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https://github.com/open-mmlab/mmdeploy.git
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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>
86 lines
2.9 KiB
Python
86 lines
2.9 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 Detector, 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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'det_model_path',
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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_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 input image')
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args = parser.parse_args()
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return args
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def visualize(frame, keypoints, filename, 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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scores = keypoints[..., 2]
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keypoints = (keypoints[..., :2] * scale).astype(int)
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img = cv2.resize(frame, (0, 0), fx=scale, fy=scale)
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for kpts, score in zip(keypoints, scores):
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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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cv2.imwrite(filename, img)
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def main():
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args = parse_args()
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# load image
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img = cv2.imread(args.image_path)
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# create object detector
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detector = Detector(
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model_path=args.det_model_path, device_name=args.device_name)
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# create pose detector
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pose_detector = PoseDetector(
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model_path=args.pose_model_path, device_name=args.device_name)
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# apply detector
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bboxes, labels, _ = detector(img)
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# filter detections
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keep = np.logical_and(labels == 0, bboxes[..., 4] > 0.6)
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bboxes = bboxes[keep, :4]
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# apply pose detector
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poses = pose_detector(img, bboxes)
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visualize(img, poses, 'det_pose_output.jpg', 0.5, 1280)
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if __name__ == '__main__':
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main()
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