def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
p.add_argument('--mesh-outfile', nargs=1, type=str, default='', help='Output label mesh file')
p.add_argument('--mesh-infiles', nargs='*', type=str, default='',
help='Input label mesh file (calculate stats / show plots only)')
p.add_argument('--reduce-frac', nargs=1, type=float, default=[0.2], metavar=('PERC'),
help='Reduce fraction for reducing meshes (decimate pro)')
#p.add_argument('--reduce-spacing', nargs=3, type=float, default=[10.0, 10.0, 5.0], metavar=('SPC'),
# help='Voxel spacing to use for mesh decimation (quadric clustering)')
p.add_argument('--reduce-nbins', nargs=1, type=int, default=[10], metavar=('NBINS'),
help='Number of bins to use for mesh decimation (quadric clustering)')
p.add_argument('--min-faces', nargs=1, type=int, default=[6], metavar=('NFACES'),
help='Minimum number of faces for each mesh (decimate pro, <= 0 to use update method)')
p.add_argument('--smooth', nargs=3, type=int, default=[3,3,3], metavar=('X', 'Y', 'Z'),
help='Size of smoothing kernel (zeros for none)')
p.add_argument('--contour-lvl', nargs=1, type=float, default=[0.25], metavar=('LVL'),
help='Level [0,1] to use to create mesh isocontours')
p.add_argument('--center-origin', action='store_true', dest='center_origin',
help='Do the weird "origin centering" transformation')
# p.add_argument('--no-flip-faces', action='store_false', dest='flip_faces',
# help='Do change the face order coming from vtk')
p.add_argument('--flip-faces', action='store_true', dest='flip_faces',
help='Change the face order coming from vtk')
p.add_argument('--seed-range', nargs=2, type=int, default=[-1,-1], metavar=('BEG', 'END'),
help='Subset of seeds to process (< 0 for beg/end)')
p.add_argument('--no-decimatePro', action='store_false', dest='decimatePro',
help='Do not use decimate pro from vtk for meshing (use quadric clustering instead)')
#p.add_argument('--decimatePro', action='store_true', dest='decimatePro',
# help='Use decimate pro from vtk for meshing (default quadric clustering)')
p.add_argument('--set-voxel-scale', action='store_true', dest='set_voxel_scale',
help='Use the voxel scale to set the data spacing to vtk (vertices in nm)')
p.add_argument('--doplots', action='store_true', help='Debugging plotting enabled for each supervoxel')
p.add_argument('--dpLabelMesher-verbose', action='store_true', help='Debugging output for dpLabelMesher')
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
# possible actions, suggest running one at a time since no easy way to specify the order
# 3d smoothing of labels (done per label)
p.add_argument('--smooth', action='store_true', help='Perform 3d smoothing on labels')
# remove components smaller than size (using voxel counts only)
p.add_argument('--minsize', nargs=1, type=int, default=[-1], metavar=('size'),
help='Minimum label size in voxels to keep')
p.add_argument('--minsize_fill', action='store_true',
help='Whether to nearest neighbor fill labels scrubbed with minsize')
# remove adjacencies
p.add_argument('--remove_adjacencies', action='store_true',
help='Perform 3d adjacency removal using fg-connectivity')
# remove cavities
p.add_argument('--cavity-fill', action='store_true', help='Remove all BG not connected to cube faces')
# rerun labeling (connected components)
p.add_argument('--relabel', action='store_true', help='Re-label components (run connected components)')
# recompute voxel type based on majority winner for each supervoxel
p.add_argument('--get-svox-type', action='store_true', help='Recompute supervoxel type using majority method')
p.add_argument('--write-voxel-type', action='store_true',
help='Perform get-svox-type and also write out voxel-type based on supervoxels')
# other options
p.add_argument('--fg-connectivity', nargs=1, type=int, default=[1], choices=[1,2,3],
help='Connectivity for foreground (where applicable)')
p.add_argument('--bg-connectivity', nargs=1, type=int, default=[1], choices=[1,2,3],
help='Connectivity for background (where applicable)')
p.add_argument('--ECS-label', nargs=1, type=int, default=[1], metavar=('size'),
