def testGridGraphSegmentationFelzenszwalbSegmentation():
dataRGB = numpy.random.random([3,3,3]).astype(numpy.float32)
dataRGB = taggedView(dataRGB,'xyc')
data = numpy.random.random([3,3]).astype(numpy.float32)
edata = numpy.random.random([3*2-1,3*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0,edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,edgeWeights=ew,k=1.0,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
data = numpy.random.random([3,3,3]).astype(numpy.float32)
edata = numpy.random.random([3*2-1,3*2-1,3*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0,edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,edgeWeights=ew,k=1.0,nodeNumStop=15)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 15
def testGridGraphSegmentationFelzenszwalbSegmentation():
dataRGB = numpy.random.random([3, 3, 3]).astype(numpy.float32)
dataRGB = taggedView(dataRGB, 'xyc')
data = numpy.random.random([3, 3]).astype(numpy.float32)
edata = numpy.random.random([3 * 2 - 1, 3 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0, edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,
edgeWeights=ew,
k=1.0,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
data = numpy.random.random([3, 3, 3]).astype(numpy.float32)
edata = numpy.random.random([3 * 2 - 1, 3 * 2 - 1,
3 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0, edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,
edgeWeights=ew,
k=1.0,
nodeNumStop=15)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 15
def testGridGraphWatersheds():
data = numpy.random.random([10, 10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1,
10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data,
seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data)
# edge weighted watershed seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,
edgeWeights=ew,
seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA, labelsNodeWeightedB)
data = numpy.random.random([10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data,
seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data)
# edge weighted watershed seeds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,
edgeWeights=ew,
seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA, labelsNodeWeightedB)
def testGridGraphAgglomerativeClustering():
dataRGB = numpy.random.random([10, 10, 3]).astype(numpy.float32)
dataRGB = vigra.taggedView(dataRGB, 'xyc')
data = numpy.random.random([10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
#ew = taggedView(ew,'xyz')
labels = graphs.agglomerativeClustering(graph=g0,
edgeWeights=ew,
nodeFeatures=dataRGB,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
labels = graphs.agglomerativeClustering(graph=g0,
edgeWeights=ew,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
dataRGB = numpy.random.random([10, 10, 10, 3]).astype(numpy.float32)
dataRGB = vigra.taggedView(dataRGB, 'xyzc')
data = numpy.random.random([10, 10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1,
10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
#ew = taggedView(ew,'xyz')
labels = graphs.agglomerativeClustering(graph=g0,
edgeWeights=ew,
nodeFeatures=dataRGB,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
labels = graphs.agglomerativeClustering(graph=g0,
edgeWeights=ew,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
def labels_to_dense_gt(labels_path, probs):
import vigra.graphs as vgraph
labels = vol_to_vol(labels_path)
gt = np.zeros_like(labels, dtype=np.uint32)
offset = 0
for z in xrange(gt.shape[2]):
hmap = vigra.filters.gaussianSmoothing(probs[:, :, z], 2.)
seeds = vigra.analysis.labelImageWithBackground(labels[:, :, z])
gt[:, :, z], _ = vigra.analysis.watershedsNew(hmap, seeds=seeds)
gt[:, :, z][gt[:, :, z] != 0] += offset
offset = gt[:, :, z].max()
# bring to 0 based indexing
gt -= 1
# remove isolated segments
rag_global = vgraph.regionAdjacencyGraph(vgraph.gridGraph(gt.shape[0:3]),
gt)
node_to_node = np.concatenate([
np.arange(rag_global.nodeNum, dtype=np.uint32)[:, None]
for _ in range(2)
],
axis=1)
for z in xrange(gt.shape[2]):
rag_local = vgraph.regionAdjacencyGraph(
vgraph.gridGraph(gt.shape[0:2]), gt[:, :, z])
for node in rag_local.nodeIter():
neighbour_nodes = []
for nnode in rag_local.neighbourNodeIter(node):
neighbour_nodes.append(nnode)
if len(neighbour_nodes) == 1:
node_coordinates = np.where(gt == node.id)
if not 0 in node_coordinates[0] and not 511 in node_coordinates[
0] and not 0 in node_coordinates[
1] and not 511 in node_coordinates[1]:
node_to_node[node.id] = neighbour_nodes[0].id
gt_cleaned = rag_global.projectLabelsToBaseGraph(node_to_node)[:, :, :, 0]
return gt_cleaned
def calc_edge_features(self, feature_matrix=None):
grid_graph = graphs.gridGraph(self._data.shape)
if feature_matrix is None:
edge_features = graphs.edgeFeaturesFromImage(grid_graph, self._data)
else:
edge_features = graphs.edgeFeaturesFromImage(grid_graph, feature_matrix)
self._edge_features = self._rag.accumulateEdgeFeatures(edge_features)
def testGridGraphAgglomerativeClustering():
dataRGB = numpy.random.random([10,10,3]).astype(numpy.float32)
dataRGB = vigra.taggedView(dataRGB,'xyc')
data = numpy.random.random([10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
#ew = taggedView(ew,'xyz')
labels = graphs.agglomerativeClustering(graph=g0,edgeWeights=ew,nodeFeatures=dataRGB,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
labels = graphs.agglomerativeClustering(graph=g0,edgeWeights=ew,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
dataRGB = numpy.random.random([10,10,10,3]).astype(numpy.float32)
dataRGB = vigra.taggedView(dataRGB,'xyzc')
data = numpy.random.random([10,10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
#ew = taggedView(ew,'xyz')
labels = graphs.agglomerativeClustering(graph=g0,edgeWeights=ew,nodeFeatures=dataRGB,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
labels = graphs.agglomerativeClustering(graph=g0,edgeWeights=ew,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
def pmapSizeFilter(neuroCCBinaryPmap, minSize):
"""
keywords:
neuroCCBinaryPmap: zero where membranes are
one where neurons are
"""
shape = neuroCCBinaryPmap.shape
# get the connected components
neuroCCMap = vigra.analysis.labelVolume(neuroCCBinaryPmap)
print "min max",neuroCCMap.min(), neuroCCMap.max()
assert neuroCCMap.min() == 1
neuroCCMap -= 1
# get region adjacency graph from super-pixel labels
gridGraph = graphs.gridGraph(shape[0:3])
rag = graphs.regionAdjacencyGraph(gridGraph, neuroCCMap)
# get all sizes
nodeSizes = rag.nodeSize()
# nodeColoring
print "neuroCCBinaryPmap",neuroCCBinaryPmap.dtype,neuroCCBinaryPmap.shape
nodeColor = rag.accumulateNodeFeatures(neuroCCBinaryPmap.astype('float32'))
# find to small nodes
toSmallNodesIds = numpy.where(nodeSizes<minSize)[0]
toSmallNodesSizes = nodeSizes[toSmallNodesIds]
toSmallNodesColor = nodeColor[toSmallNodesIds]
sortedIndices = numpy.argsort(toSmallNodesSizes)
sortedIndices.shape,sortedIndices
toSmallNodesIds = toSmallNodesIds[sortedIndices]
# todo (impl degree map as numpy array)
for nid in toSmallNodesIds:
neigbours = []
for n in rag.neighbourNodeIter(rag.nodeFromId(nid)):
neigbours.append(n)
if len(neigbours)==1:
# flip color
nodeColor[nid] = int(not bool(nodeColor[nid]))
pixelColor = rag.projectNodeFeaturesToGridGraph(nodeColor)
return pixelColor,neuroCCBinaryPmap
def testGridGraphWatersheds():
data = numpy.random.random([10,10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data,seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data)
# edge weighted watershed seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,edgeWeights=ew,seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA,labelsNodeWeightedB)
data = numpy.random.random([10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data,seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data)
# edge weighted watershed seeds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,edgeWeights=ew,seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA,labelsNodeWeightedB)
def pmapSizeFilter(neuroCCBinaryPmap, minSize):
"""
keywords:
neuroCCBinaryPmap: zero where membranes are
one where neurons are
"""
shape = neuroCCBinaryPmap.shape
# get the connected components
neuroCCMap = vigra.analysis.labelVolume(neuroCCBinaryPmap)
print "min max", neuroCCMap.min(), neuroCCMap.max()
assert neuroCCMap.min() == 1
neuroCCMap -= 1
# get region adjacency graph from super-pixel labels
gridGraph = graphs.gridGraph(shape[0:3])
rag = graphs.regionAdjacencyGraph(gridGraph, neuroCCMap)
# get all sizes
nodeSizes = rag.nodeSize()
# nodeColoring
print "neuroCCBinaryPmap", neuroCCBinaryPmap.dtype, neuroCCBinaryPmap.shape
nodeColor = rag.accumulateNodeFeatures(neuroCCBinaryPmap.astype('float32'))
# find to small nodes
toSmallNodesIds = numpy.where(nodeSizes < minSize)[0]
toSmallNodesSizes = nodeSizes[toSmallNodesIds]
toSmallNodesColor = nodeColor[toSmallNodesIds]
sortedIndices = numpy.argsort(toSmallNodesSizes)
sortedIndices.shape, sortedIndices
toSmallNodesIds = toSmallNodesIds[sortedIndices]
# todo (impl degree map as numpy array)
for nid in toSmallNodesIds:
neigbours = []
for n in rag.neighbourNodeIter(rag.nodeFromId(nid)):
neigbours.append(n)
if len(neigbours) == 1:
# flip color
nodeColor[nid] = int(not bool(nodeColor[nid]))
pixelColor = rag.projectNodeFeaturesToGridGraph(nodeColor)
return pixelColor, neuroCCBinaryPmap
def get_segmentation(predict, pmin=0.5, minMemb=10, minSeg=10, sigMin=6, sigWeights=1, sigSmooth=0.1, cleanCloseSeeds=True,
returnSeedsOnly=False, edgeLengths=None,nodeFeatures=None, nodeSizes=None, nodeLabels=None,
nodeNumStop=None, beta=0, metric='l1', wardness=0.2, out=None):
""" Get segmentation through watershed and agglomerative clustering
:param predict: prediction map
:return: segmentation map
"""
#use watershed and save superpixels map
super_pixels = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, returnSeedsOnly)
# seeds = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, True)
# save_h5(seeds, "/home/stamylew/delme/seeds.h5", "data")
print
print "#Nodes in superpixels", len(np.unique(super_pixels))
# save_h5(super_pixels, "/home/stamylew/delme/super_pixels.h5", "data")
#smooth prediction map
probs = vf.gaussianSmoothing(predict, sigSmooth)
# save_h5(probs, "/home/stamylew/delme/probs.h5", "data")
#make grid graph
grid_graph = vg.gridGraph(super_pixels.shape, False)
grid_graph_edge_indicator = vg.edgeFeaturesFromImage(grid_graph, probs)
#make region adjacency graph
rag = vg.regionAdjacencyGraph(grid_graph, super_pixels)
#accumulate edge features from grid graph node map
edge_weights = rag.accumulateEdgeFeatures(grid_graph_edge_indicator)
edge_weights_tag = "mean of the probabilities"
#do agglomerative clustering
labels = vg.agglomerativeClustering(rag, edge_weights, edgeLengths, nodeFeatures, nodeSizes,
nodeLabels, nodeNumStop, beta, metric, wardness, out)
#segmentation data
wsDt_data = np.zeros((8,1))
wsDt_data[:,0] = (pmin, minMemb, minSeg, sigMin, sigWeights, sigSmooth, cleanCloseSeeds, returnSeedsOnly)
agglCl_data = edge_weights_tag, str(edgeLengths), str(nodeFeatures), str(nodeSizes), str(nodeLabels), str(nodeNumStop), \
str(beta), metric, str(wardness), str(out)
#project labels back to data
segmentation = rag.projectLabelsToBaseGraph(labels)
print "#nodes in segmentation", len(np.unique(segmentation))
# save_h5(segmentation, "/home/stamylew/delme/segmap.h5", "data", None)
print "seg", np.unique(segmentation)
return segmentation, super_pixels, wsDt_data, agglCl_data
def shortest_paths(indicator, pairs, bounds=None, hfp=None):
# Crate the grid graph and shortest path objects
gridgr = graphs.gridGraph(indicator.shape)
indicator = indicator.astype(np.float32)
gridgr_edgeind = graphs.edgeFeaturesFromImage(gridgr, indicator)
instance = graphs.ShortestPathPathDijkstra(gridgr)
# Initialize paths image
pathsim = np.zeros(indicator.shape)
# Initialize list of path coordinates
paths = []
for pair in pairs:
source = pair[0]
target = pair[1]
if hfp is not None:
hfp.logging('Calculating path from {} to {}', source, target)
targetNode = gridgr.coordinateToNode(target)
sourceNode = gridgr.coordinateToNode(source)
instance.run(gridgr_edgeind, sourceNode, target=targetNode)
path = instance.path(pathType='coordinates')
if path.any():
