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_sizetuple of int (patch_height, patch_width), default=None
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.
See also
reconstruct_from_patches_2d
Reconstruct image from all of its patches.
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)Transform 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.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Training data.
- yIgnored
Not used, present for API consistency by convention.
- Returns
- selfobject
Returns the instance itself.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
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
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.
- transform(X)[source]¶
Transform the image samples in
X
into a matrix of patch data.- Parameters
- 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
.
- Returns
- 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 eithern_samples * max_patches
or the total number of patches that can be extracted.