/usr/lib/python2.7/dist-packages/vigra/__init__.py is in python-vigra 1.10.0+git20160211.167be93+dfsg-2+b5.
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#
# Copyright 2009-2010 by Ullrich Koethe
#
# This file is part of the VIGRA computer vision library.
# The VIGRA Website is
# http://hci.iwr.uni-heidelberg.de/vigra/
# Please direct questions, bug reports, and contributions to
# ullrich.koethe@iwr.uni-heidelberg.de or
# vigra@informatik.uni-hamburg.de
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without
# restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following
# conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
#
#######################################################################
from __future__ import division, print_function
import sys, os, time, math
from numbers import Number
from multiprocessing import cpu_count
try:
import pylab
except Exception as e:
pass
_vigra_path = os.path.abspath(os.path.dirname(__file__))
_vigra_doc_path = _vigra_path + '/doc/vigranumpy/index.html'
if sys.platform.startswith('win'):
# On Windows, add subdirectory 'dlls' to the PATH in order to find
# the DLLs vigranumpy depends upon. Since this directory appears
# at the end of PATH, already installed DLLs are always preferred.
_vigra_dll_path = _vigra_path + '/dlls'
if os.path.exists(_vigra_dll_path):
os.putenv('PATH', os.getenv('PATH') + os.pathsep + _vigra_dll_path)
def _fallbackModule(moduleName, message):
'''This function installs a fallback module with the given 'moduleName'.
All function calls into this module raise an ImportError with the
given 'message' that hopefully tells the user why the real module
was not available.
'''
import sys
moduleClass = vigranumpycore.__class__
class FallbackModule(moduleClass):
def __init__(self, name):
moduleClass.__init__(self, name)
self.__name__ = name
def __getattr__(self, name):
if name.startswith('__'):
return moduleClass.__getattribute__(self, name)
try:
return moduleClass.__getattribute__(self, name)
except AttributeError:
raise ImportError("""%s.%s: %s""" % (self.__name__, name, self.__doc__))
module = FallbackModule(moduleName)
sys.modules[moduleName] = module
module.__doc__ = """Import of module '%s' failed.\n%s""" % (moduleName, message)
if not os.path.exists(_vigra_doc_path):
_vigra_doc_path = "http://hci.iwr.uni-heidelberg.de/vigra/doc/vigranumpy/index.html"
__doc__ = '''VIGRA Computer Vision Library
HTML documentation is available in
%s
Help on individual functions can be obtained via their doc strings
as usual.
The following sub-modules group related functionality:
* arraytypes (VigraArray and axistags, automatically imported into 'vigra')
* ufunc (improved array arithmetic, automatically used by VigraArray)
* impex (image and array I/O)
* colors (color space transformations)
* filters (spatial filtering, e.g. smoothing)
* sampling (image and array re-sampling and interpolation)
* fourier (Fourier transform and Fourier domain filters)
* analysis (image analysis and segmentation)
* learning (machine learning and classification)
* noise (noise estimation and normalization)
* geometry (geometric algorithms, e.g. convex hull)
* histogram (histograms and channel representation)
* graphs (grid graphs / graphs / graph algorithms)
* utilities (priority queues)
''' % _vigra_doc_path
from .__version__ import version
import vigra.vigranumpycore as vigranumpycore
import vigra.arraytypes as arraytypes
import vigra.impex as impex
import vigra.sampling as sampling
import vigra.filters as filters
import vigra.analysis as analysis
import vigra.learning as learning
import vigra.colors as colors
import vigra.noise as noise
import vigra.geometry as geometry
import vigra.optimization as optimization
import vigra.histogram as histogram
import vigra.graphs as graphs
import vigra.utilities as utilities
import vigra.blockwise as blockwise
sampling.ImagePyramid = arraytypes.ImagePyramid
try:
import vigra.fourier as fourier
except Exception as e:
_fallbackModule('fourier',
'''
%s
Make sure that the fftw3 libraries are found during compilation and import.
They may be downloaded at http://www.fftw.org/.''' % str(e))
import fourier
class Timer:
def __init__(self, name, verbose=True):
self.name = name
self.verbose = verbose
def __enter__(self):
if self.verbose:
print(self.name, "...")
self.start = time.time()
return self
def __exit__(self, *args):
self.end = time.time()
self.interval = self.end - self.start
if self.verbose :
print("... took ", self.interval, "sec")
# portable way to inject a metaclass (taken from six.py)
def _with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(meta):
def __new__(cls, name, this_bases, d):
return meta(name, bases, d)
return type.__new__(metaclass, 'temporary_class', (), {})
# import most frequently used functions
from vigra.arraytypes import *
standardArrayType = arraytypes.VigraArray
defaultAxistags = arraytypes.VigraArray.defaultAxistags
from vigra.vigranumpycore import ChunkedArrayFull, ChunkedArrayLazy, ChunkedArrayCompressed, ChunkedArrayTmpFile, Compression
try:
from vigra.vigranumpycore import ChunkedArrayHDF5, HDF5Mode
except:
pass
from vigra.impex import readImage, readVolume
def readHDF5(filenameOrGroup, pathInFile, order=None):
'''Read an array from an HDF5 file.
'filenameOrGroup' can contain a filename or a group object
referring to an already open HDF5 file. 'pathInFile' is the name
of the dataset to be read, including intermediate groups. If the
first argument is a group object, the path is relative to this
group, otherwise it is relative to the file's root group.
If the dataset has an attribute 'axistags', the returned array
will have type :class:`~vigra.VigraArray` and will be transposed
into the given 'order' ('vigra.VigraArray.defaultOrder'
will be used if no order is given). Otherwise, the returned
array is a plain 'numpy.ndarray'. In this case, order='F' will
return the array transposed into Fortran order.
Requirements: the 'h5py' module must be installed.
'''
import h5py
if isinstance(filenameOrGroup, h5py.highlevel.Group):
file = None
group = filenameOrGroup
else:
file = h5py.File(filenameOrGroup, 'r')
group = file['/']
try:
dataset = group[pathInFile]
if not isinstance(dataset, h5py.highlevel.Dataset):
raise IOError("readHDF5(): '%s' is not a dataset" % pathInFile)
data = dataset.value
axistags = dataset.attrs.get('axistags', None)
if axistags is not None:
data = data.view(arraytypes.VigraArray)
data.axistags = arraytypes.AxisTags.fromJSON(axistags)
if order is None:
order = arraytypes.VigraArray.defaultOrder
data = data.transposeToOrder(order)
else:
if order == 'F':
data = data.transpose()
elif order not in [None, 'C', 'A']:
raise IOError("readHDF5(): unsupported order '%s'" % order)
finally:
if file is not None:
file.close()
return data
def writeHDF5(data, filenameOrGroup, pathInFile, compression=None, chunks=None):
'''Write an array to an HDF5 file.
'filenameOrGroup' can contain a filename or a group object
referring to an already open HDF5 file. 'pathInFile' is the name of the
dataset to be written, including intermediate groups. If the first
argument is a group object, the path is relative to this group,
otherwise it is relative to the file's root group. If the dataset already
exists, it will be replaced without warning.
If 'data' has an attribute 'axistags', the array is transposed to
numpy order before writing. Moreover, the axistags will be
stored along with the data in an attribute 'axistags'.
'compression' can be set to 'gzip', 'szip' or 'lzf'
gzip (standard compression),
szip (available if HDF5 is compiled with szip. Faster compression, limited types),
lzf (very fast compression, all types).
The 'lzf' compression filter is many times faster than 'gzip'
at the cost of a lower compresion ratio.
Chunking is disabled by default. When 'chunks' is set to True
chunking is enabled and a chunk shape is determined automatically.
Alternatively a chunk shape can be specified explicitly by passing
a tuple of the desired shape.
Requirements: the 'h5py' module must be installed.
'''
import h5py
if isinstance(filenameOrGroup, h5py.highlevel.Group):
file = None
group = filenameOrGroup
else:
file = h5py.File(filenameOrGroup)
group = file['/']
try:
levels = pathInFile.split('/')
for groupname in levels[:-1]:
if groupname == '':
continue
g = group.get(groupname, default=None)
if g is None:
group = group.create_group(groupname)
elif not isinstance(g, h5py.highlevel.Group):
raise IOError("writeHDF5(): invalid path '%s'" % pathInFile)
else:
group = g
dataset = group.get(levels[-1], default=None)
if dataset is not None:
if isinstance(dataset, h5py.highlevel.Dataset):
del group[levels[-1]]
else:
raise IOError("writeHDF5(): cannot replace '%s' because it is not a dataset" % pathInFile)
try:
data = data.transposeToNumpyOrder()
except:
pass
dataset = group.create_dataset(levels[-1], data=data, compression=compression, chunks=chunks)
if hasattr(data, 'axistags'):
dataset.attrs['axistags'] = data.axistags.toJSON()
finally:
if file is not None:
file.close()
impex.readHDF5 = readHDF5
readHDF5.__module__ = 'vigra.impex'
impex.writeHDF5 = writeHDF5
writeHDF5.__module__ = 'vigra.impex'
from .filters import convolve, gaussianSmoothing
from .sampling import resize
def gaussianDerivative(array, sigma, orders, out=None, window_size=0.0):
'''
Convolve 'array' with a Gaussian derivate kernel of the given 'orders'.
'orders' must contain a list of integers >= 0 for each non-channel axis.
Each channel of the array is treated independently. If 'sigma' is a single
value, the kernel size is equal in each dimension. If 'sigma' is a tuple
or list of values of appropriate length, a different size is used for each axis.
'window_size' specifies the ratio between the filter scale and the size of
the filter window. Use values around 2.0 to speed-up the computation for the
price of increased cut-off error, and values >= 4.0 for very accurate results.
The window size is automatically determined for the default value 0.0.
For the first and second derivatives, you can also use :func:`gaussianGradient`
and :func:`hessianOfGaussian`.
'''
if hasattr(array, 'dropChannelAxis'):
if array.dropChannelAxis().ndim != len(orders):
raise RuntimeError("gaussianDerivative(): len(orders) doesn't match array dimension.")
else:
if array.ndim != len(orders):
raise RuntimeError("gaussianDerivative(): len(orders) doesn't match array dimension.")
try:
len(sigma)
except:
sigma = [sigma]*len(orders)
kernels = tuple([filters.gaussianDerivativeKernel(s, o, window_size=window_size) \
for s, o in zip(sigma, orders)])
return filters.convolve(array, kernels, out)
filters.gaussianDerivative = gaussianDerivative
gaussianDerivative.__module__ = 'vigra.filters'
# import enums
CLOCKWISE = sampling.RotationDirection.CLOCKWISE
COUNTER_CLOCKWISE = sampling.RotationDirection.COUNTER_CLOCKWISE
UPSIDE_DOWN = sampling.RotationDirection.UPSIDE_DOWN
CompleteGrow = analysis.SRGType.CompleteGrow
KeepContours = analysis.SRGType.KeepContours
StopAtThreshold = analysis.SRGType.StopAtThreshold
_selfdict = globals()
def searchfor(searchstring):
'''Scan all vigra modules to find classes and functions containing
'searchstring' in their name.
