sklearn.feature_extraction.image.PatchExtractor
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class sklearn.feature_extraction.image.PatchExtractor(*, patch_size=None, max_patches=None, random_state=None)
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Extracts patches from a collection of images
Read more in the User Guide.
New in version 0.9.
- Parameters
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patch_sizetuple of int (patch_height, patch_width), default=None
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The dimensions of one patch.
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max_patchesint or float, default=None
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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.
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random_stateint, RandomState instance, default=None
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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.
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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.
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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
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Xarray-like of shape (n_samples, n_features)
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Training data.
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get_params(deep=True)
[source] -
Get parameters for this estimator.
- Parameters
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deepbool, default=True
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If True, will return the parameters for this estimator and contained subobjects that are estimators.
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- Returns
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paramsdict
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Parameter names mapped to their values.
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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
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**paramsdict
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Estimator parameters.
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- Returns
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selfestimator instance
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Estimator instance.
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transform(X)
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Transforms the image samples in X into a matrix of patch data.
- Parameters
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Xndarray of shape (n_samples, image_height, image_width) or (n_samples, image_height, image_width, n_channels)
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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
.
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- Returns
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patchesarray of shape (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels)
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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.
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© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.feature_extraction.image.PatchExtractor.html