def segmentation_for_img(raw_xy, predictions_xyc, multicut_workflow):
"""
Parameters
----------
raw_xy : vigra.VigraArray
predictions_xyc : vigra.VigraArray
multicut_workflow
Returns
-------
"""
assert are_same_xy(raw_xy, predictions_xyc)
# move these into setup_multicut?
#####
opEdgeTrainingWithMulticut = multicut_workflow.edgeTrainingWithMulticutApplet.topLevelOperator
assert isinstance(opEdgeTrainingWithMulticut, OpEdgeTrainingWithMulticut)
opDataExport = multicut_workflow.dataExportApplet.topLevelOperator
opDataExport.OutputAxisOrder.setValue('xy')
#####
role_data_dict = OrderedDict([
("Raw Data", [DatasetInfo(preloaded_array=raw_xy)]),
("Probabilities", [DatasetInfo(preloaded_array=predictions_xyc)])
])
batch_results = multicut_workflow.batchProcessingApplet.run_export(
role_data_dict, export_to_array=True)
assert len(batch_results) == 1
segmentation_xy = vigra.taggedView(batch_results[0], axistags='xy')
assert are_same_xy(segmentation_xy, raw_xy, predictions_xyc)
return segmentation_xy
def test_assert_same_xy_add_dim(vigra_arrs):
vigra_arrs += [
vigra.taggedView(np.random.random((5, 5, 3)), axistags='xyc'),
vigra.taggedView(np.random.random((5, 5, 5)), axistags='xyz'),
]
assert are_same_xy(*vigra_arrs)
def test_assert_same_xy_different_axistags(vigra_arrs):
vigra_arrs += [vigra.taggedView(np.random.random((5, 6)), axistags='yx')]
assert not are_same_xy(*vigra_arrs)
def test_assert_same_xy_same(vigra_arrs):
assert are_same_xy(*vigra_arrs)