CerebNet.datasets.utils¶

class CerebNet.datasets.utils.LTADict[source]¶

Methods

`clear`()
`copy`()
`fromkeys`(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
`get`(key[, default])	Return the value for key if key is in the dictionary, else default.
`items`()
`keys`()
`pop`(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`(/)	Remove and return a (key, value) pair as a 2-tuple.
`setdefault`(key[, default])	Insert key with a value of default if key is not in the dictionary.
`update`([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`()

CerebNet.datasets.utils.bounding_volume_offset(img, target_img_size, image_shape=None)[source]¶: Find the center of the non-zero values in img and returns offsets so this center is in the center of a bounding volume of size target_img_size.

CerebNet.datasets.utils.crop_transform(image, offsets=None, target_shape=None, out=None, pad=0)[source]¶

Perform a crop transform of the last N dimensions on the image data. Cropping does not interpolate the image, but “just removes” border pixels/voxels. Negative offsets lead to padding.

Parameters:

imagenp.ndarray, torch.Tensor: Image of size […, D_1, D_2, …, D_N], where D_1, D_2, …, D_N are the N image dimensions.
offsetsSequence[int], optional: Offset of the cropped region for the last N dimensions (default: center crop with less crop/pad towards index 0).
target_shapeSequence[int], optional: If defined, target_shape specifies the target shape of the “cropped region”, else the crop will be centered cropping offset[dim] voxels on each side (then the shape is derived by subtracting 2x the dimension-specific offset). target_shape should have the same number of elements as offsets. May be implicitly defined by out.
outnp.ndarray, torch.Tensor, optional: Array to store the cropped image in (optional), can be a view on image for memory-efficiency.
padint, str, default=0/zero-pad: Padding strategy to use when padding is required, if int, pad with that value.

Returns:

outnp.ndarray, torch.Tensor: The image (stack) cropped in the last N dimensions by offsets to the shape target_shape, or if target_shape is not given image.shape[i+2] - 2*offset[i].

Raises:

ValueError: If neither offsets nor target_shape nor out are defined.
ValueError: If out is not target_shape.
TypeError: If the type of image is not an np.ndarray or a torch.Tensor.
RuntimeError: If the dimensionality of image, out, offset or target_shape is invalid or inconsistent.