Base class for EasyLocate tool. More...

Inheritance diagram for eVision.EasyDeepLearning.ELocatorBase:

[legend]

Collaboration diagram for eVision.EasyDeepLearning.ELocatorBase:

Public Member Functions
None	SerializeSettings (self, eVision.ESerializer\|None serializer)
	Serializes the settings of the locator.
eVision.EasyDeepLearning.ELocatorMetrics	GetTrainingMetrics (self, int iteration)
	Training metrics at the given iteration.
eVision.EasyDeepLearning.ELocatorMetrics	GetValidationMetrics (self, int iteration)
	Validation metrics at the given iteration.
None	Train (self, eVision.EasyDeepLearning.EDataset dataset)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset dataset, int iterations)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation, int iterations)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, int iterations)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
None	Train (self, eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation, int iterations)
	Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.
eVision.EasyDeepLearning.ELocatorResult	Apply (self, eVision.EBaseROI img)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (self, eVision.EBaseROI img, eVision.ERegion mask)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (self, eVision.EBaseROI img, eVision.EFrame frame)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (self, eVision.EBaseROI img, eVision.EFrame frame, eVision.ERegion mask)
	Applies the tool on the given image and its mask region.
list[eVision._eVision.EasyDeepLearning.ELocatorResult]	Apply (self, list[eVision._eVision.EBaseROI\|None] imgs)
	Applies the tool on the given image and its mask region.
list[eVision._eVision.EasyDeepLearning.ELocatorResult]	Apply (self, list[eVision._eVision.EBaseROI\|None] imgs, list[eVision._eVision.ERegion] masks)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorMetrics	Evaluate (self, eVision.EasyDeepLearning.EDataset dataset)
	Evaluates the dataset.
bool	HasFeature (self, eVision.EasyDeepLearning.ELocatorFeature feature)
	Whether the given feature is enabled.
Public Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
None	Load (self, str filePath)
	Loads a deep learning tool.
None	Load (self, eVision.ESerializer\|None serializer)
	Loads a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Clone (self)
	Clones this tool.
None	Save (self, str filePath, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, str filePath)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, eVision.ESerializer\|None serializer, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
None	Save (self, eVision.ESerializer\|None serializer)
	Saves the settings, inference and training model of the deep learning tool.
None	SerializeInferenceModel (self, eVision.ESerializer\|None serializer)
	Serializes the inference model of the tool.
None	SerializeTrainingModel (self, eVision.ESerializer\|None serializer)
	Serializes the training model of the tool.
None	SaveSettings (self, str filePath)
	Saves the settings of the tool to a file.
None	SaveInferenceModel (self, str filePath)
	Saves the tool's inference model to a file.
None	SaveTrainingModel (self, str filePath)
	Saves the tool's training model to a file.
None	LoadSettings (self, str filePath)
	Loads the settings of a tool.
None	LoadInferenceModel (self, str filePath)
	Loads the tool's inference model.
None	LoadTrainingModel (self, str filePath)
	Loads the tool's training model.
None	UnloadInferenceModel (self)
	Unloads the inference model.
None	UnloadTrainingModel (self)
	Unloads the training model.
