Update video_processing.py

video_processing.py

@@ -21,7 +21,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 mtcnn = MTCNN(keep_all=False, device=device, thresholds=[0.9, 0.9, 0.9], min_face_size=50)
 
 mp_face_mesh = mp.solutions.face_mesh
-face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5)
+face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.6)
 
 mp_pose = mp.solutions.pose
 pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.6, min_tracking_confidence=0.6)
@@ -52,6 +52,7 @@ 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')])
 
@@ -64,7 +65,9 @@ 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:
@@ -85,8 +88,8 @@ def process_frames(frames_folder, aligned_faces_folder, frame_count, progress):
 
         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
-
+    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"