[Serving] PaddleSeg add triton serving && simple serving example (#1171)

* Update keypointdetection result docs

* Update im.copy() to im in examples

* Update new Api, fastdeploy::vision::Visualize to fastdeploy::vision

* Update SwapBackgroundSegmentation && SwapBackgroundMatting to SwapBackground

* Update README_CN.md

* Update README_CN.md

* Update preprocessor.h

* PaddleSeg supports triton serving

* Add PaddleSeg simple serving example

* Add PaddleSeg triton serving client code

* Update triton serving runtime config.pbtxt

* Update paddleseg grpc client

* Add paddle serving README

examples/vision/segmentation/paddleseg/python/serving/README.md

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+English | [简体中文](README_CN.md)
+
+# PaddleSegmentation Python Simple Serving Demo
+
+
+## Environment
+
+- 1. Prepare environment and install FastDeploy Python whl, refer to [download_prebuilt_libraries](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+
+Server:
+```bash
+# Download demo code
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
+
+# Download PP_LiteSeg model
+wget  https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
+tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
+
+# Launch server, change the configurations in server.py to select hardware, backend, etc.
+# and use --host, --port to specify IP and port
+fastdeploy simple_serving --app server:app
+```
+
+Client:
+```bash
+# Download demo code
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
+
+# Download test image
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# Send request and get inference result (Please adapt the IP and port if necessary)
+python client.py
+```

examples/vision/segmentation/paddleseg/python/serving/README_CN.md

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+简体中文 | [English](README.md)
+
+# PaddleSegmentation Python轻量服务化部署示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+服务端：
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
+
+# 下载PP_LiteSeg模型文件
+wget  https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
+tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
+
+# 启动服务，可修改server.py中的配置项来指定硬件、后端等
+# 可通过--host、--port指定IP和端口号
+fastdeploy simple_serving --app server:app
+```
+
+客户端：
+```bash
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/detection/paddledetection/python/serving
+
+# 下载测试图片
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# 请求服务，获取推理结果（如有必要，请修改脚本中的IP和端口号）
+python client.py
+```

examples/vision/segmentation/paddleseg/python/serving/client.py

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+import requests
+import json
+import cv2
+import fastdeploy as fd
+from fastdeploy.serving.utils import cv2_to_base64
+
+if __name__ == '__main__':
+    url = "http://127.0.0.1:8000/fd/ppliteseg"
+    headers = {"Content-Type": "application/json"}
+
+    im = cv2.imread("cityscapes_demo.png")
+    data = {"data": {"image": cv2_to_base64(im)}, "parameters": {}}
+
+    resp = requests.post(url=url, headers=headers, data=json.dumps(data))
+    if resp.status_code == 200:
+        r_json = json.loads(resp.json()["result"])
+        result = fd.vision.utils.json_to_segmentation(r_json)
+        vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+        cv2.imwrite("visualized_result.jpg", vis_im)
+        print("Visualized result save in ./visualized_result.jpg")
+    else:
+        print("Error code:", resp.status_code)
+        print(resp.text)

examples/vision/segmentation/paddleseg/python/serving/server.py

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+import fastdeploy as fd
+from fastdeploy.serving.server import SimpleServer
+import os
+import logging
+
+logging.getLogger().setLevel(logging.INFO)
+
+# Configurations
+model_dir = 'PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer'
+device = 'cpu'
+use_trt = False
+
+# Prepare model
+model_file = os.path.join(model_dir, "model.pdmodel")
+params_file = os.path.join(model_dir, "model.pdiparams")
+config_file = os.path.join(model_dir, "deploy.yaml")
+
+# Setup runtime option to select hardware, backend, etc.
+option = fd.RuntimeOption()
+if device.lower() == 'gpu':
+    option.use_gpu()
+if use_trt:
+    option.use_trt_backend()
+    option.set_trt_cache_file('pp_lite_seg.trt')
+
+# Create model instance
+model_instance = fd.vision.segmentation.PaddleSegModel(
+    model_file=model_file,
+    params_file=params_file,
+    config_file=config_file,
+    runtime_option=option)
+
+# Create server, setup REST API
+app = SimpleServer()
+app.register(
+    task_name="fd/ppliteseg",
+    model_handler=fd.serving.handler.VisionModelHandler,
+    predictor=model_instance)