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CompositeFg

class mmedit.datasets.transforms.CompositeFg(fg_dirs, alpha_dirs, interpolation='nearest')[source]

Composite foreground with a random foreground.

This class composites the current training sample with additional data randomly (could be from the same dataset). With probability 0.5, the sample will be composited with a random sample from the specified directory. The composition is performed as:

\[ \begin{align}\begin{aligned}fg_{new} = \alpha_1 * fg_1 + (1 - \alpha_1) * fg_2\\\alpha_{new} = 1 - (1 - \alpha_1) * (1 - \alpha_2)\end{aligned}\end{align} \]

where \((fg_1, \alpha_1)\) is from the current sample and \((fg_2, \alpha_2)\) is the randomly loaded sample. With the above composition, \(\alpha_{new}\) is still in [0, 1].

Required keys are “alpha” and “fg”. Modified keys are “alpha” and “fg”.

Parameters
  • fg_dirs (str | list[str]) – Path of directories to load foreground images from.

  • alpha_dirs (str | list[str]) – Path of directories to load alpha mattes from.

  • interpolation (str) – Interpolation method of mmcv.imresize to resize the randomly loaded images. Default: ‘nearest’.

transform(results: dict) dict[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

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