Merge pull request #104 from vais-ral/newdirstructure

New directory structure, Merged other changes. The build script checks old and new structure.

5 files changed, 0 insertions, 279 deletions

diff --git a/Wrappers/Python/ccpi/__init__.py b/Wrappers/Python/ccpi/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/Wrappers/Python/ccpi/__init__.py
+++ /dev/null
diff --git a/Wrappers/Python/ccpi/filters/__init__.py b/Wrappers/Python/ccpi/filters/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/Wrappers/Python/ccpi/filters/__init__.py
+++ /dev/null
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
deleted file mode 100644
index 588ea32..0000000
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ /dev/null
@@ -1,214 +0,0 @@
-"""
-script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
-"""
-
-from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
-try:
-    from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU
-    gpu_enabled = True
-except ImportError:
-    gpu_enabled = False    
-from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
-
-def ROF_TV(inputData, regularisation_parameter, iterations,
-                     time_marching_parameter,device='cpu'):
-    if device == 'cpu':
-        return TV_ROF_CPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     time_marching_parameter)
-    elif device == 'gpu' and gpu_enabled:
-        return TV_ROF_GPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     time_marching_parameter)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-
-def FGP_TV(inputData, regularisation_parameter,iterations,
-                     tolerance_param, methodTV, nonneg, printM, device='cpu'):
-    if device == 'cpu':
-        return TV_FGP_CPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     methodTV,
-                     nonneg,
-                     printM)
-    elif device == 'gpu' and gpu_enabled:
-        return TV_FGP_GPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     methodTV,
-                     nonneg,
-                     printM)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def SB_TV(inputData, regularisation_parameter, iterations,
-                     tolerance_param, methodTV, printM, device='cpu'):
-    if device == 'cpu':
-        return TV_SB_CPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     methodTV,
-                     printM)
-    elif device == 'gpu' and gpu_enabled:
-        return TV_SB_GPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     methodTV,
-                     printM)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
-                     tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'):
-    if device == 'cpu':
-        return dTV_FGP_CPU(inputData,
-                     refdata,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     eta_const,
-                     methodTV,
-                     nonneg,
-                     printM)
-    elif device == 'gpu' and gpu_enabled:
-        return dTV_FGP_GPU(inputData,
-                     refdata,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param,
-                     eta_const,
-                     methodTV,
-                     nonneg,
-                     printM)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
-        return TNV_CPU(inputData,
-                     regularisation_parameter,
-                     iterations, 
-                     tolerance_param)
-def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
-                     time_marching_parameter, penalty_type, device='cpu'):
-    if device == 'cpu':
-        return NDF_CPU(inputData,
-                     regularisation_parameter,
-                     edge_parameter,
-                     iterations, 
-                     time_marching_parameter,
-                     penalty_type)
-    elif device == 'gpu' and gpu_enabled:
-        return NDF_GPU(inputData,
-                     regularisation_parameter,
-                     edge_parameter,
-                     iterations, 
-                     time_marching_parameter,
-                     penalty_type)
-    else:
-        if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations,
-                     time_marching_parameter, device='cpu'):
-    if device == 'cpu':
-        return Diff4th_CPU(inputData,
-                     regularisation_parameter,
-                     edge_parameter,
-                     iterations, 
-                     time_marching_parameter)
-    elif device == 'gpu' and gpu_enabled:
-        return Diff4th_GPU(inputData,
-                     regularisation_parameter,
-                     edge_parameter,
-                     iterations, 
-                     time_marching_parameter)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-        
-def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter, device='cpu'):
-    if device == 'cpu':
-        return PATCHSEL_CPU(inputData,
-                     searchwindow,
-                     patchwindow,
-                     neighbours, 
-                     edge_parameter)
-    elif device == 'gpu' and gpu_enabled:
-        return PATCHSEL_GPU(inputData,
-                     searchwindow,
-                     patchwindow,
-                     neighbours, 
-                     edge_parameter)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-
-def NLTV(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
-    return NLTV_CPU(inputData,
-                     H_i,
-                     H_j,
-                     H_k, 
-                     Weights,
-                     regularisation_parameter,
-                     iterations)
-
-def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
-                     LipshitzConst, device='cpu'):
-    if device == 'cpu':
-        return TGV_CPU(inputData, 
-					regularisation_parameter, 
-					alpha1, 
-					alpha0, 
-					iterations,
-                    LipshitzConst)
-    elif device == 'gpu' and gpu_enabled:
-        return TGV_GPU(inputData, 
-					regularisation_parameter, 
-					alpha1, 
-					alpha0, 
-					iterations,
-                    LipshitzConst)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
-                     time_marching_parameter, device='cpu'):
-    if device == 'cpu':
-        return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
-    elif device == 'gpu' and gpu_enabled:
-        return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
-    else:
-        if not gpu_enabled and device == 'gpu':
-            raise ValueError ('GPU is not available')
-        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
-                         .format(device))
-def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
-                     time_marching_parameter, penalty_type):
-        return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, 
-        edge_parameter, iterations, time_marching_parameter, penalty_type)
-        
-def NVM_INP(inputData, maskData, SW_increment, iterations):
-        return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations)
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