def generate_bead_test_volume(name: str,
size: Tuple[int, int, int] = (32, 32, 32),
num_beads: int = 32,
sigmas: Tuple[float, float, float] = (0, 0, 2)):
data = np.zeros(size)
mask = np.array([True] * num_beads + [False] *
(np.prod(data.size) - num_beads))
np.random.shuffle(mask)
mask = mask.reshape(data.shape)
data[mask] = 1
blurred = gaussian_filter(data, sigmas, mode='constant')
blurred_vol = Volume(blurred, (1, 1, 1))
blurred_vol.save_tiff('test_' + name)
blurred_vol.save_tiff_single('test_single_' + name)
def generate_spheres_test_volume(name: str,
size: int = 32,
num_beads: int = 32,
sigmas: Tuple[float, float,
float] = (0, 0, 2)):
data = np.zeros((size, size, size))
for i in range(num_beads):
p = np.random.randint(8, size - 8, 3)
data[p[0] - 8:p[0] + 8, p[1] - 8:p[1] + 8,
p[1] - 8:p[1] + 8] = pymrt.geometry.sphere(16, 0.5, 8)
vol = Volume(data, (1, 1, 1))
vol.save_tiff('test_' + name)
vol.save_tiff_single('test_single_' + name)
blurred = gaussian_filter(data, sigmas, mode='constant')
blurred_vol = Volume(blurred, (1, 1, 1))
blurred_vol.save_tiff('btest_' + name)
blurred_vol.save_tiff_single('btest_single_' + name)
return data
psf = np.zeros((9, 9, 9))
for x in range(9):
for y in range(9):
for z in range(9):
psf[x, y, z] = multivariate_normal.pdf((x, y, z),
mean=(4, 4, 4),
cov=np.array([[4, 0, 0],
[0, 4, 0],
[0, 0,
4]]))
blurred = convolve(vol, psf)
blurred += (np.random.poisson(lam=25, size=blurred.shape) - 10) / 250.
blurred_vol = Volume(blurred, (1, 1, 1))
blurred_vol.save_tiff('blurred_thing')
# estimate = data
# for i in range(200):
# estimate = estimate * blur(data/(blur(estimate)+1e-6))
# estimate = restoration.richardson_lucy(blurred, psf, iterations=120)
#
# estimate_vol = Volume(estimate, (1, 1, 1))
# estimate_vol.save_tiff('estimate')
# import numpy as np
# import matplotlib.pyplot as plt
#
# from scipy.signal import convolve2d as conv2
#