Supervised segmentation tool. More...

Inheritance diagram for eVision.EasyDeepLearning.ESupervisedSegmenter:

Collaboration diagram for eVision.EasyDeepLearning.ESupervisedSegmenter:

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

Protected Member Functions
int	_Channels__getter (self)
	\cond
eVision.EasyDeepLearning.EDeepLearningToolType	_ToolType__getter (self)
	\cond
eVision.EasyDeepLearning.ESupervisedSegmenterCapacity	_Capacity__getter (self)
	\cond
	_Capacity__setter (self, eVision.EasyDeepLearning.ESupervisedSegmenterCapacity value)
	Capacity of the eVision.EasyDeepLearning.ESupervisedSegmenter.
bool	_ForceGrayscale__getter (self)
	\cond
	_ForceGrayscale__setter (self, bool value)
	Forces the eVision.EasyDeepLearning.ESupervisedSegmenter to convert all images to grayscale (default: false).
int	_PatchSize__getter (self)
	\cond
	_PatchSize__setter (self, int value)
	Patch size (width and height of the patches processed by the neural network).
float	_SamplingDensity__getter (self)
	\cond
	_SamplingDensity__setter (self, float value)
	Sampling density (default value: 1.25, its value must be equal or greater than 1).
float	_Scale__getter (self)
	\cond
	_Scale__setter (self, float value)
	Down-scaling applied to images before processing them.
float	_ClassificationThreshold__getter (self)
	\cond
	_ClassificationThreshold__setter (self, float value)
	Classification threshold for determining whether an image contains blobs with a label different than background.
bool	_ScaleDisabledAtInference__getter (self)
	\cond
	_ScaleDisabledAtInference__setter (self, bool value)
	Whether to apply the scale parameter at inference or not.
float	_InferenceScale__getter (self)
	\cond
Protected Member Functions inherited from eVision.EasyDeepLearning.EDeepLearningTool
bool	_EnableGPU__getter (self)
	\cond
	_EnableGPU__setter (self, bool value)
	Enable the use of a GPU for training and inference of the deep learning tool.
list[int]	_GPUIndexes__getter (self)
	\cond
	_GPUIndexes__setter (self, list[int] value)
	Indexes of the GPUs to use for computations.
int	_NumGPUs__getter (self)
	\cond
str	_Engine__getter (self)
	\cond
	_Engine__setter (self, str value)
	Sets the engine with which to execute or train the neural network.
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	_InferencePrecision__getter (self)
	\cond
	_InferencePrecision__setter (self, eVision.EasyDeepLearning.EDeepLearningInferencePrecision value)
	Inference precision.
int	_NumAvailableDevices__getter (self)
	\cond
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	_AvailableDevices__getter (self)
	\cond
int	_NumActiveDevices__getter (self)
	\cond
	_ActiveDeviceByName__setter (self, str value)
	\cond
	_ActiveDeviceById__setter (self, int value)
	\cond
list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice]	_ActiveDevices__getter (self)
	\cond
	_ActiveDevices__setter (self, list[eVision._eVision.EasyDeepLearning.EDeepLearningDevice] value)
	Devices with which to run or train the model.
	_ActiveDevicesById__setter (self, list[int] value)
	\cond
eVision.EasyDeepLearning.EDeepLearningExecutionSettings	_ExecutionSettings__getter (self)
	\cond
	_ExecutionSettings__setter (self, eVision.EasyDeepLearning.EDeepLearningExecutionSettings value)
	Execution settings of the tool (engine, device(s), inference precision, batch size settings).
eVision.EasyDeepLearning.EDeepLearningDevice	_EngineSetupDataDevice__getter (self)
	\cond
eVision.EasyDeepLearning.EDeepLearningInferencePrecision	_EngineSetupDataInferencePrecision__getter (self)
	\cond
int	_EngineSetupDataBatchSize__getter (self)
	\cond
bool	_OptimizeBatchSize__getter (self)
	\cond
	_OptimizeBatchSize__setter (self, bool value)
	Indicates whether to optimize the batch size (see eVision.EasyDeepLearning.EDeepLearningTool.BatchSize) to maximize the training and inference speed according to the engine, the device and the available memory.
int	_BatchSizeForMaximumInferenceSpeed__getter (self)
	\cond
int	_BatchSize__getter (self)
	\cond
	_BatchSize__setter (self, int value)
	Batch size.
float	_CurrentTrainingProgression__getter (self)
	\cond
int	_CurrentTrainingFinishedIterations__getter (self)
	\cond
int	_CurrentTrainingNumIterations__getter (self)
	\cond
int	_NumTrainedIterations__getter (self)
	\cond
int	_BestIteration__getter (self)
	\cond
int	_ImageCacheSize__getter (self)
	\cond
	_ImageCacheSize__setter (self, int value)
	Size in byte of the image cache.
str	_Path__getter (self)
	\cond
str	_InferenceModelPath__getter (self)
	\cond
str	_TrainingModelPath__getter (self)
	\cond
str	_SettingsPath__getter (self)
	\cond
int	_AutoSavePeriod__getter (self)
	\cond
	_AutoSavePeriod__setter (self, int value)
	Auto-save period.
eVision.EasyDeepLearning.EDeepLearningLearningRateType	_LearningRateType__getter (self)
	\cond
list[float]	_LearningRateParameters__getter (self)
	\cond
bool	_EnableDeterministicTraining__getter (self)
	\cond
	_EnableDeterministicTraining__setter (self, bool value)
	Whether to use deterministic training algorithm that allows to repeatable training results.
int	_DeterministicTrainingRandomSeed__getter (self)
	\cond
	_DeterministicTrainingRandomSeed__setter (self, int value)
	Random seed to use when eVision.EasyDeepLearning.EDeepLearningTool.EnableDeterministicTraining is set to true.
int	_NumLabels__getter (self)
	\cond

