/** * @file LocalFeatureCentrist.cpp * @brief Implementation of the LocalFeatureCentrist feature published in "CENTRIST: A Visual Descriptor for Scene Categorization" (PAMI 2011) * @author Alexander Freytag * @date 12/06/2011 */ // STL includes #include // nice-vislearning includes #include "vislearning/features/localfeatures/LocalFeatureCentrist.h" using namespace OBJREC; using namespace std; using namespace NICE; /* protected methods*/ /** * @brief: perform centrist transformation for a single pixel * @author Alexander Freytag * @date 12/06/2011 */ int LocalFeatureCentrist::CensusTransform(const NICE::Image & img, const int & x, const int & y) const { int index(0); if (! img.isWithinImage(x,y)) return index; if( (img.isWithinImage(x-1,y-1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x-1,y-1)) ) index |= 0x80; if( (img.isWithinImage(x-1,y)) && (img.getPixelInt(x,y)<=img.getPixelInt(x-1,y)) ) index |= 0x40; if( (img.isWithinImage(x-1,y+1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x-1,y+1)) ) index |= 0x20; if( (img.isWithinImage(x,y-1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x,y-1)) ) index |= 0x10; if( (img.isWithinImage(x,y+1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x,y+1)) ) index |= 0x08; if( (img.isWithinImage(x+1,y-1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x+1,y-1)) ) index |= 0x04; if( (img.isWithinImage(x+1,y)) && (img.getPixelInt(x,y)<=img.getPixelInt(x+1,y)) ) index |= 0x02; if( (img.isWithinImage(x+1,y+1)) && (img.getPixelInt(x,y)<=img.getPixelInt(x+1,y+1)) ) index |= 0x01; return index; } /** * @brief: perform centrist transformation for a single pixel * @author Alexander Freytag * @date 12/06/2011 */ int LocalFeatureCentrist::CensusTransform(const NICE::ColorImage & img, const int & x, const int & y, const int & channel) const { int index(0); if (! img.isWithinImage(x,y)) return index; if( (img.isWithinImage(x-1,y-1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x-1,y-1, channel)) ) index |= 0x80; if( (img.isWithinImage(x-1,y)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x-1,y, channel)) ) index |= 0x40; if( (img.isWithinImage(x-1,y+1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x-1,y+1, channel)) ) index |= 0x20; if( (img.isWithinImage(x,y-1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x,y-1, channel)) ) index |= 0x10; if( (img.isWithinImage(x,y+1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x,y+1, channel)) ) index |= 0x08; if( (img.isWithinImage(x+1,y-1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x+1,y-1, channel)) ) index |= 0x04; if( (img.isWithinImage(x+1,y)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x+1,y, channel)) ) index |= 0x02; if( (img.isWithinImage(x+1,y+1)) && (img.getPixelInt(x,y, channel)<=img.getPixelInt(x+1,y+1, channel)) ) index |= 0x01; return index; } /** * @brief: generate CT histogram for a given rectangle: pixels in [xi yi]-(xa,ya) -- including (xi,yi) but excluding (xa,ya) * @author Alexander Freytag * @date 12/06/2011 */ void LocalFeatureCentrist::GenerateHistForOneRect(const NICE::Image & img, const int & xi, const int & xa, const int & yi, const int & ya, NICE::Vector & feature) const { // double pixelSum(0.0); // double pixelSquare(0.0); feature.resize(256); feature.set(0.0); for (int j = yi; j < ya; j++) { for (int i = xi; i < xa; i++) { // pixelSum += img.getPixelInt(i,j); // pixelSquare += img.getPixelInt(i,j)*img.getPixelInt(i,j); feature[CensusTransform(img,i,j)]++; } } // normalize histogram to have 0 mean, remove the first and last entry, and normalize to have unit norm double sum(feature.Sum()/256.0);//shift more efficient? // normalize histogram to have 0 mean //but why? feature -= sum; //remove the first and last entry feature[0] = 0.0; feature[255] = 0.0; //normalization - here done using unit L2-norm feature.normalizeL2(); } /** * @brief: generate CT histogram for a given rectangle: pixels in [xi yi]-(xa,ya) -- including (xi,yi) but excluding (xa,ya) * @author Alexander Freytag * @date 12/06/2011 */ void