eVision.EasyDeepLearning.EInterestPointLocator Class Reference

EasyLocate tool - interest point version. More...

Inheritance diagram for eVision.EasyDeepLearning.EInterestPointLocator:

Collaboration diagram for eVision.EasyDeepLearning.EInterestPointLocator:

Public Member Functions
	EInterestPointLocator ()
	Constructs a EInterestPointLocator object.
	EInterestPointLocator (int objectSize)
	Constructs a EInterestPointLocator object.
	EInterestPointLocator (eVision.EasyDeepLearning.EInterestPointLocator other)
	Copy constructor of a EInterestPointLocator object.
override void	SerializeSettings (eVision.ESerializer serializer)
	Serializes the settings of the locator.
Public Member Functions inherited from eVision.EasyDeepLearning.ELocatorBase
override void	SerializeSettings (eVision.ESerializer serializer)
	Serializes the settings of the locator.
eVision.EasyDeepLearning.ELocatorMetrics	GetTrainingMetrics (int iteration)
	Training metrics at the given iteration.
eVision.EasyDeepLearning.ELocatorMetrics	GetValidationMetrics (int iteration)
	Validation metrics at the given iteration.
void	Train (eVision.EasyDeepLearning.EDataset dataset)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, int iterations)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation, int iterations)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, int iterations)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation, int iterations)
	Trains the ELocatorBase with the given dataset for the specified number of iterations.
eVision.EasyDeepLearning.ELocatorResult	Apply (eVision.EBaseROI img)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (eVision.EBaseROI img, eVision.ERegion mask)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (eVision.EBaseROI img, eVision.EFrame frame)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult	Apply (eVision.EBaseROI img, eVision.EFrame frame, eVision.ERegion mask)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageBW8[] imgs)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageBW8[] imgs, eVision.ERegion[] masks)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageBW16[] imgs)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageBW16[] imgs, eVision.ERegion[] masks)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageC24[] imgs)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EImageC24[] imgs, eVision.ERegion[] masks)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EBaseROI[] imgs)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorResult[]	Apply (eVision.EBaseROI[] imgs, eVision.ERegion[] masks)
	Applies the tool on the given image and its mask region.
eVision.EasyDeepLearning.ELocatorMetrics	Evaluate (eVision.EasyDeepLearning.EDataset dataset)
	Evaluates the dataset.
bool	HasFeature (eVision.EasyDeepLearning.ELocatorFeature feature)
	Whether the given feature is enabled.
Public Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
virtual void	Load (string filePath)
	Loads a deep learning tool. The given ESerializer must have been created for reading.
virtual void	Load (eVision.ESerializer serializer)
	Loads a deep learning tool. The given ESerializer must have been created for reading.
eVision.EasyDeepLearning.EDeepLearningTool	Clone ()
	Clones this tool.
virtual void	Save (string filePath, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (string filePath)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (eVision.ESerializer serializer, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (eVision.ESerializer serializer)
	Saves the settings, inference and training model of the deep learning tool.
void	SerializeSettings (eVision.ESerializer serializer)
	Serializes the tool settings.
void	SerializeInferenceModel (eVision.ESerializer serializer)
	Serializes the inference model of the tool.
void	SerializeTrainingModel (eVision.ESerializer serializer)
	Serializes the training model of the tool.
void	SaveSettings (string filePath)
	Saves the settings of the tool to a file.
void	SaveInferenceModel (string filePath)
	Saves the tool's inference model to a file.
void	SaveTrainingModel (string filePath)
	Saves the tool's training model to a file.
void	LoadSettings (string filePath)
	Loads the settings of a tool.
void	LoadInferenceModel (string filePath)
	Loads the tool's inference model. The inference model is required to apply the tool on new images.
void	LoadTrainingModel (string filePath)
	Loads the tool's training model. The training model is required to continue a training.
void	UnloadInferenceModel ()
	Unloads the inference model.
