Open Source Computer Vision Library https://opencv.org/
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#include "precomp.hpp"
#include <iterator>
#ifdef HAVE_IPP
#include "ipp.h"
#endif
/****************************************************************************************\
The code below is implementation of HOG (Histogram-of-Oriented Gradients)
descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.
The computed feature vectors are compatible with the
INRIA Object Detection and Localization Toolkit
(http://pascal.inrialpes.fr/soft/olt/)
\****************************************************************************************/
namespace cv
{
size_t HOGDescriptor::getDescriptorSize() const
{
CV_Assert(blockSize.width % cellSize.width == 0 &&
blockSize.height % cellSize.height == 0);
CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
(winSize.height - blockSize.height) % blockStride.height == 0 );
return (size_t)nbins*
(blockSize.width/cellSize.width)*
(blockSize.height/cellSize.height)*
((winSize.width - blockSize.width)/blockStride.width + 1)*
((winSize.height - blockSize.height)/blockStride.height + 1);
}
double HOGDescriptor::getWinSigma() const
{
return winSigma >= 0 ? winSigma : (blockSize.width + blockSize.height)/8.;
}
bool HOGDescriptor::checkDetectorSize() const
{
size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
return detectorSize == 0 ||
detectorSize == descriptorSize ||
detectorSize == descriptorSize + 1;
}
void HOGDescriptor::setSVMDetector(const vector<float>& _svmDetector)
{
svmDetector = _svmDetector;
CV_Assert( checkDetectorSize() );
}
#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"
bool HOGDescriptor::read(FileNode& obj)
{
if( !obj.isMap() )
return false;
FileNodeIterator it = obj["winSize"].begin();
it >> winSize.width >> winSize.height;
it = obj["blockSize"].begin();
it >> blockSize.width >> blockSize.height;
it = obj["blockStride"].begin();
it >> blockStride.width >> blockStride.height;
it = obj["cellSize"].begin();
it >> cellSize.width >> cellSize.height;
obj["nbins"] >> nbins;
obj["derivAperture"] >> derivAperture;
obj["winSigma"] >> winSigma;
obj["histogramNormType"] >> histogramNormType;
obj["L2HysThreshold"] >> L2HysThreshold;
obj["gammaCorrection"] >> gammaCorrection;
FileNode vecNode = obj["SVMDetector"];
if( vecNode.isSeq() )
{
vecNode >> svmDetector;
CV_Assert(checkDetectorSize());
}
return true;
}
void HOGDescriptor::write(FileStorage& fs, const String& objName) const
{
if( !objName.empty() )
fs << objName;
fs << "{" CV_TYPE_NAME_HOG_DESCRIPTOR
<< "winSize" << winSize
<< "blockSize" << blockSize
<< "blockStride" << blockStride
<< "cellSize" << cellSize
<< "nbins" << nbins
<< "derivAperture" << derivAperture
<< "winSigma" << getWinSigma()
<< "histogramNormType" << histogramNormType
<< "L2HysThreshold" << L2HysThreshold
<< "gammaCorrection" << gammaCorrection;
if( !svmDetector.empty() )
fs << "SVMDetector" << "[:" << svmDetector << "]";
fs << "}";
}
bool HOGDescriptor::load(const String& filename, const String& objname)
{
FileStorage fs(filename, FileStorage::READ);
FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
return read(obj);
}
void HOGDescriptor::save(const String& filename, const String& objName) const
{
FileStorage fs(filename, FileStorage::WRITE);
write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
}
void HOGDescriptor::copyTo(HOGDescriptor& c) const
{
c.winSize = winSize;
c.blockSize = blockSize;
c.blockStride = blockStride;
c.cellSize = cellSize;
c.nbins = nbins;
c.derivAperture = derivAperture;
c.winSigma = winSigma;
c.histogramNormType = histogramNormType;
c.L2HysThreshold = L2HysThreshold;
c.gammaCorrection = gammaCorrection;
c.svmDetector = svmDetector;
}
void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
Size paddingTL, Size paddingBR) const
{
CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );
Size gradsize(img.cols + paddingTL.width + paddingBR.width,
img.rows + paddingTL.height + paddingBR.height);
grad.create(gradsize, CV_32FC2); // <magnitude*(1-alpha), magnitude*alpha>
qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
Size wholeSize;
Point roiofs;
img.locateROI(wholeSize, roiofs);
int i, x, y;
int cn = img.channels();
Mat_<float> _lut(1, 256);
const float* lut = &_lut(0,0);
if( gammaCorrection )
for( i = 0; i < 256; i++ )
_lut(0,i) = std::sqrt((float)i);
else
for( i = 0; i < 256; i++ )
_lut(0,i) = (float)i;
AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
int* xmap = (int*)mapbuf + 1;
int* ymap = xmap + gradsize.width + 2;
const int borderType = (int)BORDER_REFLECT_101;
for( x = -1; x < gradsize.width + 1; x++ )
xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
wholeSize.width, borderType) - roiofs.x;
for( y = -1; y < gradsize.height + 1; y++ )
ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
wholeSize.height, borderType) - roiofs.y;
// x- & y- derivatives for the whole row
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);
int _nbins = nbins;
float angleScale = (float)(_nbins/CV_PI);
#ifdef HAVE_IPP
Mat lutimg(img.rows,img.cols,CV_MAKETYPE(CV_32F,cn));
Mat hidxs(1, width, CV_32F);
Ipp32f* pHidxs = (Ipp32f*)hidxs.data;
Ipp32f* pAngles = (Ipp32f*)Angle.data;
IppiSize roiSize;
roiSize.width = img.cols;
roiSize.height = img.rows;
for( y = 0; y < roiSize.height; y++ )
{
const uchar* imgPtr = img.data + y*img.step;
float* imglutPtr = (float*)(lutimg.data + y*lutimg.step);
for( x = 0; x < roiSize.width*cn; x++ )
{
imglutPtr[x] = lut[imgPtr[x]];
}
}
#endif
for( y = 0; y < gradsize.height; y++ )
{
#ifdef HAVE_IPP
const float* imgPtr = (float*)(lutimg.data + lutimg.step*ymap[y]);
const float* prevPtr = (float*)(lutimg.data + lutimg.step*ymap[y-1]);
const float* nextPtr = (float*)(lutimg.data + lutimg.step*ymap[y+1]);
#else
const uchar* imgPtr = 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];
#endif
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
if( cn == 1 )
{
for( x = 0; x < width; x++ )
{
int x1 = xmap[x];
#ifdef HAVE_IPP
dbuf[x] = (float)(imgPtr[xmap[x+1]] - imgPtr[xmap[x-1]]);
dbuf[width + x] = (float)(nextPtr[x1] - prevPtr[x1]);
#else
dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
#endif
}
}
else
{
for( x = 0; x < width; x++ )
{
int x1 = xmap[x]*3;
float dx0, dy0, dx, dy, mag0, mag;
#ifdef HAVE_IPP
const float* p2 = imgPtr + xmap[x+1]*3;
const float* p0 = imgPtr + xmap[x-1]*3;
dx0 = p2[2] - p0[2];
dy0 = nextPtr[x1+2] - prevPtr[x1+2];
mag0 = dx0*dx0 + dy0*dy0;
dx = p2[1] - p0[1];
dy = nextPtr[x1+1] - prevPtr[x1+1];
mag = dx*dx + dy*dy;
if( mag0 < mag )
{
dx0 = dx;
dy0 = dy;
mag0 = mag;
}
dx = p2[0] - p0[0];
dy = nextPtr[x1] - prevPtr[x1];
mag = dx*dx + dy*dy;
#else
const uchar* p2 = imgPtr + xmap[x+1]*3;
const uchar* p0 = imgPtr + xmap[x-1]*3;
dx0 = lut[p2[2]] - lut[p0[2]];
dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
mag0 = dx0*dx0 + dy0*dy0;
dx = lut[p2[1]] - lut[p0[1]];
dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
mag = dx*dx + dy*dy;
if( mag0 < mag )
{
dx0 = dx;
dy0 = dy;
mag0 = mag;
}
dx = lut[p2[0]] - lut[p0[0]];
dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
mag = dx*dx + dy*dy;
#endif
if( mag0 < mag )
{
dx0 = dx;
dy0 = dy;
mag0 = mag;
}
dbuf[x] = dx0;
dbuf[x+width] = dy0;
}
}
#ifdef HAVE_IPP
ippsCartToPolar_32f((const Ipp32f*)Dx.data, (const Ipp32f*)Dy.data, (Ipp32f*)Mag.data, pAngles, width);
for( x = 0; x < width; x++ )
{
if(pAngles[x] < 0.f)
pAngles[x] += (Ipp32f)(CV_PI*2.);
}
ippsNormalize_32f(pAngles, pAngles, width, 0.5f/angleScale, 1.f/angleScale);
ippsFloor_32f(pAngles,(Ipp32f*)hidxs.data,width);
ippsSub_32f_I((Ipp32f*)hidxs.data,pAngles,width);
ippsMul_32f_I((Ipp32f*)Mag.data,pAngles,width);
ippsSub_32f_I(pAngles,(Ipp32f*)Mag.data,width);
ippsRealToCplx_32f((Ipp32f*)Mag.data,pAngles,(Ipp32fc*)gradPtr,width);
#else
cartToPolar( Dx, Dy, Mag, Angle, false );
#endif
for( x = 0; x < width; x++ )
{
#ifdef HAVE_IPP
int hidx = (int)pHidxs[x];
#else
float mag = dbuf[x+width*2], angle = dbuf[x+width*3]*angleScale - 0.5f;
int hidx = cvFloor(angle);
angle -= hidx;
gradPtr[x*2] = mag*(1.f - angle);
gradPtr[x*2+1] = mag*angle;