None	UnloadModels (self)
	Unloads the inference and training model.
bool	HasInferenceModel (self)
	Whether the inference model is loaded or created when the tool is not yet trained.
bool	HasTrainingModel (self)
	Whether the training model is loaded or created when the tool is not yet trained.
bool	IsTrained (self)
	Tells whether the deep learning tool has been trained.
str	GetAvailableDeviceName (self, int i)
	Name of the i-th device of the current engine.
eVision.EasyDeepLearning.EDeepLearningDevice	GetAvailableDevice (self, int i)
	i-th eVision.EasyDeepLearning.EDeepLearningDevice of the current engine.
None	SetActiveDevice (self, eVision.EasyDeepLearning.EDeepLearningDevice device)
	Sets the device with which to run or train the model.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice (self, int i)
	Gets the active device.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice (self)
	Gets the active device.
bool	HasEngineSetupData (self)
	Whether the inference model has engine setup data.
None	ClearEngineSetupData (self)
	Clears the engine data saved in the inference model.
bool	IsTraining (self)
	Indicates whether the object is currently training.
int	WaitForTrainingCompletion (self, int timeout)
	Waits until the training is complete or the timeout is expired.
int	WaitForTrainingCompletion (self)
	Waits until the training is complete or the timeout is expired.
int	WaitForIterationCompletion (self)
	Waits until an iteration is complete.
int	StopTraining (self, bool wait)
	Stops training and returns the last completed iteration.
None	SetLearningRate (self, eVision.EasyDeepLearning.EDeepLearningLearningRateType type, list[float] parameters)
	Specifies how the value of the learning rate and how it evolves during training.
None	InitializeInference (self, int width, int height, eVision.EImageType imageType, int numImages)
	Initializes the neural network for inference (i.e.
None	InitializeInference (self, eVision.EBaseROI img)
	Initializes the neural network for inference (i.e.
None	InitializeInference (self, list[eVision._eVision.EBaseROI\|None] imgList)
	Initializes the neural network for inference (i.e.
str	GetLabel (self, int index)
	Gets the label from its index.
None	SetLabel (self, int index, str label)
	Changes a label predicted by the tool.
None	SetLabelWeight (self, int index, float weight)
	Sets the label weight.
float	GetLabelWeight (self, int index)
	Gets the label weight.
None	SetLabelColor (self, int index, eVision.ERGBColor c)
	Changes a label color.
eVision.ERGBColor	GetLabelColor (self, int index)
	Label color.
None	SetLabelOpacity (self, int index, int opacity)
	Changes a label opacity (between 0 for fully transparent and 255 for opaque color).
int	GetLabelOpacity (self, int index)
	Gets the label opacity (between 0 for fully transparent and 255 for opaque color).
int	GetOptimalNumImagesForBatchSize (self, eVision.EBaseROI sampleImage)
	Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.
int	GetOptimalNumImagesForBatchSize (self, int imageWidth, int imageHeight)
	Optimal number of images to process together for inference given the batch size and eVision.EasyDeepLearning.EDeepLearningTool.GetNumPatchesForImage.
int	GetNumPatchesForImage (self, eVision.EBaseROI sampleImage)
	Number of patches that will be extracted from an input image to perform inference.
int	GetNumPatchesForImage (self, int imageWidth, int imageHeight)
	Number of patches that will be extracted from an input image to perform inference.
None	Clean (self)
	Reset the engine and clean CPU and GPU memory allocated for running the model.

