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, 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)[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.