_REGISTRY[init_fn_name]( - module=module, - n_layers=self.config.n_layers, - d_model=self.config.d_model, - **self.config.init_config, - ) - - def fsdp_wrap_fn(self, module): - return isinstance(module, MPTBlock) - - def activation_checkpointing_fn(self, module): - return isinstance(module, MPTBlock) - - -class MPTForCausalLM(MPTPreTrainedModel): - def __init__(self, config: MPTConfig): - super().__init__(config) - if not config.tie_word_embeddings: - raise ValueError("MPTForCausalLM only supports tied word embeddings") - print(f"Instantiating an MPTForCausalLM model from {__file__}") - self.transformer = MPTModel(config) - for child in self.transformer.children(): - if isinstance(child, torch.nn.ModuleList): - continue - if isinstance(child, torch.nn.Module): - child._fsdp_wrap = True - self.logit_scale = None - if config.logit_scale is not None: - logit_scale = config.logit_scale - if isinstance(logit_scale, str): - if logit_scale == "inv_sqrt_d_model": - logit_scale = 1 / math.sqrt(config.d_model) - else: - raise ValueError( - f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'." - ) - self.logit_scale = logit_scale - - def get_input_embeddings(self): - return self.transformer.wte - - def set_input_embeddings(self, value): - self.transformer.wte = value - - def get_output_embeddings(self): - return self.transformer.wte - - def set_output_embeddings(self, new_embeddings): - self.transformer.wte = new_embeddings - - def set_decoder(self, decoder): - self.transformer = decoder - - def get_decoder(self): - return self.transformer - - def forward( - self, - input_ids: torch.LongTensor, - past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None, - attention_mask: Optional[torch.ByteTensor] = None, - prefix_mask: Optional[torch.ByteTensor] = None, - sequence_id: Optional[torch.LongTensor] = None, - labels: Optional[torch.LongTensor] = None, - return_dict: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - use_cache: Optional[bool] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - ): - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - if inputs_embeds is not None: - raise NotImplementedError( - "inputs_embeds has to be None (for hf/peft support)." - ) - outputs = self.transformer( - input_ids=input_ids, - past_key_values=past_key_values, - attention_mask=attention_mask, - prefix_mask=prefix_mask, - sequence_id=sequence_id, - return_dict=return_dict, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - use_cache=use_cache, - ) - logits = self.transformer.wte( - outputs.last_hidden_state.to(self.transformer.wte.weight.device), True - ) - if self.logit_scale is not None: - if self.logit_scale == 0: - warnings.warn( - f"Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs." - ) - logits *= self.logit_scale - loss = None - if labels is not None: - labels = torch.roll(labels, shifts=-1) - labels[:, -1] = -100 - loss = F.cross_entropy( - logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1) - ) - return CausalLMOutputWithPast( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - def param_init_fn(self, module): - init_fn_name = self.config.init_config["name"] - MODEL_INIT_REGISTRY[init_fn_name]( - module=module, - n_layers=self.config.n_layers, - d_model=self.config.d_model, - **self.config.init_config, - ) - - def fsdp_wrap_fn(self, module): - return isinstance(module, MPTBlock) - - def activation_checkpointing_fn(self, module): - return isinstance(module, MPTBlock) - - def prepare_inputs_for_generation( - self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs - ): - if inputs_embeds is not None: - raise NotImplementedError("inputs_embeds is not implemented for MPT yet") - attention_mask = kwargs["attention_mask"].bool() - if attention_mask[:, -1].sum() != attention_mask.shape[0]: - raise NotImplementedError( - "MPT does not support generation with right padding." - ) - if self.transformer.attn_uses_sequence_id and self.training: - sequence_id = torch.zeros_like(input_ids[:1]) - else: - sequence_id = None - if past_key_values is not None: - input_ids = input_ids[:, -1].unsqueeze(-1) - if self.transformer.prefix_lm: - prefix_mask = torch.ones_like(attention_mask) - if kwargs.get("use_cache") == False: - raise NotImplementedError( - "MPT with prefix_lm=True does not support use_cache=False." - ) - else: - prefix_mask = None - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "prefix_mask": prefix_mask, - "sequence_id": sequence_id, - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache", True), - } - - @staticmethod - def _reorder_cache(past_key_values, beam_idx): - """Used by HuggingFace generate when using beam search with kv-caching. - - See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133 - for an example in transformers. - """ - reordered_past = [] - for layer_past in past_key_values: - reordered_past += [ - tuple( - (past_state.index_select(0, beam_idx) for past_state in layer_past) - ) - ] - return reordered_past diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/norm.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/norm.py deleted file mode 100755 index 85291eadb..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/norm.py +++ /dev/null @@ -1,106 +0,0 @@ -import torch - - -def _cast_if_autocast_enabled(tensor): - if torch.is_autocast_enabled(): - if tensor.device.type == "cuda": - dtype = torch.get_autocast_gpu_dtype() - elif tensor.device.type == "cpu": - dtype = torch.get_autocast_cpu_dtype() - else: - raise NotImplementedError() - return tensor.to(dtype=dtype) - return tensor - - -class LPLayerNorm(torch.nn.LayerNorm): - def __init__( - self, - normalized_shape, - eps=1e-05, - elementwise_affine=True, - device=None, - dtype=None, - ): - super().__init__( - normalized_shape=normalized_shape, - eps=eps, - elementwise_affine=elementwise_affine, - device=device, - dtype=dtype, - ) - - def forward(self, x): - module_device = x.device - downcast_x = _cast_if_autocast_enabled(x) - downcast_weight = ( - _cast_if_autocast_enabled(self.weight) - if self.weight is not None - else self.weight - ) - downcast_bias = ( - _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias - ) - with torch.autocast(enabled=False, device_type=module_device.type): - return torch.nn.functional.layer_norm( - downcast_x, - self.normalized_shape, - downcast_weight, - downcast_bias, - self.eps, - ) - - -def rms_norm(x, weight=None, eps=1e-05): - output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps) - if weight is not None: - return output * weight - return output - - -class RMSNorm(torch.nn.Module): - def __init__( - self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None - ): - super().__init__() - self.eps = eps - if weight: - self.weight = torch.nn.Parameter( - torch.ones(normalized_shape, dtype=dtype, device=device) - ) - else: - self.register_parameter("weight", None) - - def forward(self, x): - return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype) - - -class LPRMSNorm(RMSNorm): - def __init__( - self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None - ): - super().__init__( - normalized_shape=normalized_shape, - eps=eps, - weight=weight, - dtype=dtype, - device=device, - ) - - def forward(self, x): - downcast_x = _cast_if_autocast_enabled(x) - downcast_weight = ( - _cast_if_autocast_enabled(self.weight) - if self.weight is not None - else self.weight - ) - with torch.autocast(enabled=False, device_type=x.device.type): - return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype) - - -NORM_CLASS_REGISTRY = { - "layernorm": torch.nn.LayerNorm, - "low_precision_layernorm": LPLayerNorm, - "rmsnorm": RMSNorm, - "low_precision_rmsnorm": LPRMSNorm, -} diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/param_init_fns.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/param_init_fns.py deleted file mode 100755 index 5c1d17a22..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/language_model/mpt/param_init_fns.py +++ /dev/null @@ -1,419 +0,0 @@ -import math -import warnings -from collections.abc import Sequence -from functools import partial -from typing import Optional, Tuple, Union - -import torch -from torch import nn - -from .norm import NORM_CLASS_REGISTRY - - -def torch_default_param_init_fn_(module: nn.Module, verbose: int = 0, **kwargs): - del kwargs - if verbose > 1: - warnings.warn(f"Initializing network using module's reset_parameters attribute") - if hasattr(module, "reset_parameters"): - module.reset_parameters() - - -def fused_init_helper_(module: nn.Module, init_fn_): - _fused = getattr(module, "_fused", None) - if _fused is None: - raise RuntimeError(f"Internal logic error") - (dim, splits) = _fused - splits = (0, *splits, module.weight.size(dim)) - for s, e in zip(splits[:-1], splits[1:]): - slice_indices = [slice(None)] * module.weight.ndim - slice_indices[dim] = slice(s, e) - init_fn_(module.weight[slice_indices]) - - -def generic_param_init_fn_( - module: nn.Module, - init_fn_, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - verbose: int = 0, - **kwargs, -): - del kwargs - if verbose > 1: - warnings.warn(f"If model has bias parameters they are initialized to 0.") - init_div_is_residual = init_div_is_residual - if init_div_is_residual is False: - div_is_residual = 1.0 - elif init_div_is_residual is True: - div_is_residual = math.sqrt(2 * n_layers) - elif isinstance(init_div_is_residual, float) or isinstance( - init_div_is_residual, int - ): - div_is_residual = init_div_is_residual - elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric(): - div_is_residual = float(init_div_is_residual) - else: - div_is_residual = 1.0 - raise ValueError( - f"Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}" - ) - if init_div_is_residual is not False: - if verbose > 1: - warnings.warn( - f"Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. " - + f"Set `init_div_is_residual: false` in init config to disable this." - ) - if isinstance(module, nn.Linear): - if hasattr(module, "_fused"): - fused_init_helper_(module, init_fn_) - else: - init_fn_(module.weight) - if module.bias is not None: - torch.nn.init.zeros_(module.bias) - if init_div_is_residual is not False and getattr(module, "_is_residual", False): - with torch.no_grad(): - module.weight.div_(div_is_residual) - elif isinstance(module, nn.Embedding): - if emb_init_std is not None: - std = emb_init_std - if std == 0: - warnings.warn(f"Embedding layer initialized to 0.") - emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std) - if verbose > 1: - warnings.warn( - f"Embedding layer initialized using normal distribution with mean=0 and std={std!r}." - ) - elif emb_init_uniform_lim is not None: - lim = emb_init_uniform_lim - if isinstance(lim, Sequence): - if len(lim) > 2: - raise ValueError( - f"Uniform init requires a min and a max limit. User input: {lim}." - ) - if lim[0] == lim[1]: - warnings.warn(f"Embedding layer initialized to {lim[0]}.") - else: - if lim == 0: - warnings.warn(f"Embedding layer initialized to 0.") - lim = [-lim, lim] - (a, b) = lim - emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b) - if verbose > 1: - warnings.warn( - f"Embedding layer initialized using uniform distribution in range {lim}." - ) - else: - emb_init_fn_ = init_fn_ - emb_init_fn_(module.weight) - elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))): - if verbose > 1: - warnings.warn( - f"Norm weights are set to 1. If norm layer has a bias it is initialized to 0." - ) - if hasattr(module, "weight") and module.weight is not None: - torch.nn.init.ones_(module.weight) - if hasattr(module, "bias") and module.bias is not None: - torch.nn.init.zeros_(module.bias) - elif isinstance(module, nn.MultiheadAttention): - if module._qkv_same_embed_dim: - assert module.in_proj_weight is not None - assert ( - module.q_proj_weight is None - and module.k_proj_weight is None - and (module.v_proj_weight is None) - ) - assert d_model is not None - _d = d_model - splits = (0, _d, 2 * _d, 3 * _d) - for s, e in zip(splits[:-1], splits[1:]): - init_fn_(module.in_proj_weight[s:e]) - else: - assert ( - module.q_proj_weight is not None - and module.k_proj_weight is not None - and (module.v_proj_weight is not None) - ) - assert module.in_proj_weight is None - init_fn_(module.q_proj_weight) - init_fn_(module.k_proj_weight) - init_fn_(module.v_proj_weight) - if module.in_proj_bias is not None: - torch.nn.init.zeros_(module.in_proj_bias) - if module.bias_k is not None: - torch.nn.init.zeros_(module.bias_k) - if module.bias_v is not None: - torch.nn.init.zeros_(module.bias_v) - init_fn_(module.out_proj.weight) - if init_div_is_residual is not False and getattr( - module.out_proj, "_is_residual", False - ): - with torch.no_grad(): - module.out_proj.weight.div_(div_is_residual) - if module.out_proj.bias is not None: - torch.nn.init.zeros_(module.out_proj.bias) - else: - for _ in module.parameters(recurse=False): - raise NotImplementedError( - f"{module.__class__.__name__} parameters are not initialized by param_init_fn." - ) - - -def _normal_init_(std, mean=0.0): - return partial(torch.nn.init.normal_, mean=mean, std=std) - - -def _normal_param_init_fn_( - module: nn.Module, - std: float, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - verbose: int = 0, - **kwargs, -): - del kwargs - init_fn_ = _normal_init_(std=std) - if verbose > 1: - warnings.warn(f"Using torch.nn.init.normal_ init fn mean=0.0, std={std}") - generic_param_init_fn_( - module=module, - init_fn_=init_fn_, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def baseline_param_init_fn_( - module: nn.Module, - init_std: float, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - verbose: int = 0, - **kwargs, -): - del kwargs - if init_std is None: - raise ValueError( - "You must set model.init_config['init_std'] to a float value to use the default initialization scheme." - ) - _normal_param_init_fn_( - module=module, - std=init_std, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def small_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: int, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - verbose: int = 0, - **kwargs, -): - del kwargs - std = math.sqrt(2 / (5 * d_model)) - _normal_param_init_fn_( - module=module, - std=std, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def neox_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: int, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - verbose: int = 0, - **kwargs, -): - """From section 2.3.1 of GPT-NeoX-20B: - - An Open-Source AutoregressiveLanguage Model โ€” Black et. al. (2022) - see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151 - and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py - """ - del kwargs - residual_div = n_layers / math.sqrt(10) - if verbose > 1: - warnings.warn(f"setting init_div_is_residual to {residual_div}") - small_param_init_fn_( - module=module, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=residual_div, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def kaiming_uniform_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - init_gain: float = 0, - fan_mode: str = "fan_in", - init_nonlinearity: str = "leaky_relu", - verbose: int = 0, - **kwargs, -): - del kwargs - if verbose > 1: - warnings.warn( - f"Using nn.init.kaiming_uniform_ init fn with parameters: " - + f"a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}" - ) - kaiming_uniform_ = partial( - nn.init.kaiming_uniform_, - a=init_gain, - mode=fan_mode, - nonlinearity=init_nonlinearity, - ) - generic_param_init_fn_( - module=module, - init_fn_=kaiming_uniform_, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def kaiming_normal_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - init_gain: float = 0, - fan_mode: str = "fan_in", - init_nonlinearity: str = "leaky_relu", - verbose: int = 0, - **kwargs, -): - del kwargs - if verbose > 1: - warnings.warn( - f"Using nn.init.kaiming_normal_ init fn with parameters: " - + f"a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}" - ) - kaiming_normal_ = partial( - torch.nn.init.kaiming_normal_, - a=init_gain, - mode=fan_mode, - nonlinearity=init_nonlinearity, - ) - generic_param_init_fn_( - module=module, - init_fn_=kaiming_normal_, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def xavier_uniform_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - init_gain: float = 0, - verbose: int = 0, - **kwargs, -): - del kwargs - xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain) - if verbose > 1: - warnings.warn( - f"Using torch.nn.init.xavier_uniform_ init fn with parameters: " - + f"gain={init_gain}" - ) - generic_param_init_fn_( - module=module, - init_fn_=xavier_uniform_, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -def xavier_normal_param_init_fn_( - module: nn.Module, - n_layers: int, - d_model: Optional[int] = None, - init_div_is_residual: Union[int, float, str, bool] = True, - emb_init_std: Optional[float] = None, - emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]] = None, - init_gain: float = 0, - verbose: int = 0, - **kwargs, -): - xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain) - if verbose > 1: - warnings.warn( - f"Using torch.nn.init.xavier_normal_ init fn with parameters: " - + f"gain={init_gain}" - ) - generic_param_init_fn_( - module=module, - init_fn_=xavier_normal_, - d_model=d_model, - n_layers=n_layers, - init_div_is_residual=init_div_is_residual, - emb_init_std=emb_init_std, - emb_init_uniform_lim=emb_init_uniform_lim, - verbose=verbose, - ) - - -MODEL_INIT_REGISTRY = { - "default_": torch_default_param_init_fn_, - "baseline_": baseline_param_init_fn_, - "kaiming_uniform_": kaiming_uniform_param_init_fn_, - "kaiming_normal_": kaiming_normal_param_init_fn_, - "neox_init_": neox_param_init_fn_, - "small_init_": small_param_init_fn_, - "xavier_uniform_": xavier_uniform_param_init_fn_, - "xavier_normal_": xavier_normal_param_init_fn_, -} diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/llava_arch.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/llava_arch.py deleted file mode 100755 index 049ea7e65..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/llava_arch.py +++ /dev/null @@ -1,398 +0,0 @@ -# Copyright 2023 Haotian Liu -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from abc import ABC, abstractmethod - -import torch -import torch.nn as nn - -# from llava.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN -from utils.utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_PATCH_TOKEN, IGNORE_INDEX, - IMAGE_TOKEN_INDEX) - -from .multimodal_encoder.builder import build_vision_tower - - -class LlavaMetaModel: - def __init__(self, config): - super(LlavaMetaModel, self).__init__(config) - - if hasattr(config, "mm_vision_tower"): - self.vision_tower = build_vision_tower(config, delay_load=True) - self.mm_projector = nn.Linear(config.mm_hidden_size, config.hidden_size) - - def get_vision_tower(self): - vision_tower = getattr(self, "vision_tower", None) - if type(vision_tower) is list: - vision_tower = vision_tower[0] - return vision_tower - - def initialize_vision_modules(self, model_args, fsdp=None): - vision_tower = model_args.vision_tower - mm_vision_select_layer = model_args.mm_vision_select_layer - mm_vision_select_feature = model_args.mm_vision_select_feature - pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter - - self.config.mm_vision_tower = vision_tower - - vision_tower = build_vision_tower(model_args) - - if fsdp is not None and len(fsdp) > 0: - self.vision_tower = [vision_tower] - else: - self.vision_tower = vision_tower - - self.config.use_mm_proj = True - self.config.mm_hidden_size = vision_tower.hidden_size - self.config.mm_vision_select_layer = mm_vision_select_layer - self.config.mm_vision_select_feature = mm_vision_select_feature - - if not hasattr(self, "mm_projector"): - self.mm_projector = nn.Linear( - self.config.mm_hidden_size, self.config.hidden_size - ) - - if pretrain_mm_mlp_adapter is not None: - mm_projector_weights = torch.load( - pretrain_mm_mlp_adapter, map_location="cpu" - ) - - def get_w(weights, keyword): - return { - k.split(keyword + ".")[1]: v - for k, v in weights.items() - if keyword in k - } - - self.mm_projector.load_state_dict( - get_w(mm_projector_weights, "mm_projector") - ) - - -class LlavaMetaForCausalLM(ABC): - @abstractmethod - def get_model(self): - pass - - def get_vision_tower(self): - return self.get_model().get_vision_tower() - - def encode_images(self, images): - image_features = self.get_model().get_vision_tower()(images) - image_features = self.get_model().mm_projector(image_features) - return image_features - - def prepare_inputs_labels_for_multimodal( - self, input_ids, attention_mask, past_key_values, labels, images - ): - vision_tower = self.get_vision_tower() - if vision_tower is None or images is None or input_ids.shape[1] == 1: - if ( - past_key_values is not None - and vision_tower is not None - and images is not None - and input_ids.shape[1] == 1 - ): - attention_mask = torch.ones( - (attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), - dtype=attention_mask.dtype, - device=attention_mask.device, - ) - return input_ids, attention_mask, past_key_values, None, labels - - if type(images) is list or images.ndim == 5: - concat_images = torch.cat([image for image in images], dim=0) - image_features = self.encode_images(concat_images) - split_sizes = [image.shape[0] for image in images] - image_features = torch.split(image_features, split_sizes, dim=0) - image_features = [x.flatten(0, 1) for x in image_features] - else: - image_features = self.encode_images(images) - - new_input_embeds = [] - new_labels = [] if labels is not None else None - cur_image_idx = 0 - for batch_idx, cur_input_ids in enumerate(input_ids): - if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: - # multimodal LLM, but the current sample is not multimodal - cur_input_embeds = self.get_model().embed_tokens(cur_input_ids) - cur_input_embeds = ( - cur_input_embeds - + ( - 0.0 * self.get_model().mm_projector(vision_tower.dummy_feature) - ).sum() - ) - new_input_embeds.append(cur_input_embeds) - if labels is not None: - new_labels.append(labels[batch_idx]) - cur_image_idx += 1 - continue - image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] - cur_new_input_embeds = [] - if labels is not None: - cur_labels = labels[batch_idx] - cur_new_labels = [] - assert cur_labels.shape == cur_input_ids.shape - while image_token_indices.numel() > 0: - cur_image_features = image_features[cur_image_idx] - image_token_start = image_token_indices[0] - if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( - self.config, "mm_use_im_start_end", False - ): - cur_new_input_embeds.append( - self.get_model() - .embed_tokens(cur_input_ids[: image_token_start - 1]) - .detach() - ) - cur_new_input_embeds.append( - self.get_model().embed_tokens( - cur_input_ids[image_token_start - 1 : image_token_start] - ) - ) - cur_new_input_embeds.append(cur_image_features) - cur_new_input_embeds.append( - self.get_model().embed_tokens( - cur_input_ids[image_token_start + 1 : image_token_start + 2] - ) - ) - if labels is not None: - cur_new_labels.append(cur_labels[:image_token_start]) - cur_new_labels.append( - torch.full( - (cur_image_features.shape[0],), - IGNORE_INDEX, - device=labels.device, - dtype=labels.dtype, - ) - ) - cur_new_labels.append( - cur_labels[image_token_start : image_token_start + 1] - ) - cur_labels = cur_labels[image_token_start + 2 :] - elif getattr(self.config, "mm_use_im_start_end", False): - cur_new_input_embeds.append( - self.get_model().embed_tokens(cur_input_ids[:image_token_start]) - ) - cur_new_input_embeds.append(cur_image_features) - cur_new_input_embeds.append( - self.get_model().embed_tokens( - cur_input_ids[image_token_start + 1 : image_token_start + 2] - ) - ) - if labels is not None: - cur_new_labels.append(cur_labels[:image_token_start]) - cur_new_labels.append( - torch.full( - (cur_image_features.shape[0],), - IGNORE_INDEX, - device=labels.device, - dtype=labels.dtype, - ) - ) - cur_new_labels.append( - cur_labels[image_token_start + 1 : image_token_start + 2] - ) - cur_labels = cur_labels[image_token_start + 2 :] - else: - cur_new_input_embeds.append( - self.get_model().embed_tokens(cur_input_ids[:image_token_start]) - ) - cur_new_input_embeds.append(cur_image_features) - if labels is not None: - cur_new_labels.append(cur_labels[:image_token_start]) - cur_new_labels.append( - torch.full( - (cur_image_features.shape[0],), - IGNORE_INDEX, - device=labels.device, - dtype=labels.dtype, - ) - ) - cur_labels = cur_labels[image_token_start + 1 :] - cur_image_idx += 1 - if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( - self.config, "mm_use_im_start_end", False - ): - cur_input_ids = cur_input_ids[image_token_start + 2 :] - elif getattr(self.config, "mm_use_im_start_end", False): - cur_input_ids = cur_input_ids[image_token_start + 2 :] - else: - cur_input_ids = cur_input_ids[image_token_start + 1 :] - image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] - if cur_input_ids.numel() > 0: - if getattr(self.config, "tune_mm_mlp_adapter", False) and getattr( - self.config, "mm_use_im_start_end", False - ): - cur_new_input_embeds.append( - self.get_model().embed_tokens(cur_input_ids).detach() - ) - elif getattr(self.config, "mm_use_im_start_end", False): - cur_new_input_embeds.append( - self.get_model().embed_tokens(cur_input_ids) - ) - else: - cur_new_input_embeds.append( - self.get_model().embed_tokens(cur_input_ids) - ) - if labels is not None: - cur_new_labels.append(cur_labels) - cur_new_input_embeds = [ - x.to(device=self.device) for x in cur_new_input_embeds - ] - cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) - new_input_embeds.append(cur_new_input_embeds) - if labels is not None: - cur_new_labels = torch.cat(cur_new_labels, dim=0) - new_labels.append(cur_new_labels) - - if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): - max_len = max(x.shape[0] for x in new_input_embeds) - - new_input_embeds_align = [] - for cur_new_embed in new_input_embeds: - cur_new_embed = torch.cat( - ( - cur_new_embed, - torch.zeros( - (max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), - dtype=cur_new_embed.dtype, - device=cur_new_embed.device, - ), - ), - dim=0, - ) - new_input_embeds_align.append(cur_new_embed) - new_input_embeds = torch.stack(new_input_embeds_align, dim=0) - - if labels is not None: - new_labels_align = [] - _new_labels = new_labels - for cur_new_label in new_labels: - cur_new_label = torch.cat( - ( - cur_new_label, - torch.full( - (max_len - cur_new_label.shape[0],), - IGNORE_INDEX, - dtype=cur_new_label.dtype, - device=cur_new_label.device, - ), - ), - dim=0, - ) - new_labels_align.append(cur_new_label) - new_labels = torch.stack(new_labels_align, dim=0) - - if attention_mask is not None: - new_attention_mask = [] - for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip( - attention_mask, _new_labels, new_labels - ): - new_attn_mask_pad_left = torch.full( - (cur_new_labels.shape[0] - labels.shape[1],), - True, - dtype=attention_mask.dtype, - device=attention_mask.device, - ) - new_attn_mask_pad_right = torch.full( - (cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), - False, - dtype=attention_mask.dtype, - device=attention_mask.device, - ) - cur_new_attention_mask = torch.cat( - ( - new_attn_mask_pad_left, - cur_attention_mask, - new_attn_mask_pad_right, - ), - dim=0, - ) - new_attention_mask.append(cur_new_attention_mask) - attention_mask = torch.stack(new_attention_mask, dim=0) - assert attention_mask.shape == new_labels.shape - else: - new_input_embeds = torch.stack(new_input_embeds, dim=0) - if labels is not None: - new_labels = torch.stack(new_labels, dim=0) - - if attention_mask is not None: - new_attn_mask_pad_left = torch.full( - ( - attention_mask.shape[0], - new_input_embeds.shape[1] - input_ids.shape[1], - ), - True, - dtype=attention_mask.dtype, - device=attention_mask.device, - ) - attention_mask = torch.cat( - (new_attn_mask_pad_left, attention_mask), dim=1 - ) - assert attention_mask.shape == new_input_embeds.shape[:2] - - return None, attention_mask, past_key_values, new_input_embeds, new_labels - - # def initialize_vision_tokenizer(self, model_args, tokenizer): - def initialize_vision_tokenizer(self, model_args, num_new_tokens): - # if model_args.mm_use_im_patch_token: - # tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True) - # self.resize_token_embeddings(len(tokenizer)) - - if model_args.mm_use_im_start_end: - # num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True) - # self.resize_token_embeddings(len(tokenizer)) - - # if num_new_tokens > 0: - # input_embeddings = self.get_input_embeddings().weight.data - # output_embeddings = self.get_output_embeddings().weight.data - - # input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( - # dim=0, keepdim=True) - # output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( - # dim=0, keepdim=True) - - # input_embeddings[-num_new_tokens:] = input_embeddings_avg - # output_embeddings[-num_new_tokens:] = output_embeddings_avg - - if model_args.tune_mm_mlp_adapter: - for p in self.get_input_embeddings().parameters(): - p.requires_grad = True - for p in self.get_output_embeddings().parameters(): - p.requires_grad = False - - if model_args.pretrain_mm_mlp_adapter: - mm_projector_weights = torch.load( - model_args.pretrain_mm_mlp_adapter, map_location="cpu" - ) - embed_tokens_weight = mm_projector_weights["model.embed_tokens.weight"] - assert num_new_tokens == 2 - if input_embeddings.shape == embed_tokens_weight.shape: - input_embeddings[-num_new_tokens:] = embed_tokens_weight[ - -num_new_tokens: - ] - elif embed_tokens_weight.shape[0] == num_new_tokens: - input_embeddings[-num_new_tokens:] = embed_tokens_weight - else: - raise ValueError( - f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}." - ) - elif model_args.mm_use_im_patch_token: - if model_args.tune_mm_mlp_adapter: - for p in self.get_input_embeddings().parameters(): - p.requires_grad = False - for p in self.get_output_embeddings().parameters(): - p.requires_grad = False diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/make_delta.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/make_delta.py deleted file mode 100755 index 26d73d247..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/make_delta.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Usage: -python3 -m llava.model.make_delta --base ~/model_weights/llama-7b --target ~/model_weights/llava-7b --delta ~/model_weights/llava-7b-delta --hub-repo-id liuhaotian/llava-7b-delta -""" -import argparse - -import torch -from llava.model.utils import auto_upgrade -from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer - - -def make_delta(base_model_path, target_model_path, delta_path, hub_repo_id): - print("Loading base model") - base = AutoModelForCausalLM.from_pretrained( - base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True - ) - - print("Loading target model") - auto_upgrade(target_model_path) - target = AutoModelForCausalLM.from_pretrained( - target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True - ) - - print("Calculating delta") - for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"): - if name not in base.state_dict(): - assert name in [ - "model.mm_projector.weight", - "model.mm_projector.bias", - ], f"{name} not in base model" - continue - if param.data.shape == base.state_dict()[name].shape: - param.data -= base.state_dict()[name] - else: - assert name in [ - "model.embed_tokens.weight", - "lm_head.weight", - ], f"{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}" - bparam = base.state_dict()[name] - param.data[: bparam.shape[0], : bparam.shape[1]] -= bparam - - print("Saving delta") - if hub_repo_id: - kwargs = {"push_to_hub": True, "repo_id": hub_repo_id} - else: - kwargs = {} - target.save_pretrained(delta_path, **kwargs) - target_tokenizer = AutoTokenizer.from_pretrained(target_model_path) - target_tokenizer.save_pretrained(delta_path, **kwargs) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--base-model-path", type=str, required=True) - parser.add_argument("--target-model-path", type=str, required=True) - parser.add_argument("--delta-path", type=str, required=True) - parser.add_argument("--hub-repo-id", type=str, default=None) - args = parser.parse_args() - - make_delta( - args.base_model_path, args.target_model_path, args.delta_path, args.hub_repo_id - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/builder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/builder.py deleted file mode 100755 index 087faa857..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/builder.py +++ /dev/null @@ -1,17 +0,0 @@ -from .clip_encoder import CLIPVisionTower - - -def build_vision_tower(vision_tower_cfg, **kwargs): - vision_tower = getattr( - vision_tower_cfg, - "mm_vision_tower", - getattr(vision_tower_cfg, "vision_tower", None), - ) - if ( - vision_tower.startswith("openai") - or vision_tower.startswith("laion") - or "clip" in vision_tower - ): - return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) - - raise ValueError(f"Unknown vision tower: {vision_tower}") diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/clip_encoder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/clip_encoder.py deleted file mode 100755 index 793b70b5f..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/multimodal_encoder/clip_encoder.py +++ /dev/null @@ -1,87 +0,0 @@ -import torch -import torch.nn as nn -from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel - - -class CLIPVisionTower(nn.Module): - def __init__(self, vision_tower, args, delay_load=False): - super().__init__() - - self.is_loaded = False - - self.vision_tower_name = vision_tower - self.select_layer = args.mm_vision_select_layer - self.select_feature = getattr(args, "mm_vision_select_feature", "patch") - - if not delay_load: - self.load_model() - else: - self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) - - def load_model(self): - self.image_processor = CLIPImageProcessor.from_pretrained( - self.vision_tower_name - ) - self.vision_tower = CLIPVisionModel.from_pretrained( - self.vision_tower_name, low_cpu_mem_usage=True - ) - self.vision_tower.requires_grad_(False) - self.is_loaded = True - - def feature_select(self, image_forward_outs): - image_features = image_forward_outs.hidden_states[self.select_layer] - if self.select_feature == "patch": - image_features = image_features[:, 1:] - elif self.select_feature == "cls_patch": - image_features = image_features - else: - raise ValueError(f"Unexpected select feature: {self.select_feature}") - return image_features - - @torch.no_grad() - def forward(self, images): - if type(images) is list: - image_features = [] - for image in images: - image_forward_out = self.vision_tower( - image.to(device=self.device, dtype=self.dtype).unsqueeze(0), - output_hidden_states=True, - ) - image_feature = self.feature_select(image_forward_out).to(image.dtype) - image_features.append(image_feature) - else: - image_forward_outs = self.vision_tower( - images.to(device=self.device, dtype=self.dtype), - output_hidden_states=True, - ) - image_features = self.feature_select(image_forward_outs).to(images.dtype) - - torch.cuda.empty_cache() - return image_features - - @property - def dummy_feature(self): - return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) - - @property - def dtype(self): - return self.vision_tower.dtype - - @property - def device(self): - return self.vision_tower.device - - @property - def config(self): - if self.is_loaded: - return self.vision_tower.config - else: - return self.cfg_only - - @property - def hidden_size(self): - return self.config.hidden_size - - @property - def num_patches(self): - return (self.config.image_size // self.config.patch_size) ** 2 diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/utils.py deleted file mode 100755 index 1ecd82db4..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/model/utils.py +++ /dev/null @@ -1,24 +0,0 @@ -from transformers import AutoConfig - - -def auto_upgrade(config): - cfg = AutoConfig.from_pretrained(config) - if "llava" in config and "llava" not in cfg.model_type: - assert cfg.model_type == "llama" - print( - "You are using newer LLaVA code base, while the checkpoint of v0 is from older code base." - ) - print( - "You must upgrade the checkpoint to the new code base (this can be done automatically)." - ) - confirm = input("Please confirm that you want to upgrade the checkpoint. [Y/N]") - if confirm.lower() in ["y", "yes"]: - print("Upgrading checkpoint...") - assert len(cfg.architectures) == 1 - setattr(cfg.__class__, "model_type", "llava") - cfg.architectures[0] = "LlavaLlamaForCausalLM" - cfg.save_pretrained(config) - print("Checkpoint upgraded.") - else: - print("Checkpoint upgrade aborted.") - exit(1) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llama_flash_attn_monkey_patch.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llama_flash_attn_monkey_patch.py deleted file mode 100755 index 312aa8769..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llama_flash_attn_monkey_patch.py +++ /dev/null @@ -1,126 +0,0 @@ -import logging -from typing import List, Optional, Tuple - -import torch -import transformers -from einops import rearrange -from torch import nn -from transformers.models.llama.modeling_llama import apply_rotary_pos_emb - -try: - from flash_attn.flash_attn_interface import \ - flash_attn_unpadded_qkvpacked_func -except ImportError: - from flash_attn.flash_attn_interface import ( - flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func, - ) - -from flash_attn.bert_padding import pad_input, unpad_input - - -def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - position_ids: Optional[torch.Tensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - output_attentions: bool = False, - use_cache: bool = False, -) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - """Input shape: Batch x Time x Channel - - attention_mask: [bsz, q_len] - """ - bsz, q_len, _ = hidden_states.size() - - query_states = ( - self.q_proj(hidden_states) - .view(bsz, q_len, self.num_heads, self.head_dim) - .transpose(1, 2) - ) - key_states = ( - self.k_proj(hidden_states) - .view(bsz, q_len, self.num_heads, self.head_dim) - .transpose(1, 2) - ) - value_states = ( - self.v_proj(hidden_states) - .view(bsz, q_len, self.num_heads, self.head_dim) - .transpose(1, 2) - ) - # [bsz, q_len, nh, hd] - # [bsz, nh, q_len, hd] - - kv_seq_len = key_states.shape[-2] - assert past_key_value is None, "past_key_value is not supported" - - cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) - query_states, key_states = apply_rotary_pos_emb( - query_states, key_states, cos, sin, position_ids - ) - # [bsz, nh, t, hd] - assert not output_attentions, "output_attentions is not supported" - assert not use_cache, "use_cache is not supported" - - # Flash attention codes from - # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py - - # transform the data into the format required by flash attention - qkv = torch.stack( - [query_states, key_states, value_states], dim=2 - ) # [bsz, nh, 3, q_len, hd] - qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd] - # We have disabled _prepare_decoder_attention_mask in LlamaModel - # the attention_mask should be the same as the key_padding_mask - key_padding_mask = attention_mask - - if key_padding_mask is None: - qkv = rearrange(qkv, "b s ... -> (b s) ...") - max_s = q_len - cu_q_lens = torch.arange( - 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device - ) - output = flash_attn_unpadded_qkvpacked_func( - qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True - ) - output = rearrange(output, "(b s) ... -> b s ...", b=bsz) - else: - nheads = qkv.shape[-2] - x = rearrange(qkv, "b s three h d -> b s (three h d)") - x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask) - x_unpad = rearrange( - x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads - ) - output_unpad = flash_attn_unpadded_qkvpacked_func( - x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True - ) - output = rearrange( - pad_input( - rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len - ), - "b s (h d) -> b s h d", - h=nheads, - ) - return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None - - -# Disable the transformation of the attention mask in LlamaModel as the flash attention -# requires the attention mask to be the same as the key_padding_mask -def _prepare_decoder_attention_mask( - self, attention_mask, input_shape, inputs_embeds, past_key_values_length -): - # [bsz, seq_len] - return attention_mask - - -def replace_llama_attn_with_flash_attn(): - cuda_major, cuda_minor = torch.cuda.get_device_capability() - if cuda_major < 8: - logging.warning( - "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." - "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" - ) - transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( - _prepare_decoder_attention_mask - ) - transformers.models.llama.modeling_llama.LlamaAttention.forward = forward diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llava_trainer.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llava_trainer.py deleted file mode 100755 index c4fd93975..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/llava_trainer.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -from typing import Optional - -import torch -from transformers import Trainer - - -def maybe_zero_3(param, ignore_status=False, name=None): - from deepspeed import zero - from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus - - if hasattr(param, "ds_id"): - if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: - if not ignore_status: - print(name, "no ignore status") - with zero.GatheredParameters([param]): - param = param.data.detach().cpu().clone() - else: - param = param.detach().cpu().clone() - return param - - -def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): - to_return = { - k: t - for k, t in named_params - if any(key_match in k for key_match in keys_to_match) - } - to_return = { - k: maybe_zero_3(v, ignore_status=True, name=k).cpu() - for k, v in to_return.items() - } - return to_return - - -class LLaVATrainer(Trainer): - def _save_checkpoint(self, model, trial, metrics=None): - if getattr(self.args, "tune_mm_mlp_adapter", False): - from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR - - checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}" - - run_dir = self._get_output_dir(trial=trial) - output_dir = os.path.join(run_dir, checkpoint_folder) - - # Only save Adapter - keys_to_match = ["mm_projector"] - if getattr(self.args, "use_im_start_end", False): - keys_to_match.extend(["embed_tokens", "embed_in"]) - - weight_to_save = get_mm_adapter_state_maybe_zero_3( - self.model.named_parameters(), keys_to_match - ) - - if self.args.local_rank == 0 or self.args.local_rank == -1: - self.model.config.save_pretrained(output_dir) - torch.save( - weight_to_save, os.path.join(output_dir, f"mm_projector.bin") - ) - else: - super(LLaVATrainer, self)._save_checkpoint(model, trial, metrics) - - def _save(self, output_dir: Optional[str] = None, state_dict=None): - if getattr(self.args, "tune_mm_mlp_adapter", False): - pass - else: - super(LLaVATrainer, self)._save(output_dir, state_dict) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train.py deleted file mode 100755 index e9ed3f8f5..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train.py +++ /dev/null @@ -1,1038 +0,0 @@ -# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: -# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: -# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import json -import logging -import os -import pathlib -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Sequence - -import torch -import transformers -from llava import conversation as conversation_lib -from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_TOKEN, IGNORE_INDEX, - IMAGE_TOKEN_INDEX) -from llava.mm_utils import tokenizer_image_token -from llava.model import * -from llava.train.llava_trainer import LLaVATrainer -from PIL import Image -from torch.utils.data import Dataset - -local_rank = None - - -def rank0_print(*args): - if local_rank == 0: - print(*args) - - -@dataclass -class ModelArguments: - model_name_or_path: Optional[str] = field(default="facebook/opt-125m") - version: Optional[str] = field(default="v0") - freeze_backbone: bool = field(default=False) - tune_mm_mlp_adapter: bool = field(default=False) - vision_tower: Optional[str] = field(default=None) - mm_vision_select_layer: Optional[int] = field( - default=-1 - ) # default to the last layer - pretrain_mm_mlp_adapter: Optional[str] = field(default=None) - mm_use_im_start_end: bool = field(default=False) - mm_use_im_patch_token: bool = field(default=True) - mm_vision_select_feature: Optional[str] = field(default="patch") - - -@dataclass -class DataArguments: - data_path: str = field( - default=None, metadata={"help": "Path to the training data."} - ) - lazy_preprocess: bool = False - is_multimodal: bool = False - image_folder: Optional[str] = field(default=None) - image_aspect_ratio: str = "square" - image_grid_pinpoints: Optional[str] = field(default=None) - - -@dataclass -class TrainingArguments(transformers.TrainingArguments): - cache_dir: Optional[str] = field(default=None) - optim: str = field(default="adamw_torch") - remove_unused_columns: bool = field(default=False) - freeze_mm_mlp_adapter: bool = field(default=False) - mpt_attn_impl: Optional[str] = field(default="triton") - model_max_length: int = field( - default=512, - metadata={ - "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)." - }, - ) - double_quant: bool = field( - default=True, - metadata={ - "help": "Compress the quantization statistics through double quantization." - }, - ) - quant_type: str = field( - default="nf4", - metadata={ - "help": "Quantization data type to use. Should be one of `fp4` or `nf4`." - }, - ) - bits: int = field(default=16, metadata={"help": "How many bits to use."}) - lora_enable: bool = False - lora_r: int = 64 - lora_alpha: int = 16 - lora_dropout: float = 0.05 - lora_weight_path: str = "" - lora_bias: str = "none" - - -def maybe_zero_3(param, ignore_status=False, name=None): - from deepspeed import zero - from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus - - if hasattr(param, "ds_id"): - if param.ds_status == ZeroParamStatus.NOT_AVAILABLE: - if not ignore_status: - logging.warning( - f"{name}: param.ds_status != ZeroParamStatus.NOT_AVAILABLE: {param.ds_status}" - ) - with zero.GatheredParameters([param]): - param = param.data.detach().cpu().clone() - else: - param = param.detach().cpu().clone() - return param - - -# Borrowed from peft.utils.get_peft_model_state_dict -def get_peft_state_maybe_zero_3(named_params, bias): - if bias == "none": - to_return = {k: t for k, t in named_params if "lora_" in k} - elif bias == "all": - to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k} - elif bias == "lora_only": - to_return = {} - maybe_lora_bias = {} - lora_bias_names = set() - for k, t in named_params: - if "lora_" in k: - to_return[k] = t - bias_name = k.split("lora_")[0] + "bias" - lora_bias_names.add(bias_name) - elif "bias" in k: - maybe_lora_bias[k] = t - for k, t in maybe_lora_bias: - if bias_name in lora_bias_names: - to_return[bias_name] = t - else: - raise NotImplementedError - to_return = {k: maybe_zero_3(v, name=k) for k, v in to_return.items()} - return to_return - - -def get_peft_state_non_lora_maybe_zero_3(named_params, require_grad_only=True): - to_return = {k: t for k, t in named_params if "lora_" not in k} - if require_grad_only: - to_return = {k: t for k, t in to_return.items() if t.requires_grad} - to_return = { - k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items() - } - return to_return - - -def get_mm_adapter_state_maybe_zero_3(named_params, keys_to_match): - to_return = { - k: t - for k, t in named_params - if any(key_match in k for key_match in keys_to_match) - } - to_return = { - k: maybe_zero_3(v, ignore_status=True).cpu() for k, v in to_return.items() - } - return to_return - - -def find_all_linear_names(model): - cls = torch.nn.Linear - lora_module_names = set() - for name, module in model.named_modules(): - if isinstance(module, cls): - names = name.split(".") - lora_module_names.add(names[0] if len(names) == 1 else names[-1]) - - if "lm_head" in lora_module_names: # needed for 16-bit - lora_module_names.remove("lm_head") - return list(lora_module_names) - - -def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str): - """Collects the state dict and dump to disk.""" - - if getattr(trainer.args, "tune_mm_mlp_adapter", False): - # Only save Adapter - keys_to_match = ["mm_projector"] - if getattr(trainer.args, "use_im_start_end", False): - keys_to_match.extend(["embed_tokens", "embed_in"]) - - weight_to_save = get_mm_adapter_state_maybe_zero_3( - trainer.model.named_parameters(), keys_to_match - ) - trainer.model.config.save_pretrained(output_dir) - - current_folder = output_dir.split("/")[-1] - parent_folder = os.path.dirname(output_dir) - if trainer.args.local_rank == 0 or trainer.args.local_rank == -1: - if current_folder.startswith("checkpoint-"): - mm_projector_folder = os.path.join(parent_folder, "mm_projector") - os.makedirs(mm_projector_folder, exist_ok=True) - torch.save( - weight_to_save, - os.path.join(mm_projector_folder, f"{current_folder}.bin"), - ) - else: - torch.save( - weight_to_save, os.path.join(output_dir, f"mm_projector.bin") - ) - return - - if trainer.deepspeed: - torch.cuda.synchronize() - trainer.save_model(output_dir) - return - - state_dict = trainer.model.state_dict() - if trainer.args.should_save: - cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()} - del state_dict - trainer._save(output_dir, state_dict=cpu_state_dict) # noqa - - -def smart_tokenizer_and_embedding_resize( - special_tokens_dict: Dict, - tokenizer: transformers.PreTrainedTokenizer, - model: transformers.PreTrainedModel, -): - """Resize tokenizer and embedding. - - Note: This is the unoptimized version that may make your embedding size not be divisible by 64. - """ - num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) - model.resize_token_embeddings(len(tokenizer)) - - if num_new_tokens > 0: - input_embeddings = model.get_input_embeddings().weight.data - output_embeddings = model.get_output_embeddings().weight.data - - input_embeddings_avg = input_embeddings[:-num_new_tokens].mean( - dim=0, keepdim=True - ) - output_embeddings_avg = output_embeddings[:-num_new_tokens].mean( - dim=0, keepdim=True - ) - - input_embeddings[-num_new_tokens:] = input_embeddings_avg - output_embeddings[-num_new_tokens:] = output_embeddings_avg - - -def _tokenize_fn( - strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer -) -> Dict: - """Tokenize a list of strings.""" - tokenized_list = [ - tokenizer( - text, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ) - for text in strings - ] - input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] - input_ids_lens = labels_lens = [ - tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() - for tokenized in tokenized_list - ] - return dict( - input_ids=input_ids, - labels=labels, - input_ids_lens=input_ids_lens, - labels_lens=labels_lens, - ) - - -def _mask_targets(target, tokenized_lens, speakers): - # cur_idx = 0 - cur_idx = tokenized_lens[0] - tokenized_lens = tokenized_lens[1:] - target[:cur_idx] = IGNORE_INDEX - for tokenized_len, speaker in zip(tokenized_lens, speakers): - if speaker == "human": - target[cur_idx + 2 : cur_idx + tokenized_len] = IGNORE_INDEX - cur_idx += tokenized_len - - -def _add_speaker_and_signal(header, source, get_conversation=True): - """Add speaker and start/end signal on each round.""" - BEGIN_SIGNAL = "### " - END_SIGNAL = "\n" - conversation = header - for sentence in source: - from_str = sentence["from"] - if from_str.lower() == "human": - from_str = conversation_lib.default_conversation.roles[0] - elif from_str.lower() == "gpt": - from_str = conversation_lib.default_conversation.roles[1] - else: - from_str = "unknown" - sentence["value"] = ( - BEGIN_SIGNAL + from_str + ": " + sentence["value"] + END_SIGNAL - ) - if get_conversation: - conversation += sentence["value"] - conversation += BEGIN_SIGNAL - return conversation - - -def preprocess_multimodal(sources: Sequence[str], data_args: DataArguments) -> Dict: - is_multimodal = data_args.is_multimodal - if not is_multimodal: - return sources - - for source in sources: - for sentence in source: - if DEFAULT_IMAGE_TOKEN in sentence["value"]: - sentence["value"] = ( - sentence["value"].replace(DEFAULT_IMAGE_TOKEN, "").strip() - ) - sentence["value"] = DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"] - sentence["value"] = sentence["value"].strip() - if "mmtag" in conversation_lib.default_conversation.version: - sentence["value"] = sentence["value"].replace( - DEFAULT_IMAGE_TOKEN, - "" + DEFAULT_IMAGE_TOKEN + "", - ) - replace_token = DEFAULT_IMAGE_TOKEN - if data_args.mm_use_im_start_end: - replace_token = ( - DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN - ) - sentence["value"] = sentence["value"].replace( - DEFAULT_IMAGE_TOKEN, replace_token - ) - - return sources - - -def preprocess_llama_2( - sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False -) -> Dict: - conv = conversation_lib.default_conversation.copy() - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - - # Apply prompt templates - conversations = [] - for i, source in enumerate(sources): - if roles[source[0]["from"]] != conv.roles[0]: - # Skip the first one if it is not from human - source = source[1:] - - conv.messages = [] - for j, sentence in enumerate(source): - role = roles[sentence["from"]] - assert role == conv.roles[j % 2], f"{i}" - conv.append_message(role, sentence["value"]) - conversations.append(conv.get_prompt()) - - # Tokenize conversations - - if has_image: - input_ids = torch.stack( - [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversations - ], - dim=0, - ) - else: - input_ids = tokenizer( - conversations, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ).input_ids - - targets = input_ids.clone() - - assert conv.sep_style == conversation_lib.SeparatorStyle.LLAMA_2 - - # Mask targets - sep = "[/INST] " - for conversation, target in zip(conversations, targets): - total_len = int(target.ne(tokenizer.pad_token_id).sum()) - - rounds = conversation.split(conv.sep2) - cur_len = 1 - target[:cur_len] = IGNORE_INDEX - for i, rou in enumerate(rounds): - if rou == "": - break - - parts = rou.split(sep) - if len(parts) != 2: - break - parts[0] += sep - - if has_image: - round_len = len(tokenizer_image_token(rou, tokenizer)) - instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 - else: - round_len = len(tokenizer(rou).input_ids) - instruction_len = len(tokenizer(parts[0]).input_ids) - 2 - - target[cur_len : cur_len + instruction_len] = IGNORE_INDEX - - cur_len += round_len - target[cur_len:] = IGNORE_INDEX - - if cur_len < tokenizer.model_max_length: - if cur_len != total_len: - target[:] = IGNORE_INDEX - print( - f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." - f" (ignored)" - ) - - return dict( - input_ids=input_ids, - labels=targets, - ) - - -def preprocess_v1( - sources, tokenizer: transformers.PreTrainedTokenizer, has_image: bool = False -) -> Dict: - conv = conversation_lib.default_conversation.copy() - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - - # Apply prompt templates - conversations = [] - for i, source in enumerate(sources): - if roles[source[0]["from"]] != conv.roles[0]: - # Skip the first one if it is not from human - source = source[1:] - - conv.messages = [] - for j, sentence in enumerate(source): - role = roles[sentence["from"]] - assert role == conv.roles[j % 2], f"{i}" - conv.append_message(role, sentence["value"]) - conversations.append(conv.get_prompt()) - - # Tokenize conversations - - if has_image: - input_ids = torch.stack( - [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversations - ], - dim=0, - ) - else: - input_ids = tokenizer( - conversations, - return_tensors="pt", - padding="longest", - max_length=tokenizer.model_max_length, - truncation=True, - ).input_ids - - targets = input_ids.clone() - - assert conv.sep_style == conversation_lib.SeparatorStyle.TWO - - # Mask targets - sep = conv.sep + conv.roles[1] + ": " - for conversation, target in zip(conversations, targets): - total_len = int(target.ne(tokenizer.pad_token_id).sum()) - - rounds = conversation.split(conv.sep2) - cur_len = 1 - target[:cur_len] = IGNORE_INDEX - for i, rou in enumerate(rounds): - if rou == "": - break - - parts = rou.split(sep) - if len(parts) != 2: - break - parts[0] += sep - - if has_image: - round_len = len(tokenizer_image_token(rou, tokenizer)) - instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 - else: - round_len = len(tokenizer(rou).input_ids) - instruction_len = len(tokenizer(parts[0]).input_ids) - 2 - - target[cur_len : cur_len + instruction_len] = IGNORE_INDEX - - cur_len += round_len - target[cur_len:] = IGNORE_INDEX - - if cur_len < tokenizer.model_max_length: - if cur_len != total_len: - target[:] = IGNORE_INDEX - print( - f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." - f" (ignored)" - ) - - return dict( - input_ids=input_ids, - labels=targets, - ) - - -def preprocess_mpt( - sources, - tokenizer: transformers.PreTrainedTokenizer, -) -> Dict: - conv = conversation_lib.default_conversation.copy() - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - - # Apply prompt templates - conversations = [] - for i, source in enumerate(sources): - if roles[source[0]["from"]] != conv.roles[0]: - # Skip the first one if it is not from human - source = source[1:] - - conv.messages = [] - for j, sentence in enumerate(source): - role = roles[sentence["from"]] - assert role == conv.roles[j % 2], f"{i}" - conv.append_message(role, sentence["value"]) - conversations.append(conv.get_prompt()) - - # Tokenize conversations - input_ids = torch.stack( - [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversations - ], - dim=0, - ) - targets = input_ids.clone() - assert conv.sep_style == conversation_lib.SeparatorStyle.MPT - - # Mask targets - sep = conv.sep + conv.roles[1] - for conversation, target in zip(conversations, targets): - total_len = int(target.ne(tokenizer.pad_token_id).sum()) - - rounds = conversation.split(conv.sep) - re_rounds = [conv.sep.join(rounds[:3])] # system + user + gpt - for conv_idx in range(3, len(rounds), 2): - re_rounds.append( - conv.sep.join(rounds[conv_idx : conv_idx + 2]) - ) # user + gpt - cur_len = 0 - target[:cur_len] = IGNORE_INDEX - for i, rou in enumerate(re_rounds): - if rou == "": - break - - parts = rou.split(sep) - if len(parts) != 2: - break - parts[0] += sep - round_len = len(tokenizer_image_token(rou, tokenizer)) + len( - tokenizer_image_token(conv.sep, tokenizer) - ) - instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - target[cur_len : cur_len + instruction_len] = IGNORE_INDEX - - cur_len += round_len - target[cur_len:] = IGNORE_INDEX - - if cur_len < tokenizer.model_max_length: - if cur_len != total_len: - target[:] = IGNORE_INDEX - print( - f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}." - f" (ignored)" - ) - - return dict( - input_ids=input_ids, - labels=targets, - ) - - -def preprocess_plain( - sources: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer, -) -> Dict: - # add end signal and concatenate together - conversations = [] - for source in sources: - assert len(source) == 2 - assert DEFAULT_IMAGE_TOKEN in source[0]["value"] - source[0]["value"] = DEFAULT_IMAGE_TOKEN - conversation = ( - source[0]["value"] - + source[1]["value"] - + conversation_lib.default_conversation.sep - ) - conversations.append(conversation) - # tokenize conversations - input_ids = [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversations - ] - targets = copy.deepcopy(input_ids) - for target, source in zip(targets, sources): - tokenized_len = len(tokenizer_image_token(source[0]["value"], tokenizer)) - target[:tokenized_len] = IGNORE_INDEX - - return dict(input_ids=input_ids, labels=targets) - - -def preprocess( - sources: Sequence[str], - tokenizer: transformers.PreTrainedTokenizer, - has_image: bool = False, -) -> Dict: - """ - Given a list of sources, each is a conversation list. This transform: - 1. Add signal '### ' at the beginning each sentence, with end signal '\n'; - 2. Concatenate conversations together; - 3. Tokenize the concatenated conversation; - 4. Make a deepcopy as the target. Mask human words with IGNORE_INDEX. - """ - if ( - conversation_lib.default_conversation.sep_style - == conversation_lib.SeparatorStyle.PLAIN - ): - return preprocess_plain(sources, tokenizer) - if ( - conversation_lib.default_conversation.sep_style - == conversation_lib.SeparatorStyle.LLAMA_2 - ): - return preprocess_llama_2(sources, tokenizer, has_image=has_image) - if conversation_lib.default_conversation.version.startswith("v1"): - return preprocess_v1(sources, tokenizer, has_image=has_image) - if conversation_lib.default_conversation.version == "mpt": - return preprocess_mpt(sources, tokenizer) - # add end signal and concatenate together - conversations = [] - for source in sources: - header = f"{conversation_lib.default_conversation.system}\n\n" - conversation = _add_speaker_and_signal(header, source) - conversations.append(conversation) - - # tokenize conversations - def get_tokenize_len(prompts): - return [len(tokenizer_image_token(prompt, tokenizer)) for prompt in prompts] - - if has_image: - input_ids = [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversations - ] - else: - conversations_tokenized = _tokenize_fn(conversations, tokenizer) - input_ids = conversations_tokenized["input_ids"] - - targets = copy.deepcopy(input_ids) - for target, source in zip(targets, sources): - if has_image: - tokenized_lens = get_tokenize_len([header] + [s["value"] for s in source]) - else: - tokenized_lens = _tokenize_fn( - [header] + [s["value"] for s in source], tokenizer - )["input_ids_lens"] - speakers = [sentence["from"] for sentence in source] - _mask_targets(target, tokenized_lens, speakers) - - return dict(input_ids=input_ids, labels=targets) - - -class LazySupervisedDataset(Dataset): - """Dataset for supervised fine-tuning.""" - - def __init__( - self, - data_path: str, - tokenizer: transformers.PreTrainedTokenizer, - data_args: DataArguments, - ): - super(LazySupervisedDataset, self).