def rotaxis_rough(proj, N_steps=10):
"""calculate the rotation axis comparing 0 and 180 projection shift
Parameters
__________
proj: 3D array
N_steps: projections per degree
by default it compares the central part of images (between 1/4 and 3/4 of shape)
"""
a = proj.shape[1] // 4
b = 3 * proj.shape[1] // 4
c = proj.shape[2] // 4
d = 3 * proj.shape[2] // 4
cent = []
N_rot = proj.shape[0] - 180 * N_steps
for i in range(N_rot):
distances = shift(proj[i, a:b, c:d],
np.flip(proj[i + N_steps * 180, a:b, c:d], 1))
cent.append(proj[i].shape[1] / 2 + distances[1] / 2)
return cent
def rotaxis_rough_filt(proj, N_steps=10, sigma=5, accuracy=10):
"""calculate the rotation axis comparing 0 and 180 projection shift
Parameters
__________
proj: 3D array
N_steps: projections per degree
by default it compares the central part of images (between 1/4 and 3/4 of shape)
"""
a = proj.shape[1] // 4
b = 3 * proj.shape[1] // 4
c = proj.shape[2] // 4
d = 3 * proj.shape[2] // 4
cent = []
N_rot = proj.shape[0] - 180 * N_steps
for i in range(N_rot):
im1 = filt_gauss_laplace(proj[i, a:b, c:d], sigma)
im2 = np.flip(
filt_gauss_laplace(proj[i + N_steps * 180, a:b, c:d], sigma), 1)
distances = shift(im1, im2, accuracy)
cent.append(proj[i].shape[1] / 2 + distances[1] / 2)
return cent
def axis_raws(image1, image2, Npad=0, RotROI=None, level=5, window=50):
"""Finds an axis of rotation by comparing the 1st and the 180deg image:
This function is based on rotaxis_rough()
It is useful when you want to search for rotaxis in different regions of
your image independently
Parameters
__________
image1: 2D array
first 2D image
image2: 2D array
second 2D image
Npad: int
type the number here if the x axis of your image was padded
RotROI: tuple
ROI where to compare the images. Note that it is better to exclude some regions at the edge of the camera
Coordinate legend: RotROI[0] - begin line (if you look at the image, numpy logic)
RotROI[1] - end line
RotROI[2] - begin column
RotROI[3] - end column
level: int
the number of pixels to be taken into account, please type None if you don't want to use it and want to export the whole data.
window: int
window is the number of pixels on the image (height) to be taken into account during comaprison
"""
if not RotROI:
RotROI = (50, image1.shape[0], Npad + image1.shape[1] // 8,
image1.shape[1] - Npad - image1.shape[1] // 8)
all_cent = []
for i in range(RotROI[0], RotROI[1], window // 2):
im_1 = image1[i:i + window, RotROI[2]:RotROI[3]]
im_2 = np.flip(image2[i:i + window, RotROI[2]:RotROI[3]], 1)
distances = shift(im_1, im_2)
cent = im_1.shape[1] / 2 + distances[1] / 2 + RotROI[2]
all_cent.append(cent)
#print('center for the slice #',i,' is: ', cent)
if level:
x = []
y = []
for i in range(len(all_cent)):
if np.absolute(all_cent[i] - np.median(all_cent)) < level:
x.append(i * window // 2)
y.append(all_cent[i])
all_cent = np.column_stack((x, y))
else:
all_cent = np.column_stack((np.linspace(0,
len(all_cent),
len(all_cent),
dtype='uint16'), all_cent))
#plt.plot(all_cent[:,0], all_cent[:,1], 'bo')
return all_cent