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67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import os
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from inference.models.utils import get_roboflow_model
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from tqdm import tqdm
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import supervision as sv
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def main(
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source_video_path: str,
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target_video_path: str,
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roboflow_api_key: str,
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model_id: str = "yolov8x-1280",
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confidence_threshold: float = 0.3,
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iou_threshold: float = 0.7,
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) -> None:
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"""
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Video Processing with Inference and ByteTrack.
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Args:
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source_video_path: Path to the source video file
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target_video_path: Path to the target video file (output)
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roboflow_api_key: Roboflow API key
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model_id: Roboflow model ID
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confidence_threshold: Confidence threshold for the model
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iou_threshold: IOU threshold for the model
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"""
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api_key = os.environ.get("ROBOFLOW_API_KEY", roboflow_api_key)
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if api_key is None:
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raise ValueError(
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"Roboflow API key is missing. Please provide it as an argument or set the "
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"ROBOFLOW_API_KEY environment variable."
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)
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model = get_roboflow_model(model_id=model_id, api_key=api_key)
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tracker = sv.ByteTrack()
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box_annotator = sv.BoxAnnotator()
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label_annotator = sv.LabelAnnotator()
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frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
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video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
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with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
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for frame in tqdm(frame_generator, total=video_info.total_frames):
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results = model.infer(
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frame, confidence=confidence_threshold, iou_threshold=iou_threshold
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)[0]
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detections = sv.Detections.from_inference(results)
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detections = tracker.update_with_detections(detections)
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annotated_frame = box_annotator.annotate(
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scene=frame.copy(), detections=detections
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)
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annotated_labeled_frame = label_annotator.annotate(
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scene=annotated_frame, detections=detections
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)
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sink.write_frame(frame=annotated_labeled_frame)
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if __name__ == "__main__":
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from jsonargparse import auto_cli, set_parsing_settings
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set_parsing_settings(parse_optionals_as_positionals=True)
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auto_cli(main, as_positional=False)
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