TextureClassificationTrain

Enriches a texture model by training it on a gray level image that has been labeled.

Access to parameter description

As an introduction:

section Image Segmentation
section Supervised Texture Classification

This algorithm can work on 3D, sequence, and spectral images.
Following the computation mode of the associated texture classification model (2D or 3D) initialized with TextureClassificationCreate, the input image can be considered as a series of 2D images or as a series of 3D images.

For all the labeled pixels of the training image, this algorithm extracts the texture features defined by the groups selected in the texture classification model. These newly extracted features increase the training-set and enrich the model.

Note: All the pixels within the analysis radius of a labeled pixel are considered for the feature extraction. It means that the number of pixels used for training is greater than the number of the labeled pixels.

See also

Function Syntax

This function returns inputOutputModel.

// Function prototype
TextureClassificationModel::Ptr
textureClassificationTrain( std::shared_ptr< iolink::ImageView > inputImage,
                            std::shared_ptr< iolink::ImageView > inputTrainingImage,
                            double minSeparationPercentage,
                            bool finalizeTraining,
                            TextureClassificationModel::Ptr inputOutputModel = NULL );

This function returns inputOutputModel.

// Function prototype.
texture_classification_train( input_image,
                              input_training_image,
                              min_separation_percentage = 5,
                              finalize_training = True,
                              input_output_model = None )

This function returns inputOutputModel.

// Function prototype.
public static TextureClassificationModel
TextureClassificationTrain( IOLink.ImageView inputImage,
                            IOLink.ImageView inputTrainingImage,
                            double minSeparationPercentage = 5,
                            bool finalizeTraining = true,
                            TextureClassificationModel inputOutputModel = null );

Class Syntax

// Command constructor.
TextureClassificationTrain();

/// Gets the inputImage parameter.
/// The input grayscale image.
std::shared_ptr< iolink::ImageView > inputImage() const;
/// Sets the inputImage parameter.
/// The input grayscale image.
void setInputImage( std::shared_ptr< iolink::ImageView > inputImage );

/// Gets the inputTrainingImage parameter.
/// The input label image.
std::shared_ptr< iolink::ImageView > inputTrainingImage() const;
/// Sets the inputTrainingImage parameter.
/// The input label image.
void setInputTrainingImage( std::shared_ptr< iolink::ImageView > inputTrainingImage );

/// Gets the minSeparationPercentage parameter.
/// This parameter controls the rejection criteria of the feature selection algorithm (FS).
double minSeparationPercentage() const;
/// Sets the minSeparationPercentage parameter.
/// This parameter controls the rejection criteria of the feature selection algorithm (FS).
void setMinSeparationPercentage( const double& minSeparationPercentage );

/// Gets the finalizeTraining parameter.
/// This parameter indicates whether the algorithm must finalize the training. This parameter must be set to true since the retraining is currently not allowed.
bool finalizeTraining() const;
/// Sets the finalizeTraining parameter.
/// This parameter indicates whether the algorithm must finalize the training. This parameter must be set to true since the retraining is currently not allowed.
void setFinalizeTraining( const bool& finalizeTraining );

/// Gets the inputOutputModel parameter.
/// The input and output texture classification model.
TextureClassificationModel::Ptr inputOutputModel() const;
/// Sets the inputOutputModel parameter.
/// The input and output texture classification model.
void setInputOutputModel( TextureClassificationModel::Ptr inputOutputModel );


// Method to launch the command.
void execute();

# Property of the inputImage parameter.
TextureClassificationTrain.input_image
# Property of the inputTrainingImage parameter.
TextureClassificationTrain.input_training_image
# Property of the minSeparationPercentage parameter.
TextureClassificationTrain.min_separation_percentage
# Property of the finalizeTraining parameter.
TextureClassificationTrain.finalize_training
# Property of the inputOutputModel parameter.
TextureClassificationTrain.input_output_model

// Method to launch the command.
execute()

// Command constructor.
TextureClassificationTrain()

// Property of the inputImage parameter.
TextureClassificationTrain.inputImage
// Property of the inputTrainingImage parameter.
TextureClassificationTrain.inputTrainingImage
// Property of the minSeparationPercentage parameter.
TextureClassificationTrain.minSeparationPercentage
// Property of the finalizeTraining parameter.
TextureClassificationTrain.finalizeTraining
// Property of the inputOutputModel parameter.
TextureClassificationTrain.inputOutputModel

// Method to launch the command.
Execute()

Parameters

	Description	Type	Supported Values	Default Value
inputImage	The input grayscale image.	Image	Grayscale	nullptr
inputTrainingImage	The input label image. Each label represents a class sample for the training.	Image	Label	nullptr
minSeparationPercentage	This parameter controls the rejection criteria of the feature selection algorithm (FS). A measure is rejected if its contribution does not increase the separation power of the classification model enough. This ratio indicates the minimal relative growth required to keep a measure. More information is available in the Feature Selection section. This value must be greater than or equal to 0.0.	Float64	[0, 100]	5
finalizeTraining	This parameter indicates whether the algorithm must finalize the training. This parameter must be set to true since the retraining is currently not allowed.	Bool		true
inputOutputModel	The input and output texture classification model.	TextureClassificationModel		nullptr

