sklearn.feature_extraction.image.PatchExtractor

class sklearn.feature_extraction.image.PatchExtractor(patch_size=None, max_patches=None, random_state=None) [source]

Extracts patches from a collection of images

Read more in the User Guide.

Parameters:	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 per image to extract. If max_patches is a float in (0, 1), it is taken to mean a proportion of the total number of patches. random_state : int or RandomState Pseudo number generator state used for random sampling.

Methods

fit(X[, y])	Do nothing and return the estimator unchanged
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transforms the image samples in X into a matrix of patch data.

__init__(patch_size=None, max_patches=None, random_state=None) [source]

fit(X, y=None) [source]

Do nothing and return the estimator unchanged

This method is just there to implement the usual API and hence work in pipelines.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self :

transform(X) [source]

Transforms the image samples in X into a matrix of patch data.

Parameters:	X : array, shape = (n_samples, image_height, image_width) or (n_samples, image_height, image_width, n_channels) Array of images from which to extract patches. For color images, the last dimension specifies the channel: a RGB image would have n_channels=3.
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 images, where n_patches is either n_samples * max_patches or the total number of patches that can be extracted.