sklearn.feature_extraction.extract_patches_2d?
sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, *, max_patches=None, random_state=None)
將二維圖像重塑為一組補丁
產生的補丁被分配在一個專用的陣列中。
在用戶指南中閱讀更多內容。
| 參數 | 說明 |
|---|---|
| image | ndarray of shape (image_height, image_width) or (image_height, image_width, n_channels) 原始圖像數據。對于彩色圖像，最后一個維度指定了通道:RGB圖像的n_channels=3。 |
| patch_size | tuple of int (patch_height, patch_width) 一個patch的尺寸。 |
| max_patches | int or float, default=None 要提取的最大patch數。如果max_patch是0到1之間的浮點數，則它被認為是補丁總數的一個比例。 |
| random_state | int, RandomState instance, default=None 確定當max_patch不是None時用于隨機采樣的隨機數生成器。使用int使隨機性具有確定性。詳見術語表。 |
| 返回值 | 說明 |
|---|---|
| patches | array of shape (n_patches, patch_height, patch_width) or (n_patches, patch_height, patch_width, n_channels) 從圖像中提取的patch集合，其中n_patch為max_patch或可提取的patch總數。 |
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]]]
