def _compute_gain_map(self, image, mask):
'''Complete the gain map of a single image.'''
from dials.algorithms.image.filter import fano_filter
# Need double image
image = image.as_double()
# Filter the image and return the mask
filteralg = fano_filter(image, mask, self._kernel_size, 0)
filtered = filteralg.fano()
mask = filteralg.mask()
mean = filteralg.mean()
# Return the filtered image and mask
return filtered, mask, mean
def _compute_gain_map(self, image, mask):
'''Complete the gain map of a single image.'''
from dials.algorithms.image.filter import fano_filter
# Need double image
image = image.as_double()
# Filter the image and return the mask
filteralg = fano_filter(image, mask, self._kernel_size, 0)
filtered = filteralg.fano()
mask = filteralg.mask()
mean = filteralg.mean()
# Return the filtered image and mask
return filtered, mask, mean
def run(self):
from dials.algorithms.image.filter import fano_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
mask = flex.random_bool(2000 * 2000, 0.99).as_int()
mask.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mask2 = mask.deep_copy()
fano_filter = fano_filter(image, mask2, (3, 3), 2)
mean = fano_filter.mean()
var = fano_filter.sample_variance()
fano = fano_filter.fano()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j, i]
v1 = var[j, i]
f1 = fano[j, i]
p = image[j - 3:j + 4, i - 3:i + 4]
m = mask[j - 3:j + 4, i - 3:i + 4]
if mask[j, i] == 0:
m2 = 0.0
v2 = 0.0
f2 = 1.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
f2 = v2 / m2
assert (abs(m1 - m2) <= eps)
assert (abs(v1 - v2) <= eps)
assert (abs(f1 - f2) <= eps)
# Test passed
print 'OK'
def run(self):
from dials.algorithms.image.filter import fano_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
mask = flex.random_bool(2000 * 2000, 0.99).as_int()
mask.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mask2 = mask.deep_copy()
fano_filter = fano_filter(image, mask2, (3, 3), 2)
mean = fano_filter.mean()
var = fano_filter.sample_variance()
fano = fano_filter.fano()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j,i]
v1 = var[j,i]
f1 = fano[j,i]
p = image[j-3:j+4,i-3:i+4]
m = mask[j-3:j+4,i-3:i+4]
if mask[j,i] == 0:
m2 = 0.0
v2 = 0.0
f2 = 1.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
f2 = v2 / m2
assert(abs(m1 - m2) <= eps)
assert(abs(v1 - v2) <= eps)
assert(abs(f1 - f2) <= eps)
# Test passed
print 'OK'