99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import cv2
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from mmdeploy_runtime import PoseTracker
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def parse_args():
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parser = argparse.ArgumentParser(
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description='show how to use SDK Python API')
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parser.add_argument('device_name', help='name of device, cuda or cpu')
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parser.add_argument(
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'det_model',
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help='path of mmdeploy SDK model dumped by model converter')
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parser.add_argument(
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'pose_model',
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help='path of mmdeploy SDK model dumped by model converter')
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parser.add_argument('video', help='video path or camera index')
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parser.add_argument('--output_dir', help='output directory', default=None)
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args = parser.parse_args()
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if args.video.isnumeric():
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args.video = int(args.video)
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return args
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def visualize(frame, results, output_dir, frame_id, thr=0.5, resize=1280):
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skeleton = [(15, 13), (13, 11), (16, 14), (14, 12), (11, 12), (5, 11),
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(6, 12), (5, 6), (5, 7), (6, 8), (7, 9), (8, 10), (1, 2),
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(0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6)]
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palette = [(255, 128, 0), (255, 153, 51), (255, 178, 102), (230, 230, 0),
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(255, 153, 255), (153, 204, 255), (255, 102, 255),
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(255, 51, 255), (102, 178, 255),
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(51, 153, 255), (255, 153, 153), (255, 102, 102), (255, 51, 51),
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(153, 255, 153), (102, 255, 102), (51, 255, 51), (0, 255, 0),
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(0, 0, 255), (255, 0, 0), (255, 255, 255)]
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link_color = [
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0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16
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]
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point_color = [16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0]
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scale = resize / max(frame.shape[0], frame.shape[1])
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keypoints, bboxes, _ = results
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scores = keypoints[..., 2]
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keypoints = (keypoints[..., :2] * scale).astype(int)
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bboxes *= scale
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img = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
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for kpts, score, bbox in zip(keypoints, scores, bboxes):
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show = [0] * len(kpts)
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for (u, v), color in zip(skeleton, link_color):
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if score[u] > thr and score[v] > thr:
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cv2.line(img, kpts[u], tuple(kpts[v]), palette[color], 1,
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cv2.LINE_AA)
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show[u] = show[v] = 1
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for kpt, show, color in zip(kpts, show, point_color):
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if show:
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cv2.circle(img, kpt, 1, palette[color], 2, cv2.LINE_AA)
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if output_dir:
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cv2.imwrite(f'{output_dir}/{str(frame_id).zfill(6)}.jpg', img)
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else:
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cv2.imshow('pose_tracker', img)
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return cv2.waitKey(1) != 'q'
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return True
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def main():
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args = parse_args()
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video = cv2.VideoCapture(args.video)
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tracker = PoseTracker(
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det_model=args.det_model,
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pose_model=args.pose_model,
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device_name=args.device_name)
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# optionally use OKS for keypoints similarity comparison
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coco_sigmas = [
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0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
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0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
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]
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state = tracker.create_state(
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det_interval=1, det_min_bbox_size=100, keypoint_sigmas=coco_sigmas)
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if args.output_dir:
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os.makedirs(args.output_dir, exist_ok=True)
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frame_id = 0
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while True:
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success, frame = video.read()
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if not success:
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break
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results = tracker(state, frame, detect=-1)
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if not visualize(frame, results, args.output_dir, frame_id):
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break
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frame_id += 1
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if __name__ == '__main__':
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main()
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