def test_fresnel_filter(self):
mat = np.random.rand(64, 64)
mat1 = filt.fresnel_filter(mat, 10, dim=1)
mat2 = filt.fresnel_filter(mat, 10, dim=2)
num1 = np.sum(np.abs(mat - mat1))
num2 = np.sum(np.abs(mat - mat2))
num3 = np.sum(np.abs(mat1 - mat2))
self.assertTrue(num1 > self.eps and num2 > self.eps and num3 > self.eps)
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))
# 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 !!!")
"Center-of-rotation: {0}. Side: {1} (0->'left', 1->'right'). Overlap: {2}".
format(center0, side, overlap))
t1 = timeit.default_timer()
print("Time cost {}".format(t1 - t0))
# Remove artifacts. They also can be passed to flat_field_correction method above as parameters.
# Remove zingers
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,