Cmake/Cython fixes to compile ROF-FGP

5 files changed, 32 insertions, 30 deletions

diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py
index b08c418..53b8538 100644
--- a/Wrappers/Python/demo/test_cpu_regularizers.py
+++ b/Wrappers/Python/demo/test_cpu_regularizers.py
@@ -11,10 +11,9 @@ import numpy as np
 import os    
 from enum import Enum
 import timeit
-from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV , FGP_TV ,\
-                                                 LLT_model, PatchBased_Regul ,\
+from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV, LLT_model, PatchBased_Regul ,\
                                                  TGV_PD
-from ccpi.filters.cpu_regularizers_cython import ROF_TV
+from ccpi.filters.cpu_regularizers_cython import TV_ROF_CPU, TV_FGP_CPU
 
 ###############################################################################
 #https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
@@ -128,21 +127,25 @@ imgplot = plt.imshow(splitbregman,\
                      )
 
 ###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+
 start_time = timeit.default_timer()
-pars = {'algorithm' : FGP_TV , \
+pars = {'algorithm' : TV_FGP_CPU , \
         'input' : u0,
         'regularization_parameter':0.05, \
         'number_of_iterations' :200 ,\
-        'tolerance_constant':1e-4,\
-        'TV_penalty': 0
+        'tolerance_constant':1e-5,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0
 }
 
-out = FGP_TV (pars['input'], 
+out = TV_FGP_CPU (pars['input'], 
               pars['regularization_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
-              pars['TV_penalty'])  
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'])  
 
 fgp = out[0]
 rms = rmse(Im, fgp)
@@ -282,13 +285,13 @@ imgplot = plt.imshow(tgv, cmap="gray")
 
 start_time = timeit.default_timer()
 
-pars = {'algorithm': ROF_TV , \
+pars = {'algorithm': TV_ROF_CPU , \
         'input' : u0,\
         'regularization_parameter':0.04,\
         'marching_step': 0.0025,\
         'number_of_iterations': 300
         }
-rof = ROF_TV(pars['input'],
+rof = TV_ROF_CPU(pars['input'],
              pars['number_of_iterations'],
              pars['regularization_parameter'],
              pars['marching_step'] 
diff --git a/Wrappers/Python/src/cpu_regularizers.cpp b/Wrappers/Python/src/cpu_regularizers.cpp
index 43d5d11..b8156d5 100644
--- a/Wrappers/Python/src/cpu_regularizers.cpp
+++ b/Wrappers/Python/src/cpu_regularizers.cpp
@@ -1040,7 +1040,6 @@ BOOST_PYTHON_MODULE(cpu_regularizers_boost)
 	np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
 
 	def("SplitBregman_TV", SplitBregman_TV);
-	def("FGP_TV", FGP_TV);
 	def("LLT_model", LLT_model);
 	def("PatchBased_Regul", PatchBased_Regul);
 	def("TGV_PD", TGV_PD);
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index b8089a8..448da31 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -18,8 +18,8 @@ import cython
 import numpy as np
 cimport numpy as np
 
-cdef extern float TV_ROF_CPU(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float flambda);
-cdef extern float TV_FGP_CPU(float *Input, float *Output, float lambda, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+cdef extern float TV_ROF_CPU_main(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float flambda);
+cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);								  
 
 # Can we use the same name here in "def" as the C function? 
 def TV_ROF_CPU(inputData, iterations, regularization_parameter, 
@@ -45,11 +45,10 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
     #/* Run ROF iterations for 2D data */
-    TV_ROF_CPU(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations,
+    TV_ROF_CPU_main(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations,
                marching_step_parameter, regularization_parameter)
     
-    return B
-        
+    return B        
             
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
                      int iterations,
@@ -65,7 +64,7 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
             np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
            
     #/* Run ROF iterations for 3D data */
-    TV_FGP_CPU(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations,
+    TV_ROF_CPU_main(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations,
              marching_step_parameter, regularization_parameter)
 
     return B    
@@ -88,11 +87,11 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
     
-    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
             np.zeros([dims[0],dims[1]], dtype='float32')
                    
     #/* Run ROF iterations for 2D data */
-    TV_FGP_CPU(&inputData[0,0], &B[0,0], regularization_parameter, 
+    TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, 
                        iterations, 
                        tolerance_param,
                        methodTV,
@@ -100,8 +99,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        printM,
                        dims[0], dims[1], 1)
     
-    return B
-        
+    return outputData        
             
 def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
  float regularization_parameter,
@@ -115,15 +113,16 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     dims[1] = inputData.shape[1]
     dims[2] = inputData.shape[2]
     
-    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B = \
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
             np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
            
     #/* Run ROF iterations for 3D data */
-    TV_FGP_CPU(&inputData[0,0, 0], &B[0,0, 0], iterations, 
+    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0, 0], regularization_parameter,
+					   iterations, 
                        tolerance_param,
                        methodTV,
                        nonneg,
                        printM,
                        dims[0], dims[1], dims[2])
 
-    return B  
+    return outputData 
diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx
index a14a20d..e99bfa7 100644
--- a/Wrappers/Python/src/gpu_regularizers.pyx
+++ b/Wrappers/Python/src/gpu_regularizers.pyx
@@ -26,7 +26,7 @@ cdef extern void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec,
                                 int SearchW, int SimilW, 
                                 int SearchW_real, float denh2, float lambdaf);
 cdef extern void TV_ROF_GPU(float* Input, float* Output, int N, int M, int Z, int iter, float tau, float lambdaf);
-cdef extern void TV_FGP_GPU(float *Input, float *Output, float lambda, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern void TV_FGP_GPU(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
 
 cdef extern float pad_crop(float *A, float *Ap, 
                            int OldSizeX, int OldSizeY, int OldSizeZ, 
diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
index 6344021..e162afa 100644
--- a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
+++ b/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os    
 import timeit
-from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML, TV_ROF_GPU
+from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML, GPU_ROF_TV
 from ccpi.filters.cpu_regularizers_cython import TV_ROF_CPU
 ###############################################################################
 def printParametersToString(pars):
@@ -56,11 +56,11 @@ imgplot = plt.imshow(u0,cmap="gray")
 
 
 # set parameters
-pars = {'algorithm': ROF_TV , \
+pars = {'algorithm': TV_ROF_CPU , \
         'input' : u0,\
         'regularization_parameter':0.04,\
         'time_marching_parameter': 0.0025,\
-        'number_of_iterations': 600
+        'number_of_iterations': 1200
         }
 print ("#################ROF TV CPU#####################")
 start_time = timeit.default_timer()
@@ -89,13 +89,14 @@ plt.title('{}'.format('CPU results'))
 
 print ("#################ROF TV GPU#####################")
 start_time = timeit.default_timer()
-rof_gpu = TV_ROF_GPU(pars['input'], 
+rof_gpu = GPU_ROF_TV(pars['input'], 
                      pars['number_of_iterations'], 
                      pars['time_marching_parameter'], 
                      pars['regularization_parameter'])
                      
 rms = rmse(Im, rof_gpu)
 pars['rmse'] = rms
+pars['algorithm'] = GPU_ROF_TV
 txtstr = printParametersToString(pars)
 txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)