Protected Member Functions
eVision.EasyDeepLearning.ELocatorCapacity	_Capacity__getter (self)
	\cond
	_Capacity__setter (self, eVision.EasyDeepLearning.ELocatorCapacity value)
	Capacity of the eVision.EasyDeepLearning.ELocatorBase.
int	_Width__getter (self)
	\cond
	_Width__setter (self, int value)
	Width of input images.
int	_Height__getter (self)
	\cond
	_Height__setter (self, int value)
	Height of input images.
int	_Channels__getter (self)
	\cond
	_Channels__setter (self, int value)
	Number of channels of input images.
list[eVision._eVision.ESize]	_PredictionAnchors__getter (self)
	\cond
	_PredictionAnchors__setter (self, list[eVision._eVision.ESize] value)
	Prediction anchors.
float	_DetectionThreshold__getter (self)
	\cond
	_DetectionThreshold__setter (self, float value)
	Detection threshold.
int	_MaxNumberOfObjects__getter (self)
	\cond
	_MaxNumberOfObjects__setter (self, int value)
	Maximum number of objects in an image (default value: 100).
float	_SameLabelMaxObjectProximity__getter (self)
	\cond
	_SameLabelMaxObjectProximity__setter (self, float value)
	Maximum proximity between two predicted objects with the same label.
float	_AbsoluteMaxObjectProximity__getter (self)
	\cond
	_AbsoluteMaxObjectProximity__setter (self, float value)
	Maximum proximity between two predicted objects regardless of their label.
int	_LocatorFeatures__getter (self)
	\cond
Protected Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
eVision.EasyDeepLearning.EDeepLearningToolType	_ToolType__getter (self)
	\cond
bool	_EnableGPU__getter (self)
	\cond
	_EnableGPU__setter (self, bool value)
	Enable the use of a GPU for training and inference of the deep learning tool.
list[int]	_GPUIndexes__getter (self)
	\cond
	_GPUIndexes__setter (self, list[int] value)
	Indexes of the GPUs to use for computations.
int	_NumGPUs__getter (self)
	\cond
str	_Engine__getter (self)
	\cond
	_Engine__setter (self, str value)
	Sets the engine with which to execute or train the neural network.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	_InferencePrecision__getter (self)
	\cond
	_InferencePrecision__setter (self, eVision.EasyDeepLearning.EDeepLearningInferencePrecision value)
	Inference precision.
int	_NumAvailableDevices__getter (self)
	\cond
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	_AvailableDevices__getter (self)
	\cond
int	_NumActiveDevices__getter (self)
	\cond
	_ActiveDeviceByName__setter (self, str value)
	\cond
	_ActiveDeviceById__setter (self, int value)
	\cond
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	_ActiveDevices__getter (self)
	\cond
	_ActiveDevices__setter (self, list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] value)
	Devices with which to run or train the model.
	_ActiveDevicesById__setter (self, list[int] value)
	\cond
eVision.EasyDeepLearning.EDeepLearningExecutionSettings	_ExecutionSettings__getter (self)
	\cond
	_ExecutionSettings__setter (self, eVision.EasyDeepLearning.EDeepLearningExecutionSettings value)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
eVision.EasyDeepLearning.EDeepLearningDevice	_EngineSetupDataDevice__getter (self)
	\cond
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	_EngineSetupDataInferencePrecision__getter (self)
	\cond
int	_EngineSetupDataBatchSize__getter (self)
	\cond
bool	_OptimizeBatchSize__getter (self)
	\cond
	_OptimizeBatchSize__setter (self, bool value)
	Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int	_BatchSizeForMaximumInferenceSpeed__getter (self)
	\cond
int	_BatchSize__getter (self)
	\cond
	_BatchSize__setter (self, int value)
	Batch size.
float	_CurrentTrainingProgression__getter (self)
	\cond
int	_CurrentTrainingFinishedIterations__getter (self)
	\cond
int	_CurrentTrainingNumIterations__getter (self)
	\cond
int	_NumTrainedIterations__getter (self)
	\cond
int	_BestIteration__getter (self)
	\cond
int	_ImageCacheSize__getter (self)
	\cond
	_ImageCacheSize__setter (self, int value)
	Size in byte of the image cache.
str	_Path__getter (self)
	\cond
str	_InferenceModelPath__getter (self)
	\cond
str	_TrainingModelPath__getter (self)
	\cond
str	_SettingsPath__getter (self)
	\cond
int	_AutoSavePeriod__getter (self)
	\cond
	_AutoSavePeriod__setter (self, int value)
	Auto-save period.
eVision.EasyDeepLearning.EDeepLearningLearningRateType	_LearningRateType__getter (self)
	\cond
list[float]	_LearningRateParameters__getter (self)
	\cond
bool	_EnableDeterministicTraining__getter (self)
	\cond
	_EnableDeterministicTraining__setter (self, bool value)
	Whether to use deterministic training algorithm that allows to repeatable training results.
int	_DeterministicTrainingRandomSeed__getter (self)
	\cond
	_DeterministicTrainingRandomSeed__setter (self, int value)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
int	_NumLabels__getter (self)
	\cond

