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Tiling

Tiling#

Image Tiler.

class anomalib.data.utils.tiler.ImageUpscaleMode(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)#

Bases: str, Enum

Type of mode when upscaling image.

exception anomalib.data.utils.tiler.StrideSizeError#

Bases: Exception

StrideSizeError to raise exception when stride size is greater than the tile size.

class anomalib.data.utils.tiler.Tiler(tile_size, stride=None, remove_border_count=0, mode=ImageUpscaleMode.PADDING)#

Bases: object

Tile Image into (non)overlapping Patches. Images are tiled in order to efficiently process large images.

Parameters:

  • tile_size (int | Sequence) – Tile dimension for each patch

  • stride (int | Sequence | None) – Stride length between patches

  • remove_border_count (int) – Number of border pixels to be removed from tile before untiling

  • mode (ImageUpscaleMode) – Upscaling mode for image resize.Supported formats: padding, interpolation

Examples

>>> import torch
>>> from torchvision import transforms
>>> from skimage.data import camera
>>> tiler = Tiler(tile_size=256,stride=128)
>>> image = transforms.ToTensor()(camera())
>>> tiles = tiler.tile(image)
>>> image.shape, tiles.shape
(torch.Size([3, 512, 512]), torch.Size([9, 3, 256, 256]))

>>> # Perform your operations on the tiles.

>>> # Untile the patches to reconstruct the image
>>> reconstructed_image = tiler.untile(tiles)
>>> reconstructed_image.shape
torch.Size([1, 3, 512, 512])
tile(image, use_random_tiling=False)#

Tiles an input image to either overlapping, non-overlapping or random patches.

Parameters:

  • image (Tensor) – Input image to tile.

  • use_random_tiling (bool) – If True, randomly crops tiles from the image. If False, tiles the image in a regular grid.

Examples

>>> from anomalib.data.utils.tiler import Tiler
>>> tiler = Tiler(tile_size=512,stride=256)
>>> image = torch.rand(size=(2, 3, 1024, 1024))
>>> image.shape
torch.Size([2, 3, 1024, 1024])
>>> tiles = tiler.tile(image)
>>> tiles.shape
torch.Size([18, 3, 512, 512])
Return type:

Tensor

Returns:

Tiles generated from the image.

untile(tiles)#

Untiles patches to reconstruct the original input image.

If patches, are overlapping patches, the function averages the overlapping pixels, and return the reconstructed image.

Parameters:

tiles (Tensor) – Tiles from the input image, generated via tile()..

Examples

>>> from anomalib.data.utils.tiler import Tiler
>>> tiler = Tiler(tile_size=512,stride=256)
>>> image = torch.rand(size=(2, 3, 1024, 1024))
>>> image.shape
torch.Size([2, 3, 1024, 1024])
>>> tiles = tiler.tile(image)
>>> tiles.shape
torch.Size([18, 3, 512, 512])
>>> reconstructed_image = tiler.untile(tiles)
>>> reconstructed_image.shape
torch.Size([2, 3, 1024, 1024])
>>> torch.equal(image, reconstructed_image)
True
Return type:

Tensor

Returns:

Output that is the reconstructed version of the input tensor.

anomalib.data.utils.tiler.compute_new_image_size(image_size, tile_size, stride)#

Check if image size is divisible by tile size and stride.

If not divisible, it resizes the image size to make it divisible.

Parameters:

  • image_size (tuple) – Original image size

  • tile_size (tuple) – Tile size

  • stride (tuple) – Stride

Examples

>>> compute_new_image_size(image_size=(512, 512), tile_size=(256, 256), stride=(128, 128))
(512, 512)

>>> compute_new_image_size(image_size=(512, 512), tile_size=(222, 222), stride=(111, 111))
(555, 555)
Returns:

Updated image size that is divisible by tile size and stride.

Return type:

tuple

anomalib.data.utils.tiler.downscale_image(image, size, mode=ImageUpscaleMode.PADDING)#

Opposite of upscaling. This image downscales image to a desired size.

Parameters:

  • image (torch.Tensor) – Input image

  • size (tuple) – Size to which image is down scaled.

  • mode (str, optional) – Downscaling mode. Defaults to “padding”.

Examples

>>> x = torch.rand(1, 3, 512, 512)
>>> y = upscale_image(image, upscale_size=(555, 555), mode="padding")
>>> y = downscale_image(y, size=(512, 512), mode='padding')
>>> torch.allclose(x, y)
True
Returns:

Downscaled image

Return type:

Tensor

anomalib.data.utils.tiler.upscale_image(image, size, mode=ImageUpscaleMode.PADDING)#

Upscale image to the desired size via either padding or interpolation.

Parameters:

  • image (torch.Tensor) – Image

  • size (tuple) – tuple to which image is upscaled.

  • mode (str, optional) – Upscaling mode. Defaults to “padding”.

Examples

>>> image = torch.rand(1, 3, 512, 512)
>>> image = upscale_image(image, size=(555, 555), mode="padding")
>>> image.shape
torch.Size([1, 3, 555, 555])

>>> image = torch.rand(1, 3, 512, 512)
>>> image = upscale_image(image, size=(555, 555), mode="interpolation")
>>> image.shape
torch.Size([1, 3, 555, 555])
Returns:

Upscaled image.

Return type:

Tensor

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