EasyLocate tool - axis aligned bounding box version. More...

Inheritance diagram for eVision.EasyDeepLearning.ELocator:

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Collaboration diagram for eVision.EasyDeepLearning.ELocator:

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Public Member Functions
None	__init__ (self)
	Constructs a eVision.EasyDeepLearning.ELocator object.
None	__init__ (self, eVision.EasyDeepLearning.ELocator other)
	Copy constructor of a eVision.EasyDeepLearning.ELocator object.
None	SerializeSettings (self, eVision.ESerializer\|None serializer)
	Serializes the settings of the locator.
list[eVision._eVision.ESize]	GenerateAnchors (self, eVision.EasyDeepLearning.EDataset dataset)
	Generates anchors based on the objects in the dataset or a formal specification.
list[eVision._eVision.ESize]	GenerateAnchors (self, float minDimension, float maxDimension, int numSubScales, list[float] aspectRatios)
	Generates anchors based on the objects in the dataset or a formal specification.
eVision.EasyDeepLearning.EDeepLearningToolType	ToolType (self)
	Type of the deep learning tool.
float	SameLabelMaxOverlap (self)
	Maximum overlap (intersection over union) between two predicted objects with the same label.
	SameLabelMaxOverlap (self, float value)
	Maximum overlap (intersection over union) between two predicted objects with the same label.
float	AbsoluteMaxOverlap (self)
	Maximum overlap (intersection over union) between two predicted objects regardless of their label.
	AbsoluteMaxOverlap (self, float value)
	Maximum overlap (intersection over union) between two predicted objects regardless of their label.
Public Member Functions inherited from eVision.EasyDeepLearning.ELocatorBase
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.
eVision.EasyDeepLearning.ELocatorCapacity	Capacity (self)
	Capacity of the eVision.EasyDeepLearning.ELocatorBase.
	Capacity (self, eVision.EasyDeepLearning.ELocatorCapacity value)
	Capacity of the eVision.EasyDeepLearning.ELocatorBase.
int	Width (self)
	Width of input images.
	Width (self, int value)
	Width of input images.
int	Height (self)
	Height of input images.
	Height (self, int value)
	Height of input images.
int	Channels (self)
	Number of channels of input images.
	Channels (self, int value)
	Number of channels of input images.
list[eVision._eVision.ESize]	PredictionAnchors (self)
	Prediction anchors.
	PredictionAnchors (self, list[eVision._eVision.ESize] value)
	Prediction anchors.
float	DetectionThreshold (self)
	Detection threshold.
	DetectionThreshold (self, float value)
	Detection threshold.
int	MaxNumberOfObjects (self)
	Maximum number of objects in an image (default value: 100).
	MaxNumberOfObjects (self, int value)
	Maximum number of objects in an image (default value: 100).
float	SameLabelMaxObjectProximity (self)
	Maximum proximity between two predicted objects with the same label.
	SameLabelMaxObjectProximity (self, float value)
	Maximum proximity between two predicted objects with the same label.
float	AbsoluteMaxObjectProximity (self)
	Maximum proximity between two predicted objects regardless of their label.
	AbsoluteMaxObjectProximity (self, float value)
	Maximum proximity between two predicted objects regardless of their label.
int	LocatorFeatures (self)
	The features supported by the locator.
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.
bool	EnableGPU (self)
	Enable the use of a GPU for training and inference of the deep learning tool.
	EnableGPU (self, bool value)
	Enable the use of a GPU for training and inference of the deep learning tool.
list[int]	GPUIndexes (self)
	Indexes of the GPUs to use for computations.
	GPUIndexes (self, list[int] value)
	Indexes of the GPUs to use for computations.
int	NumGPUs (self)
	Number of detected NVIDIA GPU.
str	Engine (self)
	Sets the engine with which to execute or train the neural network.
	Engine (self, str value)
	Sets the engine with which to execute or train the neural network.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	InferencePrecision (self)
	Inference precision.
	InferencePrecision (self, eVision.EasyDeepLearning.EDeepLearningInferencePrecision value)
	Inference precision.
int	NumAvailableDevices (self)
	Available devices for the current eVision.EasyDeepLearning.EDeepLearningTool.Engine.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	AvailableDevices (self)
	Get all available device for the current engine.
int	NumActiveDevices (self)
	Gets the number of devices with which to run or train the model.
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	ActiveDevices (self)
	Devices with which to run or train the model.
	ActiveDevices (self, list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] value)
	Devices with which to run or train the model.
eVision.EasyDeepLearning.EDeepLearningExecutionSettings	ExecutionSettings (self)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
	ExecutionSettings (self, eVision.EasyDeepLearning.EDeepLearningExecutionSettings value)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
eVision.EasyDeepLearning.EDeepLearningDevice	EngineSetupDataDevice (self)
	Device corresponding to the engine data contained in the tool.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	EngineSetupDataInferencePrecision (self)
	Inference precision corresponding to the engine data contained in the tool.
int	EngineSetupDataBatchSize (self)
	Batch size corresponding to the engine data contained in the tool.
bool	OptimizeBatchSize (self)
	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.
	OptimizeBatchSize (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 (self)
	Computes the batch size that will maximize the inference speed on NVIDIA GPU.
int	BatchSize (self)
	Batch size.
	BatchSize (self, int value)
	Batch size.
float	CurrentTrainingProgression (self)
	Current training progression as a percentage (between 0 and 1).
int	CurrentTrainingFinishedIterations (self)
	Number of iterations that are already finished in the current training.
int	CurrentTrainingNumIterations (self)
	Total number of training iterations that will be performed during the current training of the deep learning tool.
int	NumTrainedIterations (self)
	Number of iterations that were performed to train this deep learning tool.
int	BestIteration (self)
	Iteration at which the minimum validation error was reached.
int	ImageCacheSize (self)
	Size in byte of the image cache.
	ImageCacheSize (self, int value)
	Size in byte of the image cache.
str	Path (self)
	Path of the tool when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.Save or eVision.EasyDeepLearning.EDeepLearningTool.Load.
str	InferenceModelPath (self)
	Path of the inference model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveInferenceModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadInferenceModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeInferenceModel.
str	TrainingModelPath (self)
	Path of the training model when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveTrainingModel, eVision.EasyDeepLearning.EDeepLearningTool.LoadTrainingModel or eVision.EasyDeepLearning.EDeepLearningTool.SerializeTrainingModel.
str	SettingsPath (self)
	Path of the settings when loaded or saved using eVision.EasyDeepLearning.EDeepLearningTool.SaveSettings, eVision.EasyDeepLearning.EDeepLearningTool.LoadSettings or eVision.EasyDeepLearning.EDeepLearningTool.SerializeSettings.
int	AutoSavePeriod (self)
	Auto-save period.
	AutoSavePeriod (self, int value)
	Auto-save period.
eVision.EasyDeepLearning.EDeepLearningLearningRateType	LearningRateType (self)
	Learning rate type.
list[float]	LearningRateParameters (self)
	Parameters of the learning rate.
bool	EnableDeterministicTraining (self)
	Whether to use deterministic training algorithm that allows to repeatable training results.
	EnableDeterministicTraining (self, bool value)
	Whether to use deterministic training algorithm that allows to repeatable training results.
int	DeterministicTrainingRandomSeed (self)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
	DeterministicTrainingRandomSeed (self, int value)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
int	NumLabels (self)
	Number of labels of this tool.

