mirror of https://github.com/open-mmlab/mmyolo.git
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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"""Perform MMYOLO inference on a video as:
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```shell
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wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth # noqa: E501, E261.
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python demo/video_demo.py \
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demo/video_demo.mp4 \
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configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py \
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checkpoint/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth \
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--out demo_result.mp4
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```
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"""
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import argparse
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import cv2
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import mmcv
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from mmcv.transforms import Compose
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from mmdet.apis import inference_detector, init_detector
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from mmengine.utils import track_iter_progress
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from mmyolo.registry import VISUALIZERS
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def parse_args():
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parser = argparse.ArgumentParser(description='MMYOLO video demo')
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parser.add_argument('video', help='Video file')
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parser.add_argument('config', help='Config file')
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parser.add_argument('checkpoint', help='Checkpoint file')
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parser.add_argument(
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'--device', default='cuda:0', help='Device used for inference')
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parser.add_argument(
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'--score-thr', type=float, default=0.3, help='Bbox score threshold')
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parser.add_argument('--out', type=str, help='Output video file')
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parser.add_argument('--show', action='store_true', help='Show video')
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parser.add_argument(
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'--wait-time',
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type=float,
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default=1,
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help='The interval of show (s), 0 is block')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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assert args.out or args.show, \
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('Please specify at least one operation (save/show the '
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'video) with the argument "--out" or "--show"')
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# build the model from a config file and a checkpoint file
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model = init_detector(args.config, args.checkpoint, device=args.device)
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# build test pipeline
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model.cfg.test_dataloader.dataset.pipeline[
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0].type = 'mmdet.LoadImageFromNDArray'
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test_pipeline = Compose(model.cfg.test_dataloader.dataset.pipeline)
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# init visualizer
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visualizer = VISUALIZERS.build(model.cfg.visualizer)
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# the dataset_meta is loaded from the checkpoint and
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# then pass to the model in init_detector
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visualizer.dataset_meta = model.dataset_meta
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video_reader = mmcv.VideoReader(args.video)
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video_writer = None
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if args.out:
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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video_writer = cv2.VideoWriter(
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args.out, fourcc, video_reader.fps,
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(video_reader.width, video_reader.height))
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for frame in track_iter_progress(video_reader):
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result = inference_detector(model, frame, test_pipeline=test_pipeline)
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visualizer.add_datasample(
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name='video',
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image=frame,
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data_sample=result,
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draw_gt=False,
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show=False,
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pred_score_thr=args.score_thr)
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frame = visualizer.get_image()
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if args.show:
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cv2.namedWindow('video', 0)
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mmcv.imshow(frame, 'video', args.wait_time)
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if args.out:
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video_writer.write(frame)
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if video_writer:
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video_writer.release()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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