Remove ultralytics example multiple video

examples/tracking/ultralytics_example_multiple_videos.py

@@ -1,103 +0,0 @@
-import argparse
-
-from tqdm import tqdm
-from ultralytics import YOLO
-
-import supervision as sv
-
-
-def process_video(
-    source_weights_path: str,
-    source_video_path: str,
-    target_video_path: str,
-    tracker: sv.ByteTrack,
-    confidence_threshold: float = 0.3,
-    iou_threshold: float = 0.7,
-) -> None:
-    model = YOLO(source_weights_path)
-
-    box_annotator = sv.BoundingBoxAnnotator()
-    label_annotator = sv.LabelAnnotator()
-    frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
-    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
-
-    with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
-        for frame in tqdm(frame_generator, total=video_info.total_frames):
-            results = model(
-                frame, verbose=False, conf=confidence_threshold, iou=iou_threshold
-            )[0]
-            detections = sv.Detections.from_ultralytics(results)
-            detections = tracker.update_with_detections(detections)
-
-            labels = [
-                f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
-                for _, _, confidence, class_id, tracker_id, data in detections
-            ]
-
-            annotated_frame = box_annotator.annotate(
-                scene=frame.copy(), detections=detections
-            )
-
-            annotated_labeled_frame = label_annotator.annotate(
-                scene=annotated_frame, detections=detections, labels=labels
-            )
-
-            sink.write_frame(frame=annotated_labeled_frame)
-    # Reset the tracker after processing the video
-    tracker.reset()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Video Processing with YOLO and ByteTrack"
-    )
-    parser.add_argument(
-        "--source_weights_path",
-        required=True,
-        help="Path to the source weights file",
-        type=str,
-    )
-    parser.add_argument(
-        "--source_video_paths",
-        required=True,
-        help="Paths to the source video files",
-        nargs="+",
-        type=str,
-    )
-    parser.add_argument(
-        "--target_video_paths",
-        required=True,
-        help="Paths to the target video files (output)",
-        nargs="+",
-        type=str,
-    )
-    parser.add_argument(
-        "--confidence_threshold",
-        default=0.3,
-        help="Confidence threshold for the model",
-        type=float,
-    )
-    parser.add_argument(
-        "--iou_threshold", default=0.7, help="IOU threshold for the model", type=float
-    )
-
-    args = parser.parse_args()
-
-    source_video_paths = args.source_video_paths[0].split(",")
-    target_video_paths = args.target_video_paths[0].split(",")
-
-    source_video_paths = [path.strip() for path in source_video_paths]
-    target_video_paths = [path.strip() for path in target_video_paths]
-    tracker = sv.ByteTrack()
-
-    for source_video_path, target_video_path in zip(
-        source_video_paths, target_video_paths
-    ):
-        process_video(
-            source_weights_path=args.source_weights_path,
-            source_video_path=source_video_path,
-            target_video_path=target_video_path,
-            confidence_threshold=args.confidence_threshold,
-            iou_threshold=args.iou_threshold,
-            tracker=tracker,
-        )