EClassifier allows to train a classifier using an EDataset object and classify new images. More...

#include <eVision/EasyDeepLearning/EasyClassify/EClassifier.h>

Inheritance diagram for eVision::EasyDeepLearning::EClassifier:

Collaboration diagram for eVision::EasyDeepLearning::EClassifier:

Public Member Functions
	EClassifier ()
	Constructs a EClassifier object.
	EClassifier (const eVision::EasyDeepLearning::EClassifier &other)
	Copy constructor of a EClassifier object.
virtual	~EClassifier () throw ()
eVision::EasyDeepLearning::EClassifier &	operator= (const eVision::EasyDeepLearning::EClassifier &other)
	Assignment operator.
virtual void	SerializeSettings (eVision::ESerializer *serializer)
	Serializes the classifier settings.
virtual eVision::EasyDeepLearning::EDeepLearningToolType	GetToolType () const
	Type of the deep learning tool.
void	LoadAsPretrained (const std::string &path)
	Loads the classifier and use it as a pre-trained model.
void	LoadOnnxModelAsPretrained (const std::string &path)
	Loads the ONNX model as a pre-trained model.
void	LoadOnnxModel (const std::string &path)
	Loads the ONNX model for inference.
void	SetUsePretrainedModel (bool usePretrainedModel)
	Whether to use a pre-trained model when training.
bool	GetUsePretrainedModel () const
	Whether to use a pre-trained model when training.
bool	HasPretrainedModel () const
	Whether a pre-trained model for the current configuration can be found.
void	SetWidth (uint32_t width)
	Width for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.
uint32_t	GetWidth () const
	Width for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.
void	SetHeight (uint32_t height)
	Height for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.
uint32_t	GetHeight () const
	Height for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.
void	SetChannels (uint32_t channel)
	Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.
uint32_t	GetChannels () const
	Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.
void	SetEnableAutomaticImageReformat (bool val)
	Enable automatic image reformat (true by default).
bool	GetEnableAutomaticImageReformat () const
	Enable automatic image reformat (true by default).
void	SetEnableHistogramEqualization (bool val)
	Enable histogram equalization of all images passing through the classifier. (false by default).
bool	GetEnableHistogramEqualization () const
	Enable histogram equalization of all images passing through the classifier. (false by default).
uint32_t	GetMinimumWidth () const
	Minimum width for input images of the classifier. This value is equal to 1 for standard networks.
uint32_t	GetMinimumHeight () const
	Minimum height for input images of the classifier. This value is equal to 1 for standard networks.
void	SetCapacity (eVision::EasyDeepLearning::EClassifierCapacity capacity)
	Capacity of classifier to use. Be aware that changing the classifier capacity will delete the effect of any previous training. The capacity will be translated to the corresponding type (Small, Normal, or Large).
eVision::EasyDeepLearning::EClassifierCapacity	GetCapacity () const
	Capacity of classifier to use. Be aware that changing the classifier capacity will delete the effect of any previous training. The capacity will be translated to the corresponding type (Small, Normal, or Large).
std::string	GetModelType () const
	Model type for training. Default: "Normal".
void	SetModelType (const std::string &name)
	Model type for training. Default: "Normal".
eVision::EasyDeepLearning::EClassificationMetrics	GetTrainingMetrics (int id)
	Gets the metrics obtained with the training dataset at the given iteration.
eVision::EasyDeepLearning::EClassificationMetrics	GetValidationMetrics (int id)
	Gets the metrics obtained with the validation dataset at the given iteration.
void	Train (eVision::EasyDeepLearning::EDataset &dataset)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &dataset, const eVision::EasyDeepLearning::EDataAugmentation &dataAugmentation)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &trainingDataset, eVision::EasyDeepLearning::EDataset &validationDataset)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &trainingDataset, eVision::EasyDeepLearning::EDataset &validationDataset, const eVision::EasyDeepLearning::EDataAugmentation &dataAugmentation)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &dataset, int iterations)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &dataset, const eVision::EasyDeepLearning::EDataAugmentation &dataAugmentation, int iterations)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &trainingDataset, eVision::EasyDeepLearning::EDataset &validationDataset, int iterations)
	Trains the EClassifier with the given dataset for the specified number of iterations.
void	Train (eVision::EasyDeepLearning::EDataset &trainingDataset, eVision::EasyDeepLearning::EDataset &validationDataset, const eVision::EasyDeepLearning::EDataAugmentation &dataAugmentation, int iterations)
	Trains the EClassifier with the given dataset for the specified number of iterations.
eVision::EasyDeepLearning::EClassificationResult	Classify (const eVision::EBaseROI &img) const
	Classifies images and returns the complete results as an EClassificationResult object.
eVision::EasyDeepLearning::EClassificationResult	Classify (const eVision::EBaseROI &img, eVision::ERegion &mask) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< const eVision::EBaseROI * > &imgList, eVision::ERegion &mask) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< const eVision::EBaseROI * > &imgList) const
	Classifies images and returns the complete results as an EClassificationResult object.
eVision::EasyDeepLearning::EClassificationResult	Classify (const eVision::EBaseROI &img, eVision::ERegion &mask, const eVision::EFrame &frame) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< const eVision::EBaseROI * > &imgList, eVision::ERegion &mask, const eVision::EFrame &frame) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageBW8 > &imgList, eVision::ERegion &mask) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageBW8 > &imgList) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageBW16 > &imgList, eVision::ERegion &mask) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageBW16 > &imgList) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageC24 > &imgList, eVision::ERegion &mask) const
	Classifies images and returns the complete results as an EClassificationResult object.
std::vector< eVision::EasyDeepLearning::EClassificationResult >	Classify (std::vector< eVision::EImageC24 > &imgList) const
	Classifies images and returns the complete results as an EClassificationResult object.
eVision::EasyDeepLearning::EClassificationMetrics	Evaluate (const eVision::EasyDeepLearning::EDataset &dataset)
	Evaluates the EClassifier using the given EDataset.
eVision::EImageBW8	GetHeatMap (const eVision::EBaseROI &image, const std::string &label)
