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DSurf

Matrix::Mat* Impala::Core::Feature::DSurf ( Array::Array2dScalarReal64 *  image, PointDescriptorTable *  pointData, int  sampleRate, int  numberHaarSum, int  spatiality   ) image in, descriptors are added to pointList Definition at line 65 of file DSurf.h. References Impala::Core::Table::TableTem< Col1T, Col2T, Col3T, Col4T, Col5T, Col6T, Col7T, Col8T, Col9T >::Add(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CH(), Impala::Core::Array::Array2dTem< StorT, elemSize, ArithT >::CW(), ExtractFeatureIntoVector(), HaarResponse(), ILOG_DEBUG, ILOG_VAR, Impala::Core::Array::IntegrateReduce(), Impala::Core::Array::IntegrateReduceWeighted(), Impala::Core::Matrix::MatE(), and Impala::Core::Table::Table::Size(). Referenced by CalculateSurfDescriptors(). { using namespace Matrix; ILOG_VAR(Impala.Core.Feature.DSurf); ILOG_DEBUG("image w="<< image->CW() <<", h="<< image->CH()); /* In order to speed up processing reduce the image so one pixel has the same 'energy' as a block of sampleRate^2 pixels. */ Array::Array2dScalarReal64* reduced = 0; IntegrateReduce(reduced, image, sampleRate); // haar responses std::vector<Array::Array2dScalarReal64*> haar = HaarResponse(reduced); delete reduced; // sum the haar responses into component of the surf feature std::vector<Array::Array2dScalarReal64*> components; int w = haar[0]->CW() / numberHaarSum; int h = haar[0]->CH() / numberHaarSum; for(int i=0 ; i<haar.size() ; ++i) { Array::Array2dScalarReal64* comp = new Array::Array2dScalarReal64(w, h, 0, 0); IntegrateReduceWeighted(comp, haar[i], numberHaarSum); components.push_back(comp); delete haar[i]; } w = components[0]->CW(); h = components[0]->CH(); //now combine the components into actual surf features int nrFeatures = w*h; int sampleSize = sampleRate * numberHaarSum; int featureSize = sampleSize * spatiality; /* because the DSurf implementation does it's own sampling we need this loop to create the interest points with correct dimensions (actually only the middle point is needed) */ if(pointData->Size() == 0) { for(int y=0 ; y<=h-spatiality ; ++y) { for(int x=0 ; x<=w-spatiality ; ++x) { int left=x*sampleSize; int top =y*sampleSize; int centerX = static_cast<int>(((featureSize) / 2.0 + left) + 0.5); int centerY = static_cast<int>(((featureSize) / 2.0 + top) + 0.5); // this gives the same output as the old way of doing things, // though the featureSize entries are not needed *twice* pointData->Add(centerX, centerY, featureSize+1, featureSize+1, 0); //Geometry::Rectangle r(left, top, left+featureSize, top+featureSize); } } } // here comes the actual creation of surf features /* we might speed this up as follows: - first allocate a double array for all features. - then loop over the component images sepqrately and write into the buffer. This might be faster because the component images are are accessed once. - then normalize all features and copy vectors into the point list. */ int it = 0; Mat* descriptors = MatCreate<Mat>(pointData->Size(), components.size() * spatiality * spatiality); for(int y=0 ; y<=h-spatiality ; ++y) { for(int x=0 ; x<=w-spatiality ; ++x) { ExtractFeatureIntoVector(MatE(descriptors, it, 0), components, x, y, spatiality); it++; } } for(int i=0 ; i<components.size() ; ++i) delete components[i]; return descriptors; } Here is the call graph for this function:

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