mmagic.datasets.transforms.alpha

Augmentation on alpha matte.

Module Contents

Classes

GenerateSeg	Generate segmentation mask from alpha matte.
GenerateSoftSeg	Generate soft segmentation mask from input segmentation mask.

class mmagic.datasets.transforms.alpha.GenerateSeg(kernel_size=5, erode_iter_range=(10, 20), dilate_iter_range=(15, 30), num_holes_range=(0, 3), hole_sizes=[(15, 15), (25, 25), (35, 35), (45, 45)], blur_ksizes=[(21, 21), (31, 31), (41, 41)])

Bases: mmcv.transforms.BaseTransform

Generate segmentation mask from alpha matte.

Parameters

• kernel_size (int, optional) – Kernel size for both erosion and dilation. The kernel will have the same height and width. Defaults to 5.

• erode_iter_range (tuple, optional) – Iteration of erosion. Defaults to (10, 20).

• dilate_iter_range (tuple, optional) – Iteration of dilation. Defaults to (15, 30).

• num_holes_range (tuple, optional) – Range of number of holes to randomly select from. Defaults to (0, 3).

• hole_sizes (list, optional) – List of (h, w) to be selected as the size of the rectangle hole. Defaults to [(15, 15), (25, 25), (35, 35), (45, 45)].

• blur_ksizes (list, optional) – List of (h, w) to be selected as the kernel_size of the gaussian blur. Defaults to [(21, 21), (31, 31), (41, 41)].

static _crop_hole(img, start_point, hole_size)

Create a all-zero rectangle hole in the image.

Parameters

• img (np.ndarray) – Source image.

• start_point (tuple[int]) – The top-left point of the rectangle.

• hole_size (tuple[int]) – The height and width of the rectangle hole.

Returns

The cropped image.

Return type

np.ndarray

transform(results: dict) → dict

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__()

Return repr(self).

class mmagic.datasets.transforms.alpha.GenerateSoftSeg(fg_thr=0.2, border_width=25, erode_ksize=3, dilate_ksize=5, erode_iter_range=(10, 20), dilate_iter_range=(3, 7), blur_ksizes=[(21, 21), (31, 31), (41, 41)])

Bases: mmcv.transforms.BaseTransform

Generate soft segmentation mask from input segmentation mask.

Required key is “seg”, added key is “soft_seg”.

Parameters

• fg_thr (float, optional) – Threshold of the foreground in the normalized input segmentation mask. Defaults to 0.2.

• border_width (int, optional) – Width of border to be padded to the bottom of the mask. Defaults to 25.

• erode_ksize (int, optional) – Fixed kernel size of the erosion. Defaults to 5.

• dilate_ksize (int, optional) – Fixed kernel size of the dilation. Defaults to 5.

• erode_iter_range (tuple, optional) – Iteration of erosion. Defaults to (10, 20).

• dilate_iter_range (tuple, optional) – Iteration of dilation. Defaults to (3, 7).

• blur_ksizes (list, optional) – List of (h, w) to be selected as the kernel_size of the gaussian blur. Defaults to [(21, 21), (31, 31), (41, 41)].

transform(results: dict) → dict

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__()

Return repr(self).