	Gets a heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.
eVision::EImageC24	GetColorizedHeatMap (const eVision::EBaseROI &image, const std::string &label)
	Gets a colorized heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.
eVision::EImageC24A	GetColorizedHeatmapWithTransparency (const eVision::EBaseROI &image, const std::string &label, float minAlpha, float maxAlpha, eVision::EasyDeepLearning::EHeatmapColormap colormap)
	Colorized heatmap with transparency.
eVision::EImageC24A	GetColorizedHeatmapWithTransparency (const eVision::EBaseROI &image, const std::string &label, float minAlpha, float maxAlpha)
	Colorized heatmap with transparency.
float	GetOODThreshold () const
	Out-Of-Distribution (OOD) threshold below which an image is considered to be OOD.
void	SetOODThreshold (float value)
	Out-Of-Distribution (OOD) threshold below which an image is considered to be OOD.
void	LearnOODParameters (const eVision::EasyDeepLearning::EDataset &trainList, const eVision::EasyDeepLearning::EDataset &valList)
	Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.
void	LearnOODParameters (const std::vector< const eVision::EBaseROI * > &trainList, const std::vector< const eVision::EBaseROI * > &valList)
	Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.
void	LearnOODParameters (const std::vector< eVision::EImageBW8 > &trainList, const std::vector< eVision::EImageBW8 > &valList)
	Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.
void	LearnOODParameters (const std::vector< eVision::EImageBW16 > &trainList, const std::vector< eVision::EImageBW16 > &valList)
	Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.
void	LearnOODParameters (const std::vector< eVision::EImageC24 > &trainList, const std::vector< eVision::EImageC24 > &valList)
	Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.
bool	HasOODParameters () const
	Checks whether the OOD parameters have been learned.
void	SetComputeHeatmapWithResult (bool computeHeatmap)
	Whether to always compute the heatmap along the result when applying the tool. If true, the heatmap is available through EClassificationResult::GetHeatmap.
bool	GetComputeHeatmapWithResult () const
	Whether to always compute the heatmap along the result when applying the tool. If true, the heatmap is available through EClassificationResult::GetHeatmap.
void	SetComputeOODWithResult (bool computeOOD)
	Whether to compute the OOD parameters while training. Also determines whether the OOD score is computed alongside the result when applying the tool. If true, the OOD score is available through EClassificationResult::GetOODScore.
bool	GetComputeOODWithResult () const
	Whether to compute the OOD parameters while training. Also determines whether the OOD score is computed alongside the result when applying the tool. If true, the OOD score is available through EClassificationResult::GetOODScore.
	EClassifier (CppWrapperInternal::InternalConstructor)
Public Member Functions inherited from eVision::EasyDeepLearning::EDeepLearningTool
virtual	~EDeepLearningTool () throw ()
virtual void	Load (const std::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.
std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Clone () const
	Clones this tool.
virtual void	Save (const std::string &filePath, bool includeTrainingModel)
	Saves the settings, inference and training model of the deep learning tool.
virtual void	Save (const std::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	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 (const std::string &filePath)
	Saves the settings of the tool to a file.
void	SaveInferenceModel (const std::string &filePath)
	Saves the tool's inference model to a file.
void	SaveTrainingModel (const std::string &filePath)
	Saves the tool's training model to a file.
void	LoadSettings (const std::string &filePath)
	Loads the settings of a tool.
void	LoadInferenceModel (const std::string &filePath)
	Loads the tool's inference model. The inference model is required to apply the tool on new images.
void	LoadTrainingModel (const std::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 () const
	Whether the inference model is loaded or created when the tool is not yet trained.
bool	HasTrainingModel () const
	Whether the training model is loaded or created when the tool is not yet trained.
bool	IsTrained () const
	Tells whether the deep learning tool has been trained.
void	SetEnableGPU (bool enable)
	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::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.
bool	GetEnableGPU () const
	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::SetEnableGPU will be false even if the active device has a GPU device type. Setting EDeepLearningTool::SetEnableGPU to any value when the active engine is not the default one will throw an exception.
void	SetGPUIndexes (const std::vector< uint32_t > &indexes)
	Indexes of the GPUs to use for computations.
std::vector< uint32_t >	GetGPUIndexes () const
	Indexes of the GPUs to use for computations.
uint32_t	GetNumGPUs () const
	Number of detected NVIDIA GPU.
void	SetEngine (const std::string &engineName)
	Sets the engine with which to execute or train the neural network.
std::string	GetEngine () const
	Sets the engine with which to execute or train the neural network.
eVision::EasyDeepLearning::EDeepLearningInferencePrecision	GetInferencePrecision () const
	Inference precision.
void	SetInferencePrecision (eVision::EasyDeepLearning::EDeepLearningInferencePrecision prec)
	Inference precision.
int	GetNumAvailableDevices () const
	Available devices for the current EDeepLearningTool::SetEngine.
std::string	GetAvailableDeviceName (int i) const
	Name of the i-th device of the current engine.
eVision::EasyDeepLearning::EDeepLearningDevice	GetAvailableDevice (int i) const
	i-th EDeepLearningDevice of the current engine.
std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetAvailableDevices () const
	Get all available device for the current engine.
int	GetNumActiveDevices () const
	Gets the number of devices with which to run or train the model.
void	SetActiveDevice (const eVision::EasyDeepLearning::EDeepLearningDevice &device)
	Sets the device with which to run or train the model.
eVision::EasyDeepLearning::EDeepLearningDevice	GetActiveDevice (int i) const
	Gets the active device. By default, it returns the first active device.
eVision::EasyDeepLearning::EDeepLearningDevice	GetActiveDevice () const
	Gets the active device. By default, it returns the first active device.
void	SetActiveDeviceByName (const std::string &name)
	Sets the device with which to run the model using its name.
void	SetActiveDeviceById (int id)
	Sets the device with which to run the model using its index in the list returned by EDeepLearningTool::GetAvailableDevices.
std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetActiveDevices () const
	Devices with which to run or train the model.