Properties
	Channels = property(_Channels__getter, None)
	\endcond
	ToolType = property(_ToolType__getter, None)
	\endcond
	Capacity = property(_Capacity__getter, _Capacity__setter)
	\endcond
	ForceGrayscale = property(_ForceGrayscale__getter, _ForceGrayscale__setter)
	\endcond
	PatchSize = property(_PatchSize__getter, _PatchSize__setter)
	\endcond
	SamplingDensity = property(_SamplingDensity__getter, _SamplingDensity__setter)
	\endcond
	Scale = property(_Scale__getter, _Scale__setter)
	\endcond
	ClassificationThreshold = property(_ClassificationThreshold__getter, _ClassificationThreshold__setter)
	\endcond
	ScaleDisabledAtInference = property(_ScaleDisabledAtInference__getter, _ScaleDisabledAtInference__setter)
	\endcond
	InferenceScale = property(_InferenceScale__getter, None)
	\endcond
Properties inherited from eVision.EasyDeepLearning.EDeepLearningTool
	ToolType = property(_ToolType__getter, None)
	\endcond
	EnableGPU = property(_EnableGPU__getter, _EnableGPU__setter)
	\endcond
	GPUIndexes = property(_GPUIndexes__getter, _GPUIndexes__setter)
	\endcond
	NumGPUs = property(_NumGPUs__getter, None)
	\endcond
	Engine = property(_Engine__getter, _Engine__setter)
	\endcond
	InferencePrecision = property(_InferencePrecision__getter, _InferencePrecision__setter)
	\endcond
	NumAvailableDevices = property(_NumAvailableDevices__getter, None)
	\endcond
	AvailableDevices = property(_AvailableDevices__getter, None)
	\endcond
	NumActiveDevices = property(_NumActiveDevices__getter, None)
	\endcond
	ActiveDeviceByName = property(None, _ActiveDeviceByName__setter)
	\endcond
	ActiveDeviceById = property(None, _ActiveDeviceById__setter)
	\endcond
	ActiveDevices = property(_ActiveDevices__getter, _ActiveDevices__setter)
	\endcond
	ActiveDevicesById = property(None, _ActiveDevicesById__setter)
	\endcond
	ExecutionSettings = property(_ExecutionSettings__getter, _ExecutionSettings__setter)
	\endcond
	EngineSetupDataDevice = property(_EngineSetupDataDevice__getter, None)
	\endcond
	EngineSetupDataInferencePrecision = property(_EngineSetupDataInferencePrecision__getter, None)
	\endcond
	EngineSetupDataBatchSize = property(_EngineSetupDataBatchSize__getter, None)
	\endcond
	OptimizeBatchSize = property(_OptimizeBatchSize__getter, _OptimizeBatchSize__setter)
	\endcond
	BatchSizeForMaximumInferenceSpeed = property(_BatchSizeForMaximumInferenceSpeed__getter, None)
	\endcond
	BatchSize = property(_BatchSize__getter, _BatchSize__setter)
	\endcond
	CurrentTrainingProgression = property(_CurrentTrainingProgression__getter, None)
	\endcond
	CurrentTrainingFinishedIterations = property(_CurrentTrainingFinishedIterations__getter, None)
	\endcond
	CurrentTrainingNumIterations = property(_CurrentTrainingNumIterations__getter, None)
	\endcond
	NumTrainedIterations = property(_NumTrainedIterations__getter, None)
	\endcond
	BestIteration = property(_BestIteration__getter, None)
	\endcond
	ImageCacheSize = property(_ImageCacheSize__getter, _ImageCacheSize__setter)
	\endcond
	Path = property(_Path__getter, None)
	\endcond
	InferenceModelPath = property(_InferenceModelPath__getter, None)
	\endcond
	TrainingModelPath = property(_TrainingModelPath__getter, None)
	\endcond
	SettingsPath = property(_SettingsPath__getter, None)
	\endcond
	AutoSavePeriod = property(_AutoSavePeriod__getter, _AutoSavePeriod__setter)
	\endcond
	LearningRateType = property(_LearningRateType__getter, None)
	\endcond
	LearningRateParameters = property(_LearningRateParameters__getter, None)
	\endcond
	EnableDeterministicTraining = property(_EnableDeterministicTraining__getter, _EnableDeterministicTraining__setter)
	\endcond
	DeterministicTrainingRandomSeed = property(_DeterministicTrainingRandomSeed__getter, _DeterministicTrainingRandomSeed__setter)
	\endcond
	NumLabels = property(_NumLabels__getter, None)
	\endcond

