diff options
Diffstat (limited to 'Wrappers/Python/conda-recipe')
| -rw-r--r-- | Wrappers/Python/conda-recipe/run_test.py.in | 148 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/testLena.npy | bin | 0 -> 1048656 bytes |
2 files changed, 148 insertions, 0 deletions
diff --git a/Wrappers/Python/conda-recipe/run_test.py.in b/Wrappers/Python/conda-recipe/run_test.py.in new file mode 100644 index 0000000..9a6f4de --- /dev/null +++ b/Wrappers/Python/conda-recipe/run_test.py.in @@ -0,0 +1,148 @@ +import unittest +import numpy as np +from ccpi.filters.regularisers import ROF_TV, FGP_TV + +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" ,"testLena.npy") + + Im = np.load('testLena.npy'); + """ + # 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" ,"testLena.npy") + + Im = np.load('testLena.npy'); + + #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() diff --git a/Wrappers/Python/conda-recipe/testLena.npy b/Wrappers/Python/conda-recipe/testLena.npy Binary files differnew file mode 100644 index 0000000..14bc0e3 --- /dev/null +++ b/Wrappers/Python/conda-recipe/testLena.npy |