def test_masked_filter():
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
kernel = (3, 3)
image = generate_image(xsize, ysize)
mask = generate_mask(xsize, ysize)
result = median_filter(image, mask, kernel)
eps = 1e-7
for j in range(kernel[0], ysize - kernel[0]):
for i in range(kernel[1], xsize - kernel[1]):
if mask[j, i]:
j0 = j - kernel[0]
j1 = j + kernel[0] + 1
i0 = i - kernel[1]
i1 = i + kernel[1] + 1
pixels = image[j0:j1, i0:i1]
pmask = mask[j0:j1, i0:i1]
pixels = pixels.as_1d().select(pmask.as_1d())
if len(pixels) & 1:
value = median(pixels.as_numpy_array())
else:
pixels = sorted(pixels)
value = pixels[len(pixels) // 2]
assert result[j, i] == pytest.approx(value, abs=eps)
def tst_masked_filter(self):
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
image = self.generate_image(xsize, ysize)
mask = self.generate_mask(xsize, ysize)
result = median_filter(image, mask, self.kernel)
eps = 1e-7
for j in range(self.kernel[0], ysize - self.kernel[0]):
for i in range(self.kernel[1], xsize - self.kernel[1]):
if mask[j, i]:
j0 = j - self.kernel[0]
j1 = j + self.kernel[0] + 1
i0 = i - self.kernel[1]
i1 = i + self.kernel[1] + 1
pixels = image[j0:j1, i0:i1]
pmask = mask[j0:j1, i0:i1]
pixels = pixels.as_1d().select(pmask.as_1d())
if len(pixels) & 1:
value = median(pixels.as_numpy_array())
else:
pixels = sorted(list(pixels))
value = pixels[len(pixels) // 2]
assert (abs(value - result[j, i]) < eps)
else:
assert (abs(result[j, i] - 0.0) < eps)
print 'OK'
def tst_masked_filter(self):
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
image = self.generate_image(xsize, ysize)
mask = self.generate_mask(xsize, ysize)
result = median_filter(image, mask, self.kernel)
eps = 1e-7
for j in range(self.kernel[0], ysize-self.kernel[0]):
for i in range(self.kernel[1], xsize-self.kernel[1]):
if mask[j,i]:
j0 = j - self.kernel[0]
j1 = j + self.kernel[0] + 1
i0 = i - self.kernel[1]
i1 = i + self.kernel[1] + 1
pixels = image[j0:j1,i0:i1]
pmask = mask[j0:j1,i0:i1]
pixels = pixels.as_1d().select(pmask.as_1d())
if len(pixels) & 1:
value = median(pixels.as_numpy_array())
else:
pixels = sorted(list(pixels))
value = pixels[len(pixels) // 2]
assert(abs(value - result[j,i]) < eps)
else:
assert(abs(result[j,i] - 0.0) < eps)
print 'OK'
def test_filter():
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
kernel = (3, 3)
image = generate_image(xsize, ysize)
result = median_filter(image, kernel)
eps = 1e-7
for j in range(kernel[0], ysize - kernel[0]):
for i in range(kernel[1], xsize - kernel[1]):
j0 = j - kernel[0]
j1 = j + kernel[0] + 1
i0 = i - kernel[1]
i1 = i + kernel[1] + 1
pixels = image[j0:j1, i0:i1]
value = median(pixels.as_numpy_array())
assert result[j, i] == pytest.approx(value, abs=eps)
def tst_filter(self):
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
image = self.generate_image(xsize, ysize)
result = median_filter(image, self.kernel)
eps = 1e-7
for j in range(self.kernel[0], ysize - self.kernel[0]):
for i in range(self.kernel[1], xsize - self.kernel[1]):
j0 = j - self.kernel[0]
j1 = j + self.kernel[0] + 1
i0 = i - self.kernel[1]
i1 = i + self.kernel[1] + 1
pixels = image[j0:j1, i0:i1]
value = median(pixels.as_numpy_array())
assert (abs(value - result[j, i]) < eps)
print 'OK'
def tst_filter(self):
from numpy import median
from dials.algorithms.image.filter import median_filter
xsize = 200
ysize = 300
image = self.generate_image(xsize, ysize)
result = median_filter(image, self.kernel)
eps = 1e-7
for j in range(self.kernel[0], ysize-self.kernel[0]):
for i in range(self.kernel[1], xsize-self.kernel[1]):
j0 = j - self.kernel[0]
j1 = j + self.kernel[0] + 1
i0 = i - self.kernel[1]
i1 = i + self.kernel[1] + 1
pixels = image[j0:j1,i0:i1]
value = median(pixels.as_numpy_array())
assert(abs(value - result[j,i]) < eps)
print 'OK'
def finalize(self, data, mask):
'''
Finalize the model
:param data: The data array
:param mask: The mask array
'''
from dials.algorithms.image.filter import median_filter, mean_filter
from dials.algorithms.image.fill_holes import diffusion_fill
from dials.algorithms.image.fill_holes import simple_fill
from dials.array_family import flex
# Print some image properties
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Raw image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Transform to polar