#endif
if( hidx < 0 )
hidx += _nbins;
else if( hidx >= _nbins )
hidx -= _nbins;
assert( (unsigned)hidx < (unsigned)_nbins );
qanglePtr[x*2] = (uchar)hidx;
hidx++;
hidx &= hidx < _nbins ? -1 : 0;
qanglePtr[x*2+1] = (uchar)hidx;
}
}
}
struct HOGCache
{
struct BlockData
{
BlockData() : histOfs(0), imgOffset() {}
int histOfs;
Point imgOffset;
};
struct PixData
{
size_t gradOfs, qangleOfs;
int histOfs[4];
float histWeights[4];
float gradWeight;
};
HOGCache();
HOGCache(const HOGDescriptor* descriptor,
const Mat& img, Size paddingTL, Size paddingBR,
bool useCache, Size cacheStride);
virtual ~HOGCache() {};
virtual void init(const HOGDescriptor* descriptor,
const Mat& img, Size paddingTL, Size paddingBR,
bool useCache, Size cacheStride);
Size windowsInImage(Size imageSize, Size winStride) const;
Rect getWindow(Size imageSize, Size winStride, int idx) const;
const float* getBlock(Point pt, float* buf);
virtual void normalizeBlockHistogram(float* histogram) const;
vector<PixData> pixData;
vector<BlockData> blockData;
bool useCache;
vector<int> ymaxCached;
Size winSize, cacheStride;
Size nblocks, ncells;
int blockHistogramSize;
int count1, count2, count4;
Point imgoffset;
Mat_<float> blockCache;
Mat_<uchar> blockCacheFlags;
Mat grad, qangle;
const HOGDescriptor* descriptor;
};
HOGCache::HOGCache()
{
useCache = false;
blockHistogramSize = count1 = count2 = count4 = 0;
descriptor = 0;
}
HOGCache::HOGCache(const HOGDescriptor* _descriptor,
const Mat& _img, Size _paddingTL, Size _paddingBR,
bool _useCache, Size _cacheStride)
{
init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
}
void HOGCache::init(const HOGDescriptor* _descriptor,
const Mat& _img, Size _paddingTL, Size _paddingBR,
bool _useCache, Size _cacheStride)
{
descriptor = _descriptor;
cacheStride = _cacheStride;
useCache = _useCache;
descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
imgoffset = _paddingTL;
winSize = descriptor->winSize;
Size blockSize = descriptor->blockSize;
Size blockStride = descriptor->blockStride;
Size cellSize = descriptor->cellSize;
Size winSize = descriptor->winSize;
int i, j, nbins = descriptor->nbins;
int rawBlockSize = blockSize.width*blockSize.height;
nblocks = Size((winSize.width - blockSize.width)/blockStride.width + 1,
(winSize.height - blockSize.height)/blockStride.height + 1);
ncells = Size(blockSize.width/cellSize.width, blockSize.height/cellSize.height);
blockHistogramSize = ncells.width*ncells.height*nbins;
if( useCache )
{
Size cacheSize((grad.cols - blockSize.width)/cacheStride.width+1,
(winSize.height/cacheStride.height)+1);
blockCache.create(cacheSize.height, cacheSize.width*blockHistogramSize);
blockCacheFlags.create(cacheSize);
size_t i, cacheRows = blockCache.rows;
ymaxCached.resize(cacheRows);
for( i = 0; i < cacheRows; i++ )
ymaxCached[i] = -1;
}
Mat_<float> weights(blockSize);
float sigma = (float)descriptor->getWinSigma();
float scale = 1.f/(sigma*sigma*2);
for(i = 0; i < blockSize.height; i++)
for(j = 0; j < blockSize.width; j++)
{
float di = i - blockSize.height*0.5f;
float dj = j - blockSize.width*0.5f;
weights(i,j) = std::exp(-(di*di + dj*dj)*scale);
}
blockData.resize(nblocks.width*nblocks.height);
pixData.resize(rawBlockSize*3);
// Initialize 2 lookup tables, pixData & blockData.
// Here is why:
//
// The detection algorithm runs in 4 nested loops (at each pyramid layer):
// loop over the windows within the input image
// loop over the blocks within each window
// loop over the cells within each block
// loop over the pixels in each cell
//
// As each of the loops runs over a 2-dimensional array,
// we could get 8(!) nested loops in total, which is very-very slow.
//
// To speed the things up, we do the following:
// 1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
// inside we compute the current search window using getWindow() method.
// Yes, it involves some overhead (function call + couple of divisions),
// but it's tiny in fact.
// 2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
// to set up gradient and histogram pointers.
