dTV method added

1 files changed, 149 insertions, 0 deletions

diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
new file mode 100644
index 0000000..04bbd40
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -0,0 +1,149 @@
+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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