Merge pull request #53 from vais-ral/inpaint_Heat

Inpainting methods added to the toolkit

2 files changed, 90 insertions, 10 deletions

diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 7ed8fa1..c934f1d 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -25,6 +25,8 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa
 cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
 cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 
+cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
+cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ);
 #****************************************************************#
 #********************** Total-variation ROF *********************#
 #****************************************************************#
@@ -46,7 +48,7 @@ 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_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1)
+    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1)
     
     return outputData
             
@@ -99,7 +101,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        methodTV,
                        nonneg,
                        printM,
-                       dims[0], dims[1], 1)
+                       dims[1],dims[0],1)
     
     return outputData        
             
@@ -158,7 +160,7 @@ def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        tolerance_param,
                        methodTV,
                        printM,
-                       dims[0], dims[1], 1)
+                       dims[1],dims[0],1)
     
     return outputData        
             
@@ -219,7 +221,7 @@ def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        methodTV,                       
                        nonneg,
                        printM,
-                       dims[0], dims[1], 1)
+                       dims[1], dims[0], 1)
     
     return outputData        
             
@@ -298,7 +300,7 @@ def NDF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
             np.zeros([dims[0],dims[1]], dtype='float32')   
     
     # Run Nonlinear Diffusion iterations for 2D data 
-    Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1)    
+    Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
     return outputData
             
 def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
@@ -319,3 +321,81 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
 
     return outputData
+
+#*********************Inpainting WITH****************************#
+#***************Nonlinear (Isotropic) Diffusion******************#
+#****************************************************************#
+def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type):
+    if inputData.ndim == 2:
+        return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+    elif inputData.ndim == 3:
+        return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+
+def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    # Run Inpaiting by Diffusion iterations for 2D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
+    return outputData
+            
+def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+    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')
+    
+    # Run Inpaiting by Diffusion iterations for 3D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
+
+    return outputData
+#*********************Inpainting WITH****************************#
+#***************Nonlocal Vertical Marching method****************#
+#****************************************************************#
+def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterationsNumb):
+    if inputData.ndim == 2:
+        return NVM_INP_2D(inputData, maskData, SW_increment, iterationsNumb)
+    elif inputData.ndim == 3:
+        return 
+
+def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+               np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     int SW_increment,
+                     int iterationsNumb):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')   
+    
+    cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData_upd = \
+            np.zeros([dims[0],dims[1]], dtype='uint8')
+    
+    # Run Inpaiting by Nonlocal vertical marching method for 2D data 
+    NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], 
+                                  &maskData_upd[0,0],
+                                  SW_increment, iterationsNumb, 1, dims[1], dims[0], 1)
+    
+    return (outputData, maskData_upd)
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index b0775054..7eab5d5 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -157,7 +157,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        regularisation_parameter,
                        iterations , 
                        time_marching_parameter, 
-                       dims[0], dims[1], 1);   
+                       dims[1], dims[0], 1);   
      
     return outputData
     
@@ -210,7 +210,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        methodTV,
                        nonneg,
                        printM,
-                       dims[0], dims[1], 1);   
+                       dims[1], dims[0], 1);   
      
     return outputData
     
@@ -266,7 +266,7 @@ def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        tolerance_param,
                        methodTV,
                        printM,
-                       dims[0], dims[1], 1);   
+                       dims[1], dims[0], 1);   
      
     return outputData
     
@@ -325,7 +325,7 @@ def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                        methodTV,
                        nonneg,
                        printM,
-                       dims[0], dims[1], 1);   
+                       dims[1], dims[0], 1);   
      
     return outputData
     
@@ -381,7 +381,7 @@ def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     
     # Run Nonlinear Diffusion iterations for 2D data 
     # Running CUDA code here  
-    NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1)    
+    NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
     return outputData
             
 def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,