Unsupervised segmentation tool. More...

Inheritance diagram for eVision.EasyDeepLearning.EUnsupervisedSegmenter:

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Collaboration diagram for eVision.EasyDeepLearning.EUnsupervisedSegmenter:

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Public Member Functions
	EUnsupervisedSegmenter ()
	Constructs a EUnsupervisedSegmenter object.
	EUnsupervisedSegmenter (eVision.EasyDeepLearning.EUnsupervisedSegmenter other)
	Copy constructor of a EUnsupervisedSegmenter object.
override void	SerializeSettings (eVision.ESerializer serializer)
	Serializes the settings of the unsupervised segmenter.
eVision.EasyDeepLearning.EUnsupervisedSegmenterMetrics	GetTrainingMetrics (int iteration)
	Training metrics at the given iteration.
eVision.EasyDeepLearning.EUnsupervisedSegmenterMetrics	GetValidationMetrics (int iteration)
	Validation metrics at the given iteration.
void	Train (eVision.EasyDeepLearning.EDataset dataset)
	Trains the EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation)
	Trains the EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset)
	Trains the EUnsupervisedSegmenter 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 EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, int iterations)
	Trains the EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset dataset, eVision.EasyDeepLearning.EDataAugmentation dataAugmentation, int iterations)
	Trains the EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
void	Train (eVision.EasyDeepLearning.EDataset trainingDataset, eVision.EasyDeepLearning.EDataset validationDataset, int iterations)
	Trains the EUnsupervisedSegmenter 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 EUnsupervisedSegmenter with the given dataset for the specified number of iterations.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult	Apply (eVision.EBaseROI img)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult	Apply (eVision.EBaseROI img, eVision.ERegion mask)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageBW8[] imgs)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageBW8[] imgs, eVision.ERegion[] masks)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageBW16[] imgs)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageBW16[] imgs, eVision.ERegion[] masks)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageC24[] imgs)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[]	Apply (eVision.EImageC24[] imgs, eVision.ERegion[] masks)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
void	Apply (eVision.EBaseROI[] imgs, eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[] results)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
void	Apply (eVision.EBaseROI[] imgs, eVision.ERegion[] masks, eVision.EasyDeepLearning.EUnsupervisedSegmenterResult[] results)
	Applies the unsupervised segmenter on the given image and its mask region. We recommend to use pointer-based versions of this method to avoid unnecessary image and result copies.
eVision.EasyDeepLearning.EUnsupervisedSegmenterMetrics	Evaluate (eVision.EasyDeepLearning.EDataset dataset)
	Evaluates the dataset.
override int	GetNumPatchesForImage (int imageWidth, int imageHeight)
	Number of patches that will be extracted from an input image to perform inference.
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 void	Clean ()
	Reset the engine and clean CPU and GPU memory allocated for running the model.

Properties
uint	Channels `[get]`
	Number of channels of input images. It can either be 1 for grayscale images or 3 for color images.
override eVision.EasyDeepLearning.EDeepLearningToolType	ToolType `[get]`
	Type of the deep learning tool.
eVision.EasyDeepLearning.EUnsupervisedSegmenterCapacity	Capacity `[get, set]`
	Capacity of the EUnsupervisedSegmenter.
bool	ForceGrayscale `[get, set]`
	Forces the EUnsupervisedSegmenter to convert all images to grayscale (default: true).
int	PatchSize `[get, set]`
	Patch size (width and height of the patches processed by the neural network).
float	Scale `[get, set]`
	Down-scaling applied to images before processing them. Its value is between 0 and 1 (default value: 1).
float	ClassificationThreshold `[get, set]`
	Classification threshold for the score of an image (See EUnsupervisedSegmenterResult.ClassificationScore).
float	SamplingDensity `[get, set]`
	Sampling density (default value: 2.0, its value must be equal or greater than 1).
string	GoodLabel `[get, set]`
	Name of the good label in the dataset that is used for training (default value: empty).
bool	ScaleDisabledAtInference `[get, set]`
	Whether to apply the scale parameter at inference or not.
float	InferenceScale `[get]`
	The effective scale applied when performing inference.
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

Unsupervised segmentation tool.

The unsupervised segmentation tool learns a model of what is a good product and it can be used for classifying whether an image is from a good or defective product and for segmenting the defects within the image.

To learn a model of what is a good product, the tool is trained by considering only the images from the good label (EUnsupervisedSegmenter.GoodLabel). The tool labels (EDeepLearningTool.GetLabel) will always be empty.

The tool can work with images of any resolution higher than EUnsupervisedSegmenter.PatchSize by merging the results obtained by applying the deep neural network using a sliding window algorithm. The overlapping between the sliding windows is controlled by EUnsupervisedSegmenter.SamplingDensity.

The unsupervised segmenter offers a tradeoff between a high good detection rate and a high bad detection rate through a classification threshold that can be configured after training (EUnsupervisedSegmenter.ClassificationThreshold).

License(s): EasySegment or EasySegmentInference

Constructor & Destructor Documentation

◆ EUnsupervisedSegmenter() [1/2]

eVision.EasyDeepLearning.EUnsupervisedSegmenter.EUnsupervisedSegmenter ( )

Constructs a EUnsupervisedSegmenter object.

◆ EUnsupervisedSegmenter() [2/2]

eVision.EasyDeepLearning.EUnsupervisedSegmenter.EUnsupervisedSegmenter ( eVision.EasyDeepLearning.EUnsupervisedSegmenter other )

Copy constructor of a EUnsupervisedSegmenter object.

Parameters

other Reference to the EUnsupervisedSegmenter object that should be copied

Member Function Documentation

◆ Apply() [1/10]

eVision.EasyDeepLearning.EUnsupervisedSegmenterResult eVision.EasyDeepLearning.EUnsupervisedSegmenter.Apply ( eVision.EBaseROI img )