我遇到了一个令人沮丧的问题，我试图将一个边缘过滤器应用到一个图像的类分配。当我运行代码时，我收到错误“ValueError Traceback（most recent call last）
在12中，标准水平=标准垂直.T 13 ---〉14 d_水平=卷积2d（平均，标准水平，边界=“对称”，模式=“相同”，填充值=0）15 d_垂直=卷积2d（平均，标准垂直，模式=“相同”，边界=“对称”，填充值=0）16边缘=np.平方（np.平方（d_水平）+ np.平方（d_垂直））
/usr/local/lib/python3.7/dist-packages/scipy/signal/signaltools.py在convolve 2d（输入1，输入2，模式，边界，填充值）中1694 1695如果不在输入1中。ndim ==输入2。ndim == 2：- 〉1696 raise ValueError（“卷积2d输入必须都是二维数组”）1697 1698 if _inputs_swap_needed（mode，in1.shape，in2.shape）：
ValueError：convolve 2d输入必须都是2-D数组”
我知道我传递给convolve 2d的数组实际上是二维数组，但是convolve 2d似乎没有注册这一点，有什么方法可以修复这个问题吗？代码如下：

import numpy as np
import cv2 
import math
import random
from matplotlib import pyplot as plt
from scipy.signal import convolve2d

# mount drive

from google.colab import drive
drive.mount('/content/drive')

# from google.colab.patches import cv2_imshow

def in_circle(x,y, center_x, center_y, radius):
    distance = math.sqrt(math.pow(x-center_x,2)+math.pow(y-center_y,2))
    return (distance < radius)

def in_disk(x,y,center_x,center_y,inner_radius,outer_radius):
    return not in_circle(x,y,center_x,center_y,inner_radius) and in_circle(x,y,center_x,center_y,outer_radius)

img = cv2.imread('/content/mydata/circles.jpg')

# apply average filter

average_kernel = np.array(
    [[0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01],
    [0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01]]   
)
average = cv2.filter2D(img,-1,average_kernel)

# cv2.imshow('first_average',average)

plt.figure()
plt.title('first AVR')
plt.imshow(average,cmap='gray', vmin=0, vmax=255)

# apply edge filter

l_kern2 = np.array([
         [-1.0,  -1.0, -1.0]
        ,[-1.0, 8.0, -1.0]
        ,[-1.0,  -1.0, -1.0]
        ])
sobel_vert = np.array([
         [-1.0, 0.0, 1.0]
        ,[-2.0, 0.0, 2.0]
        ,[-1.0, 0.0, 1.0]
        ])
sobel_horiz = sobel_vert.T

d_horiz = convolve2d(average, sobel_horiz,  boundary = 'symm', mode='same', fillvalue=0)
d_vert = convolve2d(average, sobel_vert, mode='same', boundary = 'symm', fillvalue=0)
edgel=np.sqrt(np.square(d_horiz) + np.square(d_vert))

# edgel = cv2.filter2D(average, -1, l_kern2)

# edgel = convolve2d(average, l_kern2, mode='same', boundary = 'symm', fillvalue=0)

# edgel= np.absolute(edgel)

edgel *= 255.0 / np.max(edgel)
plt.figure()
plt.title('Edge')
plt.imshow(edgel,cmap='gray', vmin=0, vmax=255)

相关代码在#apply edge filter注解下，谢谢！