Auto-format by https://ultralytics.com/actions

ultralytics/solutions/__init__.py

@@ -1,5 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
+from .action_recognition import ActionRecognition
 from .ai_gym import AIGym
 from .analytics import Analytics
 from .distance_calculation import DistanceCalculation
@@ -9,7 +10,6 @@ from .parking_management import ParkingManagement, ParkingPtsSelection
 from .queue_management import QueueManager
 from .speed_estimation import SpeedEstimator
 from .streamlit_inference import inference
-from.action_recognition import ActionRecognition
 
 __all__ = (
     "AIGym",
@@ -22,5 +22,5 @@ __all__ = (
     "SpeedEstimator",
     "Analytics",
     "inference",
-    "ActionRecognition"
+    "ActionRecognition",
 )

ultralytics/solutions/action_recognition.py

@@ -6,18 +6,26 @@ from typing import List, Tuple
 import cv2
 import numpy as np
 import torch
-from ultralytics.utils.checks import check_imshow
 
+from ultralytics.utils.checks import check_imshow
 from ultralytics.utils.plotting import Annotator
 from ultralytics.utils.torch_utils import select_device
 
 
-
 class ActionRecognition:
     """A class to recognize actions in a real-time video stream based on object tracks."""
 
-    def __init__(self, video_classifier_model="microsoft/xclip-base-patch32", labels=None, fp16=False,
-                 crop_margin_percentage=10, num_video_sequence_samples=8, skip_frame=2, video_cls_overlap_ratio=0.25, device=""):
+    def __init__(
+        self,
+        video_classifier_model="microsoft/xclip-base-patch32",
+        labels=None,
+        fp16=False,
+        crop_margin_percentage=10,
+        num_video_sequence_samples=8,
+        skip_frame=2,
+        video_cls_overlap_ratio=0.25,
+        device="",
+    ):
         """
         Initializes the ActionRecognition with the given parameters.
 
@@ -31,9 +39,11 @@ class ActionRecognition:
             video_cls_overlap_ratio (float, optional): Overlap ratio between video sequences. Defaults to 0.25.
             device (str or torch.device, optional): The device to run the model on. Defaults to "".
         """
-        self.labels = labels if labels is not None else [
-            "walking", "running", "brushing teeth", "looking into phone", "weight lifting", "cooking", "sitting"
-        ]
+        self.labels = (
+            labels
+            if labels is not None
+            else ["walking", "running", "brushing teeth", "looking into phone", "weight lifting", "cooking", "sitting"]
+        )
         self.fp16 = fp16
         self.device = select_device(device)
 
@@ -43,7 +53,9 @@ class ActionRecognition:
 
         if video_classifier_model in TorchVisionVideoClassifier.available_model_names():
             print("'fp16' is not supported for TorchVisionVideoClassifier. Setting fp16 to False.")
-            print("'labels' is not used for TorchVisionVideoClassifier. Ignoring the provided labels and using Kinetics-400 labels.")
+            print(
+                "'labels' is not used for TorchVisionVideoClassifier. Ignoring the provided labels and using Kinetics-400 labels."
+            )
             self.video_classifier = TorchVisionVideoClassifier(video_classifier_model, device=self.device)
         else:
             self.video_classifier = HuggingFaceVideoClassifier(
@@ -164,14 +176,19 @@ class ActionRecognition:
             track_ids_to_infer = []
 
         for box, track_id in zip(self.boxes, self.track_ids):
-            if len(self.track_history[track_id]) == self.num_video_sequence_samples and self.frame_counter % self.skip_frame == 0:
+            if (
+                len(self.track_history[track_id]) == self.num_video_sequence_samples
+                and self.frame_counter % self.skip_frame == 0
+            ):
                 crops = self.video_classifier.preprocess_crops_for_video_cls(self.track_history[track_id])
                 crops_to_infer.append(crops)
                 track_ids_to_infer.append(track_id)
 
         if crops_to_infer and (
             not pred_labels
-            or self.frame_counter % int(self.num_video_sequence_samples * self.skip_frame * (1 - self.video_cls_overlap_ratio)) == 0
+            or self.frame_counter
+            % int(self.num_video_sequence_samples * self.skip_frame * (1 - self.video_cls_overlap_ratio))
+            == 0
         ):
             crops_batch = torch.cat(crops_to_infer, dim=0)
             output_batch = self.predict_action(crops_batch)