'''
for attr in _selfdict.keys():
contents = dir(_selfdict[attr])
for cont in contents:
if ( cont.upper().find(searchstring.upper()) ) >= 0:
print(attr+"."+cont)
# FIXME: use axistags here
def imshow(image,show=True, **kwargs):
'''Display a scalar or RGB image by means of matplotlib.
If the image does not have one or three channels, an exception is raised.
The image will be automatically scaled to the range 0...255 when its dtype
is not already 'uint8' and neither 'cmap' nor 'norm' are specified in kwargs
'''
import matplotlib.pylab
if not hasattr(image, 'axistags'):
plot = matplotlib.pyplot.imshow(image, **kwargs)
if show:
matplotlib.pylab.show()
return plot
image = image.transposeToNumpyOrder()
if image.channels == 1:
image = image.dropChannelAxis().view(numpy.ndarray)
if 'cmap' in kwargs.keys():
cmap = kwargs.pop('cmap')
else:
cmap = matplotlib.cm.gray
if 'norm' in kwargs.keys():
norm = kwargs.pop('norm')
else:
norm = matplotlib.cm.colors.Normalize()
plot = matplotlib.pyplot.imshow(image, cmap=cmap, norm=norm, **kwargs)
if show:
matplotlib.pylab.show()
return plot
elif image.channels == 3:
if image.dtype != numpy.uint8:
out = image.__class__(image.shape, dtype=numpy.uint8, axistags=image.axistags)
image = colors.linearRangeMapping(image, newRange=(0.0, 255.0), out=out)
plot = matplotlib.pyplot.imshow(image.view(numpy.ndarray), **kwargs)
if show:
matplotlib.pylab.show()
return plot
else:
raise RuntimeError("vigra.imshow(): Image must have 1 or 3 channels.")
def multiImshow(images,shape, show=True):
nImg = len(images)
f = pylab.figure()
s = tuple(shape)
for c,iname in enumerate(images.keys()):
data,itype = images[iname]
if itype == 'img':
ax1 = f.add_subplot(s[0],s[1],c+1)
imshow(data,show=False)
ax1.set_title(iname)
pylab.axis('off')
if show :
pylab.show()
def segShow(img,labels,edgeColor=(0,0,0),alpha=0.3,show=False,returnImg=False,r=0):
img = numpy.squeeze(img)
if img.ndim ==2:
img = numpy.concatenate( [ img[:,:,None]]*3 ,axis=2).astype(numpy.float32)
img = taggedView(img, 'xyc')
labels = numpy.squeeze(labels)
crackedEdges = analysis.regionImageToCrackEdgeImage(labels+1).squeeze()
#print("cracked shape",crackedEdges.shape)
whereEdge = numpy.where(crackedEdges==0)
whereNoEdge = numpy.where(crackedEdges!=0)
crackedEdges[whereEdge] = 1
crackedEdges[whereNoEdge] = 0
if r>0 :
res = filters.discDilation(crackedEdges.astype(numpy.uint8),int(r) )
whereEdge = numpy.where(res==1)
imgToDisplay = resize(img,numpy.squeeze(crackedEdges).shape)
imgToDisplay-=imgToDisplay.min()
imgToDisplay/=imgToDisplay.max()
for c in range(3):
ic = imgToDisplay[:,:,c]
ic[whereEdge]=(1.0-alpha)*edgeColor[c] + alpha*ic[whereEdge]
if returnImg:
return imgToDisplay
return imshow(imgToDisplay,show=show)
def nestedSegShow(img,labels,edgeColors=None,scale=1,show=False,returnImg=False):
shape=(labels.shape[0]*scale,labels.shape[1]*scale)
if scale!=1:
img=vigra.resize(img,shape)
assert numpy.squeeze(labels).ndim==3
nSegs = labels.shape[2]
if edgeColors is None :
edgeColors=numpy.ones([nSegs,4])
a =numpy.array([0,0,0.0,0.6],dtype=numpy.float32)
b =numpy.array([1,0,0,0.4],dtype=numpy.float32)
for s in range(nSegs):
f=float(s)/float(nSegs-1)
edgeColors[s,:]=f*b + (1.0-f)*a
tShape=(img.shape[0]*2-1,img.shape[1]*2-1)
imgToDisplay = resize(img,tShape)
imgToDisplay-=imgToDisplay.min()
imgToDisplay/=imgToDisplay.max()
imgIn = imgToDisplay.copy()
for si in range(nSegs):
l = labels[:,:,si].copy()
if scale!=1:
l=resize(l.astype(numpy.float32),shape,order=0).astype(numpy.uint32)
crackedEdges = analysis.regionImageToCrackEdgeImage(l)
whereEdge = numpy.where(crackedEdges==0)
if len(edgeColors[si])<4:
alpha = 0.0
else:
alpha = edgeColors[si,3]
for c in range(3):
icI = imgIn[:,:,c]
ic = imgToDisplay[:,:,c]
ic[whereEdge]=(1.0-alpha) * edgeColors[si,c] + alpha*icI[whereEdge]
if returnImg:
return imgToDisplay
return imshow(imgToDisplay,show=show)
def show():
import matplotlib.pylab
matplotlib.pylab.show()
# auto-generate code for additional Kernel generators:
def _genKernelFactories(name):
for oldName in dir(eval('filters.'+name)):
if not oldName.startswith('init'):
continue
#remove init from beginning and start with lower case character
newPrefix = oldName[4].lower() + oldName[5:]
if newPrefix == "explicitly":
newPrefix = "explict"
newName = newPrefix + 'Kernel'
if name == 'Kernel2D':
newName += '2D'
code = '''def %(newName)s(*args, **kw):
k = filters.%(name)s()
k.%(oldName)s(*args, **kw)
return k
%(newName)s.__doc__ = filters.%(name)s.%(oldName)s.__doc__
filters.%(newName)s=%(newName)s
''' % {'oldName': oldName, 'newName': newName, 'name': name}
exec(code)
_genKernelFactories('Kernel1D')
_genKernelFactories('Kernel2D')
del _genKernelFactories
# define watershedsUnionFind()
def _genWatershedsUnionFind():
def watershedsUnionFind(image, neighborhood=None, out = None):
'''Compute watersheds of an image using the union find algorithm.
If 'neighborhood' is 'None', it defaults to 8-neighborhood for 2D inputs
and 6-neighborhood for 3D inputs.
Calls :func:`watersheds` with parameters::\n\n
watersheds(image, neighborhood=neighborhood, method='UnionFind', out=out)
'''
if neighborhood is None:
neighborhood = 8 if image.spatialDimensions == 2 else 6
return analysis.watersheds(image, neighborhood=neighborhood, method='UnionFind', out=out)
watershedsUnionFind.__module__ = 'vigra.analysis'
analysis.watershedsUnionFind = watershedsUnionFind
_genWatershedsUnionFind()
del _genWatershedsUnionFind
# define watershedsReoptimization)
def _genWatershedsReoptimization():
def watershedsReoptimization(labels,edgeIndicator,shrinkN,out=None,visu=False):
# do unseeding
#if visu :
# import matplotlib,numpy
# import pylab
# # A random colormap for matplotlib
# cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( 256,3))
# pylab.imshow ( numpy.swapaxes(labels,0,1) , cmap = cmap)
# pylab.show()
seeds=analysis.segToSeeds(labels,int(shrinkN))
if visu :
import matplotlib,numpy
import pylab
# A random colormap for matplotlib
cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( 256,3))
pylab.imshow ( numpy.swapaxes(seeds,0,1) , cmap = cmap)
pylab.show()
#if seeds.ndim==2:
# seeds=analysis.labelImageWithBackground(seeds)
#elif seeds.ndim==3:
# seeds=analysis.labelVolumeWithBackground(seeds)
#else :
# raise RuntimeError("only implemented for 2d and 3d")
if visu :
import matplotlib,numpy
import pylab
# A random colormap for matplotlib
cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( 256,3))
pylab.imshow ( numpy.swapaxes(seeds,0,1) , cmap = cmap)
pylab.show()
return analysis.watersheds(edgeIndicator,seeds=seeds,out=out)
watershedsReoptimization.__module__ = 'vigra.analysis'
analysis.watershedsReoptimization = watershedsReoptimization
_genWatershedsReoptimization()
del _genWatershedsReoptimization
# define tensor convenience functions
def _genTensorConvenienceFunctions():
def hessianOfGaussianEigenvalues(image, scale, out=None,
sigma_d=0.0, step_size=1.0, window_size=0.0, roi=None):
'''Compute the eigenvalues of the Hessian of Gaussian at the given scale
for a scalar image or volume.
Calls :func:`hessianOfGaussian` and :func:`tensorEigenvalues`.
'''
hessian = filters.hessianOfGaussian(image, scale,
sigma_d=sigma_d, step_size=step_size,
window_size=window_size, roi=roi)
return filters.tensorEigenvalues(hessian, out=out)
hessianOfGaussianEigenvalues.__module__ = 'vigra.filters'
filters.hessianOfGaussianEigenvalues = hessianOfGaussianEigenvalues
def structureTensorEigenvalues(image, innerScale, outerScale, out=None,
sigma_d=0.0, step_size=1.0, window_size=0.0, roi=None):
'''Compute the eigenvalues of the structure tensor at the given scales
for a scalar or multi-channel image or volume.
Calls :func:`structureTensor` and :func:`tensorEigenvalues`.
'''
st = filters.structureTensor(image, innerScale, outerScale,
sigma_d=sigma_d, step_size=step_size,
window_size=window_size, roi=roi)
return filters.tensorEigenvalues(st, out=out)
structureTensorEigenvalues.__module__ = 'vigra.filters'
filters.structureTensorEigenvalues = structureTensorEigenvalues
_genTensorConvenienceFunctions()
del _genTensorConvenienceFunctions
# define feature convenience functions
def _genFeaturConvenienceFunctions():
def supportedFeatures(array):
'''Return a list of feature names that are available for the given array. These feature
names are the valid inputs to a call of :func:`extractFeatures`. E.g., to compute
just the first two features in the list, use::
f = vigra.analysis.supportedFeatures(array)
print("Computing features:", f[:2])
r = vigra.analysis.extractFeatures(array, features=f[:2])
'''
return analysis.extractFeatures(array, None).supportedFeatures()
supportedFeatures.__module__ = 'vigra.analysis'
analysis.supportedFeatures = supportedFeatures
def supportedRegionFeatures(array, labels):
'''Return a list of feature names that are available for the given array and label array.
