ROF_FGP_unified syntax

1 files changed, 27 insertions, 32 deletions

diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index 448da31..2654831 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -11,73 +11,68 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 
-Author: Edoardo Pasca
+Author: Edoardo Pasca, Daniil Kazantsev
 """
 
 import cython
 import numpy as np
 cimport numpy as np
 
-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);								  
+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);
 
-# Can we use the same name here in "def" as the C function? 
-def TV_ROF_CPU(inputData, iterations, regularization_parameter, 
+def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb
            marching_step_parameter):
     if inputData.ndim == 2:
-        return TV_ROF_2D(inputData, iterations, regularization_parameter, 
+        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb
                          marching_step_parameter)
     elif inputData.ndim == 3:
-        return TV_ROF_3D(inputData, iterations, regularization_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, 
-                     int iterations,
                      float regularization_parameter,
-                     float marching_step_parameter
-                     ):
-                         
+                     int iterationsNumb,                     
+                     float marching_step_parameter):
     cdef long dims[2]
     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_ROF_CPU_main(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations,
-               marching_step_parameter, regularization_parameter)
+    # Run ROF iterations for 2D data 
+    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
     
-    return B        
+    return outputData
             
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
                      int iterations,
                      float regularization_parameter,
                      float marching_step_parameter
                      ):
-    cdef long dims[2]
+    cdef long dims[3]
     dims[0] = inputData.shape[0]
     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_ROF_CPU_main(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations,
-             marching_step_parameter, regularization_parameter)
+    # Run ROF iterations for 3D data 
+    TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], dims[2])
 
-    return B    
+    return outputData    
                      
-def TV_FGP_CPU(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM):
+def TV_FGP_CPU(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM):
     if inputData.ndim == 2:
-        return TV_FGP_2D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM)
+        return TV_FGP_2D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
     elif inputData.ndim == 3:
-        return TV_FGP_3D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM)
+        return TV_FGP_3D(inputData, regularization_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
 
 def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      float regularization_parameter,
-                     int iterations, 
+                     int iterationsNumb, 
                      float tolerance_param,
                      int methodTV,
                      int nonneg,
@@ -92,7 +87,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                    
     #/* Run ROF iterations for 2D data */
     TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularization_parameter, 
-                       iterations, 
+                       iterationsNumb, 
                        tolerance_param,
                        methodTV,
                        nonneg,
@@ -103,22 +98,22 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             
 def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
  float regularization_parameter,
-                     int iterations, 
+                     int iterationsNumb, 
                      float tolerance_param,
                      int methodTV,
                      int nonneg,
                      int printM):
-    cdef long dims[2]
+    cdef long dims[3]
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
     dims[2] = inputData.shape[2]
     
     cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
-            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
            
     #/* Run ROF iterations for 3D data */
-    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0, 0], regularization_parameter,
-					   iterations, 
+    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularization_parameter,
+					   iterationsNumb, 
                        tolerance_param,
                        methodTV,
                        nonneg,