Properties
	Capacity = property(_Capacity__getter, _Capacity__setter)
	\endcond
	Width = property(_Width__getter, _Width__setter)
	\endcond
	Height = property(_Height__getter, _Height__setter)
	\endcond
	Channels = property(_Channels__getter, _Channels__setter)
	\endcond
	PredictionAnchors = property(_PredictionAnchors__getter, _PredictionAnchors__setter)
	\endcond
	DetectionThreshold = property(_DetectionThreshold__getter, _DetectionThreshold__setter)
	\endcond
	MaxNumberOfObjects = property(_MaxNumberOfObjects__getter, _MaxNumberOfObjects__setter)
	\endcond
	SameLabelMaxObjectProximity = property(_SameLabelMaxObjectProximity__getter, _SameLabelMaxObjectProximity__setter)
	\endcond
	AbsoluteMaxObjectProximity = property(_AbsoluteMaxObjectProximity__getter, _AbsoluteMaxObjectProximity__setter)
	\endcond
	LocatorFeatures = property(_LocatorFeatures__getter, None)
	\endcond
Properties inherited from eVision.EasyDeepLearning.EDeepLearningTool
	ToolType = property(_ToolType__getter, None)
	\endcond
	EnableGPU = property(_EnableGPU__getter, _EnableGPU__setter)
	\endcond
	GPUIndexes = property(_GPUIndexes__getter, _GPUIndexes__setter)
	\endcond
	NumGPUs = property(_NumGPUs__getter, None)
	\endcond
	Engine = property(_Engine__getter, _Engine__setter)
	\endcond
	InferencePrecision = property(_InferencePrecision__getter, _InferencePrecision__setter)
	\endcond
	NumAvailableDevices = property(_NumAvailableDevices__getter, None)
	\endcond
	AvailableDevices = property(_AvailableDevices__getter, None)
	\endcond
	NumActiveDevices = property(_NumActiveDevices__getter, None)
	\endcond
	ActiveDeviceByName = property(None, _ActiveDeviceByName__setter)
	\endcond
	ActiveDeviceById = property(None, _ActiveDeviceById__setter)
	\endcond
	ActiveDevices = property(_ActiveDevices__getter, _ActiveDevices__setter)
	\endcond
	ActiveDevicesById = property(None, _ActiveDevicesById__setter)
	\endcond
	ExecutionSettings = property(_ExecutionSettings__getter, _ExecutionSettings__setter)
	\endcond
	EngineSetupDataDevice = property(_EngineSetupDataDevice__getter, None)
	\endcond
	EngineSetupDataInferencePrecision = property(_EngineSetupDataInferencePrecision__getter, None)
	\endcond
	EngineSetupDataBatchSize = property(_EngineSetupDataBatchSize__getter, None)
	\endcond
	OptimizeBatchSize = property(_OptimizeBatchSize__getter, _OptimizeBatchSize__setter)
	\endcond
	BatchSizeForMaximumInferenceSpeed = property(_BatchSizeForMaximumInferenceSpeed__getter, None)
	\endcond
	BatchSize = property(_BatchSize__getter, _BatchSize__setter)
	\endcond
	CurrentTrainingProgression = property(_CurrentTrainingProgression__getter, None)
	\endcond
	CurrentTrainingFinishedIterations = property(_CurrentTrainingFinishedIterations__getter, None)
	\endcond
	CurrentTrainingNumIterations = property(_CurrentTrainingNumIterations__getter, None)
	\endcond
	NumTrainedIterations = property(_NumTrainedIterations__getter, None)
	\endcond
	BestIteration = property(_BestIteration__getter, None)
	\endcond
	ImageCacheSize = property(_ImageCacheSize__getter, _ImageCacheSize__setter)
	\endcond
	Path = property(_Path__getter, None)
	\endcond
	InferenceModelPath = property(_InferenceModelPath__getter, None)
	\endcond
	TrainingModelPath = property(_TrainingModelPath__getter, None)
	\endcond
	SettingsPath = property(_SettingsPath__getter, None)
	\endcond
	AutoSavePeriod = property(_AutoSavePeriod__getter, _AutoSavePeriod__setter)
	\endcond
	LearningRateType = property(_LearningRateType__getter, None)
	\endcond
	LearningRateParameters = property(_LearningRateParameters__getter, None)
	\endcond
	EnableDeterministicTraining = property(_EnableDeterministicTraining__getter, _EnableDeterministicTraining__setter)
	\endcond
	DeterministicTrainingRandomSeed = property(_DeterministicTrainingRandomSeed__getter, _DeterministicTrainingRandomSeed__setter)
	\endcond
	NumLabels = property(_NumLabels__getter, None)
	\endcond