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.
Protected Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
	_ActiveDeviceByName__setter (self, str value)
	Sets the device with which to run the model using its name.
	_ActiveDeviceById__setter (self, int value)
	Sets the device with which to run the model using its index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.
	_ActiveDevicesById__setter (self, list[int] value)
	Sets the devices with which to run the model using their index in the list returned by eVision.EasyDeepLearning.EDeepLearningTool.AvailableDevices.
Properties inherited from eVision.EasyDeepLearning.EDeepLearningTool
	ActiveDeviceByName
	ActiveDeviceById
	ActiveDevicesById

Detailed Description

EasyLocate tool - axis aligned bounding box version.

The eVision.EasyDeepLearning.ELocator locates and classifies objects (or defects). With this class, an object is defined by the axis-aligned bounding box that surrounds it and a label (see eVision.EasyDeepLearning.ELocatorObject class). The tool must be trained using a dataset with annotated objects. The minimum size resolution supported by the tool is 128. The maximum size of the image supported is 500 000 pixels.

The tool has 5 main parameters:

A set of anchors, i.e. pre-defined object size that it must be able to detect. The anchors can be automatically determined from the training dataset, specified manually using eVision.EasyDeepLearning.ELocatorBase.PredictionAnchors, or generated according to size information about the objects (see eVision.EasyDeepLearning.ELocator.GenerateAnchors).
A detection threshold (eVision.EasyDeepLearning.ELocatorBase.DetectionThreshold) to accept or reject predicted objects based on their score.
The maximum number of objects in an image (eVision.EasyDeepLearning.ELocatorBase.MaxNumberOfObjects)
The maximum overlap between predicted objects with the same label (eVision.EasyDeepLearning.ELocator.SameLabelMaxOverlap, eVision.EasyDeepLearning.ELocatorBase.SameLabelMaxObjectProximity)
The maximum overlap between predicted objects regardless of their label (eVision.EasyDeepLearning.ELocator.AbsoluteMaxOverlap, eVision.EasyDeepLearning.ELocatorBase.AbsoluteMaxObjectProximity). Except for the anchors, all the parameters can be changed before and after training the tool.

