kernels.py 文件源码

def convolve_mean3_gu(image, index, out_image):
    nx, ny, nk = image.shape
    for j in range(1,ny-1):
        for k in range(3):
            out_image[j-1, k] = 0.25*(image[index[0]-1,j,k]+image[index[0]+1,j,k]+image[index[0],j-1,k]+image[index[0],j+1,k])

#@numba.jit
#def convolve_mean2(image):
#    height, width = image.shape
#    out_image = np.empty((height-2, width-2))
#    i = np.arange(1, height-1)
#    j = np.arange(1, width-1)
#    convolve_mean2_gu(image, i[:, np.newaxis], j[np.newaxis, :], out_image)
#    return out_image