diff options
| -rw-r--r-- | Core/inpainters_CPU/Diffusion_Inpaint_core.c | 9 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 3 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_inpainters.py | 40 |
3 files changed, 32 insertions, 20 deletions
diff --git a/Core/inpainters_CPU/Diffusion_Inpaint_core.c b/Core/inpainters_CPU/Diffusion_Inpaint_core.c index e6972ab..56e0421 100644 --- a/Core/inpainters_CPU/Diffusion_Inpaint_core.c +++ b/Core/inpainters_CPU/Diffusion_Inpaint_core.c @@ -47,13 +47,19 @@ int signNDF_inc(float x) { float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ) { - int i; + int i,pointsone; float sigmaPar2; sigmaPar2 = sigmaPar/sqrt(2.0f); /* copy into output */ copyIm(Input, Output, dimX, dimY, dimZ); + pointsone = 0; + for (i=0; i<dimY*dimX*dimZ; i++) if (Mask[i] == 1) pointsone++; + + if (pointsone == 0) printf("%s \n", "Nothing to inpaint, zero mask!"); + else { + if (dimZ == 1) { /* running 2D diffusion iterations */ for(i=0; i < iterationsNumb; i++) { @@ -68,6 +74,7 @@ float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Outpu else NonLinearDiff_Inp_3D(Input, Mask, Output, lambdaPar, sigmaPar2, tau, penaltytype, dimX, dimY, dimZ); } } + } return *Output; } /********************************************************************/ diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index e62c020..8120f72 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -113,5 +113,4 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, penalty_type): return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, - edge_parameter, iterationsNumb, - time_marching_parameter, penalty_type) + edge_parameter, iterations, time_marching_parameter, penalty_type) diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index a022bc8..b067b11 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -20,30 +20,36 @@ def printParametersToString(pars): txt += "{0} = {1}".format(key, value.__name__) elif key == 'input': txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': + elif key == 'maskData': txt += "{0} = {1}".format(key, np.shape(value)) else: txt += "{0} = {1}".format(key, value) txt += '\n' return txt ############################################################################### -#%% + # read sinogram and the mask filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat") sino = io.loadmat(filename) -sino_no_cut = sino.get('Sinogram') +sino_full = sino.get('Sinogram') Mask = sino.get('Mask') -[angles_dim,detectors_dim] = sino_no_cut.shape -sinogram = sino_no_cut/np.max(sino_no_cut) +[angles_dim,detectors_dim] = sino_full.shape +sino_full = sino_full/np.max(sino_full) #apply mask to sinogram -sino_cut = sinogram*(1-Mask) +sino_cut = sino_full*(1-Mask) +sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32') +#sino_cut_new = sino_cut.copy(order='c') +sino_cut_new[:] = sino_cut[:] +mask = np.zeros((angles_dim,detectors_dim),'uint8') +#mask =Mask.copy(order='c') +mask[:] = Mask[:] plt.figure(1) plt.subplot(121) -plt.imshow(sino_cut,vmin=0.0, vmax=1) +plt.imshow(sino_cut_new,vmin=0.0, vmax=1) plt.title('Missing Data sinogram') plt.subplot(122) -plt.imshow(Mask) +plt.imshow(mask) plt.title('Mask') plt.show() #%% @@ -56,15 +62,15 @@ fig = plt.figure(2) plt.suptitle('Performance of linear inpainting using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Missing data sinogram') -imgplot = plt.imshow(sino_cut,cmap="gray") +imgplot = plt.imshow(sino_cut_new,cmap="gray") # set parameters pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut,\ - 'maskData' : Mask,\ - 'regularisation_parameter':6000,\ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ + 'regularisation_parameter':1000,\ 'edge_parameter':0.0,\ - 'number_of_iterations' :5000 ,\ + 'number_of_iterations' :1000 ,\ 'time_marching_parameter':0.000075,\ 'penalty_type':1 } @@ -78,7 +84,7 @@ ndf_inp_linear = NDF_INP(pars['input'], pars['time_marching_parameter'], pars['penalty_type']) -rms = rmse(sinogram, ndf_inp_linear) +rms = rmse(sino_full, ndf_inp_linear) pars['rmse'] = rms txtstr = printParametersToString(pars) @@ -107,8 +113,8 @@ imgplot = plt.imshow(sino_cut,cmap="gray") # set parameters pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut,\ - 'maskData' : Mask,\ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ 'regularisation_parameter':80,\ 'edge_parameter':0.00009,\ 'number_of_iterations' :1500 ,\ @@ -125,7 +131,7 @@ ndf_inp_nonlinear = NDF_INP(pars['input'], pars['time_marching_parameter'], pars['penalty_type']) -rms = rmse(sinogram, ndf_inp_nonlinear) +rms = rmse(sino_full, ndf_inp_nonlinear) pars['rmse'] = rms txtstr = printParametersToString(pars) |