diff options
Diffstat (limited to 'Wrappers/Python/demos')
| -rw-r--r-- | Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py | 36 |
1 files changed, 33 insertions, 3 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 d39d643..aa65cf3 100644 --- a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py +++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py @@ -23,6 +23,7 @@ import matplotlib.gridspec as gridspec import numpy as np import h5py from ccpi.supp.qualitymetrics import QualityTools +from scipy.signal import gaussian # loading the data h5f = h5py.File('data/TomoSim_data1550671417.h5','r') @@ -136,12 +137,19 @@ plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr") plt.title('FBP Reconstruction, coronal (Y-Z) view', fontsize=19) ax2.set_aspect('equal') plt.show() -#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1200) +#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1600) # calculate errors Qtools = QualityTools(phantom, recFBP) RMSE_fbp = Qtools.rmse() print("Root Mean Square Error for FBP is {}".format(RMSE_fbp)) + +# SSIM measure +Qtools = QualityTools(phantom[128,:,:]*255, recFBP[128,:,:]*235) +win = np.array([gaussian(11, 1.5)]) +win2d = win * (win.T) +ssim_fbp = Qtools.ssim(win2d) +print("Mean SSIM for FBP is {}".format(ssim_fbp[0])) #%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("Reconstructing with ADMM method using TomoRec software") @@ -196,6 +204,13 @@ plt.savefig('SBTV_phantom.pdf', format='pdf', dpi=1600) Qtools = QualityTools(phantom, RecADMM_reg_sbtv) RMSE_admm_sbtv = Qtools.rmse() print("Root Mean Square Error for ADMM-SB-TV is {}".format(RMSE_admm_sbtv)) + +# SSIM measure +Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_sbtv[128,:,:]*235) +win = np.array([gaussian(11, 1.5)]) +win2d = win * (win.T) +ssim_admm_sbtv = Qtools.ssim(win2d) +print("Mean SSIM ADMM-SBTV is {}".format(ssim_admm_sbtv[0])) #%% print ("Reconstructing with ADMM method using ROFLLT penalty") RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm, @@ -236,6 +251,13 @@ plt.show() Qtools = QualityTools(phantom, RecADMM_reg_rofllt) RMSE_admm_rofllt = Qtools.rmse() print("Root Mean Square Error for ADMM-ROF-LLT is {}".format(RMSE_admm_rofllt)) + +# SSIM measure +Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_rofllt[128,:,:]*235) +win = np.array([gaussian(11, 1.5)]) +win2d = win * (win.T) +ssim_admm_rifllt = Qtools.ssim(win2d) +print("Mean SSIM ADMM-ROFLLT is {}".format(ssim_admm_rifllt[0])) #%% print ("Reconstructing with ADMM method using TGV penalty") RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm, @@ -244,7 +266,7 @@ RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm, regularisation = 'TGV', \ regularisation_parameter = optimReg_tgv,\ regularisation_iterations = 600) - +#%% sliceSel = int(0.5*N_size) max_val = 1 plt.figure(figsize = (20,3)) @@ -269,10 +291,18 @@ plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr") plt.title('ADMM-TGV (Y-Z) view', fontsize=19) plt.colorbar(ax=ax4) plt.show() -plt.savefig('TGV_phantom.pdf', format='pdf', dpi=1200) +#plt.savefig('TGV_phantom.pdf', format='pdf', dpi=1600) # 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)) + +# SSIM measure +#Create a 2d gaussian for the window parameter +Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_tgv[128,:,:]*235) +win = np.array([gaussian(11, 1.5)]) +win2d = win * (win.T) +ssim_admm_tgv = Qtools.ssim(win2d) +print("Mean SSIM ADMM-TGV is {}".format(ssim_admm_tgv[0])) #%% |