def test_remove_blob_1d(self):
list_1d = 0.5 * np.ones(self.size)
list_1d[self.idx1:self.idx2] = 1.0
mask_1d = np.zeros_like(list_1d)
mask_1d[self.idx1:self.idx2] = 1.0
list_corr = remo.remove_blob_1d(list_1d, mask_1d)
nmean = np.mean(list_corr[self.idx1:self.idx2])
num = np.abs(nmean - 0.5)
self.assertTrue(num < self.eps)
def generate_full_sinogram_helical_scan(index,
tomo_data,
num_proj,
pixel_size,
y_start,
y_stop,
pitch,
scan_type="180",
angles=None,
flat=None,
dark=None,
mask=None,
crop=(0, 0, 0, 0)):
"""
Generate a full sinogram from a helical scan dataset which is a hdf/nxs
object (Ref. [1]_). Full sinogram is all 1D projection of the same slice of
a sample staying inside the field of view.
Parameters
----------
index : int
Index of the sinogram.
tomo_data : hdf object.
3D array.
num_proj : int
Number of projections per 180-degree.
pixel_size : float
Pixel size. The unit must be the same as y-position.
y_start : float
Y-position of the stage at the beginning of the scan.
y_stop : float
Y-position of the stage at the end of the scan.
pitch : float
The distance which the y-stage is translated in one full rotation.
scan_type : {"180", "360"}
Data acquired is the 180-degree type or 360-degree type [1].
angles : array_like, optional
1D array. List of angles (degree) corresponding to acquired projections.
flat : array_like, optional
Flat-field image used for flat-field correction.
dark : array_like, optional
Dark-field image used for flat-field correction.
mask : array_like, optional
Used for removing streak artifacts caused by blobs in the flat-field
image.
crop : tuple of int, optional
Used for cropping images.
Returns
-------
sinogram : array_like
2D array. Full sinogram.
list_angle : array_like
1D array. List of angles corresponding to the generated sinogram.
References
----------
.. [1] https://doi.org/10.1364/OE.418448
"""
(depth0, height0, width0) = tomo_data.shape
(crop_top, crop_bottom, crop_left, crop_right) = crop
top = crop_top
bottom = height0 - crop_bottom
left = crop_left
right = width0 - crop_right
width = right - left
height = bottom - top
if flat is None:
flat = np.ones((height0, width0), dtype=np.float32)
if dark is None:
dark = np.zeros((height0, width0), dtype=np.float32)
if angles is None:
step_angle = 180.0 / (num_proj - 1)
angles = np.arange(0, depth0) * step_angle
flat_dark = flat - dark
FoV = pixel_size * height
y_step = pitch / (2.0 * (num_proj - 1))
if scan_type == "180":
y_e = y_stop - pitch / 2.0
y_s = y_start + pitch / 2.0
else:
y_e = y_stop - pitch
y_s = y_start + pitch
num_proj_used = int(np.floor(FoV / y_step)) - 1
y_pos = (index - 1) * pixel_size + y_s
i0 = int(np.ceil((y_e - y_pos) / y_step))
if (i0 < 0) or (i0 >= depth0):
raise ValueError(
"Sinogram index {0} requests a projection index {1} which "
"is out of the projection range [0, {2}]".format(
index, i0, depth0 - 1))
if (i0 + num_proj_used) >= depth0:
raise ValueError(
"Sinogram index {0} requests projection-indices in the range of "
"[{1}, {2}] which is out of the data range [0, {3}]".format(
index, i0, i0 + num_proj_used, depth0 - 1))
sinogram = np.zeros((num_proj_used, width), dtype=np.float32)
for i in range(i0, i0 + num_proj_used):
j0 = (y_e + FoV - i * y_step - y_pos) / pixel_size - 1
if (j0 < 0) or (j0 >= height):
raise ValueError(
"Requested row index {0} of projection {1} is out of"
" the range [0, {2}]".format(j0, i0, height))
j0 = np.clip(j0, 0, height - 1)
jd = int(np.floor(j0))
ju = int(np.ceil(j0))
list_down = (tomo_data[i, jd + crop_top, left:right] -
dark[jd + crop_top,
left:right]) / flat_dark[jd + crop_top, left:right]
if mask is not None:
list_down = remo.remove_blob_1d(list_down, mask[jd + crop_top,
left:right])
if ju != jd:
list_up = (tomo_data[i, ju + crop_top, left:right] -
dark[ju + crop_top,
left:right]) / flat_dark[ju + crop_top, left:right]
if mask is not None:
list_up = remo.remove_blob_1d(list_up, mask[ju + crop_top,
left:right])
sinogram[i - i0] = list_down * (ju - j0) / (ju - jd) + list_up * (
j0 - jd) / (ju - jd)
else:
sinogram[i - i0] = list_down
list_angle = angles[i0:i0 + num_proj_used]
return sinogram, list_angle
def generate_sinogram_helical_scan(index,
tomo_data,
num_proj,
pixel_size,
y_start,
y_stop,
pitch,
scan_type="180",
angles=None,
flat=None,
dark=None,
mask=None,
crop=(0, 0, 0, 0)):
"""
Generate a 180-degree sinogram or a 360-degree sinogram from a helical
scan dataset which is a hdf/nxs object (Ref. [1]_).