__init__() - list_data_dict = json.load(open(data_path, "r")) - - rank0_print("Formatting inputs...Skip in lazy mode") - self.tokenizer = tokenizer - self.list_data_dict = list_data_dict - self.data_args = data_args - - def __len__(self): - return len(self.list_data_dict) - - def __getitem__(self, i) -> Dict[str, torch.Tensor]: - sources = self.list_data_dict[i] - if isinstance(i, int): - sources = [sources] - assert len(sources) == 1, "Don't know why it is wrapped to a list" # FIXME - if "image" in sources[0]: - image_file = self.list_data_dict[i]["image"] - image_folder = self.data_args.image_folder - processor = self.data_args.image_processor - image = Image.open(os.path.join(image_folder, image_file)).convert("RGB") - if self.data_args.image_aspect_ratio == "pad": - - def expand2square(pil_img, background_color): - width, height = pil_img.size - if width == height: - return pil_img - elif width > height: - result = Image.new( - pil_img.mode, (width, width), background_color - ) - result.paste(pil_img, (0, (width - height) // 2)) - return result - else: - result = Image.new( - pil_img.mode, (height, height), background_color - ) - result.paste(pil_img, ((height - width) // 2, 0)) - return result - - image = expand2square( - image, tuple(int(x * 255) for x in processor.image_mean) - ) - image = processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][0] - else: - image = processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][0] - sources = preprocess_multimodal( - copy.deepcopy([e["conversations"] for e in sources]), self.data_args - ) - else: - sources = copy.deepcopy([e["conversations"] for e in sources]) - data_dict = preprocess( - sources, self.tokenizer, has_image=("image" in self.list_data_dict[i]) - ) - if isinstance(i, int): - data_dict = dict( - input_ids=data_dict["input_ids"][0], labels=data_dict["labels"][0] - ) - - # image exist in the data - if "image" in self.list_data_dict[i]: - data_dict["image"] = image - elif self.data_args.is_multimodal: - # image does not exist in the data, but the model is multimodal - crop_size = self.data_args.image_processor.crop_size - data_dict["image"] = torch.zeros(3, crop_size["height"], crop_size["width"]) - return data_dict - - -@dataclass -class DataCollatorForSupervisedDataset(object): - """Collate examples for supervised fine-tuning.""" - - tokenizer: transformers.PreTrainedTokenizer - - def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: - input_ids, labels = tuple( - [instance[key] for instance in instances] for key in ("input_ids", "labels") - ) - input_ids = torch.nn.utils.rnn.pad_sequence( - input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id - ) - labels = torch.nn.utils.rnn.pad_sequence( - labels, batch_first=True, padding_value=IGNORE_INDEX - ) - input_ids = input_ids[:, : self.tokenizer.model_max_length] - labels = labels[:, : self.tokenizer.model_max_length] - batch = dict( - input_ids=input_ids, - labels=labels, - attention_mask=input_ids.ne(self.tokenizer.pad_token_id), - ) - - if "image" in instances[0]: - images = [instance["image"] for instance in instances] - if all(x is not None and x.shape == images[0].shape for x in images): - batch["images"] = torch.stack(images) - else: - batch["images"] = images - - return batch - - -def make_supervised_data_module( - tokenizer: transformers.PreTrainedTokenizer, data_args -) -> Dict: - """Make dataset and collator for supervised fine-tuning.""" - train_dataset = LazySupervisedDataset( - tokenizer=tokenizer, data_path=data_args.data_path, data_args=data_args - ) - data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) - return dict( - train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator - ) - - -def train(): - global local_rank - - parser = transformers.HfArgumentParser( - (ModelArguments, DataArguments, TrainingArguments) - ) - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - local_rank = training_args.local_rank - compute_dtype = ( - torch.float16 - if training_args.fp16 - else (torch.bfloat16 if training_args.bf16 else torch.float32) - ) - - bnb_model_from_pretrained_args = {} - if training_args.bits in [4, 8]: - from transformers import BitsAndBytesConfig - - bnb_model_from_pretrained_args.update( - dict( - device_map={"": training_args.device}, - load_in_4bit=training_args.bits == 4, - load_in_8bit=training_args.bits == 8, - quantization_config=BitsAndBytesConfig( - load_in_4bit=training_args.bits == 4, - load_in_8bit=training_args.bits == 8, - llm_int8_threshold=6.0, - llm_int8_has_fp16_weight=False, - bnb_4bit_compute_dtype=compute_dtype, - bnb_4bit_use_double_quant=training_args.double_quant, - bnb_4bit_quant_type=training_args.quant_type, # {'fp4', 'nf4'} - ), - ) - ) - - if model_args.vision_tower is not None: - if "mpt" in model_args.model_name_or_path: - config = transformers.AutoConfig.from_pretrained( - model_args.model_name_or_path, trust_remote_code=True - ) - config.attn_config["attn_impl"] = training_args.mpt_attn_impl - model = LlavaMPTForCausalLM.from_pretrained( - model_args.model_name_or_path, - config=config, - cache_dir=training_args.cache_dir, - **bnb_model_from_pretrained_args, - ) - else: - model = LlavaLlamaForCausalLM.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - **bnb_model_from_pretrained_args, - ) - else: - model = transformers.LlamaForCausalLM.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - **bnb_model_from_pretrained_args, - ) - model.config.use_cache = False - - if model_args.freeze_backbone: - model.model.requires_grad_(False) - - if training_args.bits in [4, 8]: - from peft import prepare_model_for_kbit_training - - model.config.torch_dtype = ( - torch.float32 - if training_args.fp16 - else (torch.bfloat16 if training_args.bf16 else torch.float32) - ) - model = prepare_model_for_kbit_training( - model, use_gradient_checkpointing=training_args.gradient_checkpointing - ) - - if training_args.gradient_checkpointing: - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - if training_args.lora_enable: - from peft import LoraConfig, get_peft_model - - lora_config = LoraConfig( - r=training_args.lora_r, - lora_alpha=training_args.lora_alpha, - target_modules=find_all_linear_names(model), - lora_dropout=training_args.lora_dropout, - bias=training_args.lora_bias, - task_type="CAUSAL_LM", - ) - if training_args.bits == 16: - if training_args.bf16: - model.to(torch.bfloat16) - if training_args.fp16: - model.to(torch.float16) - rank0_print("Adding LoRA adapters...") - model = get_peft_model(model, lora_config) - - if "mpt" in model_args.model_name_or_path: - tokenizer = transformers.AutoTokenizer.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - model_max_length=training_args.model_max_length, - padding_side="right", - ) - else: - tokenizer = transformers.AutoTokenizer.from_pretrained( - model_args.model_name_or_path, - cache_dir=training_args.cache_dir, - model_max_length=training_args.model_max_length, - padding_side="right", - use_fast=False, - ) - - if model_args.version == "v0": - if tokenizer.pad_token is None: - smart_tokenizer_and_embedding_resize( - special_tokens_dict=dict(pad_token="[PAD]"), - tokenizer=tokenizer, - model=model, - ) - elif model_args.version == "v0.5": - tokenizer.pad_token = tokenizer.unk_token - else: - tokenizer.pad_token = tokenizer.unk_token - if model_args.version in conversation_lib.conv_templates: - conversation_lib.default_conversation = conversation_lib.conv_templates[ - model_args.version - ] - else: - conversation_lib.default_conversation = conversation_lib.conv_templates[ - "vicuna_v1" - ] - - if model_args.vision_tower is not None: - model.get_model().initialize_vision_modules( - model_args=model_args, fsdp=training_args.fsdp - ) - - vision_tower = model.get_vision_tower() - vision_tower.to(dtype=torch.float16, device=training_args.device) - - data_args.image_processor = vision_tower.image_processor - data_args.is_multimodal = True - - model.config.image_aspect_ratio = data_args.image_aspect_ratio - model.config.image_grid_pinpoints = data_args.image_grid_pinpoints - - model.config.tune_mm_mlp_adapter = ( - training_args.tune_mm_mlp_adapter - ) = model_args.tune_mm_mlp_adapter - if model_args.tune_mm_mlp_adapter: - model.requires_grad_(False) - for p in model.get_model().mm_projector.parameters(): - p.requires_grad = True - - model.config.freeze_mm_mlp_adapter = training_args.freeze_mm_mlp_adapter - if training_args.freeze_mm_mlp_adapter: - for p in model.get_model().mm_projector.parameters(): - p.requires_grad = False - - if training_args.bits in [4, 8]: - model.get_model().mm_projector.to( - dtype=compute_dtype, device=training_args.device - ) - - model.config.mm_use_im_start_end = ( - data_args.mm_use_im_start_end - ) = model_args.mm_use_im_start_end - training_args.use_im_start_end = model_args.mm_use_im_start_end - model.config.mm_use_im_patch_token = model_args.mm_use_im_patch_token - model.initialize_vision_tokenizer(model_args, tokenizer=tokenizer) - - if training_args.bits in [4, 8]: - from peft.tuners.lora import LoraLayer - - for name, module in model.named_modules(): - if isinstance(module, LoraLayer): - if training_args.bf16: - module = module.to(torch.bfloat16) - if "norm" in name: - module = module.to(torch.float32) - if "lm_head" in name or "embed_tokens" in name: - if hasattr(module, "weight"): - if training_args.bf16 and module.weight.dtype == torch.float32: - module = module.to(torch.bfloat16) - - data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) - trainer = LLaVATrainer( - model=model, tokenizer=tokenizer, args=training_args, **data_module - ) - - if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): - trainer.train(resume_from_checkpoint=True) - else: - trainer.train() - trainer.save_state() - - model.config.use_cache = True - - if training_args.lora_enable: - state_dict = get_peft_state_maybe_zero_3( - model.named_parameters(), training_args.lora_bias - ) - non_lora_state_dict = get_peft_state_non_lora_maybe_zero_3( - model.named_parameters() - ) - if training_args.local_rank == 0 or training_args.local_rank == -1: - model.config.save_pretrained(training_args.output_dir) - model.save_pretrained(training_args.output_dir, state_dict=state_dict) - torch.save( - non_lora_state_dict, - os.path.join(training_args.output_dir, "non_lora_trainables.bin"), - ) - else: - safe_save_model_for_hf_trainer( - trainer=trainer, output_dir=training_args.output_dir - ) - - -if __name__ == "__main__": - train() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train_mem.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train_mem.py deleted file mode 100755 index f3940cf7f..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/train/train_mem.py +++ /dev/null @@ -1,14 +0,0 @@ -# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: -# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright: -# Make it more memory efficient by monkey patching the LLaMA model with FlashAttn. - -# Need to call this before importing transformers. -from llava.train.llama_flash_attn_monkey_patch import \ - replace_llama_attn_with_flash_attn - -replace_llama_attn_with_flash_attn() - -from llava.train.train import train - -if __name__ == "__main__": - train() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/utils.py deleted file mode 100755 index 0a2d5fd53..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/llava/utils.py +++ /dev/null @@ -1,134 +0,0 @@ -import datetime -import logging -import logging.handlers -import os -import sys - -import requests -from llava.constants import LOGDIR - -server_error_msg = ( - "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" -) -moderation_msg = ( - "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." -) - -handler = None - - -def build_logger(logger_name, logger_filename): - global handler - - formatter = logging.Formatter( - fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - ) - - # Set the format of root handlers - if not logging.getLogger().handlers: - logging.basicConfig(level=logging.INFO) - logging.getLogger().handlers[0].setFormatter(formatter) - - # Redirect stdout and stderr to loggers - stdout_logger = logging.getLogger("stdout") - stdout_logger.setLevel(logging.INFO) - sl = StreamToLogger(stdout_logger, logging.INFO) - sys.stdout = sl - - stderr_logger = logging.getLogger("stderr") - stderr_logger.setLevel(logging.ERROR) - sl = StreamToLogger(stderr_logger, logging.ERROR) - sys.stderr = sl - - # Get logger - logger = logging.getLogger(logger_name) - logger.setLevel(logging.INFO) - - # Add a file handler for all loggers - if handler is None: - os.makedirs(LOGDIR, exist_ok=True) - filename = os.path.join(LOGDIR, logger_filename) - handler = logging.handlers.TimedRotatingFileHandler( - filename, when="D", utc=True - ) - handler.setFormatter(formatter) - - for name, item in logging.root.manager.loggerDict.items(): - if isinstance(item, logging.Logger): - item.addHandler(handler) - - return logger - - -class StreamToLogger(object): - """ - Fake file-like stream object that redirects writes to a logger instance. - """ - - def __init__(self, logger, log_level=logging.INFO): - self.terminal = sys.stdout - self.logger = logger - self.log_level = log_level - self.linebuf = "" - - def __getattr__(self, attr): - return getattr(self.terminal, attr) - - def write(self, buf): - temp_linebuf = self.linebuf + buf - self.linebuf = "" - for line in temp_linebuf.splitlines(True): - # From the io.TextIOWrapper docs: - # On output, if newline is None, any '\n' characters written - # are translated to the system default line separator. - # By default sys.stdout.write() expects '\n' newlines and then - # translates them so this is still cross platform. - if line[-1] == "\n": - self.logger.log(self.log_level, line.rstrip()) - else: - self.linebuf += line - - def flush(self): - if self.linebuf != "": - self.logger.log(self.log_level, self.linebuf.rstrip()) - self.linebuf = "" - - -def disable_torch_init(): - """ - Disable the redundant torch default initialization to accelerate model creation. - """ - import torch - - setattr(torch.nn.Linear, "reset_parameters", lambda self: None) - setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) - - -def violates_moderation(text): - """ - Check whether the text violates OpenAI moderation API. - """ - url = "https://api.openai.com/v1/moderations" - headers = { - "Content-Type": "application/json", - "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"], - } - text = text.replace("\n", "") - data = "{" + '"input": ' + f'"{text}"' + "}" - data = data.encode("utf-8") - try: - ret = requests.post(url, headers=headers, data=data, timeout=5) - flagged = ret.json()["results"][0]["flagged"] - except requests.exceptions.RequestException as e: - flagged = False - except KeyError as e: - flagged = False - - return flagged - - -def pretty_print_semaphore(semaphore): - if semaphore is None: - return "None" - return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/__init__.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/__init__.py deleted file mode 100755 index e66218b2e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from .automatic_mask_generator import SamAutomaticMaskGenerator -from .build_sam import (build_sam, build_sam_vit_b, build_sam_vit_h, - build_sam_vit_l, sam_model_registry) -from .predictor import SamPredictor diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/automatic_mask_generator.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/automatic_mask_generator.py deleted file mode 100755 index aa4bc4f03..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/automatic_mask_generator.py +++ /dev/null @@ -1,372 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Any, Dict, List, Optional, Tuple - -import numpy as np -import torch -from torchvision.ops.boxes import batched_nms, box_area # type: ignore - -from .modeling import Sam -from .predictor import SamPredictor -from .utils.amg import (MaskData, area_from_rle, batch_iterator, - batched_mask_to_box, box_xyxy_to_xywh, - build_all_layer_point_grids, calculate_stability_score, - coco_encode_rle, generate_crop_boxes, - is_box_near_crop_edge, mask_to_rle_pytorch, - remove_small_regions, rle_to_mask, uncrop_boxes_xyxy, - uncrop_masks, uncrop_points) - - -class SamAutomaticMaskGenerator: - def __init__( - self, - model: Sam, - points_per_side: Optional[int] = 32, - points_per_batch: int = 64, - pred_iou_thresh: float = 0.88, - stability_score_thresh: float = 0.95, - stability_score_offset: float = 1.0, - box_nms_thresh: float = 0.7, - crop_n_layers: int = 0, - crop_nms_thresh: float = 0.7, - crop_overlap_ratio: float = 512 / 1500, - crop_n_points_downscale_factor: int = 1, - point_grids: Optional[List[np.ndarray]] = None, - min_mask_region_area: int = 0, - output_mode: str = "binary_mask", - ) -> None: - """ - Using a SAM model, generates masks for the entire image. - Generates a grid of point prompts over the image, then filters - low quality and duplicate masks. The default settings are chosen - for SAM with a ViT-H backbone. - - Arguments: - model (Sam): The SAM model to use for mask prediction. - points_per_side (int or None): The number of points to be sampled - along one side of the image. The total number of points is - points_per_side**2. If None, 'point_grids' must provide explicit - point sampling. - points_per_batch (int): Sets the number of points run simultaneously - by the model. Higher numbers may be faster but use more GPU memory. - pred_iou_thresh (float): A filtering threshold in [0,1], using the - model's predicted mask quality. - stability_score_thresh (float): A filtering threshold in [0,1], using - the stability of the mask under changes to the cutoff used to binarize - the model's mask predictions. - stability_score_offset (float): The amount to shift the cutoff when - calculated the stability score. - box_nms_thresh (float): The box IoU cutoff used by non-maximal - suppression to filter duplicate masks. - crop_n_layers (int): If >0, mask prediction will be run again on - crops of the image. Sets the number of layers to run, where each - layer has 2**i_layer number of image crops. - crop_nms_thresh (float): The box IoU cutoff used by non-maximal - suppression to filter duplicate masks between different crops. - crop_overlap_ratio (float): Sets the degree to which crops overlap. - In the first crop layer, crops will overlap by this fraction of - the image length. Later layers with more crops scale down this overlap. - crop_n_points_downscale_factor (int): The number of points-per-side - sampled in layer n is scaled down by crop_n_points_downscale_factor**n. - point_grids (list(np.ndarray) or None): A list over explicit grids - of points used for sampling, normalized to [0,1]. The nth grid in the - list is used in the nth crop layer. Exclusive with points_per_side. - min_mask_region_area (int): If >0, postprocessing will be applied - to remove disconnected regions and holes in masks with area smaller - than min_mask_region_area. Requires opencv. - output_mode (str): The form masks are returned in. Can be 'binary_mask', - 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. - For large resolutions, 'binary_mask' may consume large amounts of - memory. - """ - - assert (points_per_side is None) != ( - point_grids is None - ), "Exactly one of points_per_side or point_grid must be provided." - if points_per_side is not None: - self.point_grids = build_all_layer_point_grids( - points_per_side, - crop_n_layers, - crop_n_points_downscale_factor, - ) - elif point_grids is not None: - self.point_grids = point_grids - else: - raise ValueError("Can't have both points_per_side and point_grid be None.") - - assert output_mode in [ - "binary_mask", - "uncompressed_rle", - "coco_rle", - ], f"Unknown output_mode {output_mode}." - if output_mode == "coco_rle": - from pycocotools import \ - mask as mask_utils # type: ignore # noqa: F401 - - if min_mask_region_area > 0: - import cv2 # type: ignore # noqa: F401 - - self.predictor = SamPredictor(model) - self.points_per_batch = points_per_batch - self.pred_iou_thresh = pred_iou_thresh - self.stability_score_thresh = stability_score_thresh - self.stability_score_offset = stability_score_offset - self.box_nms_thresh = box_nms_thresh - self.crop_n_layers = crop_n_layers - self.crop_nms_thresh = crop_nms_thresh - self.crop_overlap_ratio = crop_overlap_ratio - self.crop_n_points_downscale_factor = crop_n_points_downscale_factor - self.min_mask_region_area = min_mask_region_area - self.output_mode = output_mode - - @torch.no_grad() - def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: - """ - Generates masks for the given image. - - Arguments: - image (np.ndarray): The image to generate masks for, in HWC uint8 format. - - Returns: - list(dict(str, any)): A list over records for masks. Each record is - a dict containing the following keys: - segmentation (dict(str, any) or np.ndarray): The mask. If - output_mode='binary_mask', is an array of shape HW. Otherwise, - is a dictionary containing the RLE. - bbox (list(float)): The box around the mask, in XYWH format. - area (int): The area in pixels of the mask. - predicted_iou (float): The model's own prediction of the mask's - quality. This is filtered by the pred_iou_thresh parameter. - point_coords (list(list(float))): The point coordinates input - to the model to generate this mask. - stability_score (float): A measure of the mask's quality. This - is filtered on using the stability_score_thresh parameter. - crop_box (list(float)): The crop of the image used to generate - the mask, given in XYWH format. - """ - - # Generate masks - mask_data = self._generate_masks(image) - - # Filter small disconnected regions and holes in masks - if self.min_mask_region_area > 0: - mask_data = self.postprocess_small_regions( - mask_data, - self.min_mask_region_area, - max(self.box_nms_thresh, self.crop_nms_thresh), - ) - - # Encode masks - if self.output_mode == "coco_rle": - mask_data["segmentations"] = [ - coco_encode_rle(rle) for rle in mask_data["rles"] - ] - elif self.output_mode == "binary_mask": - mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] - else: - mask_data["segmentations"] = mask_data["rles"] - - # Write mask records - curr_anns = [] - for idx in range(len(mask_data["segmentations"])): - ann = { - "segmentation": mask_data["segmentations"][idx], - "area": area_from_rle(mask_data["rles"][idx]), - "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), - "predicted_iou": mask_data["iou_preds"][idx].item(), - "point_coords": [mask_data["points"][idx].tolist()], - "stability_score": mask_data["stability_score"][idx].item(), - "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), - } - curr_anns.append(ann) - - return curr_anns - - def _generate_masks(self, image: np.ndarray) -> MaskData: - orig_size = image.shape[:2] - crop_boxes, layer_idxs = generate_crop_boxes( - orig_size, self.crop_n_layers, self.crop_overlap_ratio - ) - - # Iterate over image crops - data = MaskData() - for crop_box, layer_idx in zip(crop_boxes, layer_idxs): - crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) - data.cat(crop_data) - - # Remove duplicate masks between crops - if len(crop_boxes) > 1: - # Prefer masks from smaller crops - scores = 1 / box_area(data["crop_boxes"]) - scores = scores.to(data["boxes"].device) - keep_by_nms = batched_nms( - data["boxes"].float(), - scores, - torch.zeros_like(data["boxes"][:, 0]), # categories - iou_threshold=self.crop_nms_thresh, - ) - data.filter(keep_by_nms) - - data.to_numpy() - return data - - def _process_crop( - self, - image: np.ndarray, - crop_box: List[int], - crop_layer_idx: int, - orig_size: Tuple[int, ...], - ) -> MaskData: - # Crop the image and calculate embeddings - x0, y0, x1, y1 = crop_box - cropped_im = image[y0:y1, x0:x1, :] - cropped_im_size = cropped_im.shape[:2] - self.predictor.set_image(cropped_im) - - # Get points for this crop - points_scale = np.array(cropped_im_size)[None, ::-1] - points_for_image = self.point_grids[crop_layer_idx] * points_scale - - # Generate masks for this crop in batches - data = MaskData() - for (points,) in batch_iterator(self.points_per_batch, points_for_image): - batch_data = self._process_batch( - points, cropped_im_size, crop_box, orig_size - ) - data.cat(batch_data) - del batch_data - self.predictor.reset_image() - - # Remove duplicates within this crop. - keep_by_nms = batched_nms( - data["boxes"].float(), - data["iou_preds"], - torch.zeros_like(data["boxes"][:, 0]), # categories - iou_threshold=self.box_nms_thresh, - ) - data.filter(keep_by_nms) - - # Return to the original image frame - data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) - data["points"] = uncrop_points(data["points"], crop_box) - data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) - - return data - - def _process_batch( - self, - points: np.ndarray, - im_size: Tuple[int, ...], - crop_box: List[int], - orig_size: Tuple[int, ...], - ) -> MaskData: - orig_h, orig_w = orig_size - - # Run model on this batch - transformed_points = self.predictor.transform.apply_coords(points, im_size) - in_points = torch.as_tensor(transformed_points, device=self.predictor.device) - in_labels = torch.ones( - in_points.shape[0], dtype=torch.int, device=in_points.device - ) - masks, iou_preds, _ = self.predictor.predict_torch( - in_points[:, None, :], - in_labels[:, None], - multimask_output=True, - return_logits=True, - ) - - # Serialize predictions and store in MaskData - data = MaskData( - masks=masks.flatten(0, 1), - iou_preds=iou_preds.flatten(0, 1), - points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)), - ) - del masks - - # Filter by predicted IoU - if self.pred_iou_thresh > 0.0: - keep_mask = data["iou_preds"] > self.pred_iou_thresh - data.filter(keep_mask) - - # Calculate stability score - data["stability_score"] = calculate_stability_score( - data["masks"], - self.predictor.model.mask_threshold, - self.stability_score_offset, - ) - if self.stability_score_thresh > 0.0: - keep_mask = data["stability_score"] >= self.stability_score_thresh - data.filter(keep_mask) - - # Threshold masks and calculate boxes - data["masks"] = data["masks"] > self.predictor.model.mask_threshold - data["boxes"] = batched_mask_to_box(data["masks"]) - - # Filter boxes that touch crop boundaries - keep_mask = ~is_box_near_crop_edge( - data["boxes"], crop_box, [0, 0, orig_w, orig_h] - ) - if not torch.all(keep_mask): - data.filter(keep_mask) - - # Compress to RLE - data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) - data["rles"] = mask_to_rle_pytorch(data["masks"]) - del data["masks"] - - return data - - @staticmethod - def postprocess_small_regions( - mask_data: MaskData, min_area: int, nms_thresh: float - ) -> MaskData: - """ - Removes small disconnected regions and holes in masks, then reruns - box NMS to remove any new duplicates. - - Edits mask_data in place. - - Requires open-cv as a dependency. - """ - if len(mask_data["rles"]) == 0: - return mask_data - - # Filter small disconnected regions and holes - new_masks = [] - scores = [] - for rle in mask_data["rles"]: - mask = rle_to_mask(rle) - - mask, changed = remove_small_regions(mask, min_area, mode="holes") - unchanged = not changed - mask, changed = remove_small_regions(mask, min_area, mode="islands") - unchanged = unchanged and not changed - - new_masks.append(torch.as_tensor(mask).unsqueeze(0)) - # Give score=0 to changed masks and score=1 to unchanged masks - # so NMS will prefer ones that didn't need postprocessing - scores.append(float(unchanged)) - - # Recalculate boxes and remove any new duplicates - masks = torch.cat(new_masks, dim=0) - boxes = batched_mask_to_box(masks) - keep_by_nms = batched_nms( - boxes.float(), - torch.as_tensor(scores), - torch.zeros_like(boxes[:, 0]), # categories - iou_threshold=nms_thresh, - ) - - # Only recalculate RLEs for masks that have changed - for i_mask in keep_by_nms: - if scores[i_mask] == 0.0: - mask_torch = masks[i_mask].unsqueeze(0) - mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] - mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly - mask_data.filter(keep_by_nms) - - return mask_data diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/build_sam.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/build_sam.py deleted file mode 100755 index 788d25ad5..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/build_sam.py +++ /dev/null @@ -1,108 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from functools import partial - -import torch - -from .modeling import (ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, - TwoWayTransformer) - - -def build_sam_vit_h(checkpoint=None): - return _build_sam( - encoder_embed_dim=1280, - encoder_depth=32, - encoder_num_heads=16, - encoder_global_attn_indexes=[7, 15, 23, 31], - checkpoint=checkpoint, - ) - - -build_sam = build_sam_vit_h - - -def build_sam_vit_l(checkpoint=None): - return _build_sam( - encoder_embed_dim=1024, - encoder_depth=24, - encoder_num_heads=16, - encoder_global_attn_indexes=[5, 11, 17, 23], - checkpoint=checkpoint, - ) - - -def build_sam_vit_b(checkpoint=None): - return _build_sam( - encoder_embed_dim=768, - encoder_depth=12, - encoder_num_heads=12, - encoder_global_attn_indexes=[2, 5, 8, 11], - checkpoint=checkpoint, - ) - - -sam_model_registry = { - "default": build_sam_vit_h, - "vit_h": build_sam_vit_h, - "vit_l": build_sam_vit_l, - "vit_b": build_sam_vit_b, -} - - -def _build_sam( - encoder_embed_dim, - encoder_depth, - encoder_num_heads, - encoder_global_attn_indexes, - checkpoint=None, -): - prompt_embed_dim = 256 - image_size = 1024 - vit_patch_size = 16 - image_embedding_size = image_size // vit_patch_size - sam = Sam( - image_encoder=ImageEncoderViT( - depth=encoder_depth, - embed_dim=encoder_embed_dim, - img_size=image_size, - mlp_ratio=4, - norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), - num_heads=encoder_num_heads, - patch_size=vit_patch_size, - qkv_bias=True, - use_rel_pos=True, - global_attn_indexes=encoder_global_attn_indexes, - window_size=14, - out_chans=prompt_embed_dim, - ), - prompt_encoder=PromptEncoder( - embed_dim=prompt_embed_dim, - image_embedding_size=(image_embedding_size, image_embedding_size), - input_image_size=(image_size, image_size), - mask_in_chans=16, - ), - mask_decoder=MaskDecoder( - num_multimask_outputs=3, - transformer=TwoWayTransformer( - depth=2, - embedding_dim=prompt_embed_dim, - mlp_dim=2048, - num_heads=8, - ), - transformer_dim=prompt_embed_dim, - iou_head_depth=3, - iou_head_hidden_dim=256, - ), - pixel_mean=[123.675, 116.28, 103.53], - pixel_std=[58.395, 57.12, 57.375], - ) - sam.eval() - if checkpoint is not None: - with open(checkpoint, "rb") as f: - state_dict = torch.load(f) - sam.load_state_dict(state_dict, strict=False) - return sam diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/__init__.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/__init__.py deleted file mode 100755 index 088af386e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from .image_encoder import ImageEncoderViT -from .mask_decoder import MaskDecoder -from .prompt_encoder import PromptEncoder -from .sam import Sam -from .transformer import TwoWayTransformer diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/common.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/common.py deleted file mode 100755 index e8727816d..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/common.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Type - -import torch -import torch.nn as nn - - -class MLPBlock(nn.Module): - def __init__( - self, - embedding_dim: int, - mlp_dim: int, - act: Type[nn.Module] = nn.GELU, - ) -> None: - super().__init__() - self.lin1 = nn.Linear(embedding_dim, mlp_dim) - self.lin2 = nn.Linear(mlp_dim, embedding_dim) - self.act = act() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.lin2(self.act(self.lin1(x))) - - -# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa -# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa -class LayerNorm2d(nn.Module): - def __init__(self, num_channels: int, eps: float = 1e-6) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(num_channels)) - self.bias = nn.Parameter(torch.zeros(num_channels)) - self.eps = eps - - def forward(self, x: torch.Tensor) -> torch.Tensor: - u = x.mean(1, keepdim=True) - s = (x - u).pow(2).mean(1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.eps) - x = self.weight[:, None, None] * x + self.bias[:, None, None] - return x diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/image_encoder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/image_encoder.py deleted file mode 100755 index b472a3d6b..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/image_encoder.py +++ /dev/null @@ -1,426 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional, Tuple, Type - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from .common import LayerNorm2d, MLPBlock - - -# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa -class ImageEncoderViT(nn.Module): - def __init__( - self, - img_size: int = 1024, - patch_size: int = 16, - in_chans: int = 3, - embed_dim: int = 768, - depth: int = 12, - num_heads: int = 12, - mlp_ratio: float = 4.0, - out_chans: int = 256, - qkv_bias: bool = True, - norm_layer: Type[nn.Module] = nn.LayerNorm, - act_layer: Type[nn.Module] = nn.GELU, - use_abs_pos: bool = True, - use_rel_pos: bool = False, - rel_pos_zero_init: bool = True, - window_size: int = 0, - global_attn_indexes: Tuple[int, ...] = (), - ) -> None: - """ - Args: - img_size (int): Input image size. - patch_size (int): Patch size. - in_chans (int): Number of input image channels. - embed_dim (int): Patch embedding dimension. - depth (int): Depth of ViT. - num_heads (int): Number of attention heads in each ViT block. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool): If True, add a learnable bias to query, key, value. - norm_layer (nn.Module): Normalization layer. - act_layer (nn.Module): Activation layer. - use_abs_pos (bool): If True, use absolute positional embeddings. - use_rel_pos (bool): If True, add relative positional embeddings to the attention map. - rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. - window_size (int): Window size for window attention blocks. - global_attn_indexes (list): Indexes for blocks using global attention. - """ - super().__init__() - self.img_size = img_size - self.embed_dim = embed_dim - self.out_chans = out_chans - - self.patch_embed = PatchEmbed( - kernel_size=(patch_size, patch_size), - stride=(patch_size, patch_size), - in_chans=in_chans, - embed_dim=embed_dim, - ) - - self.pos_embed: Optional[nn.Parameter] = None - if use_abs_pos: - # Initialize absolute positional embedding with pretrain image size. - self.pos_embed = nn.Parameter( - torch.zeros( - 1, img_size // patch_size, img_size // patch_size, embed_dim - ) - ) - - self.blocks = nn.ModuleList() - for i in range(depth): - block = Block( - dim=embed_dim, - num_heads=num_heads, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, - norm_layer=norm_layer, - act_layer=act_layer, - use_rel_pos=use_rel_pos, - rel_pos_zero_init=rel_pos_zero_init, - window_size=window_size if i not in global_attn_indexes else 0, - input_size=(img_size // patch_size, img_size // patch_size), - ) - self.blocks.append(block) - - self.neck = nn.Sequential( - nn.Conv2d( - embed_dim, - out_chans, - kernel_size=1, - bias=False, - ), - LayerNorm2d(out_chans), - nn.Conv2d( - out_chans, - out_chans, - kernel_size=3, - padding=1, - bias=False, - ), - LayerNorm2d(out_chans), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.patch_embed(x) - if self.pos_embed is not None: - x = x + self.pos_embed - - for blk in self.blocks: - x = blk(x) - - dtype = x.dtype - if dtype == torch.float16: # prevent overflow - with torch.autocast(device_type="cuda", dtype=torch.float32): - x = self.neck(x.permute(0, 3, 1, 2)) - x = x.to(dtype) - else: - x = self.neck(x.permute(0, 3, 1, 2)) - return x - - -class Block(nn.Module): - """Transformer blocks with support of window attention and residual propagation blocks""" - - def __init__( - self, - dim: int, - num_heads: int, - mlp_ratio: float = 4.0, - qkv_bias: bool = True, - norm_layer: Type[nn.Module] = nn.LayerNorm, - act_layer: Type[nn.Module] = nn.GELU, - use_rel_pos: bool = False, - rel_pos_zero_init: bool = True, - window_size: int = 0, - input_size: Optional[Tuple[int, int]] = None, - ) -> None: - """ - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads in each ViT block. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool): If True, add a learnable bias to query, key, value. - norm_layer (nn.Module): Normalization layer. - act_layer (nn.Module): Activation layer. - use_rel_pos (bool): If True, add relative positional embeddings to the attention map. - rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. - window_size (int): Window size for window attention blocks. If it equals 0, then - use global attention. - input_size (tuple(int, int) or None): Input resolution for calculating the relative - positional parameter size. - """ - super().__init__() - self.norm1 = norm_layer(dim) - self.attn = Attention( - dim, - num_heads=num_heads, - qkv_bias=qkv_bias, - use_rel_pos=use_rel_pos, - rel_pos_zero_init=rel_pos_zero_init, - input_size=input_size if window_size == 0 else (window_size, window_size), - ) - - self.norm2 = norm_layer(dim) - self.mlp = MLPBlock( - embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer - ) - - self.window_size = window_size - - def forward(self, x: torch.Tensor) -> torch.Tensor: - shortcut = x - x = self.norm1(x) - # Window partition - if self.window_size > 0: - H, W = x.shape[1], x.shape[2] - x, pad_hw = window_partition(x, self.window_size) - - x = self.attn(x) - # Reverse window partition - if self.window_size > 0: - x = window_unpartition(x, self.window_size, pad_hw, (H, W)) - - x = shortcut + x - x = x + self.mlp(self.norm2(x)) - - return x - - -class Attention(nn.Module): - """Multi-head Attention block with relative position embeddings.""" - - def __init__( - self, - dim: int, - num_heads: int = 8, - qkv_bias: bool = True, - use_rel_pos: bool = False, - rel_pos_zero_init: bool = True, - input_size: Optional[Tuple[int, int]] = None, - ) -> None: - """ - Args: - dim (int): Number of input channels. - num_heads (int): Number of attention heads. - qkv_bias (bool): If True, add a learnable bias to query, key, value. - rel_pos (bool): If True, add relative positional embeddings to the attention map. - rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. - input_size (tuple(int, int) or None): Input resolution for calculating the relative - positional parameter size. - """ - super().__init__() - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = head_dim**-0.5 - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.proj = nn.Linear(dim, dim) - - self.use_rel_pos = use_rel_pos - if self.use_rel_pos: - assert ( - input_size is not None - ), "Input size must be provided if using relative positional encoding." - # initialize relative positional embeddings - self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) - self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - B, H, W, _ = x.shape - # qkv with shape (3, B, nHead, H * W, C) - qkv = ( - self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) - ) - # q, k, v with shape (B * nHead, H * W, C) - q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) - - attn = (q * self.scale) @ k.transpose(-2, -1) - - if self.use_rel_pos: - attn = add_decomposed_rel_pos( - attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W) - ) - - attn = attn.softmax(dim=-1) - x = ( - (attn @ v) - .view(B, self.num_heads, H, W, -1) - .permute(0, 2, 3, 1, 4) - .reshape(B, H, W, -1) - ) - x = self.proj(x) - - return x - - -def window_partition( - x: torch.Tensor, window_size: int -) -> Tuple[torch.Tensor, Tuple[int, int]]: - """ - Partition into non-overlapping windows with padding if needed. - Args: - x (tensor): input tokens with [B, H, W, C]. - window_size (int): window size. - - Returns: - windows: windows after partition with [B * num_windows, window_size, window_size, C]. - (Hp, Wp): padded height and width before partition - """ - B, H, W, C = x.shape - - pad_h = (window_size - H % window_size) % window_size - pad_w = (window_size - W % window_size) % window_size - if pad_h > 0 or pad_w > 0: - x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) - Hp, Wp = H + pad_h, W + pad_w - - x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) - windows = ( - x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - ) - return windows, (Hp, Wp) - - -def window_unpartition( - windows: torch.Tensor, - window_size: int, - pad_hw: Tuple[int, int], - hw: Tuple[int, int], -) -> torch.Tensor: - """ - Window unpartition into original sequences and removing padding. - Args: - windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. - window_size (int): window size. - pad_hw (Tuple): padded height and width (Hp, Wp). - hw (Tuple): original height and width (H, W) before padding. - - Returns: - x: unpartitioned sequences with [B, H, W, C]. - """ - Hp, Wp = pad_hw - H, W = hw - B = windows.shape[0] // (Hp * Wp // window_size // window_size) - x = windows.view( - B, Hp // window_size, Wp // window_size, window_size, window_size, -1 - ) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) - - if Hp > H or Wp > W: - x = x[:, :H, :W, :].contiguous() - return x - - -def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: - """ - Get relative positional embeddings according to the relative positions of - query and key sizes. - Args: - q_size (int): size of query q. - k_size (int): size of key k. - rel_pos (Tensor): relative position embeddings (L, C). - - Returns: - Extracted positional embeddings according to relative positions. - """ - max_rel_dist = int(2 * max(q_size, k_size) - 1) - # Interpolate rel pos if needed. - if rel_pos.shape[0] != max_rel_dist: - # Interpolate rel pos. - rel_pos_resized = F.interpolate( - rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), - size=max_rel_dist, - mode="linear", - ) - rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) - else: - rel_pos_resized = rel_pos - - # Scale the coords with short length if shapes for q and k are different. - q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) - k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) - relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) - - return rel_pos_resized[relative_coords.long()] - - -def add_decomposed_rel_pos( - attn: torch.Tensor, - q: torch.Tensor, - rel_pos_h: torch.Tensor, - rel_pos_w: torch.Tensor, - q_size: Tuple[int, int], - k_size: Tuple[int, int], -) -> torch.Tensor: - """ - Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. - https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 - Args: - attn (Tensor): attention map. - q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). - rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. - rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. - q_size (Tuple): spatial sequence size of query q with (q_h, q_w). - k_size (Tuple): spatial sequence size of key k with (k_h, k_w). - - Returns: - attn (Tensor): attention map with added relative positional embeddings. - """ - q_h, q_w = q_size - k_h, k_w = k_size - Rh = get_rel_pos(q_h, k_h, rel_pos_h) - Rw = get_rel_pos(q_w, k_w, rel_pos_w) - - B, _, dim = q.shape - r_q = q.reshape(B, q_h, q_w, dim) - rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) - rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) - - attn = ( - attn.view(B, q_h, q_w, k_h, k_w) - + rel_h[:, :, :, :, None] - + rel_w[:, :, :, None, :] - ).view(B, q_h * q_w, k_h * k_w) - - return attn - - -class PatchEmbed(nn.Module): - """ - Image to Patch Embedding. - """ - - def __init__( - self, - kernel_size: Tuple[int, int] = (16, 16), - stride: Tuple[int, int] = (16, 16), - padding: Tuple[int, int] = (0, 0), - in_chans: int = 3, - embed_dim: int = 768, - ) -> None: - """ - Args: - kernel_size (Tuple): kernel size of the projection layer. - stride (Tuple): stride of the projection layer. - padding (Tuple): padding size of the projection layer. - in_chans (int): Number of input image channels. - embed_dim (int): Patch embedding dimension. - """ - super().__init__() - - self.proj = nn.Conv2d( - in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.proj(x) - # B C H W -> B H W C - x = x.permute(0, 2, 3, 1) - return x diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/mask_decoder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/mask_decoder.py deleted file mode 100755 index fb104ea48..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/mask_decoder.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import List, Tuple, Type - -import torch -from torch import nn -from torch.nn import functional as F - -from .common import LayerNorm2d - - -class MaskDecoder(nn.Module): - def __init__( - self, - *, - transformer_dim: int, - transformer: nn.Module, - num_multimask_outputs: int = 3, - activation: Type[nn.Module] = nn.GELU, - iou_head_depth: int = 3, - iou_head_hidden_dim: int = 256, - ) -> None: - """ - Predicts masks given an image and prompt embeddings, using a - transformer architecture. - - Arguments: - transformer_dim (int): the channel dimension of the transformer - transformer (nn.Module): the transformer used to predict masks - num_multimask_outputs (int): the number of masks to predict - when disambiguating masks - activation (nn.Module): the type of activation to use when - upscaling masks - iou_head_depth (int): the depth of the MLP used to predict - mask quality - iou_head_hidden_dim (int): the hidden dimension of the MLP - used to predict mask quality - """ - super().__init__() - self.transformer_dim = transformer_dim - self.transformer = transformer - - self.num_multimask_outputs = num_multimask_outputs - - self.iou_token = nn.Embedding(1, transformer_dim) - self.num_mask_tokens = num_multimask_outputs + 1 - self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) - - self.output_upscaling = nn.Sequential( - nn.ConvTranspose2d( - transformer_dim, transformer_dim // 4, kernel_size=2, stride=2 - ), - LayerNorm2d(transformer_dim // 4), - activation(), - nn.ConvTranspose2d( - transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2 - ), - activation(), - ) - self.output_hypernetworks_mlps = nn.ModuleList( - [ - MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) - for i in range(self.num_mask_tokens) - ] - ) - - self.iou_prediction_head = MLP( - transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth - ) - - def forward( - self, - image_embeddings: torch.Tensor, - image_pe: torch.Tensor, - sparse_prompt_embeddings: torch.Tensor, - dense_prompt_embeddings: torch.Tensor, - multimask_output: bool, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Predict masks given image and prompt embeddings. - - Arguments: - image_embeddings (torch.Tensor): the embeddings from the image encoder - image_pe (torch.Tensor): positional encoding with the shape of image_embeddings - sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes - dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs - multimask_output (bool): Whether to return multiple masks or a single - mask. - - Returns: - torch.Tensor: batched predicted masks - torch.Tensor: batched predictions of mask quality - """ - masks, iou_pred = self.predict_masks( - image_embeddings=image_embeddings, - image_pe=image_pe, - sparse_prompt_embeddings=sparse_prompt_embeddings, - dense_prompt_embeddings=dense_prompt_embeddings, - ) - - # Select the correct mask or masks for output - if multimask_output: - mask_slice = slice(1, None) - else: - mask_slice = slice(0, 1) - masks = masks[:, mask_slice, :, :] - iou_pred = iou_pred[:, mask_slice] - - # Prepare output - return masks, iou_pred - - def predict_masks( - self, - image_embeddings: torch.Tensor, - image_pe: torch.Tensor, - sparse_prompt_embeddings: torch.Tensor, - dense_prompt_embeddings: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """Predicts masks. See 'forward' for more details.""" - # Concatenate output tokens - output_tokens = torch.cat( - [self.iou_token.weight, self.mask_tokens.weight], dim=0 - ) - output_tokens = output_tokens.unsqueeze(0).expand( - sparse_prompt_embeddings.size(0), -1, -1 - ) - - tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) - - # image_embeddings: [1, C, H, W], tokens: [B, N, C] - # dense_prompt_embeddings: [B, C, H, W] - # Expand per-image data in batch direction to be per-mask - src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) - src = src + dense_prompt_embeddings - pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) - b, c, h, w = src.shape - - # Run the transformer - hs, src = self.transformer(src, pos_src, tokens) - iou_token_out = hs[:, 0, :] - mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :] - - # Upscale mask embeddings and predict masks using the mask tokens - src = src.transpose(1, 2).view(b, c, h, w) - upscaled_embedding = self.output_upscaling(src) - hyper_in_list: List[torch.Tensor] = [] - for i in range(self.num_mask_tokens): - hyper_in_list.append( - self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) - ) - hyper_in = torch.stack(hyper_in_list, dim=1) - b, c, h, w = upscaled_embedding.shape - masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view( - b, self.num_mask_tokens, h, w - ) - - # Generate mask quality predictions - iou_pred = self.iou_prediction_head(iou_token_out) - - return masks, iou_pred - - -# Lightly adapted from -# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa -class MLP(nn.Module): - def __init__( - self, - input_dim: int, - hidden_dim: int, - output_dim: int, - num_layers: int, - sigmoid_output: bool = False, - ) -> None: - super().__init__() - self.num_layers = num_layers - h = [hidden_dim] * (num_layers - 1) - self.layers = nn.ModuleList( - nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) - ) - self.sigmoid_output = sigmoid_output - - def forward(self, x): - for i, layer in enumerate(self.layers): - x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) - if self.sigmoid_output: - x = F.sigmoid(x) - return x diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/prompt_encoder.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/prompt_encoder.py deleted file mode 100755 index 16bc3a45e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/prompt_encoder.py +++ /dev/null @@ -1,238 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Any, Optional, Tuple, Type - -import numpy as np -import torch -from torch import nn - -from .common import LayerNorm2d - - -class PromptEncoder(nn.Module): - def __init__( - self, - embed_dim: int, - image_embedding_size: Tuple[int, int], - input_image_size: Tuple[int, int], - mask_in_chans: int, - activation: Type[nn.Module] = nn.GELU, - ) -> None: - """ - Encodes prompts for input to SAM's mask decoder. - - Arguments: - embed_dim (int): The prompts' embedding dimension - image_embedding_size (tuple(int, int)): The spatial size of the - image embedding, as (H, W). - input_image_size (int): The padded size of the image as input - to the image encoder, as (H, W). - mask_in_chans (int): The number of hidden channels used for - encoding input masks. - activation (nn.Module): The activation to use when encoding - input masks. - """ - super().__init__() - self.embed_dim = embed_dim - self.input_image_size = input_image_size - self.image_embedding_size = image_embedding_size - self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) - - self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners - point_embeddings = [ - nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings) - ] - self.point_embeddings = nn.ModuleList(point_embeddings) - self.not_a_point_embed = nn.Embedding(1, embed_dim) - - self.mask_input_size = ( - 4 * image_embedding_size[0], - 4 * image_embedding_size[1], - ) - self.mask_downscaling = nn.Sequential( - nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), - LayerNorm2d(mask_in_chans // 4), - activation(), - nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), - LayerNorm2d(mask_in_chans), - activation(), - nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), - ) - self.no_mask_embed = nn.Embedding(1, embed_dim) - - def get_dense_pe(self) -> torch.Tensor: - """ - Returns the positional encoding used to encode point prompts, - applied to a dense set of points the shape of the image encoding. - - Returns: - torch.Tensor: Positional encoding with shape - 1x(embed_dim)x(embedding_h)x(embedding_w) - """ - return self.pe_layer(self.image_embedding_size).unsqueeze(0) - - def _embed_points( - self, - points: torch.Tensor, - labels: torch.Tensor, - pad: bool, - ) -> torch.Tensor: - """Embeds point prompts.""" - points = points + 0.5 # Shift to center of pixel - if pad: - padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) - padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) - points = torch.cat([points, padding_point], dim=1) - labels = torch.cat([labels, padding_label], dim=1) - point_embedding = self.pe_layer.forward_with_coords( - points, self.input_image_size - ) - point_embedding[labels == -1] = 0.0 - point_embedding[labels == -1] += self.not_a_point_embed.weight - point_embedding[labels == 0] += self.point_embeddings[0].weight - point_embedding[labels == 1] += self.point_embeddings[1].weight - return point_embedding - - def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: - """Embeds box prompts.""" - boxes = boxes + 0.5 # Shift to center of pixel - coords = boxes.reshape(-1, 2, 2) - corner_embedding = self.pe_layer.forward_with_coords( - coords, self.input_image_size - ) - corner_embedding[:, 0, :] += self.point_embeddings[2].weight - corner_embedding[:, 1, :] += self.point_embeddings[3].weight - return corner_embedding - - def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: - """Embeds mask inputs.""" - mask_embedding = self.mask_downscaling(masks) - return mask_embedding - - def _get_batch_size( - self, - points: Optional[Tuple[torch.Tensor, torch.Tensor]], - boxes: Optional[torch.Tensor], - masks: Optional[torch.Tensor], - text_embeds: Optional[torch.Tensor], - ) -> int: - """ - Gets the batch size of the output given the batch size of the input prompts. - """ - if points is not None: - return points[0].shape[0] - elif boxes is not None: - return boxes.shape[0] - elif masks is not None: - return masks.shape[0] - elif text_embeds is not None: - return text_embeds.shape[0] - else: - return 1 - - def _get_device(self) -> torch.device: - return self.point_embeddings[0].weight.device - - def forward( - self, - points: Optional[Tuple[torch.Tensor, torch.Tensor]], - boxes: Optional[torch.Tensor], - masks: Optional[torch.Tensor], - text_embeds: Optional[torch.Tensor], - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Embeds different types of prompts, returning both sparse and dense - embeddings. - - Arguments: - points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates - and labels to embed. - boxes (torch.Tensor or none): boxes to embed - masks (torch.Tensor or none): masks to embed - - Returns: - torch.Tensor: sparse embeddings for the points and boxes, with shape - BxNx(embed_dim), where N is determined by the number of input points - and boxes. - torch.Tensor: dense embeddings for the masks, in the shape - Bx(embed_dim)x(embed_H)x(embed_W) - """ - bs = self._get_batch_size(points, boxes, masks, text_embeds) - sparse_embeddings = torch.empty( - (bs, 0, self.embed_dim), device=self._get_device() - ) - if points is not None: - coords, labels = points - point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) - sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) - if boxes is not None: - box_embeddings = self._embed_boxes(boxes) - sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) - - if text_embeds is not None: - sparse_embeddings = torch.cat([sparse_embeddings, text_embeds], dim=1) - - if masks is not None: - dense_embeddings = self._embed_masks(masks) - else: - dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( - bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] - ) - - return sparse_embeddings, dense_embeddings - - -class PositionEmbeddingRandom(nn.Module): - """ - Positional encoding using random spatial frequencies. - """ - - def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: - super().__init__() - if scale is None or scale <= 0.0: - scale = 1.0 - self.register_buffer( - "positional_encoding_gaussian_matrix", - scale * torch.randn((2, num_pos_feats)), - ) - - def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: - """Positionally encode points that are normalized to [0,1].""" - # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape - coords = 2 * coords - 1 - - if coords.dtype != self.positional_encoding_gaussian_matrix.dtype: - coords = coords.to(self.positional_encoding_gaussian_matrix.dtype) - - coords = coords @ self.positional_encoding_gaussian_matrix - coords = 2 * np.pi * coords - # outputs d_1 x ... x d_n x C shape - return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) - - def forward(self, size: Tuple[int, int]) -> torch.Tensor: - """Generate positional encoding for a grid of the specified size.""" - h, w = size - device: Any = self.positional_encoding_gaussian_matrix.device - grid = torch.ones( - (h, w), device=device, dtype=self.positional_encoding_gaussian_matrix.dtype - ) - y_embed = grid.cumsum(dim=0) - 0.5 - x_embed = grid.cumsum(dim=1) - 0.5 - y_embed = y_embed / h - x_embed = x_embed / w - - pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) - return pe.permute(2, 0, 1) # C x H x W - - def forward_with_coords( - self, coords_input: torch.Tensor, image_size: Tuple[int, int] - ) -> torch.Tensor: - """Positionally encode points that are not normalized to [0,1].""" - coords = coords_input.clone() - coords[:, :, 0] = coords[:, :, 0] / image_size[1] - coords[:, :, 1] = coords[:, :, 1] / image_size[0] - return self._pe_encoding(coords.to(torch.float)) # B x N x C diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/sam.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/sam.py deleted file mode 100755 index f1d82cac3..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/sam.py +++ /dev/null @@ -1,184 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Any, Dict, List, Tuple - -import torch -from torch import nn -from torch.nn import functional as F - -from .image_encoder import ImageEncoderViT -from .mask_decoder import MaskDecoder -from .prompt_encoder import PromptEncoder - - -class Sam(nn.Module): - mask_threshold: float = 0.0 - image_format: str = "RGB" - - def __init__( - self, - image_encoder: ImageEncoderViT, - prompt_encoder: PromptEncoder, - mask_decoder: MaskDecoder, - pixel_mean: List[float] = [123.675, 116.28, 103.53], - pixel_std: List[float] = [58.395, 57.12, 57.375], - ) -> None: - """ - SAM predicts object masks from an image and input prompts. - - Arguments: - image_encoder (ImageEncoderViT): The backbone used to encode the - image into image embeddings that allow for efficient mask prediction. - prompt_encoder (PromptEncoder): Encodes various types of input prompts. - mask_decoder (MaskDecoder): Predicts masks from the image embeddings - and encoded prompts. - pixel_mean (list(float)): Mean values for normalizing pixels in the input image. - pixel_std (list(float)): Std values for normalizing pixels in the input image. - """ - super().__init__() - self.image_encoder = image_encoder - self.prompt_encoder = prompt_encoder - self.mask_decoder = mask_decoder - self.register_buffer( - "pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False - ) - self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) - - @property - def device(self) -> Any: - return self.pixel_mean.device - - @torch.no_grad() - def forward( - self, - batched_input: List[Dict[str, Any]], - multimask_output: bool, - ) -> List[Dict[str, torch.Tensor]]: - """ - Predicts masks end-to-end from provided images and prompts. - If prompts are not known in advance, using SamPredictor is - recommended over calling the model directly. - - Arguments: - batched_input (list(dict)): A list over input images, each a - dictionary with the following keys. A prompt key can be - excluded if it is not present. - 'image': The image as a torch tensor in 3xHxW format, - already transformed for input to the model. - 'original_size': (tuple(int, int)) The original size of - the image before transformation, as (H, W). - 'point_coords': (torch.Tensor) Batched point prompts for - this image, with shape BxNx2. Already transformed to the - input frame of the model. - 'point_labels': (torch.Tensor) Batched labels for point prompts, - with shape BxN. - 'boxes': (torch.Tensor) Batched box inputs, with shape Bx4. - Already transformed to the input frame of the model. - 'mask_inputs': (torch.Tensor) Batched mask inputs to the model, - in the form Bx1xHxW. - multimask_output (bool): Whether the model should predict multiple - disambiguating masks, or return a single mask. - - Returns: - (list(dict)): A list over input images, where each element is - as dictionary with the following keys. - 'masks': (torch.Tensor) Batched binary mask predictions, - with shape BxCxHxW, where B is the number of input prompts, - C is determined by multimask_output, and (H, W) is the - original size of the image. - 'iou_predictions': (torch.Tensor) The model's predictions - of mask quality, in shape BxC. - 'low_res_logits': (torch.Tensor) Low resolution logits with - shape BxCxHxW, where H=W=256. Can be passed as mask input - to subsequent iterations of prediction. - """ - input_images = torch.stack( - [self.preprocess(x["image"]) for x in batched_input], dim=0 - ) - image_embeddings = self.image_encoder(input_images) - - outputs = [] - for image_record, curr_embedding in zip(batched_input, image_embeddings): - if "point_coords" in image_record: - points = (image_record["point_coords"], image_record["point_labels"]) - else: - points = None - sparse_embeddings, dense_embeddings = self.prompt_encoder( - points=points, - boxes=image_record.get("boxes", None), - masks=image_record.get("mask_inputs", None), - ) - low_res_masks, iou_predictions = self.mask_decoder( - image_embeddings=curr_embedding.unsqueeze(0), - image_pe=self.prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings, - dense_prompt_embeddings=dense_embeddings, - multimask_output=multimask_output, - ) - masks = self.postprocess_masks( - low_res_masks, - input_size=image_record["image"].shape[-2:], - original_size=image_record["original_size"], - ) - masks = masks > self.mask_threshold - outputs.append( - { - "masks": masks, - "iou_predictions": iou_predictions, - "low_res_logits": low_res_masks, - } - ) - return outputs - - def postprocess_masks( - self, - masks: torch.Tensor, - input_size: Tuple[int, ...], - original_size: Tuple[int, ...], - ) -> torch.Tensor: - """ - Remove padding and upscale masks to the original image size. - - Arguments: - masks (torch.Tensor): Batched masks from the mask_decoder, - in BxCxHxW format. - input_size (tuple(int, int)): The size of the image input to the - model, in (H, W) format. Used to remove padding. - original_size (tuple(int, int)): The original size of the image - before resizing for input to the model, in (H, W) format. - - Returns: - (torch.Tensor): Batched masks in BxCxHxW format, where (H, W) - is given by original_size. - """ - - dtype = masks.dtype - - masks = F.interpolate( - masks.float(), - (self.image_encoder.img_size, self.image_encoder.img_size), - mode="bilinear", - align_corners=False, - ) - # masks = masks.to(dtype) - masks = masks[..., : input_size[0], : input_size[1]] - masks = F.interpolate( - masks, original_size, mode="bilinear", align_corners=False - ) - return masks - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.image_encoder.img_size - h - padw = self.image_encoder.