reconstruct_from_patches_2d

sklearn.feature_extraction.image.reconstruct_from_patches_2d(patches, image_size)[source]

Reconstruct the image from all of its patches.

Patches are assumed to overlap and the image is constructed by filling in the patches from left to right, top to bottom, averaging the overlapping regions.

Read more in the User Guide.

Parameters:

patchesndarray of shape (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels)

The complete set of patches. If the patches contain colour information, channels are indexed along the last dimension: RGB patches would have n_channels=3.

image_sizetuple of int (image_height, image_width) or (image_height, image_width, n_channels)

The size of the image that will be reconstructed.

Returns:

imagendarray of shape image_size

The reconstructed image.

Examples

>>> from sklearn.datasets import load_sample_image
>>> from sklearn.feature_extraction import image
>>> one_image = load_sample_image("china.jpg")
>>> print('Image shape: {}'.format(one_image.shape))
Image shape: (427, 640, 3)
>>> image_patches = image.extract_patches_2d(image=one_image, patch_size=(10, 10))
>>> print('Patches shape: {}'.format(image_patches.shape))
Patches shape: (263758, 10, 10, 3)
>>> image_reconstructed = image.reconstruct_from_patches_2d(
...     patches=image_patches,
...     image_size=one_image.shape
... )
>>> print(f"Reconstructed shape: {image_reconstructed.shape}")
Reconstructed shape: (427, 640, 3)

Gallery examples

Image denoising using dictionary learning