OrsClassifierEvaluator_58c4c8146d7411e78a6fc86000a21918

Segments a dataset with a classifier from the Segmentation trainer

author:ORS Team contact:http://theobjects.com email:info@theobjects.com organization:Object Research Systems (ORS), Inc. address:760 St-Paul West, suite 101, Montréal, Québec, Canada, H3C 1M4 copyright:Object Research Systems (ORS), Inc. All rights reserved 2018. date:Jul 20 2017 13:53 dragonflyVersion:  3.0.0.271 (D) UUID:58c4c8146d7411e78a6fc86000a21918

Class Code

class OrsPythonPlugins.OrsClassifierEvaluator_58c4c8146d7411e78a6fc86000a21918.OrsClassifierEvaluator_58c4c8146d7411e78a6fc86000a21918.OrsClassifierEvaluator_58c4c8146d7411e78a6fc86000a21918(varname=None, managed=True)

UIDescriptors = [<ORSServiceClass.OrsPlugin.uidescriptor.UIDescriptor object>]

canClassify() → bool

canCompare() → bool

canLaunchTraining() → bool

canPreview() -> (<class 'bool'>, <class 'str'>)

canTrain() → bool

canUseDistributedComputation()

classify(use_distributed_task: bool)

closable = True

closeTraining()

closeWidget(name)

compare()

classmethod contextualMenu(context)

deletePreview()

getCurrentModel()

getDatasetDescriptionList() → List[str]

getInputDatasetGUIDList()

getLocalSegmentationModels() → Dict[str, List[Tuple[str, Any]]]

isSelectionValidForTrainingMask(selected_structured_grid_guid: List[str]) -> (<class 'bool'>, <class 'str'>)

isSelectionValidForTrainingTarget(selected_structured_grid_guid: List[str]) -> (<class 'bool'>, <class 'str'>)

keepAlive = False

multiple = False

newFromStore()

classmethod openClassifierEvaluator(dataset_guid_list)

classmethod openGUI()

openWidget(name, dock=None, tab=None, x=-1, y=-1, w=-1, l=-1, order=-1)

preview()

resetModel()

savable = True

saveModel()

selectedObjectsChanged()

setCurrentSegmentationModel(segmentation_model_manager: OrsPythonPlugins.OrsClassifierEvaluator_58c4c8146d7411e78a6fc86000a21918.abstractsegmentationmodelmanager.AbstractSegmentationModelManager, segmentation_model: Any)

setInputDatasetGUIDList(channelList)

setRegionAndPreviewOpacity(value)

startTraining()

train(**kwargs)