void	SetActiveDevices (const std::vector< eVision::EasyDeepLearning::EDeepLearningDevice > &devices)
	Devices with which to run or train the model.
void	SetActiveDevicesById (const std::vector< int > &ids)
	Sets the devices with which to run the model using their index in the list returned by EDeepLearningTool::GetAvailableDevices.
eVision::EasyDeepLearning::EDeepLearningExecutionSettings	GetExecutionSettings () const
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
void	SetExecutionSettings (const eVision::EasyDeepLearning::EDeepLearningExecutionSettings &settings)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
bool	HasEngineSetupData () const
	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.
eVision::EasyDeepLearning::EDeepLearningDevice	GetEngineSetupDataDevice () const
	Device corresponding to the engine data contained in the tool.
eVision::EasyDeepLearning::EDeepLearningInferencePrecision	GetEngineSetupDataInferencePrecision () const
	Inference precision corresponding to the engine data contained in the tool.
int32_t	GetEngineSetupDataBatchSize () const
	Batch size corresponding to the engine data contained in the tool.
bool	GetOptimizeBatchSize () const
	Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
void	SetOptimizeBatchSize (bool optimize)
	Indicates whether to optimize the batch size (see EDeepLearningTool::SetBatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int32_t	GetBatchSizeForMaximumInferenceSpeed () const
	Computes the batch size that will maximize the inference speed on NVIDIA GPU. For all other devices, it will return 1.
void	SetBatchSize (int32_t size)
	Batch size. The batch size is the number of images that are processed together during training and batch inference.
int32_t	GetBatchSize () const
	Batch size. The batch size is the number of images that are processed together during training and batch inference.
bool	IsTraining () const
	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.
float	GetCurrentTrainingProgression () const
	Current training progression as a percentage (between 0 and 1).
uint32_t	GetCurrentTrainingFinishedIterations () const
	Number of iterations that are already finished in the current training.
uint32_t	GetCurrentTrainingNumIterations () const
	Total number of training iterations that will be performed during the current training of the deep learning tool.
uint32_t	GetNumTrainedIterations () const
	Number of iterations that were performed to train this deep learning tool.
int	GetBestIteration () const
	Iteration at which the minimum validation error was reached. After training, the tool is in the state it was at this best iteration.
void	SetImageCacheSize (uint64_t size)
	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.
uint64_t	GetImageCacheSize () const
	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.
std::string	GetPath () const
	Path of the tool when loaded or saved using EDeepLearningTool::Save or EDeepLearningTool::Load.
std::string	GetInferenceModelPath () const
	Path of the inference model when loaded or saved using EDeepLearningTool::SaveInferenceModel, EDeepLearningTool::LoadInferenceModel or EDeepLearningTool::SerializeInferenceModel.
std::string	GetTrainingModelPath () const
	Path of the training model when loaded or saved using EDeepLearningTool::SaveTrainingModel, EDeepLearningTool::LoadTrainingModel or EDeepLearningTool::SerializeTrainingModel.
std::string	GetSettingsPath () const
	Path of the settings when loaded or saved using EDeepLearningTool::SaveSettings, EDeepLearningTool::LoadSettings or EDeepLearningTool::SerializeSettings.
void	SetAutoSavePeriod (int period)
	Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.
int	GetAutoSavePeriod () const
	Auto-save period. The auto-save period is the number of training iterations between each automatic save of the model.
void	SetLearningRate (eVision::EasyDeepLearning::EDeepLearningLearningRateType type, const std::vector< float > &parameters)
	Specifies how the value of the learning rate and how it evolves during training.
eVision::EasyDeepLearning::EDeepLearningLearningRateType	GetLearningRateType () const
	Learning rate type.
std::vector< float >	GetLearningRateParameters () const
	Parameters of the learning rate.
void	SetEnableDeterministicTraining (bool enable)
	Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.
bool	GetEnableDeterministicTraining () const
	Whether to use deterministic training algorithm that allows to repeatable training results. Default value: false.
void	SetDeterministicTrainingRandomSeed (int32_t seed)
	Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.
int32_t	GetDeterministicTrainingRandomSeed () const
	Random seed to use when EDeepLearningTool::SetEnableDeterministicTraining is set to true. The default value is 42.
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 (const eVision::EBaseROI &img)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< const eVision::EBaseROI * > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageBW8 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageBW16 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
void	InitializeInference (std::vector< eVision::EImageC24 > &imgList)
	Initializes the neural network for inference (i.e. making predictions with the neural network).
uint32_t	GetNumLabels () const
	Number of labels of this tool. If the tool is not trained, the method will throw an exception.
std::string	GetLabel (uint32_t index) const
	Gets the label from its index. If the tool is not trained, the method may throw an exception.
virtual void	SetLabel (uint32_t index, const std::string &label)
	Changes a label predicted by the tool.
void	SetLabelWeight (uint32_t 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 (uint32_t index) const
	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 (uint32_t index, const eVision::ERGBColor &c)
	Changes a label color.
eVision::ERGBColor	GetLabelColor (uint32_t index) const
	Label color. The label colors affect how the results will be displayed.
void	SetLabelOpacity (uint32_t index, uint8_t opacity)
	Changes a label opacity (between 0 for fully transparent and 255 for opaque color).
uint8_t	GetLabelOpacity (uint32_t index) const
	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 (const eVision::EBaseROI &sampleImage) const
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.
int	GetOptimalNumImagesForBatchSize (int imageWidth, int imageHeight) const
	Optimal number of images to process together for inference given the batch size and EDeepLearningTool::GetNumPatchesForImage.
int	GetNumPatchesForImage (const eVision::EBaseROI &sampleImage) const
	Number of patches that will be extracted from an input image to perform inference.
virtual int	GetNumPatchesForImage (int imageWidth, int imageHeight) const
	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.
	EDeepLearningTool (CppWrapperInternal::InternalConstructor)