Additional Inherited Members
Static Public Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (str path)
	Factory method to open a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (eVision.ESerializer\|None serializer)
	Factory method to open a deep learning tool.
eVision.EasyDeepLearning.EDeepLearningTool\|None	Create (eVision.EasyDeepLearning.EDeepLearningToolType type)
	Factory method to open a deep learning tool.
int	GetNumAvailableEngines ()
	Number of available engines.
str	GetAvailableEngineName (int id)
	Gets the name of the engine given its id.
list[str]	GetAvailableEngines ()
	Available engines.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	GetAvailableDevicesForEngine (str engine)
	Available devices for the specified engine.

Detailed Description

Base class for EasyLocate tool.

The children classes of this class differs by their prediction features (eVision.EasyDeepLearning.ELocatorFeature). The ground truth objects used to train must have the same set of features as the prediction.

Remarks:

License(s): EasyLocate or EasyLocateInference

Member Function Documentation

◆ _AbsoluteMaxObjectProximity__getter()

float eVision.EasyDeepLearning.ELocatorBase._AbsoluteMaxObjectProximity__getter ( self )

protected

\cond

Maximum proximity between two predicted objects regardless of their label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter can be changed before and after training.

◆ _AbsoluteMaxObjectProximity__setter()

eVision.EasyDeepLearning.ELocatorBase._AbsoluteMaxObjectProximity__setter	(		self,
		float	value )

protected

Maximum proximity between two predicted objects regardless of their label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter can be changed before and after training.

◆ _Capacity__getter()

eVision.EasyDeepLearning.ELocatorCapacity eVision.EasyDeepLearning.ELocatorBase._Capacity__getter ( self )

protected

\cond

Capacity of the eVision.EasyDeepLearning.ELocatorBase.

A higher capacity makes the supervised segmenter capable of learning more information at the cost of a slower processing speed.

◆ _Capacity__setter()

eVision.EasyDeepLearning.ELocatorBase._Capacity__setter	(		self,
		eVision.EasyDeepLearning.ELocatorCapacity	value )

protected

Capacity of the eVision.EasyDeepLearning.ELocatorBase.

A higher capacity makes the supervised segmenter capable of learning more information at the cost of a slower processing speed.

◆ _Channels__getter()

int eVision.EasyDeepLearning.ELocatorBase._Channels__getter ( self )

protected

\cond

Number of channels of input images. It can either be 1 for grayscale images or 3 for color images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ _Channels__setter()

eVision.EasyDeepLearning.ELocatorBase._Channels__setter	(		self,
		int	value )

protected

Number of channels of input images.

It can either be 1 for grayscale images or 3 for color images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ _DetectionThreshold__getter()

float eVision.EasyDeepLearning.ELocatorBase._DetectionThreshold__getter ( self )

protected

\cond

Detection threshold. The detection threshold is set automatically during training to maximize accuracy. It can also be changed after training to obtain a different true positive/false positive tradeoff.

◆ _DetectionThreshold__setter()

eVision.EasyDeepLearning.ELocatorBase._DetectionThreshold__setter	(		self,
		float	value )

protected

Detection threshold.

The detection threshold is set automatically during training to maximize accuracy. It can also be changed after training to obtain a different true positive/false positive tradeoff.

◆ _Height__getter()

int eVision.EasyDeepLearning.ELocatorBase._Height__getter ( self )

protected

\cond

Height of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ _Height__setter()

eVision.EasyDeepLearning.ELocatorBase._Height__setter	(		self,
		int	value )

protected

Height of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ _LocatorFeatures__getter()

int eVision.EasyDeepLearning.ELocatorBase._LocatorFeatures__getter ( self )

protected

\cond

The features supported by the locator.

◆ _MaxNumberOfObjects__getter()

int eVision.EasyDeepLearning.ELocatorBase._MaxNumberOfObjects__getter ( self )

protected

\cond

Maximum number of objects in an image (default value: 100).

Remarks:

The value of this parameter will be automatically changed when training if it is lower than the maximum number of objects in an image from the training dataset.

This parameter can be changed before and after training.

◆ _MaxNumberOfObjects__setter()

eVision.EasyDeepLearning.ELocatorBase._MaxNumberOfObjects__setter	(		self,
		int	value )

protected

Maximum number of objects in an image (default value: 100).

Remarks:

The value of this parameter will be automatically changed when training if it is lower than the maximum number of objects in an image from the training dataset.

This parameter can be changed before and after training.

◆ _PredictionAnchors__getter()

list[eVision._eVision.ESize] eVision.EasyDeepLearning.ELocatorBase._PredictionAnchors__getter ( self )

protected

\cond

Prediction anchors.

The prediction anchors are a set of object bounding box sizes. Each anchor is used to detect objects with a size similar to that anchor. As such, the prediction anchors must reflect the variety of sizes of objects that must be detected.

◆ _PredictionAnchors__setter()

eVision.EasyDeepLearning.ELocatorBase._PredictionAnchors__setter	(		self,
		list[eVision._eVision.ESize]	value )

protected

Prediction anchors.