void	UnloadTrainingModel ()
	Unloads the training model.
void	UnloadModels ()
	Unloads the inference and training model.
bool	HasInferenceModel ()
	Whether the inference model is loaded or created when the tool is not yet trained.
bool	HasTrainingModel ()
	Whether the training model is loaded or created when the tool is not yet trained.
bool	IsTrained ()
	Tells whether the deep learning tool has been trained.
string	GetAvailableDeviceName (int i)
	Name of the i-th device of the current engine.
eVision.EasyDeepLearning.EDeepLearningDevice	GetAvailableDevice (int i)
	i-th EDeepLearningDevice of the current engine.
void	SetActiveDevice (eVision.EasyDeepLearning.EDeepLearningDevice device)
	Sets the device with which to run or train the model.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice (int i)
	Gets the active device. By default, it returns the first active device.
eVision.EasyDeepLearning.EDeepLearningDevice	GetActiveDevice ()
	Gets the active device. By default, it returns the first active device.
bool	HasEngineSetupData ()
	Whether the inference model has engine setup data.
void	ClearEngineSetupData ()
	Clears the engine data saved in the inference model. Use this method when your engine fails.
bool	IsTraining ()
	Indicates whether the object is currently training.
int	WaitForTrainingCompletion (int timeout)
	Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.
int	WaitForTrainingCompletion ()
	Waits until the training is complete or the timeout is expired. A call to this method will block the calling thread for the shortest time between the timeout and the time it takes for the training to complete.
int	WaitForIterationCompletion ()
	Waits until an iteration is complete. A call to this method will block the calling thread until a training iteration in the training thread is finished. This method returns the number of trained iterations.
int	StopTraining (bool wait)
	Stops training and returns the last completed iteration. If the parameter 'wait' is set to true, the method will wait for the training thread to completely stop. Otherwise, the method will return immediately.
void	SetLearningRate (eVision.EasyDeepLearning.EDeepLearningLearningRateType type, float[] parameters)
	Specifies how the value of the learning rate and how it evolves during training.
void	InitializeInference (int width, int height, eVision.EImageType imageType, int numImages)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (eVision.EBaseROI img)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (ref eVision.EBaseROI[] imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (ref eVision.EImageBW8[] imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (ref eVision.EImageBW16[] imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (ref eVision.EImageC24[] imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
string	GetLabel (uint index)
	Gets the label from its index. If the tool is not trained, the method may throw an exception.
virtual void	SetLabel (uint index, string label)
	Changes a label predicted by the tool.
void	SetLabelWeight (uint index, float weight)
	Sets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.
float	GetLabelWeight (uint index)
	Gets the label weight. If the tool is not trained, the label weights are not defined. After training, they are set to the value of the training dataset label weights.
void	SetLabelColor (uint index, eVision.ERGBColor c)
	Changes a label color.
eVision.ERGBColor	GetLabelColor (uint index)
	Label color. The label colors affect how the results will be displayed.
void	SetLabelOpacity (uint index, byte opacity)
	Changes a label opacity (between 0 for fully transparent and 255 for opaque color).
byte	GetLabelOpacity (uint index)
	Gets the label opacity (between 0 for fully transparent and 255 for opaque color). The label opacities affect with the label colors how the results will be displayed.
int	GetOptimalNumImagesForBatchSize (eVision.EBaseROI sampleImage)
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool.GetNumPatchesForImage.
int	GetOptimalNumImagesForBatchSize (int imageWidth, int imageHeight)
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool.GetNumPatchesForImage.
int	GetNumPatchesForImage (eVision.EBaseROI sampleImage)
	Number of patches that will be extracted from an input image to perform inference.
virtual int	GetNumPatchesForImage (int imageWidth, int imageHeight)
	Number of patches that will be extracted from an input image to perform inference.
virtual void	Clean ()
	Reset the engine and clean CPU and GPU memory allocated for running the model.

Properties
override eVision.EasyDeepLearning.EDeepLearningToolType	ToolType [get]
	Type of the deep learning tool.
int	ObjectSize [get, set]
	Specify the size of predicted objects when only the position is predicted. This parameter is only used to prepare the predicted objects for drawing.
float	SameLabelMinDistance [get, set]
	Minimum distance between two predicted objects with the same label.
float	AbsoluteMinDistance [get, set]
	Minimum distance between two predicted objects regardless of their label.
Properties inherited from eVision.EasyDeepLearning.ELocatorBase
eVision.EasyDeepLearning.ELocatorCapacity	Capacity [get, set]
	Capacity of the ELocatorBase.
uint	Width [get, set]
	Width of input images.
uint	Height [get, set]
	Height of input images.
uint	Channels [get, set]
	Number of channels of input images. It can either be 1 for grayscale images or 3 for color images.
eVision.ESize[]	PredictionAnchors [get, set]
	Prediction anchors.
float	DetectionThreshold [get, set]
	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.
int	MaxNumberOfObjects [get, set]
	Maximum number of objects in an image (default value: 100).
float	SameLabelMaxObjectProximity [get, set]
	Maximum proximity between two predicted objects with the same label.
float	AbsoluteMaxObjectProximity [get, set]
	Maximum proximity between two predicted objects regardless of their label.
int	LocatorFeatures [get]
	The features supported by the locator.
Properties inherited from eVision.EasyDeepLearning.EDeepLearningTool
eVision.EasyDeepLearning.EDeepLearningToolType	ToolType [get]
	Type of the deep learning tool.
bool	EnableGPU [get, set]
	Enable the use of a GPU for training and inference of the deep learning tool. If the active engine is not the default engine, EDeepLearningTool.EnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool.EnableGPU to any value when the active engine is not the default one will throw an exception.
uint[]	GPUIndexes [get, set]
	Indexes of the GPUs to use for computations.
uint	NumGPUs [get]
	Number of detected NVIDIA GPU.
string	Engine [get, set]
	Sets the engine with which to execute or train the neural network.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	InferencePrecision [get, set]
	Inference precision.
int	NumAvailableDevices [get]
	Available devices for the current EDeepLearningTool.Engine.
eVision.EasyDeepLearning.EDeepLearningDevice[]	AvailableDevices [get]
	Get all available device for the current engine.
int	NumActiveDevices [get]
	Gets the number of devices with which to run or train the model.
string	ActiveDeviceByName [set]
	Sets the device with which to run the model using its name.
int	ActiveDeviceById [set]
	Sets the device with which to run the model using its index in the list returned by EDeepLearningTool.AvailableDevices.
eVision.EasyDeepLearning.EDeepLearningDevice[]	ActiveDevices [get, set]
	Devices with which to run or train the model.
int[]	ActiveDevicesById [set]
	Sets the devices with which to run the model using their index in the list returned by EDeepLearningTool.AvailableDevices.
eVision.EasyDeepLearning.EDeepLearningExecutionSettings	ExecutionSettings [get, set]
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
eVision.EasyDeepLearning.EDeepLearningDevice	EngineSetupDataDevice [get]
	Device corresponding to the engine data contained in the tool.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	EngineSetupDataInferencePrecision [get]
	Inference precision corresponding to the engine data contained in the tool.
int	EngineSetupDataBatchSize [get]
	Batch size corresponding to the engine data contained in the tool.
bool	OptimizeBatchSize [get, set]
	Indicates whether to optimize the batch size (see EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int	BatchSizeForMaximumInferenceSpeed [get]
	Computes the batch size that will maximize the inference speed on NVIDIA GPU. For all other devices, it will return 1.
int	BatchSize [get, set]
	Batch size. The batch size is the number of images that are processed together during training and batch inference.
float	CurrentTrainingProgression [get]
	Current training progression as a percentage (between 0 and 1).
uint	CurrentTrainingFinishedIterations [get]
	Number of iterations that are already finished in the current training.
uint	CurrentTrainingNumIterations [get]
	Total number of training iterations that will be performed during the current training of the deep learning tool.
uint	NumTrainedIterations [get]
	Number of iterations that were performed to train this deep learning tool.
int	BestIteration [get]
	Iteration at which the minimum validation error was reached. After training, the tool is in the state it was at this best iteration.
ulong	ImageCacheSize [get, set]
	Size in byte of the image cache. The cache is used during training to store reformatted and normalized images. A correctly sized cache can reduce the hard drive accesses and the preprocessing time for each image.
string	Path [get]
	Path of the tool when loaded or saved using EDeepLearningTool.Save or EDeepLearningTool.Load.
string	InferenceModelPath [get]
	Path of the inference model when loaded or saved using EDeepLearningTool.SaveInferenceModel, EDeepLearningTool.LoadInferenceModel or EDeepLearningTool.SerializeInferenceModel.
string	TrainingModelPath [get]
	Path of the training model when loaded or saved using EDeepLearningTool.SaveTrainingModel, EDeepLearningTool.LoadTrainingModel or EDeepLearningTool.SerializeTrainingModel.
string	SettingsPath [get]
	Path of the settings when loaded or saved using EDeepLearningTool.SaveSettings, EDeepLearningTool.LoadSettings or EDeepLearningTool.SerializeSettings.
int	AutoSavePeriod [get, set]
	Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.
eVision.EasyDeepLearning.EDeepLearningLearningRateType	LearningRateType [get]
	Learning rate type.
float[]	LearningRateParameters [get]
	Parameters of the learning rate.
bool	EnableDeterministicTraining [get, set]
	Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.
int	DeterministicTrainingRandomSeed [get, set]
	Random seed to use when EDeepLearningTool.EnableDeterministicTraining is set to true. The default value is 42.
uint	NumLabels [get]
	Number of labels of this tool. If the tool is not trained, the method will throw an exception.
static int	NumAvailableEngines [get]
	Number of available engines.
static string[]	AvailableEngines [get]
	Available engines.

