75 lines
2.1 KiB
Python
75 lines
2.1 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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import ast
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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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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_paddle_infer_backend()
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# If use original Tensorrt, not Paddle-TensorRT,
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# please try `option.use_trt_backend()`
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option.paddle_infer_option.enable_trt = True
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option.paddle_infer_option.collect_trt_shape = True
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option.trt_option.set_shape("image", [1, 3, 640, 640], [1, 3, 640, 640],
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[1, 3, 640, 640])
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option.trt_option.set_shape("scale_factor", [1, 2], [1, 2], [1, 2])
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return option
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args = parse_arguments()
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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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runtime_option = build_option(args)
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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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