def impl():
workflow = self.shell.projectManager.workflow
pixClassApplet = workflow.pcApplet
gui = pixClassApplet.getMultiLaneGui()
# Clear all the labels
while len(gui.currentGui()._labelControlUi.labelListModel) > 0:
gui.currentGui()._labelControlUi.labelListModel.removeRow(0)
# Re-add all labels
self.test_4_AddLabels()
# Make sure the entire slice is visible
viewMenu = gui.currentGui().menus()[0]
viewMenu.actionFitToScreen.trigger()
with Timer() as timer:
# Enable interactive mode
assert gui.currentGui(
)._labelControlUi.liveUpdateButton.isChecked() == False
gui.currentGui()._labelControlUi.liveUpdateButton.click()
# Do to the way we wait for the views to finish rendering, the GUI hangs while we wait.
self.waitForViews(gui.currentGui().editor.imageViews)
logger.debug("Interactive Mode Rendering Time: {}".format(
timer.seconds()))
# Disable iteractive mode.
gui.currentGui()._labelControlUi.liveUpdateButton.click()
self.waitForViews(gui.currentGui().editor.imageViews)
def __init__(self, playback_speed=None, comment_display=None):
self._playback_speed = playback_speed
self._timer = Timer()
self._timer.unpause()
if comment_display is None:
self._comment_display = self._default_comment_display
else:
self._comment_display = comment_display
def __init__(self, parent=None, ignore_parent_events=True):
QObject.__init__(self, parent=parent)
self._ignore_parent_events = False
if parent is not None and ignore_parent_events:
self._ignore_parent_events = True
self._parent_name = get_fully_qualified_name(parent)
self._captured_events = []
self._timer = Timer()
def execute(self, slot, subindex, ignored_roi, result):
configFilePath = self.ConfigFilePath.value
config = parseClusterConfigFile(configFilePath)
blockwiseFileset = self._primaryBlockwiseFileset
# Check axis compatibility
inputAxes = self.Input.meta.getTaggedShape().keys()
outputAxes = list(blockwiseFileset.description.axes)
assert set(inputAxes) == set(outputAxes), \
"Output dataset has the wrong set of axes. Input axes: {}, Output axes: {}".format( "".join(inputAxes), "".join(outputAxes) )
roiString = self.RoiString.value
roi = Roi.loads(roiString)
if len(roi.start) != len(self.Input.meta.shape):
assert False, "Task roi: {} is not valid for this input. Did the master launch this task correctly?".format(
roiString)
logger.info("Executing for roi: {}".format(roi))
if config.use_node_local_scratch:
assert False, "FIXME."
assert (
blockwiseFileset.getEntireBlockRoi(roi.start)[1] == roi.stop
).all(
), "Each task must execute exactly one full block. ({},{}) is not a valid block roi.".format(
roi.start, roi.stop)
assert self.Input.ready()
# Convert the task subrequest shape dict into a shape for this dataset (and axisordering)
subrequest_shape = map(
lambda tag: config.task_subrequest_shape[tag.key],
self.Input.meta.axistags)
primary_subrequest_shape = self._primaryBlockwiseFileset.description.sub_block_shape
if primary_subrequest_shape is not None:
# If the output dataset specified a sub_block_shape, override the cluster config
subrequest_shape = primary_subrequest_shape
with Timer() as computeTimer:
# Stream the data out to disk.
streamer = BigRequestStreamer(self.Input, (roi.start, roi.stop),
subrequest_shape,
config.task_parallel_subrequests)
streamer.progressSignal.subscribe(self.progressSignal)
streamer.resultSignal.subscribe(self._handlePrimaryResultBlock)
streamer.execute()
# Now the block is ready. Update the status.
blockwiseFileset.setBlockStatus(roi.start,
BlockwiseFileset.BLOCK_AVAILABLE)
logger.info("Finished task in {} seconds".format(
computeTimer.seconds()))
result[0] = True
return result
def impl():
workflow = self.shell.projectManager.workflow
pixClassApplet = workflow.pcApplet
gui = pixClassApplet.getMultiLaneGui()
with Timer() as timer:
gui.currentGui().editor.posModel.slicingPos = (0,0,1)
