sklearn.feature_extraction.extract_patches_2d¶
sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None)
将二维图像重塑为一组补丁
产生的补丁被分配在一个专用的阵列中。
在用户指南中阅读更多内容。
| 参数 | 说明 |
|---|---|
| image | ndarray of shape (image_height, image_width) or (image_height, image_width, n_channels) 原始图像数据。对于彩色图像，最后一个维度指定了通道:RGB图像的n_channels=3。 |
| patch_size | tuple of int (patch_height, patch_width) 一个patch的尺寸。 |
| max_patches | int or float, default=None 要提取的最大patch数。如果max_patch是0到1之间的浮点数，则它被认为是补丁总数的一个比例。 |
| random_state | int, RandomState instance, default=None 确定当max_patch不是None时用于随机采样的随机数生成器。使用int使随机性具有确定性。详见术语表。 |
| 返回值 | 说明 |
|---|---|
| patches | array of shape (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) 从图像中提取的patch集合，其中n_patch为max_patch或可提取的patch总数。 |
示例
>>> from sklearn.datasets import load_sample_image
>>> from sklearn.feature_extraction import image
>>> # Use the array data from the first image in this dataset:
>>> one_image = load_sample_image("china.jpg")
>>> print('Image shape: {}'.format(one_image.shape))
Image shape: (427, 640, 3)
>>> patches = image.extract_patches_2d(one_image, (2, 2))
>>> print('Patches shape: {}'.format(patches.shape))
Patches shape: (272214, 2, 2, 3)
>>> # Here are just two of these patches:
>>> print(patches[1])
[[[174 201 231]
[174 201 231]]
[[173 200 230]
[173 200 230]]]
>>> print(patches[800])
[[[187 214 243]
[188 215 244]]
[[187 214 243]
[188 215 244]]]
