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.

New in version 0.9.

patch_sizetuple of int (patch_height, patch_width)

The dimensions of one patch.

max_patchesint or float, default=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_stateint, 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.


>>> from sklearn.datasets import load_sample_images
>>> from sklearn.feature_extraction import image
>>> # Use the array data from the second image in this dataset:
>>> X = load_sample_images().images[1]
>>> print('Image shape: {}'.format(X.shape))
Image shape: (427, 640, 3)
>>> pe = image.PatchExtractor(patch_size=(2, 2))
>>> pe_fit =
>>> pe_trans = pe.transform(X)
>>> print('Patches shape: {}'.format(pe_trans.shape))
Patches shape: (545706, 2, 2)


fit(X[, y])

Do nothing and return the estimator unchanged.


Get parameters for this estimator.


Set the parameters of this estimator.


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

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.

Xarray-like of shape (n_samples, n_features)

Training data.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

paramsmapping of string to any

Parameter names mapped to their values.


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.


Estimator parameters.


Estimator instance.


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

Xndarray of 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.

patchesarray of 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.