47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
import cv2
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import os
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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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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_dir", required=True, help="Path of PaddleDetection model.")
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parser.add_argument(
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"--image_file", type=str, required=True, help="Path of test image file.")
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return parser.parse_args()
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args = parse_arguments()
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runtime_option = fd.RuntimeOption()
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runtime_option.use_ascend()
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if args.model_dir is None:
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model_dir = fd.download_model(name='ppyoloe_crn_l_300e_coco')
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else:
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model_dir = args.model_dir
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model_file = os.path.join(model_dir, "model.pdmodel")
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params_file = os.path.join(model_dir, "model.pdiparams")
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config_file = os.path.join(model_dir, "infer_cfg.yml")
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# settting for runtime
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model = fd.vision.detection.PPYOLOE(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# predict
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if args.image_file is None:
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image_file = fd.utils.get_detection_test_image()
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else:
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image_file = args.image_file
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im = cv2.imread(image_file)
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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_detection(im, result, score_threshold=0.5)
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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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