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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
A class for some standard image quality metrics
"""
import numpy as np
class QualityTools:
def __init__(self, im1, im2):
if im1.size != im2.size:
print ('Error: Sizes of images/volumes are different')
raise SystemExit
self.im1 = im1 # image or volume - 1
self.im2 = im2 # image or volume - 2
def nrmse(self):
""" Normalised Root Mean Square Error """
rmse = np.sqrt(np.sum((self.im2 - self.im1) ** 2) / float(self.im1.size))
max_val = max(np.max(self.im1), np.max(self.im2))
min_val = min(np.min(self.im1), np.min(self.im2))
return 1 - (rmse / (max_val - min_val))
def rmse(self):
""" Root Mean Square Error """
rmse = np.sqrt(np.sum((self.im1 - self.im2) ** 2) / float(self.im1.size))
return rmse
def ssim(self, window, k=(0.01, 0.03), l=255):
from scipy.signal import fftconvolve
"""See https://ece.uwaterloo.ca/~z70wang/research/ssim/"""
# Check if the window is smaller than the images.
for a, b in zip(window.shape, self.im1.shape):
if a > b:
return None, None
# Values in k must be positive according to the base implementation.
for ki in k:
if ki < 0:
return None, None
c1 = (k[0] * l) ** 2
c2 = (k[1] * l) ** 2
window = window/np.sum(window)
mu1 = fftconvolve(self.im1, window, mode='valid')
mu2 = fftconvolve(self.im2, window, mode='valid')
mu1_sq = mu1 * mu1
mu2_sq = mu2 * mu2
mu1_mu2 = mu1 * mu2
sigma1_sq = fftconvolve(self.im1 * self.im1, window, mode='valid') - mu1_sq
sigma2_sq = fftconvolve(self.im2 * self.im2, window, mode='valid') - mu2_sq
sigma12 = fftconvolve(self.im1 * self.im2, window, mode='valid') - mu1_mu2
if c1 > 0 and c2 > 0:
num = (2 * mu1_mu2 + c1) * (2 * sigma12 + c2)
den = (mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)
ssim_map = num / den
else:
num1 = 2 * mu1_mu2 + c1
num2 = 2 * sigma12 + c2
den1 = mu1_sq + mu2_sq + c1
den2 = sigma1_sq + sigma2_sq + c2
ssim_map = np.ones(np.shape(mu1))
index = (den1 * den2) > 0
ssim_map[index] = (num1[index] * num2[index]) / (den1[index] * den2[index])
index = (den1 != 0) & (den2 == 0)
ssim_map[index] = num1[index] / den1[index]
mssim = ssim_map.mean()
return mssim, ssim_map
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