Cmake/Cython fixes to compile ROF-FGP

3 files changed, 13 insertions, 15 deletions

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,