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