def test_dfi_reconstruction(self):
mat_rec1 = reco.dfi_reconstruction(self.sino_180,
self.center,
apply_log=False)
num1 = np.max(np.abs(self.mat - mat_rec1))
mat_rec2 = reco.dfi_reconstruction(self.sino_360,
self.center,
angles=np.deg2rad(self.angles),
apply_log=False)
num2 = np.max(np.abs(self.mat - mat_rec2))
self.assertTrue(num1 <= 0.1 and num2 <= 0.1)
# Generate a sinogram with flat-field correction and blob removal.
# Angles corresponding to this sinogram also is generated.
index = max_index // 2
print("2 -> Generate a circular sinogram from the helical data")
(sino_360, angle_sino) = conv.generate_sinogram_helical_scan(index, proj_data, num_proj, pixel_size,
y_start, y_stop, pitch, scan_type=scan_type,
angles=angles, flat=flat_field, dark=dark_field,
mask=blob_mask, crop=(10,0,0,0))
print("3 -> Find the center of rotation, overlap-side, and overlap area between two halves of a 360-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)
# Remove partial ring artifacts
sino_180 = remo.remove_stripe_based_sorting(sino_180, 15)
# Remove zingers
sino_180 = remo.remove_zinger(sino_180, 0.08)
# Denoising
sino_180 = filt.fresnel_filter(sino_180, 250, 1)
# Perform recosntruction
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)
losa.save_image(output_base + "/reconstruction/sino_360.tif", sino_360)
losa.save_image(output_base + "/reconstruction/sino_180.tif", sino_180)
losa.save_image(output_base + "/reconstruction/recon_image.tif", img_rec)
print("!!! Done !!!")
data[proj_idx[0]:proj_idx[-1], index,:],
flat_field[index, :], dark_field[index, :])
losa.save_image(output_base + "/sinogram/sinogram_mid.tif", sinogram)
# Calculate the center-of-rotation by searching around the middle width of
# the sinogram (radius=50).
print("4 -> Calculate the center-of-rotation")
# center = calc.find_center_vo(sinogram, width//2-50, width//2+50)
center = calc.find_center_vo(sinogram)
print("Center-of-rotation is {}".format(center))
# Perform reconstruction and save the result.
# Users can choose CPU-based methods as follows
thetas = angles[proj_idx[0]:proj_idx[-1]]*np.pi/180
# # DFI method, a built-in function:
print("5 -> Perform reconstruction without artifact removal methods")
img_rec = reco.dfi_reconstruction(sinogram, center, angles=thetas, apply_log=True)
# # FBP-CPU method, a built-in function:
# img_rec = reco.fbp_reconstruction(sinogram, center, angles=thetas, apply_log=True, gpu=False)
#
# # Gridrec method in Tomopy (Tomopy must be installed before use):
# img_rec = reco.gridrec_reconstruction(sinogram, center, apply_log=True, ratio=1.0)
#
# # If GPU is available:
#
# # FBP-GPU method, a built-in function:
# img_rec = reco.fbp_reconstruction(sinogram, center, angles=thetas, apply_log=True, gpu=True)
#
# # FBP-GPU method in Astra (Astra must be installed before use):
# img_rec = reco.astra_reconstruction(sinogram, center, apply_log=True, ratio=1.0,method="FBP_CUDA")
losa.save_image(output_base + "/reconstruction/recon_mid.tif", img_rec)
# Where to save the outputs
output_base = "E:/tmp/output/"
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,
def test_make_sinogram(self):
rec_image = reco.dfi_reconstruction(self.sinogram, self.size // 2,
apply_log=False)
mat_com = np.abs(self.phantom - rec_image)
num1 = np.mean(mat_com[mat_com > 0.0])
self.assertTrue(num1 < self.error)
# Generate a sinogram without distortion correction and perform reconstruction to compare latter.
index = 800
print("3 -> Generate a sinogram without distortion correction")
sinogram = corr.flat_field_correction(proj_data[:, index, :],
flat_field[index], dark_field[index])
sinogram = remo.remove_all_stripe(sinogram, 3.0, 51, 17)
sinogram = filt.fresnel_filter(sinogram, 10, 1)
t_start = timeit.default_timer()
print("4 -> Calculate the center-of-rotation...")
center = calc.find_center_vo(sinogram, width // 2 - 50, width // 2 + 50)
t_stop = timeit.default_timer()
print("Center-of-rotation = {0}. Take {1} second".format(
center, t_stop - t_start))
t_start = timeit.default_timer()
print("5 -> Perform reconstruction")
img_rec = reco.dfi_reconstruction(sinogram, center, apply_log=True)
losa.save_image(output_base + "/rec_before_00800.tif", img_rec)
t_stop = timeit.default_timer()
print("Done reconstruction without distortion correction!!!")
# Generate a sinogram with distortion correction and perform reconstruction.
print("6 -> Generate a sinogram with distortion correction")
sinogram = corr.unwarp_sinogram(proj_data, index, xcenter, ycenter, list_fact)
sinogram = corr.flat_field_correction(sinogram, flat_discor[index],
dark_discor[index])
sinogram = remo.remove_all_stripe(sinogram, 3.0, 51, 17)
sinogram = filt.fresnel_filter(sinogram, 10, 1)
t_start = timeit.default_timer()
print("7 -> Calculate the center-of-rotation...")
# Center-of-rotation can change due to the distortion effect.
center = calc.find_center_vo(sinogram, width // 2 - 50, width // 2 + 50)
t_stop = timeit.default_timer()