mirror of
https://github.com/open-mmlab/mmdeploy.git
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2c18fbd2c8
* support-kwargs * make '__call__' as single image inference and add 'batch' API to deal with batch images inference * fix linting error and typo * fix lint
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import cv2
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import numpy as np
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from mmdeploy_python import PoseDetector
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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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'model_path',
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help='path of mmdeploy SDK model dumped by model converter')
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parser.add_argument('image_path', help='path of an image')
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parser.add_argument(
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'--bbox',
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default=None,
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nargs='+',
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type=int,
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help='bounding box of an object in format (x, y, w, h)')
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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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img = cv2.imread(args.image_path)
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detector = PoseDetector(
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model_path=args.model_path, device_name=args.device_name, device_id=0)
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if args.bbox is None:
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result = detector(img)
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else:
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# converter (x, y, w, h) -> (left, top, right, bottom)
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print(args.bbox)
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bbox = np.array(args.bbox, dtype=int)
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bbox[2:] += bbox[:2]
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result = detector(img, bbox)
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print(result)
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_, point_num, _ = result.shape
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points = result[:, :, :2].reshape(point_num, 2)
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for [x, y] in points.astype(int):
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cv2.circle(img, (x, y), 1, (0, 255, 0), 2)
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cv2.imwrite('output_pose.png', img)
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if __name__ == '__main__':
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main()
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