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/transformer.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/transformer.py deleted file mode 100755 index 8c511e4ff..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/modeling/transformer.py +++ /dev/null @@ -1,242 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Tuple, Type - -import torch -from torch import Tensor, nn - -from .common import MLPBlock - - -class TwoWayTransformer(nn.Module): - def __init__( - self, - depth: int, - embedding_dim: int, - num_heads: int, - mlp_dim: int, - activation: Type[nn.Module] = nn.ReLU, - attention_downsample_rate: int = 2, - ) -> None: - """ - A transformer decoder that attends to an input image using - queries whose positional embedding is supplied. - - Args: - depth (int): number of layers in the transformer - embedding_dim (int): the channel dimension for the input embeddings - num_heads (int): the number of heads for multihead attention. Must - divide embedding_dim - mlp_dim (int): the channel dimension internal to the MLP block - activation (nn.Module): the activation to use in the MLP block - """ - super().__init__() - self.depth = depth - self.embedding_dim = embedding_dim - self.num_heads = num_heads - self.mlp_dim = mlp_dim - self.layers = nn.ModuleList() - - for i in range(depth): - self.layers.append( - TwoWayAttentionBlock( - embedding_dim=embedding_dim, - num_heads=num_heads, - mlp_dim=mlp_dim, - activation=activation, - attention_downsample_rate=attention_downsample_rate, - skip_first_layer_pe=(i == 0), - ) - ) - - self.final_attn_token_to_image = Attention( - embedding_dim, num_heads, downsample_rate=attention_downsample_rate - ) - self.norm_final_attn = nn.LayerNorm(embedding_dim) - - def forward( - self, - image_embedding: Tensor, - image_pe: Tensor, - point_embedding: Tensor, - ) -> Tuple[Tensor, Tensor]: - """ - Args: - image_embedding (torch.Tensor): image to attend to. Should be shape - B x embedding_dim x h x w for any h and w. - image_pe (torch.Tensor): the positional encoding to add to the image. Must - have the same shape as image_embedding. - point_embedding (torch.Tensor): the embedding to add to the query points. - Must have shape B x N_points x embedding_dim for any N_points. - - Returns: - torch.Tensor: the processed point_embedding - torch.Tensor: the processed image_embedding - """ - # BxCxHxW -> BxHWxC == B x N_image_tokens x C - bs, c, h, w = image_embedding.shape - image_embedding = image_embedding.flatten(2).permute(0, 2, 1) - image_pe = image_pe.flatten(2).permute(0, 2, 1) - - # Prepare queries - queries = point_embedding - keys = image_embedding - - # Apply transformer blocks and final layernorm - for layer in self.layers: - queries, keys = layer( - queries=queries, - keys=keys, - query_pe=point_embedding, - key_pe=image_pe, - ) - - # Apply the final attention layer from the points to the image - q = queries + point_embedding - k = keys + image_pe - attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) - queries = queries + attn_out - queries = self.norm_final_attn(queries) - - return queries, keys - - -class TwoWayAttentionBlock(nn.Module): - def __init__( - self, - embedding_dim: int, - num_heads: int, - mlp_dim: int = 2048, - activation: Type[nn.Module] = nn.ReLU, - attention_downsample_rate: int = 2, - skip_first_layer_pe: bool = False, - ) -> None: - """ - A transformer block with four layers: (1) self-attention of sparse - inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp - block on sparse inputs, and (4) cross attention of dense inputs to sparse - inputs. - - Arguments: - embedding_dim (int): the channel dimension of the embeddings - num_heads (int): the number of heads in the attention layers - mlp_dim (int): the hidden dimension of the mlp block - activation (nn.Module): the activation of the mlp block - skip_first_layer_pe (bool): skip the PE on the first layer - """ - super().__init__() - self.self_attn = Attention(embedding_dim, num_heads) - self.norm1 = nn.LayerNorm(embedding_dim) - - self.cross_attn_token_to_image = Attention( - embedding_dim, num_heads, downsample_rate=attention_downsample_rate - ) - self.norm2 = nn.LayerNorm(embedding_dim) - - self.mlp = MLPBlock(embedding_dim, mlp_dim, activation) - self.norm3 = nn.LayerNorm(embedding_dim) - - self.norm4 = nn.LayerNorm(embedding_dim) - self.cross_attn_image_to_token = Attention( - embedding_dim, num_heads, downsample_rate=attention_downsample_rate - ) - - self.skip_first_layer_pe = skip_first_layer_pe - - def forward( - self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor - ) -> Tuple[Tensor, Tensor]: - # Self attention block - if self.skip_first_layer_pe: - queries = self.self_attn(q=queries, k=queries, v=queries) - else: - q = queries + query_pe - attn_out = self.self_attn(q=q, k=q, v=queries) - queries = queries + attn_out - queries = self.norm1(queries) - - # Cross attention block, tokens attending to image embedding - q = queries + query_pe - k = keys + key_pe - attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) - queries = queries + attn_out - queries = self.norm2(queries) - - # MLP block - mlp_out = self.mlp(queries) - queries = queries + mlp_out - queries = self.norm3(queries) - - # Cross attention block, image embedding attending to tokens - q = queries + query_pe - k = keys + key_pe - attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) - keys = keys + attn_out - keys = self.norm4(keys) - - return queries, keys - - -class Attention(nn.Module): - """ - An attention layer that allows for downscaling the size of the embedding - after projection to queries, keys, and values. - """ - - def __init__( - self, - embedding_dim: int, - num_heads: int, - downsample_rate: int = 1, - ) -> None: - super().__init__() - self.embedding_dim = embedding_dim - self.internal_dim = embedding_dim // downsample_rate - self.num_heads = num_heads - assert ( - self.internal_dim % num_heads == 0 - ), "num_heads must divide embedding_dim." - - self.q_proj = nn.Linear(embedding_dim, self.internal_dim) - self.k_proj = nn.Linear(embedding_dim, self.internal_dim) - self.v_proj = nn.Linear(embedding_dim, self.internal_dim) - self.out_proj = nn.Linear(self.internal_dim, embedding_dim) - - def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: - b, n, c = x.shape - x = x.reshape(b, n, num_heads, c // num_heads) - return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head - - def _recombine_heads(self, x: Tensor) -> Tensor: - b, n_heads, n_tokens, c_per_head = x.shape - x = x.transpose(1, 2) - return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C - - def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: - # Input projections - q = self.q_proj(q) - k = self.k_proj(k) - v = self.v_proj(v) - - # Separate into heads - q = self._separate_heads(q, self.num_heads) - k = self._separate_heads(k, self.num_heads) - v = self._separate_heads(v, self.num_heads) - - # Attention - _, _, _, c_per_head = q.shape - attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens - attn = attn / math.sqrt(c_per_head) - attn = torch.softmax(attn, dim=-1) - - # Get output - out = attn @ v - out = self._recombine_heads(out) - out = self.out_proj(out) - - return out diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/predictor.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/predictor.py deleted file mode 100755 index bf52d81c2..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/predictor.py +++ /dev/null @@ -1,284 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Optional, Tuple - -import numpy as np -import torch - -from .modeling import Sam -from .utils.transforms import ResizeLongestSide - - -class SamPredictor: - def __init__( - self, - sam_model: Sam, - ) -> None: - """ - Uses SAM to calculate the image embedding for an image, and then - allow repeated, efficient mask prediction given prompts. - - Arguments: - sam_model (Sam): The model to use for mask prediction. - """ - super().__init__() - self.model = sam_model - self.transform = ResizeLongestSide(sam_model.image_encoder.img_size) - self.reset_image() - - def set_image( - self, - image: np.ndarray, - image_format: str = "RGB", - ) -> None: - """ - Calculates the image embeddings for the provided image, allowing - masks to be predicted with the 'predict' method. - - Arguments: - image (np.ndarray): The image for calculating masks. Expects an - image in HWC uint8 format, with pixel values in [0, 255]. - image_format (str): The color format of the image, in ['RGB', 'BGR']. - """ - assert image_format in [ - "RGB", - "BGR", - ], f"image_format must be in ['RGB', 'BGR'], is {image_format}." - if image_format != self.model.image_format: - image = image[..., ::-1] - - # Transform the image to the form expected by the model - input_image = self.transform.apply_image(image) - input_image_torch = torch.as_tensor(input_image, device=self.device) - input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[ - None, :, :, : - ] - - self.set_torch_image(input_image_torch, image.shape[:2]) - - @torch.no_grad() - def set_torch_image( - self, - transformed_image: torch.Tensor, - original_image_size: Tuple[int, ...], - ) -> None: - """ - Calculates the image embeddings for the provided image, allowing - masks to be predicted with the 'predict' method. Expects the input - image to be already transformed to the format expected by the model. - - Arguments: - transformed_image (torch.Tensor): The input image, with shape - 1x3xHxW, which has been transformed with ResizeLongestSide. - original_image_size (tuple(int, int)): The size of the image - before transformation, in (H, W) format. - """ - assert ( - len(transformed_image.shape) == 4 - and transformed_image.shape[1] == 3 - and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size - ), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}." - self.reset_image() - - self.original_size = original_image_size - self.input_size = tuple(transformed_image.shape[-2:]) - input_image = self.model.preprocess(transformed_image) - self.features = self.model.image_encoder(input_image) - self.is_image_set = True - - def predict( - self, - point_coords: Optional[np.ndarray] = None, - point_labels: Optional[np.ndarray] = None, - box: Optional[np.ndarray] = None, - mask_input: Optional[np.ndarray] = None, - multimask_output: bool = True, - return_logits: bool = False, - ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """ - Predict masks for the given input prompts, using the currently set image. - - Arguments: - point_coords (np.ndarray or None): A Nx2 array of point prompts to the - model. Each point is in (X,Y) in pixels. - point_labels (np.ndarray or None): A length N array of labels for the - point prompts. 1 indicates a foreground point and 0 indicates a - background point. - box (np.ndarray or None): A length 4 array given a box prompt to the - model, in XYXY format. - mask_input (np.ndarray): A low resolution mask input to the model, typically - coming from a previous prediction iteration. Has form 1xHxW, where - for SAM, H=W=256. - multimask_output (bool): If true, the model will return three masks. - For ambiguous input prompts (such as a single click), this will often - produce better masks than a single prediction. If only a single - mask is needed, the model's predicted quality score can be used - to select the best mask. For non-ambiguous prompts, such as multiple - input prompts, multimask_output=False can give better results. - return_logits (bool): If true, returns un-thresholded masks logits - instead of a binary mask. - - Returns: - (np.ndarray): The output masks in CxHxW format, where C is the - number of masks, and (H, W) is the original image size. - (np.ndarray): An array of length C containing the model's - predictions for the quality of each mask. - (np.ndarray): An array of shape CxHxW, where C is the number - of masks and H=W=256. These low resolution logits can be passed to - a subsequent iteration as mask input. - """ - if not self.is_image_set: - raise RuntimeError( - "An image must be set with .set_image(...) before mask prediction." - ) - - # Transform input prompts - coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None - if point_coords is not None: - assert ( - point_labels is not None - ), "point_labels must be supplied if point_coords is supplied." - point_coords = self.transform.apply_coords(point_coords, self.original_size) - coords_torch = torch.as_tensor( - point_coords, dtype=torch.float, device=self.device - ) - labels_torch = torch.as_tensor( - point_labels, dtype=torch.int, device=self.device - ) - coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :] - if box is not None: - box = self.transform.apply_boxes(box, self.original_size) - box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device) - box_torch = box_torch[None, :] - if mask_input is not None: - mask_input_torch = torch.as_tensor( - mask_input, dtype=torch.float, device=self.device - ) - mask_input_torch = mask_input_torch[None, :, :, :] - - masks, iou_predictions, low_res_masks = self.predict_torch( - coords_torch, - labels_torch, - box_torch, - mask_input_torch, - multimask_output, - return_logits=return_logits, - ) - - masks_np = masks[0].detach().cpu().numpy() - iou_predictions_np = iou_predictions[0].detach().cpu().numpy() - low_res_masks_np = low_res_masks[0].detach().cpu().numpy() - return masks_np, iou_predictions_np, low_res_masks_np - - @torch.no_grad() - def predict_torch( - self, - point_coords: Optional[torch.Tensor], - point_labels: Optional[torch.Tensor], - boxes: Optional[torch.Tensor] = None, - mask_input: Optional[torch.Tensor] = None, - multimask_output: bool = True, - return_logits: bool = False, - ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """ - Predict masks for the given input prompts, using the currently set image. - Input prompts are batched torch tensors and are expected to already be - transformed to the input frame using ResizeLongestSide. - - Arguments: - point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the - model. Each point is in (X,Y) in pixels. - point_labels (torch.Tensor or None): A BxN array of labels for the - point prompts. 1 indicates a foreground point and 0 indicates a - background point. - boxes (np.ndarray or None): A Bx4 array given a box prompt to the - model, in XYXY format. - mask_input (np.ndarray): A low resolution mask input to the model, typically - coming from a previous prediction iteration. Has form Bx1xHxW, where - for SAM, H=W=256. Masks returned by a previous iteration of the - predict method do not need further transformation. - multimask_output (bool): If true, the model will return three masks. - For ambiguous input prompts (such as a single click), this will often - produce better masks than a single prediction. If only a single - mask is needed, the model's predicted quality score can be used - to select the best mask. For non-ambiguous prompts, such as multiple - input prompts, multimask_output=False can give better results. - return_logits (bool): If true, returns un-thresholded masks logits - instead of a binary mask. - - Returns: - (torch.Tensor): The output masks in BxCxHxW format, where C is the - number of masks, and (H, W) is the original image size. - (torch.Tensor): An array of shape BxC containing the model's - predictions for the quality of each mask. - (torch.Tensor): An array of shape BxCxHxW, where C is the number - of masks and H=W=256. These low res logits can be passed to - a subsequent iteration as mask input. - """ - if not self.is_image_set: - raise RuntimeError( - "An image must be set with .set_image(...) before mask prediction." - ) - - if point_coords is not None: - points = (point_coords, point_labels) - else: - points = None - - # Embed prompts - sparse_embeddings, dense_embeddings = self.model.prompt_encoder( - points=points, - boxes=boxes, - masks=mask_input, - ) - - # Predict masks - low_res_masks, iou_predictions = self.model.mask_decoder( - image_embeddings=self.features, - image_pe=self.model.prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embeddings, - dense_prompt_embeddings=dense_embeddings, - multimask_output=multimask_output, - ) - - # Upscale the masks to the original image resolution - masks = self.model.postprocess_masks( - low_res_masks, self.input_size, self.original_size - ) - - if not return_logits: - masks = masks > self.model.mask_threshold - - return masks, iou_predictions, low_res_masks - - def get_image_embedding(self) -> torch.Tensor: - """ - Returns the image embeddings for the currently set image, with - shape 1xCxHxW, where C is the embedding dimension and (H,W) are - the embedding spatial dimension of SAM (typically C=256, H=W=64). - """ - if not self.is_image_set: - raise RuntimeError( - "An image must be set with .set_image(...) to generate an embedding." - ) - assert ( - self.features is not None - ), "Features must exist if an image has been set." - return self.features - - @property - def device(self) -> torch.device: - return self.model.device - - def reset_image(self) -> None: - """Resets the currently set image.""" - self.is_image_set = False - self.features = None - self.orig_h = None - self.orig_w = None - self.input_h = None - self.input_w = None diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/__init__.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/__init__.py deleted file mode 100755 index 5277f4615..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/amg.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/amg.py deleted file mode 100755 index 5c3bc5d78..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/amg.py +++ /dev/null @@ -1,346 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import math -from copy import deepcopy -from itertools import product -from typing import Any, Dict, Generator, ItemsView, List, Tuple - -import numpy as np -import torch - - -class MaskData: - """ - A structure for storing masks and their related data in batched format. - Implements basic filtering and concatenation. - """ - - def __init__(self, **kwargs) -> None: - for v in kwargs.values(): - assert isinstance( - v, (list, np.ndarray, torch.Tensor) - ), "MaskData only supports list, numpy arrays, and torch tensors." - self._stats = dict(**kwargs) - - def __setitem__(self, key: str, item: Any) -> None: - assert isinstance( - item, (list, np.ndarray, torch.Tensor) - ), "MaskData only supports list, numpy arrays, and torch tensors." - self._stats[key] = item - - def __delitem__(self, key: str) -> None: - del self._stats[key] - - def __getitem__(self, key: str) -> Any: - return self._stats[key] - - def items(self) -> ItemsView[str, Any]: - return self._stats.items() - - def filter(self, keep: torch.Tensor) -> None: - for k, v in self._stats.items(): - if v is None: - self._stats[k] = None - elif isinstance(v, torch.Tensor): - self._stats[k] = v[torch.as_tensor(keep, device=v.device)] - elif isinstance(v, np.ndarray): - self._stats[k] = v[keep.detach().cpu().numpy()] - elif isinstance(v, list) and keep.dtype == torch.bool: - self._stats[k] = [a for i, a in enumerate(v) if keep[i]] - elif isinstance(v, list): - self._stats[k] = [v[i] for i in keep] - else: - raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") - - def cat(self, new_stats: "MaskData") -> None: - for k, v in new_stats.items(): - if k not in self._stats or self._stats[k] is None: - self._stats[k] = deepcopy(v) - elif isinstance(v, torch.Tensor): - self._stats[k] = torch.cat([self._stats[k], v], dim=0) - elif isinstance(v, np.ndarray): - self._stats[k] = np.concatenate([self._stats[k], v], axis=0) - elif isinstance(v, list): - self._stats[k] = self._stats[k] + deepcopy(v) - else: - raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") - - def to_numpy(self) -> None: - for k, v in self._stats.items(): - if isinstance(v, torch.Tensor): - self._stats[k] = v.detach().cpu().numpy() - - -def is_box_near_crop_edge( - boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 -) -> torch.Tensor: - """Filter masks at the edge of a crop, but not at the edge of the original image.""" - crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) - orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) - boxes = uncrop_boxes_xyxy(boxes, crop_box).float() - near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) - near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) - near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) - return torch.any(near_crop_edge, dim=1) - - -def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: - box_xywh = deepcopy(box_xyxy) - box_xywh[2] = box_xywh[2] - box_xywh[0] - box_xywh[3] = box_xywh[3] - box_xywh[1] - return box_xywh - - -def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: - assert len(args) > 0 and all( - len(a) == len(args[0]) for a in args - ), "Batched iteration must have inputs of all the same size." - n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) - for b in range(n_batches): - yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] - - -def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: - """ - Encodes masks to an uncompressed RLE, in the format expected by - pycoco tools. - """ - # Put in fortran order and flatten h,w - b, h, w = tensor.shape - tensor = tensor.permute(0, 2, 1).flatten(1) - - # Compute change indices - diff = tensor[:, 1:] ^ tensor[:, :-1] - change_indices = diff.nonzero() - - # Encode run length - out = [] - for i in range(b): - cur_idxs = change_indices[change_indices[:, 0] == i, 1] - cur_idxs = torch.cat( - [ - torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), - cur_idxs + 1, - torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), - ] - ) - btw_idxs = cur_idxs[1:] - cur_idxs[:-1] - counts = [] if tensor[i, 0] == 0 else [0] - counts.extend(btw_idxs.detach().cpu().tolist()) - out.append({"size": [h, w], "counts": counts}) - return out - - -def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: - """Compute a binary mask from an uncompressed RLE.""" - h, w = rle["size"] - mask = np.empty(h * w, dtype=bool) - idx = 0 - parity = False - for count in rle["counts"]: - mask[idx : idx + count] = parity - idx += count - parity ^= True - mask = mask.reshape(w, h) - return mask.transpose() # Put in C order - - -def area_from_rle(rle: Dict[str, Any]) -> int: - return sum(rle["counts"][1::2]) - - -def calculate_stability_score( - masks: torch.Tensor, mask_threshold: float, threshold_offset: float -) -> torch.Tensor: - """ - Computes the stability score for a batch of masks. The stability - score is the IoU between the binary masks obtained by thresholding - the predicted mask logits at high and low values. - """ - # One mask is always contained inside the other. - # Save memory by preventing unnecessary cast to torch.int64 - intersections = ( - (masks > (mask_threshold + threshold_offset)) - .sum(-1, dtype=torch.int16) - .sum(-1, dtype=torch.int32) - ) - unions = ( - (masks > (mask_threshold - threshold_offset)) - .sum(-1, dtype=torch.int16) - .sum(-1, dtype=torch.int32) - ) - return intersections / unions - - -def build_point_grid(n_per_side: int) -> np.ndarray: - """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" - offset = 1 / (2 * n_per_side) - points_one_side = np.linspace(offset, 1 - offset, n_per_side) - points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) - points_y = np.tile(points_one_side[:, None], (1, n_per_side)) - points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) - return points - - -def build_all_layer_point_grids( - n_per_side: int, n_layers: int, scale_per_layer: int -) -> List[np.ndarray]: - """Generates point grids for all crop layers.""" - points_by_layer = [] - for i in range(n_layers + 1): - n_points = int(n_per_side / (scale_per_layer**i)) - points_by_layer.append(build_point_grid(n_points)) - return points_by_layer - - -def generate_crop_boxes( - im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float -) -> Tuple[List[List[int]], List[int]]: - """ - Generates a list of crop boxes of different sizes. Each layer - has (2**i)**2 boxes for the ith layer. - """ - crop_boxes, layer_idxs = [], [] - im_h, im_w = im_size - short_side = min(im_h, im_w) - - # Original image - crop_boxes.append([0, 0, im_w, im_h]) - layer_idxs.append(0) - - def crop_len(orig_len, n_crops, overlap): - return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) - - for i_layer in range(n_layers): - n_crops_per_side = 2 ** (i_layer + 1) - overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) - - crop_w = crop_len(im_w, n_crops_per_side, overlap) - crop_h = crop_len(im_h, n_crops_per_side, overlap) - - crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] - crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] - - # Crops in XYWH format - for x0, y0 in product(crop_box_x0, crop_box_y0): - box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] - crop_boxes.append(box) - layer_idxs.append(i_layer + 1) - - return crop_boxes, layer_idxs - - -def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: - x0, y0, _, _ = crop_box - offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) - # Check if boxes has a channel dimension - if len(boxes.shape) == 3: - offset = offset.unsqueeze(1) - return boxes + offset - - -def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: - x0, y0, _, _ = crop_box - offset = torch.tensor([[x0, y0]], device=points.device) - # Check if points has a channel dimension - if len(points.shape) == 3: - offset = offset.unsqueeze(1) - return points + offset - - -def uncrop_masks( - masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int -) -> torch.Tensor: - x0, y0, x1, y1 = crop_box - if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: - return masks - # Coordinate transform masks - pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) - pad = (x0, pad_x - x0, y0, pad_y - y0) - return torch.nn.functional.pad(masks, pad, value=0) - - -def remove_small_regions( - mask: np.ndarray, area_thresh: float, mode: str -) -> Tuple[np.ndarray, bool]: - """ - Removes small disconnected regions and holes in a mask. Returns the - mask and an indicator of if the mask has been modified. - """ - import cv2 # type: ignore - - assert mode in ["holes", "islands"] - correct_holes = mode == "holes" - working_mask = (correct_holes ^ mask).astype(np.uint8) - n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) - sizes = stats[:, -1][1:] # Row 0 is background label - small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] - if len(small_regions) == 0: - return mask, False - fill_labels = [0] + small_regions - if not correct_holes: - fill_labels = [i for i in range(n_labels) if i not in fill_labels] - # If every region is below threshold, keep largest - if len(fill_labels) == 0: - fill_labels = [int(np.argmax(sizes)) + 1] - mask = np.isin(regions, fill_labels) - return mask, True - - -def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: - from pycocotools import mask as mask_utils # type: ignore - - h, w = uncompressed_rle["size"] - rle = mask_utils.frPyObjects(uncompressed_rle, h, w) - rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json - return rle - - -def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: - """ - Calculates boxes in XYXY format around masks. Return [0,0,0,0] for - an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. - """ - # torch.max below raises an error on empty inputs, just skip in this case - if torch.numel(masks) == 0: - return torch.zeros(*masks.shape[:-2], 4, device=masks.device) - - # Normalize shape to CxHxW - shape = masks.shape - h, w = shape[-2:] - if len(shape) > 2: - masks = masks.flatten(0, -3) - else: - masks = masks.unsqueeze(0) - - # Get top and bottom edges - in_height, _ = torch.max(masks, dim=-1) - in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] - bottom_edges, _ = torch.max(in_height_coords, dim=-1) - in_height_coords = in_height_coords + h * (~in_height) - top_edges, _ = torch.min(in_height_coords, dim=-1) - - # Get left and right edges - in_width, _ = torch.max(masks, dim=-2) - in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] - right_edges, _ = torch.max(in_width_coords, dim=-1) - in_width_coords = in_width_coords + w * (~in_width) - left_edges, _ = torch.min(in_width_coords, dim=-1) - - # If the mask is empty the right edge will be to the left of the left edge. - # Replace these boxes with [0, 0, 0, 0] - empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) - out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) - out = out * (~empty_filter).unsqueeze(-1) - - # Return to original shape - if len(shape) > 2: - out = out.reshape(*shape[:-2], 4) - else: - out = out[0] - - return out diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/onnx.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/onnx.py deleted file mode 100755 index 3521208f6..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/onnx.py +++ /dev/null @@ -1,157 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from typing import Tuple - -import torch -import torch.nn as nn -from torch.nn import functional as F - -from ..modeling import Sam -from .amg import calculate_stability_score - - -class SamOnnxModel(nn.Module): - """ - This model should not be called directly, but is used in ONNX export. - It combines the prompt encoder, mask decoder, and mask postprocessing of Sam, - with some functions modified to enable model tracing. Also supports extra - options controlling what information. See the ONNX export script for details. - """ - - def __init__( - self, - model: Sam, - return_single_mask: bool, - use_stability_score: bool = False, - return_extra_metrics: bool = False, - ) -> None: - super().__init__() - self.mask_decoder = model.mask_decoder - self.model = model - self.img_size = model.image_encoder.img_size - self.return_single_mask = return_single_mask - self.use_stability_score = use_stability_score - self.stability_score_offset = 1.0 - self.return_extra_metrics = return_extra_metrics - - @staticmethod - def resize_longest_image_size( - input_image_size: torch.Tensor, longest_side: int - ) -> torch.Tensor: - input_image_size = input_image_size.to(torch.float32) - scale = longest_side / torch.max(input_image_size) - transformed_size = scale * input_image_size - transformed_size = torch.floor(transformed_size + 0.5).to(torch.int64) - return transformed_size - - def _embed_points( - self, point_coords: torch.Tensor, point_labels: torch.Tensor - ) -> torch.Tensor: - point_coords = point_coords + 0.5 - point_coords = point_coords / self.img_size - point_embedding = self.model.prompt_encoder.pe_layer._pe_encoding(point_coords) - point_labels = point_labels.unsqueeze(-1).expand_as(point_embedding) - - point_embedding = point_embedding * (point_labels != -1) - point_embedding = ( - point_embedding - + self.model.prompt_encoder.not_a_point_embed.weight * (point_labels == -1) - ) - - for i in range(self.model.prompt_encoder.num_point_embeddings): - point_embedding = ( - point_embedding - + self.model.prompt_encoder.point_embeddings[i].weight - * (point_labels == i) - ) - - return point_embedding - - def _embed_masks( - self, input_mask: torch.Tensor, has_mask_input: torch.Tensor - ) -> torch.Tensor: - mask_embedding = has_mask_input * self.model.prompt_encoder.mask_downscaling( - input_mask - ) - mask_embedding = mask_embedding + ( - 1 - has_mask_input - ) * self.model.prompt_encoder.no_mask_embed.weight.reshape(1, -1, 1, 1) - return mask_embedding - - def mask_postprocessing( - self, masks: torch.Tensor, orig_im_size: torch.Tensor - ) -> torch.Tensor: - masks = F.interpolate( - masks, - size=(self.img_size, self.img_size), - mode="bilinear", - align_corners=False, - ) - - prepadded_size = self.resize_longest_image_size(orig_im_size, self.img_size).to( - torch.int64 - ) - masks = masks[..., : prepadded_size[0], : prepadded_size[1]] # type: ignore - - orig_im_size = orig_im_size.to(torch.int64) - h, w = orig_im_size[0], orig_im_size[1] - masks = F.interpolate(masks, size=(h, w), mode="bilinear", align_corners=False) - return masks - - def select_masks( - self, masks: torch.Tensor, iou_preds: torch.Tensor, num_points: int - ) -> Tuple[torch.Tensor, torch.Tensor]: - # Determine if we should return the multiclick mask or not from the number of points. - # The reweighting is used to avoid control flow. - score_reweight = torch.tensor( - [[1000] + [0] * (self.model.mask_decoder.num_mask_tokens - 1)] - ).to(iou_preds.device) - score = iou_preds + (num_points - 2.5) * score_reweight - best_idx = torch.argmax(score, dim=1) - masks = masks[torch.arange(masks.shape[0]), best_idx, :, :].unsqueeze(1) - iou_preds = iou_preds[torch.arange(masks.shape[0]), best_idx].unsqueeze(1) - - return masks, iou_preds - - @torch.no_grad() - def forward( - self, - image_embeddings: torch.Tensor, - point_coords: torch.Tensor, - point_labels: torch.Tensor, - mask_input: torch.Tensor, - has_mask_input: torch.Tensor, - orig_im_size: torch.Tensor, - ): - sparse_embedding = self._embed_points(point_coords, point_labels) - dense_embedding = self._embed_masks(mask_input, has_mask_input) - - masks, scores = self.model.mask_decoder.predict_masks( - image_embeddings=image_embeddings, - image_pe=self.model.prompt_encoder.get_dense_pe(), - sparse_prompt_embeddings=sparse_embedding, - dense_prompt_embeddings=dense_embedding, - ) - - if self.use_stability_score: - scores = calculate_stability_score( - masks, self.model.mask_threshold, self.stability_score_offset - ) - - if self.return_single_mask: - masks, scores = self.select_masks(masks, scores, point_coords.shape[1]) - - upscaled_masks = self.mask_postprocessing(masks, orig_im_size) - - if self.return_extra_metrics: - stability_scores = calculate_stability_score( - upscaled_masks, self.model.mask_threshold, self.stability_score_offset - ) - areas = (upscaled_masks > self.model.mask_threshold).sum(-1).sum(-1) - return upscaled_masks, scores, stability_scores, areas, masks - - return upscaled_masks, scores, masks diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/transforms.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/transforms.py deleted file mode 100755 index 4232d8425..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/model/segment_anything/utils/transforms.py +++ /dev/null @@ -1,113 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -from copy import deepcopy -from typing import Tuple - -import numpy as np -import torch -from torch.nn import functional as F -from torchvision.transforms.functional import resize # type: ignore -from torchvision.transforms.functional import to_pil_image - - -class ResizeLongestSide: - """ - Resizes images to the longest side 'target_length', as well as provides - methods for resizing coordinates and boxes. Provides methods for - transforming both numpy array and batched torch tensors. - """ - - def __init__(self, target_length: int) -> None: - self.target_length = target_length - - def apply_image(self, image: np.ndarray) -> np.ndarray: - """ - Expects a numpy array with shape HxWxC in uint8 format. - """ - target_size = self.get_preprocess_shape( - image.shape[0], image.shape[1], self.target_length - ) - return np.array(resize(to_pil_image(image), target_size)) - - def apply_coords( - self, coords: np.ndarray, original_size: Tuple[int, ...] - ) -> np.ndarray: - """ - Expects a numpy array of length 2 in the final dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).astype(float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes( - self, boxes: np.ndarray, original_size: Tuple[int, ...] - ) -> np.ndarray: - """ - Expects a numpy array shape Bx4. Requires the original image size - in (H, W) format. - """ - boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor: - """ - Expects batched images with shape BxCxHxW and float format. This - transformation may not exactly match apply_image. apply_image is - the transformation expected by the model. - """ - # Expects an image in BCHW format. May not exactly match apply_image. - target_size = self.get_preprocess_shape( - image.shape[0], image.shape[1], self.target_length - ) - return F.interpolate( - image, target_size, mode="bilinear", align_corners=False, antialias=True - ) - - def apply_coords_torch( - self, coords: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with length 2 in the last dimension. Requires the - original image size in (H, W) format. - """ - old_h, old_w = original_size - new_h, new_w = self.get_preprocess_shape( - original_size[0], original_size[1], self.target_length - ) - coords = deepcopy(coords).to(torch.float) - coords[..., 0] = coords[..., 0] * (new_w / old_w) - coords[..., 1] = coords[..., 1] * (new_h / old_h) - return coords - - def apply_boxes_torch( - self, boxes: torch.Tensor, original_size: Tuple[int, ...] - ) -> torch.Tensor: - """ - Expects a torch tensor with shape Bx4. Requires the original image - size in (H, W) format. - """ - boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size) - return boxes.reshape(-1, 4) - - @staticmethod - def get_preprocess_shape( - oldh: int, oldw: int, long_side_length: int - ) -> Tuple[int, int]: - """ - Compute the output size given input size and target long side length. - """ - scale = long_side_length * 1.0 / max(oldh, oldw) - newh, neww = oldh * scale, oldw * scale - neww = int(neww + 0.5) - newh = int(newh + 0.5) - return (newh, neww) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/requirements.txt b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/requirements.txt deleted file mode 100755 index 393dd8497..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/requirements.txt +++ /dev/null @@ -1,24 +0,0 @@ ---extra-index-url https://download.pytorch.org/whl/cu117 -torch==1.13.1 -torchvision==0.14.1 -packaging -sentencepiece -peft==0.4.0 -einops==0.4.1 -fastapi==0.100.1 -gradio==3.39.0 -markdown2==2.4.10 -numpy==1.24.2 -openai==0.27.8 -opencv_python==4.8.0.74 -Pillow==9.4.0 -pycocotools==2.0.6 -ray==2.6.1 -Requests==2.31.0 -shortuuid==1.0.11 -tqdm==4.64.1 -transformers==4.31.0 -uvicorn==0.23.2 -scipy==1.11.2 -bitsandbytes==0.41.1 -h5py==3.9.0 diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/ade20k_classes.json b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/ade20k_classes.json deleted file mode 100755 index 1f96e616b..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/ade20k_classes.json +++ /dev/null @@ -1,30 +0,0 @@ -[ - "wall", "building", "sky", "floor", "tree", "ceiling", "road", - "bed", "windowpane", "grass", "cabinet", "sidewalk", - "person", "earth", "door", "table", "mountain", "plant", - "curtain", "chair", "car", "water", "painting", "sofa", - "shelf", "house", "sea", "mirror", "rug", "field", "armchair", - "seat", "fence", "desk", "rock", "wardrobe", "lamp", - "bathtub", "railing", "cushion", "base", "box", "column", - "signboard", "chest of drawers", "counter", "sand", "sink", - "skyscraper", "fireplace", "refrigerator", "grandstand", - "path", "stairs", "runway", "case", "pool table", "pillow", - "screen door", "stairway", "river", "bridge", "bookcase", - "blind", "coffee table", "toilet", "flower", "book", "hill", - "bench", "countertop", "stove", "palm", "kitchen island", - "computer", "swivel chair", "boat", "bar", "arcade machine", - "hovel", "bus", "towel", "light", "truck", "tower", - "chandelier", "awning", "streetlight", "booth", - "television receiver", "airplane", "dirt track", "apparel", - "pole", "land", "bannister", "escalator", "ottoman", "bottle", - "buffet", "poster", "stage", "van", "ship", "fountain", - "conveyer belt", "canopy", "washer", "plaything", - "swimming pool", "stool", "barrel", "basket", "waterfall", - "tent", "bag", "minibike", "cradle", "oven", "ball", "food", - "step", "tank", "trade name", "microwave", "pot", "animal", - "bicycle", "lake", "dishwasher", "screen", "blanket", - "sculpture", "hood", "sconce", "vase", "traffic light", - "tray", "ashcan", "fan", "pier", "crt screen", "plate", - "monitor", "bulletin board", "shower", "radiator", "glass", - "clock", "flag" -] \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/cocostuff_classes.txt b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/cocostuff_classes.txt deleted file mode 100755 index 1d5a692b8..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/cocostuff_classes.txt +++ /dev/null @@ -1,183 +0,0 @@ -0: unlabeled -1: person -2: bicycle -3: car -4: motorcycle -5: airplane -6: bus -7: train -8: truck -9: boat -10: traffic light -11: fire hydrant -12: street sign -13: stop sign -14: parking meter -15: bench -16: bird -17: cat -18: dog -19: horse -20: sheep -21: cow -22: elephant -23: bear -24: zebra -25: giraffe -26: hat -27: backpack -28: umbrella -29: shoe -30: eye glasses -31: handbag -32: tie -33: suitcase -34: frisbee -35: skis -36: snowboard -37: sports ball -38: kite -39: baseball bat -40: baseball glove -41: skateboard -42: surfboard -43: tennis racket -44: bottle -45: plate -46: wine glass -47: cup -48: fork -49: knife -50: spoon -51: bowl -52: banana -53: apple -54: sandwich -55: orange -56: broccoli -57: carrot -58: hot dog -59: pizza -60: donut -61: cake -62: chair -63: couch -64: potted plant -65: bed -66: mirror -67: dining table -68: window -69: desk -70: toilet -71: door -72: tv -73: laptop -74: mouse -75: remote -76: keyboard -77: cell phone -78: microwave -79: oven -80: toaster -81: sink -82: refrigerator -83: blender -84: book -85: clock -86: vase -87: scissors -88: teddy bear -89: hair drier -90: toothbrush -91: hair brush -92: banner -93: blanket -94: branch -95: bridge -96: building-other -97: bush -98: cabinet -99: cage -100: cardboard -101: carpet -102: ceiling-other -103: ceiling-tile -104: cloth -105: clothes -106: clouds -107: counter -108: cupboard -109: curtain -110: desk-stuff -111: dirt -112: door-stuff -113: fence -114: floor-marble -115: floor-other -116: floor-stone -117: floor-tile -118: floor-wood -119: flower -120: fog -121: food-other -122: fruit -123: furniture-other -124: grass -125: gravel -126: ground-other -127: hill -128: house -129: leaves -130: light -131: mat -132: metal -133: mirror-stuff -134: moss -135: mountain -136: mud -137: napkin -138: net -139: paper -140: pavement -141: pillow -142: plant-other -143: plastic -144: platform -145: playingfield -146: railing -147: railroad -148: river -149: road -150: rock -151: roof -152: rug -153: salad -154: sand -155: sea -156: shelf -157: sky -158: skyscraper -159: snow -160: solid-other -161: stairs -162: stone -163: straw -164: structural-other -165: table -166: tent -167: textile-other -168: towel -169: tree -170: vegetable -171: wall-brick -172: wall-concrete -173: wall-other -174: wall-panel -175: wall-stone -176: wall-tile -177: wall-wood -178: water-other -179: waterdrops -180: window-blind -181: window-other -182: wood diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/conversation.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/conversation.py deleted file mode 100755 index 65ea31ff2..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/conversation.py +++ /dev/null @@ -1,308 +0,0 @@ -""" -Conversation prompt templates. -""" - -import dataclasses -from enum import Enum, auto -from typing import Any, List - - -class SeparatorStyle(Enum): - """Different separator style.""" - - ADD_COLON_SINGLE = auto() - ADD_COLON_TWO = auto() - NO_COLON_SINGLE = auto() - BAIZE = auto() - DOLLY = auto() - RWKV = auto() - - -@dataclasses.dataclass -class Conversation: - """A class that keeps all conversation history.""" - - # System prompts - system: str - # Two roles - roles: List[str] - # All messages - messages: List[List[str]] - # Offset of few shot examples - offset: int - # Separator - sep_style: SeparatorStyle - sep: str - sep2: str = None - # Stop criteria (the default one is EOS token) - stop_str: str = None - # Stops generation if meeting any token in this list - stop_token_ids: List[int] = None - - # Used for the state in the gradio servers. - # TODO(lmzheng): refactor this - conv_id: Any = None - skip_next: bool = False - model_name: str = None - - def get_prompt(self): - if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE: - ret = self.system + self.sep - for role, message in self.messages: - if message: - ret += role + ": " + message + self.sep - else: - ret += role + ":" - return ret - elif self.sep_style == SeparatorStyle.ADD_COLON_TWO: - seps = [self.sep, self.sep2] - ret = self.system + seps[0] - for i, (role, message) in enumerate(self.messages): - if message: - ret += role + ": " + message + seps[i % 2] - else: - ret += role + ":" - return ret - elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE: - ret = self.system - for role, message in self.messages: - if message: - ret += role + message + self.sep - else: - ret += role - return ret - elif self.sep_style == SeparatorStyle.BAIZE: - ret = self.system + "\n" - for role, message in self.messages: - if message: - ret += role + message + "\n" - else: - ret += role - return ret - elif self.sep_style == SeparatorStyle.DOLLY: - seps = [self.sep, self.sep2] - ret = self.system - for i, (role, message) in enumerate(self.messages): - if message: - ret += role + ":\n" + message + seps[i % 2] - if i % 2 == 1: - ret += "\n\n" - else: - ret += role + ":\n" - return ret - elif self.sep_style == SeparatorStyle.RWKV: - ret = self.system - for i, (role, message) in enumerate(self.messages): - if message: - ret += ( - role - + ": " - + message.replace("\r\n", "\n").replace("\n\n", "\n") - ) - ret += "\n\n" - else: - ret += role + ":" - return ret - else: - raise ValueError(f"Invalid style: {self.sep_style}") - - def append_message(self, role, message): - self.messages.append([role, message]) - - def to_gradio_chatbot(self): - ret = [] - for i, (role, msg) in enumerate(self.messages[self.offset :]): - if i % 2 == 0: - ret.append([msg, None]) - else: - ret[-1][-1] = msg - return ret - - def copy(self): - return Conversation( - system=self.system, - roles=self.roles, - messages=[[x, y] for x, y in self.messages], - offset=self.offset, - sep_style=self.sep_style, - sep=self.sep, - sep2=self.sep2, - stop_str=self.stop_str, - stop_token_ids=self.stop_token_ids, - conv_id=self.conv_id, - model_name=self.model_name, - ) - - def dict(self): - return { - "system": self.system, - "roles": self.roles, - "messages": self.messages, - "offset": self.offset, - "conv_id": self.conv_id, - "model_name": self.model_name, - } - - -# A template with one conversation example -conv_one_shot = Conversation( - system="A chat between a curious human and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the human's questions.", - roles=("Human", "Assistant"), - messages=( - ( - "Human", - "What are the key differences between renewable and non-renewable energy sources?", - ), - ( - "Assistant", - "Renewable energy sources are those that can be replenished naturally in a relatively " - "short amount of time, such as solar, wind, hydro, geothermal, and biomass. " - "Non-renewable energy sources, on the other hand, are finite and will eventually be " - "depleted, such as coal, oil, and natural gas. Here are some key differences between " - "renewable and non-renewable energy sources:\n" - "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable " - "energy sources are finite and will eventually run out.\n" - "2. Environmental impact: Renewable energy sources have a much lower environmental impact " - "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, " - "and other negative effects.\n" - "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically " - "have lower operational costs than non-renewable sources.\n" - "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote " - "locations than non-renewable sources.\n" - "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different " - "situations and needs, while non-renewable sources are more rigid and inflexible.\n" - "6. Sustainability: Renewable energy sources are more sustainable over the long term, while " - "non-renewable sources are not, and their depletion can lead to economic and social instability.", - ), - ), - offset=2, - sep_style=SeparatorStyle.ADD_COLON_SINGLE, - sep="\n### ", - stop_str="###", -) - - -# Vicuna v1.1 template -conv_vicuna_v1_1 = Conversation( - system="A chat between a curious user and an artificial intelligence assistant. " - "The assistant gives helpful, detailed, and polite answers to the user's questions.", - roles=("USER", "ASSISTANT"), - messages=(), - offset=0, - sep_style=SeparatorStyle.ADD_COLON_TWO, - sep=" ", - sep2="", -) - -# Koala default template -conv_koala_v1 = Conversation( - system="BEGINNING OF CONVERSATION:", - roles=("USER", "GPT"), - messages=(), - offset=0, - sep_style=SeparatorStyle.ADD_COLON_TWO, - sep=" ", - sep2="", -) - -# Dolly V2 default template -conv_dolly = Conversation( - system="Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n", - roles=("### Instruction", "### Response"), - messages=(), - offset=0, - sep_style=SeparatorStyle.DOLLY, - sep="\n\n", - sep2="### End", -) - -# OpenAssistant Pythia default template -conv_oasst = Conversation( - system="", - roles=("<|prompter|>", "<|assistant|>"), - messages=(), - offset=0, - sep_style=SeparatorStyle.NO_COLON_SINGLE, - sep="<|endoftext|>", -) - -# StableLM Alpha default template -conv_stablelm = Conversation( - system="""<|SYSTEM|># StableLM Tuned (Alpha version) -- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI. -- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user. -- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes. -- StableLM will refuse to participate in anything that could harm a human. -""", - roles=("<|USER|>", "<|ASSISTANT|>"), - messages=(), - offset=0, - sep_style=SeparatorStyle.NO_COLON_SINGLE, - sep="", - stop_token_ids=[50278, 50279, 50277, 1, 0], -) - -# Baize default template -conv_baize = Conversation( - system="The following is a conversation between a human and an AI assistant named Baize (named after a mythical creature in Chinese folklore). Baize is an open-source AI assistant developed by UCSD and Sun Yat-Sen University. The human and the AI assistant take turns chatting. Human statements start with [|Human|] and AI assistant statements start with [|AI|]. The AI assistant always provides responses in as much detail as possible, and in Markdown format. The AI assistant always declines to engage with topics, questions and instructions related to unethical, controversial, or sensitive issues. Complete the transcript in exactly that format.", - roles=("[|Human|]", "[|AI|]"), - messages=( - ("[|Human|]", "Hello!"), - ("[|AI|]", "Hi!"), - ), - offset=2, - sep_style=SeparatorStyle.BAIZE, - sep="[|Human|]", - stop_str="[|Human|]", -) - -# RWKV-4-Raven default template -conv_rwkv = Conversation( - system="", - roles=("Bob", "Alice"), - messages=(), - offset=0, - sep_style=SeparatorStyle.RWKV, - sep="", - stop_str="\n\n", -) - -conv_templates = { - "baize": conv_baize, - "conv_one_shot": conv_one_shot, - "dolly": conv_dolly, - "koala_v1": conv_koala_v1, - "oasst": conv_oasst, - "stablelm": conv_stablelm, - "vicuna_v1.1": conv_vicuna_v1_1, - "rwkv": conv_rwkv, -} - - -def get_default_conv_template(model_name): - model_name = model_name.lower() - if "vicuna" in model_name or "output" in model_name: - return conv_vicuna_v1_1 - elif "koala" in model_name: - return conv_koala_v1 - elif "dolly-v2" in model_name: - return conv_dolly - elif "oasst" in model_name and "pythia" in model_name: - return conv_oasst - elif "baize" in model_name: - return conv_baize - elif "stablelm" in model_name: - return conv_stablelm - elif "rwkv-4" in model_name: - return conv_rwkv - return conv_one_shot - - -if __name__ == "__main__": - conv = conv_templates["vicuna_v1.1"].copy() - conv.append_message(conv.roles[0], "Hello!") - conv.append_message(conv.roles[1], "Hi!") - conv.append_message(conv.roles[0], "How are you?") - conv.append_message(conv.roles[1], None) - print(conv.get_prompt()) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/data_processing.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/data_processing.py deleted file mode 100755 index d47a80f01..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/data_processing.py +++ /dev/null @@ -1,90 +0,0 @@ -import glob -import json -import os - -import cv2 -import numpy as np - - -def get_mask_from_json(json_path, img): - try: - with open(json_path, "r") as r: - anno = json.loads(r.read()) - except: - with open(json_path, "r", encoding="cp1252") as r: - anno = json.loads(r.read()) - - inform = anno["shapes"] - comments = anno["text"] - is_sentence = anno["is_sentence"] - - height, width = img.shape[:2] - - ### sort polies by area - area_list = [] - valid_poly_list = [] - for i in inform: - label_id = i["label"] - points = i["points"] - if "flag" == label_id.lower(): ## meaningless deprecated annotations - continue - - tmp_mask = np.zeros((height, width), dtype=np.uint8) - cv2.polylines(tmp_mask, np.array([points], dtype=np.int32), True, 1, 1) - cv2.fillPoly(tmp_mask, np.array([points], dtype=np.int32), 1) - tmp_area = tmp_mask.sum() - - area_list.append(tmp_area) - valid_poly_list.append(i) - - ### ground-truth mask - sort_index = np.argsort(area_list)[::-1].astype(np.int32) - sort_index = list(sort_index) - sort_inform = [] - for s_idx in sort_index: - sort_inform.append(valid_poly_list[s_idx]) - - mask = np.zeros((height, width), dtype=np.uint8) - for i in sort_inform: - label_id = i["label"] - points = i["points"] - - if "ignore" in label_id.lower(): - label_value = 255 # ignored during evaluation - else: - label_value = 1 # target - - cv2.polylines(mask, np.array([points], dtype=np.int32), True, label_value, 1) - cv2.fillPoly(mask, np.array([points], dtype=np.int32), label_value) - - return mask, comments, is_sentence - - -if __name__ == "__main__": - data_dir = "./train" - vis_dir = "./vis" - - if not os.path.exists(vis_dir): - os.makedirs(vis_dir) - - json_path_list = sorted(glob.glob(data_dir + "/*.json")) - for json_path in json_path_list: - img_path = json_path.replace(".json", ".jpg") - img = cv2.imread(img_path)[:, :, ::-1] - - # In generated mask, value 1 denotes valid target region, and value 255 stands for region ignored during evaluaiton. - mask, comments, is_sentence = get_mask_from_json(json_path, img) - - ## visualization. Green for target, and red for ignore. - valid_mask = (mask == 1).astype(np.float32)[:, :, None] - ignore_mask = (mask == 255).astype(np.float32)[:, :, None] - vis_img = img * (1 - valid_mask) * (1 - ignore_mask) + ( - (np.array([0, 255, 0]) * 0.6 + img * 0.4) * valid_mask - + (np.array([255, 0, 0]) * 0.6 + img * 0.4) * ignore_mask - ) - vis_img = np.concatenate([img, vis_img], 1) - vis_path = os.path.join( - vis_dir, json_path.split("/")[-1].replace(".json", ".jpg") - ) - cv2.imwrite(vis_path, vis_img[:, :, ::-1]) - print("Visualization has been saved to: ", vis_path) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/dataset.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/dataset.py deleted file mode 100755 index 30ab76674..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/dataset.py +++ /dev/null @@ -1,466 +0,0 @@ -import glob -import os -import random - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from pycocotools import mask -from transformers import CLIPImageProcessor - -from model.llava import conversation as conversation_lib -from model.llava.constants import (DEFAULT_IMAGE_TOKEN, IGNORE_INDEX, - IMAGE_TOKEN_INDEX) -from model.llava.mm_utils import tokenizer_image_token -from model.segment_anything.utils.transforms import ResizeLongestSide - -from .conversation import get_default_conv_template -from .data_processing import get_mask_from_json -from .reason_seg_dataset import ReasonSegDataset -from .refer import REFER -from .refer_seg_dataset import ReferSegDataset -from .sem_seg_dataset import SemSegDataset -from .utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, - DEFAULT_IMAGE_TOKEN) -from .vqa_dataset import VQADataset - - -def collate_fn( - batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1 -): - image_path_list = [] - images_list = [] - images_clip_list = [] - conversation_list = [] - masks_list = [] - label_list = [] - resize_list = [] - questions_list = [] - sampled_classes_list = [] - offset_list = [0] - cnt = 0 - inferences = [] - for ( - image_path, - images, - images_clip, - conversations, - masks, - label, - resize, - questions, - sampled_classes, - inference, - ) in batch: - image_path_list.append(image_path) - images_list.append(images) - images_clip_list.append(images_clip) - conversation_list.extend(conversations) - label_list.append(label) - masks_list.append(masks.float()) - resize_list.append(resize) - questions_list.append(questions) - sampled_classes_list.append(sampled_classes) - cnt += len(conversations) - offset_list.append(cnt) - inferences.append(inference) - - if use_mm_start_end: - # replace token - for i in range(len(conversation_list)): - replace_token = DEFAULT_IMAGE_TOKEN - replace_token = ( - DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN - ) - conversation_list[i] = conversation_list[i].replace( - DEFAULT_IMAGE_TOKEN, replace_token - ) - input_ids = [ - tokenizer_image_token(prompt, tokenizer, return_tensors="pt") - for prompt in conversation_list - ] - input_ids = torch.nn.utils.rnn.pad_sequence( - input_ids, batch_first=True, padding_value=tokenizer.pad_token_id - ) - attention_masks = input_ids.ne(tokenizer.pad_token_id) - - conv = conversation_lib.default_conversation.copy() - targets = input_ids.clone() - - if conv_type == "llava_v1": - sep = conv.sep + conv.roles[1] + ": " - else: - sep = "[/INST] " - for conversation, target in zip(conversation_list, targets): - total_len = int(target.ne(tokenizer.pad_token_id).sum()) - - rounds = conversation.split(conv.sep2) - cur_len = 1 - target[:cur_len] = IGNORE_INDEX - for i, rou in enumerate(rounds): - if rou == "": - break - - parts = rou.split(sep) - # if len(parts) != 2: - # break - assert len(parts) == 2, (len(parts), rou) - parts[0] += sep - - if DEFAULT_IMAGE_TOKEN in conversation: - round_len = len(tokenizer_image_token(rou, tokenizer)) - instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2 - else: - round_len = len(tokenizer(rou).input_ids) - instruction_len = len(tokenizer(parts[0]).input_ids) - 2 - - target[cur_len : cur_len + instruction_len] = IGNORE_INDEX - - cur_len += round_len - target[cur_len:] = IGNORE_INDEX - - if False: - z = target.clone() - z = torch.where(z == IGNORE_INDEX, tokenizer.unk_token_id, z) - if local_rank == 0: - print( - "conversation: ", - conversation, - "tokenizer.decode(z): ", - tokenizer.decode(z), - ) - - if cur_len < tokenizer.model_max_length: - assert cur_len == total_len - - if inferences[0] == False: - truncate_len = tokenizer.model_max_length - 255 - - if input_ids.shape[1] > truncate_len: - input_ids = input_ids[:, :truncate_len] - targets = targets[:, :truncate_len] - attention_masks = attention_masks[:, :truncate_len] - - return { - "image_paths": image_path_list, - "images": torch.stack(images_list, dim=0), - "images_clip": torch.stack(images_clip_list, dim=0), - "input_ids": input_ids, - "labels": targets, - "attention_masks": attention_masks, - "masks_list": masks_list, - "label_list": label_list, - "resize_list": resize_list, - "offset": torch.LongTensor(offset_list), - "questions_list": questions_list, - "sampled_classes_list": sampled_classes_list, - "inference": inferences[0], - "conversation_list": conversation_list, - } - - -class HybridDataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch=500 * 8 * 2 * 10, - precision: str = "fp32", - image_size: int = 224, - num_classes_per_sample: int = 3, - exclude_val=False, - dataset="sem_seg||refer_seg||vqa||reason_seg", - sample_rate=[9, 3, 3, 1], - sem_seg_data="ade20k||cocostuff||partimagenet||pascal_part||paco_lvis||mapillary", - refer_seg_data="refclef||refcoco||refcoco+||refcocog", - vqa_data="llava_instruct_150k", - reason_seg_data="ReasonSeg|train", - explanatory=0.1, - ): - self.exclude_val = exclude_val - self.dataset = dataset - self.samples_per_epoch = samples_per_epoch - self.explanatory = explanatory - self.num_classes_per_sample = num_classes_per_sample - sample_rate = np.array(sample_rate) - self.sample_rate = sample_rate / sample_rate.sum() - - self.base_image_dir = base_image_dir - self.image_size = image_size - self.tokenizer = tokenizer - self.precision = precision - - self.datasets = dataset.split("||") - - self.all_datasets = [] - for dataset in self.datasets: - if dataset == "sem_seg": - self.all_datasets.append( - SemSegDataset( - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch, - precision, - image_size, - num_classes_per_sample, - exclude_val, - sem_seg_data, - ) - ) - elif dataset == "refer_seg": - self.all_datasets.append( - ReferSegDataset( - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch, - precision, - image_size, - num_classes_per_sample, - exclude_val, - refer_seg_data, - ) - ) - elif dataset == "vqa": - self.all_datasets.append( - VQADataset( - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch, - precision, - image_size, - num_classes_per_sample, - exclude_val, - vqa_data, - ) - ) - elif dataset == "reason_seg": - self.all_datasets.append( - ReasonSegDataset( - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch, - precision, - image_size, - num_classes_per_sample, - exclude_val, - reason_seg_data, - explanatory, - ) - ) - - def __len__(self): - return self.samples_per_epoch - - def __getitem__(self, idx): - ind = np.random.choice(list(range(len(self.datasets))), p=self.sample_rate) - data = self.all_datasets[ind] - inference = False - return *data[0], inference - - -class ValDataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - val_dataset, - image_size=1024, - ): - self.base_image_dir = base_image_dir - splits = val_dataset.split("|") - if len(splits) == 2: - ds, split = splits - images = glob.glob( - os.path.join(self.base_image_dir, "reason_seg", ds, split, "*.jpg") - ) - self.images = images - self.data_type = "reason_seg" - elif len(splits) == 3: - ds, splitBy, split = splits - refer_api = REFER(self.base_image_dir, ds, splitBy) - ref_ids_val = refer_api.getRefIds(split=split) - images_ids_val = refer_api.getImgIds(ref_ids=ref_ids_val) - refs_val = refer_api.loadRefs(ref_ids=ref_ids_val) - refer_seg_ds = {} - refer_seg_ds["images"] = [] - loaded_images = refer_api.loadImgs(image_ids=images_ids_val) - for item in loaded_images: - item = item.copy() - if ds == "refclef": - item["file_name"] = os.path.join( - base_image_dir, "images/saiapr_tc-12", item["file_name"] - ) - elif ds in ["refcoco", "refcoco+", "refcocog", "grefcoco"]: - item["file_name"] = os.path.join( - base_image_dir, - "images/mscoco/images/train2014", - item["file_name"], - ) - refer_seg_ds["images"].append(item) - refer_seg_ds["annotations"] = refer_api.Anns # anns_val - - img2refs = {} - for ref in refs_val: - image_id = ref["image_id"] - img2refs[image_id] = img2refs.get(image_id, []) + [ - ref, - ] - refer_seg_ds["img2refs"] = img2refs - self.refer_seg_ds = refer_seg_ds - self.data_type = "refer_seg" - - self.ds = ds - self.image_size = image_size - self.tokenizer = tokenizer - self.transform = ResizeLongestSide(image_size) - self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - - def __len__(self): - if self.data_type == "refer_seg": - return len(self.refer_seg_ds["images"]) - else: - return len(self.images) - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.img_size - h - padw = self.