Merge remote-tracking branch 'remotes/origin/master' into newdirstructure

Conflicts:
	demos/demoMatlab_denoise.m
	demos/qualitymetrics.py

2 files changed, 65 insertions, 0 deletions

diff --git a/Wrappers/Python/ccpi/supp/__init__.py b/Wrappers/Python/ccpi/supp/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/Wrappers/Python/ccpi/supp/__init__.py
diff --git a/Wrappers/Python/ccpi/supp/qualitymetrics.py b/Wrappers/Python/ccpi/supp/qualitymetrics.py
new file mode 100644
index 0000000..f44d832
--- /dev/null
+++ b/Wrappers/Python/ccpi/supp/qualitymetrics.py
@@ -0,0 +1,65 @@
+#!/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