Static Public Member Functions
static std::string	GetAvailableModelTypes (int i)
	Get the i-th available models to be used with EClassifier::GetModelType.
static int	GetNumAvailableModelTypes ()
	Number of available models for training an EasyClassify tool.
Static Public Member Functions inherited from eVision::EasyDeepLearning::EDeepLearningTool
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (const std::string &path)
	Factory method to open a deep learning tool.
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (eVision::ESerializer *serializer)
	Factory method to open a deep learning tool.
static std::unique_ptr< eVision::EasyDeepLearning::EDeepLearningTool >	Create (eVision::EasyDeepLearning::EDeepLearningToolType type)
	Factory method to open a deep learning tool.
static int	GetNumAvailableEngines ()
	Number of available engines.
static std::string	GetAvailableEngineName (int id)
	Gets the name of the engine given its id.
static std::vector< std::string >	GetAvailableEngines ()
	Available engines.
static std::vector< eVision::EasyDeepLearning::EDeepLearningDevice >	GetAvailableDevicesForEngine (const std::string &engine)
	Available devices for the specified engine.

Additional Inherited Members
Public Attributes inherited from eVision::EasyDeepLearning::EDeepLearningTool
CppWrapperInternal::WrappedClass	wrappedClass_

Detailed Description

EClassifier allows to train a classifier using an EDataset object and classify new images.

As required by Deep Learning techniques, the input image of EClassifier must be of the same format (width, height, number of channels). By default, this format will be 224x224xC (which is the format used for pre-training our networks) with C being the number of channel of the first image of the dataset. The format can also be specified by the EClassifier::SetWidth, EClassifier::SetHeight and EClassifier::SetChannels unless its width and height is smaller than the minimum width and height supported by the classifier. In this case, the input resolution will be the minimum resolution supported by the classifier. (See EClassifier::GetMinimumWidth and EClassifier::GetMinimumHeight). methods.

By default, images that don't satisfy the image format of the classifier are automatically reformatted. This behavior can be controlled through the EClassifier::SetEnableAutomaticImageReformat method. When the automatic image reformatting is disabled, training or classifying an image that doesn't satisfy the input image format will result in an exception.

Once trained, the input image format cannot be changed.

License(s): EasyClassify or EasyClassifyInference

Constructor & Destructor Documentation

◆ EClassifier() [1/3]

eVision::EasyDeepLearning::EClassifier::EClassifier ( )

Constructs a EClassifier object.

◆ EClassifier() [2/3]

eVision::EasyDeepLearning::EClassifier::EClassifier ( const eVision::EasyDeepLearning::EClassifier & other )

Copy constructor of a EClassifier object.

