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scikit-learn 1.3.2
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# `sklearn.feature_extraction.image`.extract_patches_2d

sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None)

  Reshape a 2D image into a collection of patches.

  The resulting patches are allocated in a dedicated array.

  Read more in the User Guide.

  Parameters:
    image ndarray of shape (image_height, image_width) or (image_height, image_width, n_channels)

      The original image data. For color images, the last dimension specifies the channel: a RGB image would have `n_channels=3`.

    patch_size tuple of int (patch_height, patch_width)

      The dimensions of one patch.

    max_patches int or float, default=None

      The maximum number of patches to extract. If `max_patches` is a float between 0 and 1, it is taken to be a proportion of the total number of patches. If `max_patches` is None it corresponds to the total number of patches that can be extracted.

    random_state int, RandomState instance, default=None

      Determines the random number generator used for random sampling when `max_patches` is not None. Use an int to make the randomness deterministic. See Glossary.

  Returns:
    patches array of shape (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels)

      The collection of patches extracted from the image, where `n_patches` is either `max_patches` or the total number of patches that can be extracted.

  Examples

      >>> from sklearn.datasets import load_sample_image
      >>> from sklearn.feature_extraction import image
      >>> # Use the array data from the first image in this dataset:
      >>> one_image = load_sample_image("china.jpg")
      >>> print('Image shape: {}'.format(one_image.shape))
      Image shape: (427, 640, 3)
      >>> patches = image.extract_patches_2d(one_image, (2, 2))
      >>> print('Patches shape: {}'.format(patches.shape))
      Patches shape: (272214, 2, 2, 3)
      >>> # Here are just two of these patches:
      >>> print(patches[1])
      [[[174 201 231]
        [174 201 231]]
       [[173 200 230]
        [173 200 230]]]
      >>> print(patches[800])
      [[[187 214 243]
        [188 215 244]]
       [[187 214 243]
        [188 215 244]]]

## Examples using `sklearn.feature_extraction.image.extract_patches_2d`

Online learning of a dictionary of parts of faces

Online learning of a dictionary of parts of faces

Image denoising using dictionary learning

Image denoising using dictionary learning