dTV method added

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
deleted 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()
\ No newline at end of file