def test_sino_rytov():
myframe = sys._getframe()
sino = get_test_data_set_sino(rytov=True)
ryt = odtbrain.sinogram_as_rytov(sino)
twopi = 2*np.pi
if WRITE_RES:
write_results(myframe, ryt)
# When moving from unwrap to skimage, there was an offset introduced.
# Since this particular array is not flat at the borders, there is no
# correct way here. We just subtract 2PI.
# 2019-04-18: It turns out that on Windows, this is not the case.
# Hence, we only subtract 2PI if the minimum of the array is above
# 2PI..
if ryt.imag.min() > twopi:
ryt.imag -= twopi
assert np.allclose(np.array(ryt).flatten().view(
float), get_results(myframe))
# Check the 3D result with the 2D result. They should be the same except
# for a multiple of 2PI offset, because odtbrain._align_unwrapped
# subtracts the background such that the median phase change is closest
# to zero.
# 2D A
ryt2d = odtbrain.sinogram_as_rytov(sino[:, :, 0])
assert np.allclose(0, negative_modulo_rest_imag(
ryt2d - ryt[:, :, 0], twopi).view(float), atol=1e-6)
# 2D B
ryt2d2 = odtbrain.sinogram_as_rytov(sino[:, 0, :])
assert np.allclose(0, negative_modulo_rest_imag(
ryt2d2 - ryt[:, 0, :], twopi).view(float), atol=1e-6)
def test_correct_reproduce():
myframe = sys._getframe()
sino, angles = create_test_sino_3d(Nx=10, Ny=10)
p = get_test_parameter_set(1)[0]
sryt = odtbrain.sinogram_as_rytov(uSin=sino, u0=1, align=False)
f = odtbrain.backpropagate_3d(sryt,
angles,
padval=0,
dtype=np.float64,
copy=False,
**p)
fc = odtbrain.apple.correct(f=f,
res=p["res"],
nm=p["nm"],
enforce_envelope=.95,
max_iter=100,
min_diff=0.01)
fo = cutout(fc)
fo = np.array(fo, dtype=np.complex128)
if WRITE_RES:
write_results(myframe, fo)
data = fo.flatten().view(float)
assert np.allclose(data, get_results(myframe))
sino, angles, phantom, cfg = \
load_data("fdtd_3d_sino_A180_R6.500.tar.lzma")
A = angles.shape[0]
print("Example: Backpropagation from 3D FDTD simulations")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Number of projections:", A)
print("Performing backpropagation.")
# Apply the Rytov approximation
sinoRytov = odt.sinogram_as_rytov(sino)
# perform backpropagation to obtain object function f
f = odt.backpropagate_3d(uSin=sinoRytov,
angles=angles,
res=cfg["res"],
nm=cfg["nm"],
lD=cfg["lD"]
)
# compute refractive index n from object function
n = odt.odt_to_ri(f, res=cfg["res"], nm=cfg["nm"])
sx, sy, sz = n.shape
px, py, pz = phantom.shape
res = cfg["res"] # px/wavelengths
A = cfg["A"] # number of projections
#phantom = np.loadtxt(arc.open("mie_phantom.txt"))
x = np.arange(size)-size/2.0
X,Y = np.meshgrid(x,x)
rad_px = radius*res
phantom = np.array(((Y-lC*res)**2+X**2)<rad_px**2, dtype=np.float)*(ncyl-nmed)+nmed
# Born
u_sinB = (sino/u0*u0_single-u0_single) #fake born
fB = odt.backpropagate_2d(u_sinB, angles, res, nmed, lD*res)
nB = odt.odt_to_ri(fB, res, nmed)
# Rytov
u_sinR = odt.sinogram_as_rytov(sino/u0)
fR = odt.backpropagate_2d(u_sinR, angles, res, nmed, lD*res)
nR = odt.odt_to_ri(fR, res, nmed)
# Rytov 50
u_sinR50 = odt.sinogram_as_rytov((sino/u0)[::5,:])
fR50 = odt.backpropagate_2d(u_sinR50, angles[::5], res, nmed, lD*res)
nR50 = odt.odt_to_ri(fR50, res, nmed)
# Plot sinogram phase and amplitude
ph = unwrap.unwrap(np.angle(sino/u0))
am = np.abs(sino/u0)
## Setup background
t_RealBack = float('-1.60175172169E-02')