Remarks:

License(s): EasyLocate or EasyLocateInference

Constructor & Destructor Documentation

◆ init() [1/2]

None eVision.EasyDeepLearning.ELocator.__init__ ( self )

Constructs a eVision.EasyDeepLearning.ELocator object.

◆ init() [2/2]

None eVision.EasyDeepLearning.ELocator.__init__	(		self,
		eVision.EasyDeepLearning.ELocator	other )

Copy constructor of a eVision.EasyDeepLearning.ELocator object.

Parameters

other Reference to the eVision.EasyDeepLearning.ELocator object that should be copied

Member Function Documentation

◆ AbsoluteMaxOverlap() [1/2]

float eVision.EasyDeepLearning.ELocator.AbsoluteMaxOverlap ( self )

Maximum overlap (intersection over union) between two predicted objects regardless of their label.

The value of the parameter must be between 0 (no overlap allowed between two predicted objects with different labels) and 1 (full overlap allowed between two predicted objects with different labels).

Default value: 1.

Remarks:

This parameter can be changed before and after training.

◆ AbsoluteMaxOverlap() [2/2]

eVision.EasyDeepLearning.ELocator.AbsoluteMaxOverlap	(		self,
		float	value )

Maximum overlap (intersection over union) between two predicted objects regardless of their label.

The value of the parameter must be between 0 (no overlap allowed between two predicted objects with different labels) and 1 (full overlap allowed between two predicted objects with different labels).

Default value: 1.

Remarks:

This parameter can be changed before and after training.

◆ GenerateAnchors() [1/2]

list[eVision._eVision.ESize] eVision.EasyDeepLearning.ELocator.GenerateAnchors	(		self,
		eVision.EasyDeepLearning.EDataset	dataset )

Generates anchors based on the objects in the dataset or a formal specification.

For the version that takes a dataset, the properties eVision.EasyDeepLearning.ELocatorBase.Width and eVision.EasyDeepLearning.ELocatorBase.Height must be set.

Remarks:

The dimension of an object corresponds to the square root of its area.

Parameters

dataset Dataset

◆ GenerateAnchors() [2/2]

list[eVision._eVision.ESize] eVision.EasyDeepLearning.ELocator.GenerateAnchors	(		self,
		float	minDimension,
		float	maxDimension,
		int	numSubScales,
		list[float]	aspectRatios )

Generates anchors based on the objects in the dataset or a formal specification.

For the version that takes a dataset, the properties eVision.EasyDeepLearning.ELocatorBase.Width and eVision.EasyDeepLearning.ELocatorBase.Height must be set.

Remarks:

The dimension of an object corresponds to the square root of its area.

Parameters

minDimension	Minimum dimension of objects (minimum value: 16)
maxDimension	Maximum dimension of objects.
numSubScales	Number of sub-scales to produce for each scale covered by the given dimensions.
aspectRatios	Aspect ratios to generate at each scale. The aspect ratios are the ratios between the width and height of the anchor. Recommended value: {1.0f, 0.5f, 2.0f}.

◆ SameLabelMaxOverlap() [1/2]

float eVision.EasyDeepLearning.ELocator.SameLabelMaxOverlap ( self )

Maximum overlap (intersection over union) between two predicted objects with the same label.

The value of the parameter must be between 0 (no overlap allowed between two predicted objects with the same label) and 1 (full overlap allowed between two predicted objects with the same label). 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.

Default value: 0.5

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.

◆ SameLabelMaxOverlap() [2/2]

eVision.EasyDeepLearning.ELocator.SameLabelMaxOverlap	(		self,
		float	value )

Maximum overlap (intersection over union) between two predicted objects with the same label.

The value of the parameter must be between 0 (no overlap allowed between two predicted objects with the same label) and 1 (full overlap allowed between two predicted objects with the same label). 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.

Default value: 0.5

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.

◆ SerializeSettings()

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

Serializes the settings of the locator.

Parameters

serializer Pointer to eVision.ESerializer

Reimplemented from eVision.EasyDeepLearning.ELocatorBase.

◆ ToolType()

eVision.EasyDeepLearning.EDeepLearningToolType eVision.EasyDeepLearning.ELocator.ToolType ( self )

Type of the deep learning tool.

Reimplemented from eVision.EasyDeepLearning.EDeepLearningTool.

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ __init__() [1/2]

◆ __init__() [2/2]

Member Function Documentation

◆ AbsoluteMaxOverlap() [1/2]

◆ AbsoluteMaxOverlap() [2/2]

◆ GenerateAnchors() [1/2]

◆ GenerateAnchors() [2/2]

◆ SameLabelMaxOverlap() [1/2]

◆ SameLabelMaxOverlap() [2/2]

◆ SerializeSettings()

◆ ToolType()

◆ init() [1/2]

◆ init() [2/2]