Additional Inherited Members
Static Public Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
static eVision.EasyDeepLearning.EDeepLearningTool	Create (string path)
	Factory method to open a deep learning tool.
static eVision.EasyDeepLearning.EDeepLearningTool	Create (eVision.ESerializer serializer)
	Factory method to open a deep learning tool.
static eVision.EasyDeepLearning.EDeepLearningTool	Create (eVision.EasyDeepLearning.EDeepLearningToolType type)
	Factory method to open a deep learning tool.
static string	GetAvailableEngineName (int id)
	Gets the name of the engine given its id.
static eVision.EasyDeepLearning.EDeepLearningDevice[]	GetAvailableDevicesForEngine (string engine)
	Available devices for the specified engine.

Detailed Description

EasyLocate tool - interest point version.

The EInterestPointLocator locates and classifies objects (or defects). With this class, an object is defined by its position it and a label (see 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:

• The object size (EInterestPointLocator.ObjectSize) to indicate an approximate size for the interest point that you want to detect.
• A detection threshold (ELocatorBase.DetectionThreshold) to accept or reject predicted objects based on their score.
• The maximum number of objects in an image (ELocatorBase.MaxNumberOfObjects)
• The maximum proximity or the minimum distance between predicted objects with the same label (EInterestPointLocator.SameLabelMinDistance, ELocatorBase.SameLabelMaxObjectProximity).
• The maximum proximity or the minimum distance between predicted objects regardless of their label (EInterestPointLocator.AbsoluteMinDistance, ELocatorBase.AbsoluteMaxObjectProximity). The proximity is defined as max(0, EInterestPointLocator.ObjectSize - d) / (EInterestPointLocator.ObjectSize + d) where d is the Manhattan distance. Except for the EInterestPointLocator.ObjectSize, all the parameters can be changed before and after training the tool.

License(s): EasyLocate or EasyLocateInference

Constructor & Destructor Documentation

EInterestPointLocator() [1/3]

eVision.EasyDeepLearning.EInterestPointLocator.EInterestPointLocator

(

)

Constructs a EInterestPointLocator object.

EInterestPointLocator() [2/3]

eVision.EasyDeepLearning.EInterestPointLocator.EInterestPointLocator

(

int

objectSize

)

Constructs a EInterestPointLocator object.

Parameters

objectSize

Size of the interest point

EInterestPointLocator() [3/3]

eVision.EasyDeepLearning.EInterestPointLocator.EInterestPointLocator

(

eVision.EasyDeepLearning.EInterestPointLocator

other

)

Copy constructor of a EInterestPointLocator object.

Parameters

other

Reference to the EInterestPointLocator object that should be copied

Member Function Documentation

SerializeSettings()

override void eVision.EasyDeepLearning.EInterestPointLocator.SerializeSettings

(

eVision.ESerializer

serializer

)

Serializes the settings of the locator.

Parameters

serializer

Pointer to ESerializer

Property Documentation

AbsoluteMinDistance

float eVision.EasyDeepLearning.EInterestPointLocator.AbsoluteMinDistance

getset

Minimum distance between two predicted objects regardless of their label.

This value must be between 0 and EInterestPointLocator.ObjectSize. It is internally converted to ELocatorBase.AbsoluteMaxObjectProximity.

Default value: 0.

ObjectSize

int eVision.EasyDeepLearning.EInterestPointLocator.ObjectSize

getset

Specify the size of predicted objects when only the position is predicted. This parameter is only used to prepare the predicted objects for drawing.

SameLabelMinDistance

float eVision.EasyDeepLearning.EInterestPointLocator.SameLabelMinDistance

getset

Minimum distance between two predicted objects with the same label.

This value must be between 0 and EInterestPointLocator.ObjectSize. It is internally converted to ELocatorBase.SameLabelMaxObjectProximity.

Default value: EInterestPointLocator.ObjectSize / 3.

ToolType

override eVision.EasyDeepLearning.EDeepLearningToolType eVision.EasyDeepLearning.EInterestPointLocator.ToolType

get

Type of the deep learning tool.