These feature names are the valid inputs to a call of
:func:`extractRegionFeatures`. E.g., to compute just the first two features in the
list, use::
f = vigra.analysis.supportedRegionFeatures(array, labels)
print("Computing features:", f[:2])
r = vigra.analysis.extractRegionFeatures(array, labels, features=f[:2])
'''
return analysis.extractRegionFeatures(array, labels, None).supportedFeatures()
supportedRegionFeatures.__module__ = 'vigra.analysis'
analysis.supportedRegionFeatures = supportedRegionFeatures
def supportedConvexHullFeatures(labels):
'''Return a list of Convex Hull feature names that are available for the given 2D label array.
These Convex Hull feature names are the valid inputs to a call of
:func:`extractConvexHullFeatures`. E.g., to compute just the first two features in the
list, use::
f = vigra.analysis.supportedConvexHullFeatures(labels)
print("Computing Convex Hull features:", f[:2])
r = vigra.analysis.extractConvexHullFeatures(labels, features=f[:2])
'''
try:
return analysis.extractConvexHullFeatures(labels, list_features_only=True)
except:
return []
supportedConvexHullFeatures.__module__ = 'vigra.analysis'
analysis.supportedConvexHullFeatures = supportedConvexHullFeatures
def supportedSkeletonFeatures(labels):
'''Return a list of Skeleton feature names that are available for the given 2D label array.
These Skeleton feature names are the valid inputs to a call of
:func:`extractSkeletonFeatures`. E.g., to compute just the first two features in the
list, use::
f = vigra.analysis.supportedSkeletonFeatures(labels)
print("Computing Skeleton features:", f[:2])
r = vigra.analysis.extractSkeletonFeatures(labels, features=f[:2])
'''
try:
return analysis.extractSkeletonFeatures(labels, list_features_only=True)
except:
return []
supportedSkeletonFeatures.__module__ = 'vigra.analysis'
analysis.supportedSkeletonFeatures = supportedSkeletonFeatures
# implement the read-only part of the 'dict' API in FeatureAccumulator and RegionFeatureAccumulator
def __len__(self):
return len(self.keys())
def __iter__(self):
return self.keys().__iter__()
def __contains__(self, key):
try:
return self.isActive(key)
except:
return False
def has_key(self, key):
self.__contains__(key)
if sys.version_info[0] < 3:
def values(self):
return [self[k] for k in self.keys()]
def items(self):
return [(k, self[k]) for k in self.keys()]
else:
def values(self):
return self.itervalues()
def items(self):
return self.iteritems()
def iterkeys(self):
return self.keys().__iter__()
def itervalues(self):
for k in self.keys():
yield self[k]
def iteritems(self):
for k in self.keys():
yield (k, self[k])
for k in ['__len__', '__iter__', '__contains__', 'has_key', 'values', 'items', 'iterkeys', 'itervalues', 'iteritems']:
setattr(analysis.FeatureAccumulator, k, eval(k))
setattr(analysis.RegionFeatureAccumulator, k, eval(k))
_genFeaturConvenienceFunctions()
del _genFeaturConvenienceFunctions
MetricType = graphs.MetricType
# define grid graph convenience functions
# and extend grid graph classes
def _genGridGraphConvenienceFunctions():
def gridGraph(shape,directNeighborhood=True):
'''Return a grid graph with certain shape.
Parameters:
- shape -- shape of the image
- directNeighborhood -- use 4 (True) or 8 (False) neighborhood (default: True)
Returns:
- grid graph
use::
>>> # 4-connected
>>> g = vigra.graps.gridGraph(shape=[10,20])
>>> g.nodeNum
200
>>> # 8-connected
>>> g = vigra.graps.gridGraph(shape=[10,20],directNeighborhood=False)
'''
if(len(shape)==2):
return graphs.GridGraphUndirected2d(shape,directNeighborhood)
elif(len(shape)==3):
return graphs.GridGraphUndirected3d(shape,directNeighborhood)
else:
raise RuntimeError("GridGraph is only implemented for 2d and 3d grids")
gridGraph.__module__ = 'vigra.graphs'
graphs.gridGraph = gridGraph
# extend grid graph via meta classes
for cls in [graphs.GridGraphUndirected2d, graphs.GridGraphUndirected3d] :
metaCls = cls.__class__
class gridGraphInjectorMeta(metaCls):
def __init__(self, name, bases, dict):
for b in bases:
if type(b) not in (self, type):
for k,v in dict.items():
setattr(b,k,v)
return type.__init__(self, name, bases, dict)
class gridGraphInjector(_with_metaclass(gridGraphInjectorMeta, object)):
pass
##inject some methods in the point foo
class moreGridGraph(gridGraphInjector, cls):
@property
def shape(self):
""" shape of grid graph"""
return self.intrinsicNodeMapShape()
def nodeSize(self):
""" node map filled with 1.0"""
size = graphs.graphMap(self,item='node',dtype=numpy.float32)
size[:]=1
return size
def edgeLengths(self):
""" node map filled with 1.0"""
size = graphs.graphMap(self,item='edge',dtype=numpy.float32)
size[:]=1
return size
def mergeGraph(self):
if len(self.shape)==2:
mg = graphs.GridGraphUndirected2dMergeGraph(self)
else:
mg = graphs.GridGraphUndirected3dMergeGraph(self)
return mg
def isGridGraph(obj):
""" check if obj is gridGraph"""
return isinstance(obj,(graphs.GridGraphUndirected2d , graphs.GridGraphUndirected3d))
def isGridGraph2d(obj):
""" check if obj is gridGraph"""
return isinstance(obj,graphs.GridGraphUndirected2d)
isGridGraph.__module__ = 'vigra.graphs'
graphs.isGridGraph = isGridGraph
isGridGraph2d.__module__ = 'vigra.graphs'
graphs.isGridGraph2d = isGridGraph2d
_genGridGraphConvenienceFunctions()
del _genGridGraphConvenienceFunctions
def _genGraphConvenienceFunctions():
def listGraph(nodes=0,edges=0):
''' Return an empty directed graph
Parameters :
- nodes : number of nodes to reserveEdges
- edges : number of edges to reserve
Returns :
- graph
'''
return graphs.AdjacencyListGraph(nodes,edges)
listGraph.__module__ = 'vigra.graphs'
graphs.listGraph = listGraph
def intrinsicGraphMapShape(graph,item):
""" Intrinsic shape of node/edge/arc-map for a given graph.
Node edge and arc maps are stored in numpy arrays by default.
The instric shape may not be confused with the number
of nodes/edges/arcs. The instric shape is used to
allocate a numpy are which can store data for nodes/arcs/edgeSizes
of a given graph.
Parameters:
- graph : input graph to get the shape for
- item : item must be ``'node'`` , ``'edge'`` or ``'arc'``
Returns:
- shape as tuple
"""
if item=='edge':
return graph.intrinsicEdgeMapShape()
elif item=='node':
return graph.intrinsicNodeMapShape()
elif item=='arc':
return graph.intrinsicArcMapShape()
else :
raise RuntimeError("%s is not valid,must be 'edge','node' or 'arc' "%item)
intrinsicGraphMapShape.__module__ = 'vigra.graphs'
graphs.intrinsicGraphMapShape = intrinsicGraphMapShape
def graphMap(graph,item,dtype=numpy.float32,channels=1,addChannelDim=False):
""" Return a graph map for a given graph item (``'node'`` , ``'edge'`` or ``'arc'``).
Parameters:
- graph : graph to get a graph map for
- item : ``'node'`` , ``'edge'`` or ``'arc'``
- dtype : desired dtype
- channels : number of channels (default: 1)
- addChannelDim -- add an explicit channelDim :(default: False)
only useful if channels == 1
Returns:
- graphmap as numpy.ndarray / VigraArray
"""
s = intrinsicGraphMapShape(graph,item)
intrDim = len(s)
if(channels==1) and addChannelDim==False:
a=numpy.zeros(shape=s,dtype=dtype)
if intrDim == 1:
return taggedView(a,'x')
elif intrDim == 2:
return taggedView(a,'xy')
elif intrDim == 3:
return taggedView(a,'xyz')
elif intrDim == 4:
return taggedView(a,'xyzt')
else :
raise RuntimeError("graphs with intrisic dimension >4 are not supported")
else:
s = s+(channels,)
a=numpy.zeros(shape=s,dtype=dtype)
if intrDim == 1:
return taggedView(a,'xc')
elif intrDim == 2:
return taggedView(a,'xyc')
elif intrDim == 3:
return taggedView(a,'xyzc')
elif intrDim == 4:
return taggedView(a,'xyztc')
else :
raise RuntimeError("graphs with intrisic dimension >4 are not supported")
def graphMap2(graph,item,dtype=numpy.float32,channels=1,addChannelDim=False):
""" Return a graph map for a given graph item (``'node'`` , ``'edge'`` or ``'arc'``).
Parameters:
- graph : graph to get a graph map for
- item : ``'node'`` , ``'edge'`` or ``'arc'``
- dtype : desired dtype
- channels : number of channels (default: 1)
- addChannelDim -- add an explicit channelDim :(default: False)
only useful if channels == 1
Returns:
- graphmap as numpy.ndarray / VigraArray
"""
s = intrinsicGraphMapShape(graph,item)
intrDim = len(s)
if(channels==1) and addChannelDim==False:
a=numpy.zeros(shape=s,dtype=dtype)
if intrDim == 1:
return taggedView(a,'x')
elif intrDim == 2:
return taggedView(a,'xy')
elif intrDim == 3:
return taggedView(a,'xyz')
elif intrDim == 4:
return taggedView(a,'xyzt')
else :
raise RuntimeError("graphs with intrisic dimension >4 are not supported")
else:
s = s+(channels,)
a=numpy.zeros(shape=s,dtype=dtype)
if intrDim == 1:
return taggedView(a,'xc')
elif intrDim == 2:
return taggedView(a,'xyc')
elif intrDim == 3:
return taggedView(a,'xyzc')
elif intrDim == 4:
return taggedView(a,'xyztc')
else :
raise RuntimeError("graphs with intrisic dimension >4 are not supported")
graphMap.__module__ = 'vigra.graphs'
graphs.graphMap = graphMap
def mergeGraph(graph):
""" get a merge graph from input graph.
