work on FGP intergration

5 files changed, 30 insertions, 33 deletions

diff --git a/Core/CMakeLists.txt b/Core/CMakeLists.txt
index 68b7111..4e85002 100644
--- a/Core/CMakeLists.txt
+++ b/Core/CMakeLists.txt
@@ -88,7 +88,7 @@ add_library(cilreg SHARED
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/PatchBased_Regul_core.c
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/SplitBregman_TV_core.c
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/TGV_PD_core.c
-            ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/ROF_TV_core.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/ROF_TV_core.c
 	    ${CMAKE_CURRENT_SOURCE_DIR}/regularizers_CPU/utils.c
 	    )
 target_link_libraries(cilreg ${EXTRA_LIBRARIES} )
diff --git a/Core/regularizers_CPU/ROF_TV_core.c b/Core/regularizers_CPU/ROF_TV_core.c
index a59a3c6..a4f82a6 100644
--- a/Core/regularizers_CPU/ROF_TV_core.c
+++ b/Core/regularizers_CPU/ROF_TV_core.c
@@ -46,7 +46,7 @@ int sign(float x) {
  */
 
 /* Running iterations of TV-ROF function */
-float TV_ROF_CPU_mainfloat TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
 {
     float *D1, *D2, *D3;
     int i, DimTotal;
@@ -132,9 +132,9 @@ float D1_func(float *A, float *D1, int dimX, int dimY, int dimZ)
                 NOMy_0 = A[index] - A[(j)*dimX + i2]; /* y- */
                 
                 denom1 = NOMx_1*NOMx_1;
-                denom2 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom2 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
                 denom2 = denom2*denom2;
-                T1 = sqrt(denom1 + denom2 + EPS);
+                T1 = sqrtf(denom1 + denom2 + EPS);
                 D1[index] = NOMx_1/T1;
             }}
     }
@@ -170,11 +170,11 @@ float D2_func(float *A, float *D2, int dimX, int dimY, int dimZ)
                     
                     
                     denom1 = NOMy_1*NOMy_1;
-                    denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                    denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
                     denom2 = denom2*denom2;
-                    denom3 = 0.5*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = 0.5f*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
                     denom3 = denom3*denom3;
-                    T2 = sqrt(denom1 + denom2 + denom3 + EPS);
+                    T2 = sqrtf(denom1 + denom2 + denom3 + EPS);
                     D2[index] = NOMy_1/T2;
                 }}}
     }
@@ -196,9 +196,9 @@ float D2_func(float *A, float *D2, int dimX, int dimY, int dimZ)
                 /*NOMy_0 = A[(i)*dimY + j] - A[(i)*dimY + j2]; */  /* y- */
                 
                 denom1 = NOMy_1*NOMy_1;
-                denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
                 denom2 = denom2*denom2;
-                T2 = sqrt(denom1 + denom2 + EPS);
+                T2 = sqrtf(denom1 + denom2 + EPS);
                 D2[index] = NOMy_1/T2;
             }}
     }
@@ -233,11 +233,11 @@ float D3_func(float *A, float *D3, int dimY, int dimX, int dimZ)
                 /*NOMz_0 = A[(dimX*dimY)*k + (i)*dimY + j] - A[(dimX*dimY)*k2 + (i)*dimY + j]; */ /* z- */
                 
                 denom1 = NOMz_1*NOMz_1;
-                denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
                 denom2 = denom2*denom2;
-                denom3 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom3 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
                 denom3 = denom3*denom3;
-                T3 = sqrt(denom1 + denom2 + denom3 + EPS);
+                T3 = sqrtf(denom1 + denom2 + denom3 + EPS);
                 D3[index] = NOMz_1/T3;
             }}}
     return *D3;
diff --git a/Core/regularizers_CPU/utils.c b/Core/regularizers_CPU/utils.c
index 951fb91..cdf3d0e 100644
--- a/Core/regularizers_CPU/utils.c
+++ b/Core/regularizers_CPU/utils.c
@@ -45,10 +45,10 @@ float TV_energy2D(float *U, float *U0, float *E_val, float lambda, int dimX, int
                 j1 = j + 1; if (j == dimY-1) j1 = j;
                 
                 /* Forward differences */
-                NOMx_2 = pow(U[j1*dimX + i] - U[index],2); /* x+ */
-                NOMy_2 = pow(U[j*dimX + i1] - U[index],2); /* y+ */
-                E_Grad += sqrt(NOMx_2 + NOMy_2); /* gradient term energy */
-                E_Data += 0.5f * lambda*(pow((U[index]-U0[index]),2)); /* fidelity term energy */
+                NOMx_2 = powf(U[j1*dimX + i] - U[index],2); /* x+ */
+                NOMy_2 = powf(U[j*dimX + i1] - U[index],2); /* y+ */
+                E_Grad += sqrtf(NOMx_2 + NOMy_2); /* gradient term energy */
+                E_Data += 0.5f * lambda*(powf((U[index]-U0[index]),2)); /* fidelity term energy */
 			}
 		}
 		E_val[0] = E_Grad + E_Data;
diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py
index 53b8538..f1eb3c3 100644
--- a/Wrappers/Python/demo/test_cpu_regularizers.py
+++ b/Wrappers/Python/demo/test_cpu_regularizers.py
@@ -131,9 +131,9 @@ imgplot = plt.imshow(splitbregman,\
 start_time = timeit.default_timer()
 pars = {'algorithm' : TV_FGP_CPU , \
         'input' : u0,
-        'regularization_parameter':0.05, \
-        'number_of_iterations' :200 ,\
-        'tolerance_constant':1e-5,\
+        'regularization_parameter':0.07, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
         'methodTV': 0 ,\
         'nonneg': 0 ,\
         'printingOut': 0
@@ -156,7 +156,7 @@ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)       
 
 
-a=fig.add_subplot(2,4,3)
+a=fig.add_subplot(2,4,4)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
@@ -168,8 +168,9 @@ imgplot = plt.imshow(fgp, \
 a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
         verticalalignment='top', bbox=props)
 
-###################### LLT_model #########################################
 
+###################### LLT_model #########################################
+"""
 start_time = timeit.default_timer()
 
 pars = {'algorithm': LLT_model , \
@@ -204,7 +205,7 @@ a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(llt,\
                      cmap="gray"
                      )
-
+"""
 
 # ###################### PatchBased_Regul #########################################
 # # Quick 2D denoising example in Matlab:   
@@ -292,8 +293,8 @@ pars = {'algorithm': TV_ROF_CPU , \
         'number_of_iterations': 300
         }
 rof = TV_ROF_CPU(pars['input'],
-             pars['number_of_iterations'],
-             pars['regularization_parameter'],
+			 pars['regularization_parameter'],
+             pars['number_of_iterations'],             
              pars['marching_step'] 
              )
 #tgv = out
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index 2654831..d62ca59 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -21,14 +21,11 @@ cimport numpy as np
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 
-def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb
-           marching_step_parameter):
+def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb, marching_step_parameter):
     if inputData.ndim == 2:
-        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb
-                         marching_step_parameter)
+        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
     elif inputData.ndim == 3:
-        return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb
-                         marching_step_parameter)
+        return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
 
 def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      float regularization_parameter,
@@ -47,10 +44,9 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return outputData
             
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     int iterations,
+                     int iterationsNumb,
                      float regularization_parameter,
-                     float marching_step_parameter
-                     ):
+                     float marching_step_parameter):
     cdef long dims[3]
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]