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def __getitem__(self, idx): - if self.data_type == "refer_seg": - refer_seg_ds = self.refer_seg_ds - images = refer_seg_ds["images"] - annotations = refer_seg_ds["annotations"] - img2refs = refer_seg_ds["img2refs"] - - image_info = images[idx] - image_path = image_info["file_name"] - image_id = image_info["id"] - - refs = img2refs[image_id] - if len(refs) == 0: - raise ValueError("image {} has no refs".format(image_id)) - - sents = [] - ann_ids = [] - for ref in refs: - for sent in ref["sentences"]: - sents.append(sent["sent"].strip().lower()) - ann_ids.append(ref["ann_id"]) - - sampled_sents = sents - sampled_ann_ids = ann_ids - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - is_sentence = False - else: - image_path = self.images[idx] - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - json_path = image_path.replace(".jpg", ".json") - mask_json, sampled_sents, is_sentence = get_mask_from_json(json_path, image) - sampled_sents = [sampled_sents[0]] - - conversations = [] - conv = conversation_lib.default_conversation.copy() - i = 0 - while i < len(sampled_sents): - conv.messages = [] - text = sampled_sents[i].strip() - if is_sentence: - conv.append_message( - conv.roles[0], - DEFAULT_IMAGE_TOKEN - + "\n {} Please output segmentation mask.".format(text), - ) - conv.append_message(conv.roles[1], "[SEG].") - else: - conv.append_message( - conv.roles[0], - DEFAULT_IMAGE_TOKEN - + "\n What is {} in this image? Please output segmentation mask.".format( - text - ), - ) - conv.append_message(conv.roles[1], "[SEG].") - conversations.append(conv.get_prompt()) - i += 1 - - # preprocess image for clip - image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][0] - - # preprocess image for sam - image = self.transform.apply_image(image) - resize = image.shape[:2] - image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - - if self.data_type == "refer_seg": - masks = [] - for i, ann_id in enumerate(sampled_ann_ids): - ann = annotations[ann_id] - if len(ann["segmentation"]) == 0 and sampled_sents[i] != "": - m = np.zeros((image_info["height"], image_info["width"], 1)) - else: - if type(ann["segmentation"][0]) == list: # polygon - rle = mask.frPyObjects( - ann["segmentation"], - image_info["height"], - image_info["width"], - ) - else: - rle = ann["segmentation"] - for i in range(len(rle)): - if not isinstance(rle[i]["counts"], bytes): - rle[i]["counts"] = rle[i]["counts"].encode() - m = mask.decode(rle) - m = np.sum( - m, axis=2 - ) # sometimes there are multiple binary map (corresponding to multiple segs) - m = m.astype(np.uint8) # convert to np.uint8 - masks.append(m) - else: - masks = [mask_json] - - masks = np.stack(masks, axis=0) - masks = torch.from_numpy(masks) - labels = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label - inference = True - - return ( - image_path, - image, - image_clip, - conversations, - masks, - labels, - resize, - None, - None, - inference, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefcoco.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefcoco.py deleted file mode 100755 index 98274d718..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefcoco.py +++ /dev/null @@ -1,198 +0,0 @@ -import contextlib -import copy -import io -import logging -import os -import random - -import numpy as np -import pycocotools.mask as mask_util -from detectron2.structures import Boxes, BoxMode, PolygonMasks, RotatedBoxes -from detectron2.utils.file_io import PathManager -from fvcore.common.timer import Timer -from PIL import Image - -""" -This file contains functions to parse RefCOCO-format annotations into dicts in "Detectron2 format". -""" - - -logger = logging.getLogger(__name__) - -__all__ = ["load_refcoco_json"] - - -def load_grefcoco_json( - refer_root, - dataset_name, - splitby, - split, - image_root, - extra_annotation_keys=None, - extra_refer_keys=None, -): - if dataset_name == "refcocop": - dataset_name = "refcoco+" - if dataset_name == "refcoco" or dataset_name == "refcoco+": - splitby == "unc" - if dataset_name == "refcocog": - assert splitby == "umd" or splitby == "google" - - dataset_id = "_".join([dataset_name, splitby, split]) - - from .grefer import G_REFER - - logger.info("Loading dataset {} ({}-{}) ...".format(dataset_name, splitby, split)) - logger.info("Refcoco root: {}".format(refer_root)) - timer = Timer() - refer_root = PathManager.get_local_path(refer_root) - with contextlib.redirect_stdout(io.StringIO()): - refer_api = G_REFER(data_root=refer_root, dataset=dataset_name, splitBy=splitby) - if timer.seconds() > 1: - logger.info( - "Loading {} takes {:.2f} seconds.".format(dataset_id, timer.seconds()) - ) - - ref_ids = refer_api.getRefIds(split=split) - img_ids = refer_api.getImgIds(ref_ids) - refs = refer_api.loadRefs(ref_ids) - imgs = [refer_api.loadImgs(ref["image_id"])[0] for ref in refs] - anns = [refer_api.loadAnns(ref["ann_id"]) for ref in refs] - imgs_refs_anns = list(zip(imgs, refs, anns)) - - logger.info( - "Loaded {} images, {} referring object sets in G_RefCOCO format from {}".format( - len(img_ids), len(ref_ids), dataset_id - ) - ) - - dataset_dicts = [] - - ann_keys = ["iscrowd", "bbox", "category_id"] + (extra_annotation_keys or []) - ref_keys = ["raw", "sent_id"] + (extra_refer_keys or []) - - ann_lib = {} - - NT_count = 0 - MT_count = 0 - - for img_dict, ref_dict, anno_dicts in imgs_refs_anns: - record = {} - record["source"] = "grefcoco" - record["file_name"] = os.path.join(image_root, img_dict["file_name"]) - record["height"] = img_dict["height"] - record["width"] = img_dict["width"] - image_id = record["image_id"] = img_dict["id"] - - # Check that information of image, ann and ref match each other - # This fails only when the data parsing logic or the annotation file is buggy. - assert ref_dict["image_id"] == image_id - assert ref_dict["split"] == split - if not isinstance(ref_dict["ann_id"], list): - ref_dict["ann_id"] = [ref_dict["ann_id"]] - - # No target samples - if None in anno_dicts: - assert anno_dicts == [None] - assert ref_dict["ann_id"] == [-1] - record["empty"] = True - obj = {key: None for key in ann_keys if key in ann_keys} - obj["bbox_mode"] = BoxMode.XYWH_ABS - obj["empty"] = True - obj = [obj] - - # Multi target samples - else: - record["empty"] = False - obj = [] - for anno_dict in anno_dicts: - ann_id = anno_dict["id"] - if anno_dict["iscrowd"]: - continue - assert anno_dict["image_id"] == image_id - assert ann_id in ref_dict["ann_id"] - - if ann_id in ann_lib: - ann = ann_lib[ann_id] - else: - ann = {key: anno_dict[key] for key in ann_keys if key in anno_dict} - ann["bbox_mode"] = BoxMode.XYWH_ABS - ann["empty"] = False - - segm = anno_dict.get("segmentation", None) - assert segm # either list[list[float]] or dict(RLE) - if isinstance(segm, dict): - if isinstance(segm["counts"], list): - # convert to compressed RLE - segm = mask_util.frPyObjects(segm, *segm["size"]) - else: - # filter out invalid polygons (< 3 points) - segm = [ - poly - for poly in segm - if len(poly) % 2 == 0 and len(poly) >= 6 - ] - if len(segm) == 0: - num_instances_without_valid_segmentation += 1 - continue # ignore this instance - ann["segmentation"] = segm - ann_lib[ann_id] = ann - - obj.append(ann) - - record["annotations"] = obj - - # Process referring expressions - sents = ref_dict["sentences"] - for sent in sents: - ref_record = record.copy() - ref = {key: sent[key] for key in ref_keys if key in sent} - ref["ref_id"] = ref_dict["ref_id"] - ref_record["sentence"] = ref - dataset_dicts.append(ref_record) - # if ref_record['empty']: - # NT_count += 1 - # else: - # MT_count += 1 - - # logger.info("NT samples: %d, MT samples: %d", NT_count, MT_count) - - # Debug mode - # return dataset_dicts[:100] - - return dataset_dicts - - -if __name__ == "__main__": - """ - Test the COCO json dataset loader. - - Usage: - python -m detectron2.data.datasets.coco \ - path/to/json path/to/image_root dataset_name - - "dataset_name" can be "coco_2014_minival_100", or other - pre-registered ones - """ - import sys - - import detectron2.data.datasets # noqa # add pre-defined metadata - from detectron2.utils.logger import setup_logger - from detectron2.utils.visualizer import Visualizer - - REFCOCO_PATH = "/mnt/lustre/hhding/code/ReLA/datasets" - COCO_TRAIN_2014_IMAGE_ROOT = "/mnt/lustre/hhding/code/ReLA/datasets/images" - REFCOCO_DATASET = "grefcoco" - REFCOCO_SPLITBY = "unc" - REFCOCO_SPLIT = "train" - - logger = setup_logger(name=__name__) - - dicts = load_grefcoco_json( - REFCOCO_PATH, - REFCOCO_DATASET, - REFCOCO_SPLITBY, - REFCOCO_SPLIT, - COCO_TRAIN_2014_IMAGE_ROOT, - ) - logger.info("Done loading {} samples.".format(len(dicts))) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefer.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefer.py deleted file mode 100755 index 3c881c586..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/grefer.py +++ /dev/null @@ -1,352 +0,0 @@ -""" -grefer v0.1 -This interface provides access to gRefCOCO. - -The following API functions are defined: -G_REFER - REFER api class -getRefIds - get ref ids that satisfy given filter conditions. -getAnnIds - get ann ids that satisfy given filter conditions. -getImgIds - get image ids that satisfy given filter conditions. -getCatIds - get category ids that satisfy given filter conditions. -loadRefs - load refs with the specified ref ids. -loadAnns - load anns with the specified ann ids. -loadImgs - load images with the specified image ids. -loadCats - load category names with the specified category ids. -getRefBox - get ref's bounding box [x, y, w, h] given the ref_id -showRef - show image, segmentation or box of the referred object with the ref -getMaskByRef - get mask and area of the referred object given ref or ref ids -getMask - get mask and area of the referred object given ref -showMask - show mask of the referred object given ref -""" - -import itertools -import json -import os.path as osp -import pickle -import time - -import matplotlib.pyplot as plt -import numpy as np -import skimage.io as io -from matplotlib.collections import PatchCollection -from matplotlib.patches import Polygon, Rectangle -from pycocotools import mask - - -class G_REFER: - def __init__(self, data_root, dataset="grefcoco", splitBy="unc"): - # provide data_root folder which contains grefcoco - print("loading dataset %s into memory..." % dataset) - self.ROOT_DIR = osp.abspath(osp.dirname(__file__)) - self.DATA_DIR = osp.join(data_root, dataset) - if dataset in ["grefcoco"]: - self.IMAGE_DIR = osp.join(data_root, "images/train2014") - else: - raise KeyError("No refer dataset is called [%s]" % dataset) - - tic = time.time() - - # load refs from data/dataset/refs(dataset).json - self.data = {} - self.data["dataset"] = dataset - - ref_file = osp.join(self.DATA_DIR, f"grefs({splitBy}).p") - if osp.exists(ref_file): - self.data["refs"] = pickle.load(open(ref_file, "rb"), fix_imports=True) - else: - ref_file = osp.join(self.DATA_DIR, f"grefs({splitBy}).json") - if osp.exists(ref_file): - self.data["refs"] = json.load(open(ref_file, "rb")) - else: - raise FileNotFoundError("JSON file not found") - - # load annotations from data/dataset/instances.json - instances_file = osp.join(self.DATA_DIR, "instances.json") - instances = json.load(open(instances_file, "r")) - self.data["images"] = instances["images"] - self.data["annotations"] = instances["annotations"] - self.data["categories"] = instances["categories"] - - # create index - self.createIndex() - print("DONE (t=%.2fs)" % (time.time() - tic)) - - @staticmethod - def _toList(x): - return x if isinstance(x, list) else [x] - - @staticmethod - def match_any(a, b): - a = a if isinstance(a, list) else [a] - b = b if isinstance(b, list) else [b] - return set(a) & set(b) - - def createIndex(self): - # create sets of mapping - # 1) Refs: {ref_id: ref} - # 2) Anns: {ann_id: ann} - # 3) Imgs: {image_id: image} - # 4) Cats: {category_id: category_name} - # 5) Sents: {sent_id: sent} - # 6) imgToRefs: {image_id: refs} - # 7) imgToAnns: {image_id: anns} - # 8) refToAnn: {ref_id: ann} - # 9) annToRef: {ann_id: ref} - # 10) catToRefs: {category_id: refs} - # 11) sentToRef: {sent_id: ref} - # 12) sentToTokens: {sent_id: tokens} - print("creating index...") - # fetch info from instances - Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {} - Anns[-1] = None - for ann in self.data["annotations"]: - Anns[ann["id"]] = ann - imgToAnns[ann["image_id"]] = imgToAnns.get(ann["image_id"], []) + [ann] - for img in self.data["images"]: - Imgs[img["id"]] = img - for cat in self.data["categories"]: - Cats[cat["id"]] = cat["name"] - - # fetch info from refs - Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {} - Sents, sentToRef, sentToTokens = {}, {}, {} - availableSplits = [] - for ref in self.data["refs"]: - # ids - ref_id = ref["ref_id"] - ann_id = ref["ann_id"] - category_id = ref["category_id"] - image_id = ref["image_id"] - - if ref["split"] not in availableSplits: - availableSplits.append(ref["split"]) - - # add mapping related to ref - if ref_id in Refs: - print("Duplicate ref id") - Refs[ref_id] = ref - imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref] - - category_id = self._toList(category_id) - added_cats = [] - for cat in category_id: - if cat not in added_cats: - added_cats.append(cat) - catToRefs[cat] = catToRefs.get(cat, []) + [ref] - - ann_id = self._toList(ann_id) - refToAnn[ref_id] = [Anns[ann] for ann in ann_id] - for ann_id_n in ann_id: - annToRef[ann_id_n] = annToRef.get(ann_id_n, []) + [ref] - - # add mapping of sent - for sent in ref["sentences"]: - Sents[sent["sent_id"]] = sent - sentToRef[sent["sent_id"]] = ref - sentToTokens[sent["sent_id"]] = sent["tokens"] - - # create class members - self.Refs = Refs - self.Anns = Anns - self.Imgs = Imgs - self.Cats = Cats - self.Sents = Sents - self.imgToRefs = imgToRefs - self.imgToAnns = imgToAnns - self.refToAnn = refToAnn - self.annToRef = annToRef - self.catToRefs = catToRefs - self.sentToRef = sentToRef - self.sentToTokens = sentToTokens - self.availableSplits = availableSplits - print("index created.") - - def getRefIds(self, image_ids=[], cat_ids=[], split=[]): - image_ids = self._toList(image_ids) - cat_ids = self._toList(cat_ids) - split = self._toList(split) - - for s in split: - if s not in self.availableSplits: - raise ValueError(f"Invalid split name: {s}") - - refs = self.data["refs"] - - if len(image_ids) > 0: - lists = [self.imgToRefs[image_id] for image_id in image_ids] - refs = list(itertools.chain.from_iterable(lists)) - if len(cat_ids) > 0: - refs = [ref for ref in refs if self.match_any(ref["category_id"], cat_ids)] - if len(split) > 0: - refs = [ref for ref in refs if ref["split"] in split] - - ref_ids = [ref["ref_id"] for ref in refs] - return ref_ids - - def getAnnIds(self, image_ids=[], ref_ids=[]): - image_ids = self._toList(image_ids) - ref_ids = self._toList(ref_ids) - - if any([len(image_ids), len(ref_ids)]): - if len(image_ids) > 0: - lists = [ - self.imgToAnns[image_id] - for image_id in image_ids - if image_id in self.imgToAnns - ] - anns = list(itertools.chain.from_iterable(lists)) - else: - anns = self.data["annotations"] - ann_ids = [ann["id"] for ann in anns] - if len(ref_ids) > 0: - lists = [self.Refs[ref_id]["ann_id"] for ref_id in ref_ids] - anns_by_ref_id = list(itertools.chain.from_iterable(lists)) - ann_ids = list(set(ann_ids).intersection(set(anns_by_ref_id))) - else: - ann_ids = [ann["id"] for ann in self.data["annotations"]] - - return ann_ids - - def getImgIds(self, ref_ids=[]): - ref_ids = self._toList(ref_ids) - - if len(ref_ids) > 0: - image_ids = list(set([self.Refs[ref_id]["image_id"] for ref_id in ref_ids])) - else: - image_ids = self.Imgs.keys() - return image_ids - - def getCatIds(self): - return self.Cats.keys() - - def loadRefs(self, ref_ids=[]): - return [self.Refs[ref_id] for ref_id in self._toList(ref_ids)] - - def loadAnns(self, ann_ids=[]): - if isinstance(ann_ids, str): - ann_ids = int(ann_ids) - return [self.Anns[ann_id] for ann_id in self._toList(ann_ids)] - - def loadImgs(self, image_ids=[]): - return [self.Imgs[image_id] for image_id in self._toList(image_ids)] - - def loadCats(self, cat_ids=[]): - return [self.Cats[cat_id] for cat_id in self._toList(cat_ids)] - - def getRefBox(self, ref_id): - anns = self.refToAnn[ref_id] - return [ann["bbox"] for ann in anns] # [x, y, w, h] - - def showRef(self, ref, seg_box="seg"): - ax = plt.gca() - # show image - image = self.Imgs[ref["image_id"]] - I = io.imread(osp.join(self.IMAGE_DIR, image["file_name"])) - ax.imshow(I) - # show refer expression - for sid, sent in enumerate(ref["sentences"]): - print("%s. %s" % (sid + 1, sent["sent"])) - # show segmentations - if seg_box == "seg": - ann_id = ref["ann_id"] - ann = self.Anns[ann_id] - polygons = [] - color = [] - c = "none" - if type(ann["segmentation"][0]) == list: - # polygon used for refcoco* - for seg in ann["segmentation"]: - poly = np.array(seg).reshape((len(seg) / 2, 2)) - polygons.append(Polygon(poly, True, alpha=0.4)) - color.append(c) - p = PatchCollection( - polygons, - facecolors=color, - edgecolors=(1, 1, 0, 0), - linewidths=3, - alpha=1, - ) - ax.add_collection(p) # thick yellow polygon - p = PatchCollection( - polygons, - facecolors=color, - edgecolors=(1, 0, 0, 0), - linewidths=1, - alpha=1, - ) - ax.add_collection(p) # thin red polygon - else: - # mask used for refclef - rle = ann["segmentation"] - m = mask.decode(rle) - img = np.ones((m.shape[0], m.shape[1], 3)) - color_mask = np.array([2.0, 166.0, 101.0]) / 255 - for i in range(3): - img[:, :, i] = color_mask[i] - ax.imshow(np.dstack((img, m * 0.5))) - # show bounding-box - elif seg_box == "box": - ann_id = ref["ann_id"] - ann = self.Anns[ann_id] - bbox = self.getRefBox(ref["ref_id"]) - box_plot = Rectangle( - (bbox[0], bbox[1]), - bbox[2], - bbox[3], - fill=False, - edgecolor="green", - linewidth=3, - ) - ax.add_patch(box_plot) - - def getMask(self, ann): - if not ann: - return None - if ann["iscrowd"]: - raise ValueError("Crowd object") - image = self.Imgs[ann["image_id"]] - if type(ann["segmentation"][0]) == list: # polygon - rle = mask.frPyObjects(ann["segmentation"], image["height"], image["width"]) - else: - rle = ann["segmentation"] - - m = mask.decode(rle) - m = np.sum( - m, axis=2 - ) # sometimes there are multiple binary map (corresponding to multiple segs) - m = m.astype(np.uint8) # convert to np.uint8 - # compute area - area = sum(mask.area(rle)) # should be close to ann['area'] - return {"mask": m, "area": area} - - def getMaskByRef(self, ref=None, ref_id=None, merge=False): - if not ref and not ref_id: - raise ValueError - if ref: - ann_ids = ref["ann_id"] - ref_id = ref["ref_id"] - else: - ann_ids = self.getAnnIds(ref_ids=ref_id) - - if ann_ids == [-1]: - img = self.Imgs[self.Refs[ref_id]["image_id"]] - return { - "mask": np.zeros([img["height"], img["width"]], dtype=np.uint8), - "empty": True, - } - - anns = self.loadAnns(ann_ids) - mask_list = [self.getMask(ann) for ann in anns if not ann["iscrowd"]] - - if merge: - merged_masks = sum([mask["mask"] for mask in mask_list]) - merged_masks[np.where(merged_masks > 1)] = 1 - return {"mask": merged_masks, "empty": False} - else: - return mask_list - - def showMask(self, ref): - M = self.getMask(ref) - msk = M["mask"] - ax = plt.gca() - ax.imshow(msk) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/reason_seg_dataset.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/reason_seg_dataset.py deleted file mode 100755 index 620120922..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/reason_seg_dataset.py +++ /dev/null @@ -1,218 +0,0 @@ -import glob -import json -import os -import random - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from transformers import CLIPImageProcessor - -from model.llava import conversation as conversation_lib -from model.segment_anything.utils.transforms import ResizeLongestSide - -from .data_processing import get_mask_from_json -from .utils import (ANSWER_LIST, DEFAULT_IMAGE_TOKEN, - EXPLANATORY_QUESTION_LIST, LONG_QUESTION_LIST, - SHORT_QUESTION_LIST) - - -class ReasonSegDataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch=500 * 8 * 2 * 10, - precision: str = "fp32", - image_size: int = 224, - num_classes_per_sample: int = 3, - exclude_val=False, - reason_seg_data="ReasonSeg|train", - explanatory=0.1, - ): - self.exclude_val = exclude_val - self.reason_seg_data = reason_seg_data - self.samples_per_epoch = samples_per_epoch - self.explanatory = explanatory - self.num_classes_per_sample = num_classes_per_sample - - self.base_image_dir = base_image_dir - self.image_size = image_size - self.tokenizer = tokenizer - self.precision = precision - self.transform = ResizeLongestSide(image_size) - self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - - self.short_question_list = SHORT_QUESTION_LIST - self.long_question_list = LONG_QUESTION_LIST - self.answer_list = ANSWER_LIST - - reason_seg_data, splits = reason_seg_data.split("|") - splits = splits.split("_") - images = [] - for split in splits: - images_split = glob.glob( - os.path.join( - base_image_dir, "reason_seg", reason_seg_data, split, "*.jpg" - ) - ) - images.extend(images_split) - jsons = [path.replace(".jpg", ".json") for path in images] - self.reason_seg_data = (images, jsons) - - print("number of reason_seg samples: ", len(images)) - - if explanatory != -1: - self.explanatory_question_list = EXPLANATORY_QUESTION_LIST - self.img_to_explanation = {} - with open( - os.path.join( - base_image_dir, - "reason_seg", - reason_seg_data, - "explanatory", - "train.json", - ) - ) as f: - items = json.load(f) - for item in items: - img_name = item["image"] - self.img_to_explanation[img_name] = { - "query": item["query"], - "outputs": item["outputs"], - } - - print("len(self.img_to_explanation): ", len(self.img_to_explanation)) - - def __len__(self): - return self.samples_per_epoch - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.img_size - h - padw = self.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def __getitem__(self, idx): - images, jsons = self.reason_seg_data - idx = random.randint(0, len(images) - 1) - image_path = images[idx] - json_path = jsons[idx] - - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - ori_size = image.shape[:2] - # preprocess image for clip - image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][0] - - mask, sents, is_sentence = get_mask_from_json(json_path, image) - if len(sents) >= self.num_classes_per_sample: - sampled_inds = np.random.choice( - list(range(len(sents))), size=self.num_classes_per_sample, replace=False - ) - else: - sampled_inds = list(range(len(sents))) - sampled_sents = np.vectorize(sents.__getitem__)(sampled_inds).tolist() - sampled_masks = [ - (mask == 1).astype(np.float32) for _ in range(len(sampled_inds)) - ] - - image = self.transform.apply_image(image) # preprocess image for sam - resize = image.shape[:2] - - image_name = image_path.split("/")[-1] - if self.explanatory != -1 and image_name in self.img_to_explanation: - if random.random() < self.explanatory: - choice = 2 - else: - choice = random.randint(0, 1) - - questions = [] - answers = [] - for text in sampled_sents: - if is_sentence: - question_template = random.choice(self.long_question_list) - questions.append(question_template.format(sent=text)) - else: - question_template = random.choice(self.short_question_list) - questions.append(question_template.format(class_name=text.lower())) - - # add explanation if applicable - img_name = image_path.split("/")[-1] - if self.explanatory != -1 and img_name in self.img_to_explanation: - if choice == 0: # [SEG] token - answers.append(random.choice(self.answer_list)) - elif choice == 1: # [SEG] token + text answer - image_name = image_path.split("/")[-1] - answer = self.img_to_explanation[image_name]["outputs"] - answer = random.choice(self.answer_list) + " {}".format(answer) - questions[-1] = ( - DEFAULT_IMAGE_TOKEN - + "\n" - + text - + " {}".format(random.choice(self.explanatory_question_list)) - ) - answers.append(answer) - elif choice == 2: # vanilla text answer - image_name = image_path.split("/")[-1] - answer = self.img_to_explanation[image_name]["outputs"] - questions[-1] = DEFAULT_IMAGE_TOKEN + "\n" + text - answers.append(answer) - else: - raise ValueError("Not implemented yet.") - else: - answers.append(random.choice(self.answer_list)) - - conversations = [] - conv = conversation_lib.default_conversation.copy() - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - - i = 0 - while i < len(questions): - conv.messages = [] - conv.append_message(conv.roles[0], questions[i]) - conv.append_message(conv.roles[1], answers[i]) - conversations.append(conv.get_prompt()) - i += 1 - - image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - - image_name = image_path.split("/")[-1] - if ( - self.explanatory != -1 - and image_name in self.img_to_explanation - and choice == 2 - ): - masks = torch.rand(0, *ori_size) - label = torch.ones(ori_size) * self.ignore_label - else: - masks = np.stack(sampled_masks, axis=0) - masks = torch.from_numpy(masks) - label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label - - return ( - image_path, - image, - image_clip, - conversations, - masks, - label, - resize, - questions, - sampled_sents, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer.py deleted file mode 100755 index 3b4cea716..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer.py +++ /dev/null @@ -1,391 +0,0 @@ -__author__ = "licheng" - -""" -This interface provides access to four datasets: -1) refclef -2) refcoco -3) refcoco+ -4) refcocog -split by unc and google - -The following API functions are defined: -REFER - REFER api class -getRefIds - get ref ids that satisfy given filter conditions. -getAnnIds - get ann ids that satisfy given filter conditions. -getImgIds - get image ids that satisfy given filter conditions. -getCatIds - get category ids that satisfy given filter conditions. -loadRefs - load refs with the specified ref ids. -loadAnns - load anns with the specified ann ids. -loadImgs - load images with the specified image ids. -loadCats - load category names with the specified category ids. -getRefBox - get ref's bounding box [x, y, w, h] given the ref_id -showRef - show image, segmentation or box of the referred object with the ref -getMask - get mask and area of the referred object given ref -showMask - show mask of the referred object given ref -""" - -import itertools -import json -import os.path as osp -import pickle -import sys -import time -from pprint import pprint - -import matplotlib.pyplot as plt -import numpy as np -import skimage.io as io -from matplotlib.collections import PatchCollection -from matplotlib.patches import Polygon, Rectangle -from pycocotools import mask - - -class REFER: - def __init__(self, data_root, dataset="refcoco", splitBy="unc"): - # provide data_root folder which contains refclef, refcoco, refcoco+ and refcocog - # also provide dataset name and splitBy information - # e.g., dataset = 'refcoco', splitBy = 'unc' - print("loading dataset %s into memory..." % dataset) - self.ROOT_DIR = osp.abspath(osp.dirname(__file__)) - self.DATA_DIR = osp.join(data_root, dataset) - if dataset in ["refcoco", "refcoco+", "refcocog"]: - self.IMAGE_DIR = osp.join(data_root, "images/mscoco/images/train2014") - elif dataset == "refclef": - self.IMAGE_DIR = osp.join(data_root, "images/saiapr_tc-12") - else: - print("No refer dataset is called [%s]" % dataset) - sys.exit() - - self.dataset = dataset - - # load refs from data/dataset/refs(dataset).json - tic = time.time() - - ref_file = osp.join(self.DATA_DIR, "refs(" + splitBy + ").p") - print("ref_file: ", ref_file) - self.data = {} - self.data["dataset"] = dataset - self.data["refs"] = pickle.load(open(ref_file, "rb")) - - # load annotations from data/dataset/instances.json - instances_file = osp.join(self.DATA_DIR, "instances.json") - instances = json.load(open(instances_file, "rb")) - self.data["images"] = instances["images"] - self.data["annotations"] = instances["annotations"] - self.data["categories"] = instances["categories"] - - # create index - self.createIndex() - print("DONE (t=%.2fs)" % (time.time() - tic)) - - def createIndex(self): - # create sets of mapping - # 1) Refs: {ref_id: ref} - # 2) Anns: {ann_id: ann} - # 3) Imgs: {image_id: image} - # 4) Cats: {category_id: category_name} - # 5) Sents: {sent_id: sent} - # 6) imgToRefs: {image_id: refs} - # 7) imgToAnns: {image_id: anns} - # 8) refToAnn: {ref_id: ann} - # 9) annToRef: {ann_id: ref} - # 10) catToRefs: {category_id: refs} - # 11) sentToRef: {sent_id: ref} - # 12) sentToTokens: {sent_id: tokens} - print("creating index...") - # fetch info from instances - Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {} - for ann in self.data["annotations"]: - Anns[ann["id"]] = ann - imgToAnns[ann["image_id"]] = imgToAnns.get(ann["image_id"], []) + [ann] - for img in self.data["images"]: - Imgs[img["id"]] = img - for cat in self.data["categories"]: - Cats[cat["id"]] = cat["name"] - - # fetch info from refs - Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {} - Sents, sentToRef, sentToTokens = {}, {}, {} - for ref in self.data["refs"]: - # ids - ref_id = ref["ref_id"] - ann_id = ref["ann_id"] - category_id = ref["category_id"] - image_id = ref["image_id"] - - # add mapping related to ref - Refs[ref_id] = ref - imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref] - catToRefs[category_id] = catToRefs.get(category_id, []) + [ref] - refToAnn[ref_id] = Anns[ann_id] - annToRef[ann_id] = ref - - # add mapping of sent - for sent in ref["sentences"]: - Sents[sent["sent_id"]] = sent - sentToRef[sent["sent_id"]] = ref - sentToTokens[sent["sent_id"]] = sent["tokens"] - - # create class members - self.Refs = Refs - self.Anns = Anns - self.Imgs = Imgs - self.Cats = Cats - self.Sents = Sents - self.imgToRefs = imgToRefs - self.imgToAnns = imgToAnns - self.refToAnn = refToAnn - self.annToRef = annToRef - self.catToRefs = catToRefs - self.sentToRef = sentToRef - self.sentToTokens = sentToTokens - print("index created.") - - def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=""): - image_ids = image_ids if type(image_ids) == list else [image_ids] - cat_ids = cat_ids if type(cat_ids) == list else [cat_ids] - ref_ids = ref_ids if type(ref_ids) == list else [ref_ids] - - if len(image_ids) == len(cat_ids) == len(ref_ids) == len(split) == 0: - refs = self.data["refs"] - else: - if not len(image_ids) == 0: - refs = [self.imgToRefs[image_id] for image_id in image_ids] - else: - refs = self.data["refs"] - if not len(cat_ids) == 0: - refs = [ref for ref in refs if ref["category_id"] in cat_ids] - if not len(ref_ids) == 0: - refs = [ref for ref in refs if ref["ref_id"] in ref_ids] - if not len(split) == 0: - if split in ["testA", "testB", "testC"]: - refs = [ - ref for ref in refs if split[-1] in ref["split"] - ] # we also consider testAB, testBC, ... - elif split in ["testAB", "testBC", "testAC"]: - refs = [ - ref for ref in refs if ref["split"] == split - ] # rarely used I guess... - elif split == "test": - refs = [ref for ref in refs if "test" in ref["split"]] - elif split == "train" or split == "val": - refs = [ref for ref in refs if ref["split"] == split] - else: - print("No such split [%s]" % split) - sys.exit() - ref_ids = [ref["ref_id"] for ref in refs] - return ref_ids - - def getAnnIds(self, image_ids=[], cat_ids=[], ref_ids=[]): - image_ids = image_ids if type(image_ids) == list else [image_ids] - cat_ids = cat_ids if type(cat_ids) == list else [cat_ids] - ref_ids = ref_ids if type(ref_ids) == list else [ref_ids] - - if len(image_ids) == len(cat_ids) == len(ref_ids) == 0: - ann_ids = [ann["id"] for ann in self.data["annotations"]] - else: - if not len(image_ids) == 0: - lists = [ - self.imgToAnns[image_id] - for image_id in image_ids - if image_id in self.imgToAnns - ] # list of [anns] - anns = list(itertools.chain.from_iterable(lists)) - else: - anns = self.data["annotations"] - if not len(cat_ids) == 0: - anns = [ann for ann in anns if ann["category_id"] in cat_ids] - ann_ids = [ann["id"] for ann in anns] - if not len(ref_ids) == 0: - ids = set(ann_ids).intersection( - set([self.Refs[ref_id]["ann_id"] for ref_id in ref_ids]) - ) - return ann_ids - - def getImgIds(self, ref_ids=[]): - ref_ids = ref_ids if type(ref_ids) == list else [ref_ids] - - if not len(ref_ids) == 0: - image_ids = list(set([self.Refs[ref_id]["image_id"] for ref_id in ref_ids])) - else: - image_ids = self.Imgs.keys() - return image_ids - - def getCatIds(self): - return self.Cats.keys() - - def loadRefs(self, ref_ids=[]): - if type(ref_ids) == list: - return [self.Refs[ref_id] for ref_id in ref_ids] - elif type(ref_ids) == int: - return [self.Refs[ref_ids]] - - def loadAnns(self, ann_ids=[]): - if type(ann_ids) == list: - return [self.Anns[ann_id] for ann_id in ann_ids] - elif type(ann_ids) == int or type(ann_ids) == unicode: - return [self.Anns[ann_ids]] - - def loadImgs(self, image_ids=[]): - if type(image_ids) == list: - return [self.Imgs[image_id] for image_id in image_ids] - elif type(image_ids) == int: - return [self.Imgs[image_ids]] - - def loadCats(self, cat_ids=[]): - if type(cat_ids) == list: - return [self.Cats[cat_id] for cat_id in cat_ids] - elif type(cat_ids) == int: - return [self.Cats[cat_ids]] - - def getRefBox(self, ref_id): - ref = self.Refs[ref_id] - ann = self.refToAnn[ref_id] - return ann["bbox"] # [x, y, w, h] - - def showRef(self, ref, seg_box="seg"): - ax = plt.gca() - # show image - image = self.Imgs[ref["image_id"]] - I = io.imread(osp.join(self.IMAGE_DIR, image["file_name"])) - ax.imshow(I) - # show refer expression - for sid, sent in enumerate(ref["sentences"]): - print("%s. %s" % (sid + 1, sent["sent"])) - # show segmentations - if seg_box == "seg": - ann_id = ref["ann_id"] - ann = self.Anns[ann_id] - polygons = [] - color = [] - c = "none" - if type(ann["segmentation"][0]) == list: - # polygon used for refcoco* - for seg in ann["segmentation"]: - poly = np.array(seg).reshape((len(seg) / 2, 2)) - polygons.append(Polygon(poly, True, alpha=0.4)) - color.append(c) - p = PatchCollection( - polygons, - facecolors=color, - edgecolors=(1, 1, 0, 0), - linewidths=3, - alpha=1, - ) - ax.add_collection(p) # thick yellow polygon - p = PatchCollection( - polygons, - facecolors=color, - edgecolors=(1, 0, 0, 0), - linewidths=1, - alpha=1, - ) - ax.add_collection(p) # thin red polygon - else: - # mask used for refclef - rle = ann["segmentation"] - m = mask.decode(rle) - img = np.ones((m.shape[0], m.shape[1], 3)) - color_mask = np.array([2.0, 166.0, 101.0]) / 255 - for i in range(3): - img[:, :, i] = color_mask[i] - ax.imshow(np.dstack((img, m * 0.5))) - # show bounding-box - elif seg_box == "box": - ann_id = ref["ann_id"] - ann = self.Anns[ann_id] - bbox = self.getRefBox(ref["ref_id"]) - box_plot = Rectangle( - (bbox[0], bbox[1]), - bbox[2], - bbox[3], - fill=False, - edgecolor="green", - linewidth=3, - ) - ax.add_patch(box_plot) - - def getMask(self, ref): - # return mask, area and mask-center - ann = self.refToAnn[ref["ref_id"]] - image = self.Imgs[ref["image_id"]] - if type(ann["segmentation"][0]) == list: # polygon - rle = mask.frPyObjects(ann["segmentation"], image["height"], image["width"]) - else: - rle = ann["segmentation"] - m = mask.decode(rle) - m = np.sum( - m, axis=2 - ) # sometimes there are multiple binary map (corresponding to multiple segs) - m = m.astype(np.uint8) # convert to np.uint8 - # compute area - area = sum(mask.area(rle)) # should be close to ann['area'] - return {"mask": m, "area": area} - # # position - # position_x = np.mean(np.where(m==1)[1]) # [1] means columns (matlab style) -> x (c style) - # position_y = np.mean(np.where(m==1)[0]) # [0] means rows (matlab style) -> y (c style) - # # mass position (if there were multiple regions, we use the largest one.) - # label_m = label(m, connectivity=m.ndim) - # regions = regionprops(label_m) - # if len(regions) > 0: - # largest_id = np.argmax(np.array([props.filled_area for props in regions])) - # largest_props = regions[largest_id] - # mass_y, mass_x = largest_props.centroid - # else: - # mass_x, mass_y = position_x, position_y - # # if centroid is not in mask, we find the closest point to it from mask - # if m[mass_y, mass_x] != 1: - # print('Finding closes mask point ...') - # kernel = np.ones((10, 10),np.uint8) - # me = cv2.erode(m, kernel, iterations = 1) - # points = zip(np.where(me == 1)[0].tolist(), np.where(me == 1)[1].tolist()) # row, col style - # points = np.array(points) - # dist = np.sum((points - (mass_y, mass_x))**2, axis=1) - # id = np.argsort(dist)[0] - # mass_y, mass_x = points[id] - # # return - # return {'mask': m, 'area': area, 'position_x': position_x, 'position_y': position_y, 'mass_x': mass_x, 'mass_y': mass_y} - # # show image and mask - # I = io.imread(osp.join(self.IMAGE_DIR, image['file_name'])) - # plt.figure() - # plt.imshow(I) - # ax = plt.gca() - # img = np.ones( (m.shape[0], m.shape[1], 3) ) - # color_mask = np.array([2.0,166.0,101.0])/255 - # for i in range(3): - # img[:,:,i] = color_mask[i] - # ax.imshow(np.dstack( (img, m*0.5) )) - # plt.show() - - def showMask(self, ref): - M = self.getMask(ref) - msk = M["mask"] - ax = plt.gca() - ax.imshow(msk) - - -if __name__ == "__main__": - refer = REFER(dataset="refcocog", splitBy="google") - ref_ids = refer.getRefIds() - print(len(ref_ids)) - - print(len(refer.Imgs)) - print(len(refer.imgToRefs)) - - ref_ids = refer.getRefIds(split="train") - print("There are %s training referred objects." % len(ref_ids)) - - for ref_id in ref_ids: - ref = refer.loadRefs(ref_id)[0] - if len(ref["sentences"]) < 2: - continue - - pprint(ref) - print("The label is %s." % refer.Cats[ref["category_id"]]) - plt.figure() - refer.showRef(ref, seg_box="box") - plt.show() - - # plt.figure() - # refer.showMask(ref) - # plt.show() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer_seg_dataset.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer_seg_dataset.py deleted file mode 100755 index e28273d9d..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/refer_seg_dataset.py +++ /dev/null @@ -1,277 +0,0 @@ -import os -import random - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from pycocotools import mask -from transformers import CLIPImageProcessor - -from model.llava import conversation as conversation_lib -from model.segment_anything.utils.transforms import ResizeLongestSide - -from .grefer import G_REFER -from .refer import REFER -from .utils import ANSWER_LIST, SHORT_QUESTION_LIST - - -class ReferSegDataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch=500 * 8 * 2 * 10, - precision: str = "fp32", - image_size: int = 224, - num_classes_per_sample: int = 3, - exclude_val=False, - refer_seg_data="refclef||refcoco||refcoco+||refcocog", - ): - self.exclude_val = exclude_val - self.samples_per_epoch = samples_per_epoch - self.num_classes_per_sample = num_classes_per_sample - - self.base_image_dir = base_image_dir - self.image_size = image_size - self.tokenizer = tokenizer - self.precision = precision - self.transform = ResizeLongestSide(image_size) - self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - - self.short_question_list = SHORT_QUESTION_LIST - self.answer_list = ANSWER_LIST - - DATA_DIR = os.path.join(base_image_dir, "refer_seg") - self.refer_seg_ds_list = refer_seg_data.split( - "||" - ) # ['refclef', 'refcoco', 'refcoco+', 'refcocog'] - self.refer_seg_data = {} - for ds in self.refer_seg_ds_list: - if ds == "refcocog": - splitBy = "umd" - else: - splitBy = "unc" - - if ds == "grefcoco": - refer_api = G_REFER(DATA_DIR, ds, splitBy) - else: - refer_api = REFER(DATA_DIR, ds, splitBy) - ref_ids_train = refer_api.getRefIds(split="train") - images_ids_train = refer_api.getImgIds(ref_ids=ref_ids_train) - refs_train = refer_api.loadRefs(ref_ids=ref_ids_train) - - refer_seg_ds = {} - refer_seg_ds["images"] = [] - loaded_images = refer_api.loadImgs(image_ids=images_ids_train) - - for item in loaded_images: - item = item.copy() - if ds == "refclef": - item["file_name"] = os.path.join( - DATA_DIR, "images/saiapr_tc-12", item["file_name"] - ) - else: - item["file_name"] = os.path.join( - DATA_DIR, "images/mscoco/images/train2014", item["file_name"] - ) - refer_seg_ds["images"].append(item) - refer_seg_ds["annotations"] = refer_api.Anns # anns_train - - print( - "dataset {} (refs {}) (train split) has {} images and {} annotations.".format( - ds, - splitBy, - len(refer_seg_ds["images"]), - len(refer_seg_ds["annotations"]), - ) - ) - - img2refs = {} - for ref in refs_train: - image_id = ref["image_id"] - img2refs[image_id] = img2refs.get(image_id, []) + [ - ref, - ] - refer_seg_ds["img2refs"] = img2refs - self.refer_seg_data[ds] = refer_seg_ds - - def __len__(self): - return self.samples_per_epoch - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.img_size - h - padw = self.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def __getitem__(self, idx): - ds = random.randint(0, len(self.refer_seg_ds_list) - 1) - ds = self.refer_seg_ds_list[ds] - refer_seg_ds = self.refer_seg_data[ds] - images = refer_seg_ds["images"] - annotations = refer_seg_ds["annotations"] - img2refs = refer_seg_ds["img2refs"] - idx = random.randint(0, len(images) - 1) - image_info = images[idx] - image_path = image_info["file_name"] - image_id = image_info["id"] - refs = img2refs[image_id] - if len(refs) == 0: - return self.__getitem__(0) - - sents = [] - ann_ids = [] - for ref in refs: - for sent in ref["sentences"]: - text = sent["sent"] - sents.append(text) - ann_ids.append(ref["ann_id"]) - if len(sents) >= self.num_classes_per_sample: - sampled_inds = np.random.choice( - list(range(len(sents))), size=self.num_classes_per_sample, replace=False - ) - else: - sampled_inds = list(range(len(sents))) - sampled_sents = np.vectorize(sents.__getitem__)(sampled_inds).tolist() - # sampled_ann_ids = np.vectorize(ann_ids.__getitem__)(sampled_inds).tolist() - sampled_ann_ids = [ann_ids[ind] for ind in sampled_inds] - sampled_classes = sampled_sents - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - - # preprocess image for clip - image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][0] - - image = self.transform.apply_image(image) # preprocess image for sam - resize = image.shape[:2] - - questions = [] - answers = [] - for text in sampled_classes: - text = text.strip() - assert len(text.split("||")) == 1 - question_template = random.choice(self.short_question_list) - questions.append(question_template.format(class_name=text.lower())) - answers.append(random.choice(self.answer_list)) - - conversations = [] - conv = conversation_lib.default_conversation.copy() - - i = 0 - while i < len(questions): - conv.messages = [] - conv.append_message(conv.roles[0], questions[i]) - conv.append_message(conv.roles[1], answers[i]) - conversations.append(conv.get_prompt()) - i += 1 - - image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - - flag = False - masks = [] - for ann_id in sampled_ann_ids: - if isinstance(ann_id, list): - flag = True - if -1 in ann_id: - assert len(ann_id) == 1 - m = np.zeros((image_info["height"], image_info["width"])).astype( - np.uint8 - ) - else: - m_final = np.zeros( - (image_info["height"], image_info["width"]) - ).astype(np.uint8) - for ann_id_i in ann_id: - ann = annotations[ann_id_i] - - if len(ann["segmentation"]) == 0: - m = np.zeros( - (image_info["height"], image_info["width"]) - ).astype(np.uint8) - else: - if type(ann["segmentation"][0]) == list: # polygon - rle = mask.frPyObjects( - ann["segmentation"], - image_info["height"], - image_info["width"], - ) - else: - rle = ann["segmentation"] - for i in range(len(rle)): - if not isinstance(rle[i]["counts"], bytes): - rle[i]["counts"] = rle[i]["counts"].encode() - m = mask.decode(rle) - m = np.sum( - m, axis=2 - ) # sometimes there are multiple binary map (corresponding to multiple segs) - m = m.astype(np.uint8) # convert to np.uint8 - m_final = m_final | m - m = m_final - masks.append(m) - continue - - ann = annotations[ann_id] - - if len(ann["segmentation"]) == 0: - m = np.zeros((image_info["height"], image_info["width"])).astype( - np.uint8 - ) - masks.append(m) - continue - - if type(ann["segmentation"][0]) == list: # polygon - rle = mask.frPyObjects( - ann["segmentation"], image_info["height"], image_info["width"] - ) - else: - rle = ann["segmentation"] - for i in range(len(rle)): - if not isinstance(rle[i]["counts"], bytes): - rle[i]["counts"] = rle[i]["counts"].encode() - m = mask.decode(rle) - m = np.sum( - m, axis=2 - ) # sometimes there are multiple binary map (corresponding to multiple segs) - m = m.astype(np.uint8) # convert to np.uint8 - masks.append(m) - - masks = np.stack(masks, axis=0) - - # if ds == 'grefcoco' and flag: - # import shutil - # image_name = image_path.split("/")[-1] - # save_dir = os.path.join("/group/30042/xlai/LISA_refactor_final/debug", image_name.split(".")[0]) - # os.makedirs(save_dir, exist_ok=True) - # shutil.copy(image_path, save_dir) - # for i in range(masks.shape[0]): - # cv2.imwrite(os.path.join(save_dir, "{}_{}_{}.jpg".format(image_name, i, sampled_classes[i])), masks[i].astype(np.int32) * 100) - - masks = torch.from_numpy(masks) - label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label - - return ( - image_path, - image, - image_clip, - conversations, - masks, - label, - resize, - questions, - sampled_classes, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/sem_seg_dataset.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/sem_seg_dataset.py deleted file mode 100755 index db2ba063c..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/sem_seg_dataset.py +++ /dev/null @@ -1,335 +0,0 @@ -import glob -import json -import os -import random - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from PIL import Image -from pycocotools.coco import COCO -from transformers import CLIPImageProcessor - -from model.llava import conversation as conversation_lib -from model.segment_anything.utils.transforms import ResizeLongestSide - -from .utils import ANSWER_LIST, SHORT_QUESTION_LIST - - -def init_mapillary(base_image_dir): - mapillary_data_root = os.path.join(base_image_dir, "mapillary") - with open(os.path.join(mapillary_data_root, "config_v2.0.json")) as f: - mapillary_classes = json.load(f)["labels"] - mapillary_classes = [x["readable"].lower() for x in mapillary_classes] - mapillary_classes = np.array(mapillary_classes) - mapillary_labels = sorted( - glob.glob( - os.path.join(mapillary_data_root, "training", "v2.0", "labels", "*.png") - ) - ) - mapillary_images = [ - x.replace(".png", ".jpg").replace("v2.0/labels", "images") - for x in mapillary_labels - ] - print("mapillary: ", len(mapillary_images)) - return mapillary_classes, mapillary_images, mapillary_labels - - -def init_ade20k(base_image_dir): - with open("utils/ade20k_classes.json", "r") as f: - ade20k_classes = json.load(f) - ade20k_classes = np.array(ade20k_classes) - image_ids = sorted( - os.listdir(os.path.join(base_image_dir, "ade20k/images", "training")) - ) - ade20k_image_ids = [] - for x in image_ids: - if x.endswith(".jpg"): - ade20k_image_ids.append(x[:-4]) - ade20k_images = [] - for image_id in ade20k_image_ids: # self.descriptions: - ade20k_images.append( - os.path.join( - base_image_dir, - "ade20k", - "images", - "training", - "{}.jpg".format(image_id), - ) - ) - ade20k_labels = [ - x.replace(".jpg", ".png").replace("images", "annotations") - for x in ade20k_images - ] - print("ade20k: ", len(ade20k_images)) - return ade20k_classes, ade20k_images, ade20k_labels - - -def init_cocostuff(base_image_dir): - cocostuff_classes = [] - with open("utils/cocostuff_classes.txt") as f: - for line in f.readlines()[1:]: - cocostuff_classes.append(line.strip().split(": ")[-1]) - cocostuff_classes = np.array(cocostuff_classes) - cocostuff_images = [] - - cocostuff_labels = glob.glob( - os.path.join(base_image_dir, "cocostuff", "train2017", "*.png") - ) - cocostuff_images = [ - x.replace(".png", ".jpg").replace("cocostuff", "coco") for x in cocostuff_labels - ] - - print("cocostuff: ", len(cocostuff_images)) - return cocostuff_classes, cocostuff_images, cocostuff_labels - - -def init_paco_lvis(base_image_dir): - coco_api_paco_lvis = COCO( - os.path.join( - base_image_dir, "vlpart", "paco", "annotations", "paco_lvis_v1_train.json" - ) - ) - all_classes = coco_api_paco_lvis.loadCats(coco_api_paco_lvis.getCatIds()) - class_map_paco_lvis = {} - for cat in all_classes: - cat_split = cat["name"].strip().split(":") - if len(cat_split) == 1: - name = cat_split[0].split("_(")[0] - else: - assert len(cat_split) == 2 - obj, part = cat_split - obj = obj.split("_(")[0] - part = part.split("_(")[0] - name = (obj, part) - class_map_paco_lvis[cat["id"]] = name - img_ids = coco_api_paco_lvis.getImgIds() - print("paco_lvis: ", len(img_ids)) - return class_map_paco_lvis, img_ids, coco_api_paco_lvis - - -def init_pascal_part(base_image_dir): - coco_api_pascal_part = COCO( - os.path.join(base_image_dir, "vlpart", "pascal_part", "train.json") - ) - all_classes = coco_api_pascal_part.loadCats(coco_api_pascal_part.getCatIds()) - class_map_pascal_part = {} - for cat in all_classes: - cat_main, cat_part = cat["name"].strip().split(":") - name = (cat_main, cat_part) - class_map_pascal_part[cat["id"]] = name - img_ids = coco_api_pascal_part.getImgIds() - print("pascal_part: ", len(img_ids)) - return class_map_pascal_part, img_ids, coco_api_pascal_part - - -class SemSegDataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch=500 * 8 * 2 * 10, - precision: str = "fp32", - image_size: int = 224, - num_classes_per_sample: int = 3, - exclude_val=False, - sem_seg_data="ade20k||cocostuff||partimagenet||pascal_part||paco_lvis||mapillary", - ): - self.exclude_val = exclude_val - self.samples_per_epoch = samples_per_epoch - self.num_classes_per_sample = num_classes_per_sample - - self.base_image_dir = base_image_dir - self.image_size = image_size - self.tokenizer = tokenizer - self.precision = precision - self.transform = ResizeLongestSide(image_size) - self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - - self.short_question_list = SHORT_QUESTION_LIST - self.answer_list = ANSWER_LIST - - self.data2list = {} - self.data2classes = {} - - self.sem_seg_datas = sem_seg_data.split("||") - for ds in self.sem_seg_datas: - classes, images, labels = eval("init_{}".format(ds))(base_image_dir) - self.data2list[ds] = (images, labels) - self.data2classes[ds] = classes - - if "cocostuff" in self.sem_seg_datas: - self.cocostuff_class2index = { - c: i for i, c in enumerate(self.data2classes["cocostuff"]) - } - - def __len__(self): - return self.samples_per_epoch - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.img_size - h - padw = self.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def __getitem__(self, idx): - ds = random.randint(0, len(self.sem_seg_datas) - 1) - ds = self.sem_seg_datas[ds] - - if ds in ["paco_lvis", "pascal_part"]: - class_map = self.data2classes[ds] - img_ids, coco_api = self.data2list[ds] - idx = random.randint(0, len(img_ids) - 1) - img_id = img_ids[idx] - image_info = coco_api.loadImgs([img_id])[0] - file_name = image_info["file_name"] - if ds == "pascal_part": - file_name = os.path.join( - "VOCdevkit", "VOC2010", "JPEGImages", file_name - ) - image_path = os.path.join(self.base_image_dir, "vlpart", ds, file_name) - elif ds == "paco_lvis": - image_path = os.path.join(self.base_image_dir, "coco", file_name) - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - - # preprocess image for clip - image_clip = self.clip_image_processor.preprocess( - image, return_tensors="pt" - )["pixel_values"][0] - image = self.transform.apply_image(image) # preprocess image for sam - resize = image.shape[:2] - annIds = coco_api.getAnnIds(imgIds=image_info["id"]) - anns = coco_api.loadAnns(annIds) - if len(anns) == 0: - return self.__getitem__(0) - if len(anns) >= self.num_classes_per_sample: - sampled_anns = np.random.choice( - anns, size=self.num_classes_per_sample, replace=False - ).tolist() - else: - sampled_anns = anns - sampled_classes = [] - for ann in sampled_anns: - sampled_cls = class_map[ann["category_id"]] - if isinstance(sampled_cls, tuple): - obj, part = sampled_cls - if random.random() < 0.5: - name = obj + " " + part - else: - name = "the {} of the {}".format(part, obj) - else: - name = sampled_cls - sampled_classes.append(name) - - elif ds in ["ade20k", "cocostuff", "mapillary"]: - image, labels = self.data2list[ds] - idx = random.randint(0, len(image) - 1) - image_path = image[idx] - label_path = labels[idx] - label = Image.open(label_path) - label = np.array(label) - if ds == "ade20k": - label[label == 0] = 255 - label -= 1 - label[label == 254] = 255 - elif ds == "cocostuff": - for c, i in self.cocostuff_class2index.items(): - if "-" in c: - label[label == i] = 255 - img = cv2.imread(image_path) - image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - # preprocess image for clip - image_clip = self.clip_image_processor.preprocess( - image, return_tensors="pt" - )["pixel_values"][0] - image = self.transform.apply_image(image) # preprocess image for sam - resize = image.shape[:2] - unique_label = np.unique(label).tolist() - if 255 in unique_label: - unique_label.remove(255) - if len(unique_label) == 0: - return self.__getitem__(0) - - classes = [self.data2classes[ds][class_id] for class_id in unique_label] - if len(classes) >= self.num_classes_per_sample: - sampled_classes = np.random.choice( - classes, size=self.num_classes_per_sample, replace=False - ).tolist() - else: - sampled_classes = classes - - questions = [] - answers = [] - class_ids = [] - for sampled_cls in sampled_classes: - text = sampled_cls - - assert len(text.split("||")) == 1 - question_template = random.choice(self.short_question_list) - questions.append(question_template.format(class_name=text.lower())) - - answers.append(random.choice(self.answer_list)) - - if ds in ["paco_lvis", "pascal_part"]: - continue - - class_id = self.data2classes[ds].tolist().index(sampled_cls) - class_ids.append(class_id) - - conversations = [] - conv = conversation_lib.default_conversation.copy() - - i = 0 - while i < len(questions): - conv.messages = [] - conv.append_message(conv.roles[0], questions[i]) - conv.append_message(conv.roles[1], answers[i]) - conversations.append(conv.get_prompt()) - i += 1 - - image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - - if ds in ["paco_lvis", "pascal_part"]: - masks = [] - for ann in sampled_anns: - try: - masks.append(coco_api.annToMask(ann)) - except Exception as e: - print(e) - return self.__getitem__(0) - - masks = np.stack(masks, axis=0) - masks = torch.from_numpy(masks) - label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label - - else: - label = torch.from_numpy(label).long() - masks = [] - for class_id in class_ids: - masks.append(label == class_id) - masks = torch.stack(masks, dim=0) - return ( - image_path, - image, - image_clip, - conversations, - masks, - label, - resize, - questions, - sampled_classes, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/utils.py deleted file mode 100755 index 51930264e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/utils.py +++ /dev/null @@ -1,163 +0,0 @@ -from enum import Enum - -import numpy as np -import torch -import torch.distributed as dist - -IGNORE_INDEX = -100 -IMAGE_TOKEN_INDEX = -200 -DEFAULT_IMAGE_TOKEN = "" -DEFAULT_IMAGE_PATCH_TOKEN = "" -DEFAULT_IM_START_TOKEN = "" -DEFAULT_IM_END_TOKEN = "" - -SHORT_QUESTION_LIST = [ - DEFAULT_IMAGE_TOKEN + "\n" + "Can you segment the {class_name} in this image?", - DEFAULT_IMAGE_TOKEN + "\n" + "Please segment the {class_name} in this image.", - DEFAULT_IMAGE_TOKEN - + "\n" - + "What is {class_name} in this image? Please respond with segmentation mask.", - DEFAULT_IMAGE_TOKEN - + "\n" - + "What is {class_name} in this image? Please output segmentation mask.", -] - -LONG_QUESTION_LIST = [ - DEFAULT_IMAGE_TOKEN + "\n" + "{sent} Please respond with segmentation mask.", - DEFAULT_IMAGE_TOKEN + "\n" + "{sent} Please output segmentation mask.", -] - -EXPLANATORY_QUESTION_LIST = [ - "Please output segmentation mask and explain why.", - "Please output segmentation mask and explain the reason.", - "Please output segmentation mask and give some explanation.", -] - -ANSWER_LIST = [ - "It is [SEG].", - "Sure, [SEG].", - "Sure, it is [SEG].", - "Sure, the segmentation result is [SEG].", - "[SEG].", -] - - -class Summary(Enum): - NONE = 0 - AVERAGE = 1 - SUM = 2 - COUNT = 3 - - -class AverageMeter(object): - """Computes and stores the average and current value""" - - def __init__(self, name, fmt=":f", summary_type=Summary.AVERAGE): - self.name = name - self.fmt = fmt - self.summary_type = summary_type - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def all_reduce(self): - device = "cuda" if torch.cuda.is_available() else "cpu" - if isinstance(self.sum, np.ndarray): - total = torch.tensor( - self.sum.tolist() - + [ - self.count, - ], - dtype=torch.float32, - device=device, - ) - else: - total = torch.tensor( - [self.sum, self.count], dtype=torch.float32, device=device - ) - - dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) - if total.shape[0] > 2: - self.sum, self.count = total[:-1].cpu().numpy(), total[-1].cpu().item() - else: - self.sum, self.count = total.tolist() - self.avg = self.sum / (self.count + 1e-5) - - def __str__(self): - fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})" - return fmtstr.format(**self.__dict__) - - def summary(self): - fmtstr = "" - if self.summary_type is Summary.NONE: - fmtstr = "" - elif self.summary_type is Summary.AVERAGE: - fmtstr = "{name} {avg:.3f}" - elif self.summary_type is Summary.SUM: - fmtstr = "{name} {sum:.3f}" - elif self.summary_type is Summary.COUNT: - fmtstr = "{name} {count:.3f}" - else: - raise ValueError("invalid summary type %r" % self.summary_type) - - return fmtstr.format(**self.__dict__) - - -def intersectionAndUnionGPU(output, target, K, ignore_index=255): - # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. - assert output.dim() in [1, 2, 3] - assert output.shape == target.shape - output = output.view(-1) - target = target.view(-1) - output[target == ignore_index] = ignore_index - intersection = output[output == target] - area_intersection = torch.histc(intersection, bins=K, min=0, max=K - 1) - area_output = torch.histc(output, bins=K, min=0, max=K - 1) - area_target = torch.histc(target, bins=K, min=0, max=K - 1) - area_union = area_output + area_target - area_intersection - return area_intersection, area_union, area_target - - -class ProgressMeter(object): - def __init__(self, num_batches, meters, prefix=""): - self.batch_fmtstr = self._get_batch_fmtstr(num_batches) - self.meters = meters - self.prefix = prefix - - def display(self, batch): - entries = [self.prefix + self.batch_fmtstr.format(batch)] - entries += [str(meter) for meter in self.meters] - print("\t".join(entries)) - - def display_summary(self): - entries = [" *"] - entries += [meter.summary() for meter in self.meters] - print(" ".join(entries)) - - def _get_batch_fmtstr(self, num_batches): - num_digits = len(str(num_batches // 1)) - fmt = "{:" + str(num_digits) + "d}" - return "[" + fmt + "/" + fmt.format(num_batches) + "]" - - -def dict_to_cuda(input_dict): - for k, v in input_dict.items(): - if isinstance(input_dict[k], torch.Tensor): - input_dict[k] = v.cuda(non_blocking=True) - elif ( - isinstance(input_dict[k], list) - and len(input_dict[k]) > 0 - and isinstance(input_dict[k][0], torch.Tensor) - ): - input_dict[k] = [ele.cuda(non_blocking=True) for ele in v] - return input_dict diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/vqa_dataset.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/vqa_dataset.py deleted file mode 100755 index dd9c6e909..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/LISA_Heatmap/utils/vqa_dataset.py +++ /dev/null @@ -1,135 +0,0 @@ -import json -import os -import random - -import cv2 -import torch -import torch.nn.functional as F -from transformers import CLIPImageProcessor - -from model.llava import conversation as conversation_lib -from model.segment_anything.utils.transforms import ResizeLongestSide - -from .utils import DEFAULT_IMAGE_TOKEN - - -def preprocess_multimodal(source, mm_use_im_start_end): - for sentence in source: - if DEFAULT_IMAGE_TOKEN in sentence["value"]: - sentence["value"] = ( - sentence["value"].replace(DEFAULT_IMAGE_TOKEN, "").strip() - ) - sentence["value"] = DEFAULT_IMAGE_TOKEN + "\n" + sentence["value"] - sentence["value"] = sentence["value"].strip() - if "mmtag" in conversation_lib.default_conversation.version: - sentence["value"] = sentence["value"].replace( - DEFAULT_IMAGE_TOKEN, "" + DEFAULT_IMAGE_TOKEN + "" - ) - return source - - -class VQADataset(torch.utils.data.Dataset): - pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1) - pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1) - img_size = 1024 - ignore_label = 255 - - def __init__( - self, - base_image_dir, - tokenizer, - vision_tower, - samples_per_epoch=500 * 8 * 2 * 10, - precision: str = "fp32", - image_size: int = 224, - num_classes_per_sample: int = 3, - exclude_val=False, - vqa_data="llava_instruct_150k", - ): - self.exclude_val = exclude_val - self.samples_per_epoch = samples_per_epoch - self.num_classes_per_sample = num_classes_per_sample - - self.base_image_dir = base_image_dir - self.image_size = image_size - self.tokenizer = tokenizer - self.precision = precision - self.transform = ResizeLongestSide(image_size) - self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) - - DATA_DIR = os.path.join(base_image_dir, "llava_dataset") - self.vqa_image_root = os.path.join(base_image_dir, "coco/train2017") - with open(os.path.join(DATA_DIR, "{}.json".format(vqa_data))) as f: - vqa_data = json.load(f) - self.vqa_data = vqa_data - - print("vqa_data: ", len(self.vqa_data)) - - def __len__(self): - return self.samples_per_epoch - - def preprocess(self, x: torch.Tensor) -> torch.Tensor: - """Normalize pixel values and pad to a square input.""" - # Normalize colors - x = (x - self.pixel_mean) / self.pixel_std - - # Pad - h, w = x.shape[-2:] - padh = self.img_size - h - padw = self.img_size - w - x = F.pad(x, (0, padw, 0, padh)) - return x - - def __getitem__(self, idx): - idx = random.randint(0, len(self.vqa_data) - 1) - item = self.vqa_data[idx] - image_path = os.path.join(self.vqa_image_root, item["image"]) - image = cv2.imread(image_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - ori_size = image.shape[:2] - image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[ - "pixel_values" - ][ - 0 - ] # preprocess image for clip - - image = self.transform.apply_image(image) # preprocess image for sam - resize = image.shape[:2] - - conv = conversation_lib.default_conversation.copy() - source = item["conversations"] - source = preprocess_multimodal( - source, - mm_use_im_start_end=conv.sep_style == conversation_lib.SeparatorStyle.TWO, - ) - roles = {"human": conv.roles[0], "gpt": conv.roles[1]} - conversations = [] - if roles[source[0]["from"]] != conv.roles[0]: - # Skip the first one if it is not from human - source = source[1:] - conv.messages = [] - for j, sentence in enumerate(source): - role = roles[sentence["from"]] - assert role == conv.roles[j % 2], f"{i}" - conv.append_message(role, sentence["value"]) - conversations.append(conv.get_prompt()) - - questions = conversations - sampled_classes = conversations - - image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous()) - - masks = torch.rand(0, *ori_size) - label = torch.ones(ori_size) * self.ignore_label - - return ( - image_path, - image, - image_clip, - conversations, - masks, - label, - resize, - questions, - sampled_classes, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/README.md deleted file mode 100644 index 7f85bc106..