Update predict_det.py
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@ -175,6 +175,7 @@ class TextDetector(object):
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st = time.time()
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st = time.time()
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if args.benchmark:
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self.autolog.times.start()
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self.autolog.times.start()
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data = transform(data, self.preprocess_op)
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data = transform(data, self.preprocess_op)
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@ -185,6 +186,7 @@ class TextDetector(object):
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shape_list = np.expand_dims(shape_list, axis=0)
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shape_list = np.expand_dims(shape_list, axis=0)
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img = img.copy()
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img = img.copy()
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if args.benchmark:
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self.autolog.times.stamp()
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self.autolog.times.stamp()
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self.input_tensor.copy_from_cpu(img)
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self.input_tensor.copy_from_cpu(img)
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@ -193,7 +195,7 @@ class TextDetector(object):
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for output_tensor in self.output_tensors:
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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outputs.append(output)
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if args.benchmark:
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self.autolog.times.stamp()
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self.autolog.times.stamp()
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preds = {}
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preds = {}
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@ -218,6 +220,7 @@ class TextDetector(object):
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else:
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else:
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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if args.benchmark:
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self.autolog.times.end(stamp=True)
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self.autolog.times.end(stamp=True)
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et = time.time()
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et = time.time()
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return dt_boxes, et - st
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return dt_boxes, et - st
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@ -260,5 +263,6 @@ if __name__ == "__main__":
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cv2.imwrite(img_path, src_im)
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cv2.imwrite(img_path, src_im)
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logger.info("The visualized image saved in {}".format(img_path))
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logger.info("The visualized image saved in {}".format(img_path))
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if args.benchmark:
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text_detector.autolog.report()
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text_detector.autolog.report()
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