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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.
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)
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fit(X, y=None)
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Do nothing and return the estimator unchanged
This method is just there to implement the usual API and hence work in pipelines.
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get_params(deep=True)
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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.
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set_params(**params)
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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.Returns: self :
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transform(X)
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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.
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feature_extraction.image.PatchExtractor()
2017-01-15 04:22:07
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