t_ImagBack = float('-1.85142597885E-02')
t_ComplexBack = t_RealBack + 1j*t_ImagBack
# t_SinoBack = np.tile(t_ComplexBack, t_SudutProj*t_SensorInterp).reshape(t_SudutProj, t_SensorInterp)
t_SinoBack = np.tile(t_ComplexBack, t_SudutInterp*t_SensorInterp).reshape(t_SudutInterp, t_SensorInterp)
# print(t_SinoBack)
## Prep for Reconstruksi Citra
t_Theta = np.linspace(0., 360., t_SudutInterp, endpoint=False)
## Do Fasa background corrected
t_SinoCorrected = t_SinoSudutInterp/t_SinoBack
## Do backpropagate 2D
u_sinR = odt.sinogram_as_rytov(t_SinoCorrected)
angles = np.linspace(0, 2*np.pi, t_SudutInterp, endpoint=False)
res = 9.0
nmed = 2.4
lD = 1.0
fR = odt.backpropagate_2d(u_sinR, angles, res, nmed, lD * res)
nR = odt.odt_to_ri(fR, res, nmed)
## plot data
fig, (ax1) = plt.subplots(1, 1)
ax1.imshow(ndimage.median_filter(nR.real*-1, size=7))
ax1.set_xlabel('pixel')
ax1.set_ylabel('pixel')
plt.show()
nmed = cfg["nmed"]
ncyl = cfg["ncyl"]
lD = cfg["lD"] # measurement distance in wavelengths
lC = cfg["lC"] # displacement from center of image
size = cfg["size"]
res = cfg["res"] # px/wavelengths
A = cfg["A"] # number of projections
x = np.arange(size) - size / 2.0
X, Y = np.meshgrid(x, x)
rad_px = radius * res
phantom = np.array(((Y - lC * res)**2 + X**2) < rad_px**2,
dtype=np.float) * (ncyl - nmed) + nmed
u_sinR = odt.sinogram_as_rytov(sino / u0)
# Rytov 200 projections
# remove 50 projections from total of 250 projections
remove200 = np.argsort(angles % .0002)[:50]
angles200 = np.delete(angles, remove200, axis=0)
u_sinR200 = np.delete(u_sinR, remove200, axis=0)
ph200 = unwrap.unwrap(np.angle(sino / u0))
ph200[remove200] = 0
fR200 = odt.backpropagate_2d(u_sinR200, angles200, res, nmed, lD * res)
nR200 = odt.odt_to_ri(fR200, res, nmed)
fR200nw = odt.backpropagate_2d(u_sinR200,
angles200,
res,
nmed,
# t_SinoDeembeding = t_SinoInterp
# t_Reconstruction = iradon_sart(np.abs(t_SinoInterp.transpose()) - np.abs(t_SinoBack.transpose()), theta=t_Theta, relaxation=0.1)
# t_Reconstruction = iradon_sart(np.abs(t_Sino.transpose()), theta=t_Theta, relaxation=0.1)
print(np.min(np.abs(t_SinoDeembeding)))
print(np.max(np.abs(t_SinoDeembeding)))
print(np.mean(np.abs(t_SinoDeembeding)))
# t_Reconstruction = iradon_sart(np.abs(t_SinoDeembeding.transpose()), theta=t_Theta, relaxation=0.01)
# t_Reconstruction = iradon_sart(np.abs(t_SinoDeembeding.transpose())*-1, theta=t_Theta, relaxation=0.01, clip=[0.02,0.04])
# for i in range(50):
# t_Reconstruction = iradon_sart(np.abs(t_SinoDeembeding.transpose()), theta=t_Theta, relaxation=0.01)
# t_Reconstruction = iradon(t_SinoDeembeding.transpose(), filter="ramp" ,interpolation="cubic", circle=False, theta=t_Theta)
# u_sinR = odt.sinogram_as_rytov(t_SinoSudutInterp / t_SinoBack)
u_sinR = odt.sinogram_as_rytov(t_SinoDeembeding)
angles = np.linspace(0, 2 * np.pi, t_SudutInterp, endpoint=False)
res = 9.0
nmed = 2.4
lD = 1.0
fR = odt.backpropagate_2d(u_sinR, angles, res, nmed, lD * res)
nR = odt.odt_to_ri(fR, res, nmed)
## Interpolasi multivariate
# t_InterpVal = 100
# x = np.linspace(0, t_SensorInterp-1, t_SensorInterp)
# y = np.linspace(0, t_SensorInterp-1, t_SensorInterp)
# X, Y = np.meshgrid(x, y)
# px = np.random.choice(x, t_InterpVal)
# py = np.random.choice(y, t_InterpVal)
# print(t_Reconstruction.flatten().shape)
res = cfg["res"] # px/wavelengths