# Do not forget to correct for the offset caused by cropping!
if bounds is not None:
paths.append(path + [bounds[0].start, bounds[1].start, bounds[2].start])
else:
paths.append(path)
pathindices = np.swapaxes(path, 0, 1)
pathsim[pathindices[0], pathindices[1], pathindices[2]] = 1
return paths, pathsim
def shortest_paths(indicator, pairs, n_threads=1):
"""
This function was copied from processing_lib.py
:param indicator:
:return:
"""
gridgr = graphs.gridGraph(indicator.shape)
gridgr_edgeind = graphs.implicitMeanEdgeMap(gridgr,
indicator.astype('float32'))
def single_path(pair, instance=None):
source = pair[0]
target = pair[1]
print 'Calculating path from {} to {}'.format(source, target)
if instance == None:
instance = graphs.ShortestPathPathDijkstra(gridgr)
targetNode = gridgr.coordinateToNode(target)
sourceNode = gridgr.coordinateToNode(source)
instance.run(gridgr_edgeind, sourceNode, target=targetNode)
path = instance.path(pathType='coordinates')
if path.any():
return path
if n_threads > 1:
print "Multi-threaded w/ n-threads = ", n_threads
with futures.ThreadPoolExecutor(max_workers=n_threads) as executor:
tasks = [executor.submit(single_path, pair) for pair in pairs]
paths = [t.result() for t in tasks]
else:
print "Single threaded"
instance = graphs.ShortestPathPathDijkstra(gridgr)
paths = [single_path(pair, instance) for pair in pairs]
return paths
print "write thinned pmap transform"
vigra.impex.writeHDF5(res, opt['thinnedDistTransformPMap1'], "data")
skneuro.addHocViewer(grayData, segData)
if False:
print "make the graph and save it"
print "read segmentation"
seg = vigra.impex.readHDF5(opt['oversegL0'], "data")
segData.append([seg, "seg"])
gridGraph = graphs.gridGraph(seg.shape[0:3])
print "make rag"
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, seg, isDense=False)
print "save rag to file"
rag.writeHDF5(opt['ragL0'],'data')
if False:
print "accumulate edge weights and save them"
print "load rag"
def calculate_distances():
"""
compute distances between color markers instead of existing synapses
markers of the same color should be in the same neuron
"""
files_2d = glob.glob(inputdir + d2_pattern)
files_2d = sorted(files_2d, key=str.lower)
files_3d = glob.glob(inputdir + d3_pattern)
files_3d = sorted(files_3d, key=str.lower)
files_markers = glob.glob(inputdir + marker_pattern)
files_markers = sorted(files_markers, key=str.lower)
debug_dirs = glob.glob(debugdir)
debug_dirs = sorted(debug_dirs, key=str.lower)
files_raw = glob.glob(inputdir + raw_pattern)
files_raw = sorted(files_raw, key=str.lower)
#print files_2d, files_3d, files_markers, debug_dirs, files_raw
first = 0
last = 4
all_distances_same = []
all_distances_diff = []
nsamesame = 0
nsamediff = 0
ndiffdiff = 0
ndiffsame = 0
for f2name, f3name, mname, ddir, rawname in zip(files_2d[first:last], files_3d[first:last],
files_markers[first:last], debug_dirs[first:last],
files_raw[first:last]):
print "processing files:"
print f2name
print f3name
print mname
print ddir
print rawname
tempGraph = Graph()
edgeIndicators = []
instances = []
if debug_images:
rawim = vigra.readImage(rawname)
vigra.impex.writeImage(rawim, ddir + "/raw.tiff")
#print "processing files:", f2name, f3name, mname
f2 = h5py.File(f2name)
f3 = h5py.File(f3name)
if use_2d_only:
d2 = f2["exported_data"][5, :, :, 0]
d3 = f2["exported_data"][5, :, :, 2]
else:
d2 = f2["exported_data"][..., 0]
d3 = f3["exported_data"][5, :, :, 2] # 5 because we only want the central slice, there are 11 in total
d3 = d3.swapaxes(0, 1)
# print d2.shape, d3.shape
combined = d2 + d3
if use_2d_only:
#convert to float
combined = combined.astype(numpy.float32)
combined = combined/255.
markedNodes = extractMarkedNodes(mname)
# print
opUpsample = OpUpsampleByTwo(graph=tempGraph)
combined = numpy.reshape(combined, combined.shape + (1,) + (1,))
combined = combined.view(vigra.VigraArray)
combined.axistags = vigra.defaultAxistags('xytc')
opUpsample.Input.setValue(combined)
upsampledMembraneProbs = opUpsample.Output[:].wait()
# get rid of t
upsampledMembraneProbs = upsampledMembraneProbs[:, :, 0, :]
upsampledMembraneProbs = upsampledMembraneProbs.view(vigra.VigraArray)
upsampledMembraneProbs.axistags = vigra.defaultAxistags('xyc')
# try to filter
upsampledSmoothedMembraneProbs = computeDistanceRaw(upsampledMembraneProbs, 1.6, ddir)
upsampledMembraneProbs = filter_by_size(upsampledSmoothedMembraneProbs, ddir)
upsampledMembraneProbs = upsampledMembraneProbs.view(vigra.VigraArray)
upsampledMembraneProbs.axistags = vigra.defaultAxistags('xyc')
edgeIndicators.append(computeDistanceHessian(upsampledMembraneProbs, 5.0, ddir))
edgeIndicators.append(upsampledSmoothedMembraneProbs)
segm = superpixels(combined.squeeze())
if debug_images:
vigra.impex.writeImage(segm, ddir + "/superpixels.tiff")
gridGr = graphs.gridGraph((d2.shape[0], d2.shape[1])) # !on original pixels
for iind, indicator in enumerate(edgeIndicators):
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGr, indicator)
instance = vigra.graphs.ShortestPathPathDijkstra(gridGr)
instances.append(instance)
distances_same = []
distances_diff = []
for color, points in markedNodes.iteritems():
#going over points of *same* color
if len(points)>1:
print "Processing color", color
for i in range(len(points)):
node = map(long, points[i])
sourceNode = gridGr.coordinateToNode(node)
instance.run(gridGraphEdgeIndicator, sourceNode, target=None)
distances_all = instance.distances()
sp_this = segm[node[0], node[1]]
for j in range(i + 1, len(points)):
# go over points of the same color
other_node = map(long, points[j])
distances_same.append(distances_all[other_node[0], other_node[1]])
sp_other = segm[other_node[0], other_node[1]]
if sp_this==sp_other:
nsamesame = nsamesame + 1
#print "same color in the same superpixel!"