A merge graph might be usefull for hierarchical clustering
"""
#mg = graph.mergeGraph()
mg = graphs.__mergeGraph(graph)
#mg.__base_graph__=graph
return mg
mergeGraph.__module__ = 'vigra.graphs'
graphs.mergeGraph = mergeGraph
INVALID = graphs.Invalid()
graphs.INVALID = INVALID
class ShortestPathPathDijkstra(object):
def __init__(self,graph):
""" shortest path computer
Keyword Arguments:
- graph : input graph
"""
self.pathFinder = graphs._shortestPathDijkstra(graph)
self.graph=graph
self.source = None
self.target = None
def run(self,weights,source,target=None):
""" run shortest path search
Keyword Arguments:
- weights : edge weights encoding distance from two adjacent nodes
- source : source node
- target : target node (default: None)
If target node is None, the shortest path
to all nodes!=source is computed
"""
self.source = source
self.target = target
if target is None:
self.pathFinder.run(weights,source)
else:
self.pathFinder.run(weights,source,target)
return self
def runIgnoreLargeWeights(self,weights,source,val):
""" run shortest path search, nodes with all edge weights larger than val will be ignored
Keyword Arguments:
- weights : edge weights encoding distance from two adjacent nodes
- source : source node
- val : upper bound
"""
self.source = source
self.target = None
self.pathFinder.runIgnoreLargeWeights(weights,source,val)
return self
def path(self,target=None,pathType='coordinates'):
""" get the shortest path from source to target
Keyword Arguments:
- weights : edge weights encoding distance from two adjacent nodes
- source : source node
- target : target node (default: None)
If target node is None, the target specified
by 'run' is used.
pathType : 'coordinates' or 'ids' path (default: 'coordinates')
"""
if target is None:
assert self.target is not None
target=self.target
if pathType=='coordinates':
return self.pathFinder.nodeCoordinatePath(target)
elif pathType == 'ids':
return self.pathFinder.nodeIdPath(target)
def distance(self,target=None):
""" get distance from source to target
Keyword Arguments:
- target : target node (default: None)
If target node is None, the target specified
by 'run' is used.
"""
if target is None:
assert self.target is not None
target=self.target
return self.pathFinder.distance(target)
def distances(self,out=None):
""" return the full distance map"""
return self.pathFinder.distances(out)
def predecessors(self,out=None):
""" return the full predecessors map"""
return self.pathFinder.predecessors(out)
ShortestPathPathDijkstra.__module__ = 'vigra.graphs'
graphs.ShortestPathPathDijkstra = ShortestPathPathDijkstra
_genGraphConvenienceFunctions()
del _genGraphConvenienceFunctions
def _genRegionAdjacencyGraphConvenienceFunctions():
class RegionAdjacencyGraph(graphs.AdjacencyListGraph):
def __init__(self,graph=None ,labels=None ,ignoreLabel=None,reserveEdges=0, maxLabel=None, isDense=None):
""" Region adjacency graph
Keyword Arguments :
- graph : the base graph, the region adjacency graph should be based on
- labels : label map for the graph
- ignoreLabel : ignore a label in the labels map (default: None)
- reserveEdges : reserve a certain number of Edges
Attributes:
- labels : labels passed in constructor
- ignoreLabel : ignoreLabel passed in constructor
- baseGraphLabels : labels passed in constructor
(fixme,dublicated attribute (see labels) )
- baseGraph : baseGraph is the graph passed in constructor
- affiliatedEdges : for each edge in the region adjacency graph,
a vector of edges of the baseGraph is stored in affiliatedEdges
"""
if(graph is not None and labels is not None):
super(RegionAdjacencyGraph,self).__init__(int(labels.max()+1),int(reserveEdges))
if ignoreLabel is None and isDense is not None and isDense == True:
if ignoreLabel is None:
ignoreLabel=-1
self.labels = labels
self.ignoreLabel = ignoreLabel
self.baseGraphLabels = labels
self.baseGraph = graph
if maxLabel is None:
maxLabel = int(numpy.max(labels))
# set up rag
self.affiliatedEdges = graphs._regionAdjacencyGraphFast(graph,labels,self,maxLabel,int(reserveEdges))
else:
if ignoreLabel is None:
ignoreLabel=-1
self.labels = labels
self.ignoreLabel = ignoreLabel
self.baseGraphLabels = labels
self.baseGraph = graph
# set up rag
self.affiliatedEdges = graphs._regionAdjacencyGraph(graph,labels,self,self.ignoreLabel)
else :
super(RegionAdjacencyGraph,self).__init__(0,0)
def mergeGraph(self):
return graphs.AdjacencyListGraphMergeGraph(self)
def accumulateSeeds(self, seeds, out=None):
graph = self.baseGraph
labels = self.labels
return graphs._pyAccNodeSeeds(self, graph, labels, seeds, out)
def accumulateEdgeFeatures(self,edgeFeatures,acc='mean',out=None):
""" accumulate edge features from base graphs edges features
Keyword Argument:
- edgeFeatures : edge features of baseGraph
- acc : used accumulator (default: 'mean')
Currently only 'mean' and 'sum' are implemented
- out : preallocated edge map
Returns :
accumulated edge features
"""
graph = self.baseGraph
affiliatedEdges = self.affiliatedEdges
if isinstance(edgeFeatures, (graphs.ImplicitMEanEdgeMap_2d_float_float, graphs.ImplicitMEanEdgeMap_3d_float_float)):
if graphs.isGridGraph(graph)==False:
raise RuntimeError("implicit edge maps are only implemented for grid graphs")
return graphs._ragEdgeFeatures(self, graph, affiliatedEdges, edgeFeatures,acc, out)
else:
if self.edgeNum == 0:
raise RuntimeError("self.edgeNum == 0 => cannot accumulate edge features")
if acc == 'mean':
weights = self.baseGraph.edgeLengths()
#print("Weights",weights)
else:
weights = graphs.graphMap(self.baseGraph,'edge',dtype=numpy.float32)
weights[:] = 1
if graphs.isGridGraph2d(graph) and edgeFeatures.ndim == 4 :
return graphs._ragEdgeFeaturesMb(self,graph,affiliatedEdges,edgeFeatures,weights,acc,out)
else:
return graphs._ragEdgeFeatures(self,graph,affiliatedEdges,edgeFeatures,weights,acc,out)
def accumulateNodeFeatures(self,nodeFeatures,acc='mean',out=None):
""" accumulate edge features from base graphs edges features
Keyword Argument:
- nodeFeatures : node features of baseGraph
- acc : used accumulator (default: 'mean')
Currently only 'mean' and 'sum' are implemented
- out : preallocated node map (default: None)
Returns :
accumulated node features
"""
if self.edgeNum == 0 :
raise RuntimeError("self.edgeNum == 0 => cannot accumulate edge features")
graph = self.baseGraph
labels = self.baseGraphLabels
ignoreLabel = self.ignoreLabel
if acc == 'mean':
#print("get node size...")
weights = self.baseGraph.nodeSize()
#print("weights == ", weights)
else :
weights = graphs.graphMap(self.baseGraph,'node',dtype=numpy.float32)
weights[:]=1
return graphs._ragNodeFeatures(self,graph,labels,nodeFeatures,weights,acc,ignoreLabel,out)
def projectNodeFeatureToBaseGraph(self,features,out=None):
""" project node features from this graph, to the base graph of this graph.
Keyword Arguments:
- features : node feautres for this graph
- out : preallocated node map of baseGraph (default: None)
Returns :
projected node features of base graph
"""
out=graphs._ragProjectNodeFeaturesToBaseGraph(
rag=self,
baseGraph=self.baseGraph,
baseGraphLabels=numpy.squeeze(self.baseGraphLabels),
ragNodeFeatures=features,
ignoreLabel=self.ignoreLabel,
out=out
)
#print("out",out.shape,out.dtype)
return out
def projectLabelsBack(self,steps,labels=None,_current=0):
""" project labels from current graph to baseGraph and repeat this recursively
Keyword Arguments:
- steps : how often should the labels be projected back
- labels : labels for the current graph (default: None)
If labels is None, each node gets its own label
"""
if labels is None :
# identity segmentation on this level
labels = self.nodeIdMap()
if steps == _current :
return labels
else :
labels = self.projectLabelsToBaseGraph(labels)
return self.baseGraph.projectLabelsBack(steps,labels,_current+1)
def projectLabelsToBaseGraph(self,labels=None):
""" project node labels from this graph, to the base graph of this graph.
Keyword Arguments:
- labels : node labels for this graph (default: None)
If labels is None, each node gets its own label
- out : preallocated node map of baseGraph (default: None)
Returns :
"""
if labels is None :
# identity segmentation on this level
labels = self.nodeIdMap()
return self.projectNodeFeatureToBaseGraph(features=labels)
def projectBaseGraphGt(self, baseGraphGt, gt=None, gtQuality=None):
bggt = numpy.require(baseGraphGt,dtype=numpy.uint32)
gt, gtQuality = graphs._ragProjectGroundTruth(rag=self, graph=self.baseGraph,
labels=self.baseGraphLabels, gt=bggt,
ragGt=gt, ragGtQuality=gtQuality)
return gt, gtQuality
def edgeUVCoordinates(self, edgeId):
try :
ei = int(edgeId)
except:
ei = edgeId.id
affEdges = self.affiliatedEdges
uvCoords = affEdges.getUVCoordinates(self.baseGraph, ei)
dim = uvCoords.shape[1] // 2
uCoords = uvCoords[:,0:dim]
vCoords = uvCoords[:,dim:2*dim]
return (uCoords,vCoords)
def edgeTopologicalCoordinates(self, edgeId):
uc,vc = self.edgeUVCoordinates(edgeId)
return uc+vc
def edgeCoordinates(self, edgeId):
uc,vc = self.edgeUVCoordinates(edgeId)
return (uc+vc)/2.0
RegionAdjacencyGraph.__module__ = 'vigra.graphs'
graphs.RegionAdjacencyGraph = RegionAdjacencyGraph
class GridRegionAdjacencyGraph(graphs.RegionAdjacencyGraph):
def __init__(self,graph=None,labels=None,ignoreLabel=None,reserveEdges=0, maxLabel=None, isDense=None):
""" Grid Region adjacency graph
A region adjaceny graph,where the base graph should be
a grid graph or a GridRegionAdjacencyGraph.
Keyword Arguments :
- graph : the base graph, the region adjacency graph should be based on
- labels : label map for the graph
- ignoreLabel : ignore a label in the labels map (default: None)
- reserveEdges : reserve a certain number of Edges
Attributes :
- labels : labels passed in constructor
- ignoreLabel : ignoreLabel passed in constructor
- baseGraphLabels : labels passed in constructor
(fixme,dublicated attribute (see labels) )
- baseGraph : baseGraph is the graph passed in constructor
- affiliatedEdges : for each edge in the region adjacency graph,
a vector of edges of the baseGraph is stored in affiliatedEdges
- shape : shape of the grid graph which is a base graph in the
complete graph chain.
"""
if graph is not None and labels is not None:
if not (graphs.isGridGraph(graph) or isinstance(graph,GridRegionAdjacencyGraph)):
raise RuntimeError("graph must be a GridGraph or a GridRegionAdjacencyGraph")
super(GridRegionAdjacencyGraph, self).__init__(graph, labels, ignoreLabel, reserveEdges, maxLabel, isDense)
else:
super(GridRegionAdjacencyGraph, self).__init__()
@property
def shape(self):
""" shape of the underlying grid graph"""
return self.baseGraph.shape
def projectLabelsToGridGraph(self,labels=None):
"""project labels of this graph to the underlying grid graph.