help='Specify which label is ECS (== 0 means none, < 0 means max label)')
p.add_argument('--dpCleanLabels-verbose', action='store_true', help='Debugging output for dpCleanLabels')
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
dpCubeIter.addArgs(p)
p.add_argument('--upsample',
action='store_true',
help='Upsample mode (default downsampling)')
p.add_argument(
'--downsample-op',
nargs=1,
type=str,
default=['none'],
metavar='OP',
choices=['none', 'labels', 'mean', 'median'],
help='Specify which operation to use for downsampling method')
p.add_argument(
'--factor',
nargs=1,
type=int,
default=[2],
metavar=('F'),
help=
'Integer factor to resample, must divide size of resampled dims')
p.add_argument('--resample-dims',
nargs=3,
type=int,
default=[1, 1, 1],
metavar=('X', 'Y', 'Z'),
help='Boolean specifying which dimensions to resample')
p.add_argument('--dpResample-verbose',
action='store_true',
help='Debugging output for dpResample')
def writeLabels(cls, outfile, chunk, offset, size, datasize, chunksize, fillvalue=None, data=None, inraw='',
strbits='32', outraw='', attrs={}, subgroups=[], verbose=False):
assert( data is not None or inraw )
parser = argparse.ArgumentParser(description='class:emProbabilities',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
dpWriteh5.addArgs(parser); arg_str = ''
arg_str += ' --srcfile ' + outfile
arg_str += ' --chunk %d %d %d ' % tuple(chunk)
arg_str += ' --offset %d %d %d ' % tuple(offset)
arg_str += ' --size %d %d %d ' % tuple(size)
arg_str += ' --chunksize %d %d %d' % tuple(chunksize)
arg_str += ' --datasize %d %d %d' % tuple(datasize)
arg_str += ' --data-type %s ' % ('uint' + strbits)
if subgroups: arg_str += ' --subgroups ' + ' '.join(subgroups)
if fillvalue: arg_str += ' --fillvalue ' + str(fillvalue)
if inraw: arg_str += ' --inraw ' + inraw
if outraw: arg_str += ' --outraw ' + outraw
#if verbose: arg_str += ' --dpWriteh5-verbose --dpLoadh5-verbose '
if verbose: arg_str += ' --dpWriteh5-verbose '
if verbose: print(arg_str)
args = parser.parse_args(arg_str.split())
writeh5 = cls(args); writeh5.data_attrs = attrs
if inraw: writeh5.writeFromRaw()
else: writeh5.writeCube(data)
return writeh5
def addArgs(p):
dpWriteh5.addArgs(p)
dpCubeIter.addArgs(p)
p.add_argument('--concatenate_only', action='store_true', help='Just concatenate volumes, no stitching')
p.add_argument('--two_pass', action='store_true', help='Use two pass method')
p.add_argument('--two_pass_load', nargs=1, type=str, default='', help='Raw file to load first pass')
p.add_argument('--two_pass_save', nargs=1, type=str, default='', help='Raw file to export first pass')
p.add_argument('--dpCubeStitcher-verbose', action='store_true',
help='Debugging output for dpCubeStitcher')
def addArgs(p):
dpWriteh5.addArgs(p)
dpCubeIter.addArgs(p)
p.add_argument('--concatenate_only', action='store_true', help='Just concatenate volumes, no stitching')
p.add_argument('--two_pass', action='store_true', help='Use two pass method')
p.add_argument('--two_pass_load', nargs=1, type=str, default='', help='Raw file to load first pass')
p.add_argument('--two_pass_save', nargs=1, type=str, default='', help='Raw file to export first pass')
p.add_argument('--dpCubeStitcher-verbose', action='store_true',
help='Debugging output for dpCubeStitcher')
def readVoxType(cls, srcfile, chunk, offset, size, verbose=False):
parser = argparse.ArgumentParser(description='class:emVoxelType',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
dpWriteh5.addArgs(parser); arg_str = ''
arg_str += ' --srcfile ' + srcfile
arg_str += ' --chunk %d %d %d ' % tuple(chunk)
arg_str += ' --offset %d %d %d ' % tuple(offset)
arg_str += ' --size %d %d %d ' % tuple(size)
if verbose: arg_str += ' --dpLoadh5-verbose '
if verbose: print(arg_str)
args = parser.parse_args(arg_str.split())
loadh5 = cls(args); loadh5.readCubeToBuffers()
return loadh5
def readProbs(cls, srcfile, probName, chunk, offset, size, verbose=False):
parser = argparse.ArgumentParser(description='class:emProbabilities',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
dpWriteh5.addArgs(parser); arg_str = ''
arg_str += ' --srcfile ' + srcfile
