mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
1f56eea807
* add feature map output for mmseg * update api * update demo * fix return * update format_shape * fix lint * update csharp demo * update python demo && api * fix coreml build * fix lint * better sort * update * update cpp demo & add missing header * change to CHW * update csharp api * update isort version to 5.12.0 * fix python api * fix log * more detail api docs * isort support python3.7 * remove isort change * remove whitespace * axes check * remove FormatShapeImpl * minor * add permute tc * remove stride buffer (cherry picked from commit b85f34141b61ad0d70897cc6dcfef38928b673fb)
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 Segmentor
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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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args = parser.parse_args()
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return args
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def get_palette(num_classes=256):
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state = np.random.get_state()
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# random color
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np.random.seed(42)
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palette = np.random.randint(0, 256, size=(num_classes, 3))
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np.random.set_state(state)
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return [tuple(c) for c in palette]
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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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segmentor = Segmentor(
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model_path=args.model_path, device_name=args.device_name, device_id=0)
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seg = segmentor(img)
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if seg.dtype == np.float32:
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seg = np.argmax(seg, axis=0)
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palette = get_palette()
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
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for label, color in enumerate(palette):
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color_seg[seg == label, :] = color
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# convert to BGR
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color_seg = color_seg[..., ::-1]
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img = img * 0.5 + color_seg * 0.5
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img = img.astype(np.uint8)
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cv2.imwrite('output_segmentation.png', img)
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if __name__ == '__main__':
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main()
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