// 3. loops over cells and pixels in each cell are merged
// (since there is no overlap between cells, each pixel in the block is processed once)
// and also unrolled. Inside we use PixData[k] to access the gradient values and
// update the histogram
//
count1 = count2 = count4 = 0;
for( j = 0; j < blockSize.width; j++ )
for( i = 0; i < blockSize.height; i++ )
{
PixData* data = 0;
float cellX = (j+0.5f)/cellSize.width - 0.5f;
float cellY = (i+0.5f)/cellSize.height - 0.5f;
int icellX0 = cvFloor(cellX);
int icellY0 = cvFloor(cellY);
int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
cellX -= icellX0;
cellY -= icellY0;
if( (unsigned)icellX0 < (unsigned)ncells.width &&
(unsigned)icellX1 < (unsigned)ncells.width )
{
if( (unsigned)icellY0 < (unsigned)ncells.height &&
(unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize*2 + (count4++)];
data->histOfs[0] = (icellX0*ncells.height + icellY0)*nbins;
data->histWeights[0] = (1.f - cellX)*(1.f - cellY);
data->histOfs[1] = (icellX1*ncells.height + icellY0)*nbins;
data->histWeights[1] = cellX*(1.f - cellY);
data->histOfs[2] = (icellX0*ncells.height + icellY1)*nbins;
data->histWeights[2] = (1.f - cellX)*cellY;
data->histOfs[3] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[3] = cellX*cellY;
}
else
{
data = &pixData[rawBlockSize + (count2++)];
if( (unsigned)icellY0 < (unsigned)ncells.height )
{
icellY1 = icellY0;
cellY = 1.f - cellY;
}
data->histOfs[0] = (icellX0*ncells.height + icellY1)*nbins;
data->histWeights[0] = (1.f - cellX)*cellY;
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[1] = cellX*cellY;
data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[2] = data->histWeights[3] = 0;
}
}
else
{
if( (unsigned)icellX0 < (unsigned)ncells.width )
{
icellX1 = icellX0;
cellX = 1.f - cellX;
}
if( (unsigned)icellY0 < (unsigned)ncells.height &&
(unsigned)icellY1 < (unsigned)ncells.height )
{
data = &pixData[rawBlockSize + (count2++)];
data->histOfs[0] = (icellX1*ncells.height + icellY0)*nbins;
data->histWeights[0] = cellX*(1.f - cellY);
data->histOfs[1] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[1] = cellX*cellY;
data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[2] = data->histWeights[3] = 0;
}
else
{
data = &pixData[count1++];
if( (unsigned)icellY0 < (unsigned)ncells.height )
{
icellY1 = icellY0;
cellY = 1.f - cellY;
}
data->histOfs[0] = (icellX1*ncells.height + icellY1)*nbins;
data->histWeights[0] = cellX*cellY;
data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
}
}
data->gradOfs = (grad.cols*i + j)*2;
data->qangleOfs = (qangle.cols*i + j)*2;
data->gradWeight = weights(i,j);
}
assert( count1 + count2 + count4 == rawBlockSize );
// defragment pixData
for( j = 0; j < count2; j++ )
pixData[j + count1] = pixData[j + rawBlockSize];
for( j = 0; j < count4; j++ )
pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
count2 += count1;
count4 += count2;
// initialize blockData
for( j = 0; j < nblocks.width; j++ )
for( i = 0; i < nblocks.height; i++ )
{
BlockData& data = blockData[j*nblocks.height + i];
data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
}
}
const float* HOGCache::getBlock(Point pt, float* buf)
{
float* blockHist = buf;
assert(descriptor != 0);
Size blockSize = descriptor->blockSize;
pt += imgoffset;
CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
(unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );
if( useCache )
{
CV_Assert( pt.x % cacheStride.width == 0 &&
pt.y % cacheStride.height == 0 );
Point cacheIdx(pt.x/cacheStride.width,
(pt.y/cacheStride.height) % blockCache.rows);
if( pt.y != ymaxCached[cacheIdx.y] )
{
Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
cacheRow = (uchar)0;
ymaxCached[cacheIdx.y] = pt.y;
}
blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
if( computedFlag != 0 )
return blockHist;
computedFlag = (uchar)1; // set it at once, before actual computing
}
int k, C1 = count1, C2 = count2, C4 = count4;
const float* gradPtr = (const float*)(grad.data + grad.step*pt.y) + pt.x*2;
const uchar* qanglePtr = qangle.data + qangle.step*pt.y + pt.x*2;
CV_Assert( blockHist != 0 );
#ifdef HAVE_IPP
ippsZero_32f(blockHist,blockHistogramSize);
#else
for( k = 0; k < blockHistogramSize; k++ )
blockHist[k] = 0.f;
#endif
const PixData* _pixData = &pixData[0];
for( k = 0; k < C1; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w = pk.gradWeight*pk.histWeights[0];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
float t0 = hist[h0] + a[0]*w;
float t1 = hist[h1] + a[1]*w;
hist[h0] = t0; hist[h1] = t1;
}
for( ; k < C2; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w, t0, t1, a0 = a[0], a1 = a[1];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
for( ; k < C4; k++ )
{
const PixData& pk = _pixData[k];
const float* a = gradPtr + pk.gradOfs;
float w, t0, t1, a0 = a[0], a1 = a[1];
const uchar* h = qanglePtr + pk.qangleOfs;
int h0 = h[0], h1 = h[1];
float* hist = blockHist + pk.histOfs[0];
w = pk.gradWeight*pk.histWeights[0];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[1];
w = pk.gradWeight*pk.histWeights[1];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[2];
w = pk.gradWeight*pk.histWeights[2];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
hist = blockHist + pk.histOfs[3];
w = pk.gradWeight*pk.histWeights[3];
t0 = hist[h0] + a0*w;
t1 = hist[h1] + a1*w;
hist[h0] = t0; hist[h1] = t1;
}
normalizeBlockHistogram(blockHist);
return blockHist;
}
void HOGCache::normalizeBlockHistogram(float* _hist) const
{
float* hist = &_hist[0];
#ifdef HAVE_IPP
size_t sz = blockHistogramSize;
#else
size_t i, sz = blockHistogramSize;
#endif
float sum = 0;
#ifdef HAVE_IPP
ippsDotProd_32f(hist,hist,sz,&sum);
#else
for( i = 0; i < sz; i++ )
sum += hist[i]*hist[i];
#endif
float scale = 1.f/(std::sqrt(sum)+sz*0.1f), thresh = (float)descriptor->L2HysThreshold;
#ifdef HAVE_IPP
ippsMulC_32f_I(scale,hist,sz);
ippsThreshold_32f_I( hist, sz, thresh, ippCmpGreater );
ippsDotProd_32f(hist,hist,sz,&sum);
#else
for( i = 0, sum = 0; i < sz; i++ )
{
hist[i] = std::min(hist[i]*scale, thresh);
sum += hist[i]*hist[i];
}
#endif
scale = 1.f/(std::sqrt(sum)+1e-3f);
#ifdef HAVE_IPP
ippsMulC_32f_I(scale,hist,sz);
#else
for( i = 0; i < sz; i++ )
hist[i] *= scale;
#endif
}
Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
{
return Size((imageSize.width - winSize.width)/winStride.width + 1,
(imageSize.height - winSize.height)/winStride.height + 1);
}
Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const
{
int nwindowsX = (imageSize.width - winSize.width)/winStride.width + 1;
int y = idx / nwindowsX;
int x = idx - nwindowsX*y;
return Rect( x*winStride.width, y*winStride.height, winSize.width, winSize.height );
}
