CerebNet.datasets.utils¶

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: Image of size […, D_1, D_2, …, D_N], where D_1, D_2, …, D_N are the N image dimensions.
offsetsSequence[int]: 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, optional: Array to store the cropped image in (optional), can be a view on image for memory-efficiency.
padint, str, default=0: Padding strategy to use when padding is required, if int, pad with that value (default: zero-pad).

Returns:

outnp.ndarray: 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.