Update video_processing.py

video_processing.py

@@ -48,50 +48,6 @@ def extract_frames(video_path, output_folder, desired_fps, progress_callback=Non
     return frame_count, original_fps
 
 
-def process_frames(frames_folder, aligned_faces_folder, frame_count, progress):
-    embeddings_by_frame = {}
-    posture_scores_by_frame = {}
-    posture_landmarks_by_frame = {}
-    facial_landmarks_by_frame = {}
-    aligned_face_paths = []
-    frame_files = sorted([f for f in os.listdir(frames_folder) if f.endswith('.jpg')])
-
-    for i, frame_file in enumerate(frame_files):
-        frame_num = int(frame_file.split('_')[1].split('.')[0])
-        frame_path = os.path.join(frames_folder, frame_file)
-        frame = cv2.imread(frame_path)
-
-        if frame is not None:
-            posture_score, posture_landmarks = calculate_posture_score(frame)
-            posture_scores_by_frame[frame_num] = posture_score
-            posture_landmarks_by_frame[frame_num] = posture_landmarks
-
-            boxes, probs = mtcnn.detect(frame)
-
-            if boxes is not None and len(boxes) > 0 and probs[0] >= 0.99:
-                x1, y1, x2, y2 = [int(b) for b in boxes[0]]
-                face = frame[y1:y2, x1:x2]
-                if face.size > 0:
-                    face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
-                    results = face_mesh.process(face_rgb)
-                    if results.multi_face_landmarks:
-                        facial_landmarks_by_frame[frame_num] = results.multi_face_landmarks[0]
-                        if is_frontal_face(results.multi_face_landmarks[0].landmark):
-                            aligned_face = face
-
-                            if aligned_face is not None:
-                                aligned_face_resized = cv2.resize(aligned_face, (160, 160))
-                                output_path = os.path.join(aligned_faces_folder, f"frame_{frame_num}_face.jpg")
-                                cv2.imwrite(output_path, aligned_face_resized)
-                                aligned_face_paths.append(output_path)
-                                embedding = get_face_embedding(aligned_face_resized)
-                                embeddings_by_frame[frame_num] = embedding
-
-        progress((i + 1) / len(frame_files), f"Processing frame {i + 1} of {len(frame_files)}")
-
-    return embeddings_by_frame, posture_scores_by_frame, posture_landmarks_by_frame, aligned_face_paths, facial_landmarks_by_frame
-
-
 def process_video(video_path, anomaly_threshold, desired_fps, progress=None):
     start_time = time.time()
     output_folder = "output"
@@ -217,8 +173,7 @@ def process_video(video_path, anomaly_threshold, desired_fps, progress=None):
                 output_folder = "output"
                 heatmap_video_path = create_video_with_heatmap(
                     video_path, df, mse_embeddings, mse_posture, mse_voice,
-                    output_folder, original_fps, largest_cluster,
-                    progress=progress
+                    output_folder, original_fps, largest_cluster
                 )
                 
                 if heatmap_video_path is None: