5 files changed, 0 insertions, 905 deletions
diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat
deleted file mode 100644
index 6c84355..0000000
--- a/Wrappers/Python/conda-recipe/bld.bat
+++ /dev/null
@@ -1,20 +0,0 @@
-IF NOT DEFINED CIL_VERSION (
-ECHO CIL_VERSION Not Defined.
-exit 1
-)
-
-mkdir "%SRC_DIR%\ccpi"
-ROBOCOPY /E "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi"
-ROBOCOPY /E "%RECIPE_DIR%\..\..\..\Core" "%SRC_DIR%\Core"
-::cd %SRC_DIR%\ccpi\Python
-cd %SRC_DIR%
-
-:: issue cmake to create setup.py
-cmake -G "NMake Makefiles" %RECIPE_DIR%\..\..\..\ -DBUILD_PYTHON_WRAPPERS=ON -DCONDA_BUILD=ON -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB="%CONDA_PREFIX%\lib" -DLIBRARY_INC="%CONDA_PREFIX%" -DCMAKE_INSTALL_PREFIX="%PREFIX%\Library" 
-
-::%PYTHON% setup-regularisers.py build_ext
-::if errorlevel 1 exit 1
-::%PYTHON% setup-regularisers.py install
-::if errorlevel 1 exit 1
-nmake install
-if errorlevel 1 exit 1
-\ No newline at end of file
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
deleted file mode 100644
index 39c0f2c..0000000
--- a/Wrappers/Python/conda-recipe/build.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-
-mkdir "$SRC_DIR/ccpi"
-cp -rv "$RECIPE_DIR/../.." "$SRC_DIR/ccpi"
-cp -rv "$RECIPE_DIR/../../../Core" "$SRC_DIR/Core"
-
-cd $SRC_DIR
-##cuda=off
-
-cmake -G "Unix Makefiles" $RECIPE_DIR/../../../ -DBUILD_PYTHON_WRAPPER=ON -DCONDA_BUILD=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB=$CONDA_PREFIX/lib -DLIBRARY_INC=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$PREFIX
-
-
-make install
-
-#$PYTHON setup-regularisers.py build_ext
-#$PYTHON setup-regularisers.py install
-
-
diff --git a/Wrappers/Python/conda-recipe/conda_build_config.yaml b/Wrappers/Python/conda-recipe/conda_build_config.yaml
deleted file mode 100644
index fbe82dc..0000000
--- a/Wrappers/Python/conda-recipe/conda_build_config.yaml
+++ /dev/null
@@ -1,9 +0,0 @@
-python:
-  - 2.7 # [not win]
-  - 3.5
-  - 3.6
-#  - 3.7
-numpy:
-  - 1.12
-  - 1.14
-  - 1.15
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
deleted file mode 100644
index 7435b2b..0000000
--- a/Wrappers/Python/conda-recipe/meta.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-package:
-  name: ccpi-regulariser
-  version: {{CIL_VERSION}}
-  
-build:
-  preserve_egg_dir: False
-  number: 0
-  script_env:
-    - CIL_VERSION
-  
-test:
-  files:
-    - lena_gray_512.tif
-  requires:
-    - pillow=4.1.1
-
-requirements:
-  build:
-    - python
-    - numpy {{ numpy }}
-    - setuptools
-    - cython
-    - vc 14 # [win and py36] 
-    - vc 14 # [win and py35] 
-    - vc 9  # [win and py27]
-    - cmake 
-
-  run:
-    - {{ pin_compatible('numpy', max_pin='x.x') }}
-    - python
-    - numpy
-    - vc 14 # [win and py36] 
-    - vc 14 # [win and py35] 
-    - vc 9  # [win and py27]
-    - libgcc-ng
-
-about:
-  home: http://www.ccpi.ac.uk
-  license:  BSD license
-  summary: 'CCPi Core Imaging Library Quantification Toolbox'
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
deleted file mode 100755
index 21f3216..0000000
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import unittest
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
-from PIL import Image
-
-class TiffReader(object):
-    def imread(self, filename):
-        return np.asarray(Image.open(filename))
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-def nrmse(im1, im2):
-    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))
-    max_val = max(np.max(im1), np.max(im2))
-    min_val = min(np.min(im1), np.min(im2))
-    return 1 - (rmse / (max_val - min_val))
-    
-def rmse(im1, im2):
-    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
-    return rmse
-###############################################################################
-
-class TestRegularisers(unittest.TestCase):
-    
-
-    def test_ROF_TV_CPU_vs_GPU(self):
-        #print ("tomas debug test function")
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________ROF-TV bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        # set parameters
-        pars = {'algorithm': ROF_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 2500,\
-        'time_marching_parameter': 0.00002
-        }
-        print ("#############ROF TV CPU####################")
-        start_time = timeit.default_timer()
-        rof_cpu = ROF_TV(pars['input'],
-                     pars['regularisation_parameter'],
-                     pars['number_of_iterations'],
-                     pars['time_marching_parameter'],'cpu')
-        rms = rmse(Im, rof_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("##############ROF TV GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            rof_gpu = ROF_TV(pars['input'], 