Keyword Arguments :
- labels : node labeling of this graph (default: None)
If labels is None, each node gets its own label
Returns :
grid graph labeling
"""
if labels is None :
# identity segmentation on this level
labels = self.nodeIdMap()
if graphs.isGridGraph(self.baseGraph):
return self.projectLabelsToBaseGraph(labels)
else :
labels = self.projectLabelsToBaseGraph(labels)
return self.baseGraph.projectLabelsToGridGraph(labels)
def projectNodeFeaturesToGridGraph(self,features):
""" project features of this graph to the underlying grid graph.
Therefore project the features to an image.
Keyword Arguments :
- features : nodeFeatures of the current graph
Returns :
grid graph labeling
"""
if graphs.isGridGraph(self.baseGraph):
return self.projectNodeFeatureToBaseGraph(features)
else :
features = self.projectNodeFeatureToBaseGraph(features)
return self.baseGraph.projectNodeFeaturesToGridGraph(features)
def showNested(self,img,labels=None,returnImg=False):
""" show the complet graph chain / hierarchy given an RGB image
Keyword Arguments:
- img : RGB image
- labels : node labeling of this graph (default: None)
If labels is None, each node gets its own label
"""
ll=[]
if labels is not None:
ll.append( self.projectLabelsToGridGraph(labels) )
ll.append( self.projectLabelsToGridGraph() )
g=self.baseGraph
while graphs.isGridGraph(g)==False:
ll.append( g.projectLabelsToGridGraph() )
g=g.baseGraph
ll.reverse()
gridLabels = [l[...,numpy.newaxis] for l in ll ]
gridLabels = numpy.concatenate(gridLabels,axis=2)
return nestedSegShow(img,gridLabels,returnImg=returnImg)
def show(self,img,labels=None,edgeColor=(0,0,0),alpha=0.3,returnImg=False):
""" show the graph given an RGB image
Keyword Arguments:
- img : RGB image
- labels : node labeling of this graph (default: None)
If labels is None, each node gets its own label
- edgeColor : RGB tuple of edge color (default: (0,0,0) ).
Do not use values bigger than 1 in edgeColor.
- alpha : make edges semi transparent (default: 0.3).
0 means no transparency,1 means full transparency.
"""
pLabels = self.projectLabelsToGridGraph(labels)
return segShow(img,numpy.squeeze(pLabels),edgeColor=edgeColor,alpha=alpha,returnImg=returnImg)
def showEdgeFeature(self, img, edgeFeature, cmap='jet', returnImg=False, labelMode=False):
import matplotlib
assert graphs.isGridGraph(self.baseGraph)
imgOut = img.copy().squeeze()
if imgOut.ndim == 2:
imgOut = numpy.concatenate([imgOut[:,:,None]]*3,axis=2)
imgOut = taggedView(imgOut,'xyc')
imgOut-=imgOut.min()
imgOut/=imgOut.max()
if not labelMode:
edgeFeatureShow = edgeFeature.copy()
mi = edgeFeatureShow.min()
ma = edgeFeatureShow.max()
cm = matplotlib.cm.ScalarMappable(cmap=cmap)
rgb = cm.to_rgba(edgeFeatureShow)[:,0:3]
print(rgb.shape)
if(ma > mi):
edgeFeatureShow -=mi
edgeFeatureShow /= edgeFeatureShow.max()
else:
edgeFeatureShow[:] = 1
for e in self.edgeIter():
u,v = self.edgeUVCoordinates(e.id)
if not labelMode:
showVal = rgb[e.id,:]
else:
if edgeFeature[e.id] == 0:
showVal=[0,0,1]
elif edgeFeature[e.id] == 1:
showVal=[0,1,0]
elif edgeFeature[e.id] == -1:
showVal=[1,0,0]
imgOut[u[:,0],u[:,1],:] = showVal
imgOut[v[:,0],v[:,1],:] = showVal
#print(u.shape)
if returnImg:
return imgOut
imshow(imgOut)
def nodeSize(self):
""" get the geometric size of the nodes """
if graphs.isGridGraph(self.baseGraph):
return graphs._ragNodeSize(self, self.baseGraph, self.labels, self.ignoreLabel)
else:
baseNodeSizes = self.baseGraph.nodeSize()
return self.accumulateNodeFeatures(baseNodeSizes,acc='sum')
def edgeLengths(self):
""" get the geometric length of the edges"""
if graphs.isGridGraph(self.baseGraph):
return graphs._ragEdgeSize(self,self.affiliatedEdges)
else:
baseNodeSizes = self.baseGraph.edgeLengths()
return self.accumulateEdgeFeatures(baseNodeSizes,acc='sum')
def writeHDF5(self, filename, dset):
if(graphs.isGridGraph(self.baseGraph)):
sGraph = self.serialize()
sAffEdges = graphs._serialzieGridGraphAffiliatedEdges(self.baseGraph, self, self.affiliatedEdges )
sLabels = self.labels
writeHDF5(numpy.array([self.ignoreLabel]), filename, dset+'/ignore_label')
writeHDF5(sLabels, filename, dset+'/labels')
writeHDF5(sGraph, filename, dset+'/graph')
writeHDF5(sAffEdges, filename, dset+'/affiliated_edges')
else:
raise RuntimeError("only RAGs of Grid graph can be serialized")
#def readHdf5(self, filename, dset):
# labels = readHdf5(filename, dset+'/labels')
# shape = labels.shape
# self.baseGraph = graphs.gridGraph(shape)
GridRegionAdjacencyGraph.__module__ = 'vigra.graphs'
graphs.GridRegionAdjacencyGraph = GridRegionAdjacencyGraph
class TinyEdgeLabelGui(object):
def __init__(self, rag, img, edgeLabels = None, labelMode=True):
if labelMode and isinstance(edgeLabels, numpy.ndarray):
assert set(numpy.unique(edgeLabels)).issubset({-1, 0, 1}), 'if labelMode is true only label values of [-1, 0, 1] are permitted'
self.press = None
self.rag = rag
self.img = img
self.edgeLabels = edgeLabels
self.dim = len(img.shape)
self.zOffset = 0
self.edgeRag2dToRag = None
self.edgeRagToRag2d = None
if self.dim == 3:
self.zOffset = self.img.shape[2]//2
self.visuImg = numpy.array(img, dtype=numpy.float32)
self.visuImg -= self.visuImg.min()
self.visuImg /= self.visuImg.max()
self.rag2d = None
self.visuImg2d = None
self.labelMode = labelMode
if self.edgeLabels is None :
self.edgeLabels = numpy.zeros(self.rag.edgeNum, dtype=numpy.float32)
self.edgeLabels2d = None
self.slice2d()
self.implot = None
self.currentLabel = 1
self.brushSize = 1
def startGui(self):
from functools import partial
import pylab as plt
from matplotlib.widgets import Slider, Button, RadioButtons
ax = plt.gca()
fig = plt.gcf()
imgWithEdges =self.rag2d.showEdgeFeature(self.visuImg2d, self.edgeLabels2d, returnImg=True, labelMode=self.labelMode)
self.implot = ax.imshow(numpy.swapaxes(imgWithEdges,0,1))
ff = partial(self.onclick, self)
cid = fig.canvas.mpl_connect('button_press_event', self.onclick)
fig.canvas.mpl_connect('key_press_event', self.press_event)
fig.canvas.mpl_connect('scroll_event', self.scroll)
fig.canvas.mpl_connect('motion_notify_event', self.on_motion)
fig.canvas.mpl_connect('button_release_event', self.on_release)
if self.labelMode:
axcolor = 'lightgoldenrodyellow'
axamp = plt.axes([0.25, 0.15, 0.65, 0.03], axisbg=axcolor)
self.slideBrush = Slider(axamp, 'brush-size', 1, 20.0, valinit=2)
self.slideBrush.on_changed(self.updateBrushSize)
plt.show()
def updateBrushSize(self, val):
self.brushSize = int(val+0.5)
def press_event(self, event):
sys.stdout.flush()
if event.key=='0' or event.key=='3':
self.currentLabel = 0
if event.key=='1':
self.currentLabel = 1
if event.key=='2':
self.currentLabel = -1
def slice2d(self):
if self.dim==3:
labels = self.rag.labels[:,:,self.zOffset].squeeze()
gg = graphs.gridGraph(labels.shape)
self.rag2d = graphs.regionAdjacencyGraph(gg, labels)
# update edges 2d:
self.edgeLabels2d = numpy.zeros(self.rag2d.edgeNum, dtype=numpy.float32)
# update edge correlation
self.edgeIdRag2dToRag = dict()
self.edgeIdRagToRag2d = dict()
for edge in self.rag2d.edgeIter():
edge3d = self.rag.findEdge(edge.u, edge.v)
self.edgeIdRag2dToRag[edge.id] = edge3d.id
self.edgeIdRagToRag2d[edge3d.id] = edge.id
self.visuImg2d = self.visuImg[:,:,self.zOffset]
# update edge 2d status:
for i in numpy.arange(self.edgeLabels2d.shape[0]):
self.edgeLabels2d[i] = self.edgeLabels[self.edgeIdRag2dToRag[i]]
elif self.dim==2:
self.rag2d = self.rag
self.visuImg2d = self.visuImg
self.edgeIdRag2dToRag = dict()
for edge in self.rag.edgeIter():
self.edgeIdRag2dToRag[edge.id] = edge.id
self.edgeIdRagToRag2d = self.edgeIdRag2dToRag
self.edgeLabels2d = self.edgeLabels
else:
print('warning: bad dimension!')