logger.info('Transforming image data to polar grid')
result = self.transform.to_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Filter the image to remove noise
if self.kernel_size > 0:
if self.filter_type == 'median':
logger.info('Applying median filter')
data = median_filter(data, mask, (self.kernel_size, 0))
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Median polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
elif self.filter_type == 'mean':
logger.info('Applying mean filter')
mask_as_int = mask.as_1d().as_int()
mask_as_int.reshape(mask.accessor())
data = mean_filter(data, mask_as_int, (self.kernel_size, 0), 1)
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Mean polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
else:
raise RuntimeError('Unknown filter_type: %s' % self.filter_type)
# Fill any remaining holes
logger.info("Filling holes")
data = simple_fill(data, mask)
data = diffusion_fill(data, mask, self.niter)
mask = flex.bool(data.accessor(), True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Filled polar image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Transform back
logger.info('Transforming image data from polar grid')
result = self.transform.from_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info('Final image statistics:')
logger.info(' min: %d' % int(flex.min(sub_data)))
logger.info(' max: %d' % int(flex.max(sub_data)))
logger.info(' mean: %d' % int(flex.mean(sub_data)))
logger.info('')
# Fill in any discontinuities
# FIXME NEED TO HANDLE DISCONTINUITY
# mask = ~self.transform.discontinuity()[:-1,:-1]
# data = diffusion_fill(data, mask, self.niter)
# Get and apply the mask
mask = self.experiment.imageset.get_mask(0)[0]
mask = mask.as_1d().as_int().as_double()
mask.reshape(data.accessor())
data *= mask
# Return the result
return data
def finalize(self, data, mask):
"""
Finalize the model
:param data: The data array
:param mask: The mask array
"""
from dials.algorithms.image.filter import median_filter, mean_filter
from dials.algorithms.image.fill_holes import diffusion_fill
from dials.algorithms.image.fill_holes import simple_fill
from dials.array_family import flex
# Print some image properties
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Raw image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Transform to polar
logger.info("Transforming image data to polar grid")
result = self.transform.to_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Filter the image to remove noise
if self.kernel_size > 0:
if self.filter_type == "median":
logger.info("Applying median filter")
data = median_filter(data,
mask, (self.kernel_size, 0),
periodic=True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Median polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
elif self.filter_type == "mean":
logger.info("Applying mean filter")
mask_as_int = mask.as_1d().as_int()
mask_as_int.reshape(mask.accessor())
data = mean_filter(data, mask_as_int, (self.kernel_size, 0), 1)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Mean polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
else:
raise RuntimeError("Unknown filter_type: %s" %
self.filter_type)
# Fill any remaining holes
logger.info("Filling holes")
data = simple_fill(data, mask)
data = diffusion_fill(data, mask, self.niter)
mask = flex.bool(data.accessor(), True)
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Filled polar image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Transform back
logger.info("Transforming image data from polar grid")
result = self.transform.from_polar(data, mask)
data = result.data()
mask = result.mask()
sub_data = data.as_1d().select(mask.as_1d())
logger.info("Final image statistics:")
logger.info(" min: %d" % int(flex.min(sub_data)))
logger.info(" max: %d" % int(flex.max(sub_data)))
logger.info(" mean: %d" % int(flex.mean(sub_data)))
logger.info("")
# Fill in any discontinuities
mask = ~self.transform.discontinuity()[:-1, :-1]
data = diffusion_fill(data, mask, self.niter)
# Get and apply the mask
mask = self.experiment.imageset.get_mask(0)[0]
mask = mask.as_1d().as_int().as_double()
mask.reshape(data.accessor())
data *= mask
# Return the result
return data