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* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
import fastdeploy as fd
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import cv2
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import os
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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("--model", required=True, help="Path of model.")
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parser.add_argument(
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"--config_file", required=True, help="Path of config file.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = fd.RuntimeOption()
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runtime_option.use_sophgo()
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model_file = args.model
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params_file = ""
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config_file = args.config_file
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model = fd.vision.segmentation.PaddleSegModel(
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model_file,
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params_file,
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config_file,
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runtime_option=runtime_option,
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model_format=fd.ModelFormat.SOPHGO)
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# 预测图片分类结果
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im_org = cv2.imread(args.image)
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#bmodel 是静态模型，模型输入固定，这里设置为[512, 512]
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im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR)
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result = model.predict(im)
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print(result)
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# 预测结果可视化
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vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
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cv2.imwrite("sophgo_img.png", vis_im)
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