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 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 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)
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,
nodeNumStop=nodeNumStop)
# 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,
gamma=gamma,
edgeThreshold=edgeThreshold,
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)
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() ) )