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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
52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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from math import cos, sin
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import cv2
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import numpy as np
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from mmdeploy_python import RotatedDetector
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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', help='path of SDK model dumped by model converter')
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parser.add_argument('image_path', help='path of an image')
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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 = RotatedDetector(
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model_path=args.model_path, device_name=args.device_name, device_id=0)
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rbboxes, labels = detector(img)
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indices = [i for i in range(len(rbboxes))]
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for index, rbbox, label_id in zip(indices, rbboxes, labels):
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[cx, cy, w, h, angle], score = rbbox[0:5], rbbox[-1]
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if score < 0.1:
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continue
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[wx, wy, hx, hy] = \
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0.5 * np.array([w, w, -h, h]) * \
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np.array([cos(angle), sin(angle), sin(angle), cos(angle)])
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points = np.array([[[int(cx - wx - hx),
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int(cy - wy - hy)],
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[int(cx + wx - hx),
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int(cy + wy - hy)],
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[int(cx + wx + hx),
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int(cy + wy + hy)],
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[int(cx - wx + hx),
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int(cy - wy + hy)]]])
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cv2.drawContours(img, points, -1, (0, 255, 0), 2)
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cv2.imwrite('output_detection.png', img)
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if __name__ == '__main__':
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main()
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