The prediction anchors are a set of object bounding box sizes. Each anchor is used to detect objects with a size similar to that anchor. As such, the prediction anchors must reflect the variety of sizes of objects that must be detected.

◆ _SameLabelMaxObjectProximity__getter()

float eVision.EasyDeepLearning.ELocatorBase._SameLabelMaxObjectProximity__getter ( self )

protected

\cond

Maximum proximity between two predicted objects with the same label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter is also used to compute the matching between ground truth and predicted objects during evaluation.

This parameter can be changed before and after training.

◆ _SameLabelMaxObjectProximity__setter()

eVision.EasyDeepLearning.ELocatorBase._SameLabelMaxObjectProximity__setter	(		self,
		float	value )

protected

Maximum proximity between two predicted objects with the same label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter is also used to compute the matching between ground truth and predicted objects during evaluation.

This parameter can be changed before and after training.

◆ _Width__getter()

int eVision.EasyDeepLearning.ELocatorBase._Width__getter ( self )

protected

\cond

Width of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ _Width__setter()

eVision.EasyDeepLearning.ELocatorBase._Width__setter	(		self,
		int	value )

protected

Width of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ Apply() [1/6]

eVision.EasyDeepLearning.ELocatorResult eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		eVision.EBaseROI	img )

Applies the tool on the given image and its mask region.

Parameters

img Image.

◆ Apply() [2/6]

eVision.EasyDeepLearning.ELocatorResult eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		eVision.EBaseROI	img,
		eVision.EFrame	frame )

Applies the tool on the given image and its mask region.

Parameters

img	Image.
frame	Frame of reference in which the region was defined

◆ Apply() [3/6]

eVision.EasyDeepLearning.ELocatorResult eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		eVision.EBaseROI	img,
		eVision.EFrame	frame,
		eVision.ERegion	mask )

Applies the tool on the given image and its mask region.

Parameters

img	Image.
frame	Frame of reference in which the region was defined
mask	Mask region of the image.

◆ Apply() [4/6]

eVision.EasyDeepLearning.ELocatorResult eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		eVision.EBaseROI	img,
		eVision.ERegion	mask )

Applies the tool on the given image and its mask region.

Parameters

img	Image.
mask	Mask region of the image.

◆ Apply() [5/6]

list[eVision._eVision.EasyDeepLearning.ELocatorResult] eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		list[eVision._eVision.EBaseROI \| None]	imgs )

Applies the tool on the given image and its mask region.

Parameters

imgs	Vector of images.

◆ Apply() [6/6]

list[eVision._eVision.EasyDeepLearning.ELocatorResult] eVision.EasyDeepLearning.ELocatorBase.Apply	(		self,
		list[eVision._eVision.EBaseROI \| None]	imgs,
		list[eVision._eVision.ERegion]	masks )

Applies the tool on the given image and its mask region.

Parameters

imgs	Vector of images.
masks	Vector of mask regions for the images.

◆ Evaluate()

eVision.EasyDeepLearning.ELocatorMetrics eVision.EasyDeepLearning.ELocatorBase.Evaluate	(		self,
		eVision.EasyDeepLearning.EDataset	dataset )

Evaluates the dataset.

Remarks:

License(s): EasyLocate

Parameters

dataset Dataset to evaluate

◆ GetTrainingMetrics()

eVision.EasyDeepLearning.ELocatorMetrics eVision.EasyDeepLearning.ELocatorBase.GetTrainingMetrics	(		self,
		int	iteration )

Training metrics at the given iteration.

Parameters

iteration Iteration at which to get the metrics

◆ GetValidationMetrics()

eVision.EasyDeepLearning.ELocatorMetrics eVision.EasyDeepLearning.ELocatorBase.GetValidationMetrics	(		self,
		int	iteration )

Validation metrics at the given iteration.

Parameters

iteration Iteration at which to get the metrics

◆ HasFeature()

bool eVision.EasyDeepLearning.ELocatorBase.HasFeature	(		self,
		eVision.EasyDeepLearning.ELocatorFeature	feature )

Whether the given feature is enabled.

Parameters

feature The feature to check

◆ SerializeSettings()

None eVision.EasyDeepLearning.ELocatorBase.SerializeSettings	(		self,
		eVision.ESerializer \| None	serializer )

Serializes the settings of the locator.

