diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py | 114 |
1 files changed, 101 insertions, 13 deletions
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py index 7d22bfd..a9e45b6 100644 --- a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py +++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py @@ -21,15 +21,17 @@ import timeit import matplotlib.pyplot as plt import numpy as np import h5py -from tomophantom.supp.qualitymetrics import QualityTools +from ccpi.supp.qualitymetrics import QualityTools -# loading data +# loading the data h5f = h5py.File('data/TomoSim_data1550671417.h5','r') phantom = h5f['phantom'][:] projdata_norm = h5f['projdata_norm'][:] proj_angles = h5f['proj_angles'][:] h5f.close() +[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm) +N_size = Vert_det sliceSel = 128 #plt.gray() @@ -64,32 +66,32 @@ plt.show() from tomorec.methodsDIR import RecToolsDIR RectoolsDIR = RecToolsDIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal) DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only - AnglesVec = angles_rad, # array of angles in radians + AnglesVec = proj_angles, # array of angles in radians ObjSize = N_size, # a scalar to define reconstructed object dimensions device = 'gpu') #%% print ("Reconstruction using FBP from TomoRec") -recNumerical= RectoolsDIR.FBP(projData3D_norm) # FBP reconstruction +recFBP= RectoolsDIR.FBP(projdata_norm) # FBP reconstruction sliceSel = int(0.5*N_size) max_val = 1 #plt.gray() plt.figure() plt.subplot(131) -plt.imshow(recNumerical[sliceSel,:,:],vmin=0, vmax=max_val) +plt.imshow(recFBP[sliceSel,:,:],vmin=0, vmax=max_val) plt.title('3D Reconstruction, axial view') plt.subplot(132) -plt.imshow(recNumerical[:,sliceSel,:],vmin=0, vmax=max_val) +plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val) plt.title('3D Reconstruction, coronal view') plt.subplot(133) -plt.imshow(recNumerical[:,:,sliceSel],vmin=0, vmax=max_val) +plt.imshow(recFBP[:,:,sliceSel],vmin=0, vmax=max_val) plt.title('3D Reconstruction, sagittal view') plt.show() # calculate errors -Qtools = QualityTools(phantom_tm, recNumerical) +Qtools = QualityTools(phantom, recFBP) RMSE_fbp = Qtools.rmse() print("Root Mean Square Error for FBP is {}".format(RMSE_fbp)) #%% @@ -100,7 +102,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") from tomorec.methodsIR import RecToolsIR RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal) DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only - AnglesVec = angles_rad, # array of angles in radians + AnglesVec = proj_angles, # array of angles in radians ObjSize = N_size, # a scalar to define reconstructed object dimensions datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip) nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE') @@ -108,12 +110,41 @@ RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector d tolerance = 1e-08, # tolerance to stop outer iterations earlier device='gpu') #%% -# Run ADMM reconstrucion algorithm with 3D regularisation -RecADMM_reg_fgptv = RectoolsIR.ADMM(projData3D_norm, +print ("Reconstructing with ADMM method using ROF-TV penalty") +RecADMM_reg_roftv = RectoolsIR.ADMM(projdata_norm, rho_const = 2000.0, \ iterationsADMM = 30, \ - regularisation = 'FGP_TV', \ + regularisation = 'ROF_TV', \ regularisation_parameter = 0.003,\ + regularisation_iterations = 500) + +sliceSel = int(0.5*N_size) +max_val = 1 +plt.figure() +plt.subplot(131) +plt.imshow(RecADMM_reg_roftv[sliceSel,:,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-ROF-TV Reconstruction, axial view') + +plt.subplot(132) +plt.imshow(RecADMM_reg_roftv[:,sliceSel,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-ROF-TV Reconstruction, coronal view') + +plt.subplot(133) +plt.imshow(RecADMM_reg_roftv[:,:,sliceSel],vmin=0, vmax=max_val) +plt.title('3D ADMM-ROF-TV Reconstruction, sagittal view') +plt.show() + +# calculate errors +Qtools = QualityTools(phantom, RecADMM_reg_roftv) +RMSE_admm_rof = Qtools.rmse() +print("Root Mean Square Error for ADMM-ROF-TV is {}".format(RMSE_admm_rof)) +#%% +print ("Reconstructing with ADMM method using FGP-TV penalty") +RecADMM_reg_fgptv = RectoolsIR.ADMM(projdata_norm, + rho_const = 2000.0, \ + iterationsADMM = 30, \ + regularisation = 'FGP_TV', \ + regularisation_parameter = 0.005,\ regularisation_iterations = 250) sliceSel = int(0.5*N_size) @@ -133,8 +164,65 @@ plt.title('3D ADMM-FGP-TV Reconstruction, sagittal view') plt.show() # calculate errors -Qtools = QualityTools(phantom_tm, RecADMM_reg_fgptv) +Qtools = QualityTools(phantom, RecADMM_reg_fgptv) RMSE_admm_fgp = Qtools.rmse() print("Root Mean Square Error for ADMM-FGP-TV is {}".format(RMSE_admm_fgp)) +#%% +print ("Reconstructing with ADMM method using TGV penalty") +RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm, + rho_const = 2000.0, \ + iterationsADMM = 30, \ + regularisation = 'TGV', \ + regularisation_parameter = 0.005,\ + regularisation_iterations = 350) + +sliceSel = int(0.5*N_size) +max_val = 1 +plt.figure() +plt.subplot(131) +plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-TGV Reconstruction, axial view') + +plt.subplot(132) +plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-TGV Reconstruction, coronal view') + +plt.subplot(133) +plt.imshow(RecADMM_reg_tgv[:,:,sliceSel],vmin=0, vmax=max_val) +plt.title('3D ADMM-TGV Reconstruction, sagittal view') +plt.show() + +# calculate errors +Qtools = QualityTools(phantom, RecADMM_reg_tgv) +RMSE_admm_tgv = Qtools.rmse() +print("Root Mean Square Error for ADMM-TGV is {}".format(RMSE_admm_tgv)) +#%% +print ("Reconstructing with ADMM method using Diff4th penalty") +RecADMM_reg_diff4th = RectoolsIR.ADMM(projdata_norm, + rho_const = 2000.0, \ + iterationsADMM = 30, \ + regularisation = 'Diff4th', \ + regularisation_parameter = 0.005,\ + regularisation_iterations = 500) +sliceSel = int(0.5*N_size) +max_val = 1 +plt.figure() +plt.subplot(131) +plt.imshow(RecADMM_reg_diff4th[sliceSel,:,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-Diff4th Reconstruction, axial view') + +plt.subplot(132) +plt.imshow(RecADMM_reg_diff4th[:,sliceSel,:],vmin=0, vmax=max_val) +plt.title('3D ADMM-Diff4th Reconstruction, coronal view') + +plt.subplot(133) +plt.imshow(RecADMM_reg_diff4th[:,:,sliceSel],vmin=0, vmax=max_val) +plt.title('3D ADMM-Diff4th Reconstruction, sagittal view') +plt.show() + +# calculate errors +Qtools = QualityTools(phantom, RecADMM_reg_diff4th) +RMSE_admm_diff4th = Qtools.rmse() +print("Root Mean Square Error for ADMM-Diff4th is {}".format(RMSE_admm_diff4th)) #%% |