PatchExtractor (original) (raw)
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.
Added in version 0.9.
Parameters:
patch_sizetuple of int (patch_height, patch_width), default=None
The dimensions of one patch. If set to None, the patch size will be automatically set to (img_height // 10, img_width // 10), whereimg_height and img_width are the dimensions of the input images.
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. If set to None, extract all possible patches.
random_stateint, RandomState instance, default=None
Determines the random number generator used for random sampling whenmax_patches is not None. Use an int to make the randomness deterministic. See Glossary.
Notes
This estimator is stateless and does not need to be fitted. However, we recommend to call fit_transform instead of transform, as parameter validation is only performed in fit.
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] X = X[None, ...] print(f"Image shape: {X.shape}") Image shape: (1, 427, 640, 3) pe = image.PatchExtractor(patch_size=(10, 10)) pe_trans = pe.transform(X) print(f"Patches shape: {pe_trans.shape}") Patches shape: (263758, 10, 10, 3) X_reconstructed = image.reconstruct_from_patches_2d(pe_trans, X.shape[1:]) print(f"Reconstructed shape: {X_reconstructed.shape}") Reconstructed shape: (427, 640, 3)
Only validate the parameters of the estimator.
This method allows to: (i) validate the parameters of the estimator and (ii) be consistent with the scikit-learn transformer API.
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 haven_channels=3.
yIgnored
Not used, present for API consistency by convention.
Returns:
selfobject
Returns the instance itself.
fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_paramsand returns a transformed version of X.
Parameters:
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
**fit_paramsdict
Additional fit parameters.
Returns:
X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Returns:
routingMetadataRequest
A MetadataRequest encapsulating routing information.
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_output(*, transform=None)[source]#
Set output container.
See Introducing the set_output APIfor an example on how to use the API.
Parameters:
transform{“default”, “pandas”, “polars”}, default=None
Configure output of transform and fit_transform.
"default": Default output format of a transformer"pandas": DataFrame output"polars": Polars outputNone: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Returns:
selfestimator instance
Estimator instance.
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 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 haven_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, wheren_patches is either n_samples * max_patches or the total number of patches that can be extracted.