- 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.
image: array, 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 ints (patch_height, patch_width) :
the dimensions of one patch
max_patches: integer or float, optional default is 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.
random_state: int or RandomState :
Pseudo number generator state used for random sampling to use if max_patches is not None.
patches: array, 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.
>>> from sklearn.feature_extraction import image >>> one_image = np.arange(16).reshape((4, 4)) >>> one_image array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> patches = image.extract_patches_2d(one_image, (2, 2)) >>> print(patches.shape) (9, 2, 2) >>> patches array([[0, 1], [4, 5]]) >>> patches array([[1, 2], [5, 6]]) >>> patches array([[10, 11], [14, 15]])