void HOGDescriptor::compute(const Mat& img, vector<float>& descriptors,
Size winStride, Size padding,
const vector<Point>& locations) const
{
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
gcd(winStride.height, blockStride.height));
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
if( !nwindows )
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
const HOGCache::BlockData* blockData = &cache.blockData[0];
int nblocks = cache.nblocks.area();
int blockHistogramSize = cache.blockHistogramSize;
size_t dsize = getDescriptorSize();
descriptors.resize(dsize*nwindows);
for( size_t i = 0; i < nwindows; i++ )
{
float* descriptor = &descriptors[i*dsize];
Point pt0;
if( !locations.empty() )
{
pt0 = locations[i];
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
continue;
}
else
{
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
}
for( int j = 0; j < nblocks; j++ )
{
const HOGCache::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
float* dst = descriptor + bj.histOfs;
const float* src = cache.getBlock(pt, dst);
if( src != dst )
#ifdef HAVE_IPP
ippsCopy_32f(src,dst,blockHistogramSize);
#else
for( int k = 0; k < blockHistogramSize; k++ )
dst[k] = src[k];
#endif
}
}
}
void HOGDescriptor::detect(const Mat& img,
vector<Point>& hits, vector<double>& weights, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
{
hits.clear();
if( svmDetector.empty() )
return;
if( winStride == Size() )
winStride = cellSize;
Size cacheStride(gcd(winStride.width, blockStride.width),
gcd(winStride.height, blockStride.height));
size_t nwindows = locations.size();
padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
Size paddedImgSize(img.cols + padding.width*2, img.rows + padding.height*2);
HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);
if( !nwindows )
nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
const HOGCache::BlockData* blockData = &cache.blockData[0];
int nblocks = cache.nblocks.area();
int blockHistogramSize = cache.blockHistogramSize;
size_t dsize = getDescriptorSize();
double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
vector<float> blockHist(blockHistogramSize);
for( size_t i = 0; i < nwindows; i++ )
{
Point pt0;
if( !locations.empty() )
{
pt0 = locations[i];
if( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height )
continue;
}
else
{
pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
}
double s = rho;
const float* svmVec = &svmDetector[0];
#ifdef HAVE_IPP
int j;
#else
int j, k;
#endif
for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
{
const HOGCache::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
const float* vec = cache.getBlock(pt, &blockHist[0]);
#ifdef HAVE_IPP
Ipp32f partSum;
ippsDotProd_32f(vec,svmVec,blockHistogramSize,&partSum);
s += (double)partSum;
#else
for( k = 0; k <= blockHistogramSize - 4; k += 4 )
s += vec[k]*svmVec[k] + vec[k+1]*svmVec[k+1] +
vec[k+2]*svmVec[k+2] + vec[k+3]*svmVec[k+3];
for( ; k < blockHistogramSize; k++ )
s += vec[k]*svmVec[k];
#endif
}
if( s >= hitThreshold )
{
hits.push_back(pt0);
weights.push_back(s);
}
}
}
void HOGDescriptor::detect(const Mat& img, vector<Point>& hits, double hitThreshold,
Size winStride, Size padding, const vector<Point>& locations) const
{
vector<double> weightsV;
detect(img, hits, weightsV, hitThreshold, winStride, padding, locations);
}
struct HOGInvoker
{
HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
double _hitThreshold, Size _winStride, Size _padding,
const double* _levelScale, ConcurrentRectVector* _vec,
vector<double>* _weights=0, vector<double>* _scales=0 )
{
hog = _hog;
img = _img;
hitThreshold = _hitThreshold;
winStride = _winStride;
padding = _padding;
levelScale = _levelScale;
vec = _vec;
weights = _weights;
scales = _scales;
}
void operator()( const BlockedRange& range ) const
{
int i, i1 = range.begin(), i2 = range.end();
double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);
Size maxSz(cvCeil(img.cols/minScale), cvCeil(img.rows/minScale));
Mat smallerImgBuf(maxSz, img.type());
vector<Point> locations;
vector<double> hitsWeights;
for( i = i1; i < i2; i++ )
{
double scale = levelScale[i];
Size sz(cvRound(img.cols/scale), cvRound(img.rows/scale));
Mat smallerImg(sz, img.type(), smallerImgBuf.data);
if( sz == img.size() )
smallerImg = Mat(sz, img.type(), img.data, img.step);
else
resize(img, smallerImg, sz);
hog->detect(smallerImg, locations, hitsWeights, hitThreshold, winStride, padding);
Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
for( size_t j = 0; j < locations.size(); j++ )
{
vec->push_back(Rect(cvRound(locations[j].x*scale),
cvRound(locations[j].y*scale),
scaledWinSize.width, scaledWinSize.height));
if (scales) {
scales->push_back(scale);
}
}
if (weights && (!hitsWeights.empty()))
{
for (size_t j = 0; j < locations.size(); j++)
{
weights->push_back(hitsWeights[j]);
}
}
}
}
const HOGDescriptor* hog;
Mat img;
double hitThreshold;
Size winStride;
Size padding;
const double* levelScale;
ConcurrentRectVector* vec;
vector<double>* weights;
vector<double>* scales;
};
void HOGDescriptor::detectMultiScale(
const Mat& img, vector<Rect>& foundLocations, vector<double>& foundWeights,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
double scale = 1.;
int levels = 0;
vector<double> levelScale;
for( levels = 0; levels < nlevels; levels++ )
{
levelScale.push_back(scale);
if( cvRound(img.cols/scale) < winSize.width ||
cvRound(img.rows/scale) < winSize.height ||
scale0 <= 1 )
break;
scale *= scale0;
}
levels = std::max(levels, 1);
levelScale.resize(levels);
ConcurrentRectVector allCandidates;
vector<double> foundScales;
parallel_for(BlockedRange(0, (int)levelScale.size()),
HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &foundWeights, &foundScales));
foundLocations.resize(allCandidates.size());
std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());
if ( useMeanshiftGrouping )
{
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
}
else
{
groupRectangles(foundLocations, (int)finalThreshold, 0.2);
}
}
void HOGDescriptor::detectMultiScale(const Mat& img, vector<Rect>& foundLocations,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
{
vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
padding, scale0, finalThreshold, useMeanshiftGrouping);
}
typedef RTTIImpl<HOGDescriptor> HOGRTTI;
CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);
vector<float> HOGDescriptor::getDefaultPeopleDetector()
{
static const float detector[] = {
0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
-0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
-0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
-3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
-0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
