def test_vmi_find_image_center():
# BASEX sample image, Gaussians at 10, 15, 20, 70,85, 100, 145, 150, 155
# image width, height n = 361
IM = sample_image_dribinski()
# artificially displace center
IMx = shift(IM, (-1,2))
# find vertical center
# radial range limits comparison to smaller radial range
IMy, offset = find_image_center_by_slice(IMx, radial_range=(10,40), axis=1)
assert np.allclose(offset, (0,-1), rtol=0, atol=0.1)
# horizontal center
IMy, offset = find_image_center_by_slice(IMx, radial_range=(10,40), axis=0)
assert np.allclose(offset, (1/2,0), rtol=0, atol=0.1)
# find both
IMy, offset = find_image_center_by_slice(IMx, radial_range=(10,40),
axis=(0, 1))
assert np.allclose(offset, (1/2,-1), rtol=0, atol=0.1)
# check even image size returns odd
IM = IM[:-1, 1:]
m, n = IM.shape
IMy, offset = find_image_center_by_slice(IM, radial_range=(10,40),
axis=0)
assert IMy.shape == (m-1, n-1)
# image file
filename = 'data/O2-ANU1024.txt.bz2'
# Load as a numpy array
print('Loading ' + filename)
IM = np.loadtxt(filename)
# use plt.imread(filename) to load image formats (.png, .jpg, etc)
rows, cols = IM.shape # image size
# Image center should be mid-pixel, i.e. odd number of colums
if cols % 2 == 0:
print("HL: even pixel width image, re-adjust image centre")
# re-center image based on horizontal and vertical slice profiles
# covering the radial range [300:400] pixels from the center
IM = find_image_center_by_slice(IM, radial_range=(300, 400))[0]
rows, cols = IM.shape # new image size
c2 = cols // 2 # half-image
print('image size {:d}x{:d}'.format(rows, cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM,
dr=1,
use_quadrants=(True, True, True, True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
# image file
filename = 'data/O2-ANU1024.txt.bz2'
# Load as a numpy array
print('Loading ' + filename)
IM = np.loadtxt(filename)
# use plt.imread(filename) to load image formats (.png, .jpg, etc)
rows,cols = IM.shape # image size
# Image center should be mid-pixel, i.e. odd number of colums
if cols%2 == 0:
print ("HL: even pixel width image, re-adjust image centre")
# re-center image based on horizontal and vertical slice profiles
# covering the radial range [300:400] pixels from the center
IM = find_image_center_by_slice (IM, radial_range=(300,400))[0]
rows,cols = IM.shape # new image size
c2 = cols//2 # half-image
print ('image size {:d}x{:d}'.format(rows,cols))
# Step 2: perform the Hansen & Law transform!
print('Performing Hansen and Law inverse Abel transform:')
AIM = iabel_hansenlaw(IM, dr=1, use_quadrants=(True,True,True,True),
vertical_symmetry=False,
horizontal_symmetry=False,
verbose=True)
speeds, r = calculate_speeds (AIM)
"hansenlaw" : iabel_hansenlaw_transform,
"basex" : iabel_basex_transform,
"three_point" : iabel_three_point_transform,
}
# sort dictionary
transforms = collections.OrderedDict(sorted(transforms.items()))
ntrans = np.size(transforms.keys()) # number of transforms
# Image: O2- VMI 1024x1024 pixel ------------------
IM = np.loadtxt('data/O2-ANU1024.txt.bz2')
# this is even size, all methods except 'onion' require an odd-size
# recenter the image to an odd size
IModd, offset = find_image_center_by_slice (IM, radial_range=(300,400))
np.savetxt("O2-ANU1023.txt", IModd)
h, w = IModd.shape
print ("centered image 'data/O2-ANU2048.txt' shape = {:d}x{:d}".format(h,w))
Q0, Q1, Q2, Q3 = get_image_quadrants (IModd, reorient=True)
Q0fresh = Q0.copy() # keep clean copy
print ("quadrant shape {}".format(Q0.shape))
# Intensity mask used for intensity normalization
# quadrant image region of bright pixels
mask = np.zeros(Q0.shape,dtype=bool)
mask[500:512, 358:365] = True
"onion" : iabel_onion_transform,
"hansenlaw" : iabel_hansenlaw_transform,
"basex" : iabel_basex_transform,
"three_point" : iabel_three_point_transform,
}
# sort dictionary
transforms = collections.OrderedDict(sorted(transforms.items()))
ntrans = np.size(transforms.keys()) # number of transforms
# Image: O2- VMI 1024x1024 pixel ------------------
IM = np.loadtxt('data/O2-ANU1024.txt.bz2')
# this is even size, all methods except 'onion' require an odd-size
# recenter the image to an odd size
IModd, offset = find_image_center_by_slice(IM, radial_range=(300, 400))
np.savetxt("O2-ANU1023.txt", IModd)
h, w = IModd.shape
print("centered image 'data/O2-ANU2048.txt' shape = {:d}x{:d}".format(h, w))
Q0, Q1, Q2, Q3 = get_image_quadrants(IModd, reorient=True)
Q0fresh = Q0.copy() # keep clean copy
print("quadrant shape {}".format(Q0.shape))
# Intensity mask used for intensity normalization
# quadrant image region of bright pixels
mask = np.zeros(Q0.shape, dtype=bool)
mask[500:512, 358:365] = True
