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https://github.com/PaddlePaddle/FastDeploy.git
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02bd22422e
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
59 lines
1.5 KiB
Python
Executable File
59 lines
1.5 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("--model", default=None, help="Path of yolov8 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' or 'kunlunxin'.")
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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.device.lower() == "ascend":
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option.use_ascend()
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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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# Configure runtime, load model
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runtime_option = build_option(args)
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model = fd.vision.detection.YOLOv8(args.model, runtime_option=runtime_option)
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# Predicting image
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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)
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# Visualization
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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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