python - 计算每个图像 block 的平均值 - Python

Question

这个问题已经有答案了:

How to evaluate the sum of values within array blocks (3 个回答)

已关闭 3 年前。

我有一个如下所示的矩阵

c = [[ 1 2 3 4 5 6 7 8 9 1]
     [ 2 3 4 5 6 7 8 9 1 2]
     [ 3 4 5 6 7 8 9 1 2 3]
     [ 4 5 6 7 8 9 1 2 3 4]]

从给定的SO ANSWERS在这篇文章中，我用它把矩阵分成 block (2*5)，如下所示

def blockshaped(arr, nrows, ncols):
"""
Return an array of shape (n, nrows, ncols) where
n * nrows * ncols = arr.size

If arr is a 2D array, the returned array should look like n subblocks with
each subblock preserving the "physical" layout of arr.
"""
    h, w = arr.shape
    assert h % nrows == 0, "{} rows is not evenly divisble by {}".format(h, nrows)
    assert w % ncols == 0, "{} cols is not evenly divisble by {}".format(w, ncols)
    return (arr.reshape(h//nrows, nrows, -1, ncols)
           .swapaxes(1,2)
           .reshape(-1, nrows, ncols))


print(blockshaped(c, 2, 5))

Result:

[[[ 1 2 3 4 5 ]
  [ 2 3 4 5 6 ]]

 [[ 6 7 8 9 1 ]
  [ 7 8 9 1 2]]

 [[ 3 4 5 6 7 ]
  [ 4 5 6 7 8 ]]

 [[ 8 9 1 2 3 ]
  [ 9 1 2 3 4]]]

我有 4 个矩阵 block ，现在我需要每个 block 的平均值。如何计算每个 block 的平均值？

当我尝试使用mean()时，它将计算整个矩阵的平均值，而不是每个 block 的平均值。

Answer 1

1。具有列表理解的半行解决方案

results = blockshaped(c, 2, 5)
block_means = [np.mean(results[block,:,:]) for block in range(results.shape[0])]

print(block_means)
# [3.5, 5.8, 5.5, 4.2]

版本 2 - 更短的代码:

results = blockshaped(c, 2, 5)
block_means = [np.mean(block) for block in results]
# [3.5, 5.8, 5.5, 4.2]

In [15]: %timeit [np.mean(results[block,:,:]) for block in range(results.shape[0])]
10000 loops, best of 3: 35.9 µs per loop

In [16]: %timeit [np.mean(block) for block in results]
10000 loops, best of 3: 33.4 µs per loop

P.S:仅当 block 位于结果的第一个 (0) 维度时，第二种解决方案才有效。