else:
nsamediff += 1
#targetNode = gridGr.coordinateToNode(other_node)
#path = instance.run(gridGraphEdgeIndicator, sourceNode).path(pathType='coordinates',
# target=targetNode)
#max_on_path = numpy.max(distances_all[path])
#min_on_path = numpy.min(distances_all[path])
# print max_on_path, min_on_path
# print path.shape
#print "distance b/w", node, other_node, " = ", distances_all[other_node[0], other_node[1]]
for newcolor, newpoints in markedNodes.iteritems():
# go over points of other colors
if color == newcolor:
continue
for newi in range(len(newpoints)):
other_node = map(long, newpoints[newi])
sp_other = segm[other_node[0], other_node[1]]
if sp_this==sp_other:
ndiffsame += 1
else:
ndiffdiff += 1
distances_diff.append(distances_all[other_node[0], other_node[1]])
# highlight the source point in image
distances_all[node[0], node[1]] = numpy.max(distances_all)
outfile = ddir + "/" + str(node[0]) + "_" + str(node[1]) + "_" + str(iind) + ".tiff"
vigra.impex.writeImage(distances_all, outfile)
while len(all_distances_diff)<len(edgeIndicators):
all_distances_diff.append([])
all_distances_same.append([])
all_distances_diff[iind].extend(distances_diff)
all_distances_same[iind].extend(distances_same)
#print "summary for edge indicator:", iind
#print "points of same color:", distances_same
#print "points of other colors:", distances_diff
# print distances_same
# vigra.impex.writeImage(combined, f2name+"_combined.tiff")
# vigra.impex.writeImage(d3, f2name+"_synapse.tiff")
# vigra.impex.writeImage(d2, f2name+"_membrane.tiff")
print "same color in the same superpixels:", nsamesame
print "same color, different superpixels:", nsamediff
print "diff color, same superpixel:", ndiffsame
print "diff color, diff superpixels", ndiffdiff
analyze_distances(all_distances_same, all_distances_diff)
from vigra import Timer
iEdgeMap = graphs.implicitMeanEdgeMap
numpy.random.seed(42)
# input
shape = [200, 100, 100]
data = numpy.random.rand(*shape).astype(numpy.float32)
data = vigra.taggedView(data, "xyz")
if False:
labels = numpy.random.randint(5, size=shape[0] * shape[1] * shape[2])
labels = labels.reshape(shape).astype(numpy.uint32)
labels = vigra.analysis.labelVolume(labels)
adjListGraph = graphs.listGraph()
gridGraph = graphs.gridGraph(shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rag.writeHDF5("bla.h5", "dset")
else:
# load the region adjacency graph
rag = graphs.loadGridRagHDF5("bla.h5", "dset")
print rag.labels.shape, rag.labels.dtype, type(rag.labels)
print "accumulate edge and node features"
edgeCuesMean = rag.accumulateEdgeFeatures(iEdgeMap(rag.baseGraph, data))
edgeCuesMean = numpy.array([edgeCuesMean, edgeCuesMean]).T
def find_shortest_path(ifp, penaltypower, bounds):
# Modify distancetransform
#
# a) Invert: the lowest values (i.e. the lowest penalty for the shortest path detection) should be at the center of
# the current process
ifp.invert_image(ids='curdisttransf')
#
# b) Set all values outside the process to infinity
ifp.filter_values(ifp.amax('curdisttransf'),
type='eq',
setto=np.inf,
ids='curdisttransf',
targetids='curdisttransf_inf')
#
# c) Increase the value difference between pixels near the boundaries and pixels central within the processes
# This increases the likelihood of the paths to follow the center of processes, thus avoiding short-cuts
ifp.power(penaltypower, ids='curdisttransf_inf')
indicator = ifp.get_image('curdisttransf_inf')
gridgr = graphs.gridGraph(ifp.shape('curlabel'))
ifp.logging('gridgr.shape = {}'.format(gridgr.shape))
indicator = indicator.astype(np.float32)
gridgr_edgeind = graphs.edgeFeaturesFromImage(gridgr, indicator)
instance = graphs.ShortestPathPathDijkstra(gridgr)
# Get two local maxima
indices = np.where(ifp.get_image('curlocmax') == 1)
coords = zip(indices[0], indices[1], indices[2])
ifp.logging('Local maxima coordinates: {}'.format(coords))
ifp.set_data_dict({'pathsim': np.zeros(ifp.get_image('curlocmax').shape)},
append=True)
ifp.logging('len(coords) = {}'.format(len(coords)))
paths = []
for i in xrange(0, len(coords) - 1):
for j in xrange(i + 1, len(coords)):
ifp.logging('---')
ifp.logging('i = {0}; j = {1}'.format(i, j))
source = coords[i]
target = coords[j]
targetNode = gridgr.coordinateToNode(target)
sourceNode = gridgr.coordinateToNode(source)
ifp.logging('Source = {}'.format(source))
ifp.logging('Target = {}'.format(target))
instance.run(gridgr_edgeind, sourceNode, target=targetNode)
path = instance.path(pathType='coordinates')
# Do not forget to correct for the offset caused by cropping!
paths.append(path + [bounds[0][0], bounds[1][0], bounds[2][0]])
# for coords in path:
# # ifp.logging('coords = {}'.format(coords))
# pass
pathindices = np.swapaxes(path, 0, 1)
ifp.get_image('pathsim')[pathindices[0], pathindices[1],
pathindices[2]] = 1
# ifp.concatenate('disttransf', 'paths', target='paths_over_dist')
ifp.astype(np.uint8, ('pathsim', 'curlocmax'))
# ifp.anytask(vigra.filters.multiBinaryDilation, ('paths', 'locmax'), 3)
ifp.swapaxes(0, 2, ids=('pathsim', 'curlocmax', 'curdisttransf'))
ifp.anytask(vigra.filters.discDilation, 2, ids=('pathsim', 'curlocmax'))
ifp.set_data_dict(
{
'paths_over_dist':
np.array([
ifp.get_image('pathsim'),
ifp.get_image('curlocmax'),
ifp.get_image('curdisttransf')
])
},
append=True)
return paths
numpy.random.seed(42)
# input
shape = [200, 100, 100]
data = numpy.random.rand(*shape).astype(numpy.float32)
data = vigra.taggedView(data,"xyz")
if False:
labels = numpy.random.randint(5, size=shape[0]*shape[1]*shape[2])
labels = labels.reshape(shape).astype(numpy.uint32)
labels = vigra.analysis.labelVolume(labels)
adjListGraph = graphs.listGraph()
gridGraph = graphs.gridGraph(shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rag.writeHDF5("bla.h5", "dset")
else :
# load the region adjacency graph
rag = graphs.loadGridRagHDF5("bla.h5","dset")
print rag.labels.shape, rag.labels.dtype ,type(rag.labels)
print "accumulate edge and node features"
edgeCuesMean = rag.accumulateEdgeFeatures( iEdgeMap(rag.baseGraph, data) )
def eccentricity( img, label, distFunc = "exponential", showPathImage = False, percentageOfPaths = 100, imgSaveName = "" ):
img = img.astype(numpy.uint8)
dim = len(img.shape)
## Enlarge image by one pixel on each side
bigShape = []
for s in img.shape:
bigShape.append(s + 2)
bigImg = numpy.ones(bigShape)
slices = []
for i in range(dim):
slices.append(slice(1, bigImg.shape[i]-1))
bigImg[ slices ] = img
inside = numpy.where(bigImg==0)
outside = numpy.where(bigImg==1)
# ## Apply distanceTransform and modify (outside: high values, inside: low values)
# distImage = vigra.filters.distanceTransform2D(bigImg.astype(numpy.float32))
# if showPathImage:
# imgp = distImage.copy()
# if distFunc == "exponential":
# distImage = numpy.exp(distImage*-gamma)
# elif distFunc == "linear":
# maxDist = distImage.max()
# distImage = maxDist - distImage
# elif distFunc == "inverse":
# w = numpy.where(distImage!=0)
# distImage[w] = 1/distImage[w]
# else:
# print "wrong parameters for distFunc in eccentricity"
## Distance in the inside between two pixels is 1.0
distImage = bigImg.copy().astype(numpy.float32)
distImage[inside]=1.0
## Set the outside to a very high value
distImage[outside]=10000.0
## Image copy to draw the paths in
imgp = distImage.copy()
imgp[outside] = 100
## Get image graph and its path finder
gridGraph = vigraph.gridGraph(bigImg.shape[0:dim],False)
graphShape = []
for s in distImage.shape:
graphShape.append(s*2-1)
edgeWeights = vigra.resize(distImage, graphShape, order=0)
edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImageCorrected(gridGraph, edgeWeights)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
## Find borders in img
if dim == 2:
bigLblImg = vigra.analysis.labelImage(bigImg.astype(numpy.uint8))
else:
bigLblImg = vigra.analysis.labelVolume(bigImg.astype(numpy.uint8))
rag = vigraph.GridRegionAdjacencyGraph(gridGraph, bigLblImg)
node = vigraph.GridRegionAdjacencyGraph.nodeFromId(rag, long( bigLblImg[inside][0] ))
edges = vigraph._ragFindEdges(rag, gridGraph, rag.affiliatedEdges, bigLblImg, node)
# ## Remove duplicates
# edges_a = numpy.ascontiguousarray(edges)
# unique_edges = numpy.unique(edges_a.view([('', edges_a.dtype)]*edges_a.shape[1]))
# edges = unique_edges.view(edges_a.dtype).reshape((unique_edges.shape[0], edges_a.shape[1]))
borderImg = numpy.zeros(bigImg.shape)
for edge in edges:
slices = []
for d in range(dim):
slices.append( slice(edge[d], edge[d]+1) )
borderImg[slices] = 1
# ## Find borders in img
# borderImg = numpy.zeros(bigImg.shape)
# if dim == 2:
# for y in range(bigImg.shape[1]-1):
# for x in range(bigImg.shape[0]-1):
# if bigImg[x,y] == 0:
# if bigImg[x+1,y] == 1 or bigImg[x,y+1] == 1:
# borderImg[x, y] = 1
# else:
# if bigImg[x+1,y] == 0:
# borderImg[x+1, y] = 1
# if bigImg[x,y+1] == 0:
# borderImg[x, y+1] = 1
# else:
# for z in range(bigImg.shape[2]-1):
# for y in range(bigImg.shape[1]-1):
# for x in range(bigImg.shape[0]-1):
# if bigImg[x,y,z] == 0:
# if bigImg[x+1,y,z] == 1 or bigImg[x,y+1,z] == 1 or bigImg[x,y,z+1] == 1:
# borderImg[x, y, z] = 1
# else:
# if bigImg[x+1,y,z] == 0:
# borderImg[x+1,y,z] = 1
# if bigImg[x,y+1,z] == 0:
# borderImg[x,y+1,z] = 1
# if bigImg[x,y,z+1] == 0:
# borderImg[x,y,z+1] = 1
## End points for paths (all points on the border)
targets = numpy.where(borderImg==1)
nTargets = len(targets[0])
## Indices of start points for paths (random)
nPoints = int(numpy.ceil(percentageOfPaths * nTargets / 100.0))
numpy.random.seed(42)
starts = numpy.random.permutation(range(nTargets))[:nPoints]
## Compute paths
maxPaths = []
maxPathLengths = []
for i in range(nPoints):
sourceIndex = []
for d in range(dim):
sourceIndex.append(int(targets[d][starts[i]]))
source = gridGraph.coordinateToNode(sourceIndex)
pathFinder.run(edgeWeights, source)
maxPathLength = 0
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
path = pathFinder.path(pathType='coordinates', target=target)
pathLength = pathFinder.distance(target)
if pathLength > maxPathLength or maxPathLength == 0:
maxPathLength = pathLength
maxPath = path
maxPaths.append(maxPath)