A = cfg["A"] # number of projections
x = np.arange(size) - size / 2
X, Y = np.meshgrid(x, x)
rad_px = radius * res
phantom = np.array(((Y - lC * res)**2 + X**2) < rad_px**2,
dtype=np.float) * (ncyl - nmed) + nmed
# Born
u_sinB = (sino / u0 * u0_single - u0_single) # fake born
fB = odt.backpropagate_2d(u_sinB, angles, res, nmed, lD * res)
nB = odt.odt_to_ri(fB, res, nmed)
# Rytov
u_sinR = odt.sinogram_as_rytov(sino / u0)
fR = odt.backpropagate_2d(u_sinR, angles, res, nmed, lD * res)
nR = odt.odt_to_ri(fR, res, nmed)
# Rytov 50
u_sinR50 = odt.sinogram_as_rytov((sino / u0)[::5, :])
fR50 = odt.backpropagate_2d(u_sinR50, angles[::5], res, nmed, lD * res)
nR50 = odt.odt_to_ri(fR50, res, nmed)
# Plot sinogram phase and amplitude
ph = odt.sinogram_as_radon(sino / u0)
am = np.abs(sino / u0)
# prepare plot
vmin = np.min(np.array([phantom, nB.real, nR50.real, nR.real]))
nmed = cfg["nmed"]
ncyl = cfg["ncyl"]
lD = cfg["lD"] # measurement distance in wavelengths
lC = cfg["lC"] # displacement from center of image
size = cfg["size"]
res = cfg["res"] # px/wavelengths
A = cfg["A"] # number of projections
#phantom = np.loadtxt(arc.open("mie_phantom.txt"))
x = np.arange(size)-size/2.0
X,Y = np.meshgrid(x,x)
rad_px = radius*res
phantom = np.array(((Y-lC*res)**2+X**2)<rad_px**2, dtype=np.float)*(ncyl-nmed)+nmed
u_sinR = odt.sinogram_as_rytov(sino/u0)
# Rytov 200 projections
# remove 50 projections from total of 250 projections
remove200 = np.argsort(angles % .002)[:50]
angles200 = np.delete(angles, remove200, axis=0)
u_sinR200 = np.delete(u_sinR, remove200, axis=0)
ph200 = unwrap.unwrap(np.angle(sino/u0))
ph200[remove200] = 0
fR200 = odt.backpropagate_2d(u_sinR200, angles200, res, nmed, lD*res)
nR200 = odt.odt_to_ri(fR200, res, nmed)
fR200nw = odt.backpropagate_2d(u_sinR200, angles200, res, nmed, lD*res, weight_angles=False)
nR200nw = odt.odt_to_ri(fR200nw, res, nmed)
medium_index = 1.335
# initialize cell phantom
phantom = cellsino.phantoms.SimpleCell()
# initialize sinogram with geometric parameters
sino = cellsino.Sinogram(phantom=phantom,
wavelength=wavelength,
pixel_size=pixel_size,
grid_size=grid_size)
# compute sinogram (field according to Rytov approximation and fluorescence)
sino_field, sino_fluor = sino.compute(angles=angles, propagator="rytov")
# reconstruction of refractive index
sino_rytov = odt.sinogram_as_rytov(sino_field)
potential = odt.backpropagate_3d(uSin=sino_rytov,
angles=angles,
res=wavelength/pixel_size,
nm=medium_index)
ri = odt.odt_to_ri(f=potential,
res=wavelength/pixel_size,
nm=medium_index)
# reconstruction of fluorescence
fl = rt.backproject_3d(sinogram=sino_fluor,
angles=angles)
# reference for comparison
rimod, flmod = phantom.draw(grid_size=ri.shape,
pixel_size=pixel_size)
## Interpolation
# t_NX, t_NY = t_Data.shape
# t_X = t_Data[0]
# t_DataInterp
## Backprojection
# t_Reconstruction = iradon_sart(t_Data)
# t_Reconstruction = iradon(t_Data, filter="ramp" ,interpolation="linear", circle=True, output_size=73*2)
# for i in range(20):
# t_Reconstruction = iradon_sart(t_Data, image=t_Reconstruction)
## Backpropagation
# u_sinR = odt.sinogram_as_rytov(t_SinoInterp.reshape(t_SudutProj, t_SensorInterp), u0=t_DataBackground)
# u_sinR = odt.sinogram_as_rytov(t_SinoInterp.reshape(t_SudutProj, t_SensorInterp))
u_sinR = odt.sinogram_as_rytov(t_SinoInterp)