Parameters

serializer Pointer to eVision.ESerializer

Reimplemented from eVision.EasyDeepLearning.EDeepLearningTool.

Reimplemented in eVision.EasyDeepLearning.EInterestPointLocator, and eVision.EasyDeepLearning.ELocator.

◆ Train() [1/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	dataset )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

dataset eVision.EasyDeepLearning.EDataset with which to train and validate the deep learning tool

◆ Train() [2/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	dataset,
		eVision.EasyDeepLearning.EDataAugmentation	dataAugmentation )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

dataset	eVision.EasyDeepLearning.EDataset with which to train and validate the deep learning tool
dataAugmentation	Data augmentation to use during training

◆ Train() [3/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	dataset,
		eVision.EasyDeepLearning.EDataAugmentation	dataAugmentation,
		int	iterations )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

dataset	eVision.EasyDeepLearning.EDataset with which to train and validate the deep learning tool
dataAugmentation	Data augmentation to use during training
iterations	Number of iterations for training.

◆ Train() [4/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	dataset,
		int	iterations )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

dataset	eVision.EasyDeepLearning.EDataset with which to train and validate the deep learning tool
iterations	Number of iterations for training.

◆ Train() [5/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	trainingDataset,
		eVision.EasyDeepLearning.EDataset	validationDataset )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

trainingDataset	eVision.EasyDeepLearning.EDataset with which to train the deep learning tool
validationDataset	eVision.EasyDeepLearning.EDataset with which to validate the deep learning tool

◆ Train() [6/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	trainingDataset,
		eVision.EasyDeepLearning.EDataset	validationDataset,
		eVision.EasyDeepLearning.EDataAugmentation	dataAugmentation )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

trainingDataset	eVision.EasyDeepLearning.EDataset with which to train the deep learning tool
validationDataset	eVision.EasyDeepLearning.EDataset with which to validate the deep learning tool
dataAugmentation	Data augmentation to use during training

◆ Train() [7/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	trainingDataset,
		eVision.EasyDeepLearning.EDataset	validationDataset,
		eVision.EasyDeepLearning.EDataAugmentation	dataAugmentation,
		int	iterations )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

trainingDataset	eVision.EasyDeepLearning.EDataset with which to train the deep learning tool
validationDataset	eVision.EasyDeepLearning.EDataset with which to validate the deep learning tool
dataAugmentation	Data augmentation to use during training
iterations	Number of iterations for training.

◆ Train() [8/8]

None eVision.EasyDeepLearning.ELocatorBase.Train	(		self,
		eVision.EasyDeepLearning.EDataset	trainingDataset,
		eVision.EasyDeepLearning.EDataset	validationDataset,
		int	iterations )

Trains the eVision.EasyDeepLearning.ELocatorBase with the given dataset for the specified number of iterations.

At the end of the training, the deep learning tool is in the state it was at the iteration that gave the minimum validation error. See eVision.EasyDeepLearning.EDeepLearningTool.BestIteration.

If no iterations is given, the training will stop automatically.

Remarks:

License(s): EasyLocate

Parameters

trainingDataset	eVision.EasyDeepLearning.EDataset with which to train the deep learning tool
validationDataset	eVision.EasyDeepLearning.EDataset with which to validate the deep learning tool
iterations	Number of iterations for training.

Property Documentation

◆ AbsoluteMaxObjectProximity

eVision.EasyDeepLearning.ELocatorBase.AbsoluteMaxObjectProximity = property(_AbsoluteMaxObjectProximity__getter, _AbsoluteMaxObjectProximity__setter)

static

\endcond

Maximum proximity between two predicted objects regardless of their label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter can be changed before and after training.

◆ Capacity

eVision.EasyDeepLearning.ELocatorBase.Capacity = property(_Capacity__getter, _Capacity__setter)

static

\endcond

Capacity of the eVision.EasyDeepLearning.ELocatorBase.

A higher capacity makes the supervised segmenter capable of learning more information at the cost of a slower processing speed.