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Folder Content - -The folder would include the following files: - -- floprReplica.py is for the 2D visualisation that create a 2D white floor for further polylines drawing. -- mapTracking,py is the asembled program with real-time monitoring, 2D visualisation, and data recording. -- demo.ipynb is the final product with additional try-catch to prevent the program being shut-down when no detection triggered. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/floorReplica.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/floorReplica.py deleted file mode 100644 index 537f188a3..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/floorReplica.py +++ /dev/null @@ -1,25 +0,0 @@ -import cv2 -import numpy as np - -def floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl): - # Create a VideoCapture object - cap = cv2.VideoCapture(rtspUrl) - - # Check if camera opened successfully - success, frame = cap.read() - if not success: - cap.release() - cap = cv2.VideoCapture(rtspUrl) - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - floorReplica.py") - - tileHeight = canvasHeight // tilesY - tileWidth = canvasWidth // tilesX - - floorImage = np.ones((canvasHeight, canvasWidth, 3), dtype=np.uint8) * 255 - - for y in range(0, canvasHeight, tileHeight): - for x in range(0, canvasWidth, tileWidth): - cv2.rectangle(floorImage, (x, y), (x + tileWidth, y + tileHeight), (0, 0, 0), 1) - return floorImage \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/mapTracking.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/mapTracking.py deleted file mode 100644 index 2404ec996..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/mapTracking.py +++ /dev/null @@ -1,100 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints -from pymongo import MongoClient -from time import time - -# Load the YOLO model -model = YOLO("yolov8n.pt") - -# Connect to the MongoDB database -# and set up data recording -client = MongoClient("") -db = client["CrowdTracking"] -collection = db["Crowd"] -lastRecorded = 0 - - -# Connect to the RTSP stream -rtspUrl = 0 -cap = cv2.VideoCapture(rtspUrl) - -trackHistory = defaultdict(list) - -# Load the floor image -from floorReplica import floorReplica -canvasHeight = 1000 -canvasWidth = 700 -tilesX = 25 -tilesY = 15 -floorImage = floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl) - -height, width, channels = floorImage.shape - -# Define the codec and create a VideoWriter object -fourcc = cv2.VideoWriter_fourcc(*'mp4v') -video = cv2.VideoWriter('output.mp4', fourcc, 20.0, (width, height)) - -# Define the source and destination points for the homography matrix -# Calculate the homography matrix -ptsSRC = np.array([[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]]) -ptsDST = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) -homographyMatrix = calculateHomography(ptsSRC, ptsDST) - -# Main loop -while cap.isOpened(): - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - mapTracking.py - main loop") - - results = model.track(frame, persist=True, show=False, imgsz=1280, verbose=True) - - if results[0].boxes is not None and hasattr(results[0].boxes, 'id'): - boxes = results[0].boxes.xywh.cpu().numpy() - trackIDs = results[0].boxes.id.int().cpu().numpy() - - annotatedFrame = floorImage.copy() - - for trackID in np.unique(trackIDs): - history = trackHistory[trackID] - if len(history) > 1: - points = np.array(history, dtype=np.int32) - newPoints = transformPoints(points, homographyMatrix) - newPoints = newPoints.astype(np.int32) - - cv2.polylines(annotatedFrame, [newPoints], isClosed=False, color=(0, 0, 255), thickness=2) - - for box, trackID in zip(boxes, trackIDs): - x, y, w, h = box - center = (int(x), int(y + h / 2)) - trackHistory[trackID].append(center) - - if len(trackHistory[trackID]) > 50: - trackHistory[trackID].pop(0) - - # Record the number of people in the frame every second - currentTime = time.time() - if currentTime - lastRecorded >= 1: - frameId = int(cap.get(cv2.CAP_PROP_POS_FRAMES)) - totalPeople = len(np.unique(trackIDs)) - - record = { - "frameId": frameId, - "timestamp": currentTime.strftime("%d-%m-%Y %H:%M:%S"), - "totalPeople": totalPeople - } - collection.insert_one(record) - lastRecorded = currentTime - - video.write(annotatedFrame) - - cv2.imshow("Map Tracking", annotatedFrame) - cv2.imshow("Camera Feed", frame) - cv2.waitKey(1) - - -cap.release() -video.release() -cv2.destroyAllWindows() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/utils.py deleted file mode 100644 index a649ed8ff..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Assemble/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -import cv2 -import numpy as np - -def calculateHomography(ptsSRC, ptsDST): - return cv2.findHomography(ptsSRC, ptsDST)[0] - -def transformPoints(points, homographyMatrix): - points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) - transformedPoints = homographyMatrix.dot(points.T).T - transformedPoints /= transformedPoints[:, 2][:, np.newaxis] - return transformedPoints[:, :2] \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/README.md deleted file mode 100644 index 359597bab..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Folder Content - -The folder would include the following files: - -- **App.py** is the main program for API and route setup and running the system. -- cameraProcessingg.py has the purpose of handling the recording frame using YOLO to draw bounding boxes and polylines for every detected object on the screen. -- database.py is for MongoDB set up with functions to import the capptured data into MongoDB. -- floorReplica.py is for the 2D visualisation that create a 2D white floor for further polylines drawing. -- utils.py would include functions for calculating Hormography and transforming those points for 2D visualisation. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/app.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/app.py deleted file mode 100644 index de53f710e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/app.py +++ /dev/null @@ -1,42 +0,0 @@ -from flask import Flask, jsonify, Response, stream_with_context -from flask_cors import CORS -from cameraProcessing import CameraProcessor -from database import Database - -# Configuration -# VIDEO_SOURCE = 0 # Default camera -# IS_VIDEO = False # Set to True if using a video file -VIDEO_SOURCE = "/Users/apple/Desktop/Deakin/T2_2024/SIT764_Capstone/Crowd_Monitor/market-square.mp4" -IS_VIDEO = True - -# Create Flask app -app = Flask(__name__) -cors = CORS(app) - -# Initialize camera processor and database -camera_processor = CameraProcessor(source=VIDEO_SOURCE, is_video=IS_VIDEO) -#db = Database() - -# Routes for people count -@app.route("/api/peopleCount", methods=["GET"]) -def get_people_count(): - """Retrieve the latest people count""" - count = db.getlastestRecord() - return jsonify({"peopleCount": count}) - -# Routes for main camera display -@app.route("/LiveTracking/videoFeed", methods=["GET"]) -def video_feed(): - """Stream raw video feed""" - return Response(stream_with_context(camera_processor.get_frame()), - mimetype="multipart/x-mixed-replace; boundary=frame") - -# Routes for annotated frame or 2D floor plan -@app.route("/LiveTracking/annotatedVideoFeed", methods=["GET"]) -def annotated_video_feed(): - """Stream annotated video feed""" - return Response(stream_with_context(camera_processor.get_annotated_frame()), - mimetype="multipart/x-mixed-replace; boundary=frame") - -if __name__ == "__main__": - app.run(port=8000) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/cameraProcessing.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/cameraProcessing.py deleted file mode 100644 index b9386a228..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/cameraProcessing.py +++ /dev/null @@ -1,398 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints -#from database import Database -from floorReplica import FloorPlanAnnotator -from coordinateSelector import CoordinateSelector -import time as time_module -import os -import threading -import queue -from datetime import datetime -import platform - -class CameraProcessor: - def __init__(self, source=0, is_video=False, queue_size=20, process_every_n_frames=1): - """ - Initialize the camera processor with multi-threading support - - Args: - source (str/int): Camera index, video file path, or RTSP stream - is_video (bool): Flag to indicate if source is a video file - queue_size (int): Maximum size of frame and results queues - process_every_n_frames (int): Process every Nth frame (skip frames in between) - """ - # Initialize YOLO model for object detection (use smaller model or lower image size for performance) - self.model = YOLO("yolov8n.pt") # Consider 'yolov8n' or even 'yolov8s' for better performance - - # Set up video source - self.source = source - self.is_video = is_video - - # Process control - only process every Nth frame - self.process_every_n_frames = process_every_n_frames - self.frame_counter = 0 - - # Open video capture - if is_video and not os.path.exists(source): - raise FileNotFoundError(f"Video file not found: {source}") - - self.cap = cv2.VideoCapture(source) - - # Set lower resolution for capture if possible - if self.is_video: # Only for video files, not live cameras which might have fixed resolution - width = self.cap.get(cv2.CAP_PROP_FRAME_WIDTH) - height = self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT) - if width > 1280: # If video is high resolution, scale it down - new_width = 1280 - new_height = int(height * (new_width / width)) - self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, new_width) - self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, new_height) - print(f"Scaled video resolution to {new_width}x{new_height}") - - # Determine floor plan image path - current_dir = os.path.dirname(__file__) - floor_plan_path = os.path.join(current_dir, "/Users/apple/Desktop/Deakin/T2_2024/SIT764_Capstone/Crowd_Monitor/Stork Fountain.jpg") - - # Initialize tracking and annotation components - self.track_history = defaultdict(list) - self.floor_annotator = FloorPlanAnnotator( - background_image=floor_plan_path, - show_grid=True - ) - - # Coordinate selection and homography - self.coordinator = CoordinateSelector(source, is_video) - self.homography_matrix = self._calculate_homography() - - # Database for tracking - # self.db = Database() - self.current_frame_id = 0 - - # Threading and queue setup - self.frame_queue = queue.Queue(maxsize=queue_size) - self.results_queue = queue.Queue(maxsize=queue_size) - - # Threading control - self.running = False - self.threads = [] - - # FPS calculation - self.fps = 0 - self.frame_count = 0 - self.start_time = 0 - - # Frames for display - used to pass frames between threads safely - self.display_frame = None - self.display_floor_plan = None - self.display_lock = threading.Lock() - - # Detect operating system for platform-specific handling - self.is_macos = platform.system() == 'Darwin' - print(f"Running on: {platform.system()}") - - def _calculate_homography(self): - """Calculate homography matrix with fallback to default points""" - matrix = self.coordinator.get_homography_matrix() - if matrix is None: - print("Using default homography points as fallback") - pts_src = np.array([[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]]) - pts_dst = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) - matrix = calculateHomography(pts_src, pts_dst) - return matrix - - def capture_thread(self): - """Thread function for capturing frames""" - print("Capture thread started") - self.frame_count = 0 - self.start_time = time_module.time() - frame_skip_counter = 0 - - while self.running: - success, frame = self.cap.read() - if not success: - if self.is_video: - self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) - continue - print("Failed to read video stream") - self.running = False - break - - frame_skip_counter += 1 - - # Only process every Nth frame to reduce computational load - if frame_skip_counter >= self.process_every_n_frames: - frame_skip_counter = 0 - - if not self.frame_queue.full(): - # Optionally resize the frame to reduce processing load - # frame = cv2.resize(frame, (640, 480)) # Uncomment if needed - - self.frame_queue.put(frame) - self.frame_count += 1 - - # Calculate FPS every 30 frames - if self.frame_count % 30 == 0: - end_time = time_module.time() - self.fps = 30 / (end_time - self.start_time) - self.start_time = end_time - else: - # If queue is full, skip this frame - time_module.sleep(0.01) - # For skipped frames, we don't put them in the queue - - def processing_thread(self): - """Thread function for processing frames""" - print("Processing thread started") - - while self.running: - if not self.frame_queue.empty(): - frame = self.frame_queue.get() - try: - # Process frame - annotated_frame, floor_annotated_frame = self.process_frame(frame) - - # Add timestamp - timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - cv2.putText(annotated_frame, f"FPS: {self.fps:.2f}", (10, 30), - cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - cv2.putText(annotated_frame, timestamp, (10, 60), - cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - - # Update frames for display thread - with self.display_lock: - self.display_frame = annotated_frame - self.display_floor_plan = floor_annotated_frame - except Exception as e: - print(f"Error processing frame: {e}") - else: - # If no frames to process, wait a bit - time_module.sleep(0.01) - - def main_thread_display(self): - """Function to display frames from the main thread (macOS compatible)""" - if not self.running: - return - - with self.display_lock: - if self.display_frame is not None: - cv2.imshow("Annotated Frame", self.display_frame) - if self.display_floor_plan is not None: - cv2.imshow("Floor Annotation", self.display_floor_plan) - - # Check for exit key - must be called from main thread - key = cv2.waitKey(1) & 0xFF - if key == ord('q'): - self.running = False - - def process_frame(self, frame): - """ - Process a single video frame - - Args: - frame (numpy.ndarray): Input video frame - - Returns: - tuple: Annotated frame and floor plan annotation - """ - try: - # Process with a smaller image size for better performance (reduced from 1280) - results = self.model.track(frame, persist=True, show=False, imgsz=1280, verbose=False) - - # Prepare annotation frames - annotated_frame = frame #.copy() - floor_annotated_frame = self.floor_annotator.get_floor_plan() - total_people = 0 - - # Process detections - if results[0].boxes is not None and hasattr(results[0].boxes, 'id'): - boxes = results[0].boxes.xywh.cpu().numpy() - track_ids = results[0].boxes.id.int().cpu().numpy() - classes = results[0].boxes.cls.cpu().numpy() - - # Filter for human detections (class 0) - human_indices = classes == 0 - human_boxes = boxes[human_indices] - human_track_ids = track_ids[human_indices] - - # Pre-calculate all transformed points in batch if possible - #updated_tracks = [] - - # Annotate trajectories and draw bounding boxes - for box, track_id in zip(human_boxes, human_track_ids): - x, y, w, h = box - center = (int(x), int(y + h / 2)) - - # Update track history - self.track_history[track_id].append(center) - #updated_tracks.append(track_id) - - # Limit track history - if len(self.track_history[track_id]) > 30: # Reduced from 50 - self.track_history[track_id].pop(0) - - # Draw bounding box and ID - cv2.rectangle(annotated_frame, - (int(x - w/2), int(y - h/2)), - (int(x + w/2), int(y + h/2)), - (0, 255, 0), 2) - cv2.putText(annotated_frame, - f"ID: {int(track_id)}", - (int(x - w/2), int(y - h/2) - 10), - cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) - - total_people += 1 - - # Draw trajectories for all remaining tracks - if len(self.track_history[track_id]) > 1: - points = np.array(self.track_history[track_id], dtype=np.int32) - - if len(points) > 1: - - # Draw trajectory on original frame - cv2.polylines(annotated_frame, - [points], - isClosed=False, - color=(255, 0, 0), - thickness=2) - - # Transform points for floor plan - transformed_points = transformPoints(points, self.homography_matrix) - - # Annotate trajectory on floor plan - floor_annotated_frame = self.floor_annotator.annotate_trajectory( - transformed_points - ) - - # Record people count - self.current_frame_id += 1 - #self.db.insertRecord(total_people, self.current_frame_id) - - return annotated_frame, floor_annotated_frame - - except Exception as e: - print(f"Error processing frame: {e}") - return frame, self.floor_annotator.get_floor_plan() - - def run(self): - """Start processing with macOS-compatible threading""" - self.running = True - - # Create and start worker threads - capture_thread = threading.Thread(target=self.capture_thread) - process_thread = threading.Thread(target=self.processing_thread) - - # Set as daemon threads so they exit when main program exits - capture_thread.daemon = True - process_thread.daemon = True - - # Start threads - capture_thread.start() - process_thread.start() - - # Store threads - self.threads = [capture_thread, process_thread] - - # Main loop for UI operations (always runs on main thread) - try: - while self.running: - # Display frames from main thread only - self.main_thread_display() - - # Yield to other threads - time_module.sleep(0.01) - except KeyboardInterrupt: - print("Interrupted by user") - finally: - self.running = False - self.release() - - def get_frame(self): - """Generator for streaming annotated frames""" - # Start worker threads if not already running - if not self.running: - self.running = True - capture_thread = threading.Thread(target=self.capture_thread) - process_thread = threading.Thread(target=self.processing_thread) - capture_thread.daemon = True - process_thread.daemon = True - capture_thread.start() - process_thread.start() - self.threads = [capture_thread, process_thread] - - while True: - try: - # Wait for frame to be processed - with self.display_lock: - if self.display_frame is not None: - # Make a copy to avoid race conditions - frame_copy = self.display_frame.copy() - ret, buffer = cv2.imencode('.jpg', frame_copy) - frame_bytes = buffer.tobytes() - yield (b'--frame\r\n' - b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n') - else: - time_module.sleep(0.05) - except Exception as e: - print(f"Stream error: {e}") - time_module.sleep(0.1) - - def get_annotated_frame(self): - """Generator for streaming floor plan annotations""" - # Start worker threads if not already running - if not self.running: - self.running = True - capture_thread = threading.Thread(target=self.capture_thread) - process_thread = threading.Thread(target=self.processing_thread) - capture_thread.daemon = True - process_thread.daemon = True - capture_thread.start() - process_thread.start() - self.threads = [capture_thread, process_thread] - - while True: - try: - # Wait for floor plan to be processed - with self.display_lock: - if self.display_floor_plan is not None: - # Make a copy to avoid race conditions - floor_copy = self.display_floor_plan.copy() - ret, buffer = cv2.imencode('.jpg', floor_copy) - frame_bytes = buffer.tobytes() - yield (b'--frame\r\n' - b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n') - else: - time_module.sleep(0.05) - except Exception as e: - print(f"Stream error: {e}") - time_module.sleep(0.1) - - def release(self): - """Release video capture and close windows""" - self.running = False - - # Give threads time to finish - time_module.sleep(1) - - # Release resources - if self.cap is not None: - self.cap.release() - cv2.destroyAllWindows() - print("Resources released") - -# Example usage -if __name__ == "__main__": - # Use default camera - #processor = CameraProcessor(source=1) - - # For real-life scenarios, processing every 2-3 frames is often sufficient - # This reduces computational load while maintaining tracking accuracy - # For crowd monitoring, 5-10 FPS is typically enough, not 30 FPS - processor = CameraProcessor( - source="/Users/apple/Desktop/Deakin/T2_2024/SIT764_Capstone/Crowd_Monitor/market-square.mp4", - is_video=True, - process_every_n_frames=10 # Process every 2nd frame (reduces processing by 50%) - ) - processor.run() \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/coordinateSelector.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/coordinateSelector.py deleted file mode 100644 index 80210a109..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/coordinateSelector.py +++ /dev/null @@ -1,147 +0,0 @@ -import cv2 -import numpy as np -from utils import calculateHomography -from floorReplica import FloorPlanAnnotator -import os - -class CoordinateSelector: - def __init__(self, source, is_video=False): - """ - Initialize the coordinate selector - - Args: - source (str/int): Camera index or video file path - is_video (bool): Flag to indicate if source is a video file - """ - self.source = source - self.is_video = is_video - - # Open the video capture - if is_video: - # Validate video file exists - if not os.path.exists(source): - raise FileNotFoundError(f"Video file not found: {source}") - self.cap = cv2.VideoCapture(source) - else: - # For camera source (can be index or RTSP) - self.cap = cv2.VideoCapture(source) - - # Initialize floor plan annotator - self.floor_annotator = FloorPlanAnnotator() - - # Points for homography calculation - self.camera_points = [] - self.floor_points = [] - - # Frames and images - self.camera_frame = None - self.floorImage = self.floor_annotator.get_floor_plan() - - def mouse_callback(self, event, x, y, flags, param): - """Handle mouse events for both camera and floor plan views""" - if event == cv2.EVENT_LBUTTONDOWN: - image_type = param['type'] - if image_type == 'camera' and len(self.camera_points) < 5: - self.camera_points.append((x, y)) - cv2.circle(self.camera_frame, (x, y), 5, (0, 255, 0), -1) - cv2.putText(self.camera_frame, str(len(self.camera_points)), (x+10, y+10), - cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) - cv2.imshow('Camera View', self.camera_frame) - elif image_type == 'floor' and len(self.floor_points) < 5: - self.floor_points.append((x, y)) - cv2.circle(self.floorImage, (x, y), 5, (0, 0, 255), -1) - cv2.putText(self.floorImage, str(len(self.floor_points)), (x+10, y+10), - cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) - cv2.imshow('Floor Plan', self.floorImage) - - def select_points(self): - """ - Main function to run the coordinate selection process - - Returns: - tuple: Camera points and floor points, or None if selection is cancelled - """ - # Reset points and floor image - self.camera_points = [] - self.floor_points = [] - self.floorImage = self.floor_annotator.get_floor_plan() - - # Create windows - cv2.namedWindow('Camera View') - cv2.namedWindow('Floor Plan') - - # Set mouse callbacks - cv2.setMouseCallback('Camera View', self.mouse_callback, {'type': 'camera'}) - cv2.setMouseCallback('Floor Plan', self.mouse_callback, {'type': 'floor'}) - - # Instructions overlay - instruction_text = [ - "Select 5 points in Camera View and Floor Plan", - "Left-click to add points", - "Press 'r' to reset points", - "Press 'c' to confirm points", - "Press 'q' to quit" - ] - - while True: - # Read frame - ret, frame = self.cap.read() - if not ret: - # If video ends, reset to beginning - if self.is_video: - self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) - continue - break - - self.camera_frame = frame.copy() - - # Draw existing points - for i, point in enumerate(self.camera_points): - cv2.circle(self.camera_frame, point, 5, (0, 255, 0), -1) - cv2.putText(self.camera_frame, str(i+1), (point[0]+10, point[1]+10), - cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) - - # Draw instructions - for i, text in enumerate(instruction_text): - cv2.putText(self.camera_frame, text, (10, 30 + i*30), - cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - - # Show frames - cv2.imshow('Camera View', self.camera_frame) - cv2.imshow('Floor Plan', self.floorImage) - - # Wait for key press - key = cv2.waitKey(1) & 0xFF - - if key == ord('q'): - # Quit without selecting points - cv2.destroyAllWindows() - return None, None - - elif key == ord('r'): - # Reset points - self.camera_points = [] - self.floor_points = [] - self.floorImage = self.floor_annotator.get_floor_plan() - - elif key == ord('c'): - # Confirm points if 5 points are selected in both views - if len(self.camera_points) == 5 and len(self.floor_points) == 5: - cv2.destroyAllWindows() - return np.array(self.camera_points), np.array(self.floor_points) - else: - print("Please select exactly 5 points in both Camera View and Floor Plan") - - # Release resources - cv2.destroyAllWindows() - if self.is_video or isinstance(self.source, int): - self.cap.release() - - return None, None - - def get_homography_matrix(self): - """Get the homography matrix from selected points""" - camera_points, floor_points = self.select_points() - if camera_points is not None and floor_points is not None: - return calculateHomography(camera_points, floor_points) - return None \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/database.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/database.py deleted file mode 100644 index 0fee83231..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/database.py +++ /dev/null @@ -1,36 +0,0 @@ -from pymongo import MongoClient -import time -from datetime import datetime - -class Database: - def __init__(self): - # Initialize the MongoDB client and database - # self.client = MongoClient("") - self.db = self.client["CrowdTracking"] - self.collection = self.db["Crowd"] - self.lastRecorded = time.time() # Initialize with current timestamp - - def insertRecord(self, count, frameId): - currentTime = datetime.now() # Use datetime object for formatting - currentTimestamp = time.time() # Get current timestamp - - # Only record data every second - if currentTimestamp - self.lastRecorded >= 1: # Use timestamps for comparison - record = { - "frameId": frameId, - "peopleCount": count, - "timestamp": currentTime.strftime("%d-%m-%Y %H:%M:%S") # Format datetime object - } - try: - self.collection.insert_one(record) - print(f"Recorded: Frame {frameId}, Time {currentTime.strftime('%d-%m-%Y %H:%M:%S')}, People {count}") - except Exception as e: - print(f"Failed to insert record into database: {e}") - self.lastRecorded = currentTimestamp # Update the last recorded timestamp - - def getlastestRecord(self): - latestRecord = self.collection.find_one(sort=[("timestamp", -1)]) - return latestRecord["peopleCount"] if latestRecord else 0 - - def close(self): - self.client.close() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/floorReplica.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/floorReplica.py deleted file mode 100644 index 679662ee7..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/floorReplica.py +++ /dev/null @@ -1,149 +0,0 @@ -import cv2 -import numpy as np -import os - -class FloorPlanAnnotator: - def __init__(self, - canvas_height=700, - canvas_width=1000, - tiles_x=25, - tiles_y=15, - background_image=None, - show_grid=True): - """ - Initialize a floor plan annotator - - Args: - canvas_height (int): Height of the floor plan canvas - canvas_width (int): Width of the floor plan canvas - tiles_x (int): Number of tiles along x-axis - tiles_y (int): Number of tiles along y-axis - background_image (str/numpy.ndarray, optional): Path to image or image array to use as floor plan - show_grid (bool): Whether to overlay grid lines on the floor plan - """ - # Default floor plan image path - default_floor_plan = os.path.join( - os.path.dirname(__file__), - "/Users/apple/Desktop/Deakin/T2_2024/SIT764_Capstone/Crowd_Monitor/Stork Fountain.jpg" - ) - - self.canvas_height = canvas_height - self.canvas_width = canvas_width - self.tiles_x = tiles_x - self.tiles_y = tiles_y - self.show_grid = show_grid - - # Calculate tile dimensions - self.tile_height = canvas_height // tiles_y - self.tile_width = canvas_width // tiles_x - - # Determine background image - if background_image is None: - background_image = default_floor_plan - - # Load or create floor plan - self.floor_image = self._load_or_create_floor_plan(background_image) - - def _load_or_create_floor_plan(self, background_image=None): - """ - Load or create a floor plan image - - Args: - background_image (str/numpy.ndarray, optional): Path to image or image array - - Returns: - numpy.ndarray: Floor plan image - """ - # If no background image provided, create a white canvas - if background_image is None: - floor_image = np.ones((self.canvas_height, self.canvas_width, 3), dtype=np.uint8) * 255 - else: - # Load image from path or use provided array - if isinstance(background_image, str): - # Check if file exists - if not os.path.exists(background_image): - print(f"Warning: Background image not found: {background_image}") - floor_image = np.ones((self.canvas_height, self.canvas_width, 3), dtype=np.uint8) * 255 - else: - # Read image - floor_image = cv2.imread(background_image) - - # Resize image to match canvas dimensions - floor_image = cv2.resize(floor_image, (self.canvas_width, self.canvas_height)) - elif isinstance(background_image, np.ndarray): - # Resize provided image if needed - floor_image = cv2.resize(background_image, (self.canvas_width, self.canvas_height)) - else: - raise TypeError("Background image must be a file path or numpy array") - - # Add grid overlay if enabled - if self.show_grid: - for y in range(0, self.canvas_height, self.tile_height): - for x in range(0, self.canvas_width, self.tile_width): - cv2.rectangle(floor_image, - (x, y), - (x + self.tile_width, y + self.tile_height), - (0, 0, 0), 1) - - return floor_image - - def annotate_trajectory(self, trajectory_points, color=(0, 0, 255), thickness=2): - """ - Annotate a trajectory on the floor plan - - Args: - trajectory_points (numpy.ndarray): Array of points representing trajectory - color (tuple): Color of the trajectory line - thickness (int): Thickness of the trajectory line - - Returns: - numpy.ndarray: Annotated floor plan image - """ - # Create a copy of the floor image to avoid modifying the original - annotated_image = self.floor_image - - if len(trajectory_points) > 1: - cv2.polylines(annotated_image, - [trajectory_points.astype(np.int32)], - isClosed=False, - color=color, - thickness=thickness) - - return annotated_image - - def get_floor_plan(self): - """ - Get the current floor plan image - - Returns: - numpy.ndarray: Floor plan image - """ - return self.floor_image.copy() - - def update_floor_plan(self, new_background_image): - """ - Update the floor plan with a new background image - - Args: - new_background_image (str/numpy.ndarray): New background image - """ - self.floor_image = self._load_or_create_floor_plan(new_background_image) - -# Example usage -def main(): - # Try to load a custom floor plan image - try: - # Automatically find the image in the same directory - current_dir = os.path.dirname(__file__) - floor_plan_path = os.path.join(current_dir, "/Users/apple/Desktop/Deakin/T2_2024/SIT764_Capstone/Crowd_Monitor/Stork Fountain.jpg") - - floor_plan = FloorPlanAnnotator( - background_image=floor_plan_path, - show_grid=True # Optional grid overlay - ) - print(f"Loaded floor plan from: {floor_plan_path}") - except Exception as e: - print(f"Error loading floor plan image: {e}") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/utils.py deleted file mode 100644 index a649ed8ff..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Backend_v2/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -import cv2 -import numpy as np - -def calculateHomography(ptsSRC, ptsDST): - return cv2.findHomography(ptsSRC, ptsDST)[0] - -def transformPoints(points, homographyMatrix): - points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) - transformedPoints = homographyMatrix.dot(points.T).T - transformedPoints /= transformedPoints[:, 2][:, np.newaxis] - return transformedPoints[:, :2] \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Camo.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Camo.png deleted file mode 100644 index a882fe081..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Camo.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/.gitignore b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/.gitignore deleted file mode 100644 index 66da92e5b..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -__pycache__/ -*.py[cod] -yolov8n.pt \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/README.md deleted file mode 100644 index 7c7e1728e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Congestion Dection - -## Introduction - -The program focuses on extending the analysing features of the previous real-time crowd monitoring project. Farneback algorithm, homography transformation, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for tracking the movement of the people to provide direction and congestion predictions. - -Even though utilising the previous program, the project would solely use bulit-in camera for live tracking instead of the Real-Time Streaming Protocol. Furthermore, the data recording feature via MongoDB will be commented out, since it was not a part of the initial targets. - -The cameraProccesing.py will be the main Python script used for testing. - -## Folder Components - -### cameraProcessing.py - -The main controller used for: - -1. Objectives tracking using YOLOv8 -2. Ochestrateing other components -3. Displaying actual camera and 2D floor plan view - -### opticalFlow.py - -Utilising Farneback algorithm for: - -1. Analysing the crowd movement between consecutive frames -2. Providing future position and trajectories predictions - -### congestionDetectio.py - -This class has the purpose of: - -1. Detecting the area where the people standing close to each other -2. Predicting the potential jammed areas -3. Visualising the the zones on both camera view and 2D floor - -### dwellTime.py - -This class is majorly integrate into the 2D plan floor. Its mechanism includes: - -1. Dividing the floor into differents zones -2. OpenCV is applied to verify whether the objects are still in the zones -3. Caluclating the the dwelling time -4. Visualising the zones and their statistics diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/app.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/app.py deleted file mode 100644 index 374ed206a..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/app.py +++ /dev/null @@ -1,38 +0,0 @@ -from flask import Flask, jsonify, Response, stream_with_context -from flask_cors import CORS -from cameraProcessing import CameraProcessor -from database import Database - -app = Flask(__name__) -cors = CORS(app) -cameraProcessor = CameraProcessor() -db = Database() - - -# routes for people count -@app.route("/api/peopleCount", methods=["GET"]) -def getPeopleCount(): - count = db.getlastestRecord() - return jsonify({"peopleCount": count}) - - -# routes for main camera display -@app.route("/LiveTracking/videoFeed", methods=["GET"]) -def videoFeed(): - return Response( - stream_with_context(cameraProcessor.getFrame()), - mimetype="multipart/x-mixed-replace; boundary=frame", - ) - - -# routes for annotated frame or 2d floor plan -@app.route("/LiveTracking/annotatedVideoFeed", methods=["GET"]) -def annotatedVideoFeed(): - return Response( - stream_with_context(cameraProcessor.getAnnotatedFrame()), - mimetype="multipart/x-mixed-replace; boundary=frame", - ) - - -if __name__ == "__main__": - app.run(port=8000) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/cameraProcessing.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/cameraProcessing.py deleted file mode 100644 index b42761074..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/cameraProcessing.py +++ /dev/null @@ -1,326 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints - -# from database import Database -from floorReplica import floorReplica -import time as time_module -from opticalFlow import OpticalFlow -from congestionDetection import CongestionDetection -from dwellTime import DwellTimeAnalysis - - -class CameraProcessor: - def __init__(self): - # initialize the YOLO model - # RTSP stream URL for the video feed - # trackHistory is a dictionary to store the movement history of each person - # floorImage is a replica of the floor plan for annotation - # homographyMatrix is the homography matrix for transforming points - # db is an instance of the Database class - # lastRecorded is the timestamp of the last recorded data - self.model = YOLO("yolov8n.pt") - self.rtspUrl = 0 - self.cap = cv2.VideoCapture(self.rtspUrl) - self.trackHistory = defaultdict(list) - self.floorImage = floorReplica(1000, 700, 25, 15, self.rtspUrl) - self.homographyMatrix = self.calculateHomography() - # self.db = Database() - #self.lastRecorded = 0 - - # initialise the variables for counting the current frame ID - # initialise the congestion detection module using the homography matrix - # initialise the optical flow amd the dwell time analysis module - self.currentFrameId = 0 - self.congestionDetection = CongestionDetection( - self.homographyMatrix, debug=True - ) - self.opticalFlow = OpticalFlow(predictionStep=5, predictionScale=3) - self.dwellTimeAnalysis = DwellTimeAnalysis(self.homographyMatrix) - - - self.lastFlowVisualizationTime = 0 - self.flowVisualizationInterval = 5 # Increased to 5 seconds - self.flowVisualizationDuration = 3 # Show for 3 seconds only - self.showFlowVisualization = False - - self.showDwellTime = True - - # Function to calculate the homography matrix - def calculateHomography(self): - ptsSRC = np.array( - [[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]] - ) - ptsDST = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) - return calculateHomography(ptsSRC, ptsDST) - - def processFrame(self, frame): - # Calculate optical flow for this frame - # and initialise the current position list to store the current positions of detected people - flow = self.opticalFlow.calculateFlow(frame) - currentPositions = [] - - # Check if it's time to update the flow visualization based on the interval - currentTime = time_module.time() - - # Start visualization if interval has passed - if ( - currentTime - self.lastFlowVisualizationTime - >= self.flowVisualizationInterval - ): - self.showFlowVisualization = True - self.lastFlowVisualizationTime = currentTime - - - # the try block is used to handle exceptions, when no detections are available - try: - results = self.model.track( - frame, persist=True, show=False, imgsz=1280, verbose=False - ) - - # Create copies of the frame for annotations - # and initialise the total people count - annotatedFrame = frame.copy() - floorAnnotatedFrame = self.floorImage.copy() - totalPeople = 0 - - # Get predicted positions based on optical flow - # using the if statement to check if the flow is available - # and the visualisation is active - predictedPositions = {} - if flow is not None and self.showFlowVisualization: - predictedPositions = self.opticalFlow.predictPosition( - self.trackHistory, flow - ) - - # verify if the bounding boxes were detected and having the id attribute - # get the boxes coordinates, track IDs and classes - if results[0].boxes is not None and hasattr(results[0].boxes, "id"): - boxes = results[0].boxes.xywh.cpu().numpy() - trackIDs = results[0].boxes.id.int().cpu().numpy() - classes = results[0].boxes.cls.cpu().numpy() - - # Filter for human detections (assuming 'person' class is 0) - human_indices = classes == 0 - human_boxes = boxes[human_indices] - human_trackIDs = trackIDs[human_indices] - - # Store current positions of all detected people - currentPositions = [] - - # Process each tracked person by iterating through the unique track IDs - # and draw their history on the floor plan - for trackID in np.unique(human_trackIDs): - history = self.trackHistory[trackID] - if len(history) > 1: - points = np.array(history, dtype=np.int32) - - # Transform points to floor coordinates - floorPoints = transformPoints(points, self.homographyMatrix) - floorPoints = floorPoints.astype(np.int32) - - # Draw the history trail on floor plan - cv2.polylines( - floorAnnotatedFrame, - [floorPoints], - isClosed=False, - color=(0, 0, 255), - thickness=2, - ) - - # Calculate and visualize trajectory if predicted position exists and visualization is active - if self.showFlowVisualization and trackID in predictedPositions: - # Get predicted trajectory for this person (shorter now due to reduced predictionStep) - trajectory = self.opticalFlow.predictTrajectory( - trackID, history, predictedPositions[trackID] - ) - - # Draw predicted trajectory on annotated frame if visualization is active - if len(trajectory) >= 2: - # Draw predicted path on camera view - points = np.array(trajectory, dtype=np.int32) - cv2.polylines( - annotatedFrame, - [points], - isClosed=False, - color=(0, 255, 255), - thickness=2, - ) - - # Add arrow at the end to indicate direction - lastPoint = trajectory[-2] - endPoint = trajectory[-1] - cv2.arrowedLine( - annotatedFrame, - (int(lastPoint[0]), int(lastPoint[1])), - (int(endPoint[0]), int(endPoint[1])), - (0, 255, 255), - 3, - tipLength=0.5, - ) - - # Also draw on floor plan - floorTrajectory = transformPoints( - np.array(trajectory), self.homographyMatrix - ) - floorTrajectory = floorTrajectory.astype(np.int32) - cv2.polylines( - floorAnnotatedFrame, - [floorTrajectory], - isClosed=False, - color=(0, 255, 255), - thickness=2, - ) - - # Add arrow on floor plan as well - # Check if there are at least two points to draw an arrow - # and draw the arrow to indicate direction - if len(floorTrajectory) >= 2: - floorLast = floorTrajectory[-2] - floorEnd = floorTrajectory[-1] - cv2.arrowedLine( - floorAnnotatedFrame, - (int(floorLast[0]), int(floorLast[1])), - (int(floorEnd[0]), int(floorEnd[1])), - (0, 255, 255), - 3, - tipLength=0.5, - ) - - # Block to draw bounding boxes and IDs - for box, trackID in zip(human_boxes, human_trackIDs): - x, y, w, h = box - center = (int(x), int(y + h / 2)) - self.trackHistory[trackID].append(center) - currentPositions.append(center) - - if len(self.trackHistory[trackID]) > 50: - self.trackHistory[trackID].pop(0) - - # Draw bounding box and ID on the original frame - cv2.rectangle( - annotatedFrame, - (int(x - w / 2), int(y - h / 2)), - (int(x + w / 2), int(y + h / 2)), - (0, 255, 0), - 2, - ) - cv2.putText( - annotatedFrame, - f"ID: {int(trackID)}", - (int(x - w / 2), int(y - h / 2) - 10), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (0, 255, 0), - 2, - ) - - # If showing flow visualization, add predicted position indicator - if self.showFlowVisualization and trackID in predictedPositions: - predictedX, predictedY = predictedPositions[trackID] - cv2.circle( - annotatedFrame, (predictedX, predictedY), 5, (0, 255, 255), -1 - ) # Yellow dot - - totalPeople += 1 - - # Calculate congestion zones using the separate CongestionDetection class - self.congestionZones = self.congestionDetection.identifyCongestionZones( - currentPositions - ) - - # Draw congestion zones on both frames - self.congestionDetection.drawCongestionZones( - annotatedFrame, floorAnnotatedFrame, transformPoints - ) - - # Use the congestion detection class for future congestion prediction - futureCongestion = self.congestionDetection.predictCongestionZones( - currentPositions, flow - ) - - # Draw future congestion zones - self.congestionDetection.drawPredictedCongestion( - annotatedFrame, futureCongestion - ) - - # Update the dwell time analysis with the current positions - self.dwellTimeAnalysis.updateZones(currentPositions, self.trackHistory) - if self.showDwellTime: - # Draw dwell times on the floor plan - floorAnnotatedFrame = self.dwellTimeAnalysis.drawDwellTimes( - floorAnnotatedFrame, currentPositions, human_trackIDs - ) - # Draw dwell times on the annotated frame - annotatedFrame = self.dwellTimeAnalysis.drawDwellTimes( - annotatedFrame, currentPositions, human_trackIDs - ) - - else: - print("No human detections or IDs available.") - - except AttributeError as e: - print(f"An AttributeError occurred: {e}") - except Exception as e: - print(f"An unexpected error occurred: {e}") - - return annotatedFrame, floorAnnotatedFrame - - # Function to run the camera processor - def run(self): - while True: - success, frame = self.cap.read() - if not success: - raise Exception( - "Failed to read video stream - cameraProcessor.py - run" - ) - else: - annotatedFrame, floorAnnotatedFrame = self.processFrame(frame) - cv2.imshow("Annotated Frame", annotatedFrame) - cv2.imshow("Floor Annotation", floorAnnotatedFrame) - if cv2.waitKey(1) & 0xFF == ord("q"): - break - self.release() - - # Function to get the raw frame from the video stream - def getFrame(self): - while True: - success, frame = self.cap.read() - if not success: - raise Exception( - "Failed to read video stream - cameraProcessor.py - getFrame" - ) - else: - annotatedFrame, _ = self.processFrame(frame) - ret, buffer = cv2.imencode(".jpg", annotatedFrame) - frame = buffer.tobytes() - yield ( - b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n" - ) - - # Function to get the annotated frame with floor plan - def getAnnotatedFrame(self): - while True: - success, frame = self.cap.read() - if not success: - raise Exception( - "Failed to read video stream - cameraProcessor.py - getAnnotatedFrame" - ) - else: - _, floorAnnotatedFrame = self.processFrame(frame) - ret, buffer = cv2.imencode(".jpg", floorAnnotatedFrame) - frame = buffer.tobytes() - yield ( - b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n" - ) - - def release(self): - self.cap.release() - cv2.destroyAllWindows() - - -if __name__ == "__main__": - cameraProcessor = CameraProcessor() - cameraProcessor.run() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/congestionDetection.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/congestionDetection.py deleted file mode 100644 index 7d7fc2c20..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/congestionDetection.py +++ /dev/null @@ -1,309 +0,0 @@ -import cv2 -import numpy as np -from sklearn.cluster import DBSCAN - - -class CongestionDetection: - def __init__(self, homographyMatrix, debug=False): - # Initialize the homography matrix for transforming points - # and set 3 as the minimum people within proximity to define congestion - # and 120 as the pixel distance to consider for congestion - # define a list to store congestion zones - self.homographyMatrix = homographyMatrix - self.congestionThreshold = 3 - self.proximityThreshold = 120 - self.congestionZones = [] - self.debug = debug - - def identifyCongestionZones(self, position): - # Function to identify congestion zones based on the track histories - # Define a list to store congestion zones for the current frame - # checking if the position is empty, return an empty list - self.congestionZones = [] - - if not position: - return [] - - minCongestionThreshold = self.congestionThreshold - - # Check if the number of positions exceeds the congestion threshold - if len(position) >= minCongestionThreshold: - # Convert the positions to a numpy array - pointsArray = np.array(position, dtype=np.float32) - - try: - # Use DBSCAN clustering to identify congestion zones - # labels the points based on proximity - # get the unique cluster IDs - clustering = DBSCAN( - eps=self.proximityThreshold, min_samples=minCongestionThreshold - ).fit(pointsArray) - labels = clustering.labels_ - uniqueClusters = set(labels) - {-1} - - # Iterate through each unique cluster ID - # and calculate the center and radius of the congestion zone - for clusterID in uniqueClusters: - clusterPoints = pointsArray[labels == clusterID] - if len(clusterPoints) >= minCongestionThreshold: - centerX = int(np.mean(clusterPoints[:, 0])) - centerY = int(np.mean(clusterPoints[:, 1])) - - # Calculate the radius based on the maximum distance from the center - maxDistance = 0 - for point in clusterPoints: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - # Set the radius to the maximum distance and ensure it's at least the proximity threshold - # get the count of points in the cluster - radius = max(int(maxDistance) + 20, self.proximityThreshold) - count = len(clusterPoints) - - # add the congestion zone to the list - self.congestionZones.append(((centerX, centerY), radius, count)) - - if self.debug: - print( - f"Congestion Zone: center=({centerX}, {centerY}), Radius={radius}, Count={count}" - ) - - except ImportError as e: - # fallback if DBSCAN is not available - self._fallbackCongestionDetection(position) - else: - self._fallbackCongestionDetection(position) - - return self.congestionZones - - def _fallbackCongestionDetection(self, position): - # Fallback congestion detection logic if DBSCAN is not available - - minNearbyPoints = self.congestionThreshold - 1 - - # Iterate through each position and check for nearby points - for i, position1 in enumerate(position): - nearbyPointsCount = 0 - nearbyPositions = [] - - for j, position2 in enumerate(position): - if i != j: - distance = np.sqrt( - (position1[0] - position2[0]) ** 2 - + (position1[1] - position2[1]) ** 2 - ) - if distance < self.proximityThreshold: - nearbyPointsCount += 1 - nearbyPositions.append(position2) - - # Check if the number of nearby points exceeds the threshold - # and if so, create a congestion zone by calculating the center of the group - if nearbyPointsCount >= minNearbyPoints: - allPositions = [position1] + nearbyPositions - centerX = int(np.mean([p[0] for p in allPositions])) - centerY = int(np.mean([p[1] for p in allPositions])) - - # caculate the radius - maxDistance = 0 - for point in allPositions: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = int(maxDistance) + 20 - - # check if the zone is already in the list - # to avoid duplicates - newZone = True - for zone in self.congestionZones: - zonecenter = zone[0] - distance = np.sqrt( - (centerX - zonecenter[0]) ** 2 + (centerY - zonecenter[1]) ** 2 - ) - if distance < radius: - newZone = False - break - - if newZone: - count = nearbyPointsCount + 1 - self.congestionZones.append(((centerX, centerY), radius, count)) - if self.debug: - print( - f"Congestion Zone: center=({centerX}, {centerY}), Radius={radius}, Count={count}" - ) - - def predictCongestionZones(self, currentPositions, flow, predictScale=5): - # Function to predict congestion zones based on the current positions and flow - if not currentPositions or flow is None: - return [] - - # Define a list to store predicted congestion zones - futurePositions = [] - for x, y in currentPositions: - # Predict the future position using the flow - if 0 <= x < flow.shape[1] and 0 <= y < flow.shape[0]: - dy, dx = flow[y, x] - predictedX = int(x + dx * predictScale) - predictedY = int(y + dy * predictScale) - futurePositions.append((predictedX, predictedY)) - - # Use the same algorithm as identifyCongestionZones but on future positions - futureCongestions = [] - - # identify congestion zones based on the predicted future positions - if len(futurePositions) >= self.congestionThreshold: - # Convert future positions to numpy array - pointsArray = np.array(futurePositions, dtype=np.float32) - - try: - clustering = DBSCAN( - eps=self.proximityThreshold, min_samples=self.congestionThreshold - ).fit(pointsArray) - labels = clustering.labels_ - uniqueClusters = set(labels) - {-1} - - for clusterID in uniqueClusters: - clusterPoints = pointsArray[labels == clusterID] - if len(clusterPoints) >= self.congestionThreshold: - centerX = int(np.mean(clusterPoints[:, 0])) - centerY = int(np.mean(clusterPoints[:, 1])) - - # Calculate radius based on cluster - maxDistance = 0 - for point in clusterPoints: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = max(int(maxDistance) + 20, self.proximityThreshold) - count = len(clusterPoints) - - futureCongestions.append(((centerX, centerY), radius, count)) - except: - # Fallback if DBSCAN fails - self._fallbackFutureCongestion(futurePositions, futureCongestions) - else: - # Fallback if the number of future positions is less than the threshold - self._fallbackFutureCongestion(futurePositions, futureCongestions) - - return futureCongestions - - def _fallbackFutureCongestion(self, positions, congestionsList): - # Fallback future congestion detection - minNearbyPoints = self.congestionThreshold - 1 - processed = set() - - for i, pos1 in enumerate(positions): - if i in processed: - continue - - nearbyPointsCount = 0 - nearbyPositions = [] - - for j, pos2 in enumerate(positions): - if i != j: - distance = np.sqrt( - (pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2 - ) - if distance < self.proximityThreshold: - nearbyPointsCount += 1 - nearbyPositions.append(pos2) - processed.add(j) - - if nearbyPointsCount >= minNearbyPoints: - allPositions = [pos1] + nearbyPositions - centerX = int(np.mean([p[0] for p in allPositions])) - centerY = int(np.mean([p[1] for p in allPositions])) - - # Calculate radius - maxDistance = 0 - for point in allPositions: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = int(maxDistance) + 20 - count = nearbyPointsCount + 1 - - congestionsList.append(((centerX, centerY), radius, count)) - - def drawCongestionZones( - self, annotatedFrame, floorFrame, transformePointsFuction=None - ): - # Function to draw the congestion zones on the annotated frame - for zone in self.congestionZones: - center, radius, count = zone - - # Draw the congestion zone on the annotated frame - # the reddish circle is drawn on the floor plan with transparency - overlay = annotatedFrame.copy() - cv2.circle(overlay, center, radius, (0, 0, 255), -1) - cv2.addWeighted(overlay, 0.4, annotatedFrame, 0.6, 0, annotatedFrame) - - # Filled the circle with text and people count - cv2.circle(annotatedFrame, center, radius, (0, 0, 255), 2) - cv2.putText( - annotatedFrame, - f"CONGESTION ZONE: {count}", - (center[0] - 80, center[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Check if floorFrame and transformePointsFuction are provided - # and draw the congestion zone on the floor plan by: - # Transform the center point to the floor plan coordinates - # scale the radius and draw the circle - if floorFrame is not None and transformePointsFuction is not None: - # Draw the congestion zone on the floor plan - transformedcenter = transformePointsFuction( - np.array([center]), self.homographyMatrix - )[0] - transformedcenter = tuple(map(int, transformedcenter)) - - floorRadius = int(radius * 0.7) - - # Apply similar visualization to floor plan - overlay = floorFrame.copy() - cv2.circle(overlay, transformedcenter, floorRadius, (0, 0, 255), -1) - cv2.addWeighted(overlay, 0.4, floorFrame, 0.6, 0, floorFrame) - cv2.circle(floorFrame, transformedcenter, floorRadius, (0, 0, 255), 2) - cv2.putText( - floorFrame, - f"CONGESTION ZONE: {count}", - (transformedcenter[0] - 70, transformedcenter[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.6, - (255, 255, 255), - 2, - ) - - def drawPredictedCongestion(self, annotatedFrame, predictedCongestions): - # Function to draw the predicted congestion zones on the annotated frame with a pale blue color - for zone in predictedCongestions: - center, radius, count = zone - - # Draw a pale blue circle with transparency - overlay = annotatedFrame.copy() - - # Draw the congestion zone and count on the annotated frame - # Drawing blue circle with transparency - cv2.circle(overlay, center, radius, (255, 200, 0), -1) # Pale blue color - cv2.addWeighted(overlay, 0.3, annotatedFrame, 0.7, 0, annotatedFrame) - cv2.circle(annotatedFrame, center, radius, (255, 200, 0), 2) - cv2.putText( - annotatedFrame, - f"PREDICTED CONGESTION: {count}", - (center[0] - 80, center[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 200, 0), - 2, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/database.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/database.py deleted file mode 100644 index f7dd0e73e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/database.py +++ /dev/null @@ -1,41 +0,0 @@ -from pymongo import MongoClient -import time -from datetime import datetime - - -class Database: - def __init__(self): - # Initialize the MongoDB client and database - self.client = MongoClient("") - self.db = self.client["CrowdTracking"] - self.collection = self.db["Crowd"] - self.lastRecorded = time.time() # Initialize with current timestamp - - def insertRecord(self, count, frameId): - currentTime = datetime.now() # Use datetime object for formatting - currentTimestamp = time.time() # Get current timestamp - - # Only record data every second - if currentTimestamp - self.lastRecorded >= 1: # Use timestamps for comparison - record = { - "frameId": frameId, - "peopleCount": count, - "timestamp": currentTime.strftime( - "%d-%m-%Y %H:%M:%S" - ), # Format datetime object - } - try: - self.collection.insert_one(record) - print( - f"Recorded: Frame {frameId}, Time {currentTime.strftime('%d-%m-%Y %H:%M:%S')}, People {count}" - ) - except Exception as e: - print(f"Failed to insert record into database: {e}") - self.lastRecorded = currentTimestamp # Update the last recorded timestamp - - def getlastestRecord(self): - latestRecord = self.collection.find_one(sort=[("timestamp", -1)]) - return latestRecord["peopleCount"] if latestRecord else 0 - - def close(self): - self.client.close() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/dwellTime.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/dwellTime.py deleted file mode 100644 index 1283d529c..