Update video_processing.py

video_processing.py

@@ -65,31 +65,33 @@ def process_frames(frames_folder, aligned_faces_folder, frame_count, progress):
             posture_score, posture_landmarks = calculate_posture_score(frame)
             posture_scores_by_frame[frame_num] = posture_score
             posture_landmarks_by_frame[frame_num] = posture_landmarks
-            if results.multi_face_landmarks:
-                facial_landmarks_by_frame[frame_num] = results.multi_face_landmarks[0]
-                
+
             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:
-                    results = face_mesh.process(cv2.cvtColor(face, cv2.COLOR_BGR2RGB))
-                    if results.multi_face_landmarks and 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
+                    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"