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@ -99,7 +99,8 @@ def create_predictor(args, mode):
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config.disable_gpu()
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config.disable_glog_info()
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config.switch_ir_optim(args.ir_optim)
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# config.switch_ir_optim(args.ir_optim)
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# if args.use_tensorrt:
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# config.enable_tensorrt_engine(
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# precision_mode=AnalysisConfig.Precision.Half
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@ -127,25 +128,3 @@ def draw_text_det_res(dt_boxes, img_path):
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cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
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img_name_pure = img_path.split("/")[-1]
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cv2.imwrite("./output/%s" % img_name_pure, src_im)
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if __name__ == '__main__':
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args = parse_args()
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args.use_gpu = False
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root_path = "/Users/liuweiwei06/Desktop/TEST_CODES/icode/baidu/personal-code/PaddleOCR/"
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args.det_model_dir = root_path + "test_models/public_v1/ch_det_mv3_db"
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predictor, input_tensor, output_tensors = create_predictor(args, mode='det')
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print("det input", predictor.get_input_names())
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print("det output", predictor.get_output_names())
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# print(predictor.program(), file=open("det_program.txt", 'w'))
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outputs = []
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for output_tensor in output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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args.rec_model_dir = root_path + "test_models/public_v1/ch_rec_mv3_crnn/"
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rec_predictor, input_tensor, output_tensors = create_predictor(
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args, mode='rec')
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print("rec input", rec_predictor.get_input_names())
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print("rec output", rec_predictor.get_output_names())
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