-                             pars['regularisation_parameter'],
-                             pars['number_of_iterations'], 
-                             pars['time_marching_parameter'],'gpu')
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms = rmse(Im, rof_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = ROF_TV
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-04
-        diff_im = np.zeros(np.shape(rof_cpu))
-        diff_im = abs(rof_cpu - rof_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum() , 1)
-        
-    def test_FGP_TV_CPU_vs_GPU(self):
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________FGP-TV bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        
-        # set parameters
-        pars = {'algorithm' : FGP_TV, \
-                'input' : u0,\
-                'regularisation_parameter':0.04, \
-                'number_of_iterations' :1200 ,\
-                'tolerance_constant':0.00001,\
-                'methodTV': 0 ,\
-                'nonneg': 0 ,\
-                'printingOut': 0 
-                }
-                
-        print ("#############FGP TV CPU####################")
-        start_time = timeit.default_timer()
-        fgp_cpu = FGP_TV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['methodTV'],
-                      pars['nonneg'],
-                      pars['printingOut'],'cpu')  
-                     
-                     
-        rms = rmse(Im, fgp_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        
-        print ("##############FGP TV GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            fgp_gpu = FGP_TV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['methodTV'],
-                      pars['nonneg'],
-                      pars['printingOut'],'gpu')
-
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms = rmse(Im, fgp_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = FGP_TV
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(fgp_cpu))
-        diff_im = abs(fgp_cpu - fgp_gpu)
-        diff_im[diff_im > tolerance] = 1
-
-        self.assertLessEqual(diff_im.sum() , 1)
-
-    def test_SB_TV_CPU_vs_GPU(self):
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________SB-TV bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        
-        # set parameters
-        pars = {'algorithm' : SB_TV, \
-                'input' : u0,\
-                'regularisation_parameter':0.04, \
-                'number_of_iterations' :150 ,\
-                'tolerance_constant':1e-05,\
-                'methodTV': 0 ,\
-                'printingOut': 0 
-                }
-                
-        print ("#############SB-TV CPU####################")
-        start_time = timeit.default_timer()
-        sb_cpu = SB_TV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['methodTV'],
-                      pars['printingOut'],'cpu')  
-                     
-                     
-        rms = rmse(Im, sb_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        
-        print ("##############SB TV GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            
-            sb_gpu = SB_TV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['methodTV'],
-                      pars['printingOut'],'gpu')
-
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms = rmse(Im, sb_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = SB_TV
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(sb_cpu))
-        diff_im = abs(sb_cpu - sb_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum(), 1)
-
-    def test_TGV_CPU_vs_GPU(self):
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________TGV bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        
-        # set parameters
-        pars = {'algorithm' : TGV, \
-                'input' : u0,\
-                'regularisation_parameter':0.04, \
-                'alpha1':1.0,\
-                'alpha0':2.0,\
-                'number_of_iterations' :250 ,\
-                'LipshitzConstant' :12 ,\
-                }
-                
-        print ("#############TGV CPU####################")
-        start_time = timeit.default_timer()
-        tgv_cpu = TGV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['alpha1'],
-                      pars['alpha0'],
-                      pars['number_of_iterations'],
-                      pars['LipshitzConstant'],'cpu')
-                     
-        rms = rmse(Im, tgv_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        
-        print ("##############TGV GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            tgv_gpu = TGV(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['alpha1'],
-                      pars['alpha0'],
