我在python中使用卷积内核有问题。它是关于简单的卷积算子。我有输入矩阵和输出矩阵。我想找到一个可能的size(5x5)的卷积内核。如何使用python,numpy或tensorflow解决此问题?
import scipy.signal as ss
input_img = np.array([[94, 166, 76, 106, 152, 232],
[48, 242, 30, 98, 46, 210],
[52, 60, 86, 60, 216, 248],
[52, 236, 116, 240, 224, 184],
[138, 160, 146, 254, 236, 252],
[94, 100, 224, 246, 152, 74]], dtype=float)
output_img = np.array([[15, 49, 23, 105, 0, 0],
[43,30, 108, 124, 0, 0],
[58, 120, 112, 92, 0, 0],
[73, 127, 118, 126, 0, 0],
[112, 123, 76, 37, 0, 0],
[0, 0, 0, 0, 0, 0]], dtype=float)
# I want to find this kernel
conv = np.zeros((5,5), dtype=int)
# So if I do convolution operator, output_img will resulting a value same as I defined above
output_img = ss.convolve2d(input_img, conv, padding='same')
答案 0 :(得分:0)
据我了解,您需要通过给定的输入,输出数组和窗口大小来重构窗口权重。我认为,尤其是在输入数组(图像)足够大的情况下,这是可能的。
看下面的代码:
import scipy.signal as ss
import numpy as np
source_dataset = np.random.rand(20, 10)
sample_convolution = np.diag([1, 1, 1])
output_dataset = ss.convolve2d(data, sample_convolution, mode='same')
conv_size = c.shape[0]
# Given output_dataset, source_datset, and conv_size we need to reconstruct
# window weights.
def reconstruct(data, output, csize):
half_size = int(csize / 2)
min_row_ind = half_size
max_row_ind = int(data.shape[0]) - half_size
min_col_ind = half_size
max_col_ind = int(data.shape[1]) - half_size
A = list()
b = list()
for i in np.arange(min_row_ind, max_row_ind, dtype=int):
for j in np.arange(min_col_ind, max_col_ind, dtype=int):
A.append(data[(i - half_size):(i + half_size + 1), (j - half_size):(j + half_size + 1)].ravel().tolist())
b.append(output[i, j])
if len(A) == csize * csize and np.linalg.matrix_rank(A) == csize * csize:
return (np.linalg.pinv(A)@np.array(b)[:, np.newaxis]).reshape(csize, csize)
if len(A) < csize*csize:
raise Exception("Insufficient data")
result = reconstruct(source_dataset, output_dataset, 3)
我得到了以下结果
array([[ 1.00000000e+00, -1.77635684e-15, -1.11022302e-16],
[ 0.00000000e+00, 1.00000000e+00, -8.88178420e-16],
[ 0.00000000e+00, -1.22124533e-15, 1.00000000e+00]])
因此,它可以按预期工作;但绝对需要改进以考虑边缘效果,窗口大小均匀的情况等。