Parameters

other Reference to the EClassifier object that should be copied

◆ ~EClassifier()

eVision::EasyDeepLearning::EClassifier::~EClassifier	(		)
throw	(	)

virtual

◆ EClassifier() [3/3]

eVision::EasyDeepLearning::EClassifier::EClassifier ( CppWrapperInternal::InternalConstructor )

Member Function Documentation

◆ Classify() [1/12]

eVision::EasyDeepLearning::EClassificationResult eVision::EasyDeepLearning::EClassifier::Classify ( const eVision::EBaseROI & img ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

img	Image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [2/12]

eVision::EasyDeepLearning::EClassificationResult eVision::EasyDeepLearning::EClassifier::Classify	(	const eVision::EBaseROI &	img,
		eVision::ERegion &	mask ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

img	Image to classify
mask	Mask of image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [3/12]

eVision::EasyDeepLearning::EClassificationResult eVision::EasyDeepLearning::EClassifier::Classify	(	const eVision::EBaseROI &	img,
		eVision::ERegion &	mask,
		const eVision::EFrame &	frame ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

img	Image to classify
mask	Mask of image to classify
frame	Frame associated with the mask.

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [4/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify ( std::vector< const eVision::EBaseROI * > & imgList ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList Vector of images to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [5/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify	(	std::vector< const eVision::EBaseROI * > &	imgList,
		eVision::ERegion &	mask ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList	Vector of images to classify
mask	Mask of image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [6/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify	(	std::vector< const eVision::EBaseROI * > &	imgList,
		eVision::ERegion &	mask,
		const eVision::EFrame &	frame ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList	Vector of images to classify
mask	Mask of image to classify
frame	Frame associated with the mask.

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [7/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify ( std::vector< eVision::EImageBW16 > & imgList ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList Vector of images to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [8/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify	(	std::vector< eVision::EImageBW16 > &	imgList,
		eVision::ERegion &	mask ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList	Vector of images to classify
mask	Mask of image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [9/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify ( std::vector< eVision::EImageBW8 > & imgList ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList Vector of images to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [10/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify	(	std::vector< eVision::EImageBW8 > &	imgList,
		eVision::ERegion &	mask ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList	Vector of images to classify
mask	Mask of image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [11/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify ( std::vector< eVision::EImageC24 > & imgList ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList Vector of images to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Classify() [12/12]

std::vector< eVision::EasyDeepLearning::EClassificationResult > eVision::EasyDeepLearning::EClassifier::Classify	(	std::vector< eVision::EImageC24 > &	imgList,
		eVision::ERegion &	mask ) const

Classifies images and returns the complete results as an EClassificationResult object.

The method throws an exception if the input image does not fulfill the input specification.

Parameters

imgList	Vector of images to classify
mask	Mask of image to classify

Classifying a set of images is usually faster than classifying each image sequentially.

To maximize the classification speed on a GPU, EDeepLearningTool::SetBatchSize and the size of the set of input images must be equal to the value returned by EDeepLearningTool::GetBatchSizeForMaximumInferenceSpeed.

◆ Evaluate()

eVision::EasyDeepLearning::EClassificationMetrics eVision::EasyDeepLearning::EClassifier::Evaluate ( const eVision::EasyDeepLearning::EDataset & dataset )

Evaluates the EClassifier using the given EDataset.

Parameters

dataset EDataset with which to evaluate the classifier

This method computes various metrics (see EClassificationMetrics on the given dataset.

The method ignores the label weights and the data augmentation settings of the given dataset. However, the label weights can be taken into consideration in the returned EClassificationMetrics object.

License(s): EasyClassify

◆ GetAvailableModelTypes()

std::string eVision::EasyDeepLearning::EClassifier::GetAvailableModelTypes ( int i )

static

Get the i-th available models to be used with EClassifier::GetModelType.

Parameters

i	Index of the model between 0 and EClassifier::GetNumAvailableModelTypes - 1.

◆ GetCapacity()

eVision::EasyDeepLearning::EClassifierCapacity eVision::EasyDeepLearning::EClassifier::GetCapacity ( ) const

Capacity of classifier to use. Be aware that changing the classifier capacity will delete the effect of any previous training. The capacity will be translated to the corresponding type (Small, Normal, or Large).

Deprecated: Deprecated in version 22.12. Scheduled to be removed in version 27.6.

◆ GetChannels()

uint32_t eVision::EasyDeepLearning::EClassifier::GetChannels ( ) const

Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.

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

◆ GetColorizedHeatMap()

eVision::EImageC24 eVision::EasyDeepLearning::EClassifier::GetColorizedHeatMap	(	const eVision::EBaseROI &	image,
		const std::string &	label )

Gets a colorized heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.

Parameters

image	A reference to the image we want to generate the heatmap from.
label	A reference to the string representing the label we want to explain for the given image.

◆ GetColorizedHeatmapWithTransparency() [1/2]

eVision::EImageC24A eVision::EasyDeepLearning::EClassifier::GetColorizedHeatmapWithTransparency	(	const eVision::EBaseROI &	image,
		const std::string &	label,
		float	minAlpha,
		float	maxAlpha )

Colorized heatmap with transparency.

When the minimum and maximum alpha values are different from each other, the transparency value for a pixel will depend on the value of the heatmap at that pixel. The range of heatmap values [0, 255] will be mapped to the range [minAlpha, maxAlpha].

Parameters

image	A reference to the image we want to generate the heatmap from.
label	A reference to the string representing the label we want to explain for the given image.
minAlpha	Minimum transparency value.
maxAlpha	Maximum transparency value.

◆ GetColorizedHeatmapWithTransparency() [2/2]

eVision::EImageC24A eVision::EasyDeepLearning::EClassifier::GetColorizedHeatmapWithTransparency	(	const eVision::EBaseROI &	image,
		const std::string &	label,
		float	minAlpha,
		float	maxAlpha,
		eVision::EasyDeepLearning::EHeatmapColormap	colormap )

Colorized heatmap with transparency.

When the minimum and maximum alpha values are different from each other, the transparency value for a pixel will depend on the value of the heatmap at that pixel. The range of heatmap values [0, 255] will be mapped to the range [minAlpha, maxAlpha].

Parameters

image	A reference to the image we want to generate the heatmap from.
label	A reference to the string representing the label we want to explain for the given image.
minAlpha	Minimum transparency value.
maxAlpha	Maximum transparency value.
colormap	Color map

◆ GetComputeHeatmapWithResult()

bool eVision::EasyDeepLearning::EClassifier::GetComputeHeatmapWithResult ( ) const

Whether to always compute the heatmap along the result when applying the tool. If true, the heatmap is available through EClassificationResult::GetHeatmap.

◆ GetComputeOODWithResult()

bool eVision::EasyDeepLearning::EClassifier::GetComputeOODWithResult ( ) const

Whether to compute the OOD parameters while training. Also determines whether the OOD score is computed alongside the result when applying the tool. If true, the OOD score is available through EClassificationResult::GetOODScore.

◆ GetEnableAutomaticImageReformat()

bool eVision::EasyDeepLearning::EClassifier::GetEnableAutomaticImageReformat ( ) const

Enable automatic image reformat (true by default).

◆ GetEnableHistogramEqualization()

bool eVision::EasyDeepLearning::EClassifier::GetEnableHistogramEqualization ( ) const

Enable histogram equalization of all images passing through the classifier. (false by default).

◆ GetHeatMap()

eVision::EImageBW8 eVision::EasyDeepLearning::EClassifier::GetHeatMap	(	const eVision::EBaseROI &	image,
		const std::string &	label )

Gets a heatmap associated with the given label. A heatmap is an image that indicates which pixels in the original image best explain the given label.

Parameters

image	A reference to the image we want to generate the heatmap from.
label	A reference to the string representing the label we want to explain for the given image.

◆ GetHeight()

uint32_t eVision::EasyDeepLearning::EClassifier::GetHeight ( ) const

Height for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.

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

◆ GetMinimumHeight()

uint32_t eVision::EasyDeepLearning::EClassifier::GetMinimumHeight ( ) const

Minimum height for input images of the classifier. This value is equal to 1 for standard networks.

◆ GetMinimumWidth()

uint32_t eVision::EasyDeepLearning::EClassifier::GetMinimumWidth ( ) const

Minimum width for input images of the classifier. This value is equal to 1 for standard networks.

◆ GetModelType()

std::string eVision::EasyDeepLearning::EClassifier::GetModelType ( ) const

Model type for training. Default: "Normal".

The list of available models is available through EClassifier::GetAvailableModelTypes.

◆ GetNumAvailableModelTypes()

int eVision::EasyDeepLearning::EClassifier::GetNumAvailableModelTypes ( )

static

Number of available models for training an EasyClassify tool.

◆ GetOODThreshold()

float eVision::EasyDeepLearning::EClassifier::GetOODThreshold ( ) const

Out-Of-Distribution (OOD) threshold below which an image is considered to be OOD.

If training has been carried out with EClassifier::SetComputeOODWithResult set to True, the parameters have been automatically initialized. Otherwise, call EClassifier::LearnOODParameters with the training data before calling this method.

The OOD threshold is automatically initialized during learning. If needed, the parameter can also be changed manually.

◆ GetToolType()

eVision::EasyDeepLearning::EDeepLearningToolType eVision::EasyDeepLearning::EClassifier::GetToolType ( ) const

virtual

Type of the deep learning tool.

Implements eVision::EasyDeepLearning::EDeepLearningTool.

◆ GetTrainingMetrics()

eVision::EasyDeepLearning::EClassificationMetrics eVision::EasyDeepLearning::EClassifier::GetTrainingMetrics ( int id )

Gets the metrics obtained with the training dataset at the given iteration.

The iterations are indexed between 0 and EDeepLearningTool::GetNumTrainedIterations - 1.

Parameters

id	The iteration index

◆ GetUsePretrainedModel()

bool eVision::EasyDeepLearning::EClassifier::GetUsePretrainedModel ( ) const

Whether to use a pre-trained model when training.

Default: true if the pre-trained model are found on the disk.

◆ GetValidationMetrics()

eVision::EasyDeepLearning::EClassificationMetrics eVision::EasyDeepLearning::EClassifier::GetValidationMetrics ( int id )

Gets the metrics obtained with the validation dataset at the given iteration.

The iterations are indexed between 0 and EDeepLearningTool::GetNumTrainedIterations - 1.

Parameters

id	The iteration index

◆ GetWidth()

uint32_t eVision::EasyDeepLearning::EClassifier::GetWidth ( ) const

Width for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.

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

◆ HasOODParameters()

bool eVision::EasyDeepLearning::EClassifier::HasOODParameters ( ) const

Checks whether the OOD parameters have been learned.

◆ HasPretrainedModel()

bool eVision::EasyDeepLearning::EClassifier::HasPretrainedModel ( ) const

Whether a pre-trained model for the current configuration can be found.

