- class sklearn.feature_extraction.image.PatchExtractor(patch_size=None, max_patches=None, random_state=None)¶
Extracts patches from a collection of images
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.
fit(X[, y]) Do nothing and return the estimator unchanged get_params([deep]) Get parameters for the estimator set_params(**params) Set the parameters of the 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)¶
- 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.
Get parameters for the estimator
deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :
Transforms the image samples in X into a matrix of patch data.
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.