sklearn.feature_extraction.image.PatchExtractor¶

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

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, RandomState instance or None, optional (default=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.

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)

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)

Do nothing and return the estimator unchanged

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

Parameters: X : array-like, shape [n_samples, n_features] Training data.
get_params(deep=True)

Get parameters for this estimator.

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

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)

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. 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.