opencv/apps/traincascade/HOGfeatures.cpp

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2012-10-17 07:18:30 +08:00
#include "opencv2/core/core.hpp"
#include "opencv2/core/internal.hpp"
#include "HOGfeatures.h"
#include "cascadeclassifier.h"
CvHOGFeatureParams::CvHOGFeatureParams()
{
maxCatCount = 0;
name = HOGF_NAME;
featSize = N_BINS * N_CELLS;
}
void CvHOGEvaluator::init(const CvFeatureParams *_featureParams, int _maxSampleCount, Size _winSize)
{
CV_Assert( _maxSampleCount > 0);
int cols = (_winSize.width + 1) * (_winSize.height + 1);
for (int bin = 0; bin < N_BINS; bin++)
{
hist.push_back(Mat(_maxSampleCount, cols, CV_32FC1));
}
normSum.create( (int)_maxSampleCount, cols, CV_32FC1 );
CvFeatureEvaluator::init( _featureParams, _maxSampleCount, _winSize );
}
void CvHOGEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
{
CV_DbgAssert( !hist.empty());
CvFeatureEvaluator::setImage( img, clsLabel, idx );
vector<Mat> integralHist;
for (int bin = 0; bin < N_BINS; bin++)
{
integralHist.push_back( Mat(winSize.height + 1, winSize.width + 1, hist[bin].type(), hist[bin].ptr<float>((int)idx)) );
}
Mat integralNorm(winSize.height + 1, winSize.width + 1, normSum.type(), normSum.ptr<float>((int)idx));
integralHistogram(img, integralHist, integralNorm, (int)N_BINS);
}
//void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
//{
// _writeFeatures( features, fs, featureMap );
//}
void CvHOGEvaluator::writeFeatures( FileStorage &fs, const Mat& featureMap ) const
{
int featIdx;
int componentIdx;
const Mat_<int>& featureMap_ = (const Mat_<int>&)featureMap;
fs << FEATURES << "[";
for ( int fi = 0; fi < featureMap.cols; fi++ )
if ( featureMap_(0, fi) >= 0 )
{
fs << "{";
featIdx = fi / getFeatureSize();
componentIdx = fi % getFeatureSize();
features[featIdx].write( fs, componentIdx );
fs << "}";
}
fs << "]";
}
void CvHOGEvaluator::generateFeatures()
{
int offset = winSize.width + 1;
Size blockStep;
int x, y, t, w, h;
for (t = 8; t <= winSize.width/2; t+=8) //t = size of a cell. blocksize = 4*cellSize
{
blockStep = Size(4,4);
w = 2*t; //width of a block
h = 2*t; //height of a block
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, t, t));
}
}
w = 2*t;
h = 4*t;
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, t, 2*t));
}
}
w = 4*t;
h = 2*t;
for (x = 0; x <= winSize.width - w; x += blockStep.width)
{
for (y = 0; y <= winSize.height - h; y += blockStep.height)
{
features.push_back(Feature(offset, x, y, 2*t, t));
}
}
}
numFeatures = (int)features.size();
}
CvHOGEvaluator::Feature::Feature()
{
for (int i = 0; i < N_CELLS; i++)
{
rect[i] = Rect(0, 0, 0, 0);
}
}
CvHOGEvaluator::Feature::Feature( int offset, int x, int y, int cellW, int cellH )
{
rect[0] = Rect(x, y, cellW, cellH); //cell0
rect[1] = Rect(x+cellW, y, cellW, cellH); //cell1
rect[2] = Rect(x, y+cellH, cellW, cellH); //cell2
rect[3] = Rect(x+cellW, y+cellH, cellW, cellH); //cell3
for (int i = 0; i < N_CELLS; i++)
{
CV_SUM_OFFSETS(fastRect[i].p0, fastRect[i].p1, fastRect[i].p2, fastRect[i].p3, rect[i], offset);
}
}
void CvHOGEvaluator::Feature::write(FileStorage &fs) const
{
fs << CC_RECTS << "[";
for( int i = 0; i < N_CELLS; i++ )
{
fs << "[:" << rect[i].x << rect[i].y << rect[i].width << rect[i].height << "]";
