在Tensorflow中是否有任何卷积方法将Sobel滤波器应用于图像img(float32和rank 2类型的张量)？

sobel_x = tf.constant([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], 'float32')
result = tf.convolution(img, sobel_x) # <== TO DO THIS

我已经看过tf.nn.conv2d,但我看不出如何将它用于此操作.有没有办法使用tf.nn.conv2d来解决我的问题？

解决方法:

也许我在这里错过了一个微妙之处,但似乎你可以使用tf.expand_dims()和tf.nn.conv2d()将Sobel滤镜应用于图像,如下所示：

sobel_x = tf.constant([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], tf.float32)
sobel_x_filter = tf.reshape(sobel_x, [3, 3, 1, 1])
sobel_y_filter = tf.transpose(sobel_x_filter, [1, 0, 2, 3])

# Shape = height x width.
image = tf.placeholder(tf.float32, shape=[None, None])

# Shape = 1 x height x width x 1.
image_resized = tf.expand_dims(tf.expand_dims(image, 0), 3)

filtered_x = tf.nn.conv2d(image_resized, sobel_x_filter,
                          strides=[1, 1, 1, 1], padding='SAME')
filtered_y = tf.nn.conv2d(image_resized, sobel_y_filter,
                          strides=[1, 1, 1, 1], padding='SAME')