-6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
-0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
-4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
-0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
-0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
-7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
-0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
-0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
-0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
-0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
-5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
-0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
-0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
-0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
-4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
-0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
-0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
-0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
-0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
-0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
-0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
-0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
-0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
-4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
-8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
-0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
-0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
-0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
-8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
-0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
-0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
-8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
-0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
-0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
-0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
-3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
-5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
-0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
2.77751544e-003f, -0.05723337f, -0.04792468f, 0.07518548f,
2.77279224e-003f, 0.04211938f, 0.03100502f, 0.05278448f, 0.03954679f,
-0.03006846f, -0.03851741f, -0.02792403f, -0.02875333f, 0.01531280f,
0.02186953f, -0.01989829f, 2.50679464e-003f, -0.10258728f,
-0.04785743f, -0.02887216f, 3.85063468e-003f, 0.01112236f,
8.29218887e-003f, -0.04822981f, -0.04503597f, -0.03713100f,
-0.06988008f, -0.11002295f, -2.69209221e-003f, 1.85383670e-003f,
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0.03697936f, 0.02043072f, 0.04168725f, 0.01025975f, -0.01359980f,
-0.01600920f, 0.02581056f, 0.02329250f, 2.98100687e-003f, 0.01629762f,
0.06652115f, 0.05855627f, 0.01237463f, -0.01297135f, 0.01761587f,
0.05090865f, 0.06549342f, -0.04425945f, 2.43203156e-003f,
3.07327788e-003f, 0.06678630f, -0.04303836f, 0.01082393f, -0.06476044f,
0.04077786f, 0.12441979f, 0.08237778f, 0.07424165f, 0.04065890f,
0.06905543f, 0.09556347f, 0.12724875f, -0.02132082f, 0.08514154f,
-0.04175328f, -0.02666954f, 0.01897836f, 0.03317382f, 9.45465732e-003f,
-0.01238974f, -0.04242500f, -0.01419479f, -0.03545213f, -0.02440874f,
0.08684119f, 0.04212951f, 0.02462858f, -0.01104825f, -5.01706870e-003f,
0.02968982f, 0.02597476f, -0.01568939f, 0.04514892f, 0.06974549f,
0.08670278f, 0.06828108f, 0.10238872f, 0.05405957f, 0.06548470f,
-0.03763957f, 0.01366090f, 0.07069602f, 0.05363748f, 0.04798120f,
0.11706422f, 0.05466456f, -0.01869259f, 0.06344382f, 0.03106543f,
0.08432506f, -0.02061096f, 0.03821088f, -6.92190882e-003f,
6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
-0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
-0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
-0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
-0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
-0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
-0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
-0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
-0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
-0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
-0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
-0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
-0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
-0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
-0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
-0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
-0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
-0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
-0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
-2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
-1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
-1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
-3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
-0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
-0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
-5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
-0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
-0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
-0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
-0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
-0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
-0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
-0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
-0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
-0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
-0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
-6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
-0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
-0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
-0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
-0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
-0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
-0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
-0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
-0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
-0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
-0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
-0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
-0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
-0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
-0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
-0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f };
return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
//This function renurn 1981 SVM coeffs obtained from daimler's base.