maxPathLengths.append(maxPathLength)
imgp[sourceIndex[0], sourceIndex[1]] = 3*maxPathLength*maxPathLength
if showPathImage:
val = (imgp.max()+imgp.min())/2
for p in maxPaths:
imgp[p[:,0], p[:,1]] = val
if showPathImage:
plt.figure(distFunc)
plt.imshow(numpy.swapaxes(imgp, 1, 0), interpolation='none')
if len(imgSaveName)>1:
scipy.misc.imsave(imgSaveName, numpy.swapaxes(imgp, 1, 0))
return maxPathLengths
def eccentricity( img, distFunc = "exponential", showPathImage = False, percentageOfPaths = 100, imgSaveName = "" ):
img = img.astype(numpy.uint8)
dim = len(img.shape)
## Enlarge image by one pixel on each side
bigShape = []
for s in img.shape:
bigShape.append(s + 2)
bigImg = numpy.ones(bigShape)
slices = []
for i in range(dim):
slices.append(slice(1, bigImg.shape[i]-1))
bigImg[ slices ] = img
inside = numpy.where(bigImg==0)
outside = numpy.where(bigImg==1)
## Apply distanceTransform and modify (outside: high values, inside: low values)
distImage = vigra.filters.distanceTransform2D(bigImg.astype(numpy.float32))
imgp = distImage.copy()
# if showPathImage:
# imgp = distImage.copy()
if distFunc == "exponential":
distImage = numpy.exp(distImage*-gamma)
elif distFunc == "linear":
maxDist = distImage.max()
distImage = maxDist - distImage + 10
elif distFunc == "inverse":
w = numpy.where(distImage!=0)
distImage[w] = 1/distImage[w]
else:
print "wrong parameters for distFunc in eccentricity"
## Distance in the inside between two pixels is 1.0
#distImage = bigImg.copy().astype(numpy.float32)
#distImage[inside]=1.0
# if len(imgSaveName)>1:
# plt.imshow(numpy.swapaxes(distImage, 1, 0))
# plt.savefig(imgSaveName+"_dist.png")
# #scipy.misc.imsave(imgSaveName+"_dist.png", numpy.swapaxes(distImage, 1, 0))
## Set the outside to a very high value
distImage[outside]=1000.0
## Image copy to draw the paths in
#imgp = distImage.copy()
imgp[outside] = 100
## Get image graph and its path finder
gridGraph = vigraph.gridGraph(bigImg.shape[0:dim],False)
graphShape = []
for s in distImage.shape:
graphShape.append(s*2-1)
edgeWeights = vigra.resize(distImage, graphShape, order=0)
#edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImage(gridGraph, edgeWeights)
edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImageCorrected(gridGraph,edgeWeights)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
## Find borders in img
if dim == 2:
bigLblImg = vigra.analysis.labelImage(bigImg.astype(numpy.uint8))
else:
bigLblImg = vigra.analysis.labelVolume(bigImg.astype(numpy.uint8))
rag = vigraph.GridRegionAdjacencyGraph(gridGraph, bigLblImg)
node = vigraph.GridRegionAdjacencyGraph.nodeFromId(rag, long( bigLblImg[inside][0] ))
edges = vigraph._ragFindEdges(rag, gridGraph, rag.affiliatedEdges, bigLblImg, node)
borderImg = numpy.zeros(bigImg.shape)
for edge in edges:
slices = []
for d in range(dim):
slices.append( slice(edge[d], edge[d]+1) )
borderImg[slices] = 1
## End points for paths (all points on the border)
targets = numpy.where(borderImg==1)
nTargets = len(targets[0])
## Find the diameter (longest path)
eccLength = numpy.empty(nTargets)
eccLength.fill(-1)
eccTargetPath = {}
vpIndex = 0
vpGraphIndex = []
for d in range(dim):
vpGraphIndex.append(int(targets[d][vpIndex]))
vp = gridGraph.coordinateToNode(vpGraphIndex)
visited = numpy.zeros(nTargets)
while True:
visited[vpIndex] = 1
pathFinder.run(edgeWeights, vp)
eccChanged = False
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
pathLength = pathFinder.distance(target)
m = max(eccLength[j], pathLength)
if m > eccLength[j]:
eccChanged = True
eccLength[j] = m
eccTargetPath[j] = pathFinder.path(pathType='coordinates', target=target)
vpIndex = numpy.argmax(eccLength)
vpGraphIndex = []
for d in range(dim):
vpGraphIndex.append(int(targets[d][vpIndex]))
vp = gridGraph.coordinateToNode(vpGraphIndex)
if visited[vpIndex] or not eccChanged:
break
## Find the length of the diameter (non-weighted)
path = eccTargetPath[vpIndex]
dMax = 0
for k in range(1, len(path)):
diffCount = 0
for d in range(dim):
if path[k][d] != path[k-1][d]:
diffCount += 1
dMax += numpy.sqrt(diffCount)
## Find the midpoint of the diameter
dMax = dMax/2
if len(path) == 0:
path = numpy.empty( (1, 2), numpy.uint8 )
path[0][0] = targets[0][0]
path[0][1] = targets[1][0]
p1 = path[0]
d1 = 0
for k in range(1, len(path)):
p2 = path[k]
d2 = d1 + numpy.linalg.norm(p2-p1)
if d2 > dMax:
if (abs(d2-dMax) < abs(d1-dMax)):
p1 = p2
break
p1 = p2
d1 = d2
## Compute eccentricity from center (p1) to all points on border
sourceIndex = []
for d in range(dim):
sourceIndex.append(int(p1[d]))
source = gridGraph.coordinateToNode(sourceIndex)
pathFinder.run(edgeWeights, source)
maxPathLength = 0
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
pathLength = pathFinder.distance(target)
maxPathLength = max(maxPathLength, pathLength)
imgp[targetIndex[0], targetIndex[1]] = 40*pathLength
imgp[ path[:,0], path[:,1] ] = 12*maxPathLength
imgp[sourceIndex[0], sourceIndex[1]] = 40*maxPathLength
plt.figure(distFunc)
plt.imshow(numpy.swapaxes(imgp, 1, 0), interpolation='none')
if len(imgSaveName)>1:
scipy.misc.imsave(imgSaveName+".png", numpy.swapaxes(imgp, 1, 0))
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
superpixelDiameter = 15 # super-pixel size
slicWeight = 15.0 # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels) - 1
# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(
clusterProb.astype(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1,))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
regularizer = opengm.pottsFunction([nCluster] * 2, 0.0, beta)
fid = gm.addFunction(regularizer)
# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
uvIds = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
def find_shortest_path(ifp):
# Modify distancetransform
#
# a) Invert: the lowest values (i.e. the lowest penalty for the shortest path detection) should be at the center of
# the current process
ifp.invert_image(ids='curdisttransf')
#
# b) Set all values outside the process to infinity
ifp.filter_values(ifp.amax('curdisttransf'), type='eq', setto=np.inf, ids='curdisttransf', targetids='curdisttransf_inf')
#
# c) Increase the value difference between pixels near the boundaries and pixels central within the processes
# This increases the likelihood of the paths to follow the center of processes, thus avoiding short-cuts
ifp.power(10, ids='curdisttransf_inf')
indicator = ifp.get_image('curdisttransf_inf')
gridgr = graphs.gridGraph(ifp.shape('curlabel'))
ifp.logging('gridgr.shape = {}'.format(gridgr.shape))
indicator = indicator.astype(np.float32)
gridgr_edgeind = graphs.edgeFeaturesFromImage(gridgr, indicator)
instance = graphs.ShortestPathPathDijkstra(gridgr)
# Get two local maxima
indices = np.where(ifp.get_image('locmax') == 1)
coords = zip(indices[0], indices[1], indices[2])
ifp.logging('Local maxima coordinates: {}'.format(coords))
# ifp.deepcopy_entry('locmax', 'paths')
ifp.set_data_dict({'paths': np.zeros(ifp.get_image('locmax').shape)}, append=True)
ifp.logging('len(coords) = {}'.format(len(coords)))
for i in xrange(0, len(coords)-1):
for j in xrange(i+1, len(coords)):
ifp.logging('---')
ifp.logging('i = {0}; j = {1}'.format(i, j))
source = coords[i]
target = coords[j]
targetNode = gridgr.coordinateToNode(target)
sourceNode = gridgr.coordinateToNode(source)
ifp.logging('Source = {}'.format(source))
ifp.logging('Target = {}'.format(target))
instance.run(gridgr_edgeind, sourceNode, target=targetNode)
path = instance.path(pathType='coordinates')
# for coords in path:
# # ifp.logging('coords = {}'.format(coords))
# pass
pathindices = np.swapaxes(path, 0, 1)
ifp.get_image('paths')[pathindices[0], pathindices[1], pathindices[2]] = 1
# ifp.concatenate('disttransf', 'paths', target='paths_over_dist')
ifp.astype(np.uint8, ('paths', 'locmax'))
# ifp.anytask(vigra.filters.multiBinaryDilation, ('paths', 'locmax'), 3)
ifp.swapaxes(0, 2, ids=('paths', 'locmax', 'curdisttransf'))
ifp.anytask(vigra.filters.discDilation, 2, ids=('paths', 'locmax'))
ifp.set_data_dict({'paths_over_dist': np.array([ifp.get_image('paths'), ifp.get_image('locmax'), ifp.get_image('curdisttransf')])}, append=True)
def get_segmentation(predict, pmin=0.5, minMemb=10, minSeg=10, sigMin=6, sigWeights=1, sigSmooth=0.1, cleanCloseSeeds=True,
returnSeedsOnly=False, edgeLengths=None,nodeFeatures=None, nodeSizes=None, nodeLabels=None, nodeNumStop=None,
beta=0, metric='l1', wardness=0.2, out=None):
""" Get segmentation through watershed and agglomerative clustering
:param predict: prediction map
:return: segmentation map
"""
#use watershed and save superpixels map
super_pixels = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, returnSeedsOnly)
# seeds = wsDtSegmentation(predict, pmin, minMemb, minSeg, sigMin, sigWeights, cleanCloseSeeds, True)
# save_h5(seeds, "/home/stamylew/delme/seeds.h5", "data")
print
print "#Nodes in superpixels", len(np.unique(super_pixels))
# save_h5(super_pixels, "/home/stamylew/delme/super_pixels.h5", "data")
#smooth prediction map
probs = vf.gaussianSmoothing(predict, sigSmooth)
# save_h5(probs, "/home/stamylew/delme/probs.h5", "data")
#make grid graph
grid_graph = vg.gridGraph(super_pixels.shape, False)
grid_graph_edge_indicator = vg.edgeFeaturesFromImage(grid_graph, probs)
#make region adjacency graph
rag = vg.regionAdjacencyGraph(grid_graph, super_pixels)
#accumulate edge features from grid graph node map
edge_weights = rag.accumulateEdgeFeatures(grid_graph_edge_indicator)
edge_weights_tag = "mean of the probabilities"
#do agglomerative clustering
def agglomerativeClustering_beststop_th05(graph, edgeWeights=None, edgeStoppers=None,
edgeLengths=None, nodeFeatures=None, nodeSizes=None,
nodeLabels=None, beta=0, metric=None,
wardness=1.0, sameLabelMultiplier=1.0, out=None):
vg.ac(graph, edgeWeights=edgeWeights, edgeStoppers=edgeStoppers,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures,
nodeSizes=nodeSizes, nodeLabels=nodeLabels, nodeNumStop=0,
beta=beta, wardness=wardness, sameLabelMultiplier=sameLabelMultiplier, out=out)
f = open('/tmp/ac_bestNodenumstop.txt', 'r')
nodeNumStop = f.readline()
f.close()
