fix wrapper build

1 files changed, 35 insertions, 31 deletions

diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index e306ab3..e7ff78c 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -18,51 +18,55 @@ import cython
 import numpy as np
 cimport numpy as np
 
-cdef extern float TV_ROF(float *A, float *B, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float lambda);
+cdef extern float TV_ROF(float *A, float *B, int dimX, int dimY, int dimZ, 
+                         int iterationsNumb, float tau, float flambda);
 
 
-def ROF_TV(inputData, iterations, regularization_parameter, marching_step_parameter):
+def ROF_TV(inputData, iterations, regularization_parameter, 
+           marching_step_parameter):
     if inputData.ndim == 2:
-	    return ROF_TV_2D(inputData, iterations, regularization_parameter, 
-		                 marching_step_parameter)
-	elif inputData.ndim == 3:
-	    return ROF_TV_3D(inputData, iterations, regularization_parameter, 
-		                 marching_step_parameter)
+        return ROF_TV_2D(inputData, iterations, regularization_parameter, 
+                         marching_step_parameter)
+    elif inputData.ndim == 3:
+        return ROF_TV_3D(inputData, iterations, regularization_parameter, 
+                         marching_step_parameter)
 
 def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      int iterations,
-					 float regularization_parameter
-					 float marching_step_parameter
+                     float regularization_parameter,
+                     float marching_step_parameter
                      ):
-						 
-	cdef long dims[2]
+                         
+    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 = \
-		    np.zeros([dims[0],dims[1]], dtype='float32')
-		           
-        #/* Run ROF iterations for 2D data */
-        TV_ROF(&A[0,0], &B[0,0], dims[0], dims[1], 1, iterations, marching_step_parameter, regularization_parameter)
-	
-	return B
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run ROF iterations for 2D data */
+    TV_ROF(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations,
+               marching_step_parameter, regularization_parameter)
+    
+    return B
         
-			
+            
 def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
                      int iterations,
-					 float regularization_parameter
-					 float marching_step_parameter
+                     float regularization_parameter,
+                     float marching_step_parameter
                      ):
-	cdef long dims[2]
+    cdef long dims[2]
     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 = \
-		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] B = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
            
-      #/* Run ROF iterations for 3D data */
-      TV_ROF(&A[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(&inputData[0,0, 0], &B[0,0, 0], dims[0], dims[1], dims[2], iterations,
+             marching_step_parameter, regularization_parameter)
 
-	return B	
-					 
+    return B    
+