evaluation.py

import numpy as np

def psnr(img1, img2):
    mse = np.mean( (img1 - img2) ** 2 )
    return 20*np.log10(255/ np.sqrt(mse+1e-8))


from scipy import signal


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()
def ssim(img1, img2, cs_map=False):
   
    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))