Parameters
----------
index : int
Index of the sinogram.
tomo_data : hdf object.
3D array.
num_proj : int
Number of projections per 180-degree.
pixel_size : float
Pixel size. The unit must be the same as y-position.
y_start : float
Y-position of the stage at the beginning of the scan.
y_stop : float
Y-position of the stage at the end of the scan.
pitch : float
The distance which the y-stage is translated in one full rotation.
scan_type : {"180", "360"}
One of two options: "180" for generating a 180-degree sinogram or
"360" for generating a 360-degree sinogram.
angles : array_like, optional
1D array. List of angles (degree) corresponding to acquired projections.
flat : array_like, optional
Flat-field image used for flat-field correction.
dark : array_like, optional
Dark-field image used for flat-field correction.
mask : array_like, optional
Used for removing streak artifacts caused by blobs in the flat-field
image.
crop : tuple of int, optional
Used for cropping images.
Returns
-------
sinogram : array_like
2D array. 180-degree sinogram or 360-degree sinogram.
list_angle : array_like
1D array. List of angles corresponding to the generated sinogram.
References
----------
.. [1] https://doi.org/10.1364/OE.418448
"""
max_index = calc.calculate_maximum_index(y_start, y_stop, pitch,
pixel_size, scan_type)
(y_s, y_e) = calc.calculate_reconstructable_height(y_start, y_stop, pitch,
scan_type)
if index < 0 or index > max_index:
msg1 = "Requested index {0} is out of available index-range" \
" [0, {1}]\n".format(index, max_index)
msg2 = "corresponding to reconstructable heights" \
" [{0}, {1}]".format(y_s, y_e)
raise ValueError(msg1 + msg2)
(depth0, height0, width0) = tomo_data.shape
(crop_top, crop_bottom, crop_left, crop_right) = crop
top = crop_top
bottom = height0 - crop_bottom
left = crop_left
right = width0 - crop_right
width = right - left
height = bottom - top
if flat is None:
flat = np.ones((height0, width0), dtype=np.float32)
if dark is None:
dark = np.zeros((height0, width0), dtype=np.float32)
if angles is None:
step_angle = 180.0 / (num_proj - 1)
angles = np.arange(0, depth0) * step_angle
flat_dark = flat - dark
FoV = pixel_size * height
y_step = pitch / (2.0 * (num_proj - 1))
if scan_type == "180":
num_proj_used = num_proj
else:
num_proj_used = 2 * (num_proj - 1) + 1
y_pos = (index - 1) * pixel_size + y_s
i0 = int(np.ceil((y_e - y_pos) / y_step))
if (i0 < 0) or (i0 >= depth0):
raise ValueError("Sinogram index {0} requests a projection index {1}"
" which is out of the data range [0, {2}]".format(
index, i0, depth0 - 1))
sinogram = np.zeros((num_proj_used, width), dtype=np.float32)
for i in range(i0, i0 + num_proj_used):
j0 = (y_e + FoV - i * y_step - y_pos) / pixel_size - 1
if (j0 < 0) or (j0 >= height):
raise ValueError(
"Requested row index {0} of projection {1} is out of the"
" range [0, {2}]".format(j0, i0, height - 1))
j0 = np.clip(j0, 0, height - 1)
jd = int(np.floor(j0))
ju = int(np.ceil(j0))
list_down = (tomo_data[i, jd + crop_top, left:right] -
dark[jd + crop_top,
left:right]) / flat_dark[jd + crop_top, left:right]
if mask is not None:
list_down = remo.remove_blob_1d(list_down, mask[jd + crop_top,
left:right])
if ju != jd:
list_up = (tomo_data[i, ju + crop_top, left: right]
- dark[ju + crop_top, left: right]) \
/ flat_dark[ju + crop_top, left: right]
if mask is not None:
list_up = remo.remove_blob_1d(list_up, mask[ju + crop_top,
left:right])
sinogram[i - i0] = list_down * (ju - j0) / (ju - jd) + list_up * (
j0 - jd) / (ju - jd)
else:
sinogram[i - i0] = list_down
list_angle = angles[i0:i0 + num_proj_used]
return sinogram, list_angle