arg_str += ' --chunk %d %d %d ' % tuple(chunk)
arg_str += ' --offset %d %d %d ' % tuple(offset)
arg_str += ' --size %d %d %d ' % tuple(size)
arg_str += ' --dataset ' + emProbabilities.PROBS_DATASET + str(probName)
if verbose: arg_str += ' --dpLoadh5-verbose '
if verbose: print(arg_str)
args = parser.parse_args(arg_str.split())
loadh5 = cls(args); loadh5.readCubeToBuffers()
return loadh5
def addArgs(p):
dpWriteh5.addArgs(p)
dpCubeIter.addArgs(p)
p.add_argument(
'--annotation-file-glob',
nargs=1,
type=str,
default='',
help='Glob for a list of input annotation files from knossos')
p.add_argument(
'--labels-path',
nargs=1,
type=str,
default='',
help='Input path for superchunked supervoxel label files (to merge)'
)
p.add_argument(
'--dsfactor',
nargs=1,
type=int,
default=[1],
metavar=('F'),
help='Downsample factor, mode to write out a single label file')
#p.add_argument('--segmentation-levels', nargs=1, type=int, default=[4], metavar=('NLVLS'),
# help='Number of available segmentation levels')
p.add_argument(
'--segmentation-values',
nargs='+',
type=str,
default=[],
help='Mapping from segmentation levels to parameter value in hdf5')
p.add_argument('--smooth',
nargs=3,
type=int,
default=[7, 7, 7],
metavar=('X', 'Y', 'Z'),
help='Size of smoothing kernel (zeros for none)')
p.add_argument(
'--contour-lvl',
nargs=1,
type=float,
default=[0.2],
metavar=('LVL'),
help='Level [0,1] to use to binarize after smoothing applied')
p.add_argument('--dpLabelMerger-verbose',
action='store_true',
help='Debugging output for dpLabelMerger')
def readLabels(cls, srcfile, chunk, offset, size, data_type=None, subgroups=[], verbose=False):
if not data_type: data_type = cls.LBLS_STR_DTYPE
parser = argparse.ArgumentParser(description='class:emLabels',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
dpWriteh5.addArgs(parser); arg_str = ''
arg_str += ' --srcfile ' + srcfile
arg_str += ' --chunk %d %d %d ' % tuple(chunk)
arg_str += ' --offset %d %d %d ' % tuple(offset)
arg_str += ' --size %d %d %d ' % tuple(size)
if data_type: arg_str += ' --data-type ' + data_type
if subgroups: arg_str += ' --subgroups ' + ' '.join(subgroups)
if verbose: arg_str += ' --dpLoadh5-verbose '
if verbose: print(arg_str)
args = parser.parse_args(arg_str.split())
loadh5 = cls(args); loadh5.readCubeToBuffers()
return loadh5
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
p.add_argument('--inrawpath', nargs=1, type=str, default='', metavar='PATH', help='Raw inputs path')
# pertaining to voxel types
p.add_argument('--types', nargs='+', type=str, default=['ICS','ECS','MEM'],
metavar='TYPE', help='Names of the voxel types (prefix for raw file)')
p.add_argument('--agg-ops-types', nargs='*', type=str, default=[], metavar='OP',
help='Specify which operations to be done for each type, 0 in list delimits types')
# pertaining to probs to be merged
p.add_argument('--nmerge', nargs=1, type=int, default=[1],
metavar='N', help='Number of network probability output rawfiles to merge')
p.add_argument('--weightings', nargs='*', type=float, default=[], metavar='W',
help='Weightings for probabilities specified in each rawfile (un-reordered), default to equal weightings')
p.add_argument('--dim-orderings', nargs='*', type=str, default=[], choices=('xyz','xzy','zyx'),
metavar='ORD', help='Specify the reslice ordering of the rawfile inputs (default all xyz)')
p.add_argument('--dpAggProbs-verbose', action='store_true', help='Debugging output for dpAggProbs')
def writeVoxType(cls, outfile, chunk, offset, size, datasize, chunksize, fillvalue=None, data=None, inraw='',
outraw='', attrs={}, verbose=False):
assert( data is not None or inraw )
parser = argparse.ArgumentParser(description='class:emVoxelType',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
dpWriteh5.addArgs(parser); arg_str = ''
arg_str += ' --srcfile ' + outfile
arg_str += ' --chunk %d %d %d ' % tuple(chunk)
arg_str += ' --offset %d %d %d ' % tuple(offset)
arg_str += ' --size %d %d %d ' % tuple(size)
arg_str += ' --chunksize %d %d %d' % tuple(chunksize)
arg_str += ' --datasize %d %d %d' % tuple(datasize)
if fillvalue: arg_str += ' --fillvalue ' + str(fillvalue)