	Description	Type	Supported Values	Default Value
input_image	The input grayscale image.	image	Grayscale	None
input_training_image	The input label image. Each label represents a class sample for the training.	image	Label	None
min_separation_percentage	This parameter controls the rejection criteria of the feature selection algorithm (FS). A measure is rejected if its contribution does not increase the separation power of the classification model enough. This ratio indicates the minimal relative growth required to keep a measure. More information is available in the Feature Selection section. This value must be greater than or equal to 0.0.	float64	[0, 100]	5
finalize_training	This parameter indicates whether the algorithm must finalize the training. This parameter must be set to true since the retraining is currently not allowed.	bool		True
input_output_model	The input and output texture classification model.	TextureClassificationModel		None

	Description	Type	Supported Values	Default Value
inputImage	The input grayscale image.	Image	Grayscale	null
inputTrainingImage	The input label image. Each label represents a class sample for the training.	Image	Label	null
minSeparationPercentage	This parameter controls the rejection criteria of the feature selection algorithm (FS). A measure is rejected if its contribution does not increase the separation power of the classification model enough. This ratio indicates the minimal relative growth required to keep a measure. More information is available in the Feature Selection section. This value must be greater than or equal to 0.0.	Float64	[0, 100]	5
finalizeTraining	This parameter indicates whether the algorithm must finalize the training. This parameter must be set to true since the retraining is currently not allowed.	Bool		true
inputOutputModel	The input and output texture classification model.	TextureClassificationModel		null

Object Examples

auto classification_input = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "classification_input.vip" );
auto classification_input_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "classification_input_label.vip" );
TextureClassificationModel::Ptr modelToTrain= TextureClassificationModel::read( std::string( IMAGEDEVDATA_OBJECTS_FOLDER ) + "modelToTrain.vip" );

TextureClassificationTrain textureClassificationTrainAlgo;
textureClassificationTrainAlgo.setInputImage( classification_input );
textureClassificationTrainAlgo.setInputTrainingImage( classification_input_label );
textureClassificationTrainAlgo.setMinSeparationPercentage( 5 );
textureClassificationTrainAlgo.setFinalizeTraining( true );
textureClassificationTrainAlgo.setInputOutputModel( modelToTrain );
textureClassificationTrainAlgo.execute();

std::cout << "computeMode: " << textureClassificationTrainAlgo.inputOutputModel()->computeMode( ) ;

classification_input = imagedev.read_vip_image(imagedev_data.get_image_path("classification_input.vip"))
classification_input_label = imagedev.read_vip_image(imagedev_data.get_image_path("classification_input_label.vip"))
model_to_train = imagedev.TextureClassificationModel.read(imagedev_data.get_object_path("modelToTrain.vip"))

texture_classification_train_algo = imagedev.TextureClassificationTrain()
texture_classification_train_algo.input_image = classification_input
texture_classification_train_algo.input_training_image = classification_input_label
texture_classification_train_algo.min_separation_percentage = 5
texture_classification_train_algo.finalize_training = True
texture_classification_train_algo.input_output_model = model_to_train
texture_classification_train_algo.execute()

print( "computeMode: ", str( texture_classification_train_algo.input_output_model.compute_mode( ) ) )

ImageView classification_input = Data.ReadVipImage( @"Data/images/classification_input.vip" );
ImageView classification_input_label = Data.ReadVipImage( @"Data/images/classification_input_label.vip" );
TextureClassificationModel modelToTrain = TextureClassificationModel.Read( @"Data/objects/modelToTrain.vip" );

TextureClassificationTrain textureClassificationTrainAlgo = new TextureClassificationTrain
{
    inputImage = classification_input,
    inputTrainingImage = classification_input_label,
    minSeparationPercentage = 5,
    finalizeTraining = true,
    inputOutputModel = modelToTrain
};
textureClassificationTrainAlgo.Execute();

Console.WriteLine( "computeMode: " + textureClassificationTrainAlgo.inputOutputModel.computeMode( ) );

Function Examples

auto classification_input = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "classification_input.vip" );
auto classification_input_label = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "classification_input_label.vip" );
TextureClassificationModel::Ptr modelToTrain= TextureClassificationModel::read( std::string( IMAGEDEVDATA_OBJECTS_FOLDER ) + "modelToTrain.vip" );

auto result = textureClassificationTrain( classification_input, classification_input_label, 5, true, modelToTrain );

std::cout << "computeMode: " << result->computeMode( ) ;

classification_input = imagedev.read_vip_image(imagedev_data.get_image_path("classification_input.vip"))
classification_input_label = imagedev.read_vip_image(imagedev_data.get_image_path("classification_input_label.vip"))
model_to_train = imagedev.TextureClassificationModel.read(imagedev_data.get_object_path("modelToTrain.vip"))

result = imagedev.texture_classification_train( classification_input, classification_input_label, 5, True, model_to_train )

print( "computeMode: ", str( result.compute_mode( ) )  )

ImageView classification_input = Data.ReadVipImage( @"Data/images/classification_input.vip" );
ImageView classification_input_label = Data.ReadVipImage( @"Data/images/classification_input_label.vip" );
TextureClassificationModel modelToTrain = TextureClassificationModel.Read( @"Data/objects/modelToTrain.vip" );

TextureClassificationModel result = Processing.TextureClassificationTrain( classification_input, classification_input_label, 5, true, modelToTrain );

Console.WriteLine(  "computeMode: " + result.computeMode( )  );

TextureClassificationTrain

- Function Syntax

+ Class Syntax

Parameters

Object Examples

Function Examples

Function Syntax

Class Syntax