mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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(
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"--model_file", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--config_file", required=True, help="Path of PaddleSeg config.")
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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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def build_option(args):
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option = fd.RuntimeOption()
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option.use_rknpu2()
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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_file = args.model_file
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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.RKNN)
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model.disable_normalize_and_permute()
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# 预测图片分割结果
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im = cv2.imread(args.image)
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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_segmentation(im, result, weight=0.5)
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cv2.imwrite("vis_img.png", vis_im)
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