def genrate_sinogam_ref(image, angles):
size = image.shape[0]
sinogram = numpy.zeros((size, len(angles)), dtype="float32")
for ia, angle in enumerate(angles):
tmp_proj = project(rotate_square(image, angle))
sinogram[:, ia] = tmp_proj
return sinogram.astype("float32"), angles.astype("float32")
def genrate_sinogam(image, angles):
size = image.shape[0]
sinogram = np.zeros((size, len(angles)), dtype='float32')
for ia, angle in enumerate(angles):
tmp_proj = project(rotate_square(image, angle))
sinogram[:, ia] = tmp_proj
return sinogram.astype('float32'), angles.astype('float32')
def test_project():
N=50
x=modified_shepp_logan((N,N,3))[:,:,1]
x=numpy.array(x)
rp=ref.project(x)
ip=ispmd.project(x)
# import pylab
# pylab.figure()
# pylab.subplot(211)
# pylab.plot(rp)
# pylab.subplot(212)
# pylab.plot(ip)
# pylab.show()
assert is_equal(rp,ip)
def sart(sinogram,angles):
reconst_shape = numpy.array(sinogram.shape[0],dtype='int32')
# #normalize sinograms
# sinogram *= numpy.mean(sinogram.sum(axis=0)) / sinogram.sum(axis=0)
tomo_rec = numpy.zeros((reconst_shape, reconst_shape), dtype='float32')
tmp_backproj=numpy.zeros_like(tomo_rec)
tmp_rot=numpy.zeros_like(tomo_rec)
for coeff in numpy.array([0.8,0.3,],dtype='float32'):
shuffle_iang = numpy.arange(len(angles))
numpy.random.shuffle(shuffle_iang)
for iang in shuffle_iang:
rotate_square_ref(tomo_rec, angles[iang],tmp_rot)
tmp_proj = sinogram[:, iang] - project(tmp_rot)
back_project_ref(tmp_proj * coeff,tmp_backproj)
rotate_square_ref(tmp_backproj, -angles[iang],tmp_rot)
add_ref(tomo_rec,tmp_rot)
tomo_rec -= 0.5*tomo_rec*(tomo_rec < 0)
return tomo_rec
