Python 2.7 如何为3d卷积构造sobel滤波器？

在我的代码片段中，我想构造Sobel过滤器，它分别应用于图像（RGB）的每一层，最后粘在一起（同样是RGB，但经过过滤）

我不知道如何使用输入形状构建Sobel滤波器

[滤波器深度、滤波器高度、滤波器宽度、输入通道、输出通道]

，在我的情况下：

 sobel_x_filter = tf.reshape(sobel_x, [1, 3, 3, 3, 3]) 

整个代码如下所示：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

im0 = plt.imread('../../data/im0.png') # already divided by 255
sobel_x = tf.constant([
[[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],
[[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],
[[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]],
 [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]]], tf.float32) # is this correct? 
sobel_x_filter = tf.reshape(sobel_x, [1, 3, 3, 3, 3])
image = tf.placeholder(tf.float32, shape=[496, 718, 3])
image_resized = tf.expand_dims(tf.expand_dims(image, 0), 0)

filters_x  = tf.nn.conv3d(image_resized, filter=sobel_x_filter, strides=[1,1,1,1,1], 
                          padding='SAME', data_format='NDHWC')

with tf.Session('') as sess:
    sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    feed_dict = {image: im0}
    img  =  filters_x.eval(feed_dict=feed_dict)

plt.figure(0), plt.title('red'), plt.imshow(np.squeeze(img[...,0])),
plt.figure(1), plt.title('green'), plt.imshow(np.squeeze(img[...,1])),
plt.figure(2), plt.title('blue'), plt.imshow(np.squeeze(img[...,2]))

您可以使用：

---

使用

tf.nn.conv3d

：

im = tf.expand_dims(tf.transpose(image, [2, 0, 1]),0)
sobel_x = tf.constant([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], tf.float32)
sobel_x_filter = tf.reshape(sobel_x, [1, 3, 3, 1, 1])
conv = tf.transpose(tf.squeeze(tf.nn.conv3d(im[...,None], sobel_x_filter,
                    strides=[1,1,1,1,1],padding='SAME')), [1,2,0])

---

在我看来，你并不是在尝试对图像的每个通道应用3D卷积，而是2D卷积。确切地说，我是在尝试理解tensorlfow函数。不。我想用tf.nn。conv3d@j35t3r为tf.nn.conv3d添加了

im = tf.expand_dims(tf.transpose(image, [2, 0, 1]),0)
sobel_x = tf.constant([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], tf.float32)
sobel_x_filter = tf.reshape(sobel_x, [1, 3, 3, 1, 1])
conv = tf.transpose(tf.squeeze(tf.nn.conv3d(im[...,None], sobel_x_filter,
                    strides=[1,1,1,1,1],padding='SAME')), [1,2,0])