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

Detailed Description

Supervised segmentation tool.

The supervised segmentation tool segments the pixels of an image into various labels by learning a deep learning model on a dataset of segmented images.

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

For defect detection/foreground blobs detection, the supervised segmenter offers a tradeoff between a high good detection rate and a high defect detection rate through a classification threshold that can be configured after training (eVision.EasyDeepLearning.ESupervisedSegmenter.ClassificationThreshold).

Remarks:

License(s): EasySegment or EasySegmentInference

Constructor & Destructor Documentation

◆ init() [1/2]

None eVision.EasyDeepLearning.ESupervisedSegmenter.__init__ ( self )

Constructs a eVision.EasyDeepLearning.ESupervisedSegmenter object.

◆ init() [2/2]

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

Copy constructor of a eVision.EasyDeepLearning.ESupervisedSegmenter object.

Parameters

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

Member Function Documentation

◆ _Capacity__getter()

eVision.EasyDeepLearning.ESupervisedSegmenterCapacity eVision.EasyDeepLearning.ESupervisedSegmenter._Capacity__getter ( self )

protected

\cond

Capacity of the eVision.EasyDeepLearning.ESupervisedSegmenter.

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

◆ _Capacity__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._Capacity__setter	(		self,
		eVision.EasyDeepLearning.ESupervisedSegmenterCapacity	value )

protected

Capacity of the eVision.EasyDeepLearning.ESupervisedSegmenter.

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

◆ _Channels__getter()

int eVision.EasyDeepLearning.ESupervisedSegmenter._Channels__getter ( self )

protected

\cond

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

Remarks:

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

◆ _ClassificationThreshold__getter()

float eVision.EasyDeepLearning.ESupervisedSegmenter._ClassificationThreshold__getter ( self )

protected

\cond

Classification threshold for determining whether an image contains blobs with a label different than background.

By default, its value is set during training to maximize the weighted accuracy (see eVision.EasyDeepLearning.ESupervisedSegmenterMetrics).

◆ _ClassificationThreshold__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._ClassificationThreshold__setter	(		self,
		float	value )

protected

Classification threshold for determining whether an image contains blobs with a label different than background.

By default, its value is set during training to maximize the weighted accuracy (see eVision.EasyDeepLearning.ESupervisedSegmenterMetrics).

◆ _ForceGrayscale__getter()

bool eVision.EasyDeepLearning.ESupervisedSegmenter._ForceGrayscale__getter ( self )

protected

\cond

Forces the eVision.EasyDeepLearning.ESupervisedSegmenter to convert all images to grayscale (default: false).

Setting this property to false will make the underlying neural network operates with the same number of channels as the images in the dataset used for training. Otherwise, color images will be converted to grayscale before using them.