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/dwellTime.py +++ /dev/null @@ -1,377 +0,0 @@ -import cv2 -import numpy as np -import time -from collections import defaultdict - - -class DwellTimeAnalysis: - def __init__(self, homographyMatrix, floorWidth=700, floorHeight=1000): - self.homographyMatrix = homographyMatrix - self.floorWidth = floorWidth - self.floorHeight = floorHeight - self.zones = [] - self.personZoneData = defaultdict(dict) - self.zoneStats = defaultdict( - lambda: { - "currentCount": 0, - "totalVisits": 0, - "averageDwellTime": 0, - "maxDwellTime": 0, - } - ) - self.defineDefaultZones() - self.lastUpdateTime = time.time() - self.updateInterval = 1.0 - - - def defineDefaultZones(self): - - # Calculate the height of each zone (divide floor height by 3) - zoneHeight = self.floorHeight // 3 - - # Define the three horizontal zones with distinct colors - self.zones = [ - ( - "Emprise du Lion", - np.array( - [ - [0, 0], # Top-left - [self.floorWidth, 0], # Top-right - [self.floorWidth, zoneHeight], # Bottom-right - [0, zoneHeight], # Bottom-left - ] - ), - (204, 255, 229), - ), - ( - "Hinterlands", - np.array( - [ - [0, zoneHeight], # Top-left - [self.floorWidth, zoneHeight], # Top-right - [self.floorWidth, 2 * zoneHeight], # Bottom-right - [0, 2 * zoneHeight], # Bottom-left - ] - ), - (255, 255, 153), - ), - ( - "Chateau d'Onterre", - np.array( - [ - [0, 2 * zoneHeight], # Top-left - [self.floorWidth, 2 * zoneHeight], # Top-right - [self.floorWidth, self.floorHeight], # Bottom-right - [0, self.floorHeight], # Bottom-left - ] - ), - (102, 204, 0), - ), - ] - - def isPointInZone(self, point, zonePolygon): - """Check if a point is inside a zone polygon""" - # Make sure point is a single x,y tuple, not an array - if isinstance(point, np.ndarray) and point.ndim > 1: - # If it's an array, return array of results - return np.array( - [ - cv2.pointPolygonTest(zonePolygon, (int(x), int(y)), False) >= 0 - for x, y in point - ] - ) - else: - # For a single point - return ( - cv2.pointPolygonTest(zonePolygon, tuple(map(int, point)), False) >= 0 - ) - - def updateZones(self, currentPositions, trackIDs): - - currentTime = time.time() - - # Transform camera positions to floor plan coordinates - if currentPositions: - floorPositions = self.transformPointsToFloor( - np.array(currentPositions) - ) - else: - floorPositions = [] - - # Update person zone data - for idx, trackID in enumerate(trackIDs): - if idx >= len(floorPositions): - continue - - personPos = tuple(map(int, floorPositions[idx])) - - # Check each zone for this person - for zoneID, (zoneName, zonePoly, _) in enumerate(self.zones): - # If we haven't seen this person in this zone before, initialize their data - if zoneID not in self.personZoneData[trackID]: - self.personZoneData[trackID][zoneID] = { - "enterTime": None, - "exitTime": None, - "inZone": False, - "totalDwellTime": 0, - } - - isInZone = self.isPointInZone(personPos, zonePoly) - zoneData = self.personZoneData[trackID][zoneID] - - # Person just entered the zone - if isInZone and not zoneData["inZone"]: - zoneData["enterime"] = currentTime - zoneData["inZone"] = True - self.zoneStats[zoneID]["currentCount"] += 1 - self.zoneStats[zoneID]["totalVisits"] += 1 - - # Person just exited the zone - elif not isInZone and zoneData["inZone"]: - zoneData["exitTime"] = currentTime - zoneData["inZone"] = False - self.zoneStats[zoneID]["currentCount"] -= 1 - - # Calculate dwell time for this visit - if zoneData["enterTime"] is not None: - dwellTime = zoneData["exitTime"] - zoneData["enterTime"] - zoneData["totalDwellTime"] += dwellTime - - # Update zone statistics - self.zoneStats[zoneID]["maxDwellTime"] = max( - self.zoneStats[zoneID]["maxDwellTime"], dwellTime - ) - - # Update average dwell times periodically - if currentTime - self.lastUpdateTime >= self.updateInterval: - self.updateZoneStats() - self.lastUpdateTime = currentTime - - def updateZoneStats(self): - """Calculate updated statistics for each zone""" - for zoneID in range(len(self.zones)): - totalDwellTime = 0 - visitCount = 0 - - # Calculate total dwell time across all people who have visited this zone - for trackID, zones in self.personZoneData.items(): - if zoneID in zones: - zoneData = zones[zoneID] - - # Add completed visit times - totalDwellTime += zoneData["totalDwellTime"] - - # For people currently in the zone, add their current dwell time - if zoneData["inZone"] and zoneData["enterTime"] is not None: - currentDwell = time.time() - zoneData["enterTime"] - totalDwellTime += currentDwell - - # Count this as a visit if they've entered the zone - if zoneData["enterTime"] is not None: - visitCount += 1 - - # Update average dwell time - if visitCount > 0: - self.zoneStats[zoneID]["averageDwellTime"] = ( - totalDwellTime / visitCount - ) - - def transformPointsToFloor(self, cameraPoints): - - if len(cameraPoints) == 0: - return [] - - # Reshape points for transformation - points = np.array(cameraPoints, dtype=np.float32) - if points.ndim == 1: - points = points.reshape(1, -1, 2) - elif points.ndim == 2: - points = points.reshape(-1, 1, 2) - - # Apply homography transformation - transformedPoints = cv2.perspectiveTransform(points, self.homographyMatrix) - - # Reshape back to original format - if transformedPoints.shape[0] == 1: - transformedPoints = transformedPoints.reshape(-1, 2) - else: - transformedPoints = transformedPoints.reshape(-1, 2) - - return transformedPoints - - def getCurrentDwellTimes(self, currentTime=None): - if currentTime is None: - currentTime = time.time() - - dwellTimes = defaultdict(dict) - - for trackID, zones in self.personZoneData.items(): - for zoneID, zoneData in zones.items(): - if zoneData["inZone"] and zoneData["enterTime"] is not None: - dwellTimes[trackID][zoneID] = ( - currentTime - zoneData["enterTime"] - ) - - return dwellTimes - - def drawZones(self, floorPlanImage): - - for zoneName, zonePoly, color in self.zones: - # Create a copy of the image for overlay - overlay = floorPlanImage.copy() - - # Fill the zone polygon with semi-transparent color - alpha = 0.3 # Transparency factor - cv2.fillPoly(overlay, [zonePoly], color) - - # Apply the overlay with transparency - cv2.addWeighted( - overlay, alpha, floorPlanImage, 1 - alpha, 0, floorPlanImage - ) - - # Draw the zone polygon border - cv2.polylines(floorPlanImage, [zonePoly], True, color, 2) - - # Calculate zone centroid for text placement - centroidX = int(np.mean(zonePoly[:, 0])) - centroidY = int(np.mean(zonePoly[:, 1])) - - # Draw zone name - cv2.putText( - floorPlanImage, - zoneName, - (centroidX - 40, centroidY), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - color, - 2, - ) - - return floorPlanImage - - def drawDwellTimes( - self, image, currentPositions=None, trackIDs=None, isFloorPlan=True - ): - - # If this is floor plan, draw the zones - if isFloorPlan: - image = self.drawZones(image) - - # Draw statistics board in the top-right corner - boardWidth = 300 - boardHeight = len(self.zones) * 30 + 120 # Height based on number of zones - boardX = image.shape[1] - boardWidth - 10 # 10px padding from right edge - boardY = 10 # 10px padding from top - - # Create semi-transparent board background - cv2.rectangle( - image, - (boardX, boardY), - (boardX + boardWidth, boardY + boardHeight), - (50, 50, 50), - -1, - ) # Dark gray background - cv2.rectangle( - image, - (boardX, boardY), - (boardX + boardWidth, boardY + boardHeight), - (200, 200, 200), - 2, - ) # Light gray border - - # Add title to stats board - cv2.putText( - image, - "Zone Statistics", - (boardX + 10, boardY + 25), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Draw zone statistics in the board - statsY = boardY + 55 # Start below title - for zoneID, (zoneName, _, color) in enumerate(self.zones): - stats = self.zoneStats[zoneID] - - # Draw color swatch for this zone - cv2.rectangle( - image, (boardX + 10, statsY - 15), (boardX + 30, statsY), color, -1 - ) - - # Zone name - cv2.putText( - image, - f"{zoneName}", - (boardX + 40, statsY), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (255, 255, 255), - 1, - ) - - # Current count, average dwell time and max dwell time - cv2.putText( - image, - f"Count: {stats['currentCount']} | Avg: {stats['averageDwellTime']:.1f}s | Max: {stats['maxDwellTime']:.1f}s", - (boardX + 40, statsY + 20), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (255, 255, 255), - 1, - ) - - statsY += 55 # Move to next zone stats position - - # If we have current positions, draw them with dwell times - if currentPositions and trackIDs: - # For floor plan, transform camera points to floor coordinates - if isFloorPlan: - positionsToUse = self.transformPointsToFloor( - np.array(currentPositions) - ) - else: - # For camera view, use original camera coordinates - positionsToUse = np.array(currentPositions) - - currentDwellTimes = self.getCurrentDwellTimes() - - for idx, trackID in enumerate(trackIDs): - - if idx >= len(positionsToUse): - continue - - personPos = tuple(map(int, positionsToUse[idx])) - - # Check if this person is in any zone and display their dwell time - for zoneID, (zoneName, zonePoly, color) in enumerate(self.zones): - if ( - trackID in currentDwellTimes - and zoneID in currentDwellTimes[trackID] - ): - dwellTime = currentDwellTimes[trackID][zoneID] - - # Draw dwell time near the person's position - cv2.putText( - image, - f"{int(dwellTime)}s", - (personPos[0] + 10, personPos[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.4, - color, - 1, - ) - - # Draw circle around person colored by dwell time (redder = longer) - maxDwell = 60 # Consider 60 seconds as maximum intensity - intensity = min(dwellTime / maxDwell, 1.0) - dwellColour = ( - 0, - int(255 * (1 - intensity)), - int(255 * intensity), - ) - cv2.circle(image, personPos, 10, dwellColour, -1) - - return image - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/floorReplica.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/floorReplica.py deleted file mode 100644 index 537f188a3..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/floorReplica.py +++ /dev/null @@ -1,25 +0,0 @@ -import cv2 -import numpy as np - -def floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl): - # Create a VideoCapture object - cap = cv2.VideoCapture(rtspUrl) - - # Check if camera opened successfully - success, frame = cap.read() - if not success: - cap.release() - cap = cv2.VideoCapture(rtspUrl) - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - floorReplica.py") - - tileHeight = canvasHeight // tilesY - tileWidth = canvasWidth // tilesX - - floorImage = np.ones((canvasHeight, canvasWidth, 3), dtype=np.uint8) * 255 - - for y in range(0, canvasHeight, tileHeight): - for x in range(0, canvasWidth, tileWidth): - cv2.rectangle(floorImage, (x, y), (x + tileWidth, y + tileHeight), (0, 0, 0), 1) - return floorImage \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/opticalFlow.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/opticalFlow.py deleted file mode 100644 index d82c47627..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/opticalFlow.py +++ /dev/null @@ -1,129 +0,0 @@ -import cv2 -import numpy as np - - -class OpticalFlow: - def __init__(self, predictionStep=10, predictionScale=5): - # previousFrame and currentFrame to store the previous and current frames for optical flow calculation - # predictionSteps and predictionScale to control the prediction steps and scale - self.previousFrame = None - self.currentFrame = None - self.predictionSteps = predictionStep - self.predictionScale = predictionScale - - def calculateFlow(self, frame): - # Function to calculate the optical flow using Farneback method - # Convert the frame to grayscale - currentGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - if self.previousFrame is None: - self.previousFrame = currentGray - return frame - - # Calculate the optical flow using Farneback method - - flow = cv2.calcOpticalFlowFarneback( - self.previousFrame, currentGray, None, 0.5, 3, 15, 3, 5, 1.2, 0 - ) - self.previousFrame = currentGray - return flow - - def predictPosition(self, trackHistories, flow): - # Function to predict the next position of each trackID based on the optical flow - # Initialize a dictionary to store the predicted positions - predictedPositions = {} - - # Iterate through each trackID and its corresponding points - # if the length of points is less than 2, skip the prediction - for trackID, points in trackHistories.items(): - if len(points) < 2: - continue - - # Get the last point of the track history - # and use it to predict the next position - lastPoint = points[-1] - x, y = int(lastPoint[0]), int(lastPoint[1]) - - # Check if the point is within the bounds of the flow array - # get the flow vector at the last point - # and predict the next position - # then store the predicted position in the dictionary - if 0 <= x < flow.shape[1] and 0 <= y < flow.shape[0]: - dy, dx = flow[y, x] - predictedX = x + int(dx * self.predictionScale) - predictedY = y + int(dy * self.predictionScale) - - predictedPositions[trackID] = (predictedX, predictedY) - - return predictedPositions - - def predictTrajectory(self, trackID, points, predictedPosition): - # Function to predict the trajectory of a trackID based on the predicted position - # Check if the length of points is less than 2 and return an empty list - if len(points) < 2: - return [] - - # Initialize the trajectory with the last point of the track history - currentPosition = points[-1] - trajectory = [currentPosition] - - # Predict the next position - nextPosition = predictedPosition - trajectory.append(nextPosition) - - # Iterate through the prediction steps - # and calculate the next positions based on the flow - for _ in range(self.predictionSteps - 1): - if len(trajectory) >= 2: - - # Calculate the velocity vector based on the last two points - # and update the next position - dx = nextPosition[0] - currentPosition[0] - dy = nextPosition[1] - currentPosition[1] - nextPosition = (int(nextPosition[0] + dx), int(nextPosition[1] + dy)) - trajectory.append(nextPosition) - - # Update the current position to the last point in the trajectory - currentPosition = trajectory[-2] - - return trajectory - - def drawPredictedTrajectory(self, frame, flow): - # Function to draw the predicted trajectory on the frame - # Check if flow is None - if flow is None: - return frame.copy() - - # Get frame dimensions - h, w = frame.shape[:2] - - try: - # Extract flow components - fx, fy = flow[:, :, 0], flow[:, :, 1] - - # Make sure fx and fy have the same shape and are float32 - if fx.shape != fy.shape: - print("Flow components have mismatched shapes") - return frame.copy() - - # Convert to float32 if needed - fx = fx.astype(np.float32) - fy = fy.astype(np.float32) - - # Calculate the magnitude and angle of the flow vectors - magnitude, angle = cv2.cartToPolar(fx, fy) - - # Create an HSV image to visualize the flow - hsv = np.zeros((h, w, 3), dtype=np.uint8) - hsv[..., 0] = angle * 180 / np.pi / 2 - hsv[..., 1] = 255 - hsv[..., 2] = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX) - - # Convert the HSV image to BGR color space - bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) - bgr = cv2.addWeighted(frame, 0.5, bgr, 0.5, 0) - bgr = cv2.resize(bgr, (frame.shape[1], frame.shape[0])) - return bgr - - except Exception as e: - print(f"Error in drawPredictedTrajectory: {e}") - return frame.copy() # Return original frame if any error occurs diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/utils.py deleted file mode 100644 index eee43f75e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Dectection/utils.py +++ /dev/null @@ -1,13 +0,0 @@ -import cv2 -import numpy as np - - -def calculateHomography(ptsSRC, ptsDST): - return cv2.findHomography(ptsSRC, ptsDST)[0] - - -def transformPoints(points, homographyMatrix): - points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) - transformedPoints = homographyMatrix.dot(points.T).T - transformedPoints /= transformedPoints[:, 2][:, np.newaxis] - return transformedPoints[:, :2] diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/.gitignore b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/.gitignore deleted file mode 100644 index 66da92e5b..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -__pycache__/ -*.py[cod] -yolov8n.pt \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/Integration.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/Integration.py deleted file mode 100644 index fa4d53b6e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/Integration.py +++ /dev/null @@ -1,326 +0,0 @@ -import cv2 -import numpy as np -import time -from collections import defaultdict - - -class Integration: - def __init__(self, congestionDetection, dwellTimeAnalysis): - # Initialize the integration analysis with necessary components - # including congestion detection and dwell time analysis - self.congestionDetection = congestionDetection - self.dwellTimeAnalysis = dwellTimeAnalysis - self.homographyMatrix = congestionDetection.homographyMatrix - - # initialise the list for high risk zones - # and other necessary data structures - self.highRiskZones = [] - self.zoneCongestionCorrelation = defaultdict(float) - self.zoneHistoricalData = defaultdict(list) - - # Thresholds for triggering alerts - self.highDwellThreshold = 5 - self.highCongestionThreshold = 3 - - # Update timing settings - self.lastUpdateTime = time.time() - self.updateInterval = 5 - - def update(self, currentPositions, trackIDs, flow=None): - # Check if there are any current positions - if not currentPositions: - return self.highRiskZones - - # Identify congestion zones from current positions - congestionZones = self.congestionDetection.identifyCongestionZones( - currentPositions - ) - - # Update dwell time analysis - self.dwellTimeAnalysis.updateZones(currentPositions, trackIDs) - - # Periodic full analysis - currentTime = time.time() - if currentTime - self.lastUpdateTime > self.updateInterval: - self.integrationAnalysis(currentPositions, trackIDs, congestionZones, flow) - self.lastUpdateTime = currentTime - - # if optical flow is provided, perform prediction analysis - if flow is not None: - self.floorPrediction(currentPositions, flow) - - # return the high risk zones - return self.highRiskZones - - def integrationAnalysis(self, currentPositions, trackIDs, congestionZones, flow): - # Reset high risk zones for this update - self.highRiskZones = [] - - # Get current dwell times for all zones - dwellTimes = self.dwellTimeAnalysis.getCurrentDwellTimes() - - # Transform congestion centers to floor coordinates - floorCongestionCenters = [] - for zone in congestionZones: - centre, radius, count = zone - if centre and radius: - floorCentre = self.transformPointToFloor(centre) - floorCongestionCenters.append((floorCentre, radius, count)) - - # Analyze each zone defined in the dwell time analysis - for zoneID, (zoneName, zonePoly, zoneColor) in enumerate( - self.dwellTimeAnalysis.zones - ): - zoneStats = self.dwellTimeAnalysis.zoneStats[zoneID] - - # Calculate zone centroid - zoneCentroid = (int(np.mean(zonePoly[:, 0])), int(np.mean(zonePoly[:, 1]))) - - # Check if any congestion centers fall within this zone - zoneHasCongestion = False - congestionCount = 0 - for floorCentre, radius, count in floorCongestionCenters: - # Check if congestion center is in or near the zone - distance = np.sqrt( - (zoneCentroid[0] - floorCentre[0]) ** 2 - + (zoneCentroid[1] - floorCentre[1]) ** 2 - ) - if self.isPointInPolygon(zonePoly, floorCentre) or distance < radius: - zoneHasCongestion = True - congestionCount = max(congestionCount, count) - - # Get zone statistics - averageDwellTime = zoneStats["averageDwellTime"] - currentCount = zoneStats["currentCount"] - - # Store historical data for this zone - self.zoneHistoricalData[zoneID].append( - { - "timestamp": time.time(), - "count": currentCount, - "averageDwellTime": averageDwellTime, - "hasCongestion": zoneHasCongestion, - "congestionCount": congestionCount, - } - ) - - # Limit historical data length - if len(self.zoneHistoricalData[zoneID]) > 100: - self.zoneHistoricalData[zoneID].pop(0) - - # Determine if this is a high-risk zone based on both dwell time and congestion - if ( - zoneHasCongestion - and averageDwellTime > self.highDwellThreshold - and congestionCount >= self.highCongestionThreshold - ): - - # Calculate risk score based on dwell time and congestion count - riskScore = (averageDwellTime / self.highDwellThreshold) * ( - congestionCount / self.highCongestionThreshold - ) - - # Create high risk zone entry - self.highRiskZones.append( - { - "zoneID": zoneID, - "zoneName": zoneName, - "centroid": zoneCentroid, - "averageDwellTime": averageDwellTime, - "congestionCount": congestionCount, - "riskScore": riskScore, - "predictedCongestion": False, # Not predicted yet - "color": zoneColor, - } - ) - - def floorPrediction(self, currentPositions, flow): - # Get predicted congestion zones - futureCongestion = self.congestionDetection.predictCongestionZones( - currentPositions, flow - ) - - # Process each predicted congestion zone - for zone in futureCongestion: - centre, radius, count = zone - floorCentre = self.transformPointToFloor(centre) - - # Check which zones this predicted congestion might affect - for zoneID, (zoneName, zonePoly, zoneColor) in enumerate( - self.dwellTimeAnalysis.zones - ): - zoneStats = self.dwellTimeAnalysis.zoneStats[zoneID] - - # If dwell time is already high and predicted congestion is in this zone - if zoneStats[ - "averageDwellTime" - ] > self.highDwellThreshold and self.isPointInPolygon( - zonePoly, floorCentre - ): - - # Check if zone is already in high risk zones - zone_already_added = False - for i, riskZone in enumerate(self.highRiskZones): - if riskZone["zoneID"] == zoneID: - # Increase risk score for existing zone - self.highRiskZones[i]["riskScore"] *= 1.5 - self.highRiskZones[i]["predictedCongestion"] = True - zone_already_added = True - break - - # Add new high risk zone if not already added - if not zone_already_added: - zoneCentroid = ( - int(np.mean(zonePoly[:, 0])), - int(np.mean(zonePoly[:, 1])), - ) - riskScore = ( - zoneStats["averageDwellTime"] / self.highDwellThreshold - ) * (count / self.highCongestionThreshold) - self.highRiskZones.append( - { - "zoneID": zoneID, - "zoneName": zoneName, - "centroid": zoneCentroid, - "averageDwellTime": zoneStats["averageDwellTime"], - "congestionCount": count, - "riskScore": riskScore, - "predictedCongestion": True, - "color": zoneColor, - } - ) - - def transformPointToFloor(self, cameraPoint): - # convert camera point to floor coordinates using homography - pointArray = np.array([cameraPoint], dtype=np.float32).reshape(-1, 1, 2) - transformedPoint = cv2.perspectiveTransform(pointArray, self.homographyMatrix)[ - 0 - ][0] - return (int(transformedPoint[0]), int(transformedPoint[1])) - - def transformPolygonToCamera(self, floorPolygon): - # calculate the inverse of the homography matrix - # and transform the polygon to camera coordinates - try: - inverseHomography = np.linalg.inv(self.homographyMatrix) - floorPolygonReshaped = floorPolygon.reshape(-1, 1, 2).astype(np.float32) - cameraPolygon = cv2.perspectiveTransform( - floorPolygonReshaped, inverseHomography - ) - return cameraPolygon.reshape(-1, 2).astype(int) - - except np.linalg.LinAlgError: - print("Error: Homography matrix is singular or not invertible.") - return None - - def isPointInPolygon(self, zonePolygon, point): - return cv2.pointPolygonTest(zonePolygon, tuple(map(int, point)), False) >= 0 - - def drawAnalytics(self, cameraFrame, floorFrame): - # First draw dwell time information on the floor plan - self.dwellTimeAnalysis.drawDwellTimes(floorFrame, None, None, True) - - # Draw risk zones on both camera and floor view - for riskZone in self.highRiskZones: - zoneID = riskZone["zoneID"] - zoneName = riskZone["zoneName"] - centroid = riskZone["centroid"] - riskScore = riskZone["riskScore"] - isPredicted = riskZone.get("predictedCongestion", False) - - # Calculate color based on risk score - # Draw risk indicator on floor plan - # Circle with risk color - riskIntensity = min(riskScore / 5, 1) - riskColor = ( - 0, - int(255 * (1 - riskIntensity)), - int(255 * riskIntensity), - ) - cv2.circle(floorFrame, centroid, 25, riskColor, -1) - - # Label based on prediction status - label = "High Risk Zone" - if isPredicted: - label = "Predicted " + label - - # Draw labels on floor plan - cv2.putText( - floorFrame, - label, - (centroid[0] - 80, centroid[1] - 30), - cv2.FONT_HERSHEY_SIMPLEX, - 0.6, - riskColor, - 2, - ) - cv2.putText( - floorFrame, - f"Risk Score: {riskScore:.1f}", - (centroid[0] - 70, centroid[1] + 30), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - riskColor, - 2, - ) - - # Add additional statistics - cv2.putText( - floorFrame, - f"Dwell: {riskZone['averageDwellTime']:.1f}s", - (centroid[0] - 70, centroid[1] + 50), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - riskColor, - 2, - ) - cv2.putText( - floorFrame, - f"Count: {riskZone['congestionCount']}", - (centroid[0] - 70, centroid[1] + 70), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - riskColor, - 2, - ) - - # Transform zone polygon to camera view - zonePoly = self.dwellTimeAnalysis.zones[zoneID][1] - cameraPoly = self.transformPolygonToCamera(zonePoly) - - # Draw on camera view if polygon transformation was successful - if cameraPoly is not None: - # Create semi-transparent overlay - overlay = cameraFrame.copy() - cv2.fillPoly(overlay, [cameraPoly], riskColor) - cv2.addWeighted(overlay, 0.3, cameraFrame, 0.7, 0, cameraFrame) - - # Draw polygon outline - cv2.polylines(cameraFrame, [cameraPoly], True, riskColor, 2) - - # Calculate camera view centroid for labels - cameraCentroid = np.mean(cameraPoly, axis=0).astype(int) - - # Draw labels on camera view - cv2.putText( - cameraFrame, - label, - (cameraCentroid[0] - 80, cameraCentroid[1] - 15), - cv2.FONT_HERSHEY_SIMPLEX, - 0.6, - riskColor, - 2, - ) - - # Draw risk score on camera - cv2.putText( - cameraFrame, - f"Risk: {riskScore:.1f}", - (cameraCentroid[0] - 50, cameraCentroid[1] + 15), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - riskColor, - 2, - ) - - return cameraFrame, floorFrame diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/README.md deleted file mode 100644 index 2696d8dc0..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/README.md +++ /dev/null @@ -1,47 +0,0 @@ -# Congestion Dection - -## Introduction - -The program focuses on extending the analysing features of the previous real-time crowd monitoring project. Farneback algorithm, homography transformation, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for tracking the movement of the people to provide direction and congestion predictions. - -Even though utilising the previous program, the project would solely use bulit-in camera for live tracking instead of the Real-Time Streaming Protocol. Furthermore, the data recording feature via MongoDB will be commented out, since it was not a part of the initial targets. - -The cameraProccesing.py will be the main Python script used for testing. - -## Folder Components - -### cameraProcessing.py - -The main controller used for: - -1. Objectives tracking using YOLOv8 -2. Ochestrateing other components -3. Displaying actual camera and 2D floor plan view - -### opticalFlow.py - -Utilising Farneback algorithm for: - -1. Analysing the crowd movement between consecutive frames -2. Providing future position and trajectories predictions - -### congestionDetectio.py - -This class has the purpose of: - -1. Detecting the area where the people standing close to each other -2. Predicting the potential jammed areas -3. Visualising the the zones on both camera view and 2D floor - -### dwellTime.py - -This class is majorly integrate into the 2D plan floor. Its mechanism includes: - -1. Dividing the floor into differents zones -2. OpenCV is applied to verify whether the objects are still in the zones -3. Caluclating the the dwelling time -4. Visualising the zones and their statistics - -### integration.py - -The class is the effort of combining the congestion detection and dwelling time calculation to provide more practical outcomes. The class would retrieve the people positions on the frame and convert them to 2D floor using homography transformation. It would also calculate the risk of congestion based on the current jammed areas and the dwelling time. The integration class would also visualise the congested area on the original camera frame. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/app.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/app.py deleted file mode 100644 index 374ed206a..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/app.py +++ /dev/null @@ -1,38 +0,0 @@ -from flask import Flask, jsonify, Response, stream_with_context -from flask_cors import CORS -from cameraProcessing import CameraProcessor -from database import Database - -app = Flask(__name__) -cors = CORS(app) -cameraProcessor = CameraProcessor() -db = Database() - - -# routes for people count -@app.route("/api/peopleCount", methods=["GET"]) -def getPeopleCount(): - count = db.getlastestRecord() - return jsonify({"peopleCount": count}) - - -# routes for main camera display -@app.route("/LiveTracking/videoFeed", methods=["GET"]) -def videoFeed(): - return Response( - stream_with_context(cameraProcessor.getFrame()), - mimetype="multipart/x-mixed-replace; boundary=frame", - ) - - -# routes for annotated frame or 2d floor plan -@app.route("/LiveTracking/annotatedVideoFeed", methods=["GET"]) -def annotatedVideoFeed(): - return Response( - stream_with_context(cameraProcessor.getAnnotatedFrame()), - mimetype="multipart/x-mixed-replace; boundary=frame", - ) - - -if __name__ == "__main__": - app.run(port=8000) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/cameraProcessing.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/cameraProcessing.py deleted file mode 100644 index a97335c8d..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/cameraProcessing.py +++ /dev/null @@ -1,404 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints - -# from database import Database -from floorReplica import floorReplica -import time as time_module -from opticalFlow import OpticalFlow -from congestionDetection import CongestionDetection -from dwellTime import DwellTimeAnalysis -from Integration import Integration - - -class CameraProcessor: - def __init__(self): - # initialize the YOLO model - # RTSP stream URL for the video feed - # trackHistory is a dictionary to store the movement history of each person - # floorImage is a replica of the floor plan for annotation - # homographyMatrix is the homography matrix for transforming points - # db is an instance of the Database class - # lastRecorded is the timestamp of the last recorded data - self.model = YOLO("yolov8n.pt") - self.rtspUrl = 0 - self.cap = cv2.VideoCapture(self.rtspUrl) - self.trackHistory = defaultdict(list) - self.floorImage = floorReplica(1000, 700, 25, 15, self.rtspUrl) - self.homographyMatrix = self.calculateHomography() - # self.db = Database() - # self.lastRecorded = 0 - - # initialise the variables for counting the current frame ID - # initialise the congestion detection module using the homography matrix - # initialise the optical flow amd the dwell time analysis module - # Initialize the integration module that combines congestion and dwell time analysis - self.congestionDetection = CongestionDetection( - self.homographyMatrix, debug=True - ) - self.opticalFlow = OpticalFlow(predictionStep=5, predictionScale=3) - self.dwellTimeAnalysis = DwellTimeAnalysis(self.homographyMatrix) - self.integration = Integration(self.congestionDetection, self.dwellTimeAnalysis) - - # Visualization settings - self.lastFlowVisualizationTime = 0 - self.flowVisualizationInterval = 5 # Show every 5 seconds - self.flowVisualizationDuration = 3 # Show for 3 seconds - self.showFlowVisualization = False - - # Analysis display flags - self.showDwellTime = True - self.showIntegration = True - - # Default analysis mode - self.analysisMode = ( - "integration" # Options: "basic", "congestion", "dwell", "integration" - ) - - # Function to calculate the homography matrix - def calculateHomography(self): - ptsSRC = np.array( - [[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]] - ) - ptsDST = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) - return calculateHomography(ptsSRC, ptsDST) - - def processFrame(self, frame): - # Calculate optical flow for this frame - # and initialise the current position list to store the current positions of detected people - # and track IDs to store the IDs of detected people - flow = self.opticalFlow.calculateFlow(frame) - currentPositions = [] - trackIDs = [] - - # Check if it's time to update the flow visualization based on the interval - currentTime = time_module.time() - - # Start visualization if interval has passed - if ( - currentTime - self.lastFlowVisualizationTime - >= self.flowVisualizationInterval - ): - self.showFlowVisualization = True - self.lastFlowVisualizationTime = currentTime - - # Create copies of the frame for annotations - # and initialise the total people count - annotatedFrame = frame.copy() - floorAnnotatedFrame = self.floorImage.copy() - totalPeople = 0 - - try: - results = self.model.track( - frame, persist=True, show=False, imgsz=1280, verbose=False - ) - - # # Get predicted positions based on optical flow - # predictedPositions = {} - # if flow is not None and self.showFlowVisualization: - # predictedPositions = self.opticalFlow.predictPosition( - # self.trackHistory, flow - # ) - - # verify if the bounding boxes were detected and having the id attribute - # get the boxes coordinates, track IDs and classes - if results[0].boxes is not None and hasattr(results[0].boxes, "id"): - boxes = results[0].boxes.xywh.cpu().numpy() - trackIDs = results[0].boxes.id.int().cpu().numpy() - classes = results[0].boxes.cls.cpu().numpy() - - # Filter for human detections (assuming 'person' class is 0) - human_indices = classes == 0 - human_boxes = boxes[human_indices] - human_trackIDs = trackIDs[human_indices] - - # Store current positions of all detected people - currentPositions = [] - - # Process each tracked person - for idx, trackID in enumerate(human_trackIDs): - # Get box dimensions - x, y, w, h = human_boxes[idx] - # Define center point (feet position) - center = (int(x), int(y + h / 2)) - - # Add to current positions and tracking history - currentPositions.append(center) - self.trackHistory[trackID].append(center) - - # Limit history length - if len(self.trackHistory[trackID]) > 50: - self.trackHistory[trackID].pop(0) - - # Draw history trails - history = self.trackHistory[trackID] - if len(history) > 1: - points = np.array(history, dtype=np.int32) - - # Transform points to floor coordinates - floorPoints = transformPoints(points, self.homographyMatrix) - floorPoints = floorPoints.astype(np.int32) - - # Draw the history trail on floor plan - cv2.polylines( - floorAnnotatedFrame, - [floorPoints], - isClosed=False, - color=(0, 0, 255), - thickness=2, - ) - - # Draw bounding box and ID on the original frame - cv2.rectangle( - annotatedFrame, - (int(x - w / 2), int(y - h / 2)), - (int(x + w / 2), int(y + h / 2)), - (0, 255, 0), - 2, - ) - cv2.putText( - annotatedFrame, - f"ID: {int(trackID)}", - (int(x - w / 2), int(y - h / 2) - 10), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (0, 255, 0), - 2, - ) - - # # Show predicted trajectories when flow visualization is active - # if self.showFlowVisualization and trackID in predictedPositions: - # # Get predicted position - # pred_x, pred_y = predictedPositions[trackID] - # cv2.circle( - # annotatedFrame, (pred_x, pred_y), 5, (0, 255, 255), -1 - # ) # Yellow dot - - # # Calculate and draw trajectory - # trajectory = self.opticalFlow.predictTrajectory( - # trackID, history, predictedPositions[trackID] - # ) - - # if len(trajectory) >= 2: - # # Draw predicted path on camera view - # points = np.array(trajectory, dtype=np.int32) - # cv2.polylines( - # annotatedFrame, - # [points], - # isClosed=False, - # color=(0, 255, 255), - # thickness=2, - # ) - - # # Add direction arrow - # last_point = trajectory[-2] - # end_point = trajectory[-1] - # cv2.arrowedLine( - # annotatedFrame, - # (int(last_point[0]), int(last_point[1])), - # (int(end_point[0]), int(end_point[1])), - # (0, 255, 255), - # 3, - # tipLength=0.5, - # ) - - # # Draw on floor plan too - # floorTrajectory = transformPoints( - # np.array(trajectory), self.homographyMatrix - # ) - # floorTrajectory = floorTrajectory.astype(np.int32) - # cv2.polylines( - # floorAnnotatedFrame, - # [floorTrajectory], - # isClosed=False, - # color=(0, 255, 255), - # thickness=2, - # ) - - # # Add arrow on floor plan - # if len(floorTrajectory) >= 2: - # floor_last = floorTrajectory[-2] - # floor_end = floorTrajectory[-1] - # cv2.arrowedLine( - # floorAnnotatedFrame, - # (int(floor_last[0]), int(floor_last[1])), - # (int(floor_end[0]), int(floor_end[1])), - # (0, 255, 255), - # 3, - # tipLength=0.5, - # ) - - totalPeople += 1 - - # Process different analysis modes - if self.analysisMode == "basic" or self.analysisMode == "congestion": - # Run congestion detection - self.congestionDetection.drawCongestionZones( - annotatedFrame, floorAnnotatedFrame, transformPoints - ) - - # Predict future congestion - futureCongestion = self.congestionDetection.predictCongestionZones( - currentPositions, flow - ) - - # Draw predicted congestion - self.congestionDetection.drawPredictedCongestion( - annotatedFrame, futureCongestion - ) - - if self.analysisMode == "basic" or self.analysisMode == "dwell": - # Update and visualize dwell time analysis - self.dwellTimeAnalysis.updateZones(currentPositions, human_trackIDs) - - # Draw dwell time zones on both camera and floor frame - if self.showDwellTime: - floorAnnotatedFrame = self.dwellTimeAnalysis.drawDwellTimes( - floorAnnotatedFrame, currentPositions, human_trackIDs, True - ) - annotatedFrame = self.dwellTimeAnalysis.drawDwellTimes( - annotatedFrame, currentPositions, human_trackIDs, False - ) - - if self.analysisMode == "integration": - # Run the integrated analysis which combines congestion and dwell time - highRiskZones = self.integration.update( - currentPositions, human_trackIDs, flow - ) - - # Display integration visualization - if self.showIntegration: - # Draw analytics on both camera and floor views - annotatedFrame, floorAnnotatedFrame = ( - self.integration.drawAnalytics( - annotatedFrame, floorAnnotatedFrame - ) - ) - - else: - cv2.putText( - annotatedFrame, - "No human detections available", - (50, 50), - cv2.FONT_HERSHEY_SIMPLEX, - 1, - (0, 0, 255), - 2, - ) - - except Exception as e: - cv2.putText( - annotatedFrame, - f"Error: {str(e)}", - (50, 50), - cv2.FONT_HERSHEY_SIMPLEX, - 1, - (0, 0, 255), - 2, - ) - - # Add info overlay - self.addInfoOverlay(annotatedFrame, floorAnnotatedFrame, totalPeople) - - return annotatedFrame, floorAnnotatedFrame - - def addInfoOverlay(self, cameraFrame, floorFrame, totalPeople): - # Add mode indicator to the camera frame - cv2.putText( - cameraFrame, - f"Mode: {self.analysisMode.capitalize()}", - (20, 30), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Display total people count - cv2.putText( - cameraFrame, - f"People: {totalPeople}", - (20, 60), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Add keyboard controls info - cv2.putText( - cameraFrame, - "Press 'q' to quit, 'm' to change mode", - (20, cameraFrame.shape[0] - 20), - cv2.FONT_HERSHEY_SIMPLEX, - 0.6, - (255, 255, 255), - 1, - ) - - def togglingMode(self): - modes = ["basic", "congestion", "dwell", "integration"] - currentIndex = modes.index(self.analysisMode) - nextIndex = (currentIndex + 1) % len(modes) - self.analysisMode = modes[nextIndex] - print(f"Analysis mode changed to: {self.analysisMode}") - return self.analysisMode - - def run(self): - while True: - success, frame = self.cap.read() - if not success: - print("Failed to read video stream. Retrying...") - continue - - annotatedFrame, floorAnnotatedFrame = self.processFrame(frame) - - # Display the frames - cv2.imshow("Camera View", annotatedFrame) - cv2.imshow("Floor Plan View", floorAnnotatedFrame) - - # Handle key presses - key = cv2.waitKey(1) & 0xFF - if key == ord("q"): - break - elif key == ord("m"): - self.togglingMode() - - self.release() - - def getFrame(self): - - while True: - success, frame = self.cap.read() - if not success: - print("Failed to read video stream in getFrame(). Retrying...") - continue - - annotatedFrame, _ = self.processFrame(frame) - ret, buffer = cv2.imencode(".jpg", annotatedFrame) - frame = buffer.tobytes() - yield (b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n") - - def getAnnotatedFrame(self): - - while True: - success, frame = self.cap.read() - if not success: - print("Failed to read video stream in getAnnotatedFrame(). Retrying...") - continue - - _, floorAnnotatedFrame = self.processFrame(frame) - ret, buffer = cv2.imencode(".jpg", floorAnnotatedFrame) - frame = buffer.tobytes() - yield (b"--frame\r\n" b"Content-Type: image/jpeg\r\n\r\n" + frame + b"\r\n") - - def release(self): - self.cap.release() - cv2.destroyAllWindows() - - -if __name__ == "__main__": - cameraProcessor = CameraProcessor() - cameraProcessor.run() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/congestionDetection.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/congestionDetection.py deleted file mode 100644 index b4b9d9221..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/congestionDetection.py +++ /dev/null @@ -1,306 +0,0 @@ -import cv2 -import numpy as np -from sklearn.cluster import DBSCAN - - -class CongestionDetection: - def __init__(self, homographyMatrix, debug=False): - # Initialize the homography matrix for transforming points - # and set 3 as the minimum people within proximity to define congestion - # and 120 as the pixel distance to consider for congestion - # define a list to store congestion zones - self.homographyMatrix = homographyMatrix - self.congestionThreshold = 3 - self.proximityThreshold = 120 - self.congestionZones = [] - self.debug = debug - - def identifyCongestionZones(self, position): - # Function to identify congestion zones based on the track histories - # Define a list to store congestion zones for the current frame - # checking if the position is empty, return an empty list - self.congestionZones = [] - - if not position: - return [] - - minCongestionThreshold = self.congestionThreshold - - # Check if the number of positions exceeds the congestion threshold - if len(position) >= minCongestionThreshold: - # Convert the positions to a numpy array - pointsArray = np.array(position, dtype=np.float32) - - try: - # Use DBSCAN clustering to identify congestion zones - # labels the points based on proximity - # get the unique cluster IDs - clustering = DBSCAN( - eps=self.proximityThreshold, min_samples=minCongestionThreshold - ).fit(pointsArray) - labels = clustering.labels_ - uniqueClusters = set(labels) - {-1} - - # Iterate through each unique cluster ID - # and calculate the center and radius of the congestion zone - for clusterID in uniqueClusters: - clusterPoints = pointsArray[labels == clusterID] - if len(clusterPoints) >= minCongestionThreshold: - centerX = int(np.mean(clusterPoints[:, 0])) - centerY = int(np.mean(clusterPoints[:, 1])) - - # calculate the radius based on the cluster - maxDistance = 0 - for point in clusterPoints: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - # Set the radius to the maximum distance and ensure it's at least the proximity threshold - # get the count of points in the cluster - radius = max(int(maxDistance) + 20, self.proximityThreshold) - count = len(clusterPoints) - - # add the congestion zone to the list - self.congestionZones.append(((centerX, centerY), radius, count)) - - if self.debug: - print( - f"Congestion Zone: center=({centerX}, {centerY}), Radius={radius}, Count={count}" - ) - - except ImportError as e: - # fallback if DBSCAN is not available - self._fallbackCongestionDetection(position) - else: - # Fallback if not enough positions - self._fallbackCongestionDetection(position) - - return self.congestionZones - - def _fallbackCongestionDetection(self, position): - # Fallback congestion detection logic if DBSCAN is not available - # This is a simple distance-based clustering - - minNearbyPoints = self.congestionThreshold - 1 - - for i, position1 in enumerate(position): - nearbyPointsCount = 0 - nearbyPositions = [] - - for j, position2 in enumerate(position): - if i != j: - distance = np.sqrt( - (position1[0] - position2[0]) ** 2 - + (position1[1] - position2[1]) ** 2 - ) - if distance < self.proximityThreshold: - nearbyPointsCount += 1 - nearbyPositions.append(position2) - - # Check if the number of nearby points exceeds the threshold - # and if so, create a congestion zone by calculating the center of the group - if nearbyPointsCount >= minNearbyPoints: - allPositions = [position1] + nearbyPositions - centerX = int(np.mean([p[0] for p in allPositions])) - centerY = int(np.mean([p[1] for p in allPositions])) - - # caculate the radius - maxDistance = 0 - for point in allPositions: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = int(maxDistance) + 20 - - # check if the zone is already in the list - # to avoid duplicates - newZone = True - for zone in self.congestionZones: - zonecenter = zone[0] - distance = np.sqrt( - (centerX - zonecenter[0]) ** 2 + (centerY - zonecenter[1]) ** 2 - ) - if distance < radius: - newZone = False - break - - if newZone: - count = nearbyPointsCount + 1 - self.congestionZones.append(((centerX, centerY), radius, count)) - if self.debug: - print( - f"Congestion Zone: center=({centerX}, {centerY}), Radius={radius}, Count={count}" - ) - - def predictCongestionZones(self, currentPositions, flow, predictScale=5): - # Function to predict congestion zones based on the current positions and flow - if not currentPositions or flow is None: - return [] - - futurePositions = [] - for x, y in currentPositions: - # Predict the future position using the flow - if 0 <= x < flow.shape[1] and 0 <= y < flow.shape[0]: - dy, dx = flow[y, x] - predictedX = int(x + dx * predictScale) - predictedY = int(y + dy * predictScale) - futurePositions.append((predictedX, predictedY)) - - # Use the same algorithm as identifyCongestionZones but on future positions - futureCongestions = [] - - if len(futurePositions) >= self.congestionThreshold: - # Convert future positions to numpy array - pointsArray = np.array(futurePositions, dtype=np.float32) - - try: - clustering = DBSCAN( - eps=self.proximityThreshold, min_samples=self.congestionThreshold - ).fit(pointsArray) - labels = clustering.labels_ - uniqueClusters = set(labels) - {-1} - - for clusterID in uniqueClusters: - clusterPoints = pointsArray[labels == clusterID] - if len(clusterPoints) >= self.congestionThreshold: - centerX = int(np.mean(clusterPoints[:, 0])) - centerY = int(np.mean(clusterPoints[:, 1])) - - # Calculate radius based on cluster - maxDistance = 0 - for point in clusterPoints: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = max(int(maxDistance) + 20, self.proximityThreshold) - count = len(clusterPoints) - - futureCongestions.append(((centerX, centerY), radius, count)) - except: - # Fallback if DBSCAN fails - self._fallbackFutureCongestion(futurePositions, futureCongestions) - else: - # Fallback if not enough future positions - self._fallbackFutureCongestion(futurePositions, futureCongestions) - - return futureCongestions - - def _fallbackFutureCongestion(self, positions, congestionsList): - # Fallback future congestion detection - minNearbyPoints = self.congestionThreshold - 1 - processed = set() - - for i, pos1 in enumerate(positions): - if i in processed: - continue - - nearbyPointsCount = 0 - nearbyPositions = [] - - for j, pos2 in enumerate(positions): - if i != j: - distance = np.sqrt( - (pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2 - ) - if distance < self.proximityThreshold: - nearbyPointsCount += 1 - nearbyPositions.append(pos2) - processed.add(j) - - if nearbyPointsCount >= minNearbyPoints: - allPositions = [pos1] + nearbyPositions - centerX = int(np.mean([p[0] for p in allPositions])) - centerY = int(np.mean([p[1] for p in allPositions])) - - # Calculate radius - maxDistance = 0 - for point in allPositions: - distance = np.sqrt( - (point[0] - centerX) ** 2 + (point[1] - centerY) ** 2 - ) - maxDistance = max(maxDistance, distance) - - radius = int(maxDistance) + 20 - count = nearbyPointsCount + 1 - - congestionsList.append(((centerX, centerY), radius, count)) - - def drawCongestionZones( - self, annotatedFrame, floorFrame, transformePointsFuction=None - ): - # Function to draw the congestion zones on the annotated frame - for zone in self.congestionZones: - center, radius, count = zone - - # Draw the congestion zone and count on the annotated frame - # Drawing red circle with transparency and text - overlay = annotatedFrame.copy() - cv2.circle(overlay, center, radius, (0, 0, 255), -1) - cv2.addWeighted(overlay, 0.4, annotatedFrame, 0.6, 0, annotatedFrame) - cv2.circle(annotatedFrame, center, radius, (0, 0, 255), 2) - cv2.putText( - annotatedFrame, - f"CONGESTION ZONE: {count}", - (center[0] - 80, center[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Check if floorFrame and transformePointsFuction are provided - # and draw the congestion zone on the floor plan by: - # Transform the center point to the floor plan coordinates - # scale the radius and draw the circle - if floorFrame is not None and transformePointsFuction is not None: - transformedcenter = transformePointsFuction( - np.array([center]), self.homographyMatrix - )[0] - transformedcenter = tuple(map(int, transformedcenter)) - - floorRadius = int(radius * 0.7) - - # Apply similar visualization to floor plan - overlay = floorFrame.copy() - cv2.circle(overlay, transformedcenter, floorRadius, (0, 0, 255), -1) - cv2.addWeighted(overlay, 0.4, floorFrame, 0.6, 0, floorFrame) - cv2.circle(floorFrame, transformedcenter, floorRadius, (0, 0, 255), 2) - cv2.putText( - floorFrame, - f"CONGESTION ZONE: {count}", - (transformedcenter[0] - 70, transformedcenter[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.6, - (255, 255, 255), - 2, - ) - - def drawPredictedCongestion(self, annotatedFrame, predictedCongestions): - # Function to draw the predicted congestion zones on the annotated frame with a pale blue color - - for zone in predictedCongestions: - center, radius, count = zone - - # Draw a pale blue circle with transparency - overlay = annotatedFrame.copy() - - # Draw the congestion zone and count on the annotated frame - # Drawing blue circle with transparency - cv2.circle(overlay, center, radius, (255, 200, 0), -1) - cv2.addWeighted(overlay, 0.3, annotatedFrame, 0.7, 0, annotatedFrame) - cv2.circle(annotatedFrame, center, radius, (255, 200, 0), 2) - cv2.putText( - annotatedFrame, - f"PREDICTED CONGESTION: {count}", - (center[0] - 80, center[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 200, 0), - 2, - ) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/database.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/database.py deleted file mode 100644 index f7dd0e73e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/database.py +++ /dev/null @@ -1,41 +0,0 @@ -from pymongo import MongoClient -import time -from datetime import datetime - - -class Database: - def __init__(self): - # Initialize the MongoDB client and database - self.client = MongoClient("") - self.db = self.client["CrowdTracking"] - self.collection = self.db["Crowd"] - self.lastRecorded = time.time() # Initialize with current timestamp - - def insertRecord(self, count, frameId): - currentTime = datetime.now() # Use datetime object for formatting - currentTimestamp = time.time() # Get current timestamp - - # Only record data every second - if currentTimestamp - self.lastRecorded >= 1: # Use timestamps for comparison - record = { - "frameId": frameId, - "peopleCount": count, - "timestamp": currentTime.strftime( - "%d-%m-%Y %H:%M:%S" - ), # Format datetime object - } - try: - self.collection.insert_one(record) - print( - f"Recorded: Frame {frameId}, Time {currentTime.strftime('%d-%m-%Y %H:%M:%S')}, People {count}" - ) - except Exception as e: - print(f"Failed to insert record into database: {e}") - self.lastRecorded = currentTimestamp # Update the last recorded timestamp - - def getlastestRecord(self): - latestRecord = self.collection.find_one(sort=[("timestamp", -1)]) - return latestRecord["peopleCount"] if latestRecord else 0 - - def close(self): - self.client.close() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/dwellTime.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/dwellTime.py deleted file mode 100644 index da37a404f..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/dwellTime.py +++ /dev/null @@ -1,368 +0,0 @@ -import cv2 -import numpy as np -import time -from collections import defaultdict - - -class DwellTimeAnalysis: - def __init__(self, homographyMatrix, floorWidth=700, floorHeight=1000): - self.homographyMatrix = homographyMatrix - self.floorWidth = floorWidth - self.floorHeight = floorHeight - self.zones = [] # List of zones [(zoneName, polygon, color)] - self.personZoneData = defaultdict(dict) - self.zoneStats = defaultdict( - lambda: { - "currentCount": 0, - "totalVisits": 0, - "averageDwellTime": 0, - "maxDwellTime": 0, - } - ) - self.defineDefaultZones() - self.lastUpdateTime = time.time() - self.updateInterval = 1.0 # Update stats once per second - - def defineDefaultZones(self): - - # Calculate the height of each zone (divide floor height by 3) - zoneHeight = self.floorHeight // 3 - - # Define the three horizontal zones with distinct colors - self.zones = [ - ( - "Emprise du Lion", - np.array( - [ - [0, 0], # Top-left - [self.floorWidth, 0], # Top-right - [self.floorWidth, zoneHeight], # Bottom-right - [0, zoneHeight], # Bottom-left - ] - ), - (204, 255, 229), - ), - ( - "Hinterlands", - np.array( - [ - [0, zoneHeight], # Top-left - [self.floorWidth, zoneHeight], # Top-right - [self.floorWidth, 2 * zoneHeight], # Bottom-right - [0, 2 * zoneHeight], # Bottom-left - ] - ), - (255, 255, 153), - ), - ( - "Chateau d'Onterre", - np.array( - [ - [0, 2 * zoneHeight], # Top-left - [self.floorWidth, 2 * zoneHeight], # Top-right - [self.floorWidth, self.floorHeight], # Bottom-right - [0, self.floorHeight], # Bottom-left - ] - ), - (102, 204, 0), - ), - ] - - def isPointInZone(self, point, zonePolygon): - # Make sure point is a single x,y tuple, not an array - if isinstance(point, np.ndarray) and point.ndim > 1: - # If it's an array, return array of results - return np.array( - [ - cv2.pointPolygonTest(zonePolygon, (int(x), int(y)), False) >= 0 - for x, y in point - ] - ) - else: - # For a single point - return cv2.pointPolygonTest(zonePolygon, tuple(map(int, point)), False) >= 0 - - def updateZones(self, currentPositions, trackIDs): - - currentTime = time.time() - - # Transform camera positions to floor plan coordinates - if currentPositions: - floorPositions = self.transformPointsToFloor(np.array(currentPositions)) - else: - floorPositions = [] - - # Update person zone data - for idx, trackID in enumerate(trackIDs): - if idx >= len(floorPositions): - continue - - personPos = tuple(map(int, floorPositions[idx])) - - # Check each zone for this person - for zoneID, (zoneName, zonePoly, _) in enumerate(self.zones): - # If we haven't seen this person in this zone before, initialize their data - if zoneID not in self.personZoneData[trackID]: - self.personZoneData[trackID][zoneID] = { - "enterTime": None, - "exitTime": None, - "inZone": False, - "totalDwellTime": 0, - } - - isInZone = self.isPointInZone(personPos, zonePoly) - zoneData = self.personZoneData[trackID][zoneID] - - # Person just entered the zone - if isInZone and not zoneData["inZone"]: - zoneData["enterTime"] = currentTime - zoneData["inZone"] = True - self.zoneStats[zoneID]["currentCount"] += 1 - self.zoneStats[zoneID]["totalVisits"] += 1 - - # Person just exited the zone - elif not isInZone and zoneData["inZone"]: - zoneData["exitTime"] = currentTime - zoneData["inZone"] = False - self.zoneStats[zoneID]["currentCount"] -= 1 - - # Calculate dwell time for this visit - if zoneData["enterTime"] is not None: - dwellTime = zoneData["exitTime"] - zoneData["enterTime"] - zoneData["totalDwellTime"] += dwellTime - - # Update zone statistics - self.zoneStats[zoneID]["maxDwellTime"] = max( - self.zoneStats[zoneID]["maxDwellTime"], dwellTime - ) - - # Update average dwell times periodically - if currentTime - self.lastUpdateTime >= self.updateInterval: - self.updateZoneStats() - self.lastUpdateTime = currentTime - - def updateZoneStats(self): - - for zoneID in range(len(self.zones)): - totalDwellTime = 0 - visitCount = 0 - - # Calculate total dwell time across all people who have visited this zone - for trackID, zones in self.personZoneData.items(): - if zoneID in zones: - zoneData = zones[zoneID] - - # Add completed visit times - totalDwellTime += zoneData["totalDwellTime"] - - # For people currently in the zone, add their current dwell time - if zoneData["inZone"] and zoneData["enterTime"] is not None: - currentDwell = time.time() - zoneData["enterTime"] - totalDwellTime += currentDwell - - # Count this as a visit if they've entered the zone - if zoneData["enterTime"] is not None: - visitCount += 1 - - # Update average dwell time - if visitCount > 0: - self.zoneStats[zoneID]["averageDwellTime"] = ( - totalDwellTime / visitCount - ) - - def transformPointsToFloor(self, cameraPoints): - - if len(cameraPoints) == 0: - return [] - - # Reshape points for transformation - points = np.array(cameraPoints, dtype=np.float32) - if points.ndim == 1: - points = points.reshape(1, -1, 2) - elif points.ndim == 2: - points = points.reshape(-1, 1, 2) - - # Apply homography transformation - transformedPoints = cv2.perspectiveTransform(points, self.homographyMatrix) - - # Reshape back to original format - if transformedPoints.shape[0] == 1: - transformedPoints = transformedPoints.reshape(-1, 2) - else: - transformedPoints = transformedPoints.reshape(-1, 2) - - return transformedPoints - - def getCurrentDwellTimes(self, currentTime=None): - - if currentTime is None: - currentTime = time.time() - - dwellTimes = defaultdict(dict) - - for trackID, zones in self.personZoneData.items(): - for zoneID, zoneData in zones.items(): - if zoneData["inZone"] and zoneData["enterTime"] is not None: - dwellTimes[trackID][zoneID] = currentTime - zoneData["enterTime"] - - return dwellTimes - - def drawZones(self, floorPlanImage): - - for zoneName, zonePoly, color in self.zones: - # Create a copy of the image for overlay - overlay = floorPlanImage.copy() - - # Fill the zone polygon with semi-transparent color - alpha = 0.3 # Transparency factor - cv2.fillPoly(overlay, [zonePoly], color) - - # Apply the overlay with transparency - cv2.addWeighted( - overlay, alpha, floorPlanImage, 1 - alpha, 0, floorPlanImage - ) - - # Draw the zone polygon border - cv2.polylines(floorPlanImage, [zonePoly], True, color, 2) - - # Calculate zone centroid for text placement - centroidX = int(np.mean(zonePoly[:, 0])) - centroidY = int(np.mean(zonePoly[:, 1])) - - # Draw zone name - cv2.putText( - floorPlanImage, - zoneName, - (centroidX - 40, centroidY), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - color, - 2, - ) - - return floorPlanImage - - def drawDwellTimes( - self, image, currentPositions=None, trackIDs=None, isFloorPlan=True - ): - # If this is floor plan, draw the zones - if isFloorPlan: - image = self.drawZones(image) - - # Draw statistics board in the top-right corner - boardWidth = 300 - boardHeight = len(self.zones) * 30 + 120 # Height based on number of zones - boardX = image.shape[1] - boardWidth - 10 # 10px padding from right edge - boardY = 10 # 10px padding from top - - # Create semi-transparent board background - cv2.rectangle( - image, - (boardX, boardY), - (boardX + boardWidth, boardY + boardHeight), - (50, 50, 50), - -1, - ) # Dark gray background - cv2.rectangle( - image, - (boardX, boardY), - (boardX + boardWidth, boardY + boardHeight), - (200, 200, 200), - 2, - ) # Light gray border - - # Add title to stats board - cv2.putText( - image, - "Zone Statistics", - (boardX + 10, boardY + 25), - cv2.FONT_HERSHEY_SIMPLEX, - 0.7, - (255, 255, 255), - 2, - ) - - # Draw zone statistics in the board - statsY = boardY + 55 # Start below title - for zoneID, (zoneName, _, color) in enumerate(self.zones): - stats = self.zoneStats[zoneID] - - # Draw color swatch for this zone - cv2.rectangle( - image, (boardX + 10, statsY - 15), (boardX + 30, statsY), color, -1 - ) - - # Zone name - cv2.putText( - image, - f"{zoneName}", - (boardX + 40, statsY), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (255, 255, 255), - 1, - ) - - # Current count, average dwell time and max dwell time - cv2.putText( - image, - f"Count: {stats['currentCount']} | Avg: {stats['averageDwellTime']:.1f}s | Max: {stats['maxDwellTime']:.1f}s", - (boardX + 40, statsY + 20), - cv2.FONT_HERSHEY_SIMPLEX, - 0.5, - (255, 255, 255), - 1, - ) - - statsY += 55 # Move to next zone stats position - - # If we have current positions, draw them with dwell times - if currentPositions and trackIDs: - # For floor plan, transform camera points to floor coordinates - if isFloorPlan: - positionsToUse = self.transformPointsToFloor( - np.array(currentPositions) - ) - else: - # For camera view, use original camera coordinates - positionsToUse = np.array(currentPositions) - - currentDwellTimes = self.getCurrentDwellTimes() - - for idx, trackID in enumerate(trackIDs): - - if idx >= len(positionsToUse): - continue - - personPos = tuple(map(int, positionsToUse[idx])) - - # Check if this person is in any zone and display their dwell time - for zoneID, (zoneName, zonePoly, color) in enumerate(self.zones): - if ( - trackID in currentDwellTimes - and zoneID in currentDwellTimes[trackID] - ): - dwellTime = currentDwellTimes[trackID][zoneID] - - # Draw dwell time near the person's position - cv2.putText( - image, - f"{int(dwellTime)}s", - (personPos[0] + 10, personPos[1]), - cv2.FONT_HERSHEY_SIMPLEX, - 0.4, - color, - 1, - ) - - # Draw circle around person colored by dwell time (redder = longer) - maxDwell = 60 # Consider 60 seconds as maximum intensity - intensity = min(dwellTime / maxDwell, 1.0) - dwellColour = ( - 0, - int(255 * (1 - intensity)), - int(255 * intensity), - ) - cv2.circle(image, personPos, 10, dwellColour, -1) - - return image diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/floorReplica.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/floorReplica.py deleted file mode 100644 index 537f188a3..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/floorReplica.py +++ /dev/null @@ -1,25 +0,0 @@ -import cv2 -import numpy as np - -def floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl): - # Create a VideoCapture object - cap = cv2.VideoCapture(rtspUrl) - - # Check if camera opened successfully - success, frame = cap.read() - if not success: - cap.release() - cap = cv2.VideoCapture(rtspUrl) - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - floorReplica.py") - - tileHeight = canvasHeight // tilesY - tileWidth = canvasWidth // tilesX - - floorImage = np.ones((canvasHeight, canvasWidth, 3), dtype=np.uint8) * 255 - - for y in range(0, canvasHeight, tileHeight): - for x in range(0, canvasWidth, tileWidth): - cv2.rectangle(floorImage, (x, y), (x + tileWidth, y + tileHeight), (0, 0, 0), 1) - return floorImage \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/opticalFlow.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/opticalFlow.py deleted file mode 100644 index 8c9812830..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/opticalFlow.py +++ /dev/null @@ -1,89 +0,0 @@ -import cv2 -import numpy as np - - -class OpticalFlow: - def __init__(self, predictionStep=10, predictionScale=5): - # previousFrame and currentFrame to store the previous and current frames for optical flow calculation - # predictionSteps and predictionScale to control the prediction steps and scale - self.previousFrame = None - self.currentFrame = None - self.predictionSteps = predictionStep - self.predictionScale = predictionScale - - def calculateFlow(self, frame): - # Function to calculate the optical flow using Farneback method - # Convert the frame to grayscale - currentGray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - if self.previousFrame is None: - self.previousFrame = currentGray - return frame - - # Calculate the optical flow using Farneback method - - flow = cv2.calcOpticalFlowFarneback( - self.previousFrame, currentGray, None, 0.5, 3, 15, 3, 5, 1.2, 0 - ) - self.previousFrame = currentGray - return flow - - def predictPosition(self, trackHistories, flow): - # Function to predict the next position of each trackID based on the optical flow - # Initialize a dictionary to store the predicted positions - predictedPositions = {} - - # Iterate through each trackID and its corresponding points - # if the length of points is less than 2, skip the prediction - for trackID, points in trackHistories.items(): - if len(points) < 2: - continue - - # Get the last point of the track history - # and use it to predict the next position - lastPoint = points[-1] - x, y = int(lastPoint[0]), int(lastPoint[1]) - - # Check if the point is within the bounds of the flow array - # get the flow vector at the last point - # and predict the next position - # then store the predicted position in the dictionary - if 0 <= x < flow.shape[1] and 0 <= y < flow.shape[0]: - dy, dx = flow[y, x] - predictedX = x + int(dx * self.predictionScale) - predictedY = y + int(dy * self.predictionScale) - - predictedPositions[trackID] = (predictedX, predictedY) - - return predictedPositions - - def predictTrajectory(self, trackID, points, predictedPosition): - # Function to predict the trajectory of a trackID based on the predicted position - # Check if the length of points is less than 2 and return an empty list - if len(points) < 2: - return [] - - # Initialize the trajectory with the last point of the track history - currentPosition = points[-1] - trajectory = [currentPosition] - - # Predict the next position - nextPosition = predictedPosition - trajectory.append(nextPosition) - - # Iterate through the prediction steps - # and calculate the next positions based on the flow - for _ in range(self.predictionSteps - 1): - if len(trajectory) >= 2: - - # Calculate the velocity vector based on the last two points - # and update the next position - dx = nextPosition[0] - currentPosition[0] - dy = nextPosition[1] - currentPosition[1] - nextPosition = (int(nextPosition[0] + dx), int(nextPosition[1] + dy)) - trajectory.append(nextPosition) - - # Update the current position to the last point in the trajectory - currentPosition = trajectory[-2] - - return trajectory - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/utils.py deleted file mode 100644 index eee43f75e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Congestion_Detection_Integration/utils.py +++ /dev/null @@ -1,13 +0,0 @@ -import cv2 -import numpy as np - - -def calculateHomography(ptsSRC, ptsDST): - return cv2.findHomography(ptsSRC, ptsDST)[0] - - -def transformPoints(points, homographyMatrix): - points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) - transformedPoints = homographyMatrix.dot(points.T).T - transformedPoints /= transformedPoints[:, 2][:, np.newaxis] - return transformedPoints[:, :2] diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/Live_Tracking.