print "best nodeNumStop:", nodeNumStop
# in experiments if n of the groundtruth is n the file contains n-1. probably in the hc.hxx the
# number is written to file too late. does this make any sense??
nodeNumStop = int(nodeNumStop)
nodeNumStop += 1
ac_res, nodelabel_out_1 = vg.ac(graph, edgeWeights=edgeWeights, edgeStoppers=edgeStoppers,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures,
nodeSizes=nodeSizes, nodeLabels=nodeLabels, nodeNumStop=nodeNumStop,
beta=beta, wardness=wardness, sameLabelMultiplier=sameLabelMultiplier,
out=out)
return ac_res, nodeNumStop, nodelabel_out_1
labels, _, _ = agglomerativeClustering_beststop_th05(rag, edgeWeights=edge_weights, edgeStoppers=edge_weights,
edgeLengths=edgeLengths, nodeFeatures=nodeFeatures, nodeSizes=nodeSizes,
nodeLabels=nodeLabels, beta=beta, metric=metric,
wardness=0.1, sameLabelMultiplier=1.0, out=out)
# labels = vg.agglomerativeClustering(rag, edge_weights, edgeLengths, nodeFeatures, nodeSizes,
# nodeLabels, nodeNumStop, beta, metric, wardness, out)
#segmentation data
wsDt_data = np.zeros((8,1))
wsDt_data[:,0] = (pmin, minMemb, minSeg, sigMin, sigWeights, sigSmooth, cleanCloseSeeds, returnSeedsOnly)
agglCl_data = edge_weights_tag, str(edgeLengths), str(nodeFeatures), str(nodeSizes), str(nodeLabels), str(nodeNumStop), str(beta), metric, str(wardness), str(out)
#project labels back to data
segmentation = rag.projectLabelsToBaseGraph(labels)
print "#nodes in segmentation", len(np.unique(segmentation))
# save_h5(segmentation, "/home/stamylew/delme/segmap.h5", "data", None)
return segmentation, super_pixels, wsDt_data, agglCl_data
def eccentricity(img,
label,
distFunc="exponential",
showPathImage=False,
percentageOfPaths=100,
imgSaveName=""):
img = img.astype(numpy.uint8)
dim = len(img.shape)
## Enlarge image by one pixel on each side
bigShape = []
for s in img.shape:
bigShape.append(s + 2)
bigImg = numpy.ones(bigShape)
slices = []
for i in range(dim):
slices.append(slice(1, bigImg.shape[i] - 1))
bigImg[slices] = img
inside = numpy.where(bigImg == 0)
outside = numpy.where(bigImg == 1)
# ## Apply distanceTransform and modify (outside: high values, inside: low values)
# distImage = vigra.filters.distanceTransform2D(bigImg.astype(numpy.float32))
# if showPathImage:
# imgp = distImage.copy()
# if distFunc == "exponential":
# distImage = numpy.exp(distImage*-gamma)
# elif distFunc == "linear":
# maxDist = distImage.max()
# distImage = maxDist - distImage
# elif distFunc == "inverse":
# w = numpy.where(distImage!=0)
# distImage[w] = 1/distImage[w]
# else:
# print "wrong parameters for distFunc in eccentricity"
## Distance in the inside between two pixels is 1.0
distImage = bigImg.copy().astype(numpy.float32)
distImage[inside] = 1.0
## Set the outside to a very high value
distImage[outside] = 10000.0
## Image copy to draw the paths in
imgp = distImage.copy()
imgp[outside] = 100
## Get image graph and its path finder
gridGraph = vigraph.gridGraph(bigImg.shape[0:dim], False)
graphShape = []
for s in distImage.shape:
graphShape.append(s * 2 - 1)
edgeWeights = vigra.resize(distImage, graphShape, order=0)
edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImageCorrected(
gridGraph, edgeWeights)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
## Find borders in img
if dim == 2:
bigLblImg = vigra.analysis.labelImage(bigImg.astype(numpy.uint8))
else:
bigLblImg = vigra.analysis.labelVolume(bigImg.astype(numpy.uint8))
rag = vigraph.GridRegionAdjacencyGraph(gridGraph, bigLblImg)
node = vigraph.GridRegionAdjacencyGraph.nodeFromId(
rag, long(bigLblImg[inside][0]))
edges = vigraph._ragFindEdges(rag, gridGraph, rag.affiliatedEdges,
bigLblImg, node)
# ## Remove duplicates
# edges_a = numpy.ascontiguousarray(edges)
# unique_edges = numpy.unique(edges_a.view([('', edges_a.dtype)]*edges_a.shape[1]))
# edges = unique_edges.view(edges_a.dtype).reshape((unique_edges.shape[0], edges_a.shape[1]))
borderImg = numpy.zeros(bigImg.shape)
for edge in edges:
slices = []
for d in range(dim):
slices.append(slice(edge[d], edge[d] + 1))
borderImg[slices] = 1
# ## Find borders in img
# borderImg = numpy.zeros(bigImg.shape)
# if dim == 2:
# for y in range(bigImg.shape[1]-1):
# for x in range(bigImg.shape[0]-1):
# if bigImg[x,y] == 0:
# if bigImg[x+1,y] == 1 or bigImg[x,y+1] == 1:
# borderImg[x, y] = 1
# else:
# if bigImg[x+1,y] == 0:
# borderImg[x+1, y] = 1
# if bigImg[x,y+1] == 0:
# borderImg[x, y+1] = 1
# else:
# for z in range(bigImg.shape[2]-1):
# for y in range(bigImg.shape[1]-1):
# for x in range(bigImg.shape[0]-1):
# if bigImg[x,y,z] == 0:
# if bigImg[x+1,y,z] == 1 or bigImg[x,y+1,z] == 1 or bigImg[x,y,z+1] == 1:
# borderImg[x, y, z] = 1
# else:
# if bigImg[x+1,y,z] == 0:
# borderImg[x+1,y,z] = 1
# if bigImg[x,y+1,z] == 0:
# borderImg[x,y+1,z] = 1
# if bigImg[x,y,z+1] == 0:
# borderImg[x,y,z+1] = 1
## End points for paths (all points on the border)
targets = numpy.where(borderImg == 1)
nTargets = len(targets[0])
## Indices of start points for paths (random)
nPoints = int(numpy.ceil(percentageOfPaths * nTargets / 100.0))
numpy.random.seed(42)
starts = numpy.random.permutation(range(nTargets))[:nPoints]
## Compute paths
maxPaths = []
maxPathLengths = []
for i in range(nPoints):
sourceIndex = []
for d in range(dim):
sourceIndex.append(int(targets[d][starts[i]]))
source = gridGraph.coordinateToNode(sourceIndex)
pathFinder.run(edgeWeights, source)
maxPathLength = 0
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
path = pathFinder.path(pathType='coordinates', target=target)
pathLength = pathFinder.distance(target)
if pathLength > maxPathLength or maxPathLength == 0:
maxPathLength = pathLength
maxPath = path
maxPaths.append(maxPath)
maxPathLengths.append(maxPathLength)
imgp[sourceIndex[0],
sourceIndex[1]] = 3 * maxPathLength * maxPathLength
if showPathImage:
val = (imgp.max() + imgp.min()) / 2
for p in maxPaths:
imgp[p[:, 0], p[:, 1]] = val
if showPathImage:
plt.figure(distFunc)
plt.imshow(numpy.swapaxes(imgp, 1, 0), interpolation='none')
if len(imgSaveName) > 1:
scipy.misc.imsave(imgSaveName, numpy.swapaxes(imgp, 1, 0))
return maxPathLengths
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
superpixelDiameter = 15 # super-pixel size
slicWeight = 15.0 # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels) - 1
# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(
clusterProb.astype(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1, ))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
regularizer = opengm.pottsFunction([nCluster] * 2, 0.0, beta)
fid = gm.addFunction(regularizer)
# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
uvIds = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
Inf = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self, ):
self.labels = []
def begin(self, inference):
print("begin of inference")
def end(self, inference):
self.labels.append(inference.arg())
def visit(self, inference):
gm = inference.gm()
labelVector = inference.arg()
print("energy %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
img = vigra.impex.readImage(filepath)
# get super-pixels with slic on LAB image
imgLab = vigra.colors.transform_RGB2Lab(img)
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)
# compute gradient on interpolated image
imgLabBig = vigra.resize(imgLab,
[imgLab.shape[0] * 2 - 1, imgLab.shape[1] * 2 - 1])
gradMag = vigra.filters.gaussianGradientMagnitude(imgLabBig, sigmaGradMag)
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(
gridGraph, gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
edgeIndicator = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# accumulate node features from grid graph node map
# which is just a plain image (with channels)
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
resultFeatures = graphs.recursiveGraphSmoothing(rag,
nodeFeatures,
edgeIndicator,
def process_branch( branch_index, branch_rois ):
