sklearn.feature_extraction.image
.PatchExtractor¶
- class sklearn.feature_extraction.image.PatchExtractor(*, patch_size=None, max_patches=None, random_state=None)¶
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. If set to None, the patch size will be automatically set to
(img_height // 10, img_width // 10)
, whereimg_height
andimg_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 when
max_patches is not None
. Use an int to make the randomness deterministic. See Glossary.
См. также
reconstruct_from_patches_2d
Reconstruct image from all of its patches.
Notes
This estimator is stateless and does not need to be fitted. However, we recommend to call
fit_transform
instead oftransform
, as parameter validation is only performed infit
.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)
Methods
fit
(X[, y])Only validate the parameters of the estimator.
fit_transform
(X[, y])Fit to data, then transform it.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
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)¶
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 have
n_channels=3
.- yIgnored
Not used, present for API consistency by convention.
- Returns:
- selfobject
Returns the instance itself.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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()¶
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)¶
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)¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame output"polars"
: Polars outputNone
: Transform configuration is unchanged
New in version 1.4:
"polars"
option was added.
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)¶
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)¶
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.