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/Live_Tracking.py deleted file mode 100644 index f10f183e7..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/Live_Tracking.py +++ /dev/null @@ -1,107 +0,0 @@ -from flask import Flask, Response -import cv2 -import numpy as np -from ultralytics import YOLO -from pymongo import MongoClient -from datetime import datetime, date -import threading -from flask_cors import CORS -import os - - -app = Flask(__name__) -CORS(app) - -# Load YOLO model -model = YOLO('yolov8n.pt') # or use a different YOLO version - -# RTSP stream URL -# Retrive the RTSP stream URL from iSpy or Wireshark -# Replace the rtsp_url with your own RTSP stream URL -rtsp_url = '' -''' -# MongoDB connection -client = MongoClient('') -db = client["CrowdTracking"] -collection = db["Crowd"] -''' -frame_id = 0 -current_date = date.today() - -global_frame = None -global_result = None -def generate_frames(): - global frame_id, current_date, global_frame, global_result - cap = cv2.VideoCapture(rtsp_url) - while True: - now = datetime.now() - # Read the frame from the stream - # If the frame was not read, then break the loop and print an error - ret, frame = cap.read() - if not ret: - print('Error reading the frame') - break - - # Perform YOLO detection - results = model(frame) - - # Process results with box coordinates and confidence scores - for result in results: - boxes = result.boxes.cpu().numpy() - for box in boxes: - x1, y1, x2, y2 = map(int, box.xyxy[0]) - conf = box.conf[0] - cls = int(box.cls[0]) - - if cls == 0: # Assuming class 0 is person - cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) - cv2.putText(frame, f'Person: {conf:.2f}', (x1, y1 - 10), - cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) - - # Save the number of persons detected to MongoDB - # Save the frame_id, timestamp and the total number of persons detected - data = { - - "frame_id": frame_id, - "timestamp": now.strftime("%d/%m/%Y %H:%M:%S"), - "total_persons": len(boxes) - } - collection.insert_one(data) - - # Delete the old data from MongoDB when entering a new day - # if the current date is greater than the date of the last frame - # then reset the frame_id and delete all the data from the collection - if now.date() > current_date: - frame_id = 0 - current_date = now.date() - collection.delete_many({}) - print (f"Data will be resetted for new day: {current_date}") - - # Display the number of persons detected on the frame - cv2.rectangle(frame, (10, 10), (310, 60), (255, 255, 255), -1) - cv2.putText(frame, f'Total Persons: {len(boxes)}', (20, 40), - cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) - - frame_id += 1 - - global_frame = frame - global_result = len(boxes) - - # Encode the frame to JPEG format - ret, buffer = cv2.imencode('.jpg', frame) - frame = buffer.tobytes() - yield (b'--frame\r\n' - b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') - -# Route the display the video feed -# The video feed will be displayed on the web browser -@app.route('/video_feed') -def video_feed(): - return Response(generate_frames(), - mimetype='multipart/x-mixed-replace; boundary=frame') - - -if __name__ == '__main__': - threading.Thread(target=generate_frames, daemon=True).start() - app.run(port=8000) #nosec - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/MongoDBConnect.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/MongoDBConnect.png deleted file mode 100644 index 2f8de42e9..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/MongoDBConnect.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/README.md deleted file mode 100644 index 7b8af2c2b..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Live_Tracking/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Folder Content - -The folder would include the following files: - -- Live_Tracking.ipynb is the main structure for real-time detecting and recording captured data. -- Live_tracking.py is likewise the similar program with additional setup to run on web-interface. -git \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/Camera.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/Camera.py deleted file mode 100644 index 4442f558d..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/Camera.py +++ /dev/null @@ -1,17 +0,0 @@ -import cv2 - -def get_available_cameras(): - available_cameras = [] - # Check for 5 cameras - for i in range(10): - cap = cv2.VideoCapture(i) - if cap.isOpened(): - available_cameras.append(i) - cap.release() - return available_cameras - -cameras = get_available_cameras() -if cameras: - print("Available Cameras:", cameras) -else: - print("No cameras found.") \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/README.md deleted file mode 100644 index c7ecdfcc8..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Folder Content - -The folder would include the following files: - -- floorReplica.py is for the 2D visualisation that create a 2D white floor for further polylines drawing. -- mapTracking.py and mapTracking_v2.py are the asembled program with real-time monitoring, 2D visualisation, and data recording. -- demo.ipynb is the final product with additional try-catch to prevent the program being shut-down when no detection triggered. - diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/floorReplica.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/floorReplica.py deleted file mode 100644 index 537f188a3..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/floorReplica.py +++ /dev/null @@ -1,25 +0,0 @@ -import cv2 -import numpy as np - -def floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl): - # Create a VideoCapture object - cap = cv2.VideoCapture(rtspUrl) - - # Check if camera opened successfully - success, frame = cap.read() - if not success: - cap.release() - cap = cv2.VideoCapture(rtspUrl) - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - floorReplica.py") - - tileHeight = canvasHeight // tilesY - tileWidth = canvasWidth // tilesX - - floorImage = np.ones((canvasHeight, canvasWidth, 3), dtype=np.uint8) * 255 - - for y in range(0, canvasHeight, tileHeight): - for x in range(0, canvasWidth, tileWidth): - cv2.rectangle(floorImage, (x, y), (x + tileWidth, y + tileHeight), (0, 0, 0), 1) - return floorImage \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking.py deleted file mode 100644 index 2404ec996..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking.py +++ /dev/null @@ -1,100 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints -from pymongo import MongoClient -from time import time - -# Load the YOLO model -model = YOLO("yolov8n.pt") - -# Connect to the MongoDB database -# and set up data recording -client = MongoClient("") -db = client["CrowdTracking"] -collection = db["Crowd"] -lastRecorded = 0 - - -# Connect to the RTSP stream -rtspUrl = 0 -cap = cv2.VideoCapture(rtspUrl) - -trackHistory = defaultdict(list) - -# Load the floor image -from floorReplica import floorReplica -canvasHeight = 1000 -canvasWidth = 700 -tilesX = 25 -tilesY = 15 -floorImage = floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl) - -height, width, channels = floorImage.shape - -# Define the codec and create a VideoWriter object -fourcc = cv2.VideoWriter_fourcc(*'mp4v') -video = cv2.VideoWriter('output.mp4', fourcc, 20.0, (width, height)) - -# Define the source and destination points for the homography matrix -# Calculate the homography matrix -ptsSRC = np.array([[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]]) -ptsDST = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) -homographyMatrix = calculateHomography(ptsSRC, ptsDST) - -# Main loop -while cap.isOpened(): - success, frame = cap.read() - if not success: - raise Exception("Failed to read video stream - mapTracking.py - main loop") - - results = model.track(frame, persist=True, show=False, imgsz=1280, verbose=True) - - if results[0].boxes is not None and hasattr(results[0].boxes, 'id'): - boxes = results[0].boxes.xywh.cpu().numpy() - trackIDs = results[0].boxes.id.int().cpu().numpy() - - annotatedFrame = floorImage.copy() - - for trackID in np.unique(trackIDs): - history = trackHistory[trackID] - if len(history) > 1: - points = np.array(history, dtype=np.int32) - newPoints = transformPoints(points, homographyMatrix) - newPoints = newPoints.astype(np.int32) - - cv2.polylines(annotatedFrame, [newPoints], isClosed=False, color=(0, 0, 255), thickness=2) - - for box, trackID in zip(boxes, trackIDs): - x, y, w, h = box - center = (int(x), int(y + h / 2)) - trackHistory[trackID].append(center) - - if len(trackHistory[trackID]) > 50: - trackHistory[trackID].pop(0) - - # Record the number of people in the frame every second - currentTime = time.time() - if currentTime - lastRecorded >= 1: - frameId = int(cap.get(cv2.CAP_PROP_POS_FRAMES)) - totalPeople = len(np.unique(trackIDs)) - - record = { - "frameId": frameId, - "timestamp": currentTime.strftime("%d-%m-%Y %H:%M:%S"), - "totalPeople": totalPeople - } - collection.insert_one(record) - lastRecorded = currentTime - - video.write(annotatedFrame) - - cv2.imshow("Map Tracking", annotatedFrame) - cv2.imshow("Camera Feed", frame) - cv2.waitKey(1) - - -cap.release() -video.release() -cv2.destroyAllWindows() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking_v2.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking_v2.py deleted file mode 100644 index 787378906..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/mapTracking_v2.py +++ /dev/null @@ -1,149 +0,0 @@ -import cv2 -import numpy as np -from ultralytics import YOLO -from collections import defaultdict -from utils import calculateHomography, transformPoints -from pymongo import MongoClient -import time as time_module -from datetime import datetime -# Load the YOLO model -model = YOLO("yolov8n.pt") - -# Connect to the MongoDB database -# and set up data recording -client = MongoClient("") -db = client["Crowd_Monitoring"] -collection = db["Crowd_Count"] - -lastRecorded = time_module.time() -# Connect to the RTSP stream -# rtspUrl = "rtsp://" -rtspUrl = 1 -cap = cv2.VideoCapture(rtspUrl) - -trackHistory = defaultdict(list) - -# Load the floor image -from floorReplica import floorReplica -canvasHeight = 1000 -canvasWidth = 700 -tilesX = 25 -tilesY = 15 -floorImage = floorReplica(canvasHeight, canvasWidth, tilesX, tilesY, rtspUrl) - -height, width, channels = floorImage.shape - -# Define the codec and create a VideoWriter object -fourcc = cv2.VideoWriter_fourcc(*'mp4v') -video = cv2.VideoWriter('output.mp4', fourcc, 20.0, (width, height)) - -# Define the source and destination points for the homography matrix -# Calculate the homography matrix -ptsSRC = np.array([[28, 1158], [2120, 1112], [1840, 488], [350, 518], [468, 1144]]) -ptsDST = np.array([[0, 990], [699, 988], [693, 658], [0, 661], [141, 988]]) -homographyMatrix = calculateHomography(ptsSRC, ptsDST) - -# Main loop -while True: -#while cap.isOpened(): - success, frame = cap.read() - # if not success: - # raise Exception("Failed to read video stream - mapTracking.py - main loop") - - results = model.track(frame, persist=True, show=False, imgsz=1280, verbose=True) - annotatedFrame = floorImage.copy() - - # Process camera results with box coordinates and confidence scores - for result in results: - boxes_camera = result.boxes.cpu().numpy() - for box in boxes_camera: - x1, y1, x2, y2 = map(int, box.xyxy[0]) - conf = box.conf[0] - cls = int(box.cls[0]) - - if cls == 0: # Assuming class 0 is person - cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) - cv2.putText(frame, f'Person: {conf:.2f}', (x1, y1 - 10), - cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) - - - # if results[0].boxes is not None and hasattr(results[0].boxes, 'id'): - try: - if results[0].boxes is not None: - # Check if the boxes attribute contains IDs - if hasattr(results[0].boxes, 'id'): - # Check if there are any detected boxes - if results[0].boxes.id.numel() > 0: - # Convert tensor to NumPy array - boxes = results[0].boxes.xywh.cpu().numpy() - trackIDs = results[0].boxes.id.cpu().numpy() - print('Track IDs:', trackIDs) - # Copy floorImage only if objects are detected - annotatedFrame = floorImage.copy() - - for trackID in np.unique(trackIDs): - history = trackHistory[trackID] - if len(history) > 1: - points = np.array(history, dtype=np.int32) - newPoints = transformPoints(points, homographyMatrix) - newPoints = newPoints.astype(np.int32) - - cv2.polylines(annotatedFrame, [newPoints], isClosed=False, color=(0, 0, 255), thickness=2) - - for box, trackID in zip(boxes, trackIDs): - x, y, w, h = box - center = (int(x), int(y + h / 2)) - trackHistory[trackID].append(center) - - if len(trackHistory[trackID]) > 50: - trackHistory[trackID].pop(0) - currentTime = time_module.time() - print(currentTime) - # Record the number of people in the frame every second - if currentTime - lastRecorded > 1: - frameId = int(cap.get(cv2.CAP_PROP_POS_FRAMES)) - totalPeople = len(np.unique(trackIDs)) - print("People", totalPeople) - # Convert current time to human-readable format - # timestamp = datetime.now().strftime("%d-%m-%Y %H:%M:%S") - timestamp = time_module.strftime("%d-%m-%Y %H:%M:%S", time_module.localtime(currentTime)) - print(timestamp) - # record = { - # "frameId": frameId, - # "timestamp": timestamp, - # "totalPeople": totalPeople - # } - record = { - "frameId": frameId, - "timestamp": timestamp, - "totalPeople": totalPeople - } - - print("Before inserting record into MongoDB") - - collection.insert_one(record) - print("After inserting record into MongoDB") - lastRecorded = currentTime - print("People 2", totalPeople) - video.write(annotatedFrame) - else: - print("No objects detected. No IDs available.") - else: - print("The 'id' attribute is not present in the boxes.") - else: - print("No boxes detected. The 'boxes' attribute is None.") - except AttributeError as e: - print(f"An AttributeError occurred: {e}") - except Exception as e: - print(f"An unexpected error occurred: {e}") - - cv2.imshow("Map Tracking", annotatedFrame) - cv2.imshow("Camera Feed", frame) - if cv2.waitKey(1) & 0xFF == ord('q'): - break - - - -cap.release() -#video.release() -cv2.destroyAllWindows() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/utils.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/utils.py deleted file mode 100644 index a649ed8ff..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/Modules_Dev/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -import cv2 -import numpy as np - -def calculateHomography(ptsSRC, ptsDST): - return cv2.findHomography(ptsSRC, ptsDST)[0] - -def transformPoints(points, homographyMatrix): - points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1) - transformedPoints = homographyMatrix.dot(points.T).T - transformedPoints /= transformedPoints[:, 2][:, np.newaxis] - return transformedPoints[:, :2] \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/MongoDBConnect.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/MongoDBConnect.png deleted file mode 100644 index 2f8de42e9..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/MongoDBConnect.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/README.md deleted file mode 100644 index 259b12b65..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/README.md +++ /dev/null @@ -1,133 +0,0 @@ -## Folder Structure - -Live_Tracking -|--Assemble -|--BackendUpdate -|--Live_Tracking - -1. Modules_Dev is the program with real-time monitoring, MongoDB, and 2D visualisation features. -2. Backend_v2 is the system to run the program for web-interface. -3. Live_tracking is for testing the live-tracking with RTSP and MongoDB data importing. - -## MongoDB - -### Introduction - -MongoDB is is a NoSQL database management platform, which is utilised in this project for storing the crowd monitoring data. As the current aim of the project is to concentrate on tracking the crowd and extracting the collected data to analyse the movement trend, MongoDB would be an acceptable option. The NoSQL database platform is chosen because of its schemaless and non-relational properties. When the project gets more complicated with more components, a standard SQL database should be considered. - -### Installation - -For establishing a database on MongoDB, the database and clusters can be easliy created by following the instruction on the MongoDB website. In regard to the cluster configuration, the team would stick to the free-tier with 512 MB storage. -To connect to MongoDB, it is imporatnt to choose the current driver and version to get appropriate instruction in the below image. -Following the below command line to install MongoDB driver to the local machine -![MongoDB](MongoDBConnect.png) - -``` -python -m pip install "pymongo[srv]" -``` - -### Data Recording - -The below block has the function of connecting to the MongoDB driver. -It would directly access to the CrowdTracking database and Crowd collection - -``` -from pymongo import MongoClient - -client = MongoClient('mongo+srv:// ') -db = client["CrowTracking"] -collection = db["Crowd"] -``` - -In regard to real-time crowd monitoring there would be two main approachs. - -``` -now = datetime.now() -data = { - "frame_id": frame_id, - "timestamp": now.strftime("%d/%m/%Y %H:%M:%S"), - "total_persons": len(boxes) -} -collection.insert_one(data) -``` - -This code would record the captured data based on every round of loop. The advantage of this approach is that the data would be imported into MongoDB in every frame ID. However, as the recursion os executed hastely, YOLO could process mutiple of frames in a second leading to the burdern of storage. - -``` -if current_time - last_update_time < update_interval: - now = datetime.now() - data = { - "frame_id": frame_id, - "timestamp": now.strftime("%d/%m/%Y %H:%M:%S"), - "total_persons": len(boxes) - } - collection.insert_one(data) - last_update_time = current_time -``` - -With the above code, by setting up a variable for interval time, we can easily adjust this variable to update the recorded data on MongoDB in every second, minute or hour. This approach will put less stress on the data storage section, but lack of detail. - -## Live-Tracking Method - -### Introduction - -Currently there are three different methods of real-time crowd monitoring including using the computer bulit in web-cam, the external camera, and the IP camera. This project would solely focus on the Real-Time Streaming Protocol of an IP camera and utilising OpenCV to process the recording frame. - -During testing the program, we had used built-in camera or external devices with cameras for convenience. Those two could be utilised by assigning "0" and "1" in the video capture statement. 0 is for the bulit-in camera whilst 1 is for connected devices (mobile camera - Camo App). - -``` -cap = cv2.VideoCapture(0) -cap = cv2.VideoCapture(1) -``` -### Camo App setup -Download Camo app through App Store/Google Play for your computer and the mobile device. After installed, open the app and pair your mobile device with your computer. - ![CamoApp](Camo.png) - -### Retrieving RTSP - -There are two ways of retrieving the livestream protocol: - -1. The simpliest one is using iSpy application. The advantage of this method is providing a full form RTSP, whiles merely be available on Window system is its cirtical drawbacks to condiser. In regard to see the RTSP, first filling out the authentication and choose the network. - ![iSpy](iSpy.png) - -2. The second method is to use the network anaylsing application, WireShark. The application is widely aviable on different operating systems. By accessing to the current network that the machine is connecting to and filter the protocol, we can retrieve these networking components including IP address, port, stream, and channel.With those pieces of data, we can form a complete RTSP: - -``` -rtsp://:@:/Streaming/Channels -``` - -![WireShark](WireShark.png) - -### Using OpenCV to load the camera frame with RTSP - -The following code is the example of how to load the camera frame using OpenCV: - -- Using VideoCapture to read the RTSP -- Using Loop accompanying imshow to open the frame -- Closing all opened winows when finishing the program - -``` -import cv2 - -rtsp_url = 'Fill in your RTSP' -cap = cv2.VideoCapture(rtsp_url) - -while True: - ret, frame = cap.read() - if not ret: - print('Error reading the frame') - break - - cv2.imshow('Crowd Detection', frame) - if cv2.waitKey(1) & 0xFF == ord('q'): - break - -cap.release() -cv2.destroyAllWindows() -``` - -### Important Notice - -1. It is remarkable that these two methods will require the useername and password from the camera. Furthermore, the camera would always run on port 554 and 2.5 GHz band. -2. Both iSpy and WireShark RTSP retrieving methods solely work, when the computer used to search them connect to a 2.5GHz network. -3. There should be less than two devices or programs using the same RTSP. For three to four entities, two different RTSP can be used with two devices connecting to the 480p protocol and the other two connecting the the 720p option. diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/WireShark.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/WireShark.png deleted file mode 100644 index 87472149a..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/WireShark.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/iSpy.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/iSpy.png deleted file mode 100644 index f96ddb779..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/Live_Tracking/iSpy.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/test/seq_000015.jpg b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/test/seq_000015.jpg deleted file mode 100644 index 811ef9bcf..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/test/seq_000015.jpg and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.jpg b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.jpg deleted file mode 100644 index 614b47b1d..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.jpg and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.xml b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.xml deleted file mode 100644 index f4d23e995..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/TensorFlow Object Detection/images/train/seq_000011.xml +++ /dev/null @@ -1,314 +0,0 @@ - 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-## Project Overview -This project aims to develop an efficient and robust **face detection system** using the YOLOv8 framework. The system can process images, videos, and real-time camera feeds, making it versatile for various applications such as surveillance, user authentication, and video analysis. The project demonstrates the integration of state-of-the-art machine learning techniques with real-world deployment. - ---- - -## Objectives - -### Core Functionality -- Implement face detection on images, videos, and real-time streams with high accuracy. -- Ensure a modular design for scalability and further enhancements. - -### Efficiency and Usability -- Optimize the YOLOv8 model for deployment on systems with constrained resources. -- Provide clear and detailed documentation for reproducibility and collaboration. - -### Collaboration and Knowledge Sharing -- Assist team members in improving detection systems through advanced techniques. -- Contribute to smooth project handovers with comprehensive documentation. - ---- - -## Completed Deliveries - -### Dataset Selection and Preparation -- Selected and configured a **face detection dataset** from Roboflow. -- Ensured compliance with the dataset license (**CC BY 4.0**) for ethical use. - -### Model Training and Evaluation -- Trained the YOLOv8 model on the prepared dataset with hyperparameter optimization. -- Evaluated model performance using metrics like precision, recall, mAP50, and mAP50-95 to ensure reliability. - -### Feature Integration -- Implemented functionalities for: - - **Image Prediction**: Detect faces in single images. - - **Video Prediction**: Process videos for face detection, frame by frame. - - **Real-Time Prediction**: Perform live detection using a connected camera. - -### Documentation and Collaboration -- Documented all program code and testing results. -- Shared the project on GitHub for team collaboration and review. - ---- - -## Performance Analysis -### Key Metrics -- **Precision (P):** 0.959 -- **Recall (R):** 0.942 -- **mAP50:** 0.979 -- **mAP50-95:** 0.879 - -The YOLOv8 model achieved high accuracy, making it suitable for real-world applications. - ---- - -## How to Use - -### Requirements -- Python 3.8+ -- Install the following libraries manually: - ```python - pip install torch==1.4.0 - pip install opencv-python - pip install numpy - pip install matplotlib - pip install moviepy - pip install ultralytics - ``` - -### Instructions -1. **Set Up Your Environment**: Ensure Python is installed, and manually install the required libraries as listed above. -2. **Run Image Prediction**: -- Open the `image_prediction.py` script, and set the paths for the input image and YOLOv8 model directly in the code: -```python -img_path = "path_to_image.jpg" -model_path = "face_detector.pt" -``` -- Run the script: -```bash -python image_prediction.py -``` -3. **Run Video Prediction**: -- Open the `video_prediction.py` script, and set the paths for the input video and YOLOv8 model: -```python -video_path = "path_to_video.mp4" -model_path = "face_detector.pt" -``` -- Run the script: -```bash -python video_prediction.py -``` -4. **Run Real-Time Prediction**: Open the `realtime_prediction.py` script, ensure your webcam is accessible, and run the script: -```bash -python realtime_prediction.py -``` - ---- - -## Example Code - -### Image Prediction -```python -from ultralytics import YOLO -import cv2 - -model = YOLO("face_detector.pt") -img = cv2.imread("path_to_image.jpg") -results = model(img) -results.show() -``` - -### Real-Time Prediction -```python -import cv2 -from ultralytics import YOLO - -model = YOLO("face_detector.pt") -cap = cv2.VideoCapture(0) - -while True: - ret, frame = cap.read() - if not ret: - break - results = model(frame) - results.show() - if cv2.waitKey(1) & 0xFF == ord('q'): - break - -cap.release() -cv2.destroyAllWindows() -``` - -## Open Issues -- **Optimization**: Improve real-time processing speed to enhance performance in live environments. -- **Resource Efficiency**: Reduce memory usage and computational overhead to make the model more suitable for deployment on devices with limited hardware capabilities. -- **Error Handling**: Add better error-handling mechanisms for edge cases, such as unsupported file formats or camera connection failures. -- **Scalability**: Ensure the system can handle larger datasets and more complex scenarios in future iterations. - -## Contributors -- Yuekai Zhang diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/image_prediction.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/image_prediction.py deleted file mode 100644 index 42607a0c5..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/image_prediction.py +++ /dev/null @@ -1,93 +0,0 @@ -# coding:utf-8 -from ultralytics import YOLO -import cv2 -import os - - -def validate_file_path(file_path, base_dir="."): - """ - Validate if a file path is within a specific base directory. - Prevents directory traversal attacks. - """ - abs_path = os.path.abspath(file_path) - base_dir = os.path.abspath(base_dir) - - if not abs_path.startswith(base_dir): - raise ValueError(f"Invalid file path: {file_path}") - - return abs_path - - -def img_cvread(img_path): - """Read the image and return the image array""" - return cv2.imread(img_path) - - -def face_detect(cv_img, face_model): - """Perform face detection and return detected face images and their locations""" - results = face_model(cv_img) - faces = [] - locations = [] - for result in results: - for bbox in result.boxes: - x1, y1, x2, y2 = map(int, bbox.xyxy[0].cpu().numpy()) - face = cv_img[y1:y2, x1:x2] - faces.append(face) - locations.append((x1, y1, x2, y2)) - return cv_img, faces, locations - - -def adjust_parameters(width, height): - """Adjust parameters based on image size""" - base_width = 640 - scale = min(width / base_width, height / base_width) - box_thickness = int(10 * scale) - return box_thickness - - -if __name__ == '__main__': - base_dir = "." # Define a base directory to validate file paths - - img_path = "" # Input image file path - output_path = "" # Output image file path - - # Validate file paths - try: - img_path = validate_file_path(img_path, base_dir) - output_path = validate_file_path(output_path, base_dir) - except ValueError as e: - print(e) - exit() - - # Path to the face detection model - face_model_path = 'face_detector.pt' - - # Load the face detection model - face_model = YOLO(face_model_path, task='detect') - - cv_img = img_cvread(img_path) - if cv_img is None: - print(f"Error: Could not read image from path: {img_path}") - exit() - - height, width = cv_img.shape[:2] - - # Adjust parameters - box_thickness = adjust_parameters(width, height) - - # Perform face detection - face_cvimg, faces, locations = face_detect(cv_img, face_model) - - if faces: - for i in range(len(faces)): - left, top, right, bottom = locations[i] - # Draw rectangle around the detected face - face_cvimg = cv2.rectangle(face_cvimg, (left, top), (right, bottom), (50, 50, 250), box_thickness) - - # Save the predicted image - cv2.imwrite(output_path, face_cvimg) - - # Display the predicted image - cv2.imshow('yolov8_detections', face_cvimg) - cv2.waitKey(0) - cv2.destroyAllWindows() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/realtime_prediction.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/realtime_prediction.py deleted file mode 100644 index faa08844c..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/realtime_prediction.py +++ /dev/null @@ -1,70 +0,0 @@ -# coding:utf-8 -from ultralytics import YOLO -import cv2 - - -def face_detect(cv_img, face_model): - """Perform face detection and return detected face images and their locations""" - results = face_model(cv_img) - faces = [] - locations = [] - for result in results: - for bbox in result.boxes: - x1, y1, x2, y2 = map(int, bbox.xyxy[0].cpu().numpy()) - face = cv_img[y1:y2, x1:x2] - faces.append(face) - locations.append((x1, y1, x2, y2)) - return cv_img, locations - - -def adjust_parameters(width, height): - """Adjust parameters based on image size""" - base_width = 640 - scale = min(width / base_width, height / base_width) - box_thickness = int(10 * scale) - return box_thickness - - -if __name__ == '__main__': - # Path to the face detection model - face_model_path = 'face_detector.pt' - - # Load the face detection model - face_model = YOLO(face_model_path, task='detect') - - # Open the camera, 0 indicates the default camera - cap = cv2.VideoCapture(0) - if not cap.isOpened(): - print("Error: Could not open camera.") - exit() - - while True: - ret, frame = cap.read() - if not ret: - print("Error: Failed to read frame.") - break - - height, width = frame.shape[:2] - - # Adjust parameters - box_thickness = adjust_parameters(width, height) - - # Perform face detection - face_cvimg, locations = face_detect(frame, face_model) - - if locations: - for (left, top, right, bottom) in locations: - # Draw rectangle around detected faces - face_cvimg = cv2.rectangle(face_cvimg, (left, top), (right, bottom), (50, 50, 250), box_thickness) - - # Display the frame with detection results - cv2.imshow('Face Detection System', face_cvimg) - - # Exit the loop when 'q' key is pressed - if cv2.waitKey(1) == ord('q'): - break - - # Release the camera resource - cap.release() - # Close all OpenCV windows - cv2.destroyAllWindows() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/video_prediction.py b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/video_prediction.py deleted file mode 100644 index ef071dfde..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/code/video_prediction.py +++ /dev/null @@ -1,108 +0,0 @@ -# coding:utf-8 -from ultralytics import YOLO -import cv2 -import os -from moviepy.editor import VideoFileClip, AudioFileClip - - -def validate_file_path(file_path, base_dir="."): - """ - Validate if a file path is within a specific base directory. - Prevents directory traversal attacks. - """ - abs_path = os.path.abspath(file_path) - base_dir = os.path.abspath(base_dir) - - if not abs_path.startswith(base_dir): - raise ValueError(f"Invalid file path: {file_path}") - - return abs_path - - -def face_detect(cv_img, face_model): - """Perform face detection and return detected face images and their locations""" - results = face_model(cv_img) - faces = [] - locations = [] - for result in results: - for bbox in result.boxes: - x1, y1, x2, y2 = map(int, bbox.xyxy[0].cpu().numpy()) - face = cv_img[y1:y2, x1:x2] - faces.append(face) - locations.append((x1, y1, x2, y2)) - return cv_img, locations - - -def process_frame(frame, face_model): - """Process video frame for face detection""" - face_cvimg, locations = face_detect(frame, face_model) - - if locations: - for (left, top, right, bottom) in locations: - # Draw rectangle around detected faces - face_cvimg = cv2.rectangle(face_cvimg, (left, top), (right, bottom), (50, 50, 250), 2) - - return face_cvimg - - -if __name__ == '__main__': - base_dir = "." # Define a base directory to validate file paths - - video_path = "" # Input video file path - output_path = "" # Output video file path - - # Validate file paths - try: - video_path = validate_file_path(video_path, base_dir) - output_path = validate_file_path(output_path, base_dir) - except ValueError as e: - print(e) - exit() - - # Path to the face detection model - face_model_path = 'face_detector.pt' - - # Load the face detection model - face_model = YOLO(face_model_path) - - # Open the video file - cap = cv2.VideoCapture(video_path) - if not cap.isOpened(): - print(f"Error: Could not open video file: {video_path}") - exit() - - # Get video properties - width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - fps = cap.get(cv2.CAP_PROP_FPS) - - # Open video writer - out = cv2.VideoWriter('temp_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height)) - - while cap.isOpened(): - ret, frame = cap.read() - if not ret: - break - - # Process each frame - processed_frame = process_frame(frame, face_model) - - # Display the result - cv2.imshow('Face Detection', processed_frame) - - # Write the processed frame - out.write(processed_frame) - - # Exit on 'q' key press - if cv2.waitKey(1) & 0xFF == ord('q'): - break - - cap.release() - out.release() - cv2.destroyAllWindows() - - # Combine processed video with original audio - video_clip = VideoFileClip("temp_video.mp4") - audio_clip = AudioFileClip(video_path) - final_clip = video_clip.set_audio(audio_clip) - final_clip.write_videofile(output_path, codec='libx264') diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/dataset/data.yaml b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/dataset/data.yaml deleted file mode 100644 index cf826ed02..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/dataset/data.yaml +++ /dev/null @@ -1,15 +0,0 @@ -path: ../ - -train: train -val: valid -test: test - -nc: 1 -names: ['face'] - -# roboflow: -# workspace: uniform-fhonp -# project: face-detection-quvrj -# version: 1 -# license: CC BY 4.0 -# url: https://universe.roboflow.com/uniform-fhonp/face-detection-quvrj/dataset/1 \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/image_predict/input1.png b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/image_predict/input1.png deleted file mode 100644 index 05460518f..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/image_predict/input1.png and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/face_detection/image_predict/input2.jpg 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b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/detected by haar cascade/output_9.jpg deleted file mode 100644 index 9b53fe365..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/detected by haar cascade/output_9.jpg and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/doc.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/doc.md deleted file mode 100644 index dba3cee1f..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/doc.md +++ /dev/null @@ -1,95 +0,0 @@ -# Crowd Monitoring and Analyses -Mustafa Tariq (223124219) -S Abdul Ahad Bin Abrar Aslam Shah (223735551) -## Project Overview -This project focuses on developing a face detection and characteristics extraction model using deep learning techniques. The model is capable of processing video frames, detecting faces, extracting facial landmarks, and computing face embeddings for recognition purposes. The project integrates YOLOv3 for face detection and Dlib for facial landmark prediction and embedding extraction. -Code Overview -The code is structured to process a video file, detect faces in each frame, extract facial characteristics, and generate a JSON-like object containing the details of detected faces. The key components of the code are: - -## 1. Loading YOLO Model: -โ€ข The load_yolo_model function initializes the YOLOv3 model with the given weights and configuration files. This model is used for face detection. -### load_yolo_model(weights_path, config_path) -### Purpose: Loads the YOLO model using the specified weights and configuration files. -## Parameters: -### weights_path: Path to the YOLO weights file. -### config_path: Path to the YOLO configuration file. -### Returns: The YOLO network and output layers. - - -## 2. Face Detection: -The `detect_faces` function processes each frame to detect faces using the YOLOv3 model. It returns the coordinates of detected faces along with the confidence scores for each detection. A face is considered detected if the confidence score exceeds the 50% threshold. - -### `detect_faces(img, net, output_layers, confidence_threshold=0.5)` -- **Purpose**: Detects faces in an image using the YOLO model. -- **Parameters**: - - `img`: Image in which faces are to be detected. - - `net`: Loaded YOLO network. - - `output_layers`: Layers from the YOLO model. - - `confidence_threshold`: Minimum confidence to consider a detection valid. -- **Returns**: - - Processed image with bounding boxes. - - List of detected face coordinates. - - Their confidence scores. -##3. Facial Characteristics Extraction: -The `extract_face_characteristics` function extracts facial landmarks and computes the embedding vector for each detected face using Dlib. The landmarks are also highlighted in the output video. - -### `extract_face_characteristics(img, faces, shape_predictor, face_rec_model)` -- **Purpose**: Extracts facial landmarks and embeddings for detected faces. -- **Parameters**: - - `img`: Image containing detected faces. - - `faces`: List of face coordinates. - - `shape_predictor`: Dlib shape predictor for facial landmarks. - - `face_rec_model`: Dlib face recognition model. -- **Returns**: - - List of face characteristics including bounding box, landmarks, and embeddings. - -## 4.Processing Video Frames: -The `process_video` function processes the video file frame by frame, detects faces, extracts characteristics, and stores the results in a JSON-like object. The results are printed for each frame and can also be saved to a JSON file. - -### `process_video(video_path, output_video_path, net, output_layers, shape_predictor, face_rec_model)` -- **Purpose**: Processes a video to detect faces in each frame, extracts characteristics, and outputs a video and JSON data. -- **Parameters**: - - `video_path`: Path to the input video. - - `output_video_path`: Path to save the output video. - - `net`, `output_layers`, `shape_predictor`, `face_rec_model`: Preloaded models. -- **Returns**: - - JSON-like object with details of detected faces per frame. - -## 5. Main Function: -The `main` function serves as the entry point of the code, loading the models and calling the video processing function. The output video is saved with detected faces highlighted, and the results are saved to a JSON file. - -### `main()` -- **Purpose**: Entry point for the script to process the video using the above functions. - -## Confidence Scores and Accuracy Assessment: -In this project, the accuracy of face detection is assessed using the confidence scores provided by the YOLO model. The model is configured with a confidence threshold of 50%, meaning that only faces detected with a confidence score above this threshold are considered valid detections. While this method does not provide a direct accuracy metric, it ensures that only faces detected with reasonable certainty are included in the results. - -The confidence scores are included in the JSON-like object generated for each frame, providing a measure of the reliability of each detected face. - -## Progress - -### 1: -- **Activity**: Completed the implementation of the face detection model using YOLOv3. -- **Challenges**: Encountered issues with setting up the YOLO environment, including installation of necessary libraries and configuration of the model weights and configuration files. -- **Outcome**: Successfully set up the environment and completed the face detection module, capable of detecting faces in images and video frames. - -### 2: -- **Activity**: Developed the facial characteristics and shape predictor models using Dlib. -- **Challenges**: Faced difficulties with the installation of Dlib and its dependencies, requiring manual compilation and troubleshooting of environment issues. -- **Outcome**: Completed the facial landmark detection and embedding extraction modules, enabling detailed analysis of detected faces. - -### 3: -- **Activity**: Integrated the face detection and facial characteristics models into a video processing pipeline. -- **Challenges**: Encountered issues with processing large video files, managing memory, and ensuring the performance of the integrated system. -- **Outcome**: Successfully integrated a video module that processes each frame, detects faces, extracts characteristics, and generates a JSON-like object with detailed information about frames, detected faces, including bounding boxes, landmarks, embeddings, and confidence scores. - - -## Installation and Environment Setup: -Throughout the project, significant effort was invested in setting up the development environment, which involved: - -- Installing **OpenCV** for image and video processing tasks. -- Setting up the **YOLOv3** model, including downloading and configuring the necessary weights and configuration files. -- Installing **Dlib**, which required manual compilation due to compatibility issues with the existing system setup. -- Ensuring all dependencies were correctly installed and configured, allowing the models to function smoothly together in an integrated pipeline. - -The environment setup was a critical step in the project, as it allowed for the successful implementation and integration of advanced computer vision techniques. \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/haarcascade_image_result.jpg b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/haarcascade_image_result.jpg deleted file mode 100644 index 9b53fe365..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/harcascade_implementation/haarcascade_image_result.jpg and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/small_object_detection/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/small_object_detection/README.md deleted file mode 100644 index 01ceb2c46..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/small_object_detection/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# What is small object detection? -In computer vision, a small object does not necessarily correspond to small objects in reality; rather, it indicates the object's size in relation to the entire image. This distinction is important, especially in aerial imaging, where objects may appear small because of the camera's distance. - -Small object in computer vision is not imply as small objects in real life; it state the object's size relative to the overall image. This is especially important in aerial imaging, where objects might be small due to the distance of the camera. - -## Why Is Detecting Small Objects Hard? -The small object is a universal problem for various object, as the COCO evaluation results for some common models YOLOv3, EfficientDet, and YOLOv4 (Akyon et al., 2022): - -![performance](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/small%20object%20perfomance.png) - -From the results, focus on APs and APl column, which represent average precision scores. For instance, in EfficientDet, the AP for small objects is just 12%, compared to 51% for large objects. This shows a significant gap, nearly five-fold difference! - -The difficulty YOLO models face in detecting small objects can be attributed to the model's architecture. According to Dario et al. (2021), YOLO includes multiple convolutional neural network (CNN) layers, after each CNN its result in a feature map and it get smaller till the final one. This small feature map tends to lose spatial context, which is crucial for detecting small objects. As a result, YOLO often misses these small objects in an image. More detail explanationis [here](https://learnopencv.com/slicing-aided-hyper-inference/). - -![yolo](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/yolo.jpg) - -Morover, current object detection model such as Faster RCNN, YOLO, SSD, RetinaNet, EfficientDet, etc are trained on COCO (Common Objects in Context) dataset, which may contain biases toward larger objects due to their prevalence. As a consequence, the model may not have been exposed to enough diverse training examples of small objects. - -## How to detect small objects (inference optimisations) -One of the approach is using is InferenceSlicer. Instead of running the model on the whole scene, InferenceSlicer splits it into smaller parts (slices), runs the model on each one, and then stitches the results together. - -Taken this idea, SAHI was introduced in a research paper "Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection" from Akyon et al. (2022). SAHI works by dividing an image into slices that completely cover it and running inference on each of these slices with a specified detection model. The predictions across all of these slices are then merged together to generate one list of detections across the entire image. The โ€œhyperโ€ in SAHI comes from the fact that SAHIโ€™s output is not the result of model inference but a result of computations involving multiple model inferences. - -SAHI slices are allowed to overlap (as illustrated in the GIF below), which can help ensure that enough of an object is in at least one slice to be detected. - -SAHI framework - -![sahi_framework](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/390262ac-d9c3-4987-add6-b910cbf4bc89_12.avif) - -Illustration of framework, [source](https://supervision.roboflow.com/develop/how_to/detect_small_objects/#input-resolution) - -![sahi_framework_illustration](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/Small%20object%20detection/resources/supervision_detect_small_objects_example_2-ezgif.com-video-to-gif-converter.gif) - -The key advantage of using SAHI is that it is model-agnostic. SAHI can leverage todayโ€™s SOTA object detection models and whatever the SOTA model happens to be tomorrow! - -## Result on my implementation - -**YOLOv5 from T1/2024** -![yolov5_t124](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/base-yolov5.png) - -**YOLOv8 + SAHI** -![yolov8_sahi](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/yolov8%2Bsahi.png) - -Only YOLOv8 - -![yolo_predict](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/yolov8_predict.png) - -YOLOv8 and SAHI - -![yolo_predict](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/resources/yolov8nsahi.png) - -[source_code](https://github.com/milieureka/redback-orion/blob/main/Crowd_Monitoring/small_object_detection/model.ipynb) -## Evaluation -# Reference -Hereโ€™s the corrected version of your references, formatted consistently: - -1. Akyon, F., Altinuc, S. O., & Temizel, A. (2022). *Slicing aided hyper inference and fine-tuning for small object detection*. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 966-970). - -2. Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). *YOLOv4: Optimal speed and accuracy of object detection*. Retrieved from [https://arxiv.org/abs/2004.10934](https://arxiv.org/abs/2004.10934) - -3. Oliveira, D. A. B., Pereira, L. G. R., Bresolin, T., Ferreira, R. E. P., & Dorea, J. R. R. (2021). *A review of deep learning algorithms for computer vision systems in livestock*. 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using AI and computer vision to enhance **crowd safety monitoring** and **player tracking** during Australian Football League (AFL) matches. +Project 4 Orion is part of the Redback Company that focuses on AI computer visions of VFL game footafge to enhance +- **Player Tracking** +- **Crowd Monitoring** +- **VFL Analytics Dashboards** ---- +The system follows a microservices architecture with a FastAPI backend acting as an API gateway between the frontend and ML services. ## ๐ŸŽฏ Project Objectives -- ๐Ÿƒโ€โ™‚๏ธ **Track individual AFL players** in match footage using YOLOv11 and DeepSORT. -- ๐Ÿ‘ฅ **Estimate crowd density and movement** in stadium environments. -- ๐ŸŽฅ **Overlay visual analytics** (bounding boxes, heatmaps) on match videos. -- ๐Ÿ“Š **Generate dashboards** with statistics and visualizations. -- ๐Ÿง  **Collaborate with sports analytics team** to align player events (e.g., tackles, kicks, marks) with visual data. +- ๐Ÿƒโ€โ™‚๏ธ **Track individual VFL players** +- ๐Ÿ‘ฅ **Monitor crowd density and movement** +- ๐ŸŽฅ **Overlay visual analytics (bounding boxes, heatmaps** +- ๐Ÿ“Š **Generate dashboards** +- ๐Ÿง  **Integrate multiple services through a backend API gateway** --- @@ -18,28 +21,65 @@ This project is a combined submodule of Redback Project 4 focused on using AI a | Layer | Tech Stack | |-------------|------------| -| Frontend | React, Vite, Tailwind CSS, Chart.js, Leaflet.js | -| Backend | Python, FastAPI, OpenCV, Uvicorn, YOLOv11, DeepSORT | -| Models | Ultralytics YOLOv11, OpenCV background subtraction | -| Others | GitHub, VS Code, Google Colab, Jupyter | +| Frontend | React, Vite, Tailwind | +| Backend | Python, FastAPI, Uvicorn | +| ML Models | YOLO, DeepSORT | +| Dev Tools | GitHub, VS Code, Docker.. | --- + +## Architecture Overview + + Frontend (React) + โ”‚ + โ–ผ + Backend API (FastAPI) + โ”‚ + โ–ผ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ”‚ API Gateway Layer โ”‚ + โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ + โ”‚ + โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” + โ–ผ โ–ผ +Player Service Crowd Service + (YOLO) (Density) + ## ๐Ÿš€ How to Run the Project ### 1. Clone the Repository ```bash git clone https://github.com//redback-project4.git -cd redback-project4/Player_Tracking/afl_player_tracking_and_crowd_monitoring +cd redback-orion/26_T1/afl_player_tracking_and_crowd_monitoring ``` ### 2. Run the backend -```bash +**Navigate to the backend folder** cd backend + +**Create and activate virtual environment** +**(Mac)** +python -m venv .venv +source .venv/bin/activate +**(Windows)** +.venv\Scripts\activate + +**Install required Python packages** pip install -r requirements.txt -uvicorn app.main:app --reload -``` + +**Start the FastAPI server** +uvicorn app.main:app --reload --port 8000 + ### 3. Run the frontend -```bash +**Open a new terminal (command + t)** +cd frontend + +**Install the required Node.js dependencies** +npm install + +**Start the development server** +npm run dev + cd frontend npm install npm run dev @@ -49,9 +89,10 @@ npm run dev | Feature | Description | |--------------------|-----------------------------------------------------------------------------| -| ๐ŸŽฏ **Player Tracking** | Detect and track players in AFL match footage using YOLOv8 + DeepSORT | -| ๐Ÿ”ฅ **Heatmaps** | Visualize crowd intensity or player movement using density overlays | +| ๐ŸŽฏ **Player Tracking** | Detect and track players in VFL match footage using YOLO + tracking algs | +| ๐Ÿ‘ฅ **Crowd Monitoring | Analyse crowd density, movement and distribution across statium areas | +| ๐Ÿ”ฅ **Heatmaps** | Visualise crowd intensity or player movement using spatial heatmaps | | ๐Ÿ“ˆ **Dashboard** | Live stats on tackles, movement, player positions, and crowd data | -| ๐ŸŽฌ **Annotated Video** | Render bounding boxes, player IDs, and heatmaps onto match videos | -| ๐Ÿ”„ **API Integration** | Backend APIs expose results for the frontend to visualize | +| ๐ŸŽฌ **Annotated Video** | Render bounding boxes, player IDs, directly onto video footage | +| ๐Ÿ”„ **API Integration** | FastAPI backend connects frontend with player and crowd services through a unified API | diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.env.example b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.env.example index 061470413..79f182dde 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.env.example +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.env.example @@ -19,3 +19,4 @@ JWT_EXPIRE_MINUTES=60 # Logging DEBUG=True LOG_LEVEL=INFO + diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.gitignore b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.gitignore index d3c280c5e..34a0943cb 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.gitignore +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/.gitignore @@ -3,3 +3,12 @@ __pycache__/ *.pyc uploads/ .pytest_cache/ + +# Office / presentation files +*.pptx +*.xlsx +*.csv + +# Dev tooling +GIT_GUIDE.txt +.claude/ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/API_CONTRACT.md b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/API_CONTRACT.md new file mode 100644 index 000000000..d67149a65 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/API_CONTRACT.md @@ -0,0 +1,625 @@ +# Project Orion โ€” API Contract +**Version:** 1.2.0 +**Date:** 2026-04-27 +**Backend Lead:** Tomin Jose +**Base URL:** `http://localhost:8000` +**Interactive Docs:** `http://localhost:8000/docs` + +--- + +## Overview + +This document defines every HTTP endpoint the frontend must integrate with. All endpoints return JSON. All timestamps are ISO 8601 UTC strings. + +--- + +## Authentication + +All protected endpoints require a JWT token in the `Authorization` header: + +``` +Authorization: Bearer +``` + +The system uses **two tokens**: + +| Token | Lifetime | Purpose | +|-------|----------|---------| +| `access_token` | 60 minutes | Attached to every API request | +| `refresh_token` | 7 days | Used only to silently get a new access token | + +Both tokens are returned on login and register. When an `access_token` expires the frontend receives a `401` โ€” at that point call `POST /auth/refresh` with the `refresh_token` to get a new pair silently. Do **not** redirect to login unless the refresh token is also expired or revoked. + +--- + +## Error Response Format + +All errors follow this consistent shape: + +```json +{ + "detail": "Human-readable error message" +} +``` + +| HTTP Status | Meaning | +|-------------|---------| +| `400` | Bad request โ€” validation error or invalid input | +| `401` | Unauthorized โ€” missing or expired token | +| `403` | Forbidden โ€” valid token but insufficient role | +| `404` | Resource not found | +| `500` | Internal server error | + +--- + +## Endpoints + +--- + +### 1. Health Check + +#### `GET /health` +No authentication required. + +**Response `200`:** +```json +{ + "gateway": "ok", + "player_service": "ok | pending | error", + "crowd_service": "ok | pending | error" +} +``` + +**Use:** Check on app startup to verify services are reachable. + +--- + +#### `GET /` +No authentication required. + +**Response `200`:** +```json +{ + "status": "success", + "message": "Backend is running!" +} +``` + +--- + +### 2. Authentication + +--- + +#### `POST /auth/register` +Create a new user account. Returns a token immediately โ€” no separate login needed after registration. + +**Request body:** +```json +{ + "username": "johndoe", + "email": "john@example.com", + "password": "securepassword" +} +``` + +| Field | Type | Required | Notes | +|-------|------|----------|-------| +| `username` | string | Yes | Must be unique | +| `email` | string (email) | Yes | Must be unique, valid email format | +| `password` | string | Yes | Stored as bcrypt hash | + +**Response `200`:** +```json +{ + "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "token_type": "bearer", + "expires_in": 3600, + "user": { + "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "username": "johndoe", + "email": "john@example.com", + "role": "user", + "created_at": "2026-04-06T10:00:00Z" + } +} +``` + +**Error responses:** +```json +// 400 โ€” email already registered +{ "detail": "Email already registered" } + +// 400 โ€” username already taken +{ "detail": "Username already taken" } +``` + +--- + +#### `POST /auth/login` +Authenticate an existing user and receive both tokens. + +**Request body:** +```json +{ + "email": "john@example.com", + "password": "securepassword" +} +``` + +**Response `200`:** +```json +{ + "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "token_type": "bearer", + "expires_in": 3600, + "user": { + "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "username": "johndoe", + "email": "john@example.com", + "role": "user", + "created_at": "2026-04-06T10:00:00Z" + } +} +``` + +**Error responses:** +```json +// 401 โ€” wrong credentials +{ "detail": "Invalid email or password" } +``` + +--- + +#### `POST /auth/refresh` +Exchange a valid refresh token for a new access token + refresh token pair. The old refresh token is revoked immediately after use. + +**Request body:** +```json +{ + "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." +} +``` + +**Response `200`:** +```json +{ + "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", + "token_type": "bearer", + "expires_in": 3600, + "user": { ... } +} +``` + +**Error responses:** +```json +// 401 โ€” token invalid, expired, or already revoked +{ "detail": "Invalid or expired refresh token" } +{ "detail": "Refresh token has been revoked" } +{ "detail": "Refresh token has expired" } +``` + +> Always replace both stored tokens with the new pair returned. The old refresh token cannot be reused. + +--- + +#### `POST /auth/logout` +Revoke the refresh token. After this the user must log in again when their access token expires. + +**Request body:** +```json +{ + "refresh_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." +} +``` + +**Response `200`:** +```json +{ + "message": "Logged out successfully" +} +``` + +> After calling logout, clear both tokens from storage on the frontend. + +--- + +#### `GET /auth/me` +**Protected.** Get the currently logged-in user's profile. + +**Response `200`:** +```json +{ + "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "username": "johndoe", + "email": "john@example.com", + "role": "user", + "created_at": "2026-04-06T10:00:00Z" +} +``` + +> `role` is either `"user"` or `"admin"`. Use this to conditionally show admin-only UI. + +--- + +### 3. Video Upload + +--- + +#### `POST /upload` +**Protected.** Upload a video file for analysis. Returns a `job_id` immediately โ€” processing happens in the background. + +**Request:** `multipart/form-data` + +| Field | Type | Required | Notes | +|-------|------|----------|-------| +| `file` | file | Yes | Accepted: `.mp4`, `.avi`, `.mov` | + +**Example (JavaScript fetch):** +```js +const formData = new FormData(); +formData.append('file', videoFile); + +const response = await fetch('http://localhost:8000/upload', { + method: 'POST', + headers: { 'Authorization': `Bearer ${token}` }, + body: formData +}); +``` + +**Response `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "processing", + "created_at": "2026-04-06T10:00:00Z" +} +``` + +**Error responses:** +```json +// 400 โ€” unsupported file format +{ "detail": "Invalid video format. Accepted formats: .mp4, .avi, .mov" } + +// 401 โ€” not logged in +{ "detail": "Not authenticated" } +``` + +> After receiving `job_id`, immediately begin polling `/status/{job_id}`. + +--- + +### 4. Job Status & Results + +--- + +#### `GET /status/{job_id}` +**Protected.** Poll this endpoint to check processing progress. + +**Path parameter:** `job_id` โ€” UUID returned from `/upload` + +**Response while processing `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "processing" +} +``` + +**Response when complete `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "done", + "results": { + "player": { ... }, + "crowd": { ... } + } +} +``` + +**Response when partial (one service failed) `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "partial", + "results": { + "player": { ... }, + "crowd": null + }, + "error": "Crowd service timed out" +} +``` + +**Response when failed `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "failed", + "error": "Player: timeout | Crowd: timeout" +} +``` + +**Job status values:** + +| Status | Meaning | Frontend action | +|--------|---------|-----------------| +| `processing` | Both services still running | Keep polling | +| `done` | Both services succeeded | Display full results | +| `partial` | One service failed, one succeeded | Display partial results + show retry button | +| `failed` | Both services failed | Show error message | + +**Recommended polling interval:** every **4 seconds** until status is not `processing`. + +**Error responses:** +```json +// 404 +{ "detail": "Job not found" } + +// 403 โ€” trying to access another user's job +{ "detail": "Access denied" } +``` + +--- + +#### `GET /jobs` +**Protected.