PatchExtractor

Extracts patches from a collection of images.

Constructors

constructor()

Signature

new PatchExtractor(opts?: object): PatchExtractor;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.max_patches?`	`number`	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 `undefined`, extract all possible patches.
`opts.patch_size?`	`any`	The dimensions of one patch. If set to `undefined`, the patch size will be automatically set to `(img\_height // 10, img\_width // 10)`, where `img\_height` and `img\_width` are the dimensions of the input images.
`opts.random_state?`	`number`	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.

Returns

PatchExtractor

Defined in: generated/feature_extraction/image/PatchExtractor.ts:23 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:98 (opens in a new tab)

fit()

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.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[][]	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`.
`opts.y?`	`any`	Not used, present for API consistency by convention.

Returns

Promise<any>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:117 (opens in a new tab)

fit_transform()

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.

Signature

fit_transform(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Input samples.
`opts.fit_params?`	`any`	Additional fit parameters.
`opts.y?`	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns

Promise<any[]>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:157 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:206 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:56 (opens in a new tab)

set_output()

Set output container.

See Introducing the set_output API for an example on how to use the API.

Signature

set_output(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.transform?`	`"default"` \| `"pandas"`	Configure output of `transform` and `fit\_transform`.

Returns

Promise<any>

Defined in: generated/feature_extraction/image/PatchExtractor.ts:243 (opens in a new tab)

transform()

Transform the image samples in X into a matrix of patch data.

Signature

transform(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[][]	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`.