succesfull variable regulariser implementation

1 files changed, 22 insertions, 9 deletions

diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index a63ecfa..a2c4c32 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -18,7 +18,7 @@ import cython
 import numpy as np
 cimport numpy as np
 
-cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
+cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float *infovector, float *lambdaPar, int arrayscal, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector, float mu, int iter, float epsil, int methodTV, int dimX, int dimY, int dimZ);
 cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
@@ -45,26 +45,33 @@ def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_ste
         return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param)
 
 def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
-                     float regularisation_parameter,
+                     regularisation_parameter,
                      int iterationsNumb,
                      float marching_step_parameter,
                      float tolerance_param):
     cdef long dims[2]
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
-
+    cdef float lambdareg
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] reg
     cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
             np.zeros([dims[0],dims[1]], dtype='float32')
     cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \
             np.ones([2], dtype='float32')
 
     # Run ROF iterations for 2D data
-    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
-
+    # TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
+     # Run ROF iterations for 2D data
+    if isinstance (regularisation_parameter, np.ndarray):
+        reg = regularisation_parameter.copy()
+        TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], &reg[0,0],  1, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
+    else: # supposedly this would be a float
+        lambdareg = regularisation_parameter;
+        TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], &lambdareg,  0, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
     return (outputData,infovec)
 
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
-                     float regularisation_parameter,
+                     regularisation_parameter,
                      int iterationsNumb,
                      float marching_step_parameter,
                      float tolerance_param):
@@ -72,15 +79,21 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
     dims[2] = inputData.shape[2]
-
+    cdef float lambdareg
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] reg
     cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
             np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
     cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \
             np.ones([2], dtype='float32')
 
     # Run ROF iterations for 3D data
-    TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0])
-
+    #TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0])
+    if isinstance (regularisation_parameter, np.ndarray):
+        reg = regularisation_parameter.copy()
+        TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], &reg[0,0,0], 1, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0])
+    else: # supposedly this would be a float
+        lambdareg = regularisation_parameter
+        TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], &lambdareg, 0, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0])
     return (outputData,infovec)
 
 #****************************************************************#