GPU version, this completes implementation of nltv #68

9 files changed, 189 insertions, 17 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 54b8bac..3506cca 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -138,17 +138,17 @@ figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)');
 fprintf('Weights pre-calculation for Non-local TV (takes time on CPU) \n');
 SearchingWindow = 7;
 PatchWindow = 2;
-NeighboursNumber = 15; % the number of neibours to include
+NeighboursNumber = 20; % the number of neibours to include
 h = 0.23; % edge related parameter for NLM
-[H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, NeighboursNumber, h);
+tic; [H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, NeighboursNumber, h); toc;
 %%
 fprintf('Denoise using Non-local Total Variation (CPU) \n');
-iter_nltv = 2; % number of nltv iterations
-lambda_nltv = 0.085; % regularisation parameter for nltv
+iter_nltv = 3; % number of nltv iterations
+lambda_nltv = 0.05; % regularisation parameter for nltv
 tic; u_nltv = Nonlocal_TV(single(u0), H_i, H_j, 0, Weights, lambda_nltv, iter_nltv); toc; 
 rmse_nltv = (RMSE(u_nltv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for Non-local Total Variation is:', rmse_nltv);
-figure; imshow(u_nltv, [0 1]); title('Non-local Total Variation denoised image (CPU)');
+figure; imagesc(u_nltv, [0 1]); colormap(gray); daspect([1 1 1]); title('Non-local Total Variation denoised image (CPU)');
 %%
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index bf7e23c..0a65590 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -4,7 +4,7 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gp
 
 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
 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
+    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
 except ImportError:
     gpu_enabled = False    
@@ -153,7 +153,11 @@ def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter
                      neighbours, 
                      edge_parameter)
     elif device == 'gpu' and gpu_enabled:
-        return 1
+        return PATCHSEL_GPU(inputData,
+                     searchwindow,
+                     patchwindow,
+                     neighbours, 
+                     edge_parameter)
     else:
         if not gpu_enabled and device == 'gpu':
     	    raise ValueError ('GPU is not available')
diff --git a/Wrappers/Python/conda-recipe/run_test.py b/Wrappers/Python/conda-recipe/run_test.py
index 6ffaca1..499ae7f 100755
--- a/Wrappers/Python/conda-recipe/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
@@ -2,7 +2,7 @@ import unittest
 import numpy as np

 import os

 import timeit

-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th

+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
 from PIL import Image

 

 class TiffReader(object):

diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index 31e4cad..78e9aff 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -400,20 +400,29 @@ plt.title('{}'.format('CPU results'))
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Nonlocal patches pre-calculation____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
 # set parameters
 pars = {'algorithm' : PatchSelect, \
         'input' : u0,\
         'searchwindow': 7, \
         'patchwindow': 2,\
         'neighbours' : 15 ,\
-        'edge_parameter':0.23}
+        'edge_parameter':0.18}
 
 H_i, H_j, Weights = PatchSelect(pars['input'], 
               pars['searchwindow'],
               pars['patchwindow'], 
               pars['neighbours'],
               pars['edge_parameter'],'cpu')
-
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Nonlocal Total Variation penalty____")
@@ -431,10 +440,9 @@ pars2 = {'algorithm' : NLTV, \
         'H_j': H_j,\
         'H_k' : 0,\
         'Weights' : Weights,\
-        'regularisation_parameter': 0.085,\
-        'iterations': 2
+        'regularisation_parameter': 0.04,\
+        'iterations': 3
         }
-#%%
 start_time = timeit.default_timer()
 nltv_cpu = NLTV(pars2['input'], 
               pars2['H_i'],
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index 3d6e92f..616eab0 100644
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
@@ -13,6 +13,7 @@ import numpy as np
 import os
 import timeit
 from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import PatchSelect
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -732,4 +733,58 @@ if (diff_im.sum() > 1):
     print ("Arrays do not match!")
 else:
     print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____Non-local regularisation bench_________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of Nonlocal TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+print ("############## Nonlocal Patches on CPU##################")
+start_time = timeit.default_timer()
+H_i, H_j, WeightsCPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'cpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("############## Nonlocal Patches on GPU##################")
+start_time = timeit.default_timer()
+start_time = timeit.default_timer()
+H_i, H_j, WeightsGPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(u0))
+diff_im = abs(WeightsCPU[0,:,:] - WeightsGPU[0,:,:])
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,2,2)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
 #%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index de0cbde..2ada559 100644
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_gpu_regularisers.py
@@ -13,6 +13,7 @@ import numpy as np
 import os
 import timeit
 from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -84,7 +85,7 @@ pars = {'algorithm': ROF_TV, \
         'input' : u0,\
         'regularisation_parameter':0.04,\
         'number_of_iterations': 1200,\
-        'time_marching_parameter': 0.0025        
+        'time_marching_parameter': 0.0025
         }
 print ("##############ROF TV GPU##################")
 start_time = timeit.default_timer()
@@ -394,6 +395,77 @@ plt.title('{}'.format('GPU results'))
 
 #%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+H_i, H_j, Weights = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+        'input' : u0,\
+        'H_i': H_i, \
+        'H_j': H_j,\
+        'H_k' : 0,\
+        'Weights' : Weights,\
+        'regularisation_parameter': 0.02,\
+        'iterations': 3
+        }
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'], 
+              pars2['H_i'],
+              pars2['H_j'], 
+              pars2['H_k'],
+              pars2['Weights'],
+              pars2['regularisation_parameter'],
+              pars2['iterations'])
+
+rms = rmse(Im, nltv_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(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________FGP-dTV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in
index 542dcb4..462edda 100644
--- a/Wrappers/Python/setup-regularisers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -45,6 +45,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"),
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "NDF" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "dTV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "DIFF4th" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "PatchSelect" ) ,
 						   "."]
 
 if platform.system() == 'Windows':				   
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index e51e6d8..4aa3251 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -456,7 +456,7 @@ def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_paramete
     if inputData.ndim == 2:
         return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
     elif inputData.ndim == 3:
-        return PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+        return 1
 def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                      int searchwindow,
                      int patchwindow,
@@ -480,7 +480,7 @@ def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     # Run patch-based weight selection function
     PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow,  neighbours,  edge_parameter, 1)
     return H_i, H_j, Weights
-
+"""
 def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                      int searchwindow,
                      int patchwindow,
@@ -507,7 +507,7 @@ def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     # Run patch-based weight selection function
     PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow,  neighbours, edge_parameter, 1)
     return H_i, H_j, H_k, Weights
-
+"""
 
 #****************************************************************#
 #***************Non-local Total Variation******************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 82d3e01..302727e 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -26,6 +26,7 @@ cdef extern void LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF,
 cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
 cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
 cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern void PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
 
 # Total-variation Rudin-Osher-Fatemi (ROF)
 def TV_ROF_GPU(inputData,
@@ -542,3 +543,34 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     Diffus4th_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])
 
     return outputData
+#****************************************************************#
+#************Patch-based weights pre-selection******************#
+#****************************************************************#
+def PATCHSEL_GPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter):
+    if inputData.ndim == 2:
+        return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+    elif inputData.ndim == 3:
+        return 1
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                     int searchwindow,
+                     int patchwindow,
+                     int neighbours,
+                     float edge_parameter):
+    cdef long dims[3]
+    dims[0] = neighbours
+    dims[1] = inputData.shape[0]
+    dims[2] = inputData.shape[1]    
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
+    
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+    # Run patch-based weight selection function
+    PatchSelect_GPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], searchwindow, patchwindow,  neighbours,  edge_parameter)
+    
+    return H_i, H_j, Weights