Update video_processing.py

video_processing.py

@@ -53,7 +53,7 @@ def process_frames(frames_folder, faces_folder, frame_count, progress):
     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)
+        frame = cv2.cvtColor(cv2.imread(frame_path), cv2.COLOR_BGR2RGB)
 
         if frame is not None:
             posture_score, posture_landmarks = calculate_posture_score(frame)
@@ -66,10 +66,10 @@ def process_frames(frames_folder, faces_folder, frame_count, progress):
                 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)
-                    face_resized = cv2.resize(face_rgb, (160, 160))
+                    face_resized = cv2.resize(face, (160, 160))
                     output_path = os.path.join(faces_folder, f"frame_{frame_num}_face.jpg")
                     cv2.imwrite(output_path, face_resized)
+                    cv2.imwrite(output_path, cv2.cvtColor(face_resized, cv2.COLOR_RGB2BGR))
                     face_paths.append(output_path)
                     embedding = get_face_embedding(face_resized)
                     embeddings_by_frame[frame_num] = embedding
@@ -309,10 +309,10 @@ def get_all_face_samples(organized_faces_folder, output_folder, largest_cluster,
                     for i, sample in enumerate(face_files[:max_samples]):
                         face_path = os.path.join(person_folder, sample)
                         output_path = os.path.join(output_folder, f"face_sample_most_frequent_{i:04d}.jpg")
-                        face_img = cv2.imread(face_path)
+                        face_img = cv2.cvtColor(cv2.imread(face_path), cv2.COLOR_BGR2RGB)
                         if face_img is not None:
                             small_face = cv2.resize(face_img, (160, 160))
-                            cv2.imwrite(output_path, small_face)
+                            cv2.imwrite(output_path, cv2.cvtColor(small_face, cv2.COLOR_RGB2BGR))
                             face_samples["most_frequent"].append(output_path)
                         if len(face_samples["most_frequent"]) >= max_samples:
                             break
@@ -322,10 +322,10 @@ def get_all_face_samples(organized_faces_folder, output_folder, largest_cluster,
                         for i, sample in enumerate(face_files[:remaining_samples]):
                             face_path = os.path.join(person_folder, sample)
                             output_path = os.path.join(output_folder, f"face_sample_other_{cluster_id:02d}_{i:04d}.jpg")
-                            face_img = cv2.imread(face_path)
+                            face_img = cv2.cvtColor(cv2.imread(face_path), cv2.COLOR_BGR2RGB)
                             if face_img is not None:
                                 small_face = cv2.resize(face_img, (160, 160))
-                                cv2.imwrite(output_path, small_face)
+                                cv2.imwrite(output_path, cv2.cvtColor(small_face, cv2.COLOR_RGB2BGR))
                                 face_samples["others"].append(output_path)
                             if len(face_samples["others"]) >= max_samples:
                                 break