def test_fbp_reconstruction(self):
mat_rec1 = reco.fbp_reconstruction(self.sino_180,
self.center,
apply_log=False,
gpu=False)
num1 = np.max(np.abs(self.mat - mat_rec1))
mat_rec2 = reco.fbp_reconstruction(self.sino_360,
self.center,
angles=np.deg2rad(self.angles),
apply_log=False,
gpu=False)
num2 = np.max(np.abs(self.mat - mat_rec2))
check = True
if cuda.is_available() is True:
mat_rec1 = reco.fbp_reconstruction(self.sino_180,
self.center,
apply_log=False,
gpu=True)
num3 = np.max(np.abs(self.mat - mat_rec1))
mat_rec2 = reco.fbp_reconstruction(self.sino_360,
self.center,
angles=np.deg2rad(self.angles),
apply_log=False,
gpu=True)
num4 = np.max(np.abs(self.mat - mat_rec2))
if num3 > 0.1 or num4 > 0.1:
check = False
self.assertTrue(num1 <= 0.1 and num2 <= 0.1 and check)
print("2 -> Calculate the center-of-rotation...")
sinogram = corr.flat_field_correction(data[0:num_proj, mid_slice, left:right],
flat_field[mid_slice, left:right],
dark_field[mid_slice, left:right])
center = calc.find_center_vo(sinogram)
print("Center-of-rotation = {0}".format(center))
for i in range(num_tomo):
folder_name = "tomo_" + ("0000" + str(i))[-5:]
thetas = np.deg2rad(angles[i * num_proj:(i + 1) * num_proj])
for slice_idx in range(start_slice, stop_slice + 1, step_slice):
sinogram = corr.flat_field_correction(
data[i * num_proj:(i + 1) * num_proj, slice_idx, left:right],
flat_field[slice_idx, left:right], dark_field[slice_idx,
left:right])
sinogram = remo.remove_zinger(sinogram, 0.05, 1)
sinogram = remo.remove_all_stripe(sinogram, 3.0, 51, 17)
sinogram = filt.fresnel_filter(sinogram, 100)
# img_rec = reco.dfi_reconstruction(sinogram, center, angles=thetas, apply_log=True)
# img_rec = reco.gridrec_reconstruction(sinogram, center, angles=thetas, apply_log=True)
img_rec = reco.fbp_reconstruction(sinogram,
center,
angles=thetas,
apply_log=True)
file_name = "rec_slice_" + ("0000" + str(slice_idx))[-5:] + ".tif"
losa.save_image(output_base + "/" + folder_name + "/" + file_name,
img_rec)
print("Done tomograph {0}".format(i))
time_stop = timeit.default_timer()
print("All done!!! Total time cost: {}".format(time_stop - time_start))
start_sino = i * slice_chunk + offset
stop_sino = start_sino + slice_chunk
sinograms = corr.flat_field_correction(
data[ii * num_proj:(ii + 1) * num_proj, start_sino:stop_sino,
left:right],
flat_field[start_sino:stop_sino, left:right],
dark_field[start_sino:stop_sino, left:right],
option1=opt1,
option2=opt2,
option3=opt3)
for j in range(start_sino, stop_sino):
# img_rec = reco.dfi_reconstruction(sinograms[:, j - start_sino, :], center, angles=thetas, apply_log=True)
# img_rec = reco.gridrec_reconstruction(sinograms[:, j - start_sino, :], center, angles=thetas,
# apply_log=True)
img_rec = reco.fbp_reconstruction(sinograms[:, j - start_sino, :],
center,
angles=thetas,
apply_log=True)
name = "0000" + str(j)
losa.save_image(
output_base + "/" + folder_name + "/rec_" + name[-5:] + ".tif",
img_rec)
t_stop = timeit.default_timer()
print("Tomograph {0} -> Done slice: {1} - {2} . Time {3}".format(
ii, start_sino, stop_sino, t_stop - time_start))
if num_rest != 0:
for i in range(num_rest):
start_sino = num_iter * slice_chunk + offset
stop_sino = start_sino + num_rest
sinograms = corr.flat_field_correction(
data[ii * num_proj:(ii + 1) * num_proj, start_sino:stop_sino,
artifact_rem = False # Remove ring artifacts
t0 = timeit.default_timer()
for i in np.arange(start_slice, stop_slice + 1, step):
name = ("0000" + str(i))[-5:]
sino_phase = phase_hdf[:, i, :]
if fluct_correct:
sino_phase = filt.double_wedge_filter(sino_phase, center)
sino_trans = trans_hdf[:, i, :]
sino_dark = dark_hdf[:, i, :]
if artifact_rem:
sino_phase = rem.remove_all_stripe(sino_phase, 2.0, 51, 17)
sino_trans = rem.remove_all_stripe(sino_trans, 2.0, 51, 17)
sino_dark = rem.remove_all_stripe(sino_dark, 2.0, 51, 17)
# Change to CPU methods (DFI or gridrec) if GPU not available
rec_phase = reco.fbp_reconstruction(sino_phase,
center,
apply_log=False,
filter_name="hann")
rec_trans = reco.fbp_reconstruction(sino_trans,
center,
apply_log=True,
filter_name="hann")
rec_dark = reco.fbp_reconstruction(sino_dark,
center,
apply_log=True,
filter_name="hann")
losa.save_image(output_base + "/phase/rec_" + name + ".tif", rec_phase)
