summaryrefslogtreecommitdiffstats
path: root/Wrappers
diff options
context:
space:
mode:
Diffstat (limited to 'Wrappers')
-rw-r--r--Wrappers/Matlab/demos/demoMatlab_denoise.m11
-rw-r--r--Wrappers/Matlab/mex_compile/compileCPU_mex.m5
-rw-r--r--Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c73
-rw-r--r--Wrappers/Python/ccpi/filters/regularisers.py7
-rw-r--r--Wrappers/Python/demos/demo_cpu_regularisers.py53
-rw-r--r--Wrappers/Python/src/cpu_regularisers.pyx26
6 files changed, 172 insertions, 3 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 129bedc..dab98dc 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -70,3 +70,14 @@ figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)');
% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc;
% figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)');
%%
+fprintf('Denoise using the TNV prior (CPU) \n');
+slices = 5; N = 512;
+vol3D = zeros(N,N,slices, 'single');
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im));
+end
+vol3D(vol3D < 0) = 0;
+
+iter_tnv = 200; % number of TNV iterations
+tic; u_tnv = TNV(single(vol3D), lambda_reg, iter_tnv); toc;
+figure; imshow(u_tnv(:,:,3), [0 1]); title('TNV denoised stack of channels (CPU)');
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
index c3c82ff..9892d73 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m
@@ -17,7 +17,10 @@ movefile SB_TV.mex* ../installed/
mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
movefile FGP_dTV.mex* ../installed/
-delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* utils* CCPiDefines.h
+mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile TNV.mex* ../installed/
+
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* CCPiDefines.h
fprintf('%s \n', 'All successfully compiled!');
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
new file mode 100644
index 0000000..e0584c4
--- /dev/null
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
@@ -0,0 +1,73 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "TNV_core.h"
+/*
+ * C-OMP implementation of Total Nuclear Variation regularisation model (2D + channels) [1]
+ * The code is modified from the implementation by Joan Duran <joan.duran@uib.es> see
+ * "denoisingPDHG_ipol.cpp" in Joans Collaborative Total Variation package
+ *
+ * Input Parameters:
+ * 1. Noisy volume of 2D + channel dimension, i.e. 3D volume
+ * 2. lambda - regularisation parameter
+ * 3. Number of iterations [OPTIONAL parameter]
+ * 4. eplsilon - tolerance constant [OPTIONAL parameter]
+ * 5. print information: 0 (off) or 1 (on) [OPTIONAL parameter]
+ *
+ * Output:
+ * 1. Filtered/regularized image
+ *
+ * [1]. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
+ */
+void mexFunction(
+ int nlhs, mxArray *plhs[],
+ int nrhs, const mxArray *prhs[])
+
+{
+ int number_of_dims, iter, dimX, dimY, dimZ;
+ const int *dim_array;
+ float *Input, *Output=NULL, lambda, epsil;
+
+ number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+ dim_array = mxGetDimensions(prhs[0]);
+
+ /*Handling Matlab input data*/
+ if ((nrhs < 2) || (nrhs > 4)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D + channels), Regularisation parameter, Regularization parameter, iterations number, tolerance");
+
+ Input = (float *) mxGetData(prhs[0]); /* noisy sequence of channels (2D + channels) */
+ lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */
+ iter = 1000; /* default iterations number */
+ epsil = 1.00e-05; /* default tolerance constant */
+
+ if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+
+ if ((nrhs == 3) || (nrhs == 4)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+ if (nrhs == 4) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */
+
+ /*Handling Matlab output data*/
+ dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+
+ if (number_of_dims == 2) mexErrMsgTxt("The input must be 3D: [X,Y,Channels]");
+ if (number_of_dims == 3) {
+ Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+ /* running the function */
+ TNV_CPU_main(Input, Output, lambda, iter, epsil, dimX, dimY, dimZ);
+ }
+} \ No newline at end of file
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
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-07 10:42:45 +0000