def scroll(self, event):
import pylab as plt
if self.dim==3:
if event.button == 'up':
self.zOffset += 1
else:
self.zOffset -= 1
self.zOffset = self.zOffset % self.visuImg.shape[2]
self.slice2d()
imgWithEdges = self.rag2d.showEdgeFeature(self.visuImg2d, self.edgeLabels2d,returnImg=True, labelMode=self.labelMode)
self.implot.set_data(numpy.swapaxes(imgWithEdges,0,1))
plt.draw()
def on_motion(self, event):
if self.press is None:
return
print(event.xdata, event.ydata)
self.handle_click(event)
def on_release(self, event):
self.press = None
def onclick(self, event):
self.press = event.xdata, event.ydata
print(event.xdata, event.ydata)
try:
self.handle_click(event)
except:
pass
def handle_click(self, event):
import pylab as plt
if event.button==1:
self.currentLabel = 1
if event.button==2:
self.currentLabel = 0
if event.button==3:
self.currentLabel = -1
img = self.img
rag = self.rag2d
labels = rag.baseGraphLabels
shape = img.shape
if event.xdata != None and event.ydata != None:
xRaw,yRaw = event.xdata,event.ydata
if xRaw >=0.0 and yRaw>=0.0 and xRaw<img.shape[0] and yRaw<img.shape[1]:
x,y = int(math.floor(event.xdata)),int(math.floor(event.ydata))
#print("X,Y",x,y)
l = labels[x,y]
others = []
bs = self.brushSize
for xo in range(-1*bs, bs+1):
for yo in range(-1*bs, bs+1):
xx = x+xo
yy = y+yo
if xo is not 0 or yo is not 0:
if xx >=0 and xx<shape[0] and \
yy >=0 and yy<shape[0]:
otherLabel = labels[xx, yy]
if l != otherLabel:
edge = rag.findEdge(int(l), int(otherLabel))
#print(edge)
others.append((xx,yy,edge))
#break
#if other is not None:
# pass
if self.labelMode:
for other in others:
eid = other[2].id
oldLabel = self.edgeLabels[self.edgeIdRag2dToRag[eid]]
if self.currentLabel == oldLabel:
newLabel = oldLabel
else:
newLabel = self.currentLabel
self.edgeLabels[self.edgeIdRag2dToRag[eid]] = newLabel
self.edgeLabels2d[eid] = newLabel
imgWithEdges = rag.showEdgeFeature(self.visuImg2d, self.edgeLabels2d,returnImg=True, labelMode=self.labelMode)
self.implot.set_data(numpy.swapaxes(imgWithEdges,0,1))
plt.draw()
TinyEdgeLabelGui.__module__ = 'vigra.graphs'
graphs.TinyEdgeLabelGui = TinyEdgeLabelGui
def loadGridRagHDF5(filename , dset):
#print("load labels and make grid graph")
labels = readHDF5(filename, dset+'/labels')
shape = labels.shape
gridGraph = graphs.gridGraph(shape)
#print(gridGraph)
#print("load graph serialization")
graphSerialization = readHDF5(filename, dset+'/graph')
#print("make empty grid rag")
gridRag = GridRegionAdjacencyGraph()
#print("deserialize")
gridRag.deserialize(graphSerialization)
#print("load affiliatedEdges")
affEdgeSerialization = readHDF5(filename, dset+'/affiliated_edges')
#print("deserialize")
affiliatedEdges = graphs._deserialzieGridGraphAffiliatedEdges(gridGraph, gridRag, affEdgeSerialization)
ignoreLabel = readHDF5(filename, dset+'/ignore_label')
gridRag.affiliatedEdges = affiliatedEdges
gridRag.labels = taggedView(labels,"xyz")
gridRag.ignoreLabel = int(ignoreLabel[0])
gridRag.baseGraphLabels = taggedView(labels,"xyz")
gridRag.baseGraph = gridGraph
return gridRag
loadGridRagHDF5.__module__ = 'vigra.graphs'
graphs.loadGridRagHDF5 = loadGridRagHDF5
def regionAdjacencyGraph(graph,labels,ignoreLabel=None,reserveEdges=0, maxLabel=None, isDense=None):
""" Return a region adjacency graph for a labeld graph.
Parameters:
- graph -- input graph
- lables -- node-map with labels for each nodeSumWeights
- ignoreLabel -- label to ingnore (default: None)
- reserveEdges -- reverse a certain number of edges (default: 0)
Returns:
- rag -- instance of RegionAdjacencyGraph or GridRegionAdjacencyGraph
If graph is a GridGraph or a GridRegionAdjacencyGraph, a GridRegionAdjacencyGraph
will be returned.
Otherwise a RegionAdjacencyGraph will be returned
"""
if isinstance(graph , graphs.GridRegionAdjacencyGraph) or graphs.isGridGraph(graph):
return GridRegionAdjacencyGraph(graph=graph, labels=labels, ignoreLabel=ignoreLabel,
reserveEdges=reserveEdges, maxLabel=maxLabel, isDense=isDense)
else:
return RegionAdjacencyGraph(graph=graph, labels=labels, ignoreLabel=ignoreLabel,
reserveEdges=reserveEdges, maxLabel=maxLabel, isDense=isDense)
regionAdjacencyGraph.__module__ = 'vigra.graphs'
graphs.regionAdjacencyGraph = regionAdjacencyGraph
def gridRegionAdjacencyGraph(labels,ignoreLabel=None,reserveEdges=0, maxLabel=None, isDense=None):
""" get a region adjacency graph and a grid graph from a labeling.
This function will call 'graphs.gridGraph' and 'graphs.regionAdjacencyGraph'
Keyword Arguments:
- labels : label image
- ignoreLabel : label to ingnore (default: None)
- reserveEdges : reserve a number of edges (default: 0)
"""
_gridGraph=graphs.gridGraph(numpy.squeeze(labels).shape)
rag=graphs.regionAdjacencyGraph(graph=_gridGraph, labels=labels, ignoreLabel=ignoreLabel,
reserveEdges=reserveEdges, maxLabel=maxLabel, isDense=isDense)
return _gridGraph, rag
gridRegionAdjacencyGraph.__module__ = 'vigra.graphs'
graphs.gridRegionAdjacencyGraph = gridRegionAdjacencyGraph
_genRegionAdjacencyGraphConvenienceFunctions()
del _genRegionAdjacencyGraphConvenienceFunctions
def _genGraphSegmentationFunctions():
def getNodeSizes(graph):
""" get size of nodes:
This functions will try to call 'graph.nodeSize()' .
If this fails, a node map filled with 1.0 will be
returned
Keyword Arguments:
- graph : input graph
"""
try:
return graph.nodeSize()
except:
size = graphs.graphMap(graph,'node',dtype=numpy.float32)
size[:]=1
return size
getNodeSizes.__module__ = 'vigra.graphs'
graphs.getNodeSizes = getNodeSizes
def getEdgeLengths(graph):
""" get lengths/sizes of edges:
This functions will try to call 'graph.edgeLength()' .
If this fails, an edge map filled with 1.0 will be
returned
Keyword Arguments:
- graph : input graph
"""
try:
return graph.edgeLengths()
except:
size = graphs.graphMap(graph,'edge',dtype=numpy.float32)
size[:]=1
return size
getEdgeLengths.__module__ = 'vigra.graphs'
graphs.getEdgeLengths = getEdgeLengths
def felzenszwalbSegmentation(graph,edgeWeights,nodeSizes=None,k=1.0,nodeNumStop=None,out=None):
""" felzenszwalbs segmentation method
Keyword Arguments :
- graph : input graph
- edgeWeights : edge weights / indicators
- nodeSizes : size of each node (default: None)
If nodeSizes is None, 'getNodeSizes' will be called
- k : free parameter in felzenszwalbs algorithms (default : 1.0)
(todo: write better docu)
- nodeNumStop : stop the agglomeration at a given nodeNum (default :None)
If nodeNumStop is None, the resulting number of nodes does depends on k.
- backgroundBias : backgroundBias (default : None)
"""
if nodeNumStop is None :
nodeNumStop=-1
if nodeSizes is None :
nodeSizes=graphs.getNodeSizes(graph)
return graphs._felzenszwalbSegmentation(graph=graph,edgeWeights=edgeWeights,nodeSizes=nodeSizes,
k=k,nodeNumStop=nodeNumStop,out=out)
felzenszwalbSegmentation.__module__ = 'vigra.graphs'
graphs.felzenszwalbSegmentation = felzenszwalbSegmentation
def edgeWeightedWatersheds(graph,edgeWeights,seeds,backgroundLabel=None,backgroundBias=None,out=None):
""" edge weighted seeded watersheds
Keyword Arguments :
- graph : input graph
- edgeWeights : evaluation weights
- seeds : node map with seeds .
For at least one node, seeds must be nonzero
- backgroundLabel : a specific backgroundLabel (default : None)
- backgroundBias : backgroundBias (default : None)
"""
if backgroundLabel is None and backgroundBias is None:
return graphs._edgeWeightedWatershedsSegmentation(graph=graph,edgeWeights=edgeWeights,seeds=seeds,
out=out)
else :
if backgroundLabel is None or backgroundBias is None:
raise RuntimeError("if backgroundLabel or backgroundBias is not None, the other must also be not None")
return graphs._carvingSegmentation(graph=graph,edgeWeights=edgeWeights,seeds=seeds,
backgroundLabel=backgroundLabel,backgroundBias=backgroundBias,out=out)
edgeWeightedWatersheds.__module__ = 'vigra.graphs'
graphs.edgeWeightedWatersheds = edgeWeightedWatersheds
def nodeWeightedWatershedsSeeds(graph,nodeWeights,out=None):
""" generate watersheds seeds
Keyword Arguments :
- graph : input graph
- nodeWeights : node height map
- out : seed map
"""
return graphs._nodeWeightedWatershedsSeeds(graph=graph,nodeWeights=nodeWeights,out=out)
nodeWeightedWatershedsSeeds.__module__ = 'vigra.graphs'
graphs.nodeWeightedWatershedsSeeds = nodeWeightedWatershedsSeeds
def shortestPathSegmentation(graph, edgeWeights, nodeWeights, seeds=None, out=None):
""" node weighted seeded watersheds
Keyword Arguments :
- graph : input graph
- edgeWeights : edge weight map
- nodeWeights : node weight map
- seeds : node map with seeds (default: None)
If seeds are None, 'nodeWeightedWatershedsSeeds' will be called
"""
if seeds is None:
seeds = graphs.nodeWeightedWatershedsSeeds(graph=graph,nodeWeights=nodeWeights)
return graphs._shortestPathSegmentation(graph=graph, edgeWeights=edgeWeights, nodeWeights=nodeWeights,
seeds=seeds, out=out)
shortestPathSegmentation.__module__ = 'vigra.graphs'
graphs.shortestPathSegmentation = shortestPathSegmentation
def nodeWeightedWatersheds(graph,nodeWeights,seeds=None,method='regionGrowing',out=None):
""" node weighted seeded watersheds
Keyword Arguments :
- graph : input graph
- nodeWeights : node height map / evaluation weights
- seeds : node map with seeds (default: None)
If seeds are None, 'nodeWeightedWatershedsSeeds' will be called
"""
if seeds is None:
seeds = graphs.nodeWeightedWatershedsSeeds(graph=graph,nodeWeights=nodeWeights)
if method!='regionGrowing':
raise RuntimeError("currently only 'regionGrowing' is supported")
return graphs._nodeWeightedWatershedsSegmentation(graph=graph,nodeWeights=nodeWeights,seeds=seeds,method=method,out=out)
nodeWeightedWatersheds.__module__ = 'vigra.graphs'
graphs.nodeWeightedWatersheds = nodeWeightedWatersheds
def seededSegmentation(graph, nodeMap=None, edgeMap=None, seeds=None, alg='ws',out=None,**kwargs):
"""
alg:
- 'ws' watershed
- 'sp' shortest path
- 'crf' crf/mrf method
- 'hc' hierarchical-clustering method
"""
if alg == 'ws':
# "default" node weighted watershed
if nodeMap is not None and edgeMap is None:
seg = graphs.nodeWeightedWatersheds(graph=graph,
nodeWeights=nodeMap,
seeds=seeds,out=out)
# edge weighted watershed
elif nodeMap is None and edgeMap is not None:
seg = graphs.edgeWeightedWatersheds(graph=graph,
edgeWeights=edgeMap,
seeds=seeds,out=out)
# hybrid (not yet implemented)
elif nodeMap is not None and edgeMap is not None:
raise RuntimeError("Not Yet Implemented")
else :
# error
raise RuntimeError("error")
elif alg == 'sp':
# "default" shortest path
if nodeMap is None and edgeMap is None:
raise RuntimeError("Not Yet Implemented")
elif nodeMap is not None or edgeMap is not None:
if nodeMap is None:
nodeMap = graphs.graphMap(graph,'node',dtype='float32')
nodeMap[:] = 0
if edgeMap is None:
edgeMap = graphs.graphMap(graph,'edge',dtype='float32')
edgeMap[:] = 0
seg = graphs.shortestPathSegmentation(graph=graph,
edgeWeights=edgeMap,
nodeWeights=nodeMap,
seeds=seeds,out=out)
else :
# error
raise RuntimeError("error")
elif alg == 'crf':
raise RuntimeError("Not Yet Implemented")
return seg
seededSegmentation.__module__ = 'vigra.graphs'
graphs.seededSegmentation = seededSegmentation
def wsDtSegmentation(pmap, pmin, minMembraneSize, minSegmentSize, sigmaMinima, sigmaWeights, cleanCloseSeeds=True):
"""A probability map 'pmap' is provided and thresholded using pmin.