# opFeatures and opThreshold are declared locally so this whole block can be parallelized!
# (We use Input.setValue() instead of Input.connect() here.)
opFeatures = OpPixelFeaturesPresmoothed(graph=tempGraph)
# Compute the Hessian slicewise and create gridGraphs
standard_scales = [0.3, 0.7, 1.0, 1.6, 3.5, 5.0, 10.0]
standard_feature_ids = ['GaussianSmoothing', 'LaplacianOfGaussian', \
'GaussianGradientMagnitude', 'DifferenceOfGaussians', \
'StructureTensorEigenvalues', 'HessianOfGaussianEigenvalues']
opFeatures.Scales.setValue(standard_scales)
opFeatures.FeatureIds.setValue(standard_feature_ids)
# Select Hessian Eigenvalues at scale 5.0
scale_index = standard_scales.index(5.0)
feature_index = standard_feature_ids.index('HessianOfGaussianEigenvalues')
selection_matrix = numpy.zeros( (6,7), dtype=bool ) # all False
selection_matrix[feature_index][scale_index] = True
opFeatures.Matrix.setValue(selection_matrix)
# opFeatures and opThreshold are declared locally so this whole block can be parallelized!
# (We use Input.setValue() instead of Input.connect() here.)
opThreshold = OpThresholdTwoLevels(graph=tempGraph)
opThreshold.Channel.setValue(0) # We select SYNAPSE_CHANNEL before the data is given to opThreshold
opThreshold.SmootherSigma.setValue({'x': 2.0, 'y': 2.0, 'z': 1.0}) #NOTE: two-level is much better. Maybe we can afford it?
#opThreshold.CurOperator.setValue(0) # 0==one-level
#opThreshold.SingleThreshold.setValue(0.4) #FIXME: solve the mess with uint8/float in predictions
opThreshold.CurOperator.setValue(1) # 1==two-level
opThreshold.HighThreshold.setValue(0.4)
opThreshold.LowThreshold.setValue(0.2)
previous_slice_objects = None
previous_slice_roi = None
conn_ids = [x.id for x in connector_infos]
connector_infos_dict = dict(zip(conn_ids, connector_infos))
branch_node_count = len(branch_rois)
for node_index_in_branch, (node_info, roi) in enumerate(branch_rois):
with Timer() as timer:
skeletonCoord = (node_info.x_px, node_info.y_px, node_info.z_px)
logger.debug("skeleton point: {}".format( skeletonCoord ))
#Add channel dimension
roi_with_channel = numpy.zeros((2, roi.shape[1]+1), dtype=numpy.uint32)
roi_with_channel[:, :-1] = roi[:]
roi_with_channel[0, -1] = 0
roi_with_channel[1, -1] = 1
iz = roi[0][2]
roi_hessian = (roi_with_channel[0]*2, roi_with_channel[1]*2-1)
for x in range(roi.shape[1]):
if roi[0][x] == 0:
roi_hessian[0][x] = 0
roi_hessian[0][2] = iz
roi_hessian[1][2] = iz+1
#we need the second eigenvalue
roi_hessian[0][-1] = 1
roi_hessian[1][-1] = 2
WITH_CONNECTORS_ONLY = True
if WITH_CONNECTORS_ONLY:
if not node_info.id in node_to_connector.keys():
continue
if debug_images:
outdir1 = outdir+"raw/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".png"
data = opPixelClassification3d.InputImages[-1](roi_with_channel[0], roi_with_channel[1]).wait()
vigra.impex.writeImage(data.squeeze().astype(numpy.uint8), outfile)
'''
outdir2 = outdir + "synapse_pred/"
outfile = outdir2+"%.02d"%iz + ".png"
data = opThreshold.InputImage(roi_with_channel[0], roi_with_channel[1]).wait()
vigra.impex.writeImage(data.squeeze().astype(numpy.uint8), outfile)
'''
start_pred = time.time()
prediction_roi = numpy.append( roi_with_channel[:,:-1], [[0],[4]], axis=1 )
synapse_prediction_roi = numpy.append( prediction_roi[:,:-1], [[SYNAPSE_CHANNEL],[SYNAPSE_CHANNEL+1]], axis=1 )
membrane_prediction_roi = numpy.append( prediction_roi[:,:-1], [[MEMBRANE_CHANNEL],[MEMBRANE_CHANNEL+1]], axis=1 )
#synapse_predictions = opPixelClassification3d.PredictionProbabilities[-1](*prediction_roi).wait()
synapse_predictions = opSynapsePredictionCache.Output(*synapse_prediction_roi).wait()
synapse_predictions = vigra.taggedView( synapse_predictions, "xytc" )
if debug_images:
outdir1 = outdir+"membrane/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".png"
#membrane_predictions = opPixelClassification2d.HeadlessPredictionProbabilities[-1](*prediction_roi).wait()
membrane_predictions = opMembranePredictionCache.Output(*membrane_prediction_roi).wait()
vigra.impex.writeImage(membrane_predictions[..., 0].squeeze(), outfile)
stop_pred = time.time()
timing_logger.debug( "spent in first 3d prediction: {}".format( stop_pred-start_pred ) )
opThreshold.InputImage.setValue(synapse_predictions)
opThreshold.InputImage.meta.drange = opPixelClassification3d.PredictionProbabilities[-1].meta.drange
synapse_cc = opThreshold.Output[:].wait()
if debug_images:
outdir1 = outdir+"predictions_roi/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".tiff"
#norm = numpy.where(synapse_cc[:, :, 0, 0]>0, 255, 0)
vigra.impex.writeImage(synapse_predictions[...,0,0], outfile)
if debug_images:
outdir1 = outdir+"synapses_roi/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".tiff"
norm = numpy.where(synapse_cc[:, :, 0, 0]>0, 255, 0)
vigra.impex.writeImage(norm.astype(numpy.uint8), outfile)
if numpy.sum(synapse_cc)==0:
print "NO SYNAPSES IN THIS SLICE:", iz
timing_logger.debug( "ROI TIMER: {}".format( timer.seconds() ) )
continue
# Distances over Hessian
start_hess = time.time()
roi_hessian = ( tuple(map(long, roi_hessian[0])), tuple(map(long, roi_hessian[1])) )
upsampled_combined_membranes = opUpsample.Output(*roi_hessian).wait()
upsampled_combined_membranes = vigra.taggedView(upsampled_combined_membranes, opUpsample.Output.meta.axistags )
opFeatures.Input.setValue(upsampled_combined_membranes)
eigenValues = opFeatures.Output[...,1:2].wait() #we need the second eigenvalue
eigenValues = numpy.abs(eigenValues[:, :, 0, 0])
stop_hess = time.time()
timing_logger.debug( "spent for hessian: {}".format( stop_hess-start_hess ) )
shape_x = roi[1][0]-roi[0][0]
shape_y = roi[1][1]-roi[0][1]
shape_x = long(shape_x)
shape_y = long(shape_y)
start_gr = time.time()
gridGr = graphs.gridGraph((shape_x, shape_y )) # !on original pixels
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGr, eigenValues)
#gridGraphs.append(gridGr)
#graphEdges.append(gridGraphEdgeIndicator)
stop_gr = time.time()
timing_logger.debug( "creating graph: {}".format( stop_gr - start_gr ) )
if debug_images:
outdir1 = outdir+"hessianUp/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".tiff"
logger.debug( "saving hessian to file: {}".format( outfile ) )
vigra.impex.writeImage(eigenValues, outfile )
instance = vigra.graphs.ShortestPathPathDijkstra(gridGr)
relative_coord = [skeletonCoord[0]-roi[0][0], skeletonCoord[1]-roi[0][1]]
relative_coord = map(long, relative_coord)
sourceNode = gridGr.coordinateToNode(relative_coord)
start_dij = time.time()
instance.run(gridGraphEdgeIndicator, sourceNode, target=None)
distances = instance.distances()
stop_dij = time.time()
timing_logger.debug( "spent in dijkstra {}".format( stop_dij - start_dij ) )
if debug_images:
outdir1 = outdir+"distances/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".tiff"
logger.debug( "saving distances to file:".format( outfile ) )
# Create a "white" pixel at the source node
distances[skeletonCoord[0]-roi[0][0], skeletonCoord[1]-roi[0][1]] = numpy.max(distances)
vigra.impex.writeImage(distances, outfile )
# Distances over raw membrane probabilities
roi_upsampled_membrane = numpy.asarray( roi_hessian )
roi_upsampled_membrane[:, -1] = [0,1]
roi_upsampled_membrane = (map(long, roi_upsampled_membrane[0]), map(long, roi_upsampled_membrane[1]))
connector_distances = None
connector_coords = None
if node_info.id in node_to_connector.keys():
connectors = node_to_connector[node_info.id]
connector_info = connector_infos_dict[connectors[0]]
#Convert to pixels
con_x_px = int(connector_info.x_nm / float(X_RES))
con_y_px = int(connector_info.y_nm / float(Y_RES))
con_z_px = int(connector_info.z_nm / float(Z_RES))
connector_coords = (con_x_px-roi[0][0], con_y_px-roi[0][1])
if con_x_px>roi[0][0] and con_x_px<roi[1][0] and con_y_px>roi[0][1] and con_y_px<roi[1][1]:
#this connector is inside our prediction roi, compute the distance field "
con_relative = [long(con_x_px-roi[0][0]), long(con_y_px-roi[0][1])]
sourceNode = gridGr.coordinateToNode(con_relative)
instance.run(gridGraphEdgeIndicator, sourceNode, target=None)
connector_distances = instance.distances()
else:
connector_distances = None
upsampled_membrane_probabilities = opUpsample.Output(*roi_upsampled_membrane).wait().squeeze()
upsampled_membrane_probabilities = vigra.filters.gaussianSmoothing(upsampled_membrane_probabilities, sigma=1.0)
#print "UPSAMPLED MEMBRANE SHAPE: {} MAX: {} MIN: {}".format( upsampled_membrane_probabilities.shape, upsampled_membrane_probabilities.max(), upsampled_membrane_probabilities.min() )
