reconstruct_from_patches_2d

sklearn.feature_extraction.image.reconstruct_from_patches_2d(patches, image_size)

Reconstruct the image from all of its patches.

假设斑块重叠，图像是通过从左到右、从上到下填充斑块并对重叠区域进行平均来构建的。

阅读更多的 User Guide .

参数:

patches形状的nd数组（n_patches、patch_height、patch_宽度）或 （n_patches、patches_height、patch_宽度、n_channels）

完整的补丁集。如果补丁包含颜色信息，则通道沿最后一个维度进行索引：RGB补丁将具有 n_channels=3 .

image_sizeint（Image_height，Image_root）或 （图像_高度、图像_宽度、n_通道）

将重建的图像的大小。

返回:

image形状图片大小的nd数组

重建的图像。

示例

>>> from sklearn.datasets import load_sample_image
>>> from sklearn.feature_extraction import image
>>> one_image = load_sample_image("china.jpg")
>>> print('Image shape: {}'.format(one_image.shape))
Image shape: (427, 640, 3)
>>> image_patches = image.extract_patches_2d(image=one_image, patch_size=(10, 10))
>>> print('Patches shape: {}'.format(image_patches.shape))
Patches shape: (263758, 10, 10, 3)
>>> image_reconstructed = image.reconstruct_from_patches_2d(
...     patches=image_patches,
...     image_size=one_image.shape
... )
>>> print(f"Reconstructed shape: {image_reconstructed.shape}")
Reconstructed shape: (427, 640, 3)