def _generate_random_labels(self, labels_per_image, label_value):
"""
Inject random labels into the project file.
(This is a special feature requested by the FlyEM proofreaders.)
"""
logger.info("Injecting {} labels of value {} into all images.".format(
labels_per_image, label_value))
opTopLevelClassify = self.pcApplet.topLevelOperator
label_names = copy.copy(opTopLevelClassify.LabelNames.value)
while len(label_names) < label_value:
label_names.append("Label {}".format(len(label_names) + 1))
opTopLevelClassify.LabelNames.setValue(label_names)
for image_index in range(len(opTopLevelClassify.LabelImages)):
logger.info("Injecting labels into image #{}".format(image_index))
# For reproducibility of label generation
SEED = 1
numpy.random.seed([SEED, image_index])
label_input_slot = opTopLevelClassify.LabelInputs[image_index]
label_output_slot = opTopLevelClassify.LabelImages[image_index]
shape = label_output_slot.meta.shape
random_labels = numpy.zeros(shape=shape, dtype=numpy.uint8)
num_pixels = len(random_labels.flat)
current_progress = -1
for sample_index in range(labels_per_image):
flat_index = numpy.random.randint(0, num_pixels)
# Don't overwrite existing labels
# Keep looking until we find a blank pixel
while random_labels.flat[flat_index]:
flat_index = numpy.random.randint(0, num_pixels)
random_labels.flat[flat_index] = label_value
# Print progress every 10%
progress = float(sample_index) // labels_per_image
progress = 10 * (int(100 * progress) // 10)
if progress != current_progress:
current_progress = progress
sys.stdout.write("{}% ".format(current_progress))
sys.stdout.flush()
sys.stdout.write("100%\n")
# Write into the operator
label_input_slot[fullSlicing(shape)] = random_labels
logger.info("Done injecting labels")
def _generate_random_labels(self, labels_per_image, label_value):
"""
Inject random labels into the project file.
(This is a special feature requested by the FlyEM proofreaders.)
"""
logger.info( "Injecting {} labels of value {} into all images.".format( labels_per_image, label_value ) )
opTopLevelClassify = self.pcApplet.topLevelOperator
label_names = copy.copy(opTopLevelClassify.LabelNames.value)
while len(label_names) < label_value:
label_names.append( "Label {}".format( len(label_names)+1 ) )
opTopLevelClassify.LabelNames.setValue( label_names )
for image_index in range(len(opTopLevelClassify.LabelImages)):
logger.info( "Injecting labels into image #{}".format( image_index ) )
# For reproducibility of label generation
SEED = 1
numpy.random.seed([SEED, image_index])
label_input_slot = opTopLevelClassify.LabelInputs[image_index]
label_output_slot = opTopLevelClassify.LabelImages[image_index]
shape = label_output_slot.meta.shape
random_labels = numpy.zeros( shape=shape, dtype=numpy.uint8 )
num_pixels = len(random_labels.flat)
current_progress = -1
for sample_index in range(labels_per_image):
flat_index = numpy.random.randint(0,num_pixels)
# Don't overwrite existing labels
# Keep looking until we find a blank pixel
while random_labels.flat[flat_index]:
flat_index = numpy.random.randint(0,num_pixels)
random_labels.flat[flat_index] = label_value
# Print progress every 10%
progress = float(sample_index) / labels_per_image
progress = 10 * (int(100*progress)/10)
if progress != current_progress:
current_progress = progress
sys.stdout.write( "{}% ".format( current_progress ) )
sys.stdout.flush()
sys.stdout.write( "100%\n" )
# Write into the operator
label_input_slot[fullSlicing(shape)] = random_labels
logger.info( "Done injecting labels" )
def impl():
shell = self.shell
workflow = shell.projectManager.workflow
carvingApplet = workflow.carvingApplet
gui = carvingApplet.getMultiLaneGui()
op_carving = carvingApplet.topLevelOperator.getLane(0)
# activate the carving applet
shell.setSelectedAppletDrawer(2)
# let the gui catch up
QApplication.processEvents()
self.waitForViews(gui.currentGui().editor.imageViews)
# inject the labels
op5 = OpReorderAxes(parent=op_carving.parent)
opReader = OpInputDataReader(parent=op_carving.parent)
try:
opReader.FilePath.setValue(f"{self.reference_files['carving_label_file']}/exported_data")
op5.AxisOrder.setValue(op_carving.WriteSeeds.meta.getAxisKeys())
op5.Input.connect(opReader.Output)
label_data = op5.Output[:].wait()
finally:
op5.cleanUp()
opReader.cleanUp()
slicing = roi.fullSlicing(label_data.shape)
op_carving.WriteSeeds[slicing] = label_data
gui.currentGui().labelingDrawerUi.segment.click()
QApplication.processEvents()
op_carving.saveObjectAs("Object 1")
op_carving.deleteObject("<not saved yet>")
# export the mesh:
req = gui.currentGui()._exportMeshes(["Object 1"], [self.output_obj_file])
req.wait()
# compare meshes
with open(self.output_obj_file, "r") as f:
left = f.read()
with open(self.reference_files["output_obj_file"], "r") as f:
right = f.read()
# TODO: might result in errors due to rounding on different systems
assert left == right
# export the completed segments layer
layermatch = [
x.name.startswith("Completed segments (unicolor)") for x in gui.currentGui().editor.layerStack
]
assert sum(layermatch) == 1, "Completed segments (unicolor) Layer expected."
