Scipy ndimage median_filter原点

origin表示它只接受标量,但对我来说,它也接受类似数组的输入 scipy.ndimage.filters.convolve 作用通过0确实是足迹的中心。原点的值相对于中心。对于3x3封装,您可以指定值-1.0到1.0。以下是一些示例。请注意,在未指定原点的示例中,过滤器按预期居中。

import numpy as np
import scipy.ndimage

a= np.zeros((5, 5))
a[1:4, 1:4] = np.arange(3*3).reshape((3, 3))

default_out = scipy.ndimage.median_filter(a, size=(3, 3))
shift_pos_x = scipy.ndimage.median_filter(a, size=(3, 3), origin=(0, 1))
shift_neg_x = scipy.ndimage.median_filter(a, size=(3, 3), origin=(0, -1))

print(a)
print(default_out)
print(shift_pos_x)
print(shift_neg_x)

输出:

输入数组:

[[ 0.  0.  0.  0.  0.]
 [ 0.  0.  1.  2.  0.]
 [ 0.  3.  4.  5.  0.]
 [ 0.  6.  7.  8.  0.]
 [ 0.  0.  0.  0.  0.]]

居中输出:

[[ 0.  0.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.]
 [ 0.  1.  4.  2.  0.]
 [ 0.  0.  4.  0.  0.]
 [ 0.  0.  0.  0.  0.]]

向右移位输出:

[[ 0.  0.  0.  0.  0.]
 [ 0.  0.  0.  1.  0.]
 [ 0.  0.  1.  4.  2.]
 [ 0.  0.  0.  4.  0.]
 [ 0.  0.  0.  0.  0.]]

左移输出:

[[ 0.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.]
 [ 1.  4.  2.  0.  0.]
 [ 0.  4.  0.  0.  0.]
 [ 0.  0.  0.  0.  0.]]