# for x_back in range(72):
# for y_back in range(72):
# if t_DataBackground[x_back,y_back] == 0. :
# t_DataBackground[x_back, y_back] = 1.
# u_sinR = t_SinoInterp.reshape(t_SudutProj, t_SensorInterp)/t_DataBackground
# u_sinR = t_SinoInterp.reshape(t_SudutProj, t_SensorInterp)
t_Theta = np.linspace(0., 360., t_SudutProj, endpoint=False)
# t_Reconstruction = iradon(t_DataInterp.transpose(), filter="ramp" ,interpolation="linear", circle=True, theta=t_Theta)
# t_Reconstruction = iradon(t_DataInterp.transpose(), filter="ramp" ,interpolation="linear", circle=False, theta=t_Theta)
# t_Reconstruction = radontea.backproject(t_DataInterp.transpose(), t_Theta)
t_Reconstruction = radontea.fan.radon_fan(t_DataInterp.transpose(), det_size=9, det_spacing=2)
print(t_Reconstruction.shape)
# t_Reconstruction = iradon_sart(t_DataInterp.transpose(), theta=t_Theta, relaxation=0.01)
# for i in range(10):
if autofocus:
Ex, d = nrefocus.autofocus(Ex,
cfg["nm"],
cfg["res"],
(-1.5*cfg["lD"]*cfg["res"],0),
ret_d=True)
lD = 0
print("Autofocusing distance [px]: ", d)
else:
lD = cfg["lD"]*cfg["res"]
# Create sinogram
u_sin = np.tile(Ex.flat,A).reshape(A, cfg["size"], cfg["size"])
# Apply the Rytov approximation
u_sinR = odt.sinogram_as_rytov(u_sin)
# Backpropagation
fR = odt.backpropagate_3d(uSin = u_sinR,
angles = angles,
res = cfg["res"],
nm = cfg["nm"],
lD = lD,
padfac=2.1)
# RI computation
nR = odt.odt_to_ri(fR, cfg["res"], cfg["nm"])
# Plotting
fig, axes = plt.subplots(2, 3, figsize=(12,8), dpi=300)
axes = np.array(axes).flatten()
def backpropagate_sinogram(
sinogram,
angles,
approx,
res,
nm,
ld=0,
):
"""Backpropagate a 2D or 3D sinogram
Parameters
----------
sinogram: complex ndarray
The scattered field data
angles: 1d ndarray
The angles at which the sinogram data were recorded
approx: str
Approximation to use, one of ["radon", "born", "rytov"]
res: float
Size of vacuum wavelength in pixels
nm: float
Refractive index of surrounding medium
ld: float
Reconstruction distance. Values !=0 only make sense for the
Born approximation (which itself is not very usable).
See the ODTbrain documentation for more information.
Returns
-------
ri: ndarray
The 2D or 3D reconstructed refractive index
"""
sshape = len(sinogram.shape)
assert sshape in [2, 3], "sinogram must have dimension 2 or 3"
uSin = sinogram
assert approx in ["radon", "born", "rytov"]
if approx == "rytov":
uSin = odt.sinogram_as_rytov(uSin)
elif approx == "radon":
uSin = odt.sinogram_as_radon(uSin)
if approx in ["born", "rytov"]:
# Perform reconstruction with ODT
if sshape == 2:
f = odt.backpropagate_2d(uSin,
angles=angles,
res=res,
nm=nm,
lD=ld)
else:
f = odt.backpropagate_3d(uSin,
angles=angles,
res=res,
nm=nm,
lD=ld)
ri = odt.odt_to_ri(f, res, nm)
else:
# Perform reconstruction with OPT
# works in 2d and 3d
f = rt.backproject(uSin, angles=angles)
ri = odt.opt_to_ri(f, res, nm)
return ri
cfg = {}
for l in info.readlines():
l = l.decode()
if l.count("=") == 1:
key, val = l.split("=")
cfg[key.strip()] = float(val.strip())
print("Example: Backpropagation from 2d FDTD simulations")
print("Refractive index of medium:", cfg["nm"])
print("Measurement position from object center:", cfg["lD"])
print("Wavelength sampling:", cfg["res"])
print("Performing backpropagation.")
## Apply the Rytov approximation
sinoRytov = odt.sinogram_as_rytov(sino)
## perform backpropagation to obtain object function f
f = odt.backpropagate_2d( uSin=sinoRytov,
angles=angles,
res=cfg["res"],
nm=cfg["nm"],
lD=cfg["lD"]*cfg["res"]
)
## compute refractive index n from object function
n = odt.odt_to_ri(f, res=cfg["res"], nm=cfg["nm"])
# Reconstruction angles
angles = np.linspace(0, 2 * np.pi, A, endpoint=False)
# Perform focusing
Ex = nrefocus.refocus(
Ex,
d=-cfg["lD"] * cfg["res"],
nm=cfg["nm"],
res=cfg["res"],
)
# Create sinogram
u_sin = np.tile(Ex.flat, A).reshape(A, int(cfg["size"]), int(cfg["size"]))
# Apply the Rytov approximation
u_sinR = odt.sinogram_as_rytov(u_sin)
# Backpropagation
fR = odt.backpropagate_3d(uSin=u_sinR,
angles=angles,
res=cfg["res"],
nm=cfg["nm"],
lD=0,
padfac=2.1,
save_memory=True)
# RI computation
nR = odt.odt_to_ri(fR, cfg["res"], cfg["nm"])
# Plotting
fig, axes = plt.subplots(2, 3, figsize=(8, 5))