◆ Channels

eVision.EasyDeepLearning.ELocatorBase.Channels = property(_Channels__getter, _Channels__setter)

static

\endcond

Number of channels of input images. It can either be 1 for grayscale images or 3 for color images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ DetectionThreshold

eVision.EasyDeepLearning.ELocatorBase.DetectionThreshold = property(_DetectionThreshold__getter, _DetectionThreshold__setter)

static

\endcond

Detection threshold. The detection threshold is set automatically during training to maximize accuracy. It can also be changed after training to obtain a different true positive/false positive tradeoff.

◆ Height

eVision.EasyDeepLearning.ELocatorBase.Height = property(_Height__getter, _Height__setter)

static

\endcond

Height of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

◆ LocatorFeatures

eVision.EasyDeepLearning.ELocatorBase.LocatorFeatures = property(_LocatorFeatures__getter, None)

static

\endcond

The features supported by the locator.

◆ MaxNumberOfObjects

eVision.EasyDeepLearning.ELocatorBase.MaxNumberOfObjects = property(_MaxNumberOfObjects__getter, _MaxNumberOfObjects__setter)

static

\endcond

Maximum number of objects in an image (default value: 100).

Remarks:

The value of this parameter will be automatically changed when training if it is lower than the maximum number of objects in an image from the training dataset.

This parameter can be changed before and after training.

◆ PredictionAnchors

eVision.EasyDeepLearning.ELocatorBase.PredictionAnchors = property(_PredictionAnchors__getter, _PredictionAnchors__setter)

static

\endcond

Prediction anchors.

The prediction anchors are a set of object bounding box sizes. Each anchor is used to detect objects with a size similar to that anchor. As such, the prediction anchors must reflect the variety of sizes of objects that must be detected.

◆ SameLabelMaxObjectProximity

eVision.EasyDeepLearning.ELocatorBase.SameLabelMaxObjectProximity = property(_SameLabelMaxObjectProximity__getter, _SameLabelMaxObjectProximity__setter)

static

\endcond

Maximum proximity between two predicted objects with the same label.

The value of the parameter can be between

0 when the two objects are completely outside their respective zone of influence; and
1 when the two predicted objects are the same. The actual implementation of the proximity depends on the type of locator tool.

A low value will reduce the amount of falsely detected objects but may increase the number of missed objects. A high value will increase the number of falsely detected objects and reduce the number of missed objects.

Remarks:

This parameter is also used to compute the matching between ground truth and predicted objects during evaluation.

This parameter can be changed before and after training.

◆ Width

eVision.EasyDeepLearning.ELocatorBase.Width = property(_Width__getter, _Width__setter)

static

\endcond

Width of input images.

Remarks:

If the locator tool is not trained or the value was not explicitly set, its value will be 0.

Public Member Functions

Protected Member Functions

Properties

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ _AbsoluteMaxObjectProximity__getter()

◆ _AbsoluteMaxObjectProximity__setter()

◆ _Capacity__getter()

◆ _Capacity__setter()

◆ _Channels__getter()

◆ _Channels__setter()

◆ _DetectionThreshold__getter()

◆ _DetectionThreshold__setter()

◆ _Height__getter()

◆ _Height__setter()

◆ _LocatorFeatures__getter()

◆ _MaxNumberOfObjects__getter()

◆ _MaxNumberOfObjects__setter()

◆ _PredictionAnchors__getter()

◆ _PredictionAnchors__setter()

◆ _SameLabelMaxObjectProximity__getter()

◆ _SameLabelMaxObjectProximity__setter()

◆ _Width__getter()

◆ _Width__setter()

◆ Apply() [1/6]

◆ Apply() [2/6]

◆ Apply() [3/6]

◆ Apply() [4/6]

◆ Apply() [5/6]

◆ Apply() [6/6]

◆ Evaluate()

◆ GetTrainingMetrics()

◆ GetValidationMetrics()

◆ HasFeature()

◆ SerializeSettings()

◆ Train() [1/8]

◆ Train() [2/8]

◆ Train() [3/8]

◆ Train() [4/8]

◆ Train() [5/8]

◆ Train() [6/8]

◆ Train() [7/8]

◆ Train() [8/8]

Property Documentation

◆ AbsoluteMaxObjectProximity

◆ Capacity

◆ Channels

◆ DetectionThreshold

◆ Height

◆ LocatorFeatures

◆ MaxNumberOfObjects

◆ PredictionAnchors

◆ SameLabelMaxObjectProximity

◆ Width