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mmedit.datasets.transforms.normalization

Module Contents

Classes

Normalize

Normalize images with the given mean and std value.

RescaleToZeroOne

Transform the images into a range between 0 and 1.

class mmedit.datasets.transforms.normalization.Normalize(keys, mean, std, to_rgb=False, save_original=False)[source]

Bases: mmcv.transforms.BaseTransform

Normalize images with the given mean and std value.

Required keys are the keys in attribute “keys”, added or modified keys are the keys in attribute “keys” and these keys with postfix ‘_norm_cfg’. It also supports normalizing a list of images.

Parameters
  • keys (Sequence[str]) – The images to be normalized.

  • mean (np.ndarray) – Mean values of different channels.

  • std (np.ndarray) – Std values of different channels.

  • to_rgb (bool) – Whether to convert channels from BGR to RGB. Default: False.

  • save_original (bool) – Whether to save original images. Default: False.

transform(results)[source]

transform function.

Parameters

results (dict) – A dict containing the necessary information and data for augmentation.

Returns

A dict containing the processed data and information.

Return type

dict

__repr__()[source]

Return repr(self).

class mmedit.datasets.transforms.normalization.RescaleToZeroOne(keys)[source]

Bases: mmcv.transforms.BaseTransform

Transform the images into a range between 0 and 1.

Required keys are the keys in attribute “keys”, added or modified keys are the keys in attribute “keys”. It also supports rescaling a list of images.

Parameters

keys (Sequence[str]) – The images to be transformed.

transform(results)[source]

transform function.

Parameters

results (dict) – A dict containing the necessary information and data for augmentation.

Returns

A dict containing the processed data and information.

Return type

dict

__repr__()[source]

Return repr(self).

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