Ultralytics Code Refactor https://ultralytics.com/actions (#14109)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

examples/YOLOv8-Action-Recognition/action_recognition.py

@@ -69,7 +69,7 @@ class TorchVisionVideoClassifier:
         """
         return list(TorchVisionVideoClassifier.model_name_to_model_and_weights.keys())
 
-    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = [224, 224]) -> torch.Tensor:
+    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = None) -> torch.Tensor:
         """
         Preprocess a list of crops for video classification.
 
@@ -80,6 +80,8 @@ class TorchVisionVideoClassifier:
         Returns:
             torch.Tensor: Preprocessed crops as a tensor with dimensions (1, T, C, H, W).
         """
+        if input_size is None:
+            input_size = [224, 224]
         from torchvision.transforms import v2
 
         transform = v2.Compose(
@@ -156,7 +158,7 @@ class HuggingFaceVideoClassifier:
             model = model.half()
         self.model = model.eval()
 
-    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = [224, 224]) -> torch.Tensor:
+    def preprocess_crops_for_video_cls(self, crops: List[np.ndarray], input_size: list = None) -> torch.Tensor:
         """
         Preprocess a list of crops for video classification.
 
@@ -167,6 +169,8 @@ class HuggingFaceVideoClassifier:
         Returns:
             torch.Tensor: Preprocessed crops as a tensor (1, T, C, H, W).
         """
+        if input_size is None:
+            input_size = [224, 224]
         from torchvision.transforms import v2
 
         transform = v2.Compose(
@@ -266,15 +270,7 @@ def run(
     video_cls_overlap_ratio: float = 0.25,
     fp16: bool = False,
     video_classifier_model: str = "microsoft/xclip-base-patch32",
-    labels: List[str] = [
-        "walking",
-        "running",
-        "brushing teeth",
-        "looking into phone",
-        "weight lifting",
-        "cooking",
-        "sitting",
-    ],
+    labels: List[str] = None,
 ) -> None:
     """
     Run action recognition on a video source using YOLO for object detection and a video classifier.
@@ -295,6 +291,16 @@ def run(
     Returns:
         None</edit>
     """
+    if labels is None:
+        labels = [
+            "walking",
+            "running",
+            "brushing teeth",
+            "looking into phone",
+            "weight lifting",
+            "cooking",
+            "sitting",
+        ]
     # Initialize models and device
     device = select_device(device)
     yolo_model = YOLO(weights).to(device)
@@ -312,9 +318,7 @@ def run(
     # Initialize video capture
     if source.startswith("http") and urlparse(source).hostname in {"www.youtube.com", "youtube.com", "youtu.be"}:
         source = get_best_youtube_url(source)
-    elif source.endswith(".mp4"):
-        pass
-    else:
+    elif not source.endswith(".mp4"):
         raise ValueError("Invalid source. Supported sources are YouTube URLs and MP4 files.")
     cap = cv2.VideoCapture(source)
 

examples/YOLOv8-OpenCV-int8-tflite-Python/main.py

@@ -18,6 +18,7 @@ class LetterBox:
     def __init__(
         self, new_shape=(img_width, img_height), auto=False, scaleFill=False, scaleup=True, center=True, stride=32
     ):
+        """Initializes LetterBox with parameters for reshaping and transforming image while maintaining aspect ratio."""
         self.new_shape = new_shape
         self.auto = auto
         self.scaleFill = scaleFill