This results in a mask. Every connected component which has fewer pixel
than 'minMembraneSize' is deleted from the mask. The mask is used to
calculate the signed distance transformation.
From this distance transformation the segmentation is computed using
a seeded watershed algorithm. The seeds are placed on the local maxima
of the distanceTrafo after smoothing with 'sigmaMinima'.
The weights of the watershed are defined by the inverse of the signed
distance transform smoothed with 'sigmaWeights'.
'minSegmentSize' determines how small the smallest segment in the final
segmentation is allowed to be. If there are smaller ones the corresponding
seeds are deleted and the watershed is done again.
If 'cleanCloseSeeds' is True, multiple seed points that are clearly in the
same neuron will be merged with a heuristik that ensures that no seeds of
two different neurons are merged.
"""
def cdist(xy1, xy2):
# influenced by: http://stackoverflow.com/a/1871630
d = numpy.zeros((xy1.shape[1], xy1.shape[0], xy1.shape[0]))
for i in numpy.arange(xy1.shape[1]):
d[i,:,:] = numpy.square(numpy.subtract.outer(xy1[:,i], xy2[:,i]))
d = numpy.sum(d, axis=0)
return numpy.sqrt(d)
def findBestSeedCloserThanMembrane(seeds, distances, distanceTrafo, membraneDistance):
""" finds the best seed of the given seeds, that is the seed with the highest value distance transformation."""
closeSeeds = distances <= membraneDistance
numpy.zeros_like(closeSeeds)
# iterate over all close seeds
maximumDistance = -numpy.inf
mostCentralSeed = None
for seed in seeds[closeSeeds]:
if distanceTrafo[seed[0], seed[1], seed[2]] > maximumDistance:
maximumDistance = distanceTrafo[seed[0], seed[1], seed[2]]
mostCentralSeed = seed
return mostCentralSeed
def nonMaximumSuppressionSeeds(seeds, distanceTrafo):
""" removes all seeds that have a neigbour that is closer than the the next membrane
seeds is a list of all seeds, distanceTrafo is array-like
return is a list of all seeds that are relevant.
works only for 3d
"""
seedsCleaned = set()
# calculate the distances from each seed to the next seeds.
distances = cdist(seeds, seeds)
for i in numpy.arange(len(seeds)):
membraneDistance = distanceTrafo[seeds[i,0], seeds[i,1], seeds[i,2]]
bestAlternative = findBestSeedCloserThanMembrane(seeds, distances[i,:], distanceTrafo, membraneDistance)
seedsCleaned.add(tuple(bestAlternative))
return numpy.array(list(seedsCleaned))
def volumeToListOfPoints(seedsVolume, threshold=0.):
return numpy.array(numpy.where(seedsVolume > threshold)).transpose()
def placePointsInVolumen(points, shape):
volumen = numpy.zeros(shape)
points = numpy.maximum(points, numpy.array((0, 0, 0)))
points = numpy.minimum(points, numpy.array(shape) - 1)
for point in (numpy.floor(points)).astype(int):
volumen[point[0], point[1], point[2]] = 1
return volumen
# get the thresholded pmap
binary = numpy.zeros_like(pmap, dtype=numpy.uint32)
binary[pmap >= pmin] = 1
# delete small CCs
labeled = analysis.labelVolumeWithBackground(binary)
analysis.sizeFilterSegInplace(labeled, int(numpy.max(labeled)), int(minMembraneSize), checkAtBorder=True)
# use cleaned binary image as mask
mask = numpy.zeros_like(binary, dtype = numpy.float32)
mask[labeled > 0] = 1.
# perform signed dt on mask
dt = filters.distanceTransform3D(mask)
dtInv = filters.distanceTransform3D(mask, background=False)
dtInv[dtInv>0] -= 1
dtSigned = dt.max() - dt + dtInv
dtSignedSmoothMinima = filters.gaussianSmoothing(dtSigned, sigmaMinima)
dtSignedSmoothWeights = filters.gaussianSmoothing(dtSigned, sigmaWeights)
seeds = analysis.localMinima3D(dtSignedSmoothMinima, neighborhood=26, allowAtBorder=True)
if cleanCloseSeeds:
seeds = nonMaximumSuppressionSeeds(volumeToListOfPoints(seeds), dt)
seeds = placePointsInVolumen(seeds, mask.shape).astype(numpy.uint32)
seedsLabeled = analysis.labelVolumeWithBackground(seeds)
segmentation = analysis.watershedsNew(dtSignedSmoothWeights, seeds = seedsLabeled, neighborhood=26)[0]
analysis.sizeFilterSegInplace(segmentation, int(numpy.max(segmentation)), int(minSegmentSize), checkAtBorder=True)
segmentation = analysis.watershedsNew(dtSignedSmoothWeights, seeds = segmentation, neighborhood=26)[0]
return segmentation
wsDtSegmentation.__module__ = 'vigra.analysis'
analysis.wsDtSegmentation = wsDtSegmentation
def agglomerativeClustering(graph,edgeWeights=None,edgeLengths=None,nodeFeatures=None,nodeSizes=None,
nodeLabels=None,nodeNumStop=None,beta=0.5,metric='l1',wardness=1.0,out=None):
""" agglomerative hierarchicalClustering
Keyword Arguments :
- graph : input graph
- edgeWeights : edge weights / indicators (default : None)
- edgeLengths : length / weight of each edge (default : None)
Since we do weighted mean agglomeration, a length/weight
is needed for each edge to merge 2 edges w.r.t. weighted mean.
If no edgeLengths is given, 'getEdgeLengths' is called.
- nodeFeatures : a feature vector for each node (default: None)
A feature vector as RGB values,or a histogram for each node.
Within the agglomeration, an additional edge weight will be
computed from the "difference" between the features of two adjacent nodes.
The metric specified in the keyword 'metric' is used to compute this
difference
- nodeSizes : size / weight of each node (default : None)
Since we do weighted mean agglomeration, a size / weight
is needed for each node to merge 2 edges w.r.t. weighted mean.
If no nodeSizes is given, 'getNodeSizes' is called.