if inraw: arg_str += ' --inraw ' + inraw
if outraw: arg_str += ' --outraw ' + outraw
if verbose: arg_str += ' --dpWriteh5-verbose '
if verbose: print(arg_str)
args = parser.parse_args(arg_str.split())
writeh5 = cls(args); writeh5.data_attrs = attrs
if inraw: writeh5.writeFromRaw()
else: writeh5.writeCube(data)
return writeh5
def addArgs(p):
dpWriteh5.addArgs(p)
# adds arguments required for this object to specified ArgumentParser object
p.add_argument('--rawfile', nargs=1, type=str, default='raw.h5', help='Path/name of hdf5 raw EM (input) file')
p.add_argument('--raw-dataset', nargs=1, type=str, default='data', help='Name of the raw EM dataset to read')
p.add_argument('--dpWarp-verbose', action='store_true', help='Debugging output for dpWarp')
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
p.add_argument('--probfile',
nargs=1,
type=str,
default='',
help='Path/name of hdf5 probability (input) file')
p.add_argument(
'--prob_types',
nargs='+',
type=str,
default=['MEM'],
metavar='TYPE',
help=
'Dataset names of the voxel types to use from the probabilities')
p.add_argument('--typefile',
nargs=1,
type=str,
default='',
help='Path/name of hdf5 with voxel types')
p.add_argument('--train-chunks',
nargs='+',
type=int,
default=[0, 0, 0],
metavar='X_Y_Z',
help='Training chunks (specify x0 y0 z0 x1 y1 ...) ')
p.add_argument(
'--train-offsets',
nargs='*',
type=int,
default=[],
metavar='X_Y_Z',
help=
'Training offsets (specify x0 y0 z0 x1 y1 ..., default all zero) ')
p.add_argument('--train-size',
nargs=3,
type=int,
default=[0, 0, 0],
metavar=('X', 'Y', 'Z'),
help='Size of training chunks (default chunksize)')
p.add_argument('--test-size',
nargs=2,
type=int,
default=[64, 64],
metavar='S',
help='Size of the sliding correlation windows')
p.add_argument('--savefile',
nargs=1,
type=str,
default='',
help='Path/name npz file to save outputs to')
p.add_argument('--loadfile',
nargs=1,
type=str,
default='',
help='Load previous run saved in npz for plotting')
p.add_argument('--nthreads',
nargs=1,
type=int,
default=[8],
help='Number of threads to use in fftw')
# arguments for concatenate mode (concatenate runs over savefiles that were created for independent volumes
# and reduces down to specified size)
p.add_argument('--loadfiles-path',
nargs=1,
type=str,
default='',
help='Path to saved runs to concatenate')
p.add_argument('--concat-chunk',
nargs=3,
type=int,
default=[0, 0, 0],
metavar=('X', 'Y', 'Z'),
help='Starting chunk alignment for concatenate')
p.add_argument('--concat-nchunks',
nargs=3,
type=int,
default=[0, 0, 0],
metavar=('X', 'Y', 'Z'),
help='Total area for concatenation (chunks)')
p.add_argument(
'--reduce-size',
nargs=3,
type=int,
default=[0, 0, 0],
metavar=('X', 'Y', 'Z'),
help='Block size to reduce down to after concantenation (voxels)')
p.add_argument(
'--concat-loadfile',
nargs=1,
type=str,
default='',
help='Load previous concat saved in npz for analysis/plotting')
p.add_argument('--dpVolumeXcorr-verbose',
action='store_true',
help='Debugging output for dpVolumeXcorr')
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
# possible actions, suggest running one at a time since no easy way to specify the order
# 3d smoothing of labels (done per label)
p.add_argument('--smooth',
action='store_true',
help='Perform 3d smoothing on labels')
p.add_argument('--smooth-size',
nargs=3,
type=int,
default=[3, 3, 3],
metavar=('X', 'Y', 'Z'),
help='Size of smoothing kernel')
p.add_argument(
'--contour-lvl',
nargs='*',
type=float,
default=[0.25],
metavar=('LVL'),
help=
'Level [0,1] to use to create mesh isocontours, specify range for "auto" mode'
)
# remove components smaller than size (using voxel counts only)
p.add_argument('--minsize',
nargs=1,
type=int,
default=[-1],
metavar=('size'),
help='Minimum label size in voxels to keep')
p.add_argument(
'--minsize_fill',
action='store_true',
help='Whether to nearest neighbor fill labels scrubbed with minsize'
)
# remove adjacencies
p.add_argument(
'--remove_adjacencies',
nargs=1,
type=int,
default=[0],
metavar=('NBHD'),
help=
'Perform 3d adjacency removal using specified neighborhood size')