-                      pars['number_of_iterations'],
-                      pars['LipshitzConstant'],'gpu')
-
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms = rmse(Im, tgv_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = TGV
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(tgv_gpu))
-        diff_im = abs(tgv_cpu - tgv_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum() , 1)
-
-    def test_LLT_ROF_CPU_vs_GPU(self):
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________LLT-ROF bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        
-        # set parameters
-        pars = {'algorithm' : LLT_ROF, \
-                'input' : u0,\
-                'regularisation_parameterROF':0.04, \
-                'regularisation_parameterLLT':0.01, \
-                'number_of_iterations' :1000 ,\
-                'time_marching_parameter' :0.0001 ,\
-                }
-                
-        print ("#############LLT- ROF CPU####################")
-        start_time = timeit.default_timer()
-        lltrof_cpu = LLT_ROF(pars['input'], 
-                      pars['regularisation_parameterROF'],
-                      pars['regularisation_parameterLLT'],
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'],'cpu')
-        
-        rms = rmse(Im, lltrof_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("#############LLT- ROF GPU####################")
-        start_time = timeit.default_timer()
-        try:
-            lltrof_gpu = LLT_ROF(pars['input'], 
-                      pars['regularisation_parameterROF'],
-                      pars['regularisation_parameterLLT'],
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'],'gpu')
-        
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms = rmse(Im, lltrof_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = LLT_ROF
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-04
-        diff_im = np.zeros(np.shape(lltrof_gpu))
-        diff_im = abs(lltrof_cpu - lltrof_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum(), 1)
-
-    def test_NDF_CPU_vs_GPU(self):
-        print(__name__)
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("_______________NDF bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        
-        # set parameters
-        pars = {'algorithm' : NDF, \
-                'input' : u0,\
-                'regularisation_parameter':0.06, \
-                'edge_parameter':0.04,\
-                'number_of_iterations' :1000 ,\
-                'time_marching_parameter':0.025,\
-                'penalty_type':  1
-                }
-                
-        print ("#############NDF CPU####################")
-        start_time = timeit.default_timer()
-        ndf_cpu = NDF(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['edge_parameter'], 
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'], 
-                      pars['penalty_type'],'cpu')
-                     
-        rms = rmse(Im, ndf_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        
-        print ("##############NDF GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            ndf_gpu = NDF(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['edge_parameter'], 
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'], 
-                      pars['penalty_type'],'gpu')
-                     
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-        rms = rmse(Im, ndf_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = NDF
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(ndf_cpu))
-        diff_im = abs(ndf_cpu - ndf_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum(), 1)
-
-        
-    def test_Diff4th_CPU_vs_GPU(self):
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("___Anisotropic Diffusion 4th Order (2D)____")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        # set parameters
-        pars = {'algorithm' : Diff4th, \
-        'input' : u0,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.001
-        }
-        
-        print ("#############Diff4th CPU####################")
-        start_time = timeit.default_timer()
-        diff4th_cpu = Diff4th(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['edge_parameter'], 
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'],'cpu')
-                     
-        rms = rmse(Im, diff4th_cpu)
-        pars['rmse'] = rms
-
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("##############Diff4th GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            diff4th_gpu = Diff4th(pars['input'], 
-                      pars['regularisation_parameter'],
-                      pars['edge_parameter'], 
-                      pars['number_of_iterations'],