//To use these coeffs the detection window size should be (48,96)
vector<float> HOGDescriptor::getDaimlerPeopleDetector()
{
static const float detector[] = {
0.294350f, -0.098796f, -0.129522f, 0.078753f,
0.387527f, 0.261529f, 0.145939f, 0.061520f,
0.328699f, 0.227148f, -0.066467f, -0.086723f,
0.047559f, 0.106714f, 0.037897f, 0.111461f,
-0.024406f, 0.304769f, 0.254676f, -0.069235f,
0.082566f, 0.147260f, 0.326969f, 0.148888f,
0.055270f, -0.087985f, 0.261720f, 0.143442f,
0.026812f, 0.238212f, 0.194020f, 0.056341f,
-0.025854f, -0.034444f, -0.156631f, 0.205174f,
0.089008f, -0.139811f, -0.100147f, -0.037830f,
-0.029230f, -0.055641f, 0.033248f, -0.016512f,
0.155244f, 0.247315f, -0.124694f, -0.048414f,
-0.062219f, 0.193683f, 0.004574f, 0.055089f,
0.093565f, 0.167712f, 0.167581f, 0.018895f,
0.215258f, 0.122609f, 0.090520f, -0.067219f,
-0.049029f, -0.099615f, 0.241804f, -0.094893f,
-0.176248f, 0.001727f, -0.134473f, 0.104442f,
0.050942f, 0.081165f, 0.072156f, 0.121646f,
0.002656f, -0.297974f, -0.133587f, -0.060121f,
-0.092515f, -0.048974f, -0.084754f, -0.180111f,
-0.038590f, 0.086283f, -0.134636f, -0.107249f,
0.132890f, 0.141556f, 0.249425f, 0.130273f,
-0.030031f, 0.073212f, -0.008155f, 0.019931f,
0.071688f, 0.000300f, -0.019525f, -0.021725f,
-0.040993f, -0.086841f, 0.070124f, 0.240033f,
0.265350f, 0.043208f, 0.166754f, 0.091453f,
0.060916f, -0.036972f, -0.091043f, 0.079873f,
0.219781f, 0.158102f, -0.140618f, -0.043016f,
0.124802f, 0.093668f, 0.103208f, 0.094872f,
0.080541f, 0.137711f, 0.160566f, -0.169231f,
0.013983f, 0.309508f, -0.004217f, -0.057200f,
-0.064489f, 0.014066f, 0.361009f, 0.251328f,
-0.080983f, -0.044183f, 0.061436f, -0.037381f,
-0.078786f, 0.030993f, 0.066314f, 0.037683f,
0.152325f, -0.091683f, 0.070203f, 0.217856f,
0.036435f, -0.076462f, 0.006254f, -0.094431f,
0.154829f, -0.023038f, -0.196961f, -0.024594f,
0.178465f, -0.050139f, -0.045932f, -0.000965f,
0.109112f, 0.046165f, -0.159373f, -0.008713f,
0.041307f, 0.097129f, -0.057211f, -0.064599f,
0.077165f, 0.176167f, 0.138322f, 0.065753f,
-0.104950f, 0.017933f, 0.136255f, -0.011598f,
0.047007f, 0.080550f, 0.068619f, 0.084661f,
-0.035493f, -0.091314f, -0.041411f, 0.060971f,
-0.101912f, -0.079870f, -0.085977f, -0.022686f,
0.079788f, -0.098064f, -0.054603f, 0.040383f,
0.300794f, 0.128603f, 0.094844f, 0.047407f,
0.101825f, 0.061832f, -0.162160f, -0.204553f,
-0.035165f, 0.101450f, -0.016641f, -0.027140f,
-0.134392f, -0.008743f, 0.102331f, 0.114853f,
0.009644f, 0.062823f, 0.237339f, 0.167843f,
0.053066f, -0.012592f, 0.043158f, 0.002305f,
0.065001f, -0.038929f, -0.020356f, 0.152343f,
0.043469f, -0.029967f, -0.042948f, 0.032481f,
0.068488f, -0.110840f, -0.111083f, 0.111980f,
-0.002072f, -0.005562f, 0.082926f, 0.006635f,
-0.108153f, 0.024242f, -0.086464f, -0.189884f,
-0.017492f, 0.191456f, -0.007683f, -0.128769f,
-0.038017f, -0.132380f, 0.091926f, 0.079696f,
-0.106728f, -0.007656f, 0.172744f, 0.011576f,
0.009883f, 0.083258f, -0.026516f, 0.145534f,
0.153924f, -0.130290f, -0.108945f, 0.124490f,
-0.003186f, -0.100485f, 0.015024f, -0.060512f,
0.026288f, -0.086713f, -0.169012f, 0.076517f,
0.215778f, 0.043701f, -0.131642f, -0.012585f,
-0.045181f, -0.118183f, -0.241544f, -0.167293f,
-0.020107f, -0.019917f, -0.101827f, -0.107096f,
-0.010503f, 0.044938f, 0.189680f, 0.217119f,
-0.046086f, 0.044508f, 0.199716f, -0.036004f,
-0.148927f, 0.013355f, -0.078279f, 0.030451f,
0.056301f, -0.024609f, 0.083224f, 0.099533f,
-0.039432f, -0.138880f, 0.005482f, -0.024120f,
-0.140468f, -0.066381f, -0.017057f, 0.009260f,
-0.058004f, -0.028486f, -0.061610f, 0.007483f,
-0.158309f, -0.150687f, -0.044595f, -0.105121f,
