def execute(self, slot, subindex, roi, result):
assert slot == self.MST, "Invalid output slot: {}".format(slot.name)
#first thing, show the user that we are waiting for computations to finish
self.applet.progressSignal.emit(0)
volume_feat = self.Image(*roiFromShape(self.Image.meta.shape)).wait()
labelVolume = self.LabelImage(
*roiFromShape(self.LabelImage.meta.shape)).wait()
self.applet.progress = 0
def updateProgressBar(x):
#send signal iff progress is significant
if x - self.applet.progress > 1 or x == 100:
self.applet.progressSignal.emit(x)
self.applet.progress = x
mst = MSTSegmentor(labelVolume[0, ..., 0],
numpy.asarray(volume_feat[0, ..., 0],
numpy.float32),
edgeWeightFunctor="minimum",
progressCallback=updateProgressBar)
#mst.raw is not set here in order to avoid redundant data storage
mst.raw = None
#Output is of shape 1
result[0] = mst
return result
def preprocess(inputf, outputf, sigma=1.6):
print "preprocessing file %s to outputfile %s" % (inputf, outputf)
h5f = h5py.File(inputf, "r")
volume = h5f["raw"][:35, :35, :35]
print "input volume shape: ", volume.shape
print "input volume size: ", volume.nbytes / 1024**2, "MB"
fvol = volume.astype(numpy.float32)
#volume_feat = vigra.filters.gaussianGradientMagnitude(fvol,sigma)
volume_feat = vigra.filters.hessianOfGaussianEigenvalues(fvol,
sigma)[:, :, :, 0]
volume_ma = numpy.max(volume_feat)
volume_mi = numpy.min(volume_feat)
volume_feat = (volume_feat - volume_mi) * 255.0 / (volume_ma - volume_mi)
print "Watershed..."
labelVolume = vigra.analysis.watersheds(volume_feat)[0].astype(numpy.int32)
print labelVolume
print labelVolume.shape, labelVolume.dtype
mst = MSTSegmentor(labelVolume,
volume_feat.astype(numpy.float32),
edgeWeightFunctor="minimum")
mst.raw = volume
mst.saveH5("C:/Users/Ben/Desktop/carvingData/unprecarv_part35.h5", "graph")
outputf = sys.argv[2] else: outputf = "test.graph5" print "preprocessing file %s to outputfile %s" % (inputf, outputf) sigma = 1.6 h5f = h5py.File(inputf,"r") #volume = h5f["volume/data"][0,:,:,:,0] volume = h5f["sbfsem"][:,:450,:450] print "input volume shape: ", volume.shape print "input volume size: ", volume.nbytes / 1024**2, "MB" fvol = volume.astype(numpy.float32) volume_feat = vigra.filters.hessianOfGaussianEigenvalues(fvol,sigma)[:,:,:,0] volume_ma = numpy.max(volume_feat) volume_mi = numpy.min(volume_feat) volume_feat = (volume_feat - volume_mi) * 255.0 / (volume_ma-volume_mi) print "Watershed..." labelVolume = vigra.analysis.watersheds(volume_feat)[0].astype(numpy.int32) print labelVolume.shape, labelVolume.dtype mst = MSTSegmentor(labelVolume, volume_feat.astype(numpy.float32), edgeWeightFunctor = "minimum") mst.raw = volume mst.saveH5(outputf,"graph")