- nodeNumStop : stop the agglomeration at a given nodeNum (default : graph.nodeNum/2)
- beta : weight between edgeWeights and nodeFeatures based edgeWeights (default:0.5) :
0.0 means only edgeWeights (from keyword edge weights) and 1.0 means only edgeWeights
from nodeFeatures differences
- metric : metric used to compute node feature difference (default : 'l1')
- wardness : 0 means do not apply wards critrion, 1.0 means fully apply wards critrion (default : 1.0)
- out : preallocated nodeMap for the resulting labeling (default : None)
Returns:
A node labele map encoding the segmentation
"""
assert edgeWeights is not None or nodeFeatures is not None
print("prepare ")
if nodeNumStop is None:
nodeNumStop = max(graph.nodeNum//2,min(graph.nodeNum,2))
if edgeLengths is None :
print("get edge length")
edgeLengths = graphs.getEdgeLengths(graph)
if nodeSizes is None:
print("get node size")
nodeSizes = graphs.getNodeSizes(graph)
if edgeWeights is None :
print("get wegihts length")
edgeWeights = graphs.graphMap(graph,'edge')
edgeWeights[:]=0
if nodeFeatures is None :
print("get node feat")
nodeFeatures = graphs.graphMap(graph,'node',addChannelDim=True)
nodeFeatures[:]=0
if nodeLabels is None:
nodeLabels = graphs.graphMap(graph,'node',dtype='uint32')
#import sys
#print("graph refcout", sys.getrefcount(graph))
mg = graphs.mergeGraph(graph)
#print("graph refcout", sys.getrefcount(graph))
#mg = []
#del mg
#import gc
#gc.collect()
#print("graph refcout", sys.getrefcount(graph))
#sys.exit(0)
clusterOp = graphs.minEdgeWeightNodeDist(mg,edgeWeights=edgeWeights,edgeLengths=edgeLengths,
nodeFeatures=nodeFeatures,nodeSizes=nodeSizes,
nodeLabels=nodeLabels,
beta=float(beta),metric=metric,wardness=wardness)
hc = graphs.hierarchicalClustering(clusterOp, nodeNumStopCond=nodeNumStop,
buildMergeTreeEncoding=False)
hc.cluster()
labels = hc.resultLabels(out=out)
#del hc
#del clusterOp
#del mg
return labels
agglomerativeClustering.__module__ = 'vigra.graphs'
graphs.agglomerativeClustering = agglomerativeClustering
def minEdgeWeightNodeDist(mergeGraph,edgeWeights=None,edgeLengths=None,nodeFeatures=None,nodeSizes=None,
nodeLabels=None,outWeight=None,
beta=0.5,metric='squaredNorm',wardness=1.0, gamma=10000000.0):
graph=mergeGraph.graph()
assert edgeWeights is not None or nodeFeatures is not None
if edgeLengths is None :
edgeLengths = graphs.getEdgeLengths(graph,addChannelDim=True)
if nodeSizes is None:
nodeSizes = graphs.getNodeSizes(graph,addChannelDim=True)
if edgeWeights is None :
edgeWeights = graphs.graphMap(graph,'edge',addChannelDim=True)
edgeWeights[:]=0
if nodeFeatures is None :
nodeFeatures = graphs.graphMap(graph,'node',addChannelDim=True)
nodeFeatures[:]=0
if outWeight is None:
outWeight=graphs.graphMap(graph,item='edge',dtype=numpy.float32)
if nodeLabels is None :
nodeLabels = graphs.graphMap(graph,'node',dtype='uint32')
nodeLabels[:]=0
if metric=='squaredNorm':
nd=graphs.MetricType.squaredNorm
elif metric=='norm':
nd=graphs.MetricType.norm
elif metric=='chiSquared':
nd=graphs.MetricType.chiSquared
elif metric in ('l1','manhattan'):
nd=graphs.MetricType.manhattan
elif isinstance(metric,graphs.MetricType):
nd=metric
else :
raise RuntimeError("'%s' is not a supported distance type"%str(metric))
# call unsave c++ function and make it sav
print("nodeLabels ",nodeLabels.shape, nodeLabels.dtype)
op = graphs.__minEdgeWeightNodeDistOperator(mergeGraph,edgeWeights,edgeLengths,nodeFeatures,nodeSizes,outWeight,nodeLabels,
float(beta),nd,float(wardness),float(gamma))
op.__base_object__=mergeGraph
op.__outWeightArray__=outWeight
op.edgeLengths=edgeLengths
op.nodeSizes=nodeSizes
op.edgeWeights=edgeWeights
op.nodeFeatures=nodeFeatures
return op
minEdgeWeightNodeDist.__module__ = 'vigra.graphs'
graphs.minEdgeWeightNodeDist = minEdgeWeightNodeDist
def pythonClusterOperator(mergeGraph,operator,useMergeNodeCallback=True,useMergeEdgesCallback=True,useEraseEdgeCallback=True):
#call unsave function and make it save
op = graphs.__pythonClusterOperator(mergeGraph,operator,useMergeNodeCallback,useMergeEdgesCallback,useEraseEdgeCallback)
#op.__dict__['__base_object__']=mergeGraph
#op.__base_object__=mergeGraph
return op
pythonClusterOperator.__module__ = 'vigra.graphs'
graphs.pythonClusterOperator = pythonClusterOperator
def hierarchicalClustering(clusterOperator,nodeNumStopCond,buildMergeTreeEncoding=True):
# call unsave c++ function and make it save
hc = graphs.__hierarchicalClustering(clusterOperator,int(nodeNumStopCond),bool(buildMergeTreeEncoding))
#hc.__dict__['__base_object__']=clusterOperator
hc.__base_object__ = clusterOperator
return hc
hierarchicalClustering.__module__ = 'vigra.graphs'
graphs.hierarchicalClustering = hierarchicalClustering
_genGraphSegmentationFunctions()
del _genGraphSegmentationFunctions
def _genHistogram():
def gaussianHistogram(image,minVals,maxVals,bins=30,
sigma=3.0,sigmaBin=2.0,out=None):
"""
"""
spatialDim = image.ndim - 1
out = histogram.gaussianHistogram_(image=image, minVals=minVals, maxVals=maxVals,
bins=bins, sigma=sigma, sigmaBin=sigmaBin,
out=out)
out = out.reshape(image.shape[0:spatialDim]+(-1,))
if spatialDim == 2:
out /= numpy.sum(out,axis=spatialDim)[:,:, numpy.newaxis]
elif spatialDim == 3:
out /= numpy.sum(out,axis=spatialDim)[:,:,:, numpy.newaxis]
elif spatialDim == 4:
out /= numpy.sum(out,axis=spatialDim)[:,:,:, :,numpy.newaxis]
return out
gaussianHistogram.__module__ = 'vigra.histogram'
histogram.gaussianHistogram = gaussianHistogram
def gaussianRankOrder(image, minVal=None, maxVal=None,
bins=20, sigmas=None, ranks=[0.1,0.25,0.5,0.75,0.9],
out=None):
# FIXME: crashes on Python3
image = numpy.require(image.squeeze(),dtype='float32')
nDim = image.ndim
if sigmas is None:
sigmas = (2.0,)*nDim + (float(bins)/10.0,)
ranks = numpy.require(ranks,dtype='float32')
sigmas = numpy.require(sigmas,dtype='float32')
assert len(sigmas) == image.ndim + 1
if minVal is None :
minVal = image.min()
if maxVal is None :
maxVal = image.max()
#print("image",image.shape,image.dtype)
#print("ranks",ranks.shape,ranks.dtype)
#print("sigmas",sigmas)
return histogram._gaussianRankOrder(image=image,
minVal=float(minVal),
maxVal=float(maxVal),
bins=int(bins),
sigmas=sigmas,ranks=ranks,
out=out)
gaussianRankOrder.__module__ = 'vigra.histogram'
histogram.gaussianRankOrder = gaussianRankOrder
_genHistogram()
del _genHistogram
def _genGraphSmoothingFunctions():
def recursiveGraphSmoothing( graph,nodeFeatures,edgeIndicator,gamma,
edgeThreshold,scale=1.0,iterations=1,out=None):
""" recursive graph smoothing to smooth node features.
Each node feature is smoothed with the features of neighbor nodes.
The strength of the smoothing is computed from:
"edgeIndicator > edgeThreshold ? 0 : exp(-1.0*gamma*edgeIndicator)*scale"
Therefore this filter is edge preserving.
Keyword Arguments :
- graph : input graph
- nodeFeatures : node features which should be smoothed
- edgeIndicator : edge indicator
- gamma : scale edgeIndicator by gamma bevore taking the negative exponent
- scale : how much should a node be mixed with its neighbours per iteration
- iteration : how often should recursiveGraphSmoothing be called recursively
Returns :
smoothed nodeFeatures
"""
return graphs._recursiveGraphSmoothing(graph=graph,nodeFeatures=nodeFeatures,edgeIndicator=edgeIndicator,
gamma=gamma,edgeThreshold=edgeThreshold,scale=scale,iterations=iterations,out=out)
recursiveGraphSmoothing.__module__ = 'vigra.graphs'
graphs.recursiveGraphSmoothing = recursiveGraphSmoothing
_genGraphSmoothingFunctions()
del _genGraphSmoothingFunctions
def _genGraphMiscFunctions():
def nodeFeaturesToEdgeWeights(graph,nodeFeatures,metric='l1',out=None):
""" compute an edge indicator from node features .
Keyword Arguments :
- graph : input graph
- nodeFeatures : node map with feature vector for each node
- metric : metric / distance used to convert 2 node features to
an edge weight
Returns :
edge indicator
"""
return graphs._nodeFeatureDistToEdgeWeight(graph=graph,nodeFeatures=nodeFeatures,metric=metric,out=out)
nodeFeaturesToEdgeWeights.__module__ = 'vigra.graphs'
graphs.nodeFeaturesToEdgeWeights = nodeFeaturesToEdgeWeights
_genGraphMiscFunctions()
del _genGraphMiscFunctions
def _genBlockwiseFunctions():
def makeTuple(val, ndim):
tvals = None
if isinstance(val, Number):
tvals = (float(val),)*ndim
else :
tvals = tuple(val)
if len(tvals) != ndim:
raise RuntimeError("sigma/innerScale/outerScale must be as long as ndim, or must be a scalar")
return tvals
def getConvolutionOptionsClass(ndim):
assert ndim >=2 and ndim <= 5
if ndim == 2 :
return blockwise.BlockwiseConvolutionOptions2D
elif ndim == 3 :
return blockwise.BlockwiseConvolutionOptions3D
elif ndim == 4 :
return blockwise.BlockwiseConvolutionOptions4D
elif ndim == 5 :
return blockwise.BlockwiseConvolutionOptions5D
def convolutionOptions(blockShape, sigma=None,innerScale=None, outerScale=None, numThreads = cpu_count()):
ndim = len(blockShape)
options = getConvolutionOptionsClass(ndim)()
options.blockShape = blockShape
options.numThreads = numThreads
if sigma is not None:
sigma = makeTuple(sigma,ndim)
options.stdDev = sigma
if innerScale is not None:
options.innerScale = makeTuple(innerScale,ndim)
if outerScale is not None:
options.outerScale = makeTuple(outerScale,ndim)
return options
convolutionOptions.__module__ = 'vigra.blockwise'
blockwise.convolutionOptions = convolutionOptions
blockwise.convOpts = convolutionOptions
def gaussianSmooth(image,options,out=None):
out = blockwise._gaussianSmooth(image,options,out)
return out
gaussianSmooth.__module__ = 'vigra.blockwise'
blockwise.gaussianSmooth = gaussianSmooth
def gaussianGradient(image,options,out=None):
out = blockwise._gaussianGradient(image,options,out)
return out
gaussianGradient.__module__ = 'vigra.blockwise'
blockwise.gaussianGradient = gaussianGradient
def gaussianGradientMagnitude(image,options,out=None):
out = blockwise._gaussianGradientMagnitude(image,options,out)
return out
gaussianGradientMagnitude.__module__ = 'vigra.blockwise'
blockwise.gaussianGradientMagnitude = gaussianGradientMagnitude
def hessianOfGaussianEigenvalues(image,options,out=None):
out = blockwise._hessianOfGaussianEigenvalues(image,options,out)
return out
hessianOfGaussianEigenvalues.__module__ = 'vigra.blockwise'
blockwise.hessianOfGaussianEigenvalues = hessianOfGaussianEigenvalues
def hessianOfGaussianFirstEigenvalue(image,options,out=None):
out = blockwise._hessianOfGaussianFirstEigenvalue(image,options,out)
return out
hessianOfGaussianFirstEigenvalue.__module__ = 'vigra.blockwise'
blockwise.hessianOfGaussianFirstEigenvalue = hessianOfGaussianFirstEigenvalue
def hessianOfGaussianLastEigenvalue(image,options,out=None):
out = blockwise._hessianOfGaussianLastEigenvalue(image,options,out)
return out
hessianOfGaussianLastEigenvalue.__module__ = 'vigra.blockwise'
blockwise.hessianOfGaussianLastEigenvalue = hessianOfGaussianLastEigenvalue
_genBlockwiseFunctions()
del _genBlockwiseFunctions
def loadBSDGt(filename):
import scipy.io as sio
matContents = sio.loadmat(filename)
ngt = len(matContents['groundTruth'][0])
gts = []
for gti in range(ngt):
gt = matContents['groundTruth'][0][gti][0]['Segmentation'][0]
gt = numpy.swapaxes(gt,0,1)
gt = gt.astype(numpy.uint32)
print(gt.min(),gt.max())
gts.append(gt[:,:,None])
gtArray = numpy.concatenate(gts,axis=2)
print(gtArray.shape)
return gtArray
def pmapSeeds(pmap):
pass
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