Scripts for daily image collection and decryption.

scripts/collect_images.py

@@ -0,0 +1,98 @@
+import argparse
+import datetime
+import json
+import os
+import subprocess
+
+from src.db.db_connection import sql_cursor
+
+"""
+This script should be set up with a cron job to run daily.
+Each user will have to set up their rclone config, in our case pointing to a Google Drive folder: https://rclone.org/drive/
+This can be done with /local/b/embedvis/rclone-v1.52.2-linux-amd64/rclone config
+
+Collect images of non-goggle detections from the database.
+Upload images and metadata to Google Drive.
+"""
+
+METADATA_FILE = 'metadata.json'
+
+
+def get_metadata():
+    """
+    Get image filenames and other relevant metadata from the database.
+    Save metadata to a file for future decryption.
+    @return: A list of dictionaries with the metadata for each image
+
+    Example list: [
+    {'image_name': "0.jpg", 'x_min': 0.0, 'y_min': 0.0, 'x_max': 100.0, 'y_max': 100.0, 'init_vector': "example"}
+    {'image_name': "1.jpg", 'x_min': 25.0, 'y_min': 25.0, 'x_max': 120.0, 'y_max': 140.0, 'init_vector': "example2"}]
+    """
+
+    metadata = []
+    current_date = (datetime.date.today(),)
+
+    # make sql connection
+    # execute query
+    with sql_cursor() as cursor:
+        try:
+            cursor.execute('USE goggles')
+            cursor.execute('SELECT b.image_name, b.X_Min, b.Y_Min, b.X_Max, b.Y_Max, '
+                           'b.init_vector, b.goggles from BBOX AS b, IMAGE as i where '
+                           'b.image_name=i.image_name and i.image_date=%s and b.goggles=False', current_date)
+
+            for (image_name, x_min, y_min, x_max, y_max, init_vector, goggles) in cursor:
+                metadata.append({'image_name': image_name,
+                                 'x_min': float(x_min),
+                                 'y_min': float(y_min),
+                                 'x_max': float(x_max),
+                                 'y_max': float(y_max),
+                                 'init_vector': init_vector
+                                 })
+        except Exception as e:
+            print(e)
+
+    with open(METADATA_FILE, 'w') as meta_file:
+        json.dump(metadata, meta_file)
+    return metadata
+
+
+def upload_files(metadata, dir, rclone_path, remote_name):
+    """
+    For each filename returned by get_metadata, upload image
+    to Drive. Upload the day's metadata file.
+    @param metadata: the list of dictionaries returned by get_metadata
+    @param dir: the folder containing the images to upload
+    @param remote_name: name of remote location in rclone
+    """
+
+    images = []
+    today = datetime.datetime.today().strftime('%Y-%m-%d')
+
+    # send images to the Drive
+    for image in metadata:
+        # prevent sending the same image twice (if two faces are detected)
+        if image not in images:
+            images.append(image)
+            image_path = os.path.join(dir, image['image_name'])
+            subprocess.run([rclone_path, 'copy', image_path, '{}:{}'.format(remote_name, today)])
+
+    # upload metadata.json to the Drive
+    subprocess.run([rclone_path, 'copy', METADATA_FILE, '{}:{}'.format(remote_name, today)])
+    os.remove(METADATA_FILE)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser('Collect images.')
+    parser.add_argument('--directory', '-d', type=str, required=True, help='Folder containing images to upload')
+    parser.add_argument('--rclone_path', '-r', type=str, default='/local/b/embedvis/rclone-v1.52.2-linux-amd64/rclone',
+                        help='Location of rclone binary. Default version on ee220clnx1 doesn\'t support copying to '
+                             'shared folders.')
+    parser.add_argument('--remote_name', type=str, help='Name of remote location according to rclone config. You must '
+                                                        'create your own config.')
+    args = parser.parse_args()
+
+    metadata = get_metadata()
+    upload_files(metadata, args.directory, args.rclone_path, args.remote_name)
+
+    exit(0)

scripts/decrypt_images.py

@@ -0,0 +1,42 @@
+import argparse
+import getpass
+import json
+import os
+
+from src.jetson.AES import Encryption
+
+"""
+After having collect_images has run and the output folder has been downloaded,
+decrypt the associated images.
+This file is assumed to be on the end user's machine.
+"""
+
+# the metadata file generated by collect_images
+METADATA_FILE = 'metadata.json'
+
+
+def decrypt_images(dir):
+    # ask for decryption key
+    decrypt_key = getpass.getpass('Decryption password: ')
+
+    # convert to PKBDF2 or whatever
+
+    # make decryptor; probably changes once Jason finishes
+    decryptor = Encryption
+
+    with open(os.path.join(dir, METADATA_FILE)) as meta_file:
+        metadata = json.load(meta_file)
+        # use face coords to find where to decrypt in video frame
+        for image in metadata:
+            # TODO handle multiple faces in one frame. append to coords list
+            coords = [(image['x_min'], image['y_min'], image['x_max'], image['y_max'])]
+            init_vector = image['init_vector']
+            # overwrite encrypted image
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser('Decrypt images.')
+    parser.add_argument('--directory', '-d', type=str, required=True, help='Folder of images to be decrypted.')
+    args = parser.parse_args()
+
+    decrypt_images(args.directory)

src/db/data_insertion.py

@@ -3,6 +3,9 @@
 from decimal import Decimal
 import datetime
 
+# location where the images will be stored (on the HELPS machine)
+IMAGE_DIR = '/local/b/embedvis/Nano_Images'
+
 
 def data_insert(image_name: str, image_date: datetime, image_time: datetime, init_vecs: list, bboxes: list, input_dir: str, labels: list):
     """Transfer image to remote storage then inserts image metadata and bounding boxes data in database
@@ -18,8 +21,8 @@ def data_insert(image_name: str, image_date: datetime, image_time: datetime, ini
     """
 
     # Below ftp transfer has been commented out for testing purposes
-    #with ftp_transfer() as transfer:
-        #transfer(input_dir, './Documents', image_name)
+    with ftp_transfer() as transfer:
+        transfer(input_dir, IMAGE_DIR, image_name)
 
     sql_insert(IMAGE(image_name, image_date, image_time))
 

src/db/db_connection.py

@@ -1,5 +1,4 @@
 import mysql.connector
-import datetime
 
 from src.db.config import get_config
 from contextlib import contextmanager, closing

src/jetson/face_detector.py

@@ -1,3 +1,5 @@
+import os
+
 import numpy as np
 import torch
 
@@ -41,7 +43,7 @@ def __init__(self, detector: str, detector_type: str, detection_threshold=0.7, c
 
             self.net = BlazeFace(self.device == torch.device("cuda:0"))
             self.net.load_weights(detector)
-            self.net.load_anchors("models/BlazeFace/anchors.npy")
+            self.net.load_anchors(os.path.join(os.path.dirname(__file__), 'models/BlazeFace/anchors.npy'))
             self.model_name = 'blazeface'
             self.net.min_score_thresh = 0.75
             self.net.min_suppression_threshold = 0.3
@@ -110,7 +112,7 @@ def detect(self,
                 xmax = detections[i, 3] * frame.shape[1]
                 conf = detections[i, 16]
 
-                transformed_frame = img / 127.5 - 1.0
+                transformed_frame = transformed_frame / 127.5 - 1.0
 
                 for k in range(6):
                     kp_x = detections[i, 4 + k * 2] * transformed_frame.shape[1]