# remove cavities
p.add_argument('--cavity-fill',
action='store_true',
help='Remove all BG not connected to cube faces')
# remove any labels less than specified size that are within cavities
p.add_argument(
'--cavity-fill-minsize',
nargs=1,
type=int,
default=[1],
metavar=('SIZE'),
help=
'Minimum label size to replace labels in cavities (force cavity fill)'
)
# rerun labeling (connected components)
p.add_argument('--relabel',
action='store_true',
help='Re-label components (run connected components)')
# write background (membrane mask) using the voxel type
p.add_argument(
'--apply-bg-mask',
action='store_true',
help='Write voxel-type background (membrane) mask to supervoxels')
# recompute voxel type based on majority winner for each supervoxel
p.add_argument('--get-svox-type',
action='store_true',
help='Recompute supervoxel type using majority method')
p.add_argument(
'--write-voxel-type',
action='store_true',
help=
'Perform get-svox-type and also write out voxel-type based on supervoxels'
)
p.add_argument('--replace-ECS',
action='store_true',
help='Replace all ECS supervoxels with ECS-label')
# make overlay that traces minpath between particular label value
p.add_argument(
'--minpath',
nargs=1,
type=int,
default=[-1],
metavar=('label'),
help=
'Calculate min foreground path between specified label (default off)'
)
p.add_argument(
'--minpath-perc',
nargs=1,
type=float,
default=[1.01],
metavar=('perc'),
help='Percentage away from bwmin to label in minpath overlay')
p.add_argument(
'--minpath-skel',
action='store_true',
help=
'Whether to skeletonize min foreground path (for use with minpath)'
)
# used to set min label value for both relabel and minsize
p.add_argument(
'--min-label',
nargs=1,
type=int,
default=[1],
metavar=('min'),
help='First label after relabel if relabeling (also minsize)')
# xxx - new switch intended to have actions performed per label instead of the whole volume at once.
# intended for medium-large volumes but in situations where superchunking is not desireable.
# currently supported for: cavity_fill
p.add_argument('--labelwise',
action='store_true',
help='Perform operations on each label sequentially')
# other options
p.add_argument('--fg-connectivity',
nargs=1,
type=int,
default=[1],
choices=[1, 2, 3],
help='Connectivity for foreground (where applicable)')
p.add_argument('--bg-connectivity',
nargs=1,
type=int,
default=[1],
choices=[1, 2, 3],
help='Connectivity for background (where applicable)')
p.add_argument(
'--ECS-label',
nargs=1,
type=int,
default=[1],
metavar=('size'),
help=
'Specify which label is ECS (== 0 means none, < 0 means max label)'
)
p.add_argument('--dpCleanLabels-verbose',
action='store_true',
help='Debugging output for dpCleanLabels')
def addArgs(p):
# adds arguments required for this object to specified ArgumentParser object
dpWriteh5.addArgs(p)
p.add_argument('--inrawpath',
nargs=1,
type=str,
default='',
metavar='PATH',
help='Raw inputs path')
# pertaining to voxel types
p.add_argument('--types',
nargs='+',
type=str,
default=['ICS', 'ECS', 'MEM'],
metavar='TYPE',
help='Names of the voxel types (prefix for raw file)')
p.add_argument(
'--agg-ops-types',
nargs='*',
type=str,
default=[],
metavar='OP',
help=
'Specify which operations to be done for each type, 0 in list delimits types'
)
# pertaining to probs to be merged
p.add_argument(
'--nmerge',
nargs=1,
type=int,
default=[1],
metavar='N',
help='Number of network probability output rawfiles to merge')
p.add_argument(
'--weightings',
nargs='*',
type=float,
default=[],
metavar='W',
help=
'Weightings for probabilities specified in each rawfile (un-reordered), default to equal weightings'
)
p.add_argument(
'--dim-orderings',
nargs='*',
type=str,
default=[],
choices=('xyz', 'xzy', 'zyx'),
metavar='ORD',
help=
'Specify the reslice ordering of the rawfile inputs (default all xyz)'
)
# added this feature to optimize overlap support
p.add_argument(
'--overlap',
nargs=3,
type=int,
default=[0, 0, 0],
metavar=('X', 'Y', 'Z'),
help='Select out portion of raw cube to support overlaps')
p.add_argument('--dpAggProbs-verbose',
action='store_true',
help='Debugging output for dpAggProbs')
