PatchExtractor#

class sklearn.feature_extraction.image.PatchExtractor(*, patch_size=None, max_patches=None, random_state=None)[source]#

从图像集合中提取补丁。

Read more in the User Guide.

Added in version 0.9.

参数:

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), where img_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 when max_patches is not None. Use an int to make the randomness deterministic. See Glossary.

另请参阅

reconstruct_from_patches_2d: Reconstruct image from all of its patches.

注意事项

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.

示例

>>> 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)

fit(X, y=None)[source]#

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.

参数:

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.
y被忽略: 未使用，按照惯例为保持 API 一致性而存在。

返回:

selfobject: 返回实例本身。

fit_transform(X, y=None, **fit_params)[source]#

拟合数据，然后对其进行转换。

使用可选参数 fit_params 将转换器拟合到 X 和 y，并返回 X 的转换版本。

参数:

Xshape 为 (n_samples, n_features) 的 array-like: 输入样本。
y形状为 (n_samples,) 或 (n_samples, n_outputs) 的类数组对象，默认=None: 目标值（对于无监督转换，为 None）。
**fit_paramsdict: 额外的拟合参数。仅当估计器在其 fit 方法中接受额外的参数时才传递。

返回:

X_newndarray array of shape (n_samples, n_features_new): 转换后的数组。

get_metadata_routing()[source]#

获取此对象的元数据路由。

请查阅用户指南，了解路由机制如何工作。

返回:

routingMetadataRequest: 封装路由信息的 MetadataRequest。

get_params(deep=True)[source]#

获取此估计器的参数。

参数:

deepbool, default=True: 如果为 True，将返回此估计器以及包含的子对象（如果它们是估计器）的参数。

返回:

paramsdict: 参数名称映射到其值。

set_output(*, transform=None)[source]#

设置输出容器。

有关如何使用 API 的示例，请参阅引入 set_output API。

参数:

transform{“default”, “pandas”, “polars”}, default=None

配置 transform 和 fit_transform 的输出。

"default": 转换器的默认输出格式
"pandas": DataFrame 输出
"polars": Polars 输出
None: 转换配置保持不变

1.4 版本新增: 添加了 "polars" 选项。

返回:

selfestimator instance: 估计器实例。

set_params(**params)[source]#

设置此估计器的参数。

此方法适用于简单的估计器以及嵌套对象（如 Pipeline）。后者具有 <component>__<parameter> 形式的参数，以便可以更新嵌套对象的每个组件。

参数:

**paramsdict: 估计器参数。

返回:

selfestimator instance: 估计器实例。

transform(X)[source]#

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

参数:

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.

返回:

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 either n_samples * max_patches or the total number of patches that can be extracted.

PatchExtractor#

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