-                      pars['time_marching_parameter'], 'gpu')
-                     
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-        rms = rmse(Im, diff4th_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = Diff4th
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(diff4th_cpu))
-        diff_im = abs(diff4th_cpu - diff4th_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum() , 1)
-
-    def test_FDGdTV_CPU_vs_GPU(self):
-        filename = os.path.join("lena_gray_512.tif")
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        
-        Im = Im/255
-        perc = 0.05
-        u0 = Im + np.random.normal(loc = 0 ,
-                                          scale = perc * Im , 
-                                          size = np.shape(Im))
-        u_ref = Im + np.random.normal(loc = 0 ,
-                                          scale = 0.01 * Im , 
-                                          size = np.shape(Im))
-        
-        # map the u0 u0->u0>0
-        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-        u0 = u0.astype('float32')
-        u_ref = u_ref.astype('float32')
-        
-
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("____________FGP-dTV bench___________________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        
-        # set parameters
-        pars = {'algorithm' : FGP_dTV, \
-                'input' : u0,\
-                'refdata' : u_ref,\
-                'regularisation_parameter':0.04, \
-                'number_of_iterations' :1000 ,\
-                'tolerance_constant':1e-07,\
-                'eta_const':0.2,\
-                'methodTV': 0 ,\
-                'nonneg': 0 ,\
-                'printingOut': 0 
-                }
-                
-        print ("#############FGP dTV CPU####################")
-        start_time = timeit.default_timer()
-        fgp_dtv_cpu = FGP_dTV(pars['input'], 
-                      pars['refdata'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['eta_const'], 
-                      pars['methodTV'],
-                      pars['nonneg'],
-                      pars['printingOut'],'cpu')
-                     
-                     
-        rms = rmse(Im, fgp_dtv_cpu)
-        pars['rmse'] = rms
-        
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("##############FGP dTV GPU##################")
-        start_time = timeit.default_timer()
-        try:
-            fgp_dtv_gpu = FGP_dTV(pars['input'], 
-                      pars['refdata'], 
-                      pars['regularisation_parameter'],
-                      pars['number_of_iterations'],
-                      pars['tolerance_constant'], 
-                      pars['eta_const'], 
-                      pars['methodTV'],
-                      pars['nonneg'],
-                      pars['printingOut'],'gpu')
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-        rms = rmse(Im, fgp_dtv_gpu)
-        pars['rmse'] = rms
-        pars['algorithm'] = FGP_dTV
-        txtstr = printParametersToString(pars)
-        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-        print (txtstr)
-        print ("--------Compare the results--------")
-        tolerance = 1e-05
-        diff_im = np.zeros(np.shape(fgp_dtv_cpu))
-        diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
-        diff_im[diff_im > tolerance] = 1
-        self.assertLessEqual(diff_im.sum(), 1)
-
-    def test_cpu_ROF_TV(self):
-        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
-        
-        filename = os.path.join("lena_gray_512.tif")
-
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        Im = Im/255
-        
-        """
-        # read noiseless image
-        Im = plt.imread(filename)
-        Im = np.asarray(Im, dtype='float32')
-        """
-        tolerance = 1e-05
-        rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model
-
-        # set parameters for ROF-TV
-        pars_rof_tv = {'algorithm': ROF_TV, \
-                            'input' : Im,\
-                            'regularisation_parameter':0.04,\
-                            'number_of_iterations': 50,\
-                            'time_marching_parameter': 0.00001
-                            }
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("_________testing ROF-TV (2D, CPU)__________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        rof_cpu = ROF_TV(pars_rof_tv['input'],
-             pars_rof_tv['regularisation_parameter'],
-             pars_rof_tv['number_of_iterations'],
-             pars_rof_tv['time_marching_parameter'],'cpu')
-        rms_rof = rmse(Im, rof_cpu)
-        
-        # now compare obtained rms with the expected value
-        self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)
-    def test_cpu_FGP_TV(self):
-        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
-        
-        filename = os.path.join("lena_gray_512.tif")
-
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        Im = Im/255
-        """
-        # read noiseless image
-        Im = plt.imread(filename)
-        Im = np.asarray(Im, dtype='float32')
-        """
-        tolerance = 1e-05
-        rms_fgp_exp = 0.019152347 #expected value for FGP model
-        
-        pars_fgp_tv = {'algorithm' : FGP_TV, \
-                            'input' : Im,\
-                            'regularisation_parameter':0.04, \
-                            'number_of_iterations' :50 ,\
-                            'tolerance_constant':1e-06,\
-                            'methodTV': 0 ,\
-                            'nonneg': 0 ,\
-                            'printingOut': 0 
-                            }
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("_________testing FGP-TV (2D, CPU)__________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        fgp_cpu = FGP_TV(pars_fgp_tv['input'], 
-              pars_fgp_tv['regularisation_parameter'],
-              pars_fgp_tv['number_of_iterations'],
-              pars_fgp_tv['tolerance_constant'], 
-              pars_fgp_tv['methodTV'],
-              pars_fgp_tv['nonneg'],
-              pars_fgp_tv['printingOut'],'cpu')  
-        rms_fgp = rmse(Im, fgp_cpu)
-        # now compare obtained rms with the expected value
-        self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)
-
-    def test_gpu_ROF(self):
-        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
-        filename = os.path.join("lena_gray_512.tif")
-
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)
-        Im = np.asarray(Im, dtype='float32')
-        Im = Im/255
-        
-        tolerance = 1e-05
-        rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model
-        
-        # set parameters for ROF-TV
-        pars_rof_tv = {'algorithm': ROF_TV, \
-                            'input' : Im,\
-                            'regularisation_parameter':0.04,\
-                            'number_of_iterations': 50,\
-                            'time_marching_parameter': 0.00001
-                            }
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("_________testing ROF-TV (2D, GPU)__________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        try:
-            rof_gpu = ROF_TV(pars_rof_tv['input'],
-             pars_rof_tv['regularisation_parameter'],
-             pars_rof_tv['number_of_iterations'],
-             pars_rof_tv['time_marching_parameter'],'gpu')
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-
-        rms_rof = rmse(Im, rof_gpu)
-        # now compare obtained rms with the expected value
-        self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)
-    
-    def test_gpu_FGP(self):
-        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")
-        filename = os.path.join("lena_gray_512.tif")
-
-        plt = TiffReader()
-        # read image
-        Im = plt.imread(filename)                     
-        Im = np.asarray(Im, dtype='float32')
-        Im = Im/255
-        tolerance = 1e-05
-        
-        rms_fgp_exp = 0.019152347 #expected value for FGP model
-        
-        # set parameters for FGP-TV
-        pars_fgp_tv = {'algorithm' : FGP_TV, \
-                            'input' : Im,\
-                            'regularisation_parameter':0.04, \
-                            'number_of_iterations' :50 ,\
-                            'tolerance_constant':1e-06,\
-                            'methodTV': 0 ,\
-                            'nonneg': 0 ,\
-                            'printingOut': 0 
-                            }
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        print ("_________testing FGP-TV (2D, GPU)__________")
-        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-        try:
-            fgp_gpu = FGP_TV(pars_fgp_tv['input'], 
-              pars_fgp_tv['regularisation_parameter'],
-              pars_fgp_tv['number_of_iterations'],
-              pars_fgp_tv['tolerance_constant'], 
-              pars_fgp_tv['methodTV'],
-              pars_fgp_tv['nonneg'],
-              pars_fgp_tv['printingOut'],'gpu')  
-        except ValueError as ve:
-            self.skipTest("Results not comparable. GPU computing error.")
-        rms_fgp = rmse(Im, fgp_gpu)
-        # now compare obtained rms with the expected value
-
-        self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)
-
-if __name__ == '__main__':
-    unittest.main()