completed_segments_layer = gui.currentGui().editor.layerStack[layermatch.index(True)]
opExport = get_export_operator(completed_segments_layer)
try:
opExport.OutputFilenameFormat.setValue(self.output_file)
opExport.run_export()
finally:
opExport.cleanUp()
assert os.path.exists(self.output_file)
# compare completed segments
with h5py.File(self.reference_files["output_file"], "r") as f_left:
data_left = f_left["exported_data"][:]
with h5py.File(self.output_file, "r") as f_right:
data_right = f_right["exported_data"][:]
numpy.testing.assert_array_almost_equal(data_left, data_right)
# Save the project
saveThread = self.shell.onSaveProjectActionTriggered()
saveThread.join()
def impl():
shell = self.shell
workflow = shell.projectManager.workflow
carvingApplet = workflow.carvingApplet
gui = carvingApplet.getMultiLaneGui()
op_carving = carvingApplet.topLevelOperator.getLane(0)
# activate the carving applet
shell.setSelectedAppletDrawer(2)
# let the gui catch up
QApplication.processEvents()
self.waitForViews(gui.currentGui().editor.imageViews)
# inject the labels
op5 = OpReorderAxes(parent=op_carving.parent)
opReader = OpInputDataReader(parent=op_carving.parent)
try:
opReader.FilePath.setValue(f"{self.reference_files['carving_label_file']}/exported_data")
op5.AxisOrder.setValue(op_carving.WriteSeeds.meta.getAxisKeys())
op5.Input.connect(opReader.Output)
label_data = op5.Output[:].wait()
finally:
op5.cleanUp()
opReader.cleanUp()
slicing = roi.fullSlicing(label_data.shape)
op_carving.WriteSeeds[slicing] = label_data
gui.currentGui().labelingDrawerUi.segment.click()
QApplication.processEvents()
op_carving.saveObjectAs("Object 1")
op_carving.deleteObject("<not saved yet>")
# export the mesh:
req = gui.currentGui()._exportMeshes(["Object 1"], [self.output_obj_file])
req.wait()
# compare meshes
with open(self.output_obj_file, "r") as f:
left = f.read()
with open(self.reference_files["output_obj_file"], "r") as f:
right = f.read()
# TODO: might result in errors due to rounding on different systems
assert left == right
# export the completed segments layer
layermatch = [
x.name.startswith("Completed segments (unicolor)") for x in gui.currentGui().editor.layerStack
]
assert sum(layermatch) == 1, "Completed segments (unicolor) Layer expected."
completed_segments_layer = gui.currentGui().editor.layerStack[layermatch.index(True)]
opExport = get_export_operator(completed_segments_layer)
try:
opExport.OutputFilenameFormat.setValue(self.output_file)
opExport.run_export()
finally:
opExport.cleanUp()
assert os.path.exists(self.output_file)
# compare completed segments
with h5py.File(self.reference_files["output_file"], "r") as f_left:
data_left = f_left["exported_data"][:]
with h5py.File(self.output_file, "r") as f_right:
data_right = f_right["exported_data"][:]
numpy.testing.assert_array_almost_equal(data_left, data_right)
# Save the project
saveThread = self.shell.onSaveProjectActionTriggered()
saveThread.join()