import numpy as np
import cv2
import supervision as sv
from ultralytics import RTDETR
def process_video_with_cmc():
 # Setup models, video paths, etc.
 helmet_model = RTDETR(<HELMET_MODEL_PATH>)
 clip_path = <VIDEO_PATH>
 output_path = "result.mp4"
 # Initialize tools
 box_annotator = sv.BoxAnnotator()
 label_annotator = sv.LabelAnnotator()
 
 # Initialize tracker and wrap it with CameraMotionCompensator
 byte_tracker = sv.ByteTrack(frame_rate=<FRAME_RATE>)
 cmc = sv.CameraMotionCompensator(tracker=byte_tracker)
 cmc.reset()
 def callback(frame: np.ndarray, index: int) -> np.ndarray:
 # 1. Perform detection
 results = helmet_model(frame)[0]
 detections = sv.Detections.from_ultralytics(results)
 # 2. Update with detections using the simple wrapper method
 final_detections = cmc.update_with_detections(detections, frame=frame)
 # 3. Annotation
 labels = [
 f"#{tracker_id} {class_name} {confidence:.2f}"
 for class_name, tracker_id, confidence
 in zip(final_detections.data["class_name"], final_detections.tracker_id, final_detections.confidence)
 ]
 annotated_frame = box_annotator.annotate(frame.copy(), detections=final_detections)
 return label_annotator.annotate(annotated_frame, detections=final_detections, labels=labels)
 sv.process_video(
 source_path=clip_path,
 target_path=output_path,
 callback=callback
 )
if __name__ == "__main__":
 process_video_with_cmc()