◆ _ForceGrayscale__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._ForceGrayscale__setter	(		self,
		bool	value )

protected

Forces the eVision.EasyDeepLearning.ESupervisedSegmenter to convert all images to grayscale (default: false).

Setting this property to false will make the underlying neural network operates with the same number of channels as the images in the dataset used for training. Otherwise, color images will be converted to grayscale before using them.

◆ _InferenceScale__getter()

float eVision.EasyDeepLearning.ESupervisedSegmenter._InferenceScale__getter ( self )

protected

\cond

The effective scale applied when performing inference.

If eVision.EasyDeepLearning.ESupervisedSegmenter.ScaleDisabledAtInference is true, it is equal to 1.0. Otherwise, it is equal to eVision.EasyDeepLearning.ESupervisedSegmenter.Scale.

◆ _PatchSize__getter()

int eVision.EasyDeepLearning.ESupervisedSegmenter._PatchSize__getter ( self )

protected

\cond

Patch size (width and height of the patches processed by the neural network).

Remarks:

There are three supported patch size: 64x64, 128x128, and 256x256. By default, the patch size is 0 and it means that the patch size will be 128x128 if all images in the training and validation dataset have a higher resolution or the patch size will be 64x64.

◆ _PatchSize__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._PatchSize__setter	(		self,
		int	value )

protected

Patch size (width and height of the patches processed by the neural network).

Remarks:

There are three supported patch size: 64x64, 128x128, and 256x256. By default, the patch size is 0 and it means that the patch size will be 128x128 if all images in the training and validation dataset have a higher resolution or the patch size will be 64x64.

◆ _SamplingDensity__getter()

float eVision.EasyDeepLearning.ESupervisedSegmenter._SamplingDensity__getter ( self )

protected

\cond

Sampling density (default value: 1.25, its value must be equal or greater than 1).

The sampling density is the parameter of the sliding window algorithm used for processing a whole image using subwindows of size eVision.EasyDeepLearning.ESupervisedSegmenter.PatchSize. It indicates how much overlap there will be between the image patches: the stride between two consecutive patches is eVision.EasyDeepLearning.ESupervisedSegmenter.PatchSize / eVision.EasyDeepLearning.ESupervisedSegmenter.SamplingDensity.

◆ _SamplingDensity__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._SamplingDensity__setter	(		self,
		float	value )

protected

Sampling density (default value: 1.25, its value must be equal or greater than 1).

The sampling density is the parameter of the sliding window algorithm used for processing a whole image using subwindows of size eVision.EasyDeepLearning.ESupervisedSegmenter.PatchSize. It indicates how much overlap there will be between the image patches: the stride between two consecutive patches is eVision.EasyDeepLearning.ESupervisedSegmenter.PatchSize / eVision.EasyDeepLearning.ESupervisedSegmenter.SamplingDensity.

◆ _Scale__getter()

float eVision.EasyDeepLearning.ESupervisedSegmenter._Scale__getter ( self )

protected

\cond

Down-scaling applied to images before processing them. Its value is between 0 and 1 (default value: 1).

◆ _Scale__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._Scale__setter	(		self,
		float	value )

protected

Down-scaling applied to images before processing them.

Its value is between 0 and 1 (default value: 1).

◆ _ScaleDisabledAtInference__getter()

bool eVision.EasyDeepLearning.ESupervisedSegmenter._ScaleDisabledAtInference__getter ( self )

protected

\cond

Whether to apply the scale parameter at inference or not.

If you disable the scale at inference, make sure your images are already scaled. For example, if you trained with images of 1024x1024 and a scale of 0.5, the inference images should be 512 x 512 images with the same field of view as the training images.

◆ _ScaleDisabledAtInference__setter()

eVision.EasyDeepLearning.ESupervisedSegmenter._ScaleDisabledAtInference__setter	(		self,
		bool	value )

protected

Whether to apply the scale parameter at inference or not.

If you disable the scale at inference, make sure your images are already scaled. For example, if you trained with images of 1024x1024 and a scale of 0.5, the inference images should be 512 x 512 images with the same field of view as the training images.

◆ _ToolType__getter()

eVision.EasyDeepLearning.EDeepLearningToolType eVision.EasyDeepLearning.ESupervisedSegmenter._ToolType__getter ( self )

protected

\cond

Type of the deep learning tool.

Reimplemented from eVision.EasyDeepLearning.EDeepLearningTool.