只有 size-1 arrays 可以转换为 Python 标量错误

Question

我有这个 function 将过滤器 3x3 应用于矩阵 3x3，但它给了我这个错误：

Traceback (most recent call last):
  File "/Users/matteo/opt/anaconda3/lib/python3.9/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/Users/matteo/opt/anaconda3/lib/python3.9/multiprocessing/pool.py", line 48, in mapstar
    return list(map(*args))
  File "/Users/matteo/Desktop/erasmus/massive computing/Session5_2022/myfunctions2.py", line 93, in parallel_filtering_image
    Sum = prow[c - 1][j]*my_filter[0][0] +  prow[c + 0][j]*my_filter[0][1] + prow[c + 1][j]*my_filter[0][2] + srow[c - 1]*my_filter[1][0][j] + srow[c + 0][j]*my_filter[1][1] + srow[c + 1][j]*my_filter[1][2] +nrow[c-1][j]*my_filter[2][0] +nrow[c + 0][j]*my_filter[2][1] + nrow[c + 1][j]*my_filter[2][2];
IndexError: invalid index to scalar variable.

这是 function：

def parallel_filtering_image(r):
    # r: is the image row to filter
    # image is the global memory array
    # my_filter is the filter shape to apply to the image

    
    global image
    global my_filter

    #from the global variaable, gets the image shape
    (rows,cols,depth) = image.shape

    #fetch the r row from the original ima)ge
    srow=image[r,:,:]
    if ( r>0 ):
        prow=image[r-1,:,:]
    else:
        prow=image[r,:,:]
    
    if ( r == (rows-1)):
        nrow=image[r,:,:]
    else:
        nrow=image[r+1,:,:]
        
        
    #defines the result vector, and set the initial value to 0
    frow=np.zeros((cols,depth))
    
    print(my_filter[0][0])

    #range from 1 cause cannot manage column 0 borders
    x = range(1, cols-1)
    for c in x:
        #range for the 3 layers(in this case the layer were equal so i could to this without looping)
        for j in range(3):
            Sum = prow[c - 1][j]*my_filter[0][0] +  prow[c + 0][j]*my_filter[0][1] + prow[c + 1][j]*my_filter[0][2] + srow[c - 1]*my_filter[1][0][j] + srow[c + 0][j]*my_filter[1][1] + srow[c + 1][j]*my_filter[1][2] +nrow[c-1][j]*my_filter[2][0] +nrow[c + 0][j]*my_filter[2][1] + nrow[c + 1][j]*my_filter[2][2];
            #adding to the filtered row the mean
            frow[c][j]=int(Sum)
            
            
            
        

    #return the filtered row
    return frow

类型：变量prow,nrow,srow为numpy.uint8矩阵元素为numpy.float64

我该如何解决它，还有一种方法可以使过滤器产品更具可读性？