MOVE: Wrappers/Python/supp to src/Python/ccpi/supp

2 files changed, 0 insertions, 65 deletions

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