gridGrRaw = graphs.gridGraph((shape_x, shape_y )) # !on original pixels
gridGraphRawEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGrRaw, upsampled_membrane_probabilities)
#gridGraphs.append(gridGrRaw)
#graphEdges.append(gridGraphRawEdgeIndicator)
instance_raw = vigra.graphs.ShortestPathPathDijkstra(gridGrRaw)
sourceNode = gridGrRaw.coordinateToNode(relative_coord)
instance_raw.run(gridGraphRawEdgeIndicator, sourceNode, target=None)
distances_raw = instance_raw.distances()
stop_dij = time.time()
timing_logger.debug( "spent in dijkstra (raw probs) {}".format( stop_dij - start_dij ) )
if debug_images:
outdir1 = outdir+"distances_raw/"
try:
os.makedirs(outdir1)
except os.error:
pass
outfile = outdir1+"/{}-{}".format( iz, node_info.id ) + ".tiff"
logger.debug( "saving distances (raw probs) to file:".format( outfile ) )
# Create a "white" pixel at the source node
distances_raw[skeletonCoord[0]-roi[0][0], skeletonCoord[1]-roi[0][1]] = numpy.max(distances_raw)
vigra.impex.writeImage(distances_raw, outfile )
if numpy.sum(synapse_cc)==0:
continue
with max_label_lock:
synapse_objects_4d, maxLabelCurrent = normalize_synapse_ids( synapse_cc,
roi,
previous_slice_objects,
previous_slice_roi,
maxLabelSoFar[0] )
maxLabelSoFar[0] = maxLabelCurrent
synapse_objects = synapse_objects_4d.squeeze()
#add this synapse to the exported list
previous_slice_objects = synapse_objects
previous_slice_roi = roi
'''
if numpy.sum(synapse_cc)==0:
print "NO SYNAPSES IN THIS SLICE:", iz
timing_logger.debug( "ROI TIMER: {}".format( timer.seconds() ) )
continue
'''
synapseIds = set(synapse_objects.flat)
synapseIds.remove(0)
for sid in synapseIds:
#find the pixel positions of this synapse
syn_pixel_coords = numpy.where(synapse_objects == sid)
synapse_size = len( syn_pixel_coords[0] )
#syn_pixel_coords = numpy.unravel_index(syn_pixels, distances.shape)
#FIXME: offset by roi
syn_average_x = numpy.average(syn_pixel_coords[0])+roi[0][0]
syn_average_y = numpy.average(syn_pixel_coords[1])+roi[0][1]
syn_distances = distances[syn_pixel_coords]
mindist = numpy.min(syn_distances)
syn_distances_raw = distances_raw[syn_pixel_coords]
mindist_raw = numpy.min(syn_distances_raw)
if connector_distances is not None:
syn_distances_connector = connector_distances[syn_pixel_coords]
min_conn_distance = numpy.min(syn_distances_connector)
elif connector_coords is not None:
euclidean_dists = [scipy.spatial.distance.euclidean(connector_coords, xy) for xy in zip(syn_pixel_coords[0], syn_pixel_coords[1])]
min_conn_distance = numpy.min(euclidean_dists)
else:
min_conn_distance = 99999.0
# Determine average uncertainty
# Get probabilities for this synapse's pixels
flat_predictions = synapse_predictions.view(numpy.ndarray)[synapse_objects_4d[...,0] == sid]
# If we pulled the data from cache, there may be only one channel.
# In that case, we can't quite compute a proper uncertainty,
# so we'll just pretend there were only two prediction channels to begin with.
if flat_predictions.shape[-1] > 1:
# Sort along channel axis
flat_predictions.sort(axis=-1)
# What's the difference between the highest and second-highest class?
certainties = flat_predictions[:,-1] - flat_predictions[:,-2]
else:
# Pretend there were only two channels
certainties = flat_predictions[:,0] - (1 - flat_predictions[:,0])
avg_certainty = numpy.average(certainties)
avg_uncertainty = 1.0 - avg_certainty
fields = {}
fields["synapse_id"] = int(sid)
fields["x_px"] = int(syn_average_x + 0.5)
fields["y_px"] = int(syn_average_y + 0.5)
fields["z_px"] = iz
fields["size_px"] = synapse_size
fields["distance_hessian"] = mindist
fields["distance_raw_probs"] = mindist_raw
fields["detection_uncertainty"] = avg_uncertainty
fields["node_id"] = node_info.id
fields["node_x_px"] = node_info.x_px
fields["node_y_px"] = node_info.y_px
fields["node_z_px"] = node_info.z_px
if min_conn_distance!=99999.0:
connectors = node_to_connector[node_info.id]
connector_info = connector_infos_dict[connectors[0]]
fields["nearest_connector_id"] = connector_info.id
fields["nearest_connector_distance_nm"] = min_conn_distance
fields["nearest_connector_x_nm"] = connector_info.x_nm
fields["nearest_connector_y_nm"] = connector_info.y_nm
fields["nearest_connector_z_nm"] = connector_info.z_nm
else:
fields["nearest_connector_id"] = -1
fields["nearest_connector_distance_nm"] = min_conn_distance
fields["nearest_connector_x_nm"] = -1
fields["nearest_connector_y_nm"] = -1
fields["nearest_connector_z_nm"] = -1
with f_out_lock:
csv_writer.writerow( fields )
fout.flush()
with f_out_lock:
node_overall_index[0] += 1
progress_callback( ProgressInfo( node_overall_index[0],
skeleton_node_count,
branch_index,
skeleton_branch_count,
node_index_in_branch,
branch_node_count,
maxLabelCurrent ) )
#Sanity check
#outfile = outdir+"hessianUp/"+ "%.02d"%iz + ".tiff"
#vigra.impex.writeImage(eigenValues, outfile)
#outfile = outdir+"distances/"+ "%.02d"%iz + ".tiff"
#vigra.impex.writeImage(distances, outfile)
timing_logger.debug( "ROI TIMER: {}".format( timer.seconds() ) )
def eccentricity(img,
distFunc="exponential",
showPathImage=False,
percentageOfPaths=100,
imgSaveName=""):
img = img.astype(numpy.uint8)
dim = len(img.shape)
## Enlarge image by one pixel on each side
bigShape = []
for s in img.shape:
bigShape.append(s + 2)
bigImg = numpy.ones(bigShape)
slices = []
for i in range(dim):
slices.append(slice(1, bigImg.shape[i] - 1))
bigImg[slices] = img
inside = numpy.where(bigImg == 0)
outside = numpy.where(bigImg == 1)
## Apply distanceTransform and modify (outside: high values, inside: low values)
distImage = vigra.filters.distanceTransform2D(bigImg.astype(numpy.float32))
imgp = distImage.copy()
# if showPathImage:
# imgp = distImage.copy()
if distFunc == "exponential":
distImage = numpy.exp(distImage * -gamma)
elif distFunc == "linear":
maxDist = distImage.max()
distImage = maxDist - distImage + 10
elif distFunc == "inverse":
w = numpy.where(distImage != 0)
distImage[w] = 1 / distImage[w]
else:
print "wrong parameters for distFunc in eccentricity"
## Distance in the inside between two pixels is 1.0
#distImage = bigImg.copy().astype(numpy.float32)
#distImage[inside]=1.0
# if len(imgSaveName)>1:
# plt.imshow(numpy.swapaxes(distImage, 1, 0))
# plt.savefig(imgSaveName+"_dist.png")
# #scipy.misc.imsave(imgSaveName+"_dist.png", numpy.swapaxes(distImage, 1, 0))
## Set the outside to a very high value
distImage[outside] = 1000.0
## Image copy to draw the paths in
#imgp = distImage.copy()
imgp[outside] = 100
## Get image graph and its path finder
gridGraph = vigraph.gridGraph(bigImg.shape[0:dim], False)
graphShape = []
for s in distImage.shape:
graphShape.append(s * 2 - 1)
edgeWeights = vigra.resize(distImage, graphShape, order=0)
#edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImage(gridGraph, edgeWeights)
edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImageCorrected(
gridGraph, edgeWeights)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
## Find borders in img
if dim == 2:
bigLblImg = vigra.analysis.labelImage(bigImg.astype(numpy.uint8))
else:
bigLblImg = vigra.analysis.labelVolume(bigImg.astype(numpy.uint8))
rag = vigraph.GridRegionAdjacencyGraph(gridGraph, bigLblImg)
node = vigraph.GridRegionAdjacencyGraph.nodeFromId(
rag, long(bigLblImg[inside][0]))
edges = vigraph._ragFindEdges(rag, gridGraph, rag.affiliatedEdges,
bigLblImg, node)
borderImg = numpy.zeros(bigImg.shape)
for edge in edges:
slices = []
for d in range(dim):
slices.append(slice(edge[d], edge[d] + 1))
borderImg[slices] = 1
## End points for paths (all points on the border)
targets = numpy.where(borderImg == 1)
nTargets = len(targets[0])
## Find the diameter (longest path)
eccLength = numpy.empty(nTargets)
eccLength.fill(-1)
eccTargetPath = {}
vpIndex = 0
vpGraphIndex = []
for d in range(dim):
vpGraphIndex.append(int(targets[d][vpIndex]))
vp = gridGraph.coordinateToNode(vpGraphIndex)
visited = numpy.zeros(nTargets)
while True:
visited[vpIndex] = 1
pathFinder.run(edgeWeights, vp)
eccChanged = False
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
pathLength = pathFinder.distance(target)
m = max(eccLength[j], pathLength)
if m > eccLength[j]:
eccChanged = True
eccLength[j] = m
eccTargetPath[j] = pathFinder.path(pathType='coordinates',
target=target)
vpIndex = numpy.argmax(eccLength)
vpGraphIndex = []
for d in range(dim):
vpGraphIndex.append(int(targets[d][vpIndex]))
vp = gridGraph.coordinateToNode(vpGraphIndex)
if visited[vpIndex] or not eccChanged:
break
## Find the length of the diameter (non-weighted)
path = eccTargetPath[vpIndex]
