metric.py
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import numpy as np
from scipy import signal
from scipy import ndimage
def psnr(img1, img2):
mse = np.mean( (img1 - img2) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def ssim(img1, img2, cs_map=False):
"""Return the Structural Similarity Map corresponding to input images img1
and img2 (images are assumed to be uint8)
This function attempts to mimic precisely the functionality of ssim.m a
MATLAB provided by the author's of SSIM
https://ece.uwaterloo.ca/~z70wang/research/ssim/ssim_index.m
"""
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
size = 11
sigma = 1.5
window = fspecial_gauss(size, sigma)
K1 = 0.01
K2 = 0.03
L = 255 #bitdepth of image
C1 = (K1*L)**2
C2 = (K2*L)**2
mu1 = signal.fftconvolve(window, img1, mode='valid')
mu2 = signal.fftconvolve(window, img2, mode='valid')
mu1_sq = mu1*mu1
mu2_sq = mu2*mu2
mu1_mu2 = mu1*mu2
sigma1_sq = signal.fftconvolve(window, img1*img1, mode='valid') - mu1_sq
sigma2_sq = signal.fftconvolve(window, img2*img2, mode='valid') - mu2_sq
sigma12 = signal.fftconvolve(window, img1*img2, mode='valid') - mu1_mu2
if cs_map:
return (((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:
return ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*
(sigma1_sq + sigma2_sq + C2))
def ms_ssim(img1, img2):
"""This function implements Multi-Scale Structural Similarity (MSSSIM) Image
Quality Assessment according to Z. Wang's "Multi-scale structural similarity
for image quality assessment" Invited Paper, IEEE Asilomar Conference on
Signals, Systems and Computers, Nov. 2003
Author's MATLAB implementation:-
http://www.cns.nyu.edu/~lcv/ssim/msssim.zip
"""
level = 5
weight = np.array([0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
downsample_filter = np.ones((2, 2))/4.0
im1 = img1.astype(np.float64)
im2 = img2.astype(np.float64)
mssim = np.array([])
mcs = np.array([])
for l in range(level):
ssim_map, cs_map = ssim(im1, im2, cs_map=True)
mssim = np.append(mssim, ssim_map.mean())
mcs = np.append(mcs, cs_map.mean())
filtered_im1 = ndimage.filters.convolve(im1, downsample_filter,
mode='reflect')
filtered_im2 = ndimage.filters.convolve(im2, downsample_filter,
mode='reflect')
im1 = filtered_im1[::2, ::2]
im2 = filtered_im2[::2, ::2]
return (np.prod(mcs[0:level-1]**weight[0:level-1])*
(mssim[level-1]**weight[level-1]))
def fspecial_gauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function
"""
x, y = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]
g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))
return g/g.sum()