2D CPU version for constrained diffusion

2 files changed, 63 insertions, 1 deletions

diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 398e11c..1e427bf 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -2,7 +2,7 @@
 script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
 """
 
-from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_MASK_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
 try:
     from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU
     gpu_enabled = True
@@ -127,6 +127,31 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
     	    raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
+def NDF_MASK(inputData, diffuswindow, regularisation_parameter, edge_parameter, iterations,
+                     time_marching_parameter, penalty_type, tolerance_param, device='cpu'):
+    if device == 'cpu':
+        return NDF_MASK_CPU(inputData,
+                     diffuswindow,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations,
+                     time_marching_parameter,
+                     penalty_type,
+                     tolerance_param)
+    elif device == 'gpu' and gpu_enabled:
+        return NDF_MASK_CPU(inputData,
+                     diffuswindow,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations,
+                     time_marching_parameter,
+                     penalty_type,
+                     tolerance_param)
+    else:
+        if not gpu_enabled and device == 'gpu':
+    	    raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
 def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations,
                      time_marching_parameter, tolerance_param, device='cpu'):
     if device == 'cpu':
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index add641b..305ee1f 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -24,6 +24,7 @@ cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector,
 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);
 cdef extern float TGV_main(float *Input, float *Output, float *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ);
+cdef extern float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, float *Output, float *infovector, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float Diffus4th_CPU_main(float *Input, float *Output,  float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ);
 cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
 cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
@@ -379,6 +380,42 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     tolerance_param,
     dims[2], dims[1], dims[0])
     return (outputData,infovec)
+
+#****************************************************************#
+#********Constrained Nonlinear(Isotropic) Diffusion**************#
+#****************************************************************#
+def NDF_MASK_CPU(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type, tolerance_param):
+    if inputData.ndim == 2:
+        return NDF_MASK_2D(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param)
+    elif inputData.ndim == 3:
+        return 0
+
+def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                    np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     int diffuswindow,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type,
+                     float tolerance_param):
+    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.float32_t, ndim=1, mode="c"] infovec = \
+                np.zeros([2], dtype='float32')
+
+    # Run constrained nonlinear diffusion iterations for 2D data
+    DiffusionMASK_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &infovec[0],
+    diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,
+    time_marching_parameter, penalty_type,
+    tolerance_param,
+    dims[1], dims[0], 1)
+    return (outputData,infovec)
+
 #****************************************************************#
 #*************Anisotropic Fourth-Order diffusion*****************#
 #****************************************************************#