sklearn.feature_extraction.image.extract_patches_2d¶
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sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, max_patches=None, random_state=None)[source]¶ 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 : 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, RandomState instance or None, optional (default=None)
Pseudo number generator state used for random sampling to use if max_patches is not None. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Returns: - 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.
Examples
>>> 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[0] array([[0, 1], [4, 5]]) >>> patches[1] array([[1, 2], [5, 6]]) >>> patches[8] array([[10, 11], [14, 15]])
