TNV module added

3 files changed, 84 insertions, 2 deletions

diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 50c4374..81deea9 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/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_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU
+from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU 
 from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
@@ -86,3 +86,8 @@ def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
+def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
+        return TNV_CPU_pyx(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param)
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 0e4355b..e74fa58 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -12,7 +12,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import os
 import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, TNV
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -242,6 +242,57 @@ imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
 
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("__________Total nuclear Variation__________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(4)
+plt.suptitle('Performance of TNV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+channelsNo = 5
+N = 512
+noisyVol = np.zeros((channelsNo,N,N),dtype='float32')
+idealVol = np.zeros((channelsNo,N,N),dtype='float32')
+
+for i in range (slices):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+# set parameters
+pars = {'algorithm' : TNV, \
+        'input' : noisyVol,\
+        'regularisation_parameter': 0.04, \
+        'number_of_iterations' : 200 ,\
+        'tolerance_constant':1e-05
+        }
+        
+print ("#############TNV CPU#################")
+start_time = timeit.default_timer()
+tnv_cpu = TNV(pars['input'],           
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'])
+             
+rms = rmse(idealVol, tnv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tnv_cpu[3,:,:], cmap="gray")
+plt.title('{}'.format('CPU results'))
+
 
 # Uncomment to test 3D regularisation performance 
 #%%
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 417670d..898bb40 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,6 +21,7 @@ cimport numpy as np
 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);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+cdef extern float TNV_CPU_main(float *Input, float *u, float lambda, 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);
 
 
@@ -249,3 +250,28 @@ def dTV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        printM,
                        dims[2], dims[1], dims[0])
     return outputData
+    
+#****************************************************************#
+#*********************Total Nuclear Variation********************#
+#****************************************************************#
+def TNV_CPU_pyx(inputData, regularisation_parameter, iterationsNumb, tolerance_param):
+    if inputData.ndim == 2:
+        return 
+    elif inputData.ndim == 3:
+        return TNV_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param)
+
+def TNV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb,
+                     float tolerance_param):
+    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 TNV iterations for 3D (X,Y,Channels) data 
+    TNV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, tolerance_param, dims[2], dims[1], dims[0])
+    return outputData