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BoundedScaleAspectJitter

class mmocr.datasets.transforms.BoundedScaleAspectJitter(long_size_bound, short_size_bound, ratio_range=(0.7, 1.3), aspect_ratio_range=(0.9, 1.1), resize_type='Resize', **resize_kwargs)[source]

First randomly rescale the image so that the longside and shortside of the image are around the bound; then jitter its aspect ratio.

Required Keys:

  • img

  • img_shape

  • gt_bboxes (optional)

  • gt_polygons (optional)

Modified Keys:

  • img

  • img_shape

  • gt_bboxes (optional)

  • gt_polygons (optional)

Added Keys:

  • scale

  • scale_factor

  • keep_ratio

Parameters
  • long_size_bound (int) – The approximate bound for long size.

  • short_size_bound (int) – The approximate bound for short size.

  • size_jitter_range (tuple(float, float)) – Range of the ratio used to jitter the size. Defaults to (0.7, 1.3).

  • aspect_ratio_jitter_range (tuple(float, float)) – Range of the ratio used to jitter its aspect ratio. Defaults to (0.9, 1.1).

  • resize_type (str) – The type of resize class to use. Defaults to “Resize”.

  • **resize_kwargs – Other keyword arguments for the resize_type.

  • ratio_range (Tuple[float, float]) –

  • aspect_ratio_range (Tuple[float, float]) –

Return type

None

transform(results)[source]

The transform function. All subclass of BaseTransform should override this method.

This function takes the result dict as the input, and can add new items to the dict or modify existing items in the dict. And the result dict will be returned in the end, which allows to concate multiple transforms into a pipeline.

Parameters

results (dict) – The result dict.

Returns

The result dict.

Return type

dict

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