# read projections
prjs = np.load(args.sino)
prjs = np.copy(prjs[args.slices[0]:args.slices[1], ...], 'C') / args.imgNorm
# add noise to projections
np.random.seed(0)
if args.N0 > 0:
prjs = prjs + np.sqrt(
(1 - args.doseRate) / args.doseRate * np.exp(prjs * args.imgNorm) /
args.N0) * np.random.normal(size=prjs.shape) / args.imgNorm
# In[11]:
# mask for network training
masks = helper.GetMasks2D(reconNet, [resolution] * refs.shape[0])
# In[12]:
def CalcProjectorNorm(reconNet, weight, nIter=20):
weight = np.sqrt(weight)
x = np.random.random_sample([1, reconNet.nx, reconNet.ny,
1]).astype(np.float32)
x = x / np.linalg.norm(x)
for i in range(nIter):
print(i, end=',', flush=True)
fp = reconNet.cDDFanProjection3d(x) * weight
projectorNorm = np.linalg.norm(fp)
# read projections
prjs = np.load(args.sino)
prjs = np.copy(prjs[inds[:args.nSlices], ...], 'C') / args.imgNorm
# add noise to projections
np.random.seed(0)
if args.N0 > 0:
prjs = prjs + np.sqrt((1 - args.doseRate) / args.doseRate * np.exp(prjs * args.imgNorm) / args.N0) * np.random.normal(size = prjs.shape) / args.imgNorm
# In[8]:
# mask for network training
masks = helper.GetMasks2D(reconNet, resolutions)
# In[9]:
# fbp
imgs = []
for i in range(prjs.shape[0]):
# for i in range(10):
if i % 10 == 0:
print (i, end=', ')
reconNet.dx = resolutions[i]
reconNet.dy = resolutions[i]
reconNet.rotview = len(setToUse)
reconNet.angles = [reconNet.angles[i] for i in setToUse]
# support only one resolution
resolution = np.load(args.resolutionFile)
resolution = np.mean(resolution[args.slices[0]:args.slices[1]])
reconNet.dx = resolution
reconNet.dy = resolution
# In[38]:
# mask for network training
masks = helper.GetMasks2D(reconNet, [0.8] * refs.shape[0])
# In[39]:
# fbp
imgs = []
for i in range(prjs.shape[0]):
# for i in range(10):
if i % 10 == 0:
print (i, end=', ')
fp = np.copy(prjs[[i],...], 'C')