[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/cpu-gpu/python/infer.py

@@ -0,0 +1,61 @@
+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 PaddleSeg model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        # If use original Tensorrt, not Paddle-TensorRT,
+        # comment the following two lines
+        option.enable_paddle_to_trt()
+        option.enable_paddle_trt_collect_shape()
+        option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
+                                   [1, 3, 2048, 2048])
+    return option
+
+
+args = parse_arguments()
+
+# settting for runtime
+runtime_option = build_option(args)
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+config_file = os.path.join(args.model, "deploy.yaml")
+model = fd.vision.segmentation.PaddleSegModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# predict
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)
+
+# visualize
+vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+cv2.imwrite("vis_img.png", vis_im)