mmagic.datasets.transforms.crop

Module Contents

Classes

Crop	Crop data to specific size for training.
CropLike	Crop/pad the image in the target_key according to the size of image in
FixedCrop	Crop paired data (at a specific position) to specific size for training.
ModCrop	Mod crop images, used during testing.
PairedRandomCrop	Paired random crop.
RandomResizedCrop	Crop data to random size and aspect ratio.
CropAroundCenter	Randomly crop the images around unknown area in the center 1/4 images.
CropAroundFg	Crop around the whole foreground in the segmentation mask.
CropAroundUnknown	Crop around unknown area with a randomly selected scale.
RandomCropLongEdge	Random crop the given image by the long edge.
CenterCropLongEdge	Center crop the given image by the long edge.
InstanceCrop	Use maskrcnn to detect instances on image.

Attributes

mmdet_apis

mmagic.datasets.transforms.crop.mmdet_apis

class mmagic.datasets.transforms.crop.Crop(keys, crop_size, random_crop=True, is_pad_zeros=False)

Bases: mmcv.transforms.BaseTransform

Crop data to specific size for training.

Parameters

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

• crop_size (Tuple[int]) – Target spatial size (h, w).

• random_crop (bool) – If set to True, it will random crop image. Otherwise, it will work as center crop. Default: True.

• is_pad_zeros (bool, optional) – Whether to pad the image with 0 if crop_size is greater than image size. Default: False.

_crop(data)

Crop the data.

Parameters

data (Union[List, np.ndarray]) – Input data to crop.

Returns

cropped data and corresponding crop box.

Return type

tuple

transform(results)

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.crop.CropLike(target_key, reference_key=None)

Bases: mmcv.transforms.BaseTransform

Crop/pad the image in the target_key according to the size of image in the reference_key .

Parameters

• target_key (str) – The key needs to be cropped.

• reference_key (str | None) – The reference key, need its size. Default: None.

transform(results)

Transform function.

Parameters

results (dict) – A dict containing the necessary information and data for augmentation. Require self.target_key and self.reference_key.

Returns

A dict containing the processed data and information.

Modify self.target_key.

Return type

dict

__repr__()

Return repr(self).

class mmagic.datasets.transforms.crop.FixedCrop(keys, crop_size, crop_pos=None)

Bases: mmcv.transforms.BaseTransform

Crop paired data (at a specific position) to specific size for training.

Parameters

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

• crop_size (Tuple[int]) – Target spatial size (h, w).

• crop_pos (Tuple[int]) – Specific position (x, y). If set to None, random initialize the position to crop paired data batch. Default: None.

_crop(data, x_offset, y_offset, crop_w, crop_h)

Crop the data.

Parameters

• data (Union[List, np.ndarray]) – Input data to crop.

• x_offset (int) – The offset of x axis.

• y_offset (int) – The offset of y axis.

• crop_w (int) – The width of crop bbox.

• crop_h (int) – The height of crop bbox.

Returns

cropped data and corresponding crop box.

Return type

tuple

transform(results)

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.crop.ModCrop(key='gt')

Bases: mmcv.transforms.BaseTransform

Mod crop images, used during testing.

Required keys are “scale” and “KEY”, added or modified keys are “KEY”.

Parameters

key (str) – The key of image. Default: ‘gt’

transform(results)

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.crop.PairedRandomCrop(gt_patch_size, lq_key='img', gt_key='gt')

Bases: mmcv.transforms.BaseTransform

Paired random crop.

It crops a pair of img and gt images with corresponding locations. It also supports accepting img list and gt list. Required keys are “scale”, “lq_key”, and “gt_key”, added or modified keys are “lq_key” and “gt_key”.

Parameters

• gt_patch_size (int) – cropped gt patch size.

• lq_key (str) – Key of LQ img. Default: ‘img’.

• gt_key (str) – Key of GT img. Default: ‘gt’.

transform(results)

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.crop.RandomResizedCrop(keys, crop_size, scale=(0.08, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0), interpolation='bilinear')

Bases: mmcv.transforms.BaseTransform

Crop data to random size and aspect ratio.

A crop of a random proportion of the original image and a random aspect ratio of the original aspect ratio is made. The cropped image is finally resized to a given size specified by ‘crop_size’. Modified keys are the attributes specified in “keys”.

This code is partially adopted from torchvision.transforms.RandomResizedCrop: [https://pytorch.org/vision/stable/_modules/torchvision/transforms/ transforms.html#RandomResizedCrop].

