solved pr comments: restore comments and used one line if

deepface/DeepFace.py

@@ -450,7 +450,7 @@ def stream(
     time_threshold: int = 5,
     frame_threshold: int = 5,
     anti_spoofing: bool = False,
-    output_path: Optional[str] = None,  # New parameter
+    output_path: Optional[str] = None,
 ) -> None:
     """
     Run real time face recognition and facial attribute analysis
@@ -480,7 +480,7 @@ def stream(
 
         anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
 
-        output_path (str): Path to save the output video. If None, no video is saved.
+        output_path (str): Path to save the output video. If None, no video is saved (default is None).
 
     Returns:
         None
@@ -499,7 +499,7 @@ def stream(
         time_threshold=time_threshold,
         frame_threshold=frame_threshold,
         anti_spoofing=anti_spoofing,
-        output_path=output_path,  # Pass the output_path to analysis
+        output_path=output_path,
     )
 
 

deepface/modules/streaming.py

@@ -34,29 +34,45 @@ def analysis(
     time_threshold=5,
     frame_threshold=5,
     anti_spoofing: bool = False,
-    output_path: Optional[str] = None,  # New parameter
+    output_path: Optional[str] = None,
 ):
     """
-    Run real-time face recognition and facial attribute analysis, with optional video output.
+    Run real time face recognition and facial attribute analysis
 
     Args:
-        db_path (str): Path to the folder containing image files.
+        db_path (string): Path to the folder containing image files. All detected faces
-        model_name (str): Model for face recognition.
+            in the database will be considered in the decision-making process.
-        detector_backend (str): Face detector backend.
+
-        distance_metric (str): Metric for measuring similarity.
+        model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
-        enable_face_analysis (bool): Flag to enable face analysis.
+            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face)
-        source (Any): The source for the video stream (camera index or video file path).
+
-        time_threshold (int): Time threshold (in seconds) for face recognition.
+        detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
-        frame_threshold (int): Frame threshold for face recognition.
+            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
-        anti_spoofing (bool): Flag to enable anti-spoofing.
+            'centerface' or 'skip' (default is opencv).
-        output_path (str): Path to save the output video. If None, no video is saved.
+
+        distance_metric (string): Metric for measuring similarity. Options: 'cosine',
+            'euclidean', 'euclidean_l2' (default is cosine).
+
+        enable_face_analysis (bool): Flag to enable face analysis (default is True).
+
+        source (Any): The source for the video stream (default is 0, which represents the
+            default camera).
+
+        time_threshold (int): The time threshold (in seconds) for face recognition (default is 5).
+
+        frame_threshold (int): The frame threshold for face recognition (default is 5).
+
+        anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
+
+        output_path (str): Path to save the output video. If None, no video is saved (default is None).
 
     Returns:
         None
     """
-    # Initialize models
+    # initialize models
     build_demography_models(enable_face_analysis=enable_face_analysis)
     build_facial_recognition_model(model_name=model_name)
+    # call a dummy find function for db_path once to create embeddings before starting webcam
     _ = search_identity(
         detected_face=np.zeros([224, 224, 3]),
         db_path=db_path,
@@ -77,9 +93,11 @@ def analysis(
     fourcc = cv2.VideoWriter_fourcc(*"mp4v")  # Codec for output file
 
     # Initialize video writer if output_path is provided
-    video_writer = None
+    video_writer = (
-    if output_path:
+        cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
-        video_writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        if output_path
+        else None
+    )
 
     freezed_img = None
     freeze = False
@@ -91,6 +109,8 @@ def analysis(
         if not has_frame:
             break
 
+        # we are adding some figures into img such as identified facial image, age, gender
+        # that is why, we need raw image itself to make analysis
         raw_img = img.copy()
         faces_coordinates = []
 
@@ -98,6 +118,9 @@ def analysis(
             faces_coordinates = grab_facial_areas(
                 img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing
             )
+
+            # we will pass img to analyze modules (identity, demography) and add some illustrations
+            # that is why, we will not be able to extract detected face from img clearly
             detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
             img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
             img = countdown_to_freeze(
@@ -111,15 +134,19 @@ def analysis(
             freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
 
             if freeze:
+                # add analyze results into img - derive from raw_img
                 img = highlight_facial_areas(
                     img=raw_img, faces_coordinates=faces_coordinates, anti_spoofing=anti_spoofing
                 )
+
+                # age, gender and emotion analysis
                 img = perform_demography_analysis(
                     enable_face_analysis=enable_face_analysis,
                     img=raw_img,
                     faces_coordinates=faces_coordinates,
                     detected_faces=detected_faces,
                 )
+                # facial recogntion analysis
                 img = perform_facial_recognition(
                     img=img,
                     faces_coordinates=faces_coordinates,
@@ -129,13 +156,18 @@ def analysis(
                     distance_metric=distance_metric,
                     model_name=model_name,
                 )
+
+                # freeze the img after analysis
                 freezed_img = img.copy()
+
+                # start counter for freezing
                 tic = time.time()
-                logger.info("Image frozen for analysis")
+                logger.info("freezed")
 
         elif freeze and time.time() - tic > time_threshold:
             freeze = False
             freezed_img = None
+            # reset counter for freezing
             tic = time.time()
             logger.info("Freeze released")
 
@@ -222,10 +254,10 @@ def search_identity(
     # detected face is coming from parent, safe to access 1st index
     df = dfs[0]
 
-    if df.shape[0] == 0:
+    if df.shape[0] == 0:  # type: ignore
         return None, None
 
-    candidate = df.iloc[0]
+    candidate = df.iloc[0]  # type: ignore
     target_path = candidate["identity"]
     logger.info(f"Hello, {target_path}")