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64 lines
1.5 KiB
Python
64 lines
1.5 KiB
Python
import cv2
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import fastdeploy as fd
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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", default=None, help="Path of yolor onnx model.")
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parser.add_argument(
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"--image", default=None, 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, 640, 640])
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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if args.model is None:
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model = fd.download_model(name='YOLOR-W6')
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else:
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model = args.model
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model = fd.vision.detection.YOLOR(model, runtime_option=runtime_option)
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# 预测图片检测结果
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if args.image is None:
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image = fd.utils.get_detection_test_image()
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else:
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image = args.image
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im = cv2.imread(image)
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result = model.predict(im.copy())
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print(result)
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# 预测结果可视化
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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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