Parameters

• keys (list[str]) – The images to be resized and random-cropped.

• crop_size (int | tuple[int]) – Target spatial size (h, w).

• scale (tuple[float], optional) – Range of the proportion of the original image to be cropped. Default: (0.08, 1.0).

• ratio (tuple[float], optional) – Range of aspect ratio of the crop. Default: (3. / 4., 4. / 3.).

• interpolation (str, optional) – Algorithm used for interpolation. It can be only either one of the following: “nearest” | “bilinear” | “bicubic” | “area” | “lanczos”. Default: “bilinear”.

get_params(data)

Get parameters for a random sized crop.

Parameters

data (np.ndarray) – Image of type numpy array to be cropped.

Returns

A tuple containing the coordinates of the top left corner and the chosen crop size.

transform(results)

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.crop.CropAroundCenter(crop_size)

Bases: mmcv.transforms.BaseTransform

Randomly crop the images around unknown area in the center 1/4 images.

This cropping strategy is adopted in GCA matting. The unknown area is the same as semi-transparent area. https://arxiv.org/pdf/2001.04069.pdf

It retains the center 1/4 images and resizes the images to ‘crop_size’. Required keys are “fg”, “bg”, “trimap” and “alpha”, added or modified keys are “crop_bbox”, “fg”, “bg”, “trimap” and “alpha”.

Parameters

crop_size (int | tuple) – Desired output size. If int, square crop is applied.

transform(results)

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.crop.CropAroundFg(keys, bd_ratio_range=(0.1, 0.4), test_mode=False)

Bases: mmcv.transforms.BaseTransform

Crop around the whole foreground in the segmentation mask.

Required keys are “seg” and the keys in argument keys. Meanwhile, “seg” must be in argument keys. Added or modified keys are “crop_bbox” and the keys in argument keys.

Parameters

• keys (Sequence[str]) – The images to be cropped. It must contain ‘seg’.

• bd_ratio_range (tuple, optional) – The range of the boundary (bd) ratio to select from. The boundary ratio is the ratio of the boundary to the minimal bbox that contains the whole foreground given by segmentation. Default to (0.1, 0.4).

• test_mode (bool) – Whether use test mode. In test mode, the tight crop area of foreground will be extended to the a square. Default to False.

transform(results)

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

class mmagic.datasets.transforms.crop.CropAroundUnknown(keys, crop_sizes, unknown_source='alpha', interpolations='bilinear')

Bases: mmcv.transforms.BaseTransform

Crop around unknown area with a randomly selected scale.

Randomly select the w and h from a list of (w, h). Required keys are the keys in argument keys, added or modified keys are “crop_bbox” and the keys in argument keys. This class assumes value of “alpha” ranges from 0 to 255.

Parameters

• keys (Sequence[str]) – The images to be cropped. It must contain ‘alpha’. If unknown_source is set to ‘trimap’, then it must also contain ‘trimap’.

• crop_sizes (list[int | tuple[int]]) – List of (w, h) to be selected.

• unknown_source (str, optional) – Unknown area to select from. It must be ‘alpha’ or ‘trimap’. Default to ‘alpha’.

• interpolations (str | list[str], optional) – Interpolation method of mmcv.imresize. The interpolation operation will be applied when image size is smaller than the crop_size. If given as a list of str, it should have the same length as keys. Or if given as a str all the keys will be resized with the same method. Default to ‘bilinear’.

transform(results)

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.crop.RandomCropLongEdge(keys='img')

Bases: mmcv.transforms.BaseTransform

Random crop the given image by the long edge.

Parameters

keys (list[str]) – The images to be cropped.

transform(results)

Call 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.crop.CenterCropLongEdge(keys='img')

Bases: mmcv.transforms.BaseTransform

Center crop the given image by the long edge.

Parameters

keys (list[str]) – The images to be cropped.

transform(results)

Call 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.crop.InstanceCrop(config_file, from_pretrained=None, key='img', box_num_upbound=- 1, finesize=256)

Bases: mmcv.transforms.BaseTransform

Use maskrcnn to detect instances on image.

Mask R-CNN is used to detect the instance on the image pred_bbox is used to segment the instance on the image

Parameters

• config_file (str) – config file name relative to detectron2’s “configs/”

• key (str) – Unused

• box_num_upbound (int) – The upper limit on the number of instances in the figure

transform(results: dict) → dict

The transform function of InstanceCrop.

Parameters

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

Returns

A dict containing the processed data

and information.

Return type

results (dict)

predict_bbox(image)