◆ LearnOODParameters() [1/5]

void eVision::EasyDeepLearning::EClassifier::LearnOODParameters	(	const eVision::EasyDeepLearning::EDataset &	trainList,
		const eVision::EasyDeepLearning::EDataset &	valList )

Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.

Parameters

trainList	List of images used for training the EClassifier.
valList	List of images used as validation during the training of the EClassifier.

◆ LearnOODParameters() [2/5]

void eVision::EasyDeepLearning::EClassifier::LearnOODParameters	(	const std::vector< const eVision::EBaseROI * > &	trainList,
		const std::vector< const eVision::EBaseROI * > &	valList )

Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.

Parameters

trainList	List of images used for training the EClassifier.
valList	List of images used as validation during the training of the EClassifier.

◆ LearnOODParameters() [3/5]

void eVision::EasyDeepLearning::EClassifier::LearnOODParameters	(	const std::vector< eVision::EImageBW16 > &	trainList,
		const std::vector< eVision::EImageBW16 > &	valList )

Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.

Parameters

trainList	List of images used for training the EClassifier.
valList	List of images used as validation during the training of the EClassifier.

◆ LearnOODParameters() [4/5]

void eVision::EasyDeepLearning::EClassifier::LearnOODParameters	(	const std::vector< eVision::EImageBW8 > &	trainList,
		const std::vector< eVision::EImageBW8 > &	valList )

Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.

Parameters

trainList	List of images used for training the EClassifier.
valList	List of images used as validation during the training of the EClassifier.

◆ LearnOODParameters() [5/5]

void eVision::EasyDeepLearning::EClassifier::LearnOODParameters	(	const std::vector< eVision::EImageC24 > &	trainList,
		const std::vector< eVision::EImageC24 > &	valList )

Learns OOD parameters using training data. The dataset must correspond to the dataset used to train the classifier.

Parameters

trainList	List of images used for training the EClassifier.
valList	List of images used as validation during the training of the EClassifier.

◆ LoadAsPretrained()

void eVision::EasyDeepLearning::EClassifier::LoadAsPretrained ( const std::string & path )

Loads the classifier and use it as a pre-trained model.

Parameters

path	Path to the pre-trained model.

◆ LoadOnnxModel()

void eVision::EasyDeepLearning::EClassifier::LoadOnnxModel ( const std::string & path )

Loads the ONNX model for inference.

The methods will automatically create labels according to the number of classes in the ONNX model. The labels are named according to their position, i.e. "0", "1", etc. You can change the labels afterward using the method EDeepLearningTool::SetLabel.

◆ LoadOnnxModelAsPretrained()

void eVision::EasyDeepLearning::EClassifier::LoadOnnxModelAsPretrained ( const std::string & path )

Loads the ONNX model as a pre-trained model.

This method loads the architecture of the neural network as well as the trained weights in the model but discards the weights and parameters of some of the last layers to allow retraining on a new dataset with a different number of labels.

◆ operator=()

eVision::EasyDeepLearning::EClassifier & eVision::EasyDeepLearning::EClassifier::operator= ( const eVision::EasyDeepLearning::EClassifier & other )

Assignment operator.

Parameters

other Reference to the EClassifier object used for the assignment

◆ SerializeSettings()

void eVision::EasyDeepLearning::EClassifier::SerializeSettings ( eVision::ESerializer * serializer )

virtual

Serializes the classifier settings.

Parameters

serializer Pointer to ESerializer

Implements eVision::EasyDeepLearning::EDeepLearningTool.

◆ SetCapacity()

void eVision::EasyDeepLearning::EClassifier::SetCapacity ( eVision::EasyDeepLearning::EClassifierCapacity capacity )

Capacity of classifier to use. Be aware that changing the classifier capacity will delete the effect of any previous training. The capacity will be translated to the corresponding type (Small, Normal, or Large).

Parameters

capacity Capacity of classifier

Deprecated

Use EClassifier::GetModelType instead.

Deprecated in version 22.12. Scheduled to be removed in version 27.6.

◆ SetChannels()

void eVision::EasyDeepLearning::EClassifier::SetChannels ( uint32_t channel )

Number of channels for input images of the classifier. The number of channels can be either 1 (monochrome image) or 3 (RGB image). By default, this value will be set from the format of the first image added to the training dataset.

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

◆ SetComputeHeatmapWithResult()

void eVision::EasyDeepLearning::EClassifier::SetComputeHeatmapWithResult ( bool computeHeatmap )

Whether to always compute the heatmap along the result when applying the tool. If true, the heatmap is available through EClassificationResult::GetHeatmap.

Parameters

computeHeatmap Whether to compute the heatmap with the result

◆ SetComputeOODWithResult()

void eVision::EasyDeepLearning::EClassifier::SetComputeOODWithResult ( bool computeOOD )

Whether to compute the OOD parameters while training. Also determines whether the OOD score is computed alongside the result when applying the tool. If true, the OOD score is available through EClassificationResult::GetOODScore.

Parameters

computeOOD Whether to compute the OOD score with the result.

◆ SetEnableAutomaticImageReformat()

void eVision::EasyDeepLearning::EClassifier::SetEnableAutomaticImageReformat ( bool val )

Enable automatic image reformat (true by default).

