[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/server.py

@@ -0,0 +1,38 @@
+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)