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mmedit.datasets.transforms.generate_frame_indices

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Classes

GenerateFrameIndices

Generate frame index for REDS datasets. It also performs temporal

GenerateFrameIndiceswithPadding

Generate frame index with padding for REDS dataset and Vid4 dataset

GenerateSegmentIndices

Generate frame indices for a segment. It also performs temporal

class mmedit.datasets.transforms.generate_frame_indices.GenerateFrameIndices(interval_list, frames_per_clip=99)[source]

Bases: mmcv.transforms.BaseTransform

Generate frame index for REDS datasets. It also performs temporal augmention with random interval.

Required Keys:

  • img_path

  • gt_path

  • key

  • num_input_frames

Modified Keys:

  • img_path

  • gt_path

Added Keys:

  • interval

  • reverse

Parameters
  • interval_list (list[int]) – Interval list for temporal augmentation. It will randomly pick an interval from interval_list and sample frame index with the interval.

  • frames_per_clip (int) – Number of frames per clips. Default: 99 for REDS dataset.

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.generate_frame_indices.GenerateFrameIndiceswithPadding(padding, filename_tmpl='{:08d}')[source]

Bases: mmcv.transforms.BaseTransform

Generate frame index with padding for REDS dataset and Vid4 dataset during testing.

Required Keys:

  • img_path

  • gt_path

  • key

  • num_input_frames

  • sequence_length

Modified Keys:

  • img_path

  • gt_path

Parameters

padding

padding mode, one of ‘replicate’ | ‘reflection’ | ‘reflection_circle’ | ‘circle’.

Examples: current_idx = 0, num_input_frames = 5 The generated frame indices under different padding mode:

replicate: [0, 0, 0, 1, 2] reflection: [2, 1, 0, 1, 2] reflection_circle: [4, 3, 0, 1, 2] circle: [3, 4, 0, 1, 2]

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.generate_frame_indices.GenerateSegmentIndices(interval_list, start_idx=0, filename_tmpl='{:08d}.png')[source]

Bases: mmcv.transforms.BaseTransform

Generate frame indices for a segment. It also performs temporal augmention with random interval.

Required Keys:

  • img_path

  • gt_path

  • key

  • num_input_frames

  • sequence_length

Modified Keys:

  • img_path

  • gt_path

Added Keys:

  • interval

  • reverse

Parameters
  • interval_list (list[int]) – Interval list for temporal augmentation. It will randomly pick an interval from interval_list and sample frame index with the interval.

  • start_idx (int) – The index corresponds to the first frame in the sequence. Default: 0.

  • filename_tmpl (str) – Template for file name. Default: ‘{:08d}.png’.

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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