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https://github.com/PaddlePaddle/FastDeploy.git
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866d044898
* model done, CLA fix * remove letter_box and ConvertAndPermute, use resize hwc2chw and convert in preprocess * remove useless values in preprocess * remove useless values in preprocess * fix reviewed problem * fix reviewed problem pybind * fix reviewed problem pybind * postprocess fix * add test_fastestdet.py, coco_val2017_500 fixed done, ready to review * fix reviewed problem * python/.../fastestdet.py * fix infer.cc, preprocess, python/fastestdet.py * fix examples/python/infer.py
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
import fastdeploy as fd
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import cv2
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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 FastestDet onnx model.")
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parser.add_argument(
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"--image", 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 '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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option.set_trt_input_shape("images", [1, 3, 352, 352])
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return option
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args = parse_arguments()
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# Configure runtime and load model
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runtime_option = build_option(args)
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model = fd.vision.detection.FastestDet(args.model, runtime_option=runtime_option)
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# Predict picture detection results
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im = cv2.imread(args.image)
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result = model.predict(im)
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# Visualization of prediction results
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vis_im = fd.vision.vis_detection(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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