/** * @file SemSegContextTree.h * @brief Context Trees -> Combination of decision tree and context information * @author Björn Fröhlich * @date 29.11.2011 */ #ifndef SemSegContextTreeINCLUDE #define SemSegContextTreeINCLUDE // nice-core includes #include // nice-vislearning includes #include #include // nice-segmentation includes #include // nice-semseg includes #include "semseg/semseg/operations/Operations.h" #include "SemanticSegmentation.h" namespace OBJREC { /** Localization system */ class SemSegContextTree : public SemanticSegmentation { /** Segmentation Method */ RegionSegmentationMethod *segmentation; /** tree -> saved as vector of nodes */ std::vector > forest; /** local features */ LocalFeatureColorWeijer *lfcw; /** number of featuretype -> currently: local and context features = 2 */ int ftypes; /** maximum samples for tree */ int maxSamples; /** size for neighbourhood */ int windowSize; /** how many feats should be considered for a split */ int featsPerSplit; /** count samples per label */ std::map labelcounter; /** map of labels */ std::map labelmap; /** map of labels inverse*/ std::map labelmapback; /** scalefactor for balancing for each class */ std::vector a; /** counter for used operations */ std::vector opOverview; /** relative use of context vs raw features per tree level*/ std::vector > contextOverview; /** the minimum number of features allowed in a leaf */ int minFeats; /** maximal depth of tree */ int maxDepth; /** current depth for training */ int depth; /** how many splittests */ int randomTests; /** operations for pairwise features */ std::vector > ops; std::vector calcVal; /** use alternative calculation for information gain */ bool useShannonEntropy; /** Classnames */ ClassNames classnames; /** train selection */ std::set forbidden_classes; /** Configfile */ const NICE::Config *conf; /** use pixelwise labeling or regionlabeling with additional segmenation */ bool pixelWiseLabeling; /** Number of trees used for the forest */ int nbTrees; /** use Gradient image or not */ bool useGradient; /** use Color features from van de Weijer or not */ bool useWeijer; /** use Regions as extra feature channel or not */ bool useRegionFeature; /** use external image categorization to avoid some classes */ bool useCategorization; /** categorization information for external categorization */ std::string cndir; /** how to handle each channel * 0: simple grayvalue features * 1: which pixel belongs to which region * 2: graycolor integral images * 3: context integral images * 4: context features (not in MultiChannelImageT encoded) */ std::vector channelType; /** list of channels per feature type */ std::vector > channelsPerType; /** whether we should use the geometric features of Hoeim (only offline computation with MATLAB supported) */ bool useHoiemFeatures; /** directory of the geometric features */ std::string hoiemDirectory; /** first iteration or not */ bool firstiteration; /** which IntegralImage channel belongs to which raw value channel */ std::vector > integralMap; /** amount of grayvalue Channels */ int rawChannels; /** classifier for categorization */ OBJREC::FPCGPHIK *fasthik; /** unique numbers for nodes */ int uniquenumber; public: /** simple constructor */ SemSegContextTree ( const NICE::Config *conf, const MultiDataset *md ); /** simple destructor */ virtual ~SemSegContextTree(); /** * test a single image * @param ce input data * @param segresult segmentation results * @param probabilities probabilities for each pixel */ void semanticseg ( CachedExample *ce, NICE::ImageT & segresult, NICE::MultiChannelImageT & probabilities ); void semanticseg ( CachedExample *ce, NICE::MultiChannelImageT & segresult, NICE::MultiChannelImage3DT & probabilities ) {} /** * the main training method * @param md training data */ void train ( const MultiDataset *md ); /** * @brief computes integral image of given feats * * @param currentfeats input features * @param integralImage output image (must be initilized) * @return void **/ void computeIntegralImage ( const NICE::MultiChannelImageT ¤tfeats, NICE::MultiChannelImageT &lfeats,int firstChannel ); /** * @brief reads image and does some simple convertions * * @param feats output image * @param currentFile image filename * @return void **/ void extractBasicFeatures ( NICE::MultiChannelImageT &feats, const NICE::ColorImage &img, const std::string ¤tFile, int &amountRegions); /** * compute best split for current settings * @param feats features * @param currentfeats matrix with current node for each feature * @param labels labels for each feature * @param node current node * @param splitfeat output feature position * @param splitval * @return best information gain */ double getBestSplit ( std::vector > &feats, std::vector > ¤tfeats, const std::vector > &labels, int node, Operation *&splitop, double &splitval, const int &tree, std::vector > > ®ionProbs ); /** * @brief computes the mean probability for a given class over all trees * @param x x position * @param y y position * @param channel current class * @param currentfeats information about the nodes * @return double mean value **/ inline double getMeanProb ( const int &x, const int &y, const int &channel, const NICE::MultiChannelImageT ¤tfeats ); /** * @brief load all data to is stream * * @param is input stream * @param format has no influence * @return void **/ virtual void restore ( std::istream & is, int format = 0 ); /** * @brief save all data to is stream * * @param os output stream * @param format has no influence * @return void **/ virtual void store ( std::ostream & os, int format = 0 ) const; /** * @brief clean up * * @return void **/ virtual void clear () {} }; } // namespace #endif