From e53d631a2d0c34915459028e3db64153c3a936c3 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Wed, 23 May 2018 15:41:35 +0100 Subject: TGV for CPU and GPU added with demos --- Wrappers/Python/ccpi/filters/regularisers.py | 24 ++++- Wrappers/Python/demos/demo_cpu_regularisers.py | 67 +++++++++++-- .../Python/demos/demo_cpu_vs_gpu_regularisers.py | 106 +++++++++++++++++++-- Wrappers/Python/demos/demo_gpu_regularisers.py | 70 ++++++++++++-- Wrappers/Python/setup-regularisers.py.in | 1 + Wrappers/Python/src/cpu_regularisers.pyx | 34 +++++++ Wrappers/Python/src/gpu_regularisers.pyx | 34 +++++++ 7 files changed, 306 insertions(+), 30 deletions(-) (limited to 'Wrappers/Python') diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index 0b79dac..0e435a6 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,8 +2,8 @@ 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 -from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_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 +from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU def ROF_TV(inputData, regularisation_parameter, iterations, @@ -128,6 +128,26 @@ def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations, else: raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations, + LipshitzConst, device='cpu'): + if device == 'cpu': + return TGV_CPU(inputData, + regularisation_parameter, + alpha1, + alpha0, + iterations, + LipshitzConst) + elif device == 'gpu': + return TGV_GPU(inputData, + regularisation_parameter, + alpha1, + alpha0, + iterations, + LipshitzConst) + else: + raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ + .format(device)) + def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, penalty_type): return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index ff500ae..5c20244 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, TNV, NDF, DIFF4th +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, TNV, NDF, DIFF4th from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -74,7 +74,7 @@ print ("_______________ROF-TV (2D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(1) +fig = plt.figure() plt.suptitle('Performance of ROF-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -109,13 +109,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(rof_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-TV (2D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(2) +fig = plt.figure() plt.suptitle('Performance of FGP-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -159,12 +159,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_cpu, cmap="gray") plt.title('{}'.format('CPU results')) +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________SB-TV (2D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure() plt.suptitle('Performance of SB-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -205,14 +206,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) imgplot = plt.imshow(sb_cpu, cmap="gray") plt.title('{}'.format('CPU results')) +#%% + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_____Total Generalised Variation (2D)______") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure() +plt.suptitle('Performance of TGV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : TGV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'alpha1':1.0,\ + 'alpha0':0.7,\ + 'number_of_iterations' :250 ,\ + 'LipshitzConstant' :12 ,\ + } + +print ("#############TGV CPU####################") +start_time = timeit.default_timer() +tgv_cpu = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], + pars['number_of_iterations'], + pars['LipshitzConstant'],'cpu') + + +rms = rmse(Im, tgv_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(tgv_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("________________NDF (2D)___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure() plt.suptitle('Performance of NDF regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -259,7 +308,7 @@ print ("___Anisotropic Diffusion 4th Order (2D)____") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure() plt.suptitle('Performance of DIFF4th regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -304,7 +353,7 @@ print ("_____________FGP-dTV (2D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure() plt.suptitle('Performance of FGP-dTV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -356,7 +405,7 @@ print ("__________Total nuclear Variation__________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(7) +fig = plt.figure() plt.suptitle('Performance of TNV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py index 4611522..46b8ffc 100644 --- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_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, NDF, DIFF4th +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -50,12 +50,13 @@ u_ref = Im + np.random.normal(loc = 0 , u0 = u0.astype('float32') u_ref = u_ref.astype('float32') +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(1) +fig = plt.figure() plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -90,7 +91,6 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(rof_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - print ("##############ROF TV GPU##################") start_time = timeit.default_timer() rof_gpu = ROF_TV(pars['input'], @@ -128,12 +128,13 @@ if (diff_im.sum() > 1): else: print ("Arrays match") +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-TV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(2) +fig = plt.figure() plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -218,12 +219,13 @@ if (diff_im.sum() > 1): else: print ("Arrays match") +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________SB-TV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure() plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -303,14 +305,98 @@ if (diff_im.sum() > 1): print ("Arrays do not match!") else: print ("Arrays match") +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________TGV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure() +plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations') +a=fig.add_subplot(1,4,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : TGV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'alpha1':1.0,\ + 'alpha0':0.7,\ + 'number_of_iterations' :250 ,\ + 'LipshitzConstant' :12 ,\ + } + +print ("#############TGV CPU####################") +start_time = timeit.default_timer() +tgv_cpu = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], + pars['number_of_iterations'], + pars['LipshitzConstant'],'cpu') + +rms = rmse(Im, tgv_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,2) + +# 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(tgv_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + +print ("##############SB TV GPU##################") +start_time = timeit.default_timer() +tgv_gpu = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], + pars['number_of_iterations'], + pars['LipshitzConstant'],'gpu') + +rms = rmse(Im, tgv_gpu) +pars['rmse'] = rms +pars['algorithm'] = TGV +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,3) + +# 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(tgv_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) +print ("--------Compare the results--------") +tolerance = 1e-05 +diff_im = np.zeros(np.shape(tgv_gpu)) +diff_im = abs(tgv_cpu - tgv_gpu) +diff_im[diff_im > tolerance] = 1 +a=fig.add_subplot(1,4,4) +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") +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________NDF bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure() plt.suptitle('Comparison of NDF regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -390,13 +476,13 @@ if (diff_im.sum() > 1): else: print ("Arrays match") - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("___Anisotropic Diffusion 4th Order (2D)____") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure() plt.suptitle('Comparison of Diff4th regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -472,12 +558,13 @@ if (diff_im.sum() > 1): else: print ("Arrays match") +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-dTV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure() plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') @@ -565,3 +652,4 @@ 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 3179428..792a019 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_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, NDF, DIFF4th +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -66,13 +66,14 @@ Im2[:,0:M] = Im[:,0:M] Im = Im2 del Im2 """ +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(1) +fig = plt.figure() plt.suptitle('Performance of the ROF-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -108,13 +109,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(rof_gpu, cmap="gray") plt.title('{}'.format('GPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-TV regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(2) +fig = plt.figure() plt.suptitle('Performance of the FGP-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -157,13 +158,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_gpu, cmap="gray") plt.title('{}'.format('GPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________SB-TV regulariser______________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure() plt.suptitle('Performance of the SB-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -203,14 +204,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) imgplot = plt.imshow(sb_gpu, cmap="gray") plt.title('{}'.format('GPU results')) +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_____Total Generalised Variation (2D)______") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +## plot +fig = plt.figure() +plt.suptitle('Performance of TGV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : TGV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'alpha1':1.0,\ + 'alpha0':0.7,\ + 'number_of_iterations' :250 ,\ + 'LipshitzConstant' :12 ,\ + } + +print ("#############TGV CPU####################") +start_time = timeit.default_timer() +tgv_gpu = TGV(pars['input'], + pars['regularisation_parameter'], + pars['alpha1'], + pars['alpha0'], + pars['number_of_iterations'], + pars['LipshitzConstant'],'gpu') + + +rms = rmse(Im, tgv_gpu) +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(tgv_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________NDF regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure() plt.suptitle('Performance of the NDF regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -251,13 +300,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(ndf_gpu, cmap="gray") plt.title('{}'.format('GPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("___Anisotropic Diffusion 4th Order (2D)____") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure() plt.suptitle('Performance of DIFF4th regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -296,12 +345,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(diff4_gpu, cmap="gray") plt.title('{}'.format('GPU results')) +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-dTV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure() plt.suptitle('Performance of the FGP-dTV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in index 76dfecf..89ebaf9 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -38,6 +38,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"), os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_ROF" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_SB" ) , + os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TGV" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "NDF" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "dTV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "DIFF4th" ) , diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index bdb1eff..cf81bec 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 TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY); cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, 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); @@ -189,6 +190,39 @@ def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, printM, dims[2], dims[1], dims[0]) return outputData + +#***************************************************************# +#***************** Total Generalised Variation *****************# +#***************************************************************# +def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst): + if inputData.ndim == 2: + return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst) + elif inputData.ndim == 3: + return 0 + +def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + float alpha1, + float alpha0, + int iterationsNumb, + float LipshitzConst): + + 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 TGV iterations for 2D data */ + TGV_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, + alpha1, + alpha0, + iterationsNumb, + LipshitzConst, + dims[1],dims[0]) + return outputData + #****************************************************************# #**************Directional Total-variation FGP ******************# #****************************************************************# diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index b67e62b..4a202d7 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -21,6 +21,7 @@ cimport numpy as np cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z); cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z); cdef extern void TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z); +cdef extern void TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY); 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); @@ -86,6 +87,12 @@ def TV_SB_GPU(inputData, tolerance_param, methodTV, printM) +# Total Generilised Variation (TGV) +def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst): + if inputData.ndim == 2: + return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst) + elif inputData.ndim == 3: + return 0 # Directional Total-variation Fast-Gradient-Projection (FGP) def dTV_FGP_GPU(inputData, refdata, @@ -315,6 +322,33 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, dims[2], dims[1], dims[0]); return outputData + +#***************************************************************# +#***************** Total Generalised Variation *****************# +#***************************************************************# +def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + float alpha1, + float alpha0, + int iterationsNumb, + float LipshitzConst): + + 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 TGV iterations for 2D data */ + TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, + alpha1, + alpha0, + iterationsNumb, + LipshitzConst, + dims[1],dims[0]) + return outputData + #****************************************************************# #**************Directional Total-variation FGP ******************# #****************************************************************# -- cgit v1.2.3