def load_dataset(self, corrected=True):
self.stack = [
np.array(axitom.read_image(image, flat_corrected=corrected))
for image in self.dataset
]
if self.debug:
print(self.stack)
return self.stack
def reconstruct_tomogram():
config = axitom.config_from_xtekct("./example_data/radiogram.xtekct")
radiogram = axitom.read_image(r"./example_data/radiogram.tif", flat_corrected=True)
# Remove some edges that are in field of view
radiogram[:250, :] = 0.95
radiogram[1800:, :] = 0.95
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
tomogram = axitom.fdk(radiogram, config)
return tomogram
def run_reconstruction():
dir_path = os.path.dirname(os.path.realpath(__file__))
config = axitom.config_from_xtekct(dir_path +
"/example_data/settings.xtekct")
radiogram = axitom.read_image(dir_path + "/example_data/radiogram.tif",
flat_corrected=True)
# Remove some edges that are in field of view
radiogram[:250, :] = 0.95
radiogram[1800:, :] = 0.95
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(
radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
reconstruction = axitom.fdk(radiogram, config)
return reconstruction
def test_compare_to_external_software(self):
"""
Compare the results obtained by using only a single radiogram from a full dataset to the results
obtained by using the whole dataset and external reconstruction software. The reconstruction by
the external software is done in advance and only the result is used here.
"""
dir_path = os.path.dirname(os.path.realpath(__file__))
self.tol = 2e-2
recon = run_reconstruction()
recon_crop = recon.transpose()[:, ::-1][1:, :400]
recon_crop_norm = normalize_grey_scales(recon_crop)
correct = axitom.read_image(
dir_path + "/example_data/recon_by_external_software.tif")
correct_norm = normalize_grey_scales(correct.transpose())
error_field = np.abs(recon_crop_norm[::-1, :] - correct_norm)
if np.average(error_field) > self.tol:
self.fail(
"The reconstruction did not match the reconstruction performed by another software and the error was: %f"
% np.average(error_field))
radiogram[1800:, :] = 0.95
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
tomogram = axitom.fdk(radiogram, config)
return tomogram
recon_tomo = reconstruct_tomogram()
correct = axitom.read_image(r"./example_data/recon_by_external_software.tif")
correct_norm = normalize_grey_scales(correct.transpose())
Reconimg_crop = recon_tomo.transpose()[:, ::-1][1:, :400]
Reconimg_crop_norm = normalize_grey_scales(Reconimg_crop)
plt.figure()
plt.subplot(1, 3, 1)
plt.title("Nikon CT-PRO")
plt.imshow(correct_norm, cmap=plt.cm.magma)
plt.axis('off')
plt.clim(vmin=0.5, vmax=1.0)
plt.colorbar()
plt.subplot(1, 3, 2)
plt.title("AXITOM")
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(
radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
tomogram = axitom.fdk(radiogram, config)
return tomogram
recon_tomo = reconstruct_tomogram()
correct = axitom.read_image(
join(path_to_data, "recon_by_external_software.tif"))
correct_norm = normalize_grey_scales(correct.transpose())
Reconimg_crop = recon_tomo.transpose()[:, ::-1][1:, :400]
Reconimg_crop_norm = normalize_grey_scales(Reconimg_crop)
plt.figure()
plt.subplot(1, 3, 1)
plt.title("Nikon CT-PRO")
plt.imshow(correct_norm, cmap=plt.cm.magma)
plt.axis('off')
plt.clim(vmin=0.5, vmax=1.0)
plt.colorbar()
plt.subplot(1, 3, 2)
plt.title("AXITOM")