CropPad

vision_architectures.transforms.croppad.get_updated_crop_start(current_crop_start, new_crop_start)

class vision_architectures.transforms.croppad.CropForegroundWithCropTrackingd(crop_offset_key='crop_offset', *args, **kwargs)

Bases: CropForegroundd

__init__(crop_offset_key='crop_offset', *args, **kwargs)

Parameters:

• keys – keys of the corresponding items to be transformed. See also: monai.transforms.compose.MapTransform

• source_key – data source to generate the bounding box of foreground, can be image or label, etc.

• select_fn – function to select expected foreground, default is to select values > 0.

• channel_indices – if defined, select foreground only on the specified channels of image. if None, select foreground on the whole image.

• margin – add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.

• allow_smaller – when computing box size with margin, whether to allow the image edges to be smaller than the final box edges. If False, part of a padded output box might be outside of the original image, if True, the image edges will be used as the box edges. Default to False. The default value is changed from True to False in v1.5.0.

• k_divisible – make each spatial dimension to be divisible by k, default to 1. if k_divisible is an int, the same k be applied to all the input spatial dimensions.

• mode – available modes for numpy array:{"constant", "edge", "linear_ramp", "maximum", "mean", "median", "minimum", "reflect", "symmetric", "wrap", "empty"} available modes for PyTorch Tensor: {"constant", "reflect", "replicate", "circular"}. One of the listed string values or a user supplied function. Defaults to "constant". See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html it also can be a sequence of string, each element corresponds to a key in keys.

• start_coord_key – key to record the start coordinate of spatial bounding box for foreground.

• end_coord_key – key to record the end coordinate of spatial bounding box for foreground.

• allow_missing_keys – don’t raise exception if key is missing.

• lazy – a flag to indicate whether this transform should execute lazily or not. Defaults to False.

• pad_kwargs – other arguments for the np.pad or torch.pad function. note that np.pad treats channel dimension as the first dimension.

class vision_architectures.transforms.croppad.RandSpatialCropSamplesWithCropTracking(crop_offset_key='crop_offset', *args, **kwargs)

Bases: RandSpatialCropSamples

__init__(crop_offset_key='crop_offset', *args, **kwargs)

__call__(img, lazy=None)

Apply the transform to img, assuming img is channel-first and cropping doesn’t change the channel dim.

Return type:

list[Tensor]

class vision_architectures.transforms.croppad.RandSpatialCropSamplesWithCropTrackingd(keys, roi_size, num_samples, max_roi_size=None, random_center=True, random_size=False, allow_missing_keys=False, lazy=False, crop_offset_key='crop_offset')

Bases: RandSpatialCropSamplesd

__init__(keys, roi_size, num_samples, max_roi_size=None, random_center=True, random_size=False, allow_missing_keys=False, lazy=False, crop_offset_key='crop_offset')