使用最常见的值更改numpy数组的结构 [英] Changing structure of numpy array using most common value

问题描述

如何使用模式"将4 * 6尺寸的栅格数据缩小为2 * 3尺寸，即2 * 2像素以内的最常用值?

How can I downscale the raster data of 4*6 size into 2*3 size using 'mode' i.e., most common value with in 2*2 pixels?

import numpy as np
data=np.array([
[0,0,1,1,1,1],
[1,0,0,1,1,1],
[1,0,1,1,0,1],
[1,1,0,1,0,0]])

结果应为:

result = np.array([
    [0,1,1],
    [1,1,0]])

推荐答案

请参考

Please refer to this thread for a full explanation. The following code will calculate your desired result.

from sklearn.feature_extraction.image import extract_patches

data=np.array([
    [0,0,1,1,1,1],
    [1,0,0,1,1,1],
    [1,0,1,1,0,1],
    [1,1,0,1,0,0]])

patches = extract_patches(data, patch_shape=(2, 2), extraction_step=(2, 2))
most_frequent_number = ((patches > 0).sum(axis=-1).sum(axis=-1) > 2).astype(int)
print most_frequent_number

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