}
fs << "]";
}
//cell and bin idx writing
//void CvHOGEvaluator::Feature::write(FileStorage &fs, int varIdx) const
//{
// int featComponent = varIdx % (N_CELLS * N_BINS);
// int cellIdx = featComponent / N_BINS;
// int binIdx = featComponent % N_BINS;
//
// fs << CC_RECTS << "[:" << rect[cellIdx].x << rect[cellIdx].y <<
// rect[cellIdx].width << rect[cellIdx].height << binIdx << "]";
//}
//cell[0] and featComponent idx writing. By cell[0] it's possible to recover all block
//All block is nessesary for block normalization
void CvHOGEvaluator::Feature::write(FileStorage &fs, int featComponentIdx) const
{
fs << CC_RECT << "[:" << rect[0].x << rect[0].y <<
rect[0].width << rect[0].height << featComponentIdx << "]";
}
void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, Mat &norm, int nbins) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
int x, y, binIdx;
Size gradSize(img.size());
Size histSize(histogram[0].size());
Mat grad(gradSize, CV_32F);
Mat qangle(gradSize, CV_8U);
AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
int* xmap = (int*)mapbuf + 1;
int* ymap = xmap + gradSize.width + 2;
const int borderType = (int)BORDER_REPLICATE;
for( x = -1; x < gradSize.width + 1; x++ )
xmap[x] = borderInterpolate(x, gradSize.width, borderType);
for( y = -1; y < gradSize.height + 1; y++ )
ymap[y] = borderInterpolate(y, gradSize.height, borderType);
int width = gradSize.width;
AutoBuffer<float> _dbuf(width*4);
float* dbuf = _dbuf;
Mat Dx(1, width, CV_32F, dbuf);
Mat Dy(1, width, CV_32F, dbuf + width);
Mat Mag(1, width, CV_32F, dbuf + width*2);
Mat Angle(1, width, CV_32F, dbuf + width*3);
float angleScale = (float)(nbins/CV_PI);
for( y = 0; y < gradSize.height; y++ )
{
const uchar* currPtr = img.data + img.step*ymap[y];
const uchar* prevPtr = img.data + img.step*ymap[y-1];
const uchar* nextPtr = img.data + img.step*ymap[y+1];
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
for( x = 0; x < width; x++ )
{
dbuf[x] = (float)(currPtr[xmap[x+1]] - currPtr[xmap[x-1]]);
dbuf[width + x] = (float)(nextPtr[xmap[x]] - prevPtr[xmap[x]]);
}
cartToPolar( Dx, Dy, Mag, Angle, false );
for( x = 0; x < width; x++ )
{
float mag = dbuf[x+width*2];
float angle = dbuf[x+width*3];
angle = angle*angleScale - 0.5f;
int bidx = cvFloor(angle);
angle -= bidx;
if( bidx < 0 )
bidx += nbins;
else if( bidx >= nbins )
bidx -= nbins;
qanglePtr[x] = (uchar)bidx;
gradPtr[x] = mag;
}
}
integral(grad, norm, grad.depth());
float* histBuf;
const float* magBuf;
const uchar* binsBuf;
int binsStep = (int)( qangle.step / sizeof(uchar) );
int histStep = (int)( histogram[0].step / sizeof(float) );
int magStep = (int)( grad.step / sizeof(float) );
for( binIdx = 0; binIdx < nbins; binIdx++ )
{
histBuf = (float*)histogram[binIdx].data;
magBuf = (const float*)grad.data;
binsBuf = (const uchar*)qangle.data;
memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
histBuf += histStep + 1;
for( y = 0; y < qangle.rows; y++ )
{
histBuf[-1] = 0.f;
float strSum = 0.f;
for( x = 0; x < qangle.cols; x++ )
{
if( binsBuf[x] == binIdx )
strSum += magBuf[x];
histBuf[x] = histBuf[-histStep + x] + strSum;
}
histBuf += histStep;
binsBuf += binsStep;
magBuf += magStep;
}
}
}