def runner(parser, options, args):
smoothness = int(options.smoothness or 1)
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len (args)==1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
elif len(args)==2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error("incorrect number of arguments (expected upto 2 but got %s)" % (len(args)))
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
new_images = stack.images.copy()
background = (stack.pathinfo.get_background() or [0,0])[0]
print 'Image has background', background
window_width = options.window_width or None
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (1e6*dr, dr/voxel_sizes[1], dr/voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6*dz, dz / voxel_sizes[0])
vz,vy,vx = voxel_sizes
m = 3
scales = (m*vz/dz, m*vy/dr, m*vx/dr)
window_width = '%.1fx%.1f' % (dz/m/vz, dr/m/vy)
else:
window_width = options.window_width
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1/s for s in scales]
apply_window_inplace (new_images, scales, smoothness, background)
if options.output_path is None:
b,e = os.path.splitext(options.input_path)
suffix = '_window%s_%s' % (window_width, smoothness)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if verbose:
print 'Leak: %.3f%%' % ( 100*(1-new_images.sum ()/stack.images.sum ()))
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to',options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def runner(parser, options, args):
smoothness = int(options.smoothness or 1)
verbose = options.verbose if options.verbose is not None else True
if not hasattr(parser, 'runner'):
options.output_path = None
if args:
if len(args) == 1:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
elif len(args) == 2:
if options.input_path:
print >> sys.stderr, "WARNING: overwriting input path %r with %r" % (
options.input_path, args[0])
options.input_path = args[0]
options.output_path = args[1]
else:
parser.error(
"incorrect number of arguments (expected upto 2 but got %s)" %
(len(args)))
options.input_path = fix_path(options.input_path)
stack = ImageStack.load(options.input_path, options=options)
voxel_sizes = stack.get_voxel_sizes()
new_images = stack.images.copy()
background = (stack.pathinfo.get_background() or [0, 0])[0]
print 'Image has background', background
window_width = options.window_width or None
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple(
[s / (window_width * min(voxel_sizes)) for s in voxel_sizes])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (
1e6 * dr, dr / voxel_sizes[1], dr / voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6 * dz,
dz / voxel_sizes[0])
vz, vy, vx = voxel_sizes
m = 3
scales = (m * vz / dz, m * vy / dr, m * vx / dr)
window_width = '%.1fx%.1f' % (dz / m / vz, dr / m / vy)
else:
window_width = options.window_width
scales = tuple(
[s / (window_width * min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1 / s for s in scales]
apply_window_inplace(new_images, scales, smoothness, background)
if options.output_path is None:
b, e = os.path.splitext(options.input_path)
suffix = '_window%s_%s' % (window_width, smoothness)
options.output_path = b + suffix + (e or '.tif')
options.output_path = fix_path(options.output_path)
if verbose:
print 'Leak: %.3f%%' % (100 *
(1 - new_images.sum() / stack.images.sum()))
print 'MSE:', ((new_images - stack.images)**2).mean()
print 'Energy:', ((stack.images)**2).sum()
print 'Saving result to', options.output_path
ImageStack(new_images, stack.pathinfo).save(options.output_path)
def deconvolve(psf, stack, working_dir=None, data_type=None, options=None):
"""Deconvolve stack of images against given PSF.
Parameters
----------
psf : `iocbio.io.image_stack.ImageStack`
PSF
stack : `iocbio.io.image_stack.ImageStack`
Scanned images.
working_dir : {None, str}
Directory name where to save intermediate results.
When None then a temporary directory will be created.
data_type : {None, str}
Desired data type of deconvolution estimate to be returned.
options : {None, `iocbio.utils.Options`}
See :ref:`iocbio-deconvolve` for information about options.
The following options attributes are used:
float_type, apply_window, rltv_algorithm_type, degrade_data,
first_estimate, rltv_stop_tau, save_intermediate_results
Returns
-------
estimate : `iocbio.io.image_stack.ImageStack`
Last deconvolution estimate. See ``working_dir`` for other estimates.
