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			58 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
			
		
		
	
	
			58 lines
		
	
	
		
			1.4 KiB
		
	
	
	
		
			Python
		
	
	
		
			Executable File
		
	
	
	
	
| import fastdeploy as fd
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| import cv2
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| import os
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| from fastdeploy import ModelFormat
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| 
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| 
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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 yolov7 paddle 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', 'kunlunxin' or 'gpu'.")
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|     return parser.parse_args()
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| 
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| 
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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(0)
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| 
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|     if args.device.lower() == "kunlunxin":
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|         option.use_kunlunxin()
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| 
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|     if args.device.lower() == "ascend":
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|         option.use_ascend()
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| 
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|     return option
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| 
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| 
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| args = parse_arguments()
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| 
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| model_file = os.path.join(args.model, "model.pdmodel")
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| params_file = os.path.join(args.model, "model.pdiparams")
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| # 配置runtime,加载模型
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| runtime_option = build_option(args)
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| model = fd.vision.detection.YOLOv7(
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|     model_file,
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|     params_file,
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|     runtime_option=runtime_option,
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|     model_format=ModelFormat.PADDLE)
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| 
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| # 预测图片检测结果
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| im = cv2.imread(args.image)
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| result = model.predict(im)
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| print(result)
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| 
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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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