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msssim_tf.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
def _FSpecialGauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function."""
radius = size // 2
offset = 0.0
start, stop = -radius, radius + 1
if size % 2 == 0:
offset = 0.5
stop -= 1
x, y = np.mgrid[offset + start:stop, offset + start:stop]
assert len(x) == size
g = np.exp(-((x**2 + y**2) / (2.0 * sigma**2)))
window = g / g.sum()
window = np.reshape(window, (size, size, 1, 1))
window = tf.constant(window, dtype=tf.float32)
return window
def _Conv2dIndividual(input_tensor, filter_tensor, strides, padding):
if input_tensor.get_shape().as_list()[3] == 3:
input_split = tf.unstack(input_tensor, 3, axis=3)
result_split = [tf.nn.conv2d(tf.expand_dims(in_t, -1),
filter_tensor,
strides=strides,
padding=padding)
for in_t in input_split]
result = tf.stack(result_split, axis=3)
result = tf.squeeze(result, axis=4)
elif input_tensor.get_shape().as_list()[3] == 1:
result = tf.nn.conv2d(input_tensor,
filter_tensor,
strides=strides,
padding=padding)
else:
raise Exception('nb_channels must be 1 or 3, got %s instead'
% str(input_tensor.get_shape().as_list()))
return result
def _SSIMForMultiScale(img1, img2, max_val=255, filter_size=11,
filter_sigma=1.5, k1=0.01, k2=0.03):
"""Return the Structural Similarity Map between `img1` and `img2`.
This function attempts to match the functionality of ssim_index_new.m by
Zhou Wang: http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
Arguments:
img1: Numpy array holding the first RGB image batch.
img2: Numpy array holding the second RGB image batch.
max_val: the dynamic range of the images (i.e., the difference
between the maximum the and minimum allowed values).
filter_size: Size of blur kernel to use
(will be reduced for small images).
filter_sigma: Standard deviation for Gaussian blur kernel
(will be reduced for small images).
k1: Constant used to maintain stability in the SSIM calculation
(0.01 in the original paper).
k2: Constant used to maintain stability in the SSIM calculation
(0.03 in the original paper).
Returns:
Pair containing the mean SSIM and contrast sensitivity between
`img1` and `img2`.
Raises:
RuntimeError: If input images don't have the same shape or don't
have four dimensions: [batch_size, height, width, depth].
"""
if img1.get_shape().as_list() != img2.get_shape().as_list():
raise RuntimeError(
'Input images must have the same shape (%s vs. %s).',
img1.get_shape().as_list(), img2.get_shape().as_list())
if len(img1.get_shape().as_list()) != 4:
raise RuntimeError('Input images must have four dimensions, not %d',
len(img1.get_shape().as_list()))
_, height, width, _ = img1.get_shape().as_list()
# Filter size can't be larger than height or width of images.
size = min(filter_size, height, width)
# Scale down sigma if a smaller filter size is used.
sigma = size * filter_sigma / filter_size if filter_size else 0
if filter_size:
window = _FSpecialGauss(size, sigma)
mu1 = _Conv2dIndividual(img1,
window,
strides=[1, 1, 1, 1],
padding='VALID')
mu2 = _Conv2dIndividual(img2,
window,
strides=[1, 1, 1, 1],
padding='VALID')
sigma11 = _Conv2dIndividual(img1 * img1,
window,
strides=[1, 1, 1, 1],
padding='VALID')
sigma22 = _Conv2dIndividual(img2 * img2,
window,
strides=[1, 1, 1, 1],
padding='VALID')
sigma12 = _Conv2dIndividual(img1 * img2,
window,
strides=[1, 1, 1, 1],
padding='VALID')
else:
# Empty blur kernel so no need to convolve.
mu1, mu2 = img1, img2
sigma11 = img1 * img1
sigma22 = img2 * img2
sigma12 = img1 * img2
# return mu1[0, 0, 0, 0], sigma11[0, 0, 0, 0]
mu11 = mu1 * mu1
mu22 = mu2 * mu2
mu12 = mu1 * mu2
sigma11 -= mu11
sigma22 -= mu22
sigma12 -= mu12
# Calculate intermediate values used by both ssim and cs_map.
c1 = (k1 * max_val) ** 2
c2 = (k2 * max_val) ** 2
v1 = 2.0 * sigma12 + c2
v2 = sigma11 + sigma22 + c2
ssim = tf.reduce_mean(((2.0 * mu12 + c1) * v1) / ((mu11 + mu22 + c1) * v2))
cs = tf.reduce_mean(v1 / v2)
return ssim, cs
def MultiScaleSSIM(img1, img2, max_val=255, filter_size=11, filter_sigma=1.5,
k1=0.01, k2=0.03, weights=None):
"""Return the MS-SSIM score between `img1` and `img2`.
This function implements Multi-Scale Structural Similarity (MS-SSIM) Image
Quality Assessment according to Zhou Wang's paper, "Multi-scale structural
similarity for image quality assessment" (2003).
Link: https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf
Author's MATLAB implementation:
http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
Arguments:
img1: Numpy array holding the first RGB image batch.
img2: Numpy array holding the second RGB image batch.
max_val: the dynamic range of the images (i.e., the difference
between the maximum the and minimum allowed values).
filter_size: Size of blur kernel to use
(will be reduced for small images).
filter_sigma: Standard deviation for Gaussian blur kernel
(will be reduced for small images).
k1: Constant used to maintain stability in the SSIM calculation
(0.01 in the original paper).
k2: Constant used to maintain stability in the SSIM calculation
(0.03 in the original paper).
weights: List of weights for each level; if none,
use five levels and the weights from the original paper.
Returns:
MS-SSIM score between `img1` and `img2`.
Raises:
RuntimeError: If input images don't have the same shape or
don't have four dimensions: [batch_size, height, width, depth].
"""
if img1.get_shape().as_list() != img2.get_shape().as_list():
raise RuntimeError(
'Input images must have the same shape (%s vs. %s).',
img1.get_shape().as_list(), img2.get_shape().as_list())
if len(img1.get_shape().as_list()) != 4:
raise RuntimeError('Input images must have four dimensions, not %d',
len(img1.get_shape().as_list()))
# Note: default weights don't sum to 1.0 but do match
# the paper / matlab code.
weights = tf.constant(np.array(weights if weights else
[0.0448, 0.2856, 0.3001, 0.2363, 0.1333]),
dtype=tf.float32)
levels = weights.get_shape().as_list()[0]
downsample_filter = [1, 2, 2, 1]
mssim = []
mcs = []
for _ in range(levels):
ssim, cs = _SSIMForMultiScale(
img1, img2, max_val=max_val, filter_size=filter_size,
filter_sigma=filter_sigma, k1=k1, k2=k2)
mssim.append(ssim)
mcs.append(cs)
filtered = [tf.nn.avg_pool(im,
downsample_filter,
strides=[1, 2, 2, 1],
padding='SAME')
for im in [img1, img2]]
img1, img2 = filtered
return (tf.reduce_prod(tf.pow(mcs[0:levels - 1], weights[0:levels - 1])) *
(mssim[levels - 1]**weights[levels - 1]))