ssim佔比alpha
l1佔比1-alpha
#%%
import tensorflow as tf
import numpy as np
import torch
#模仿matlab的fspecial函式，建立濾波運算元（計算ssim用）
def _tf_fspecial_gauss(size, sigma, channels=1):
"""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)))
window = g / tf.reduce_sum(g)
return tf.tile(window, (1,1,channels,channels))
#高斯卷積
def tf_gauss_conv(img, filter_size=11, filter_sigma=1.5):
_, height, width, ch = img.get_shape().as_list()
size = min(filter_size, height, width)
sigma = size * filter_sigma / filter_size if filter_size else 0
window = _tf_fspecial_gauss(size, sigma, ch) # window shape [size, size]
padded_img = tf.pad(img, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="constant")
return tf.nn.conv2d(padded_img, window, strides=[1,1,1,1], padding='valid')
#高斯加權的l1正則化
def tf_gauss_weighted_l1(img1, img2, mean_metric=true, filter_size=11, filter_sigma=1.5):
diff = tf.abs(img1 - img2)
l1 = tf_gauss_conv(diff, filter_size=filter_size, filter_sigma=filter_sigma)
if mean_metric:
return tf.reduce_mean(l1)
else:
return l1
#計算ssim
def tf_ssim(img1, img2, cs_map=false, mean_metric=true, filter_size=11, filter_sigma=1.5):
_, height, width, ch = img1.get_shape().as_list()
size = min(filter_size, height, width)
sigma = size * filter_sigma / filter_size if filter_size else 0
window = _tf_fspecial_gauss(size, sigma, ch) # 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
#求取滑塊內均值ux uy，均方值ux_sq
padded_img1 = tf.pad(img1, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="constant") #img1 上下左右補零
padded_img2 = tf.pad(img2, [[0, 0], [size//2, size//2], [size//2, size//2], [0, 0]], mode="constant") #img2 上下左右補零
mu1 = tf.nn.conv2d(padded_img1, window, strides=[1,1,1,1], padding='valid') #利用滑動視窗，求取視窗內影象的的加權平均
mu2 = tf.nn.conv2d(padded_img2, window, strides=[1,1,1,1], padding='valid')
mu1_sq = mu1*mu1 #img(x,y) ux*ux 均方
mu2_sq = mu2*mu2 #img(x,y) uy*uy
mu1_mu2 = mu1*mu2 #img(x,y) ux*uy
#求取方差，方差等於平方的期望減去期望的平方，平方的均值減去均值的平方
paddedimg11 = padded_img1*padded_img1
paddedimg22 = padded_img2*padded_img2
paddedimg12 = padded_img1*padded_img2
sigma1_sq = tf.nn.conv2d(paddedimg11, window, strides=[1,1,1,1],padding='valid') - mu1_sq #sigma1方差
sigma2_sq = tf.nn.conv2d(paddedimg22, window, strides=[1,1,1,1],padding='valid') - mu2_sq #sigma2方差
sigma12 = tf.nn.conv2d(paddedimg12, window, strides=[1,1,1,1],padding='valid') - mu1_mu2 #sigma12協方差，乘積的均值減去均值的乘積
ssim_value = tf.clip_by_value(((2*mu1_mu2 + c1)*(2*sigma12 + c2))/((mu1_sq + mu2_sq + c1)*(sigma1_sq + sigma2_sq + c2)), 0, 1)
if cs_map: #只考慮contrast對比度，structure結構，不考慮light亮度
cs_map_value = tf.clip_by_value((2*sigma12 + c2)/(sigma1_sq + sigma2_sq + c2), 0, 1) #對比度結構map
value = (ssim_value, cs_map_value)
else:
value = ssim_value
if mean_metric: #求取矩陣的均值，否則返回ssim矩陣
value = tf.reduce_mean(value)
return value
def tf_ssim_l1_loss(img1, img2, mean_metric=true, filter_size=11, filter_sigma=1.5, alpha=0.84):
l1 = tf_gauss_weighted_l1(img1, img2, mean_metric=false, filter_size=filter_size, filter_sigma=filter_sigma)
if mean_metric:
loss_ssim= 1 - tf_ssim(img1, img2, cs_map=false, mean_metric=true, filter_size=filter_size, filter_sigma=filter_sigma)
loss_l1 = tf.reduce_mean(l1)
value = loss_ssim * alpha + loss_l1 * (1-alpha)
else:
loss_ssim= 1 - tf_ssim(img1, img2, cs_map=false, mean_metric=false, filter_size=filter_size, filter_sigma=filter_sigma)
value = loss_ssim * alpha + l1 * (1-alpha)
return value, loss_ssim
#%%img1 = np.arange(10000,dtype=np.float32).reshape([1,100,100,1])
img2 = np.arange(10000,dtype=np.float32).reshape([1,100,100,1])-1
l1_loss = tf_ssim_l1_loss(tf.constant(img1),tf.constant(img2))
with tf.session() as sess:
print(sess.run(l1_loss))
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