Added HOG features to the traincascade module
This commit is contained in:
Alexey Kazakov
245
modules/traincascade/HOGfeatures.cpp
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245
modules/traincascade/HOGfeatures.cpp
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#include "HOGfeatures.h"
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#include "cascadeclassifier.h"
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CvHOGFeatureParams::CvHOGFeatureParams()
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{
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maxCatCount = 0;
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name = HOGF_NAME;
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featSize = N_BINS * N_CELLS;
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}
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void CvHOGEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
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{
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CV_Assert( _maxSampleCount > 0);
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int cols = (_winSize.width + 1) * (_winSize.height + 1);
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for (int bin = 0; bin < N_BINS; bin++)
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{
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hist.push_back(Mat(_maxSampleCount, cols, CV_32FC1));
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}
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normSum.create( (int)_maxSampleCount, cols, CV_32FC1 );
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CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
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}
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void CvHOGEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
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{
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CV_DbgAssert( !hist.empty());
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CvFeatureEvaluator::setImage( img, clsLabel, idx );
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vector<Mat> integralHist;
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for (int bin = 0; bin < N_BINS; bin++)
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{
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integralHist.push_back( Mat(winSize.height + 1, winSize.width + 1, hist[bin].type(), hist[bin].ptr<float>((int)idx)) );
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}
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Mat integralNorm(winSize.height + 1, winSize.width + 1, normSum.type(), normSum.ptr<float>((int)idx));
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integralHistogram(img, integralHist, integralNorm, (int)N_BINS);
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}
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//void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
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//{
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// _writeFeatures( features, fs, featureMap );
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//}
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void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
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{
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int featIdx;
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int componentIdx;
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const Mat_<int>& featureMap_ = (const Mat_<int>&)featureMap;
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fs << FEATURES << "[";
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for ( int fi = 0; fi < featureMap.cols; fi++ )
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if ( featureMap_(0, fi) >= 0 )
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{
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fs << "{";
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featIdx = fi / getFeatureSize();
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componentIdx = fi % getFeatureSize();
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features[featIdx].write( fs, componentIdx );
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fs << "}";
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}
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fs << "]";
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}
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void CvHOGEvaluator::generateFeatures()
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{
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int offset = winSize.width + 1;
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Size blockStep;
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int x, y, t, w, h;
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for (t = 8; t <= winSize.width/2; t+=8) //t = size of a cell. blocksize = 4*cellSize
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{
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blockStep = Size(4,4);
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w = 2*t; //width of a block
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h = 2*t; //height of a block
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for (x = 0; x <= winSize.width - w; x += blockStep.width)
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{
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for (y = 0; y <= winSize.height - h; y += blockStep.height)
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{
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features.push_back(Feature(offset, x, y, t, t));
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}
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}
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w = 2*t;
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h = 4*t;
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for (x = 0; x <= winSize.width - w; x += blockStep.width)
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{
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for (y = 0; y <= winSize.height - h; y += blockStep.height)
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{
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features.push_back(Feature(offset, x, y, t, 2*t));
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}
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}
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w = 4*t;
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h = 2*t;
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for (x = 0; x <= winSize.width - w; x += blockStep.width)
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{
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for (y = 0; y <= winSize.height - h; y += blockStep.height)
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{
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features.push_back(Feature(offset, x, y, 2*t, t));
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}
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}
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}
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numFeatures = (int)features.size();
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}
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CvHOGEvaluator::Feature::Feature()
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{
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for (int i = 0; i < N_CELLS; i++)
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{
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rect[i] = Rect(0, 0, 0, 0);
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}
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}
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CvHOGEvaluator::Feature::Feature( int offset, int x, int y, int cellW, int cellH )
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{
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rect[0] = Rect(x, y, cellW, cellH); //cell0
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rect[1] = Rect(x+cellW, y, cellW, cellH); //cell1
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rect[2] = Rect(x, y+cellH, cellW, cellH); //cell2
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rect[3] = Rect(x+cellW, y+cellH, cellW, cellH); //cell3
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for (int i = 0; i < N_CELLS; i++)
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{
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CV_SUM_OFFSETS(fastRect[i].p0, fastRect[i].p1, fastRect[i].p2, fastRect[i].p3, rect[i], offset);
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}
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}
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void CvHOGEvaluator::Feature::write(FileStorage &fs) const
