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.
New in version 0.9.
- 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)
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.
Examples
>>> 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.fit(X) >>> pe_trans = pe.transform(X) >>> print('Patches shape: {}'.format(pe_trans.shape)) Patches shape: (545706, 2, 2)
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]¶ Initialize self. See help(type(self)) for accurate signature.
-
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.
- Parameters
X : array-like, shape [n_samples, n_features]
Training data.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep : bool, default=True
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.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.
-
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 eithern_samples * max_patches
or the total number of patches that can be extracted.