A random forest for RGB-D images. More...

#include <random_forest_image.h>

Public Member Functions
	RandomForestImage (const std::vector< std::string > &treeFiles, const std::vector< int > &deviceIds=std::vector< int >(1, 0), const AccelerationMode accelerationMode=GPU_ONLY, const double histogramBias=0.0)
	Load the random forest from disk.
	RandomForestImage (unsigned int treeCount, const TrainingConfiguration &configuration)
	Prepare a random forest with the given number of trees and the configuration.
	RandomForestImage (const std::vector< boost::shared_ptr< RandomTreeImage > > &ensemble, const TrainingConfiguration &configuration)
	Construct a random forest from the given list of existing trees.
void	train (const std::vector< LabeledRGBDImage > &trainLabelImages, size_t numLabels=0, bool trainTreesSequentially=false)
	Starts the training process of the random forest.
LabelImage	predict (const RGBDImage &image, cuv::ndarray< float, cuv::host_memory_space > *prediction=0, const bool onGPU=true, bool useDepthImages=true) const
LabelImage	improveHistograms (const RGBDImage &trainingImage, const LabelImage &labelImage, const bool onGPU=true, bool useDepthImages=true) const
	classifies all images at the end of training - used to improve leaf distributions
void	updateTreesHistograms ()
	uses the allPixelsHistograms to update the trees leaves histograms - used to improve leaf distributions
std::map< std::string, size_t >	countFeatures () const
LabelType	getNumClasses () const
const boost::shared_ptr < RandomTreeImage >	getTree (size_t treeNr) const
const std::vector < boost::shared_ptr < RandomTreeImage > > &	getTrees () const
const TrainingConfiguration &	getConfiguration () const
bool	shouldIgnoreLabel (const LabelType &label) const
std::map< LabelType, RGBColor >	getLabelColorMap () const
void	normalizeHistograms (const double histogramBias)
	goes over all trees and normalizes the histograms

Detailed Description

A random forest for RGB-D images.

Definition at line 17 of file random_forest_image.h.

Constructor & Destructor Documentation

curfil::RandomForestImage::RandomForestImage	(	const std::vector< std::string > &	treeFiles,
		const std::vector< int > &	deviceIds = `std::vector< int >(1, 0)`,
		const AccelerationMode	accelerationMode = `GPU_ONLY`,
		const double	histogramBias = `0.0`
	)

explicit

Load the random forest from disk.

Parameters

treeFiles	paths of JSON tree files
deviceIds	the GPU device IDs to use for prediction
accelerationMode	the acceleration mode to use for prediction. CPU or GPU.
histogramBias	if larger than 0.0, apply a histogram bias (see the Wiki) after loading the forest.

curfil::RandomForestImage::RandomForestImage	(	unsigned int	treeCount,
		const TrainingConfiguration &	configuration
	)

explicit

Prepare a random forest with the given number of trees and the configuration.

Parameters

treeCount	the number of trees in the forest
configuration	the configuration to use for training

curfil::RandomForestImage::RandomForestImage	(	const std::vector< boost::shared_ptr< RandomTreeImage > > &	ensemble,
		const TrainingConfiguration &	configuration
	)

explicit

Construct a random forest from the given list of existing trees.

Parameters

ensemble	list of trees that should be used to construct the random forest from
configuration	the configuration the trees were trained with

Member Function Documentation

std::map<std::string, size_t> curfil::RandomForestImage::countFeatures ( ) const

Returns: a recursive sum of per-feature type count

const TrainingConfiguration& curfil::RandomForestImage::getConfiguration ( ) const

inline

Returns: the configuration the random forest was trained with

Definition at line 113 of file random_forest_image.h.

std::map<LabelType, RGBColor> curfil::RandomForestImage::getLabelColorMap ( ) const

Returns: a map where keys are the labels and the values are the colors associated with them

LabelType curfil::RandomForestImage::getNumClasses ( ) const

Returns: the number of classes (labels) that random forest was trained from

const boost::shared_ptr<RandomTreeImage> curfil::RandomForestImage::getTree ( size_t treeNr ) const

inline

Returns: the n-th tree in the forest where 0 ≤ treeNr < numTrees

Definition at line 95 of file random_forest_image.h.

const std::vector<boost::shared_ptr<RandomTreeImage> >& curfil::RandomForestImage::getTrees ( ) const

inline

Returns: the list of trees in the forest

Definition at line 106 of file random_forest_image.h.

LabelImage curfil::RandomForestImage::predict	(	const RGBDImage &	image,
		cuv::ndarray< float, cuv::host_memory_space > *	prediction = `0`,
		const bool	onGPU = `true`,
		bool	useDepthImages = `true`
	)		const

Parameters

image	the image which should be classified
prediction	if not null, probabilities per class in a C×H×W matrix for C classes and an image of size W×H
onGPU	whether prediction is done using GPUs
useDepthImages	whether depth images are used when classifying images

Returns: prediction image which has the same size as 'image'

bool curfil::RandomForestImage::shouldIgnoreLabel ( const LabelType & label ) const

Returns: whether the passed label is one of those specified by the user to be ignored

void curfil::RandomForestImage::train	(	const std::vector< LabeledRGBDImage > &	trainLabelImages,
		size_t	numLabels = `0`,
		bool	trainTreesSequentially = `false`
	)

Starts the training process of the random forest.

Parameters

trainLabelImages	the list of labeled images to train the random forest from
trainTreesSequentially	whether to train the trees in the random forest in parallel or sequentially.
numLabels	number of classes in the images

The documentation for this class was generated from the following file:

src/curfil/random_forest_image.h

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation