del pad
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0ee6137d75
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@ -49,12 +49,11 @@ class DBPostProcess(object):
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self.dilation_kernel = None if not use_dilation else np.array(
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[[1, 1], [1, 1]])
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def boxes_from_bitmap(self, pred, _bitmap, shape):
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def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
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'''
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_bitmap: single map with shape (1, H, W),
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whose values are binarized as {0, 1}
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'''
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dest_height, dest_width, ratio_h, ratio_w = shape
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bitmap = _bitmap
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height, width = bitmap.shape
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@ -89,9 +88,9 @@ class DBPostProcess(object):
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box = np.array(box)
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box[:, 0] = np.clip(
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np.round(box[:, 0] / ratio_w), 0, dest_width)
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np.round(box[:, 0] / width * dest_width), 0, dest_width)
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box[:, 1] = np.clip(
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np.round(box[:, 1] / ratio_h), 0, dest_height)
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np.round(box[:, 1] / height * dest_height), 0, dest_height)
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boxes.append(box.astype(np.int16))
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scores.append(score)
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return np.array(boxes, dtype=np.int16), scores
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@ -175,6 +174,7 @@ class DBPostProcess(object):
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boxes_batch = []
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for batch_index in range(pred.shape[0]):
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src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
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if self.dilation_kernel is not None:
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mask = cv2.dilate(
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np.array(segmentation[batch_index]).astype(np.uint8),
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@ -182,7 +182,7 @@ class DBPostProcess(object):
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else:
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mask = segmentation[batch_index]
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boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
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shape_list[batch_index])
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src_w, src_h)
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boxes_batch.append({'points': boxes})
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return boxes_batch
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