[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

examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/README.md

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+[English](README.md) | 简体中文
+# PaddleSeg 算能 Python部署示例
+
+## 1. 部署环境准备
+
+在部署前，需自行编译基于算能硬件的FastDeploy python wheel包并安装，参考文档[算能硬件部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#算能硬件部署环境)
+
+本目录下提供`infer.py`快速完成 pp_liteseg 在SOPHGO TPU上部署的示例。执行如下脚本即可完成
+
+## 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](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)。
+
+## 3. 运行部署示例
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/semantic_segmentation/sophgo/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/sophgo/python
+
+# 下载图片
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# PaddleSeg模型转换为bmodel模型
+将Paddle模型转换为SOPHGO bmodel模型，转换步骤参考[文档](../README_CN.md)
+
+# 推理
+python3 infer.py --model_file ./bmodel/pp_liteseg_1684x_f32.bmodel --config_file ./bmodel/deploy.yaml --image cityscapes_demo.png
+
+# 运行完成后返回结果如下所示
+运行结果保存在sophgo_img.png中
+```
+
+## 4. 更多指南
+- [PP-LiteSeg SOPHGO C++部署](../cpp)
+- [转换 PP-LiteSeg SOPHGO模型文档](../README.md)
+
+## 5. 常见问题
+- [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)

examples/vision/segmentation/paddleseg/semantic_segmentation/sophgo/python/infer.py

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+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=True, help="Path of model.")
+    parser.add_argument(
+        "--config_file", required=True, help="Path of config file.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+
+    return parser.parse_args()
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = fd.RuntimeOption()
+runtime_option.use_sophgo()
+
+model_file = args.model
+params_file = ""
+config_file = args.config_file
+
+model = fd.vision.segmentation.PaddleSegModel(
+    model_file,
+    params_file,
+    config_file,
+    runtime_option=runtime_option,
+    model_format=fd.ModelFormat.SOPHGO)
+
+# 预测图片分类结果
+im_org = cv2.imread(args.image)
+#bmodel 是静态模型，模型输入固定，这里设置为[512, 512]
+im = cv2.resize(im_org, [512, 512], interpolation=cv2.INTER_LINEAR)
+result = model.predict(im)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+cv2.imwrite("sophgo_img.png", vis_im)