See also
--------
iocbio.microscope.deconvolution
"""
if VERBOSE > 9:
print 'Entering %s.deconvolve' % (__file__)
options = Options(options)
if working_dir is None:
import tempfile
working_dir = tempfile.mkdtemp('-iocbio.deconvolve')
if data_type is None:
data_type = stack.images.dtype
dtype = float2dtype(options.get(float_type='single'))
phf0, data = get_coherent_images(psf, stack, dtype)
if options.get(apply_window=False):
background = (stack.pathinfo.get_background() or [0, 0])[0]
voxel_sizes = stack.get_voxel_sizes()
smoothness = int(options.get(smoothness=1))
window_width = options.get(window_width=None)
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple([
s / (window_width * min(voxel_sizes)) for s in voxel_sizes
])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (
1e6 * dr, dr / voxel_sizes[1], dr / voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6 * dz, dz /
voxel_sizes[0])
vz, vy, vx = voxel_sizes
m = 0.3
scales = (m * vz / dz, m * vy / dr, m * vx / dr)
else:
scales = tuple(
[s / (window_width * min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1 / s for s in scales]
from iocbio.ops.apply_window_ext import apply_window_inplace
apply_window_inplace(data, scales, smoothness, background)
mode = options.get(rltv_algorithm_type='multiplicative').lower()
Cls = dict(multiplicative=DeconvolveRLPoisson,
additive=DeconvolveRLGauss)[mode]
task = Cls(phf0, data, stack.get_voxel_sizes(), options)
task.set_cache_dir(working_dir)
task.set_save_data(stack.pathinfo, data.shape, data_type)
task.set_test_data()
estimate = task.deconvolve()
estimate = contract_to_shape(estimate, stack.images.shape, data_type)
return ImageStack(estimate,
stack.pathinfo,
suffix=task.get_suffix(),
options=options)
def deconvolve(psf, stack, working_dir = None, data_type = None,
options = None):
"""Deconvolve stack of images against given PSF.
Parameters
----------
psf : `iocbio.io.image_stack.ImageStack`
PSF
stack : `iocbio.io.image_stack.ImageStack`
Scanned images.
working_dir : {None, str}
Directory name where to save intermediate results.
When None then a temporary directory will be created.
data_type : {None, str}
Desired data type of deconvolution estimate to be returned.
options : {None, `iocbio.utils.Options`}
See :ref:`iocbio-deconvolve` for information about options.
The following options attributes are used:
float_type, apply_window, rltv_algorithm_type, degrade_data,
first_estimate, rltv_stop_tau, save_intermediate_results
Returns
-------
estimate : `iocbio.io.image_stack.ImageStack`
Last deconvolution estimate. See ``working_dir`` for other estimates.
See also
--------
iocbio.microscope.deconvolution
"""
if VERBOSE>9:
print 'Entering %s.deconvolve' % (__file__)
options = Options(options)
if working_dir is None:
import tempfile
working_dir = tempfile.mkdtemp('-iocbio.deconvolve')
if data_type is None:
data_type = stack.images.dtype
dtype = float2dtype(options.get(float_type='single'))
phf0, data = get_coherent_images(psf, stack, dtype)
if options.get(apply_window=False):
background = (stack.pathinfo.get_background() or [0,0])[0]
voxel_sizes = stack.get_voxel_sizes()
smoothness = int(options.get(smoothness=1))
window_width = options.get(window_width=None)
if window_width is None:
dr = stack.get_lateral_resolution()
dz = stack.get_axial_resolution()
if dr is None or dz is None:
window_width = 3.0
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
else:
print 'lateral resolution: %.3f um (%.1f x %.1f px^2)' % (1e6*dr, dr/voxel_sizes[1], dr/voxel_sizes[2])
print 'axial resolution: %.3f um (%.1fpx)' % (1e6*dz, dz / voxel_sizes[0])
vz,vy,vx = voxel_sizes
m = 0.3
scales = (m*vz/dz, m*vy/dr, m*vx/dr)
else:
scales = tuple([s/(window_width*min(voxel_sizes)) for s in voxel_sizes])
print 'Window size in pixels:', [1/s for s in scales]
from iocbio.ops.apply_window_ext import apply_window_inplace
apply_window_inplace(data, scales, smoothness, background)
mode = options.get(rltv_algorithm_type='multiplicative').lower()
Cls = dict(multiplicative=DeconvolveRLPoisson,
additive = DeconvolveRLGauss)[mode]
task = Cls(phf0, data, stack.get_voxel_sizes(), options)
task.set_cache_dir(working_dir)
task.set_save_data(stack.pathinfo, data.shape, data_type)
task.set_test_data()
estimate = task.deconvolve()
estimate = contract_to_shape(estimate,
stack.images.shape,
data_type)
return ImageStack(estimate, stack.pathinfo, suffix=task.get_suffix(),
options = options)