# Do to the way we wait for the views to finish rendering, the GUI hangs while we wait.
self.waitForViews(gui.currentGui().editor.imageViews)
logger.debug("New Slice Rendering Time: {}".format( timer.seconds() ))
def setInSlot(self, slot, subindex, roi, value):
key = roi.toSlice()
if slot == self.WriteSeeds:
with Timer() as timer:
print "Writing seeds to label array"
self.opLabelArray.LabelSinkInput[roi.toSlice()] = value
print "Writing seeds to label array took {} seconds".format( timer.seconds() )
assert self._mst is not None
# Important: mst.seeds will requires erased values to be 255 (a.k.a -1)
value[:] = numpy.where(value == 100, 255, value)
with Timer() as timer:
print "Writing seeds to MST"
if hasattr(key, '__len__'):
self._mst.seeds[key[1:4]] = value
else:
self._mst.seeds[key] = value
print "Writing seeds to MST took {} seconds".format( timer.seconds() )
self.has_seeds = True
else:
raise RuntimeError("unknown slots")
def setupClass(cls):
# Base class first
super(TestPixelClassificationGui, cls).setupClass()
if hasattr(cls, 'SAMPLE_DATA'):
cls.using_random_data = False
else:
cls.using_random_data = True
cls.SAMPLE_DATA = os.path.split(__file__)[0] + '/random_data.npy'
data = numpy.random.random((1, 200, 200, 50, 1))
data *= 256
numpy.save(cls.SAMPLE_DATA, data.astype(numpy.uint8))
# Start the timer
cls.timer = Timer()
cls.timer.unpause()
def impl():
workflow = self.shell.projectManager.workflow
pixClassApplet = workflow.pcApplet
gui = pixClassApplet.getMultiLaneGui()
# Make sure the entire slice is visible
gui.currentGui().menuGui.actionFitToScreen.trigger()
with Timer() as timer:
# Enable interactive mode
assert gui.currentGui()._labelControlUi.liveUpdateButton.isChecked() == False
gui.currentGui()._labelControlUi.liveUpdateButton.click()
# Do to the way we wait for the views to finish rendering, the GUI hangs while we wait.
self.waitForViews(gui.currentGui().editor.imageViews)
logger.debug("Interactive Mode Rendering Time: {}".format( timer.seconds() ))
def _buildDone(self):
"""
Builds the done segmentation anew, for example after saving an object or
deleting an object.
"""
if self._mst is None:
return
with Timer() as timer:
self._done_lut = numpy.zeros(len(self._mst.objects.lut), dtype=numpy.int32)
self._done_seg_lut = numpy.zeros(len(self._mst.objects.lut), dtype=numpy.int32)
print "building 'done' luts"
for name, objectSupervoxels in self._mst.object_lut.iteritems():
if name == self._currObjectName:
continue
self._done_lut[objectSupervoxels] += 1
assert name in self._mst.object_names, "%s not in self._mst.object_names, keys are %r" % (name, self._mst.object_names.keys())
self._done_seg_lut[objectSupervoxels] = self._mst.object_names[name]
print "building the 'done' luts took {} seconds".format( timer.seconds() )
def execute(self, slot, subindex, roi, result):
assert roi.stop - roi.start == self.Output.meta.shape, "Watershed must be run on the entire volume."
input_image = self.Input(roi.start, roi.stop).wait()
volume_feat = input_image[0, ..., 0]
result_view = result[0, ..., 0]
with Timer() as watershedTimer:
if self.Input.meta.getTaggedShape()['z'] > 1:
sys.stdout.write("Watershed...")
sys.stdout.flush()
#result_view[...] = vigra.analysis.watersheds(volume_feat[:,:])[0].astype(numpy.int32)
result_view[...] = vigra.analysis.watersheds(
volume_feat[:, :].astype(numpy.uint8))[0]
print "done", numpy.max(result[...])
else:
sys.stdout.write("Watershed...")
sys.stdout.flush()
labelVolume = vigra.analysis.watersheds(
volume_feat[:, :, 0])[0].view(dtype=numpy.int32)
result_view[...] = labelVolume[:, :, numpy.newaxis]
print "done", numpy.max(labelVolume)
print "Watershed took {} seconds".format(watershedTimer.seconds())
return result
def loadObject(self, name):
print "want to load object with name = %s" % name
if not self.hasObjectWithName(name):
print " --> no such object '%s'" % name
return False
if self.hasCurrentObject():
self.saveCurrentObject()
self._clearLabels()
fgVoxels, bgVoxels = self.loadObject_impl(name)
fg_bounding_box_start = numpy.array( map( numpy.min, fgVoxels ) )
fg_bounding_box_stop = 1 + numpy.array( map( numpy.max, fgVoxels ) )
bg_bounding_box_start = numpy.array( map( numpy.min, bgVoxels ) )
bg_bounding_box_stop = 1 + numpy.array( map( numpy.max, bgVoxels ) )
bounding_box_start = numpy.minimum( fg_bounding_box_start, bg_bounding_box_start )
bounding_box_stop = numpy.maximum( fg_bounding_box_stop, bg_bounding_box_stop )
bounding_box_slicing = roiToSlice( bounding_box_start, bounding_box_stop )
bounding_box_shape = tuple(bounding_box_stop - bounding_box_start)
dtype = self.opLabelArray.Output.meta.dtype
# Convert coordinates to be relative to bounding box
fgVoxels = numpy.array(fgVoxels)
fgVoxels = fgVoxels - numpy.array( [bounding_box_start] ).transpose()
fgVoxels = list(fgVoxels)
bgVoxels = numpy.array(bgVoxels)
bgVoxels = bgVoxels - numpy.array( [bounding_box_start] ).transpose()
bgVoxels = list(bgVoxels)
with Timer() as timer:
print "Loading seeds...."
