Merge pull request #1038 from serengil/feat-task-2402-bug-fixes-and-improvements

Feat task 2402 bug fixes and improvements

deepface/DeepFace.py

@@ -19,7 +19,7 @@ from deepface.modules import (
     recognition,
     demography,
     detection,
-    realtime,
+    streaming,
 )
 from deepface import __version__
 
@@ -409,7 +409,7 @@ def stream(
     time_threshold = max(time_threshold, 1)
     frame_threshold = max(frame_threshold, 1)
 
-    realtime.analysis(
+    streaming.analysis(
         db_path=db_path,
         model_name=model_name,
         detector_backend=detector_backend,

deepface/detectors/Yolo.py

@@ -43,8 +43,15 @@ class YoloClient(Detector):
 
         # Download the model's weights if they don't exist
         if not os.path.isfile(weight_path):
-            gdown.download(WEIGHT_URL, weight_path, quiet=False)
-            logger.info(f"Downloaded YOLO model {os.path.basename(weight_path)}")
+            logger.info(f"Downloading Yolo weights from {WEIGHT_URL} to {weight_path}...")
+            try:
+                gdown.download(WEIGHT_URL, weight_path, quiet=False)
+            except Exception as err:
+                raise ValueError(
+                    f"Exception while downloading Yolo weights from {WEIGHT_URL}."
+                    f"You may consider to download it to {weight_path} manually."
+                ) from err
+            logger.info(f"Yolo model is just downloaded to {os.path.basename(weight_path)}")
 
         # Return face_detector
         return YOLO(weight_path)

deepface/modules/detection.py

@@ -76,6 +76,9 @@ def extract_faces(
     # img might be path, base64 or numpy array. Convert it to numpy whatever it is.
     img, img_name = preprocessing.load_image(img_path)
 
+    if img is None:
+        raise ValueError(f"Exception while loading {img_name}")
+
     base_region = FacialAreaRegion(x=0, y=0, w=img.shape[1], h=img.shape[0], confidence=0)
 
     if detector_backend == "skip":

deepface/modules/streaming.py

@@ -91,7 +91,7 @@ def analysis(
                 faces = []
                 for face_obj in face_objs:
                     facial_area = face_obj["facial_area"]
-                    if facial_area["w"] <= 130: # discard small detected faces
+                    if facial_area["w"] <= 130:  # discard small detected faces
                         continue
                     faces.append(
                         (
@@ -176,7 +176,7 @@ def analysis(
 
                             demographies = DeepFace.analyze(
                                 img_path=custom_face,
-                                detector_backend=detector_backend,
+                                detector_backend="skip",
                                 enforce_detection=False,
                                 silent=True,
                             )
@@ -411,7 +411,7 @@ def analysis(
                             img_path=custom_face,
                             db_path=db_path,
                             model_name=model_name,
-                            detector_backend=detector_backend,
+                            detector_backend="skip",
                             distance_metric=distance_metric,
                             enforce_detection=False,
                             silent=True,
@@ -429,7 +429,7 @@ def analysis(
                                 display_img = cv2.imread(label)
                                 # to use extracted face
                                 source_objs = DeepFace.extract_faces(
-                                    img_path=label,
+                                    img_path=display_img,
                                     target_size=(pivot_img_size, pivot_img_size),
                                     detector_backend=detector_backend,
                                     enforce_detection=False,