Backend: integrate player & crowd services with full pipeline

.gitignore

@@ -30,7 +30,13 @@ runs/
 # Model weights & large files
 *.pt
 *.pth
-*.zipobj/
+*.zip
+*.mp4
+*.json
+*.csv
+/Player_Tracking/Pose_Matching_project/Pose_Estimation/pose-estimation-fitness/src/models/FitnessTracking.v1i.yolov5pytorch/test/images
+/Player_Tracking/Pose_Matching_project/Pose_Estimation/pose-estimation-fitness/src/models/FitnessTracking.v1i.yolov5pytorch
+obj/
 bin/
 *.mp4
 *.mov
@@ -42,3 +48,5 @@ bin/
 obj/
 bin/
 .DS_Store
+/Player_Tracking/Pose_Matching_project/Pose_Estimation/pose-estimation-fitness
+*.jpg

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/Dockerfile

@@ -0,0 +1,23 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    libxcb1 \
+    libx11-6 \
+    libxext6 \
+    libsm6 \
+    libxrender1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY Crowd_Monitoring/2026_T1/requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY Crowd_Monitoring/2026_T1/ .
+
+EXPOSE 8002
+
+CMD ["uvicorn", "shared.services.main:app", "--host", "0.0.0.0", "--port", "8002"]

