我不知道有没有直接的方法，但可以尝试将遮罩缩小到一半大小，即每个低分辨率像素Map到4个原始像素（根据需要修改为任何比例）：

import numpy as np

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

# first make the original mask divisible by 2
pad_row = orig_mask.shape[0] % 2
pad_col = orig_mask.shape[1] % 2

# i.e. pad the right and bottom of the mask with zeros
orig_mask_padded = np.pad(orig_mask, ((0,pad_row), (0,pad_col)))

# get the new shape
new_rows = orig_mask_padded.shape[0] // 2
new_cols = orig_mask_padded.shape[1] // 2

# group the original pixels by fours and max each group 
shrunk_mask = orig_mask_padded.reshape(new_rows, 2, new_cols, 2).max(axis=(1,3))

print(shrunk_mask)

在此处检查使用子矩阵：Numpy: efficiently sum sub matrix m of M
以下是用于收缩到任何所需形状的完整函数：

def shrink_max(mask, shrink_to_shape):
    r, c = shrink_to_shape
    m, n = mask.shape
    padded_mask = np.pad(mask, ((0, -m % r), (0, -n % c)))
    pr, pc = padded_mask.shape
    return padded_mask.reshape(r, pr // r, c, pc // c).max(axis=(1, 3))

例如，print(shrink_max(orig_mask, (2,1)))会传回：

[[1]
 [0]]