** List all jobs for the current user (paginated). Admins see all jobs. + +**Query parameters:** + +| Parameter | Type | Default | Notes | +|-----------|------|---------|-------| +| `page` | integer | `1` | Page number | +| `limit` | integer | `10` | Results per page | + +**Example:** `GET /jobs?page=2&limit=5` + +**Response `200`:** +```json +{ + "total": 42, + "page": 2, + "limit": 5, + "jobs": [ + { + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "done", + "created_at": "2026-04-06T10:00:00Z", + "updated_at": "2026-04-06T10:01:30Z" + }, + ... + ] +} +``` + +--- + +#### `GET /jobs/{job_id}` +**Protected.** Get full detail of a single job including results. + +**Response `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "done", + "created_at": "2026-04-06T10:00:00Z", + "updated_at": "2026-04-06T10:01:30Z", + "results": { + "player": { + "players": [ + { + "player_id": 1, + "team": "Team A", + "position": { "x": 120, "y": 340 }, + "speed": 6.4, + "distance_covered": 3.2, + "sprints": 4 + } + ], + "heatmap": null + }, + "crowd": { + "video_id": "8d41b321-2870-45a0-9bf7-4faa05293511", + "summary": { + "total_frames_processed": 65, + "peak_person_count": 11, + "crowd_state": "increasing_density", + "highest_density_zone": "B2", + "highest_risk_zone": "B2" + }, + "peak_crowd_frame": { + "frame_id": 49, + "timestamp": 8.0, + "person_count": 11, + "annotated_frame_path": "crowd_detection_output/people_detection_results/frame_0049.jpg" + }, + "anomaly_visual": { + "event_type": "walking_or_running_activity", + "image_path": "crowd_behaviour_analytics/output/.../motion_frame_0009.jpg" + }, + "heatmap": { + "image_path": "output/heatmap_8d41b321-....png" + }, + "time_series_chart": { + "image_path": "analytics_output/charts/8d41b321-..._crowd_activity_chart.png" + }, + "density_extremes": { + "highest_density_zone": { + "zone_id": "B2", + "person_count": 227, + "density": 1.0, + "risk_level": "critical", + "flagged": true + }, + "lowest_density_zone": { + "zone_id": "A1", + "person_count": 0, + "density": 0.0, + "risk_level": "very_low", + "flagged": false + } + } + } + }, + "errors": { + "player": null, + "crowd": null + } +} +``` + +**Crowd state values:** + +| Value | Meaning | +|-------|---------| +| `increasing_density` | Crowd is growing in density | +| `stable` | Crowd density is steady | +| `decreasing_density` | Crowd is thinning out | + +**Risk level values:** `very_low`, `low`, `medium`, `high`, `critical` + +> Do **not** use `crowd.heatmap.image_path` directly โ€” it is a server-side file path. Use `GET /jobs/{job_id}/heatmap` to fetch the heatmap image. + +--- + +#### `GET /jobs/{job_id}/heatmap` +**Protected.** Fetch the heatmap PNG image for a completed job. + +**Response `200`:** PNG image (`Content-Type: image/png`) + +**Usage in frontend:** +```js +const url = `http://localhost:8000/jobs/${jobId}/heatmap`; +// Use directly as with the Authorization header via fetch +``` + +**Error responses:** +```json +// 404 โ€” heatmap not ready yet or job not found +{ "detail": "Heatmap not available for this job" } + +// 502 โ€” crowd service unreachable +{ "detail": "Could not fetch heatmap from crowd service" } +``` + +--- + +### 5. Job Actions + +--- + +#### `POST /jobs/{job_id}/retry` +**Protected.** Re-run only the failed service for a `partial` job. Only works when `status === "partial"`. + +**Request body:** none + +**Response `200`:** +```json +{ + "job_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", + "status": "done" +} +``` + +**Error responses:** +```json +// 400 โ€” job is not partial +{ "detail": "Only partial jobs can be retried" } + +// 404 +{ "detail": "Job not found" } +``` + +> After retry, resume polling `/status/{job_id}` until status changes from `processing`. + +--- + +#### `DELETE /jobs/{job_id}` +**Protected.** Permanently delete a job record. + +**Response `200`:** +```json +{ + "message": "job deleted" +} +``` + +**Error responses:** +```json +// 404 +{ "detail": "Job not found" } + +// 403 +{ "detail": "Access denied" } +``` + +--- + +## CORS + +The backend is configured to accept requests from `http://localhost:3000`. No proxy configuration is needed. + +Allowed: +- **Origins:** `http://localhost:3000` +- **Methods:** All (`GET`, `POST`, `PUT`, `DELETE`, etc.) +- **Headers:** All +- **Credentials:** Yes + +--- + +## Frontend Integration Checklist + +``` +Auth +[ ] Store access_token AND refresh_token on login/register +[ ] Attach Authorization: Bearer to every request +[ ] On 401 response โ†’ call POST /auth/refresh with refresh_token +[ ] If refresh succeeds โ†’ retry the original request with new access_token +[ ] If refresh fails (401) โ†’ clear tokens and redirect to /login +[ ] On logout โ†’ call POST /auth/logout, then clear both tokens from storage +[ ] Read role from /auth/me to show/hide admin UI + +Upload +[ ] Send multipart/form-data โ€” do NOT set Content-Type header manually +[ ] Show upload progress indicator +[ ] Save job_id from response and start polling immediately + +Polling +[ ] Poll /status/{job_id} every 4 seconds +[ ] Stop polling when status !== "processing" +[ ] Handle all 4 status values: processing / done / partial / failed +[ ] Show retry button only when status === "partial" + +Jobs History +[ ] Implement pagination using page + limit query params +[ ] Calculate total pages: Math.ceil(total / limit) + +Error Handling +[ ] Handle 400, 401, 403, 404, 500 globally +[ ] Show user-friendly messages from the detail field +[ ] Never expose raw token values in UI or logs +``` + +--- + +## Notes + +- The `results.crowd` schema is **confirmed** โ€” see `GET /jobs/{job_id}` above for the full structure returned by the real crowd model. +- The `results.player` schema is **TBC** โ€” pending player service integration. Currently returns mock data with `players[]` array and `heatmap`. +- Do **not** use image paths from `crowd_result` directly. Always use `GET /jobs/{job_id}/heatmap` to fetch images via the gateway. +- Swagger UI at `http://localhost:8000/docs` is live and can be used to test all endpoints directly. +- All `job_id` and `user_id` values are **UUIDs** (string format: `"3fa85f64-5717-4562-b3fc-2c963f66afa6"`). +- Refresh tokens are **single use** โ€” each call to `/auth/refresh` revokes the old token and issues a new pair. + +--- + +## Changelog + +| Version | Date | Changes | +|---------|------|---------| +| 1.2.0 | 2026-04-27 | Added confirmed crowd response schema. Added `GET /jobs/{job_id}/heatmap` proxy endpoint. Added split mock flags (`USE_MOCK_PLAYER` / `USE_MOCK_CROWD`). Crowd service integrated and tested end-to-end. | +| 1.1.0 | 2026-04-13 | Added refresh token flow โ€” `POST /auth/refresh`, `POST /auth/logout`. Updated `/auth/register` and `/auth/login` responses to include `refresh_token`. Updated auth checklist. | +| 1.0.0 | 2026-04-06 | Initial contract โ€” health, auth, upload, jobs, CORS, checklist. | diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/GIT_GUIDE.txt b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/GIT_GUIDE.txt deleted file mode 100644 index 63f286aee..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/GIT_GUIDE.txt +++ /dev/null @@ -1,110 +0,0 @@ -======================================================== - PROJECT ORION โ€” BACKEND TEAM GIT GUIDE - SIT782 โ€“ Team Project (B) โ€“ Execution and Delivery -======================================================== - -OUR BRANCH ----------- -Branch name : backend-api-gateway -Our folder : 26_T1/afl_player_tracking_and_crowd_monitoring/backend/ -Repo : https://github.com/lucastargett/redback-orion - -RULE: Only touch files inside the backend/ folder. -RULE: Never commit directly to main. - - -FIRST TIME SETUP (new team member) ------------------------------------- -1. Clone the repo: - git clone https://github.com/lucastargett/redback-orion.git - -2. Switch to our branch: - git checkout backend-api-gateway - -3. Confirm you are on the right branch: - git branch - (should show * backend-api-gateway) - - -DAILY WORKFLOW --------------- -Before starting work every day: - git pull origin backend-api-gateway - -Do your work... - -When done: - git add backend/ (only add backend folder) - git commit -m "your message" - git push - - -WEEKLY SYNC (every Monday) ---------------------------- -Get latest changes from other teams: - git checkout main - git pull origin main - git checkout backend-api-gateway - git merge main - -This brings other teams' merged work into your branch. -Resolve any conflicts if they arise (unlikely since we own backend/). - - -COMMIT MESSAGE GUIDE ---------------------- -Keep messages short and clear. Examples: - "Add auth register and login endpoints" - "Implement JWT token validation" - "Add PostgreSQL database connection" - "Fix job status not updating after processing" - - -BRANCH STRUCTURE (whole team) ------------------------------- -Branch | Owner | Folder ------------------------|---------------|--------------------------- -main | Everyone | โ€” (merge target only) -backend-api-gateway | Backend team | backend/ -crowd-monitoring | Crowd team | crowd_model/ -player-tracking | Player team | player_model/ -frontend | Frontend team | frontend/ - - -MERGING TO MAIN (end of each week) ------------------------------------- -When a phase is complete and ready to share: - 1. Go to GitHub - 2. Open a Pull Request: backend-api-gateway โ†’ main - 3. Get team lead to review and approve - 4. Merge - - -THINGS TO NEVER DO -------------------- -- Never commit .env file (contains secrets) -- Never commit uploads/ folder (contains video files) -- Never force push to main -- Never commit directly to main -- Never edit other teams' folders - - -IF YOU GET A CONFLICT ----------------------- -1. Open the conflicting file -2. Look for these markers: - <<<<<<< your branch - your changes - ======= - their changes - >>>>>>> main - -3. Keep the correct version, remove the markers -4. git add the file -5. git commit - - -======================================================== - Backend Team โ€” Project Orion - Created: 2026-03-24 -======================================================== diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/dependencies.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/dependencies.py index e402e7b00..bf9862c37 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/dependencies.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/dependencies.py @@ -1,10 +1,11 @@ from fastapi import Depends, HTTPException, status -from fastapi.security import OAuth2PasswordBearer +from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials from app.auth.jwt import decode_access_token -oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/login") +http_bearer = HTTPBearer() -def get_current_user(token: str = Depends(oauth2_scheme)) -> dict: +def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(http_bearer)) -> dict: + token = credentials.credentials payload = decode_access_token(token) if payload is None: raise HTTPException( diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/jwt.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/jwt.py index e86dc0d87..24dc5b5d6 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/jwt.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/auth/jwt.py @@ -1,16 +1,39 @@ from datetime import datetime, timedelta, timezone from jose import JWTError, jwt -from app.config import JWT_SECRET_KEY, JWT_ALGORITHM, JWT_EXPIRE_MINUTES +from app.config import JWT_SECRET_KEY, JWT_ALGORITHM, JWT_EXPIRE_MINUTES, REFRESH_TOKEN_EXPIRE_DAYS + def create_access_token(data: dict) -> str: to_encode = data.copy() expire = datetime.now(timezone.utc) + timedelta(minutes=JWT_EXPIRE_MINUTES) - to_encode.update({"exp": expire}) + to_encode.update({"exp": expire, "type": "access"}) return jwt.encode(to_encode, JWT_SECRET_KEY, algorithm=JWT_ALGORITHM) + +def create_refresh_token(data: dict) -> tuple[str, datetime]: + """Returns (token, expires_at)""" + to_encode = data.copy() + expires_at = datetime.now(timezone.utc) + timedelta(days=REFRESH_TOKEN_EXPIRE_DAYS) + to_encode.update({"exp": expires_at, "type": "refresh"}) + token = jwt.encode(to_encode, JWT_SECRET_KEY, algorithm=JWT_ALGORITHM) + return token, expires_at + + def decode_access_token(token: str) -> dict: try: payload = jwt.decode(token, JWT_SECRET_KEY, algorithms=[JWT_ALGORITHM]) + if payload.get("type") != "access": + return None + return payload + except JWTError: + return None + + +def decode_refresh_token(token: str) -> dict: + try: + payload = jwt.decode(token, JWT_SECRET_KEY, algorithms=[JWT_ALGORITHM]) + if payload.get("type") != "refresh": + return None return payload except JWTError: return None diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/config.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/config.py index 99ace030a..da20037ed 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/config.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/config.py @@ -9,12 +9,18 @@ UPLOAD_DIR = os.getenv("UPLOAD_DIR", "uploads") USE_MOCK_SERVICES = os.getenv("USE_MOCK_SERVICES", "true").lower() == "true" +USE_MOCK_PLAYER = os.getenv("USE_MOCK_PLAYER", str(USE_MOCK_SERVICES)).lower() == "true" +USE_MOCK_CROWD = os.getenv("USE_MOCK_CROWD", str(USE_MOCK_SERVICES)).lower() == "true" -DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://user:password@localhost:5432/orion_db") +# Have just added async driver ('+asyncpg') to URL to match app - Lucas +DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://user:password@localhost:5432/orion_db") JWT_SECRET_KEY = os.getenv("JWT_SECRET_KEY", "your-secret-key-here") JWT_ALGORITHM = os.getenv("JWT_ALGORITHM", "HS256") JWT_EXPIRE_MINUTES = int(os.getenv("JWT_EXPIRE_MINUTES", 60)) +REFRESH_TOKEN_EXPIRE_DAYS = int(os.getenv("REFRESH_TOKEN_EXPIRE_DAYS", 7)) DEBUG = os.getenv("DEBUG", "True").lower() == "true" LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO") + + diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/main.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/main.py index b90ffeb01..e6933ecda 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/main.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/main.py @@ -13,7 +13,7 @@ ) logger = logging.getLogger(__name__) -Base.metadata.create_all(bind=engine) +# Base.metadata.create_all(bind=engine.sync_engine) app = FastAPI( title="Project Orion Backend API", diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/models.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/models.py index 2384d5b50..dd4213220 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/models.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/models.py @@ -1,7 +1,8 @@ import uuid from datetime import datetime, timezone -from sqlalchemy import Column, String, DateTime, ForeignKey +from sqlalchemy import Column, String, DateTime, ForeignKey, Boolean from sqlalchemy.dialects.postgresql import UUID, JSONB +from sqlalchemy.orm import relationship # โœ… ADDED from app.database import Base @@ -15,6 +16,19 @@ class User(Base): role = Column(String, default="user", nullable=False) created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc)) + jobs = relationship("Job", back_populates="user") + + +class RefreshToken(Base): + __tablename__ = "refresh_tokens" + + id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4) + user_id = Column(UUID(as_uuid=True), ForeignKey("users.user_id"), nullable=False) + token = Column(String, unique=True, nullable=False) + is_active = Column(Boolean, default=True, nullable=False) + created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc)) + expires_at = Column(DateTime, nullable=False) + class Job(Base): __tablename__ = "jobs" @@ -28,3 +42,5 @@ class Job(Base): error = Column(String, nullable=True) created_at = Column(DateTime, default=lambda: datetime.now(timezone.utc)) updated_at = Column(DateTime, default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc)) + + user = relationship("User", back_populates="jobs") \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/auth.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/auth.py index 53bd0f251..cdcdabb41 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/auth.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/auth.py @@ -1,16 +1,44 @@ +from datetime import datetime, timezone from fastapi import APIRouter, Depends, HTTPException, status from sqlalchemy.orm import Session from app.database import get_db -from app.models import User -from app.schemas.auth import RegisterRequest, LoginRequest, AuthResponse, UserResponse +from app.models import User, RefreshToken +from app.schemas.auth import RegisterRequest, LoginRequest, AuthResponse, UserResponse, RefreshRequest, LogoutRequest from app.auth.hashing import hash_password, verify_password -from app.auth.jwt import create_access_token +from app.auth.jwt import create_access_token, create_refresh_token, decode_refresh_token from app.auth.dependencies import get_current_user from app.config import JWT_EXPIRE_MINUTES router = APIRouter() +def _issue_tokens(user: User, db: Session) -> dict: + """Create access + refresh tokens, store refresh token in DB.""" + token_data = { + "sub": str(user.user_id), + "email": user.email, + "role": user.role + } + access_token = create_access_token(token_data) + refresh_token_str, expires_at = create_refresh_token(token_data) + + db_refresh = RefreshToken( + user_id=user.user_id, + token=refresh_token_str, + expires_at=expires_at + ) + db.add(db_refresh) + db.commit() + + return { + "access_token": access_token, + "refresh_token": refresh_token_str, + "token_type": "bearer", + "expires_in": JWT_EXPIRE_MINUTES * 60, + "user": user + } + + @router.post("/register", response_model=AuthResponse) def register(user: RegisterRequest, db: Session = Depends(get_db)): if db.query(User).filter(User.email == user.email).first(): @@ -27,18 +55,7 @@ def register(user: RegisterRequest, db: Session = Depends(get_db)): db.commit() db.refresh(new_user) - token = create_access_token({ - "sub": str(new_user.user_id), - "email": new_user.email, - "role": new_user.role - }) - - return { - "access_token": token, - "token_type": "bearer", - "user": new_user, - "expires_in": JWT_EXPIRE_MINUTES * 60 - } + return _issue_tokens(new_user, db) @router.post("/login", response_model=AuthResponse) @@ -47,18 +64,50 @@ def login(user: LoginRequest, db: Session = Depends(get_db)): if not db_user or not verify_password(user.password, db_user.password): raise HTTPException(status_code=401, detail="Invalid email or password") - token = create_access_token({ - "sub": str(db_user.user_id), - "email": db_user.email, - "role": db_user.role - }) + return _issue_tokens(db_user, db) - return { - "access_token": token, - "token_type": "bearer", - "user": db_user, - "expires_in": JWT_EXPIRE_MINUTES * 60 - } + +@router.post("/refresh", response_model=AuthResponse) +def refresh(body: RefreshRequest, db: Session = Depends(get_db)): + payload = decode_refresh_token(body.refresh_token) + if not payload: + raise HTTPException(status_code=401, detail="Invalid or expired refresh token") + + db_token = db.query(RefreshToken).filter( + RefreshToken.token == body.refresh_token, + RefreshToken.is_active == True + ).first() + + if not db_token: + raise HTTPException(status_code=401, detail="Refresh token has been revoked") + + if db_token.expires_at.replace(tzinfo=timezone.utc) < datetime.now(timezone.utc): + db_token.is_active = False + db.commit() + raise HTTPException(status_code=401, detail="Refresh token has expired") + + user = db.query(User).filter(User.user_id == db_token.user_id).first() + if not user: + raise HTTPException(status_code=404, detail="User not found") + + # Revoke old refresh token and issue a new pair + db_token.is_active = False + db.commit() + + return _issue_tokens(user, db) + + +@router.post("/logout") +def logout(body: LogoutRequest, db: Session = Depends(get_db)): + db_token = db.query(RefreshToken).filter( + RefreshToken.token == body.refresh_token + ).first() + + if db_token: + db_token.is_active = False + db.commit() + + return {"message": "Logged out successfully"} @router.get("/me", response_model=UserResponse) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/jobs.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/jobs.py index e76b83c8d..8a428e86c 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/jobs.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/jobs.py @@ -1,6 +1,8 @@ import asyncio +import httpx from datetime import datetime, timezone from fastapi import APIRouter, Depends, HTTPException +from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session from app.database import get_db from app.models import Job @@ -8,6 +10,7 @@ from app.auth.dependencies import get_current_user from app.services.player_client import get_player_data from app.services.crowd_client import get_crowd_data +from app.config import CROWD_SERVICE_URL router = APIRouter() @@ -129,6 +132,32 @@ async def retry_job( return {"job_id": str(job.job_id), "status": job.status} +@router.get("/jobs/{job_id}/heatmap") +async def get_heatmap( + job_id: str, + current_user: dict = Depends(get_current_user), + db: Session = Depends(get_db) +): + job = db.query(Job).filter(Job.job_id == job_id).first() + if not job: + raise HTTPException(status_code=404, detail="Job not found") + check_job_access(job, current_user) + + crowd = job.crowd_result + if not crowd or not crowd.get("heatmap") or not crowd["heatmap"].get("image_path"): + raise HTTPException(status_code=404, detail="Heatmap not available for this job") + + image_path = crowd["heatmap"]["image_path"].replace("\\", "/") + url = f"{CROWD_SERVICE_URL}/artifacts/{image_path}" + + async with httpx.AsyncClient(timeout=10.0) as client: + r = await client.get(url) + if r.status_code != 200: + raise HTTPException(status_code=502, detail="Could not fetch heatmap from crowd service") + + return StreamingResponse(iter([r.content]), media_type="image/png") + + @router.delete("/jobs/{job_id}") def delete_job( job_id: str, diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/upload.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/upload.py index 9e059af9a..e2860b43f 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/upload.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/routes/upload.py @@ -12,6 +12,8 @@ from app.services.player_client import get_player_data from app.services.crowd_client import get_crowd_data +from sqlalchemy import select + router = APIRouter() ALLOWED_EXTENSIONS = {".mp4", ".avi", ".mov"} @@ -44,16 +46,17 @@ async def process_video(job_id: str, file_path: str): status = "done" error = None - job = db.query(Job).filter(Job.job_id == job_id).first() + result = await db.execute(select(Job).where(Job.job_id == job_id)) + job = result.scalar_one_or_none() if job: job.status = status job.player_result = player_data job.crowd_result = crowd_data job.error = error job.updated_at = datetime.now(timezone.utc) - db.commit() + await db.commit() finally: - db.close() + await db.close() if os.path.exists(file_path): os.remove(file_path) diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/auth.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/auth.py index a104f317d..aae717cfa 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/auth.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/auth.py @@ -27,11 +27,20 @@ class UserResponse(BaseModel): class AuthResponse(BaseModel): access_token: str + refresh_token: str token_type: str user: Optional[UserResponse] = None expires_in: int +class RefreshRequest(BaseModel): + refresh_token: str + + +class LogoutRequest(BaseModel): + refresh_token: str + + diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/jobs.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/jobs.py index c39934f47..8810c1499 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/jobs.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/schemas/jobs.py @@ -1,22 +1,31 @@ -from pydantic import BaseModel, ConfigDict +from pydantic import BaseModel, ConfigDict, field_serializer from typing import Optional, List, Any from datetime import datetime +from uuid import UUID class UploadResponse(BaseModel): - job_id: str + job_id: UUID status: str created_at: datetime + @field_serializer("job_id") + def serialize_job_id(self, v: UUID) -> str: + return str(v) + class JobSummary(BaseModel): model_config = ConfigDict(from_attributes=True) - job_id: str + job_id: UUID status: str created_at: datetime updated_at: datetime + @field_serializer("job_id") + def serialize_job_id(self, v: UUID) -> str: + return str(v) + class JobResults(BaseModel): player: Optional[Any] = None @@ -31,13 +40,17 @@ class JobErrors(BaseModel): class JobDetail(BaseModel): model_config = ConfigDict(from_attributes=True) - job_id: str + job_id: UUID status: str created_at: datetime updated_at: datetime results: Optional[JobResults] = None errors: Optional[JobErrors] = None + @field_serializer("job_id") + def serialize_job_id(self, v: UUID) -> str: + return str(v) + class JobListResponse(BaseModel): total: int diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/crowd_client.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/crowd_client.py index 6a28c3477..6b5b60e7c 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/crowd_client.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/crowd_client.py @@ -1,29 +1,67 @@ +import os import httpx -from app.config import USE_MOCK_SERVICES, CROWD_SERVICE_URL +from app.config import USE_MOCK_CROWD, CROWD_SERVICE_URL -def get_mock_crowd_data(): +def get_mock_crowd_data(video_id: str): return { - "crowd_count": 15432, - "density": 0.72, - "movement": "steady", - "sections": [ - {"section_id": 1, "count": 3200, "density": 0.65}, - {"section_id": 2, "count": 4100, "density": 0.78}, - {"section_id": 3, "count": 3900, "density": 0.74}, - {"section_id": 4, "count": 4232, "density": 0.81} - ] + "video_id": video_id, + "summary": { + "total_frames_processed": 65, + "peak_person_count": 11, + "crowd_state": "stable", + "highest_density_zone": "A1", + "highest_risk_zone": None + }, + "peak_crowd_frame": { + "frame_id": 18, + "timestamp": 12.4, + "person_count": 11, + "annotated_frame_path": None + }, + "anomaly_visual": { + "event_type": "walking_detection", + "image_path": f"output/anomaly_{video_id}.jpg" + }, + "heatmap": { + "image_path": f"output/heatmap_{video_id}.png" + }, + "time_series_chart": { + "image_path": f"analytics_output/charts/{video_id}_crowd_activity_chart.png" + }, + "density_extremes": { + "highest_density_zone": { + "zone_id": "A1", + "person_count": 8, + "density": 0.72, + "risk_level": "low", + "flagged": False + }, + "lowest_density_zone": { + "zone_id": "A2", + "person_count": 2, + "density": 0.18, + "risk_level": "very_low", + "flagged": False + } + } } -async def get_crowd_data(file_path: str = None): - if USE_MOCK_SERVICES: - return get_mock_crowd_data() +async def get_crowd_data(file_path: str = None, video_id: str = None): + if video_id is None: + video_id = os.path.splitext(os.path.basename(file_path))[0] if file_path else "unknown" - async with httpx.AsyncClient(timeout=60.0) as client: + if USE_MOCK_CROWD: + return get_mock_crowd_data(video_id) + + # Send absolute path so the crowd service can locate the file regardless of its CWD + abs_path = os.path.abspath(file_path) if file_path else None + + async with httpx.AsyncClient(timeout=120.0) as client: response = await client.post( - f"{CROWD_SERVICE_URL}/analyze", - data={"file_path": file_path} + f"{CROWD_SERVICE_URL}/process-crowd-detection", + json={"video_id": video_id, "video_path": abs_path} ) response.raise_for_status() return response.json() diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/player_client.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/player_client.py index d35c9f172..783e0b5e4 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/player_client.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/app/services/player_client.py @@ -1,5 +1,5 @@ import httpx -from app.config import USE_MOCK_SERVICES, PLAYER_SERVICE_URL +from app.config import USE_MOCK_PLAYER, PLAYER_SERVICE_URL def get_mock_player_data(): @@ -27,7 +27,7 @@ def get_mock_player_data(): async def get_player_data(file_path: str = None): - if USE_MOCK_SERVICES: + if USE_MOCK_PLAYER: return get_mock_player_data() async with httpx.AsyncClient(timeout=60.0) as client: diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.csv b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.csv deleted file mode 100644 index f038dc79e..000000000 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.csv +++ /dev/null @@ -1,73 +0,0 @@ -BRANCH OWNERSHIP,,,, -MEMBER,BRANCH,STATUS,, -Tomin,backend-api-gateway,Main backend branch,, -Lucas,backend-feature/auth-schemas,Active,, -Prabhnoor,config-error-handling,Active,, -William,feature/mock-services,Already merged into backend-api-gateway,, -,,,, -Bi-WEEKLY SPRINT 1: 23rd March - 3rd April,,,, -Backend,,,, -,WEEK 1: 23 - 28 March,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Backend Architecture & Project Setup,"Define the backend architecture, create the project folder structure, set up the FastAPI application, establish main API routes and share the API contract with all teams.","Working FastAPI base project with organised folder structure, main.py, basic routes (/ and /health), and BACKEND_PLAN.txt shared with team" -Lucas,API Routes Implementation,"Create the backend route files for player, crowd, and test functionality and implement the initial API endpoints required for frontend and integration testing.","Route files created (players, crowd, test) with working endpoints /api/test, /api/players, and /api/crowd" -William,Mock Service Layer โ€” Setup,"Create player_client.py and crowd_client.py returning mock JSON responses for player and crowd data. Add USE_MOCK_SERVICES flag to .env so mocks can be toggled on/off without code changes.","player_client.py and crowd_client.py created with structured mock JSON responses and USE_MOCK_SERVICES flag supported" -Prabhnoor,Configuration Setup,"Implement config.py to load all environment variables from .env using python-dotenv. Fill in .env with local dev values including USE_MOCK_SERVICES=true as default.",".env and config.py completed with all required environment variables including mock service toggle" -,,,, -,WEEK 2: 29 March - 3 April,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,CORS + Route Wiring + Frontend Checkpoint 1 + Contract Push,"Configure CORS in main.py to allow frontend on localhost:3000. Wire all completed routes into main.py. Share BACKEND_PLAN.txt and Swagger URL with frontend team. Send contract template to player and crowd teams and request confirmation of ports, endpoints, and response format. Review and merge all week 1 PRs.","CORS configured, all routes wired, API contract shared with frontend team, contract confirmation requested from player and crowd teams" -Lucas,Auth Schemas,"Implement schemas/auth.py (RegisterRequest, LoginRequest, AuthResponse, UserResponse) and schemas/health.py (HealthResponse).","Pydantic schemas ready for use in auth routes" -William,Mock Services Connected to Routes,"Connect player_client.py and crowd_client.py to the API routes. Verify mock responses return correctly through the gateway when USE_MOCK_SERVICES=true.","Mock services integrated and returning structured data through the routes" -Prabhnoor,Database Setup,"Implement database.py (PostgreSQL async connection via SQLAlchemy) and models.py (users and jobs table definitions). Set up local PostgreSQL and confirm connection works.","Working database connection with users and jobs tables created" -,,,, -,,,, -Bi-WEEKLY SPRINT 2: 4th - 17th April,,,, -Backend,,,, -,WEEK 3: 4 - 10 April,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Auth Core Implementation + Contract Follow-up,"Implement auth/hashing.py (bcrypt password hashing) and auth/jwt.py (JWT token creation and validation). Implement RBAC dependencies get_current_user and require_admin. Chase up player and crowd teams for contract confirmation if not received. Review all PRs.","Working auth system with password hashing, JWT tokens, and role-based access control. Contract status known." -Lucas,Auth Routes,"Implement routes/auth.py with POST /auth/register, POST /auth/login, and GET /auth/me endpoints.","Auth endpoints working and returning correct responses" -William,Job Schemas,"Implement schemas/jobs.py with JobSummary, JobDetail, JobListResponse, and UploadResponse Pydantic models.","Job schemas ready for use in upload and job routes" -Prabhnoor,Global Error Handler & Logging Middleware,"Add global exception handler to main.py to catch all unhandled errors and return clean JSON. Add request logging middleware to log all incoming requests.","All unhandled errors return clean JSON responses, all requests logged" -,,,, -,WEEK 4: 11 - 17 April,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Video Upload Route + Frontend Checkpoint 2,"Implement routes/upload.py to validate video format, save to uploads/, create a job in the database, and return job_id immediately. Wire route into main.py. Verify frontend can connect to /health and /auth/login โ€” confirm CORS and connection working.","POST /upload working, job created in DB, frontend connection verified" -Lucas,Job Routes,"Implement routes/jobs.py with GET /status/{job_id}, GET /jobs, GET /jobs/{job_id}, POST /jobs/{job_id}/retry, and DELETE /jobs/{job_id} with RBAC enforced.","All job endpoints working with correct role-based access control" -William,Background Task โ€” process_video(),"Implement process_video() background task using asyncio.gather to call player and crowd services in parallel. Respect USE_MOCK_SERVICES flag โ€” use mock clients if true, real clients if false. Handle partial results if one service fails. Update job status in DB to done, partial, or failed.","Background processing working with mock toggle support, job status updated correctly" -Prabhnoor,Auth & Health Tests,"Write tests/test_auth.py (register, login, token validation) and tests/test_health.py.","Auth and health tests passing" -,,,, -,,,, -Bi-WEEKLY SPRINT 3: 17th April - 1st May,,,, -Backend,,,, -,WEEK 5: 17 - 24 April,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Finalise API Contracts + Integration Decision,"Finalise confirmed contracts with player and crowd teams. Update BACKEND_PLAN.txt and .env with real service URLs. Decide whether to proceed with real integration or stay on mocks based on other teams' readiness. Review all PRs.","Contracts finalised, integration decision made, .env updated accordingly" -Lucas,Upload & Job Tests,"Write tests/test_upload.py (file format validation, job creation) and tests/test_jobs.py (job status, pagination, retry) using mocked service responses.","Upload and job tests passing with mocked services" -William,Real Service Integration (if contracts confirmed),"If player team contract is confirmed, update player_client.py to call real player service. Keep USE_MOCK_SERVICES flag โ€” real client used only when flag is false. If contract not confirmed, keep mock and document blockers.","player_client.py supports both mock and real mode via USE_MOCK_SERVICES flag" -Prabhnoor,Real Service Integration (if contracts confirmed),"If crowd team contract is confirmed, update crowd_client.py to call real crowd service. Keep USE_MOCK_SERVICES flag โ€” real client used only when flag is false. If contract not confirmed, keep mock and document blockers.","crowd_client.py supports both mock and real mode via USE_MOCK_SERVICES flag" -,,,, -,WEEK 6: 25 April - 1 May,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Core Frontend Integration Checkpoint 3 + Demo Mode Prep,"Run full pipeline locally with both real and mock modes. Verify frontend can hit /upload and /status/{job_id} end-to-end. Confirm demo can run in Mode A (real services) or Mode B (mock fallback). Review and merge all integration PRs.","Both demo modes verified โ€” Mode A with real services and Mode B with mock fallback" -Lucas,Route Error Handling,"Handle timeout errors from player and crowd services, return 400 for bad video formats, and return partial or failed status when a service is down.","All error cases handled with correct HTTP status codes and clean responses" -William,Retry Endpoint Testing,"Test POST /jobs/{job_id}/retry with a real partial failure scenario. Verify asyncio.gather handles one service failing correctly and only the failed service is retried.","Retry working correctly in real partial failure scenarios" -Prabhnoor,Integration Test Suite,"Run all tests against real services if available. If not, run against mocks. Fix any failures and document integration status clearly.","All tests passing, integration status documented" -,,,, -,,,, -Bi-WEEKLY SPRINT 4: 1st - 15th May,,,, -Backend,,,, -,WEEK 7: 1 - 8 May โ€” BUFFER / CATCH-UP WEEK,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,Buffer โ€” Fix Integration Issues or Start Docker,"If real service integration is behind, use this week to resolve blockers and stabilise integration. If integration is on track, begin Dockerfile and docker-compose.yml setup. Review all PRs.","Integration stable or Docker setup started depending on progress" -Lucas,Buffer โ€” Fix Failing Tests or Docker Config,"Fix any remaining test failures or integration issues. If on track, swap .env URLs from localhost to Docker service names and begin Docker testing.","Tests passing or Docker config started" -William,Buffer โ€” Service Client Fixes or Docker Testing,"Resolve any service client issues from integration. If on track, test player_client.py and crowd_client.py with Docker service names.","Service clients stable or Docker-ready" -Prabhnoor,Buffer โ€” Test Suite Fixes or Docker Test Run,"Fix any outstanding test failures. If on track, run the full test suite inside Docker and fix any Docker-specific issues.","Test suite stable or passing inside Docker" -,,,, -,WEEK 8: 8 - 15 May,,, -MEMBER,TASK,DESCRIPTION,OUTPUT -Tomin,README + Final Demo Checkpoint 4,"Write README.md with local and Docker setup instructions. Do final review of all code. Run full end-to-end demo โ€” attempt Mode A (real services) first, fall back to Mode B (mock) if needed. Coordinate final demo run with frontend and all teams.","Complete README, all code reviewed, demo confirmed working in at least one mode" -Lucas,Swagger Docs Polish,"Verify /docs looks clean and accurate. Add any missing response descriptions to all endpoints.","Clean and accurate Swagger UI at /docs" -William,Logging & Cleanup,"Add request and response logging to all service clients. Clean up any leftover debug code.","All service client calls logged, codebase clean" -Prabhnoor,Final Test Run & Cleanup,"Run the complete test suite one final time. Ensure all tests pass. Remove any leftover debug code.","All tests green, codebase clean and demo-ready" diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.xlsx b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.xlsx deleted file mode 100644 index 7881f7704..000000000 Binary files a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/planner.xlsx and /dev/null differ diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_jobs.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_jobs.py index e69de29bb..8ccf087d9 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_jobs.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_jobs.py @@ -0,0 +1,180 @@ +from datetime import datetime, timezone +from types import SimpleNamespace + +from app.main import app +from app.auth.dependencies import get_current_user + +# Bypass authentication +def override_get_current_user(): + return {"sub": "test_user", "role": "admin"} + +# Mock DB record +def make_job( + job_id="job-123", + status="done", + user_id="test_user", + player_result=None, + crowd_result=None, + error=None, + video_path="uploads/test.mp4", + ): + now = datetime.now(timezone.utc) + + return SimpleNamespace( + job_id=job_id, + status=status, + user_id=user_id, + player_result=player_result, + crowd_result=crowd_result, + error=error, + video_path=video_path, + created_at=now, + updated_at=now, + ) + +# Test: Get job status (success) +def test_get_status_success(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + fake_job = make_job( + status="done", + player_result={"players": 10}, + crowd_result={"crowd": 50}, + ) + mock_db.query.return_value.filter.return_value.first.return_value = fake_job # Mock DB query to return fake job + response = client.get("/status/job-123") # Send GET request to endpoint + assert response.status_code == 200 + data = response.json() + assert data["job_id"] == "job-123" + assert data["status"] == "done" + assert "results" in data + +# Test: Job status not found +def test_get_status_not_found(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + mock_db.query.return_value.filter.return_value.first.return_value = None + + response = client.get("/status/missing-job") + + assert response.status_code == 404 + assert response.json()["detail"] == "Job not found" + +# Test: List jobs (pagination) +def test_list_jobs_with_pagination(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + # Create fake jobs list + jobs = [ + make_job(job_id="job-1"), + make_job(job_id="job-2"), + make_job(job_id="job-3"), + ] + + mock_db.query.return_value.count.return_value = 3 + mock_db.query.return_value.order_by.return_value.offset.return_value.limit.return_value.all.return_value = jobs[:2] + + response = client.get("/jobs?page=1&limit=2") + assert response.status_code == 200 + data = response.json() + + # Check pagination fields + assert data["total"] == 3 + assert data["page"] == 1 + assert data["limit"] == 2 + assert len(data["jobs"]) == 2 # only 2 returned + +# Test: Get job details +def test_get_job_success(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + # Fake completed job + fake_job = make_job( + status="done", + player_result={"players": 10}, + crowd_result={"crowd": 50}, + ) + + mock_db.query.return_value.filter.return_value.first.return_value = fake_job + + response = client.get("/jobs/job-123") + assert response.status_code == 200 + data = response.json() + # Validate job data + assert data["job_id"] == "job-123" + assert data["status"] == "done" + assert "results" in data + +# Test Get job not found +def test_get_job_not_found(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + # No job returned + mock_db.query.return_value.filter.return_value.first.return_value = None + + response = client.get("/jobs/missing-job") + assert response.status_code == 404 + assert response.json()["detail"] == "Job not found" + +# Test: Retry job (success case) +def test_retry_job_success(client, mock_db, monkeypatch): + app.dependency_overrides[get_current_user] = override_get_current_user + + # Partial job (only one result missing) + fake_job = make_job( + status="partial", + player_result=None, + crowd_result={"crowd": 50}, + ) + + mock_db.query.return_value.filter.return_value.first.return_value = fake_job + + # Mock async player service response + async def fake_player_data(video_path): + return {"players": 12} + + # Replace real service call with fake one + monkeypatch.setattr("app.routes.jobs.get_player_data", fake_player_data) + + response = client.post("/jobs/job-123/retry") + assert response.status_code == 200 + data = response.json() + + # Job should now be completed + assert data["job_id"] == "job-123" + assert data["status"] == "done" + +# Test: Retry invalid job +def test_retry_job_not_partial(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + # Job already complete + fake_job = make_job( + status="done", + player_result={"players": 10}, + crowd_result={"crowd": 50}, + ) + + mock_db.query.return_value.filter.return_value.first.return_value = fake_job + + response = client.post("/jobs/job-123/retry") + + # Should fail + assert response.status_code == 400 + assert response.json()["detail"] == "Only partial jobs can be retried" + +# Test: Delete job +def test_delete_job_success(client, mock_db): + app.dependency_overrides[get_current_user] = override_get_current_user + + fake_job = make_job() + + # Mock DB returning a job + mock_db.query.return_value.filter.return_value.first.return_value = fake_job + + response = client.delete("/jobs/job-123") + assert response.status_code == 200 + assert response.json()["message"] == "job deleted" + + # Ensure DB methods were called + mock_db.delete.assert_called_once_with(fake_job) + mock_db.commit.assert_called() \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_upload.py b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_upload.py index e69de29bb..f401fdbf4 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_upload.py +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/backend/tests/test_upload.py @@ -0,0 +1,28 @@ +from app.main import app +from app.auth.dependencies import get_current_user + +def override_get_current_user(): + return {"sub": "test_user", "role": "admin"} + +async def fake_process_video(job_id, file_path): + return None + +def test_upload_valid_file(client, monkeypatch): + app.dependency_overrides[get_current_user] = override_get_current_user + monkeypatch.setattr("app.routes.upload.process_video", fake_process_video) + response = client.post("/upload", files={"file": ("test.mp4", b"fake video content", "video/mp4")}) + assert response.status_code == 200 + assert "job_id" in response.json() + assert response.json()["status"] == "processing" + +def test_upload_invalid_file_type(client): + app.dependency_overrides[get_current_user] = override_get_current_user + response = client.post("/upload", files={"file": ("text.txt", b"dummy,data", "text/plain")}) + assert response.status_code == 400 + assert "invalid" in str(response.json()).lower() + +def test_missing_file(client): + app.dependency_overrides[get_current_user] = override_get_current_user + response = client.post("/upload", files={}) + assert response.status_code == 422 + assert "file" in str(response.json()) \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/frontend/client/pages/AFLDashboard.tsx b/26_T1/afl_player_tracking_and_crowd_monitoring/frontend/client/pages/AFLDashboard.tsx index 284b9921a..90c807476 100644 --- a/26_T1/afl_player_tracking_and_crowd_monitoring/frontend/client/pages/AFLDashboard.tsx +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/frontend/client/pages/AFLDashboard.tsx @@ -79,6 +79,7 @@ import { Settings, LogOut, ChevronDown, + Shield, } from "lucide-react"; // Mock data for the dashboard @@ -201,7 +202,27 @@ const crowdZones = [ trend: "stable", }, ]; +const safestZone = crowdZones.reduce((min, zone) => zone.density < min.density ? zone : min, crowdZones[0]); +const BackToTopButton = () => { + const [visible, setVisible] = useState(false); + useEffect(() => { + const handleScroll = () => { + setVisible(window.scrollY > 300); + }; + window.addEventListener("scroll", handleScroll); + return () => window.removeEventListener("scroll", handleScroll); + }, []); + + return visible ? ( + + ) : null; +}; export default function AFLDashboard() { const navigate = useNavigate(); const [selectedPlayer, setSelectedPlayer] = useState(mockPlayers[0]); @@ -224,6 +245,7 @@ export default function AFLDashboard() { const [videoAnalysisError, setVideoAnalysisError] = useState( null, ); + const [currentJobId, setCurrentJobId] = useState(null); const [selectedAnalysisType, setSelectedAnalysisType] = useState("highlights"); const [selectedFocusAreas, setSelectedFocusAreas] = useState([]); @@ -334,7 +356,25 @@ export default function AFLDashboard() { const diffHours = Math.floor(diffMins / 60); return `${diffHours}h ${diffMins % 60}m remaining`; }; +useEffect(() => { + if (!currentJobId) return; + + const interval = setInterval(async () => { + try { + const response = await fetch(`http://localhost:8000/status/${currentJobId}`); + const data = await response.json(); + + if (data.status !== "processing") { + clearInterval(interval); + setCurrentJobId(null); + } + } catch (error) { + console.error("Polling error:", error); + } + }, 4000); + return () => clearInterval(interval); + }, [currentJobId]); // Generate dynamic chart data for analysis results const generateAnalysisChartData = (item: any) => { // Player performance data for charts @@ -1683,11 +1723,27 @@ Export ID: ${Date.now()}-${Math.random().toString(36).substr(2, 9)}
Goals
-
+
{selectedPlayer.efficiency}%
Efficiency
+
+
+ {selectedPlayer.efficiency >= 90 ? "๐Ÿ† Excellent" : + selectedPlayer.efficiency >= 80 ? "โญ Good" : + selectedPlayer.efficiency >= 70 ? "๐Ÿ‘ Average" : "๐Ÿ“ˆ Needs Improvement"} +
+
+ {selectedPlayer.efficiency >= 90 ? "Elite level performance. Top 10% of all players." : + selectedPlayer.efficiency >= 80 ? "Strong performance. Above average player." : + selectedPlayer.efficiency >= 70 ? "Solid performance. Room to improve." : "Below average. Focus on consistency."} +
+
Score: {selectedPlayer.efficiency}/100
+
@@ -2091,6 +2147,22 @@ Export ID: ${Date.now()}-${Math.random().toString(36).substr(2, 9)} + + +
+
+

Safest Zone

+

+ {safestZone.zone} +

+

+ {safestZone.density}% full ยท {safestZone.current.toLocaleString()} people +

+
+ +
+ + {/* Analytics Report Download */} @@ -3342,6 +3414,7 @@ Generated on: ${new Date().toLocaleString()} )} + ); } diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/Player_Detection_Nithin.ipynb b/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/Player_Detection_Nithin.ipynb new file mode 100644 index 000000000..3bd278e52 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/Player_Detection_Nithin.ipynb @@ -0,0 +1,437 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Player Detection using YOLOv11 (AFL)" + ], + "metadata": { + "id": "bYn_vNpoiz97" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Mounting the Google Drive" + ], + "metadata": { + "id": "MviPtkLyjSL4" + } + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')\n", + "print(\"Drive mounted!\")" + ], + "metadata": { + "id": "aKPlZkkhi5Xj" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Install Ultralytics" + ], + "metadata": { + "id": "1LBZsLNXjaiK" + } + }, + { + "cell_type": "markdown", + "source": [ + "Installing Ultralytics to run the YOLO" + ], + "metadata": { + "id": "WgNW3HYmj0Fc" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install ultralytics\n", + "print(\"Ultralytics installed!\")" + ], + "metadata": { + "id": "2FNHbXi3j9jh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Loading the Model" + ], + "metadata": { + "id": "sjQj1hJskBKv" + } + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "\n", + "# Load pretrained YOLOv11 nano model\n", + "model = YOLO('yolo11n.pt')" + ], + "metadata": { + "id": "_BCE3Jt6kJuT" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Training the model using the annotated dataset" + ], + "metadata": { + "id": "aCQMSaiUkMUP" + } + }, + { + "cell_type": "markdown", + "source": [ + "### creating the data.yml file" + ], + "metadata": { + "id": "gQgzmEIVoBhg" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "# Create data.yaml directly in the correct Drive folder\n", + "yaml_content = \"\"\"path: /content/drive/MyDrive/Colab Notebooks/Project_Orion/Labelled_Data/yolo_train_data\n", + "train: images\n", + "val: images\n", + "\n", + "nc: 3\n", + "names:\n", + " 0: CAR\n", + " 1: GCS\n", + " 2: REF\n", + "\"\"\"\n", + "\n", + "yaml_path = '/content/drive/MyDrive/Colab Notebooks/Project_Orion/Labelled_Data/yolo_train_data/data.yaml'\n", + "\n", + "with open(yaml_path, 'w') as f:\n", + " f.write(yaml_content)\n", + "\n", + "print(\"data.yaml created successfully at:\")\n", + "print(yaml_path)" + ], + "metadata": { + "id": "CrC8J2L1oFo7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train on your AFL annotated dataset\n", + "model.train(\n", + " data='/content/drive/MyDrive/Colab Notebooks/Project_Orion/Labelled_Data/yolo_train_data/data.yaml',\n", + " epochs=50,\n", + " imgsz=640,\n", + " batch=16,\n", + " name='afl_player_detection'\n", + ")" + ], + "metadata": { + "id": "IDQ6GpF8kXtx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "* We have the manually labelled dataset to train the model.\n", + "* Used Label-studio to manually label the dataset (200 images each)" + ], + "metadata": { + "id": "bXHM_MLbka5T" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Saving the best.pt to Drive" + ], + "metadata": { + "id": "7hP8bRGmkwve" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "for root, dirs, files in os.walk('/content/runs'):\n", + " for file in files:\n", + " if file == 'best.pt':\n", + " print(os.path.join(root, file))" + ], + "metadata": { + "id": "RFDtlFxrrF3N" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import shutil\n", + "import os\n", + "\n", + "os.makedirs('/content/drive/MyDrive/Colab Notebooks/Project_Orion/AFL_Model', exist_ok=True)\n", + "\n", + "shutil.copy(\n", + " '/content/runs/detect/afl_player_detection2/weights/best.pt',\n", + " '/content/drive/MyDrive/Colab Notebooks/Project_Orion/AFL_Model/best.pt'\n", + ")\n", + "print(\"New AFL model saved to Drive!\")" + ], + "metadata": { + "id": "Rc7EgDeMk2XK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Load the new AFL model for inference test:" + ], + "metadata": { + "id": "Acs3Fop6md05" + } + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "from IPython.display import Image as IPImage\n", + "import glob\n", + "\n", + "# Load YOUR new AFL trained model\n", + "model = YOLO('/content/drive/MyDrive/Colab Notebooks/Project_Orion/AFL_Model/best.pt')\n", + "\n", + "# Test on one image from your training data\n", + "results = model.predict(\n", + " source='/content/drive/MyDrive/Colab Notebooks/Project_Orion/Labelled_Data/yolo_train_data/images',\n", + " conf=0.3,\n", + " save=True,\n", + " max_det=50\n", + ")\n", + "\n", + "# Show one result\n", + "output_images = glob.glob(\"/content/runs/detect/predict*/*.jpg\")\n", + "IPImage(output_images[0])" + ], + "metadata": { + "id": "taWNNubkmgrH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Loading the model" + ], + "metadata": { + "id": "ilsjtcPQPkHS" + } + }, + { + "cell_type": "code", + "source": [ + "from ultralytics import YOLO\n", + "\n", + "# Load your AFL trained model\n", + "model = YOLO('/content/drive/MyDrive/Colab Notebooks/Project_Orion/AFL_Model/best.pt')\n", + "print(\"Model loaded successfully!\")" + ], + "metadata": { + "id": "DkgWGbPePm1K" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Upload multiple images" + ], + "metadata": { + "id": "VogjWeSGP1I2" + } + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "\n", + "print(\"Upload 6-7 AFL frame images...\")\n", + "uploaded = files.upload()\n", + "image_filenames = list(uploaded.keys())\n", + "print(f\"\\nUploaded {len(image_filenames)} images:\")\n", + "for img in image_filenames:\n", + " print(f\" โ†’ {img}\")" + ], + "metadata": { + "id": "9jYlMeOSP2SX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### inference on all images" + ], + "metadata": { + "id": "_Cvr0pufP7Bp" + } + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Image as IPImage, display\n", + "import glob, os\n", + "\n", + "# Summary table\n", + "summary = []\n", + "\n", + "for image_file in image_filenames:\n", + " print(f\"\\n{'='*40}\")\n", + " print(f\"Image: {image_file}\")\n", + " print(f\"{'='*40}\")\n", + "\n", + " results = model.predict(\n", + " source=image_file,\n", + " conf=0.3,\n", + " save=True,\n", + " verbose=False\n", + " )\n", + "\n", + " boxes = results[0].boxes\n", + " num_detections = len(boxes)\n", + "\n", + " # Count each class detected\n", + " car_count = 0\n", + " gcs_count = 0\n", + " ref_count = 0\n", + "\n", + " for box in boxes:\n", + " cls = int(box.cls[0])\n", + " conf = float(box.conf[0])\n", + " class_name = model.names[cls]\n", + " print(f\" โ†’ {class_name}: {conf:.2f} confidence\")\n", + "\n", + " if class_name == 'CAR':\n", + " car_count += 1\n", + " elif class_name == 'GCS':\n", + " gcs_count += 1\n", + " elif class_name == 'REF':\n", + " ref_count += 1\n", + "\n", + " print(f\"\\nSummary for this image:\")\n", + " print(f\" Carlton players (CAR): {car_count}\")\n", + " print(f\" Gold Coast players (GCS): {gcs_count}\")\n", + " print(f\" Referees (REF): {ref_count}\")\n", + " print(f\" Total detections: {num_detections}\")\n", + "\n", + " summary.append({\n", + " 'image': image_file,\n", + " 'CAR': car_count,\n", + " 'GCS': gcs_count,\n", + " 'REF': ref_count,\n", + " 'total': num_detections\n", + " })\n", + "\n", + "# Show all result images\n", + "print(\"\\n\\n--- DISPLAYING ALL RESULTS ---\")\n", + "output_images = sorted(glob.glob(\"/content/runs/detect/predict*/*.jpg\"))\n", + "for img_path in output_images[-len(image_filenames):]:\n", + " print(f\"\\n{os.path.basename(img_path)}\")\n", + " display(IPImage(img_path, width=800))" + ], + "metadata": { + "id": "d0k-MQF1P8Ho" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Model confidence metrics" + ], + "metadata": { + "id": "9GY0V-X2Qv0l" + } + }, + { + "cell_type": "code", + "source": [ + "print(\"\\n=== MODEL CONFIDENCE METRICS ===\\n\")\n", + "\n", + "all_confs = {'CAR': [], 'GCS': [], 'REF': []}\n", + "\n", + "for image_file in image_filenames:\n", + " results = model.predict(\n", + " source=image_file,\n", + " conf=0.3,\n", + " verbose=False\n", + " )\n", + " for box in results[0].boxes:\n", + " cls = int(box.cls[0])\n", + " conf = float(box.conf[0])\n", + " class_name = model.names[cls]\n", + " all_confs[class_name].append(conf)\n", + "\n", + "for class_name, confs in all_confs.items():\n", + " if confs:\n", + " print(f\"{class_name}:\")\n", + " print(f\" Average confidence: {sum(confs)/len(confs):.2f}\")\n", + " print(f\" Highest confidence: {max(confs):.2f}\")\n", + " print(f\" Lowest confidence: {min(confs):.2f}\")\n", + " print(f\" Total detections: {len(confs)}\")\n", + " else:\n", + " print(f\"{class_name}: No detections found\")\n", + " print()\n" + ], + "metadata": { + "id": "Q1mOg_3mQxN4" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/README.md b/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/README.md new file mode 100644 index 000000000..a252be7c0 --- /dev/null +++ b/26_T1/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Notebooks/2026_T1_Nithin/README.md @@ -0,0 +1,90 @@ +# AFL Player Detection โ€” YOLOv11 + +**Author:** Nithin JS +**Sprint:** Sprint 2 (4 โ€“ 17 April) +**Branch:** `player-tracking-sp2/nithin-yolo-training` + +## Overview + +This notebook trains a custom YOLOv11 object detection model to identify Australian Football League (AFL) match participants across three classes: + +- `CAR` โ€” Carlton Football Club players +- `GCS` โ€” Gold Coast Suns players +- `REF` โ€” Match referees + +The trained model is the detection stage of the broader AFL Player Tracking pipeline, which uses DeepSORT for tracking identified detections across frames. + +## Files + +- `Player_Detection_Nithin.ipynb` โ€” end-to-end training and inference notebook + +## Dataset + +The training dataset is **not included in this repository** due to size and licensing considerations. It is stored at: + +``` +Google Drive: Colab Notebooks/Project_Orion/Labelled_Data/yolo_train_data/ +``` + +- **Source:** AFL broadcast frames extracted by the team, annotated in Label Studio +- **Size:** ~200 frames +- **Classes:** 3 (CAR, GCS, REF) +- **Format:** YOLO format (one `.txt` label file per image) + +To run the notebook, you must have Drive access to the Project Orion folder. + +## How to Run + +1. Open `Player_Detection_Nithin.ipynb` in Google Colab with GPU runtime enabled. +2. Mount Google Drive when prompted. +3. Run cells in order. Training takes roughly 30โ€“40 minutes on a Colab T4 GPU. +4. Trained weights are saved to `Colab Notebooks/Project_Orion/AFL_Model/best.pt`. + +## Training Configuration + +| Parameter | Value | +|--------------|-------| +| Base model | YOLOv11 (Ultralytics, COCO-pretrained) | +| Epochs | 50 | +| Image size | 640 | +| Batch size | 16 | +| Hardware | Colab T4 GPU | + +## Results (Sprint 2 โ€” Initial Baseline) + +> **Caveat:** These metrics were produced with train and validation sets pointing to the same folder. Numbers are inflated. A proper train/val split is planned for Sprint 3. + +| Metric | Value | +|---------------|-------| +| Precision | 0.949 | +| Recall | 0.932 | +| mAP@50 | 0.976 | +| mAP@50-95 | 0.574 | + +**Inference confidence across 6 test images:** + +| Class | Avg Conf | Max | Min | Detections | +|-------|----------|------|------|------------| +| CAR | 0.73 | 0.90 | 0.43 | 33 | +| GCS | 0.73 | 0.94 | 0.34 | 31 | +| REF | 0.74 | 0.93 | 0.31 | 14 | + +## Known Limitations + + +- Classes are tied to specific teams (Carlton, Gold Coast). Model does not generalise to other matches. +- Dataset size (200 frames) is small; more data needed for robust performance. + +## Sprint 3 Plan + +- Evaluate merging teammate-annotated data (pending class schema alignment). +- Explore generalisation of class labels to support different matches. + +## Dependencies + +- `ultralytics` (YOLOv11) +- `torch`, `torchvision` +- Standard scientific Python stack (numpy, matplotlib, opencv-python) + +Installed via `!pip install ultralytics` in the notebook. + diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/App.tsx b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/App.tsx index 3a56b3cb4..0c0b3e2fd 100644 --- a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/App.tsx +++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/App.tsx @@ -13,6 +13,7 @@ import Analytics from "./pages/Analytics"; import Reports from "./pages/Reports"; import ApiDiagnostics from "./pages/ApiDiagnostics"; import ErrorDemo from "./pages/ErrorDemo"; +import About from "./pages/About"; import NotFound from "./pages/NotFound"; const queryClient = new QueryClient({ @@ -83,6 +84,7 @@ export default function App() { } /> } /> } /> + } /> } /> } /> {/* ADD ALL CUSTOM ROUTES ABOVE THE CATCH-ALL "*" ROUTE */} diff --git a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/components/MobileNavigation.tsx b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/components/MobileNavigation.tsx index 0376f6ac3..8f0c79bbe 100644 --- a/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/components/MobileNavigation.tsx +++ b/Player_Tracking/afl_player_tracking_and_crowd_monitoring/frontend/client/components/MobileNavigation.tsx @@ -51,6 +51,12 @@ const navigationItems = [ icon: Terminal, description: "System monitoring", }, + { + name: "About", + href: "/about", + icon: Zap, // or any icon you like + description: "About this system", + }, ]; export default function MobileNavigation() { @@ -191,8 +197,8 @@ export default function MobileNavigation() { {/* Bottom Navigation for Mobile */}