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aae9f32623
* [Feature]: Add github prebuild workflow after new release. (#1852) * add prebuild dockerfile * add prebuild test workflw * update * update * rm other workflow for test * Update docker image * add win1o prebuild * add test prebuild * add windows scripts in prebuilt package * add linux scripts in prebuilt package * generate_build_config.py * fix cudnn search * fix env * fix script * fix rpath * fix cwd * fix windows * fix lint * windows prebuild ci * linux prebuild ci * fix * update trigger * Revert "rm other workflow for test" This reverts commit 0a0387275014efab71046d33a0e52904672b4012. * update sdk build readme * update prebuild * fix dll deps for python >= 3.8 on windows * fix ci * test prebuild * update test script to avoid modify upload folder * add onnxruntime.dll to mmdeploy_python * update prebuild workflow * update prebuild * Update loader.cpp.in * remove exists prebuild files * fix opencv env * update cmake options for mmdeploy python build * remove test code * fix lint --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: RunningLeon <maningsheng@sensetime.com> * rename mmdeploy_python -> mmdeploy_runtime * test master prebuild * fix trt net build * Revert "test master prebuild" This reverts commit aad5258648f5f2c410c965b295c309fd1166da22. * add master branch * fix linux set_env script * update package_tools docs * fix gcc 7.3 aligned_alloc * comment temporarily as text_det_recog can't be built with prebuild package built under manylinux --------- Co-authored-by: RunningLeon <mnsheng@yeah.net> Co-authored-by: RunningLeon <maningsheng@sensetime.com>
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_runtime 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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