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
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)¶
- 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_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.
Returns : 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 former 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.
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.