# python - TensorFlow的python SSIM/ms SSIM

TensorFlow是否有SSIM或ms SSIM的实现？

SSIM (结构相似性指标)是衡量图像质量或图像相似性的度量指标，例如，请参见神经网络图像处理的损失函数

``````
import tensorflow as tf

import numpy as np

def _tf_fspecial_gauss(size, sigma):

"""Function to mimic the 'fspecial' gaussian MATLAB function

"""

x_data, y_data = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]

x_data = np.expand_dims(x_data, axis=-1)

x_data = np.expand_dims(x_data, axis=-1)

y_data = np.expand_dims(y_data, axis=-1)

y_data = np.expand_dims(y_data, axis=-1)

x = tf.constant(x_data, dtype=tf.float32)

y = tf.constant(y_data, dtype=tf.float32)

g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))

return g / tf.reduce_sum(g)

def tf_ssim(img1, img2, cs_map=False, mean_metric=True, size=11, sigma=1.5):

window = _tf_fspecial_gauss(size, sigma) # window shape [size, size]

K1 = 0.01

K2 = 0.03

L = 1 # depth of image (255 in case the image has a differnt scale)

C1 = (K1*L)**2

C2 = (K2*L)**2

mu1 = tf.nn.conv2d(img1, window, strides=[1,1,1,1], padding='VALID')

mu1_sq = mu1*mu1

mu2_sq = mu2*mu2

mu1_mu2 = mu1*mu2

sigma1_sq = tf.nn.conv2d(img1*img1, window, strides=[1,1,1,1],padding='VALID') - mu1_sq

sigma2_sq = tf.nn.conv2d(img2*img2, window, strides=[1,1,1,1],padding='VALID') - mu2_sq

sigma12 = tf.nn.conv2d(img1*img2, window, strides=[1,1,1,1],padding='VALID') - mu1_mu2

if cs_map:

value = (((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*

(sigma1_sq + sigma2_sq + C2)),

(2.0*sigma12 + C2)/(sigma1_sq + sigma2_sq + C2))

else:

value = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*

(sigma1_sq + sigma2_sq + C2))

if mean_metric:

value = tf.reduce_mean(value)

return value

def tf_ms_ssim(img1, img2, mean_metric=True, level=5):

weight = tf.constant([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=tf.float32)

mssim = []

mcs = []

for l in range(level):

ssim_map, cs_map = tf_ssim(img1, img2, cs_map=True, mean_metric=False)

mssim.append(tf.reduce_mean(ssim_map))

mcs.append(tf.reduce_mean(cs_map))

filtered_im1 = tf.nn.avg_pool(img1, [1,2,2,1], [1,2,2,1], padding='SAME')

filtered_im2 = tf.nn.avg_pool(img2, [1,2,2,1], [1,2,2,1], padding='SAME')

img1 = filtered_im1

img2 = filtered_im2

# list to tensor of dim D+1

mssim = tf.pack(mssim, axis=0)

mcs = tf.pack(mcs, axis=0)

value = (tf.reduce_prod(mcs[0:level-1]**weight[0:level-1])*

(mssim[level-1]**weight[level-1]))

if mean_metric:

value = tf.reduce_mean(value)

return value

``````

``````
import numpy as np

import tensorflow as tf

from skimage import data, img_as_float

image = data.camera()

img = img_as_float(image)

rows, cols = img.shape

noise = np.ones_like(img) * 0.2 * (img.max() - img.min())

noise[np.random.random(size=noise.shape) > 0.5] *= -1

img_noise = img + noise

## TF CALC START

BATCH_SIZE = 1

CHANNELS = 1

image1 = tf.placeholder(tf.float32, shape=[rows, cols])

image2 = tf.placeholder(tf.float32, shape=[rows, cols])

def image_to_4d(image):

image = tf.expand_dims(image, 0)

image = tf.expand_dims(image, -1)

return image

image4d_1 = image_to_4d(image1)

image4d_2 = image_to_4d(image2)

ssim_index = tf_ssim(image4d_1, image4d_2)

msssim_index = tf_ms_ssim(image4d_1, image4d_2)

with tf.Session() as sess:

sess.run(tf.initialize_all_variables())

tf_ssim_none = sess.run(ssim_index,

feed_dict={image1: img, image2: img})

tf_ssim_noise = sess.run(ssim_index,

feed_dict={image1: img, image2: img_noise})

tf_msssim_none = sess.run(msssim_index,

feed_dict={image1: img, image2: img})

tf_msssim_noise = sess.run(msssim_index,

feed_dict={image1: img, image2: img_noise})

###TF CALC END

print('tf_ssim_none', tf_ssim_none)

print('tf_ssim_noise', tf_ssim_noise)

print('tf_msssim_none', tf_msssim_none)

print('tf_msssim_noise', tf_msssim_noise)

``````

msssim.py

``````
python msssim.py --original_image=original.png --compared_image=distorted.png

``````