◆ SetEnableHistogramEqualization()

void eVision::EasyDeepLearning::EClassifier::SetEnableHistogramEqualization ( bool val )

Enable histogram equalization of all images passing through the classifier. (false by default).

◆ SetHeight()

void eVision::EasyDeepLearning::EClassifier::SetHeight ( uint32_t height )

Height for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.

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

◆ SetModelType()

void eVision::EasyDeepLearning::EClassifier::SetModelType ( const std::string & name )

Model type for training. Default: "Normal".

The list of available models is available through EClassifier::GetAvailableModelTypes.

◆ SetOODThreshold()

void eVision::EasyDeepLearning::EClassifier::SetOODThreshold ( float value )

Out-Of-Distribution (OOD) threshold below which an image is considered to be OOD.

If training has been carried out with EClassifier::SetComputeOODWithResult set to True, the parameters have been automatically initialized. Otherwise, call EClassifier::LearnOODParameters with the training data before calling this method.

The OOD threshold is automatically initialized during learning. If needed, the parameter can also be changed manually.

◆ SetUsePretrainedModel()

void eVision::EasyDeepLearning::EClassifier::SetUsePretrainedModel ( bool usePretrainedModel )

Whether to use a pre-trained model when training.

Default: true if the pre-trained model are found on the disk.

◆ SetWidth()

void eVision::EasyDeepLearning::EClassifier::SetWidth ( uint32_t width )

Width for input images of the classifier. By default, this value will be set to 224 if it wasn't explicitly set before training.

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

◆ Train() [1/8]

void eVision::EasyDeepLearning::EClassifier::Train ( eVision::EasyDeepLearning::EDataset & dataset )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [2/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	dataset,
		const eVision::EasyDeepLearning::EDataAugmentation &	dataAugmentation )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [3/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	dataset,
		const eVision::EasyDeepLearning::EDataAugmentation &	dataAugmentation,
		int	iterations )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [4/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	dataset,
		int	iterations )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [5/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	trainingDataset,
		eVision::EasyDeepLearning::EDataset &	validationDataset )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [6/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	trainingDataset,
		eVision::EasyDeepLearning::EDataset &	validationDataset,
		const eVision::EasyDeepLearning::EDataAugmentation &	dataAugmentation )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [7/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	trainingDataset,
		eVision::EasyDeepLearning::EDataset &	validationDataset,
		const eVision::EasyDeepLearning::EDataAugmentation &	dataAugmentation,
		int	iterations )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

◆ Train() [8/8]

void eVision::EasyDeepLearning::EClassifier::Train	(	eVision::EasyDeepLearning::EDataset &	trainingDataset,
		eVision::EasyDeepLearning::EDataset &	validationDataset,
		int	iterations )

Trains the EClassifier 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 EDeepLearningTool::GetBestIteration.

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

Parameters

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

License(s): EasyClassify

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ EClassifier() [1/3]

◆ EClassifier() [2/3]

◆ ~EClassifier()

◆ EClassifier() [3/3]

Member Function Documentation

◆ Classify() [1/12]

◆ Classify() [2/12]

◆ Classify() [3/12]

◆ Classify() [4/12]

◆ Classify() [5/12]

◆ Classify() [6/12]

◆ Classify() [7/12]

◆ Classify() [8/12]

◆ Classify() [9/12]

◆ Classify() [10/12]

◆ Classify() [11/12]

◆ Classify() [12/12]

◆ Evaluate()

◆ GetAvailableModelTypes()

◆ GetCapacity()

◆ GetChannels()

◆ GetColorizedHeatMap()

◆ GetColorizedHeatmapWithTransparency() [1/2]

◆ GetColorizedHeatmapWithTransparency() [2/2]

◆ GetComputeHeatmapWithResult()

◆ GetComputeOODWithResult()

◆ GetEnableAutomaticImageReformat()

◆ GetEnableHistogramEqualization()

◆ GetHeatMap()

◆ GetHeight()

◆ GetMinimumHeight()

◆ GetMinimumWidth()

◆ GetModelType()

◆ GetNumAvailableModelTypes()

◆ GetOODThreshold()

◆ GetToolType()

◆ GetTrainingMetrics()

◆ GetUsePretrainedModel()

◆ GetValidationMetrics()

◆ GetWidth()

◆ HasOODParameters()

◆ HasPretrainedModel()

◆ LearnOODParameters() [1/5]

◆ LearnOODParameters() [2/5]

◆ LearnOODParameters() [3/5]

◆ LearnOODParameters() [4/5]

◆ LearnOODParameters() [5/5]

◆ LoadAsPretrained()

◆ LoadOnnxModel()

◆ LoadOnnxModelAsPretrained()

◆ operator=()

◆ SerializeSettings()

◆ SetCapacity()

◆ SetChannels()

◆ SetComputeHeatmapWithResult()

◆ SetComputeOODWithResult()

◆ SetEnableAutomaticImageReformat()

◆ SetEnableHistogramEqualization()

◆ SetHeight()

◆ SetModelType()

◆ SetOODThreshold()

◆ SetUsePretrainedModel()

◆ SetWidth()

◆ Train() [1/8]

◆ Train() [2/8]

◆ Train() [3/8]

◆ Train() [4/8]

◆ Train() [5/8]

◆ Train() [6/8]

◆ Train() [7/8]

◆ Train() [8/8]