阈值比较2个不同大小的numpy数组错误

Question

在比较这两个不同但相似的numpy数组时，我一直试图获得True的评估。它们是我将路径与地图其余部分隔离开的图像，我试图让它识别（在一定阈值内）相同的路径交叉点。从我的研究中，我认为np.allclose（）是可行的方法，但我不断获得

ValueError：操作数不能与形状一起广播（477,1920）（588,1920） 我已经尝试过np.testing_assert_almost_equal（），但是遇到类似的错误。

np.allclose（）是正确的方向吗，只是做错了，还是我想做的事情有更好的功能。

附件是2个numpy数组文本文件的并排图片。任何帮助是极大的赞赏。

import numpy as np

# I have a numpy array that has isolated an intersection in a trial map/path 
from an image,
# I'm trying to get it to recognise the intersection (to a certain degree as 
it approaches) so
# I have a second numpy array that represents the trail map/path slightly 
before you are actually to
# the point where the intersection is.  So the 2 numpy arrays are different 
sizes because the second one
# has the intersection slightly higher up in the image because you just 
havent reached that point yet. But the
# arrays are still pretty similar. So I would want it to return True that 
they are almost the same Array and
# so I figured using np.allclose() with a threshold would work just fine. 
However
# I keep getting "ValueError: operands could not be broadcast together with 
shapes (477,1920) (588,1920)".  When I try np.allclose()

def main():

    # Intersection array I am looking for
    intersectArray = np.loadtxt('Intersection.txt', dtype=int)

    # an array just before I've reached the same point at the intersection
    intersectArrayApproach = np.loadtxt('IntersectionApproach.txt', 
    dtype=int)

    print(np.allclose(intersectArray, intersectArrayApproach, .5, 
     equal_nan=True))

main()