diff options
Diffstat (limited to 'Wrappers/Python/test')
| -rw-r--r-- | Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc | bin | 823 -> 0 bytes | |||
| -rw-r--r-- | Wrappers/Python/test/run_test.py | 149 |
2 files changed, 0 insertions, 149 deletions
diff --git a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc b/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc Binary files differdeleted file mode 100644 index 2196a53..0000000 --- a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc +++ /dev/null diff --git a/Wrappers/Python/test/run_test.py b/Wrappers/Python/test/run_test.py deleted file mode 100644 index 04bbd40..0000000 --- a/Wrappers/Python/test/run_test.py +++ /dev/null @@ -1,149 +0,0 @@ -import unittest -import numpy as np -import os -from ccpi.filters.regularisers import ROF_TV, FGP_TV -import matplotlib.pyplot as plt - -def rmse(im1, im2): - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) - return rmse - -class TestRegularisers(unittest.TestCase): - - def setUp(self): - pass - - def test_cpu_regularisers(self): - filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - tolerance = 1e-05 - rms_rof_exp = 0.006812507 #expected value for ROF model - rms_fgp_exp = 0.019152347 #expected value for FGP 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': 0.0025 - } - # set parameters for FGP-TV - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-08,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, CPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - res = True - 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) - """ - if abs(rms_rof-self.rms_rof_exp) > self.tolerance: - raise TypeError('ROF-TV (2D, CPU) test FAILED') - else: - print ("test PASSED") - """ - 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) - """ - if abs(rms_fgp-self.rms_fgp_exp) > self.tolerance: - raise TypeError('FGP-TV (2D, CPU) test FAILED') - else: - print ("test PASSED") - """ - self.assertTrue(res) - def test_gpu_regularisers(self): - filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') - - Im = Im/255 - tolerance = 1e-05 - rms_rof_exp = 0.006812507 #expected value for ROF model - rms_fgp_exp = 0.019152347 #expected value for FGP 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': 0.0025 - } - # set parameters for FGP-TV - pars_fgp_tv = {'algorithm' : FGP_TV, \ - 'input' : Im,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :50 ,\ - 'tolerance_constant':1e-08,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing ROF-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - res = True - 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') - rms_rof = rmse(Im, rof_gpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance) - """ - if abs(rms_rof-self.rms_rof_exp) > self.tolerance: - raise TypeError('ROF-TV (2D, GPU) test FAILED') - else: - print ("test PASSED") - """ - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - print ("_________testing FGP-TV (2D, GPU)__________") - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - 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') - rms_fgp = rmse(Im, fgp_gpu) - # now compare obtained rms with the expected value - self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance) - """ - if abs(rms_fgp-self.rms_fgp_exp) > self.tolerance: - raise TypeError('FGP-TV (2D, GPU) test FAILED') - else: - print ("test PASSED") - """ - self.assertTrue(res) -if __name__ == '__main__': - unittest.main()
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