mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 08:21:53 +08:00
huangjianhui
376fdbfe2c
* Update keypointdetection result docs * Update im.copy() to im in examples * Update new Api, fastdeploy::vision::Visualize to fastdeploy::vision * Update SwapBackgroundSegmentation && SwapBackgroundMatting to SwapBackground * Update README_CN.md * Update README_CN.md
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
import fastdeploy as fd
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import cv2
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of modnet onnx model.")
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parser.add_argument(
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"--image", required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--bg",
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type=str,
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required=True,
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default=None,
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help="Path of test background image file.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("input", [1, 3, 256, 256])
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model = fd.vision.matting.MODNet(args.model, runtime_option=runtime_option)
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#设置推理size, 必须和模型文件一致
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model.size = (256, 256)
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# 预测图片抠图结果
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im = cv2.imread(args.image)
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bg = cv2.imread(args.bg)
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result = model.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_matting_alpha(im, result)
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vis_im_with_bg = fd.vision.swap_background(im, bg, result)
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cv2.imwrite("visualized_result_fg.jpg", vis_im)
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cv2.imwrite("visualized_result_replaced_bg.jpg", vis_im_with_bg)
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print(
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"Visualized result save in ./visualized_result_replaced_bg.jpg and ./visualized_result_fg.jpg"
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)
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