losa.save_image(output_base + "/transmission/rec_" + name + ".tif",
rec_trans)
losa.save_image(output_base + "/dark_signal/rec_" + name + ".tif",
rec_dark)
print("Done slice: {}".format(i))
size = 1024
# Generate a built-in phantom
phantom = sim.make_face_phantom(size)
losa.save_image(output_base + "/face_phantom.tif", phantom)
angles = np.linspace(0.0, 180.0, size) * np.pi / 180.0
# Generate sinogram
sinogram = sim.make_sinogram(phantom, angles)
losa.save_image(output_base + "/sinogram.tif", sinogram)
# Find center of rotation
center = calc.find_center_vo(sinogram)
# Reconstruct using the DFI method
rec_image = reco.dfi_reconstruction(sinogram, center, apply_log=False)
losa.save_image(output_base + "/recon_dfi.tif", rec_image)
# Reconstruct using the FBP (GPU) method
rec_image = reco.fbp_reconstruction(sinogram, center, apply_log=False)
losa.save_image(output_base + "/recon_fbp.tif", rec_image)
# Convert to X-ray image
sinogram = sim.convert_to_Xray_image(sinogram)
# Add noise
sinogram = sim.add_noise(sinogram, noise_ratio=0.1)
# Add stripe artifacts
sinogram = sim.add_stripe_artifact(sinogram, 2, size // 4, strength_ratio=0.3,
stripe_type="partial")
sinogram = sim.add_stripe_artifact(sinogram, 1, size // 3, strength_ratio=0.3,
stripe_type="full")
sinogram = sim.add_stripe_artifact(sinogram, 2, size // 2 + size // 3,
strength_ratio=0.7, stripe_type="dead")
sinogram = sim.add_stripe_artifact(sinogram, 1, size // 2 - size // 4,
strength_ratio=0.2,
sino_360 = conv.stitch_image_multiple(list_sino,
list_overlap,
norm=True,
total_width=None)
losa.save_image(output_base + "/reconstruction/sino_360_stitched.tif",
sino_360)
print(
"3 -> Calculate the center of rotation and convert a 360-degree sinogram to a 180-degree sinogram"
)
(center0, overlap, side, _) = calc.find_center_360(sino_360, 100)
print(
"Center-of-rotation: {0}. Side: {1} (0->'left', 1->'right'). Overlap: {2}".
format(center0, side, overlap))
# Convert the 360-degree sinogram to the 180-degree sinogram.
sino_180, center1 = conv.convert_sinogram_360_to_180(sino_360, center0)
losa.save_image(output_base + "/reconstruction/sino_180_converted.tif",
sino_180)
# Apply the Fresnel filter
print("4 -> Apply a denoising method because the sinogram is undersampled")
sino_180 = filt.fresnel_filter(sino_180, 250, 1)
# Perform reconstruction
print("5 -> Perform reconstruction")
# img_rec = reco.dfi_reconstruction(sino_180, center1, apply_log=True)
## Use gpu for fast reconstruction
img_rec = reco.fbp_reconstruction(sino_180, center1, apply_log=True, gpu=True)
## Using gridrec code for faster CPU reconstruction if Tomopy installed
# img_rec = reco.gridrec_reconstruction(sino_180, center1, apply_log=True)
losa.save_image(output_base + "/reconstruction/recon_image.tif", img_rec)
time_stop = timeit.default_timer()
print("!!! Done !!! Time cost: {}".format(time_stop - time_start))
sino_360 = remo.remove_zinger(sino_360, 0.08)
# Remove ring artifacts
sino_360 = remo.remove_all_stripe(sino_360, 3, 51, 17)
# 1st way: Convert the 360-degree sinogram to the 180-degree sinogram.
sino_180, center1 = conv.convert_sinogram_360_to_180(sino_360, center0)
losa.save_image(output_base + "/reconstruction/sino_180.tif", sino_180)
## Denoising
sino_180 = filt.fresnel_filter(sino_180, 250, 1, apply_log=True)
# Perform reconstruction
img_rec = reco.dfi_reconstruction(sino_180, center1, apply_log=True)
## Using fbp with GPU for faster reconstruction
# img_rec = reco.fbp_reconstruction(sino_180, center1, apply_log=True, gpu=True)
losa.save_image(output_base + "/reconstruction/recon_image_1.tif", img_rec)
# 2nd way: Extending the 360-degree sinogram (by weighting and padding).
(sino_ext, center2) = conv.extend_sinogram(sino_360, center0)
losa.save_image(output_base + "/reconstruction/sino_360_extened.tif", sino_ext)
# Denoising
sino_ext = filt.fresnel_filter(sino_ext, 250, 1, apply_log=False)
# Perform reconstruction
# img_rec = reco.dfi_reconstruction(sino_ext, center2, angles=angles*np.pi/180.0,apply_log=False)
## Using fbp with GPU for faster reconstruction
img_rec = reco.fbp_reconstruction(sino_ext,
center2,
angles=angles * np.pi / 180.0,
apply_log=False,
gpu=True)
losa.save_image(output_base + "/reconstruction/recon_image_2.tif", img_rec)
print("!!! Done !!!")