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[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)
* [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
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[English](README.md) | 简体中文
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# PaddleSeg Ascend NPU Python部署示例
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本目录下提供`infer.py`快速完成PP-LiteSeg在华为昇腾上部署的示例。
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## 1. 部署环境准备
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在部署前,需自行编译基于华为昇腾NPU的FastDeploy python wheel包并安装,参考文档[华为昇腾NPU部署环境编译](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
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## 2. 部署模型准备
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在部署前,请准备好您所需要运行的推理模型,你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md),如果你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)。
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## 3. 运行部署示例
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/semantic_segmentation/ascend/python
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# 如果您希望从PaddleSeg下载示例代码,请运行
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# git clone https://github.com/PaddlePaddle/PaddleSeg.git
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# # 注意:如果当前分支找不到下面的fastdeploy测试代码,请切换到develop分支
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# # git checkout develop
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# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/ascend/python
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# 下载PP-LiteSeg模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# 华为昇腾推理
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python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png
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```
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运行完成可视化结果如下图所示
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
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</div>
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## 4. 更多指南
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- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
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- [FastDeploy部署PaddleSeg模型概览](..)
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- [PaddleSeg C++部署](../cpp)
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## 5. 常见问题
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- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
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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", required=True, help="Path of PaddleSeg model.")
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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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runtime_option = fd.RuntimeOption()
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runtime_option.use_ascend()
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# setup runtime
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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model = fd.vision.segmentation.PaddleSegModel(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# predict
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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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# visualize
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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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