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DefTruth 5b143219ce [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

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PaddleSeg Ascend NPU Python部署示例

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。

1. 部署环境准备

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

2. 部署模型准备

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或者自行导出PaddleSeg部署模型，如果你部署的为PP-Matting、PP-HumanMatting以及ModNet请参考Matting模型部署。

3. 运行部署示例

# 下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/semantic_segmentation/ascend/python
# 如果您希望从PaddleSeg下载示例代码，请运行
# git clone https://github.com/PaddlePaddle/PaddleSeg.git
# # 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
# # git checkout develop
# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/ascend/python

# 下载PP-LiteSeg模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

# 华为昇腾推理
python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png

运行完成可视化结果如下图所示

4. 更多指南

5. 常见问题

如何将模型预测结果SegmentationResult转为numpy格式

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