#fname = "../../sparse_ct/data/benchmark_ellipses/6.png"
fname = args.fname # "../../sparse_ct/data/shepp_logan.jpg"
res_name = fname.split('/')[-1]
gt, sinogram, theta, FOCUS = image_to_sparse_sinogram(fname, channel=1,
n_proj=64, size=512, angle1=0.0, angle2=180.0, noise_pow=39.0 )
dgr_iter = 4001
sart_iter = 40
recon_fbp = IRadonReconstructor('FBP')
recon_sart = SartReconstructor('SART', sart_n_iter=sart_iter, sart_relaxation=0.15)
recon_sart_tv = SartTVReconstructor('SART+TV',
sart_n_iter=sart_iter, sart_relaxation=0.15,
tv_weight=0.8, tv_n_iter=100)
recon_dip1 = DgrReconstructor('DGR1',
dip_n_iter=dgr_iter,
net='skip',
lr=0.01,
reg_std=1./100,
w_proj_loss=1.00,
# w_perceptual_loss=0.01,
w_tv_loss=0.0,
w_ssim_loss=0.00
)
recon_dip2 = DgrReconstructor('DGR2',
dip_n_iter=dgr_iter,
net='skip',
def test(fname, label, n_proj=32, noise_pow=25.0):
dgr_iter = 4000
lr = 0.01
net = 'skip'
noise_std = 1./100
gt, sinogram, theta, FOCUS = image_to_sparse_sinogram(fname,
channel=1, n_proj=n_proj, size=512,
angle1=0.0, angle2=180.0, noise_pow=noise_pow)
logging.warning('Starting')
logging.warning('fname: %s %s',label, fname)
logging.warning('n_proj: %s', n_proj)
logging.warning('noise_pow: %s', noise_pow)
logging.warning('dgr_n_iter: %s', dgr_iter)
logging.warning('dgr_lr: %s', lr)
logging.warning('dgr_net: %s', net)
logging.warning('dgr_noise_std: %s', noise_std)
recons = [
IRadonReconstructor('FBP'),
SartReconstructor('SART', sart_n_iter=40, sart_relaxation=0.15),
SartTVReconstructor('SART+TV',
sart_n_iter=40, sart_relaxation=0.15,
tv_weight=0.5, tv_n_iter=100),
SartBM3DReconstructor('SART+BM3D',
sart_n_iter=40, sart_relaxation=0.15,
bm3d_sigma=0.5),
DgrReconstructor('DIP_1.00_0.00_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=1.0,
w_perceptual_loss=0.0,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.99_0.01_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.99,
w_perceptual_loss=0.01,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.90_0.10_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.90,
w_perceptual_loss=0.10,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.50_0.50_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.5,
w_perceptual_loss=0.5,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.10_0.90_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.10,
w_perceptual_loss=0.90,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.01_0.99_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.99,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.00_1.00_0.00_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.0,
w_perceptual_loss=1.0,
w_tv_loss=0.0
),
DgrReconstructor('DIP_0.99_0.00_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.99,
w_perceptual_loss=0.0,
w_tv_loss=0.01
),
DgrReconstructor('DIP_0.90_0.00_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.9,
w_perceptual_loss=0.0,
w_tv_loss=0.1
),
DgrReconstructor('DIP_0.50_0.00_0.50_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.5,
w_perceptual_loss=0.0,
w_tv_loss=0.5
),
DgrReconstructor('DIP_0.10_0.00_0.90_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.1,
w_perceptual_loss=0.0,
w_tv_loss=0.9
),
DgrReconstructor('DIP_0.01_0.00_0.99_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.0,
w_tv_loss=0.99
),
DgrReconstructor('DIP_0.00_0.00_1.0_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.00,
w_perceptual_loss=0.0,
w_tv_loss=1.0
),
DgrReconstructor('DIP_0.33_0.33_0.33_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.33,
w_perceptual_loss=0.33,
w_tv_loss=0.33
),
DgrReconstructor('DIP_0.8_0.10_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.8,
w_perceptual_loss=0.1,
w_tv_loss=0.1
),
DgrReconstructor('DIP_0.98_0.01_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.98,
w_perceptual_loss=0.01,
w_tv_loss=0.01
),
DgrReconstructor('DIP_0.10_0.80_0.10_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.10,
w_perceptual_loss=0.80,
w_tv_loss=0.10
),
DgrReconstructor('DIP_0.01_0.98_0.01_0.00',
dip_n_iter=dgr_iter,
net=net,
lr=lr,
reg_std=noise_std,
w_proj_loss=0.01,
w_perceptual_loss=0.98,
w_tv_loss=0.01
),
]
img_sart_bm3d = recons[3].calc(sinogram, theta)
imgs = []
for recon in recons:
if type(recon) == DgrReconstructor:
recon.set_for_metric(gt, img_sart_bm3d, FOCUS=FOCUS, log_dir='../log/dip')
imgs.append(recon.calc(sinogram))
mse, psnr, ssim = recon.eval(gt)
recon.save_result()
logstr = "{}: MSE:{:.5f} PSNR:{:.5f} SSIM:{:.5f}".format(
recon.name, mse, psnr, ssim
)
logging.info(logstr)
plot_grid([gt] + imgs,
FOCUS=FOCUS, save_name=label+'.png', dpi=500)
logging.warning('Done. Results saved as %s', label+'.png')
fname = "../data/ct1.jpg"
gt, sinogram, FOCUS = image_to_sparse_sinogram(fname,
channel=1,
n_proj=32,
size=512,
noise_pow=25.0)
n_iter = 40
recons = [
IRadonReconstructor('FBP'),
SartReconstructor('SART', sart_n_iter=n_iter, sart_relaxation=0.15),
SartTVReconstructor('SART+TVw0.2',
sart_n_iter=n_iter,
sart_relaxation=0.15,
tv_weight=0.2,
tv_n_iter=100),
SartTVReconstructor('SART+TVw0.5',
sart_n_iter=n_iter,
sart_relaxation=0.15,
tv_weight=0.5,
tv_n_iter=100),
SartTVReconstructor('SART+TVw0.8',
sart_n_iter=n_iter,
sart_relaxation=0.15,
tv_weight=0.8,
tv_n_iter=100),
SartTVReconstructor('SART+TVw0.95',
sart_n_iter=n_iter,
sart_relaxation=0.15,
data,
recon,
theta,
40.0
)
recon_fbp = IRadonReconstructor('FBP')
recon_sart = SartReconstructor('SART',
sart_n_iter=40,
sart_relaxation=0.15)
recon_sart_tv = SartTVReconstructor('SART+TVw0.9',
sart_n_iter=40, sart_relaxation=0.15,
tv_weight=0.9, tv_n_iter=100)
recon_sart_bm3d = SartBM3DReconstructor('SART+BM3Ds0.35',
sart_n_iter=40, sart_relaxation=0.15,
bm3d_sigma=0.35)
recon_dgr = DgrReconstructor('DGR_0.80_0.00_0.10_0.10',
dip_n_iter=4001,
net='skip',
lr=0.01,
reg_std=1./100,
w_proj_loss=0.80,
w_perceptual_loss=0.00,
w_tv_loss=0.10,
w_ssim_loss=0.10)
recon_rdgr = DgrReconstructor('RDGR_1.00_0.00_0.00',