diff options
Diffstat (limited to 'recipe/run_test.py')
| -rwxr-xr-x | recipe/run_test.py | 821 |
1 files changed, 0 insertions, 821 deletions
diff --git a/recipe/run_test.py b/recipe/run_test.py deleted file mode 100755 index f551616..0000000 --- a/recipe/run_test.py +++ /dev/null @@ -1,821 +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()
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