LocalFeatureCentrist::GenerateHistForOneRect(const NICE::ColorImage & img, const int & xi, const int & xa, const int & yi, const int & ya, NICE::Vector & feature) const { // double pixelSum(0.0); // double pixelSquare(0.0); int nrChannel (img.channels()); feature.resize(256*nrChannel); feature.set(0.0); for (int channel = 0; channel < nrChannel; channel++) { for (int j = yi; j < ya; j++) { for (int i = xi; i < xa; i++) { // pixelSum += img.getPixelInt(i,j, channel); // pixelSquare += img.getPixelInt(i,j, channel)*img.getPixelInt(i,j, channel); feature[256*channel+this->CensusTransform(img,i,j,channel)]++; } } } // normalize histogram to have 0 mean, remove the first and last entry, and normalize to have unit norm double sum(feature.Sum()/256.0);//shift more efficient? // normalize histogram to have 0 mean //but why? feature -= sum; //remove the first and last entry feature[0] = 0.0; feature[255] = 0.0; //normalization - here done using unit L2-norm feature.normalizeL2(); } /** * @brief Computes several CENTRIST descriptors for each of the given positions om a local neighborhood * @author Alexander Freytag * @date 12/06/2011 */ void LocalFeatureCentrist::computeDesc( const NICE::Image & img, NICE::VVector & positions, NICE::VVector & descriptors ) const { descriptors.clear(); for (NICE::VVector::const_iterator it = positions.begin(); it != positions.end(); it++) { NICE::Vector descriptor; this->GenerateHistForOneRect(img, (*it)[0]-i_sizeNeighborhood/2, (*it)[0]+i_sizeNeighborhood/2, (*it)[1]-i_sizeNeighborhood/2, (*it)[1]+i_sizeNeighborhood/2, descriptor); descriptors.push_back(descriptor); } } /** * @brief Computes several CENTRIST descriptors for each of the given positions om a local neighborhood * @author Alexander Freytag * @date 12/06/2011 */ void LocalFeatureCentrist::computeDesc( const NICE::ColorImage & img, NICE::VVector & positions, NICE::VVector & descriptors ) const { descriptors.clear(); for (NICE::VVector::const_iterator it = positions.begin(); it != positions.end(); it++) { NICE::Vector descriptor; this->GenerateHistForOneRect(img, (*it)[0]-i_sizeNeighborhood/2, (*it)[0]+i_sizeNeighborhood/2, (*it)[1]-i_sizeNeighborhood/2, (*it)[1]+i_sizeNeighborhood/2, descriptor); descriptors.push_back(descriptor); } } /* public methods*/ /** * @brief Default constructor * @author Alexander Freytag * @date 12/06/2011 */ LocalFeatureCentrist::LocalFeatureCentrist() { this->i_sizeNeighborhood = 16; } /** * @brief Recommended constructor * @author Alexander Freytag * @date 12/06/2011 */ LocalFeatureCentrist::LocalFeatureCentrist( const Config *conf, const std::string & section ) { this->i_sizeNeighborhood = conf->gI(section, "i_sizeNeighborhood", 16); } /** * @brief Default destructor * @author Alexander Freytag * @date 12/06/2011 */ LocalFeatureCentrist::~LocalFeatureCentrist() { } int LocalFeatureCentrist::getDescSize() const { // we have 8 neighbors, so the size is always 256 with the given implementation (without spatial levels, ...) return 256; } /** * @brief Computes several CENTRIST descriptors for each of the given positions on a local neighborhood * @author Alexander Freytag * @date 12/06/2011 */ int LocalFeatureCentrist::getDescriptors ( const NICE::Image & img, VVector & positions, VVector & descriptors ) const { this->computeDesc( img, positions, descriptors ); return 0; } /** * @brief Computes several CENTRIST descriptors for each of the given positions on a local neighborhood * @author Alexander Freytag * @date 12/06/2011 */ int LocalFeatureCentrist::getDescriptors ( const NICE::ColorImage & img, NICE::VVector & positions, NICE::VVector & descriptors) const { this->computeDesc( img, positions, descriptors ); return 0; } /** * @brief Visualizes CENTRIST descriptors, not implemented yet! * @author Alexander Freytag * @date 12/06/2011 */ void LocalFeatureCentrist::visualizeFeatures ( NICE::Image & mark, const VVector & positions, size_t color ) const { throw NICE::Exception ( "LocalFeatureCentrist::visualizeFeatures -- currently not implemented." ); }