diff options
Diffstat (limited to 'recipe')
| -rw-r--r-- | recipe/bld.bat | 20 | ||||
| -rw-r--r-- | recipe/build.sh | 18 | ||||
| -rw-r--r-- | recipe/conda_build_config.yaml | 9 | ||||
| -rw-r--r-- | recipe/meta.yaml | 40 | ||||
| -rwxr-xr-x | recipe/run_test.py | 819 |
5 files changed, 906 insertions, 0 deletions
diff --git a/recipe/bld.bat b/recipe/bld.bat new file mode 100644 index 0000000..6c84355 --- /dev/null +++ b/recipe/bld.bat @@ -0,0 +1,20 @@ +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/recipe/build.sh b/recipe/build.sh new file mode 100644 index 0000000..a156193 --- /dev/null +++ b/recipe/build.sh @@ -0,0 +1,18 @@ + +#mkdir "$SRC_DIR/ccpi" +#cp -rv "$RECIPE_DIR/../src/Matlab" "$SRC_DIR/ccpi" +#cp -rv "$RECIPE_DIR/../src/Python" "$SRC_DIR/ccpi" +#cp -rv "$RECIPE_DIR/../src/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/recipe/conda_build_config.yaml b/recipe/conda_build_config.yaml new file mode 100644 index 0000000..fbe82dc --- /dev/null +++ b/recipe/conda_build_config.yaml @@ -0,0 +1,9 @@ +python: + - 2.7 # [not win] + - 3.5 + - 3.6 +# - 3.7 +numpy: + - 1.12 + - 1.14 + - 1.15 diff --git a/recipe/meta.yaml b/recipe/meta.yaml new file mode 100644 index 0000000..61d17bd --- /dev/null +++ b/recipe/meta.yaml @@ -0,0 +1,40 @@ +package: + name: ccpi-regulariser + version: {{CIL_VERSION}} + +build: + preserve_egg_dir: False + number: 0 + script_env: + - CIL_VERSION + +test: + files: + - ../test/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/recipe/run_test.py b/recipe/run_test.py new file mode 100755 index 0000000..21f3216 --- /dev/null +++ b/recipe/run_test.py @@ -0,0 +1,819 @@ +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()
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