I am just trying to make a Gaussian filter (like the 'scipy.ndimage.gaussian_filter') to process a 4-D tensor in TensorFlow, the 4-D tensor has a shape of: [16,96,96,3] (16 is the batch size, 96 is the image block size, and 3 is the number of channels). How could I achieve this?
You just need to create a Gaussian 2D kernel and use a 2D convolution:
import tensorflow as tf
# Make Gaussian kernel following SciPy logic
def make_gaussian_2d_kernel(sigma, truncate=4.0, dtype=tf.float32):
radius = tf.to_int32(sigma * truncate)
x = tf.cast(tf.range(-radius, radius + 1), dtype=dtype)
k = tf.exp(-0.5 * tf.square(x / sigma))
k = k / tf.reduce_sum(k)
return tf.expand_dims(k, 1) * k
# Input data
image = tf.placeholder(tf.float32, [16, 96, 96, 3])
# Convolution kernel
kernel = make_gaussian_2d_kernel(5)
# Apply kernel to each channel (see https://stackoverflow.com/q/55687616/1782792)
kernel = tf.tile(kernel[:, :, tf.newaxis, tf.newaxis], [1, 1, 3, 1])
image_filtered = tf.nn.separable_conv2d(
image, kernel, tf.eye(3, batch_shape=[1, 1]),
strides=[1, 1, 1, 1], padding='SAME')