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/README.md

@@ -37,6 +37,49 @@ Heatmap
 Crowd Behaviour Analytics
     ->
 Crowd Allocation / Risk Zone
+This repository contains the Crowd Monitoring module for the Orion project under Redback Operations.
+
+The purpose of this module is to analyse stadium crowd behaviour and density using computer vision and data analytics. The expected outputs include:
+
+- crowd heatmaps
+- density metrics
+- zone-based analytics
+- behaviour insights for integration into the Orion analytics dashboard
+
+## Objectives
+
+- Detect and analyse spectators in stadium footage
+- Estimate crowd density and distribution
+- Generate zone-based crowd analytics
+- Produce crowd heatmaps and visualisations
+- Develop AI-based crowd behaviour analysis
+- Optionally predict future attendance using machine learning models
+
+## Proposed Pipeline
+
+```text
+Video Input
+    |
+    v
+Frame Extraction (OpenCV)
+    |
+    v
+Person Detection (YOLOv8)
+    |
+    v
+Crowd Density Estimation
+    |
+    v
+Zone-based Analysis
+    |
+    v
+Heatmap Generation
+    |
+    v
+Analytics Output (JSON / CSV)
+    |
+    v
+Behaviour Analysis / Prediction
 ```
 
 ## Tech Stack
@@ -183,3 +226,97 @@ In the current setup, Swagger UI is served on the root URL.
 - FastAPI Swagger is generated from `shared/services/models.py`
 - implementation teams should follow their task `SCHEMA.md`
 - if a field name changes, update schema first and then update service and task code
+## 👥 Team Tasks (7 Members)
+
+Each team member owns one task. Follow the README in each task folder for detailed objectives and deliverables.
+
+| # | Task Folder | Team Member | Objective |
+|---|-------------|-------------|-----------|
+| 1 | **video_processing** | Member 1 | Extract frames and prepare video data |
+| 2 | **crowd_detection** | Member 2 | Detect persons using YOLOv8 |
+| 3 | **density_zoning** | Member 3 | Calculate density and zone-based analytics |
+| 4 | **heatmap** | Member 4 | Generate crowd heatmaps |
+| 5 | **analytics_output** | Member 5 | Create reports and output files (JSON/CSV) |
+| 6 | **crowd_behaviour_analytics** | Member 6 | Analyze crowd movement and behavior patterns |
+| 7 | **crowd_allocation_risk_zone** | Member 7 | Assess risk levels and crowd allocation strategy |
+
+**Optional:** `prediction_optional/` - Attendance prediction (if time permits)
+
+---
+
+## Task Guidance
+
+Each task folder contains its own `README.md` with:
+
+- 🎯 Task objective
+- 📥 Expected inputs and outputs
+- 💡 Implementation notes
+- ✅ Suggested deliverables
+
+This keeps the semester plan split into smaller, easier-to-manage work packages.
+
+---
+
+## Project Structure
+
+```text
+2026_T1/
+|-- data/                           # Sample videos and frames
+|-- docs/                           # Documentation and notes
+|-- shared/                         # Common utilities and configs
+|-- requirements.txt                # Dependencies
+|-- README.md                       # This file
+|
+|-- Task Folders (one per member):
+|-- video_processing/               # Task 1: Video extraction & preparation
+|-- crowd_detection/                # Task 2: Person detection (YOLOv8)
+|-- density_zoning/                 # Task 3: Density & zone analysis
+|-- heatmap/                        # Task 4: Heatmap generation
+|-- analytics_output/               # Task 5: Report generation
+|-- crowd_behaviour_analytics/      # Task 6: Behavior analysis
+|-- crowd_allocation_risk_zone/     # Task 7: Risk assessment & allocation
+|
+|-- Optional:
+`-- prediction_optional/            # Optional: Attendance prediction
+```
+
+---
+
+## 🚀 Getting Started
+
+1. **Find your task** in the table above
+2. **Read the README** in your task folder
+3. **Follow the implementation notes**
+4. **Deliver outputs** as specified
+5. **Update your README** with progress
+
+---
+
+## 📋 Integration Points
+
+Tasks run in sequence:
+
+```
+video_processing (Task 1)
+         ↓
+crowd_detection (Task 2)
+         ↓
+density_zoning (Task 3)
+         ↓
+heatmap (Task 4) + analytics_output (Task 5)
+         ↓
+crowd_behaviour_analytics (Task 6)
+crowd_allocation_risk_zone (Task 7)
+         ↓
+         ↓
+Dashboard / Report Integration
+```
+
+---
+
+## 💬 Collaboration
+
+- **Shared utilities:** Use files in `shared/` folder
+- **Input/Output:** Check task README for I/O specifications
+- **Questions:** Document in docs/ folder
+- **Results:** Save outputs to designated location in your task folder

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/analytics_output/README.md

@@ -67,5 +67,9 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script for exporting results
+
+## Suggested Deliverables
+
+- A serializer or export script
 - Example JSON and CSV output
 - A short schema note in this folder or `docs/`

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_allocation_risk_zone/README.md

@@ -102,3 +102,10 @@ The module has been tested with the following scenarios:
 ### Test Results
 
 All validation tests passed (5/5). Module is ready for integration.
+
+## Suggested Deliverables
+
+- Risk zone map visualization
+- Allocation recommendation report (JSON/CSV)
+- Risk threshold configuration file
+- Examples showing critical scenarios and responses

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_behaviour_analytics/README.md

@@ -67,5 +67,9 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script for behaviour analysis
+
+## Suggested Deliverables
+
+- A prototype behaviour analysis script
 - A few example scenarios or simulated cases
 - A list of event definitions used by the module

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/crowd_detection/README.md

@@ -67,5 +67,9 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script for person detection
+
+## Suggested Deliverables
+
+- A detection script or notebook
 - Sample visual output with boxes drawn on frames
 - A saved set of person coordinates or detections

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/density_zoning/README.md

@@ -67,5 +67,9 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script that maps people into zones
+
+## Suggested Deliverables
+
+- A script that maps people into zones
 - A table of zone counts and densities
 - Example output using sample coordinates or detections

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/heatmap/README.md

@@ -65,5 +65,9 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script for heatmap generation
+
+## Suggested Deliverables
+
+- A heatmap generation script or notebook
 - Example heatmap image
 - Optional frame overlay showing density distribution

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/shared/services/main.py

@@ -4,6 +4,7 @@
 
 from fastapi import FastAPI
 from fastapi import Request
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 
@@ -19,6 +20,15 @@
 )
 
 
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3000"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
 @app.exception_handler(Exception)
 async def unhandled_exception_handler(request: Request, exc: Exception):
     """Return actual runtime errors as JSON instead of generic 500 pages."""

26_T1/afl_player_tracking_and_crowd_monitoring/Crowd_Monitoring/2026_T1/video_processing/README.md

@@ -66,5 +66,10 @@ Do not create extra files too early. Start simple, then split only when needed.
 ## Suggested Deliverables
 
 - A simple `main.py` script that extracts frames from a video
+- Store reusable helpers in `shared/` when possible
+
+## Suggested Deliverables
+
+- A script or notebook that extracts frames from a video
 - A short note on frame sampling strategy
 - Example output for one sample video

26_T1/afl_player_tracking_and_crowd_monitoring/README.md

@@ -29,7 +29,7 @@ The system follows a microservices architecture with a FastAPI backend acting as
 ---
 
 ## Architecture Overview
-
+```text
       Frontend (React)
               │
               ▼
@@ -44,6 +44,7 @@ The system follows a microservices architecture with a FastAPI backend acting as
    ▼                    ▼
 Player Service     Crowd Service
    (YOLO)           (Density)
+```
 
 ## 🚀 How to Run the Project