dMax = 0
for k in range(1, len(path)):
diffCount = 0
for d in range(dim):
if path[k][d] != path[k - 1][d]:
diffCount += 1
dMax += numpy.sqrt(diffCount)
## Find the midpoint of the diameter
dMax = dMax / 2
if len(path) == 0:
path = numpy.empty((1, 2), numpy.uint8)
path[0][0] = targets[0][0]
path[0][1] = targets[1][0]
p1 = path[0]
d1 = 0
for k in range(1, len(path)):
p2 = path[k]
d2 = d1 + numpy.linalg.norm(p2 - p1)
if d2 > dMax:
if (abs(d2 - dMax) < abs(d1 - dMax)):
p1 = p2
break
p1 = p2
d1 = d2
## Compute eccentricity from center (p1) to all points on border
sourceIndex = []
for d in range(dim):
sourceIndex.append(int(p1[d]))
source = gridGraph.coordinateToNode(sourceIndex)
pathFinder.run(edgeWeights, source)
maxPathLength = 0
for j in range(nTargets):
targetIndex = []
for d in range(dim):
targetIndex.append(int(targets[d][j]))
target = gridGraph.coordinateToNode(targetIndex)
pathLength = pathFinder.distance(target)
maxPathLength = max(maxPathLength, pathLength)
imgp[targetIndex[0], targetIndex[1]] = 40 * pathLength
imgp[path[:, 0], path[:, 1]] = 12 * maxPathLength
imgp[sourceIndex[0], sourceIndex[1]] = 40 * maxPathLength
plt.figure(distFunc)
plt.imshow(numpy.swapaxes(imgp, 1, 0), interpolation='none')
if len(imgSaveName) > 1:
scipy.misc.imsave(imgSaveName + ".png", numpy.swapaxes(imgp, 1, 0))
import vigra.graphs as graphs
import pylab
import numpy as np
import sys
import matplotlib
import pylab as plt
import math
from matplotlib.widgets import Slider, Button, RadioButtons
# parameter:
imPath = ('holyRegion.h5', 'im') # input image path
labPath = ('segMaskOnly.h5', 'data') # labeled image path
# load volume
labels = vigra.impex.readHDF5(*labPath).astype(np.uint32)[:, :, 0:20]
volume = vigra.impex.readHDF5(*imPath)[:, :, 0:20]
gridGraph = graphs.gridGraph(labels.shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rand = np.random.rand(rag.edgeNum) * 2 - 1
gui = vigra.graphs.TinyEdgeLabelGui(rag=rag,
img=volume,
edgeLabels=None,
labelMode=True)
gui.startGui()
print gui.edgeLabels
# load image and convert to LAB
img = vigra.impex.readImage(filepath)
# get super-pixels with slic on LAB image
imgLab = vigra.colors.transform_RGB2Lab(img)
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)
# compute gradient on interpolated image
imgLabBig = vigra.resize(imgLab, [imgLab.shape[0]*2-1, imgLab.shape[1]*2-1])
gradMag = vigra.filters.gaussianGradientMagnitude(imgLabBig, sigmaGradMag)
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGraph,
gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
edgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# accumulate node features from grid graph node map
# which is just a plain image (with channels)
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
# do agglomerativeClustering
labels = graphs.agglomerativeClustering(graph=rag, edgeWeights=edgeWeights,
beta=beta, nodeFeatures=nodeFeatures,
print imgLab.shape
print "interpolate image"
imgLabBig = vigra.resize(imgLab,
[imgLab.shape[0] * 2 - 1, imgLab.shape[1] * 2 - 1])
# make a few edge weights
gradmag = numpy.squeeze(
vigra.filters.gaussianGradientMagnitude(imgLabBig, sigma))
hessian = numpy.squeeze(
vigra.filters.hessianOfGaussianEigenvalues(imgLabBig[:, :, 0],
sigma))[:, :, 0]
hessian -= hessian.min()
raw = 256 - imgLabBig[:, :, 0].copy()
gridGraph = vigraph.gridGraph(imgLab.shape[:2], False)
weights = makeWeights(3.0)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
visuimg = img.copy()
ax = plt.gca()
fig = plt.gcf()
visuimg -= visuimg.min()
visuimg /= visuimg.max()
implot = ax.imshow(numpy.swapaxes(visuimg, 0, 1), cmap='gray')
clickList = []
frozen = False
options.stdDev = (4.5, )*3
print "smooth"
ev = bw.gaussianSmooth(ev, options)
vigra.impex.writeHDF5(ev,"growmap.h5",'data')
else:
ev = vigra.impex.readHDF5("growmap.h5",'data')
with vigra.Timer("watershedsNew"):
labels, nseg = vigra.analysis.unionFindWatershed3D(ev,(100,100,100))
print "gridGraph"
gridGraph = graphs.gridGraph(labels.shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rag.writeHDF5("rag.h5",'data')
else:
rag = vigra.graphs.loadGridRagHDF5("rag.h5",'data')
labels=rag.labels
img/=img.max()
img*=255
print(imgLab.shape)
print("interpolate image")
imgLabSmall = imgLab
# make a few edge weights
gradmag = numpy.squeeze(vigra.filters.gaussianGradientMagnitude(imgLabSmall,sigma))
hessian = numpy.squeeze(vigra.filters.hessianOfGaussianEigenvalues(imgLabSmall[:,:,0],sigma))[:,:,0]
hessian-=hessian.min()
raw = 256-imgLabSmall[:,:,0].copy()
gridGraph = vigraph.gridGraph(imgLab.shape[:2],False)
weights = makeWeights(3.0)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
visuimg =img.copy()
ax = plt.gca()
fig = plt.gcf()
visuimg-=visuimg.min()
visuimg/=visuimg.max()
implot = ax.imshow(numpy.swapaxes(visuimg,0,1),cmap='gray')
grayData.append([dist, "dist"])
grayData.append([res, "thinned"])
print "write thinned pmap transform"
vigra.impex.writeHDF5(res, opt['thinnedDistTransformPMap1'], "data")
skneuro.addHocViewer(grayData, segData)
if False:
print "make the graph and save it"
print "read segmentation"
seg = vigra.impex.readHDF5(opt['oversegL0'], "data")
segData.append([seg, "seg"])
gridGraph = graphs.gridGraph(seg.shape[0:3])
print "make rag"
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, seg, isDense=False)
print "save rag to file"
rag.writeHDF5(opt['ragL0'], 'data')
if False:
print "accumulate edge weights and save them"
print "load rag"
# get region adjacency graph from super-pixel labels
rag = graphs.loadGridRagHDF5(opt['ragL0'], 'data')
gridGraph = rag.baseGraph
def eccentricity( img, distFunc = "exponential", showPathImage = False, percentageOfPaths = 100, imgSaveName = "" ):
## Enlarge image by one pixel on each side
img = img.astype(numpy.uint8)
bigImg = numpy.ones( (img.shape[0]+2, img.shape[1]+2) )
bigImg[1:bigImg.shape[0]-1, 1:bigImg.shape[1]-1] = img
## Find borders in img (replace with graph functions)
borderImg = numpy.zeros(bigImg.shape)
for y in range(bigImg.shape[1]-1):
for x in range(bigImg.shape[0]-1):
if bigImg[x,y] == 0:
if bigImg[x+1,y] == 1 or bigImg[x,y+1] == 1:
borderImg[x, y] = 1
else:
if bigImg[x+1,y] == 0:
borderImg[x+1, y] = 1
if bigImg[x,y+1] == 0:
borderImg[x, y+1] = 1
## regionImageToCrackEdgeImage ( labelImage )
# ## Apply distanceTransform and modify (outside: high values, inside: low values)
# distImage = vigra.filters.distanceTransform2D(bigImg.astype(numpy.float32))
# if showPathImage:
# imgp = distImage.copy()
# if distFunc == "exponential":
# distImage = numpy.exp(distImage*-gamma)
# elif distFunc == "linear":
# maxDist = distImage.max()
# distImage = maxDist - distImage
# elif distFunc == "inverse":
# w = numpy.where(distImage!=0)
# distImage[w] = 1/distImage[w]
# else:
# print "wrong parameters for distFunc in eccentricity"
## Distance in the inside between two pixels is 1.0
distImage = bigImg.copy().astype(numpy.float32)
distImage[numpy.where(bigImg==0)]=1.0
## Set the outside to a very high value
distImage[numpy.where(bigImg==1)]=10000.0
imgp = distImage.copy()
## Get image graph and its path finder
gridGraph = vigraph.gridGraph(bigImg.shape[0:2],False)
edgeWeights = vigra.resize(distImage,[distImage.shape[0]*2-1,distImage.shape[1]*2-1],order=0)
edgeWeights = vigra.graphs.edgeFeaturesFromInterpolatedImageCorrected(gridGraph,edgeWeights)
pathFinder = vigraph.ShortestPathPathDijkstra(gridGraph)
## End points for paths (all points on the border)
targets = numpy.where(borderImg==1)
tx,ty = targets
nTargets = len(tx)
## Indices of start points for paths (random)
nPoints = int(numpy.ceil(percentageOfPaths * nTargets / 100.0))
numpy.random.seed(42)
starts = numpy.random.permutation(range(nTargets))[:nPoints]
## Compute paths
maxPaths = []
maxPathLengths = []
for i in range(nPoints):
source = gridGraph.coordinateToNode((int(tx[starts[i]]), int (ty[starts[i]])))
pathFinder.run(edgeWeights, source)
maxPathLength = 0
for j in range(nTargets):
target = gridGraph.coordinateToNode((int(tx[j]), int(ty[j])))
path = pathFinder.path(pathType='coordinates', target=target)
pathLength = pathFinder.distance(target)
if pathLength > maxPathLength or maxPathLength == 0:
maxPathLength = pathLength
maxPath = path
maxPaths.append(maxPath)
maxPathLengths.append(maxPathLength)
if showPathImage or len(imgSaveName)>1:
val = (imgp.max()+imgp.min())/2
for p in maxPaths:
imgp[p[:,0], p[:,1]] = val
if showPathImage:
plt.figure(distFunc)
plt.imshow(imgp, interpolation='none')
if len(imgSaveName)>1:
scipy.misc.imsave(imgSaveName, imgp)
return maxPathLengths