z = numpy.zeros(bounding_box_shape, dtype=dtype)
print "Allocating seed array took {} seconds".format( timer.seconds() )
z[fgVoxels] = 2
z[bgVoxels] = 1
self.WriteSeeds[(slice(0,1),) + bounding_box_slicing + (slice(0,1),)] = z[numpy.newaxis, :,:,:, numpy.newaxis]
print "Loading seeds took a total of {} seconds".format( timer.seconds() )
#restore the correct parameter values
mst = self._mst
assert name in mst.object_lut
assert name in mst.object_seeds_fg_voxels
assert name in mst.object_seeds_bg_voxels
assert name in mst.bg_priority
assert name in mst.no_bias_below
assert name in mst.bg_priority
assert name in mst.no_bias_below
self.BackgroundPriority.setValue( mst.bg_priority[name] )
self.NoBiasBelow.setValue( mst.no_bias_below[name] )
self.updatePreprocessing()
# The entire segmentation layer needs to be refreshed now.
self.Segmentation.setDirty()
return True
def execute(self, slot, subindex, roi, result):
#make sure raw data is 5D: t,{x,y,z},c
ax = self.Input.meta.axistags
sh = self.Input.meta.shape
assert len(ax) == 5
assert ax[0].key == "t" and sh[0] == 1
for i in range(1, 4):
assert ax[i].isSpatial()
assert ax[4].key == "c" and sh[4] == 1
volume5d = self.Input.value
sigma = self.Sigma.value
volume = volume5d[0, :, :, :, 0]
result_view = result[0, :, :, :, 0]
print "input volume shape: ", volume.shape
print "input volume size: ", volume.nbytes / 1024**2, "MB"
fvol = numpy.asarray(volume, numpy.float32)
#Choose filter selected by user
volume_filter = self.Filter.value
print "applying filter", fvol.shape
with Timer() as filterTimer:
if fvol.shape[2] > 1:
# true 3D volume
if volume_filter == OpFilter.HESSIAN_BRIGHT:
print "lowest eigenvalue of Hessian of Gaussian"
result_view[
...] = vigra.filters.hessianOfGaussianEigenvalues(
fvol, sigma)[:, :, :, 2]
result_view[:] = numpy.max(result_view) - result_view
elif volume_filter == OpFilter.HESSIAN_DARK:
print "greatest eigenvalue of Hessian of Gaussian"
result_view[
...] = vigra.filters.hessianOfGaussianEigenvalues(
fvol, sigma)[:, :, :, 0]
elif volume_filter == OpFilter.STEP_EDGES:
print "Gaussian Gradient Magnitude"
result_view[...] = vigra.filters.gaussianGradientMagnitude(
fvol, sigma)
elif volume_filter == OpFilter.RAW:
print "Gaussian Smoothing"
result_view[...] = vigra.filters.gaussianSmoothing(
fvol, sigma)
elif volume_filter == OpFilter.RAW_INVERTED:
print "negative Gaussian Smoothing"
result_view[...] = vigra.filters.gaussianSmoothing(
-fvol, sigma)
print "Filter took {} seconds".format(filterTimer.seconds())
else:
# 2D Image
fvol = fvol[:, :, 0]
if volume_filter == OpFilter.HESSIAN_BRIGHT:
print "lowest eigenvalue of Hessian of Gaussian"
volume_feat = vigra.filters.hessianOfGaussianEigenvalues(
fvol, sigma)[:, :, 1]
volume_feat[:] = numpy.max(volume_feat) - volume_feat
elif volume_filter == OpFilter.HESSIAN_DARK:
print "greatest eigenvalue of Hessian of Gaussian"
volume_feat = vigra.filters.hessianOfGaussianEigenvalues(
fvol, sigma)[:, :, 0]
elif volume_filter == OpFilter.STEP_EDGES:
print "Gaussian Gradient Magnitude"
volume_feat = vigra.filters.gaussianGradientMagnitude(
fvol, sigma)
elif volume_filter == OpFilter.RAW:
print "Gaussian Smoothing"
volume_feat = vigra.filters.gaussianSmoothing(fvol, sigma)
elif volume_filter == OpFilter.RAW_INVERTED:
print "negative Gaussian Smoothing"
volume_feat = vigra.filters.gaussianSmoothing(-fvol, sigma)
result_view[:, :, 0] = volume_feat
print "Filter took {} seconds".format(filterTimer.seconds())
return result