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{
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fs << CC_RECTS << "[";
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for( int i = 0; i < N_CELLS; i++ )
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{
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fs << "[:" << rect[i].x << rect[i].y << rect[i].width << rect[i].height << "]";
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}
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fs << "]";
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}
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//cell and bin idx writing
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//void CvHOGEvaluator::Feature::write(FileStorage &fs, int varIdx) const
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//{
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// int featComponent = varIdx % (N_CELLS * N_BINS);
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// int cellIdx = featComponent / N_BINS;
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// int binIdx = featComponent % N_BINS;
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//
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// fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y <<
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// rect[cellIdx].width << rect[cellIdx].height << binIdx << "]";
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//}
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//cell[0] and featComponent idx writing. By cell[0] it's possible to recover all block
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//All block is nessesary for block normalization
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void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const
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{
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fs << CC_RECT << "[:" << rect[0].x << rect[0].y <<
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rect[0].width << rect[0].height << featComponentIdx << "]";
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}
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void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
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{
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CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
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int x, y, binIdx;
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Size gradSize(img.size());
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Size histSize(histogram[0].size());
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Mat grad(gradSize, CV_32F);
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Mat qangle(gradSize, CV_8U);
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AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
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int* xmap = (int*)mapbuf + 1;
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int* ymap = xmap + gradSize.width + 2;
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const int borderType = (int)BORDER_REPLICATE;
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for( x = -1; x < gradSize.width + 1; x++ )
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xmap[x] = borderInterpolate(x, gradSize.width, borderType);
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for( y = -1; y < gradSize.height + 1; y++ )
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ymap[y] = borderInterpolate(y, gradSize.height, borderType);
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int width = gradSize.width;
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AutoBuffer<float> _dbuf(width*4);
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float* dbuf = _dbuf;
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Mat Dx(1, width, CV_32F, dbuf);
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Mat Dy(1, width, CV_32F, dbuf + width);
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Mat Mag(1, width, CV_32F, dbuf + width*2);
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Mat Angle(1, width, CV_32F, dbuf + width*3);
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float angleScale = (float)(nbins/CV_PI);
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for( y = 0; y < gradSize.height; y++ )
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{
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const uchar* currPtr = img.data + img.step*ymap[y];
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const uchar* prevPtr = img.data + img.step*ymap[y-1];
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const uchar* nextPtr = img.data + img.step*ymap[y+1];
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float* gradPtr = (float*)grad.ptr(y);
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uchar* qanglePtr = (uchar*)qangle.ptr(y);
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for( x = 0; x < width; x++ )
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{
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dbuf[x] = (float)(currPtr[xmap[x+1]] - currPtr[xmap[x-1]]);
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dbuf[width + x] = (float)(nextPtr[xmap[x]] - prevPtr[xmap[x]]);
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}
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cartToPolar( Dx, Dy, Mag, Angle, false );
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for( x = 0; x < width; x++ )
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{
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float mag = dbuf[x+width*2];
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float angle = dbuf[x+width*3];
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angle = angle*angleScale - 0.5f;
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int bidx = cvFloor(angle);
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angle -= bidx;
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if( bidx < 0 )
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bidx += nbins;
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else if( bidx >= nbins )
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bidx -= nbins;
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qanglePtr[x] = (uchar)bidx;
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gradPtr[x] = mag;
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}
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}
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integral(grad, norm, grad.depth());
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float* histBuf;
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const float* magBuf;
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const uchar* binsBuf;
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int binsStep = (int)( qangle.step / sizeof(uchar) );
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int histStep = (int)( histogram[0].step / sizeof(float) );
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int magStep = (int)( grad.step / sizeof(float) );
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for( binIdx = 0; binIdx < nbins; binIdx++ )
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{
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histBuf = (float*)histogram[binIdx].data;
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magBuf = (const float*)grad.data;
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binsBuf = (const uchar*)qangle.data;
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memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
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histBuf += histStep + 1;
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for( y = 0; y < qangle.rows; y++ )
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{
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histBuf[-1] = 0.f;
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float strSum = 0.f;
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for( x = 0; x < qangle.cols; x++ )
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{
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if( binsBuf[x] == binIdx )
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strSum += magBuf[x];
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histBuf[x] = histBuf[-histStep + x] + strSum;
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}
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histBuf += histStep;
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binsBuf += binsStep;
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magBuf += magStep;
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}
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}
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}
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