-0.045763f, -0.006618f, -0.024419f, -0.117713f,
-0.119366f, -0.175941f, -0.071542f, 0.119027f,
0.111362f, 0.043080f, 0.034889f, 0.093003f,
0.007842f, 0.057368f, -0.108834f, -0.079968f,
0.230959f, 0.020205f, 0.011470f, 0.098877f,
0.101310f, -0.030215f, -0.018018f, -0.059552f,
-0.106157f, 0.021866f, -0.036471f, 0.080051f,
0.041165f, -0.082101f, 0.117726f, 0.030961f,
-0.054763f, -0.084102f, -0.185778f, -0.061305f,
-0.038089f, -0.110728f, -0.264010f, 0.076675f,
-0.077111f, -0.137644f, 0.036232f, 0.277995f,
0.019116f, 0.107738f, 0.144003f, 0.080304f,
0.215036f, 0.228897f, 0.072713f, 0.077773f,
0.120168f, 0.075324f, 0.062730f, 0.122478f,
-0.049008f, 0.164912f, 0.162450f, 0.041246f,
0.009891f, -0.097827f, -0.038700f, -0.023027f,
-0.120020f, 0.203364f, 0.248474f, 0.149810f,
-0.036276f, -0.082814f, -0.090343f, -0.027143f,
-0.075689f, -0.320310f, -0.000500f, -0.143334f,
-0.065077f, -0.186936f, 0.129372f, 0.116431f,
0.181699f, 0.170436f, 0.418854f, 0.460045f,
0.333719f, 0.230515f, 0.047822f, -0.044954f,
-0.068086f, 0.140179f, -0.044821f, 0.085550f,
0.092483f, -0.107296f, -0.130670f, -0.206629f,
0.114601f, -0.317869f, -0.076663f, 0.038680f,
0.212753f, -0.016059f, -0.126526f, -0.163602f,
0.210154f, 0.099887f, -0.126366f, 0.118453f,
0.019309f, -0.021611f, -0.096499f, -0.111809f,
-0.200489f, 0.142854f, 0.228840f, -0.353346f,
-0.179151f, 0.116834f, 0.252389f, -0.031728f,
-0.188135f, -0.158998f, 0.386523f, 0.122315f,
0.209944f, 0.394023f, 0.359030f, 0.260717f,
0.170335f, 0.013683f, -0.142596f, -0.026138f,
-0.011878f, -0.150519f, 0.047159f, -0.107062f,
-0.147347f, -0.187689f, -0.186027f, -0.208048f,
0.058468f, -0.073026f, -0.236556f, -0.079788f,
-0.146216f, -0.058563f, -0.101361f, -0.071294f,
-0.071093f, 0.116919f, 0.234304f, 0.306781f,
0.321866f, 0.240000f, 0.073261f, -0.012173f,
0.026479f, 0.050173f, 0.166127f, 0.228955f,
0.061905f, 0.156460f, 0.205990f, 0.120672f,
0.037350f, 0.167884f, 0.290099f, 0.420900f,
-0.012601f, 0.189839f, 0.306378f, 0.118383f,
-0.095598f, -0.072360f, -0.132496f, -0.224259f,
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0.013593f, 0.115616f, 0.014616f, -0.281710f,
-0.237597f, -0.117305f, -0.000034f, -0.136739f,
-0.196275f, -0.095225f, -0.125310f, -0.250514f,
0.236804f, -0.071805f, -0.037421f, 0.048230f,
0.321596f, 0.063632f, 0.024039f, -0.029133f,
0.230983f, 0.160593f, -0.154355f, -0.013086f,
-0.079929f, 0.094692f, 0.160391f, 0.180239f,
0.053895f, 0.100759f, 0.288631f, 0.038191f,
0.181692f, 0.229682f, 0.440166f, 0.063401f,
0.006273f, 0.020865f, 0.338695f, 0.256244f,
-0.043927f, 0.115617f, 0.003296f, 0.173965f,
0.021318f, -0.040936f, -0.118932f, 0.182380f,
0.235922f, -0.053233f, -0.015053f, -0.101057f,
0.095341f, 0.051111f, 0.161831f, 0.032614f,
0.159496f, 0.072375f, 0.025089f, 0.023748f,
0.029151f, 0.161284f, -0.117717f, -0.036191f,
-0.176822f, -0.162006f, 0.226542f, -0.078329f,
0.043079f, -0.119172f, 0.054614f, -0.101365f,
-0.064541f, -0.115304f, 0.135170f, 0.298872f,
0.098060f, 0.089428f, -0.007497f, 0.110391f,
-0.028824f, 0.020835f, -0.036804f, 0.125411f,
0.192105f, -0.048931f, 0.003086f, -0.010681f,
0.074698f, -0.016263f, 0.096063f, 0.060267f,
-0.007277f, 0.139139f, -0.080635f, 0.036628f,
0.086058f, 0.131979f, 0.085707f, 0.025301f,
0.226094f, 0.194759f, 0.042193f, -0.157846f,
-0.068402f, -0.141450f, -0.112659f, -0.076305f,
-0.069085f, -0.114332f, -0.102005f, 0.132193f,
-0.067042f, 0.106643f, 0.198964f, 0.171616f,
0.167237f, -0.033730f, -0.026755f, 0.083621f,
0.149459f, -0.002799f, -0.000318f, 0.011753f,
0.065889f, -0.089375f, -0.049610f, 0.224579f,
0.216548f, -0.034908f, -0.017851f, -0.088144f,
0.007530f, 0.240268f, 0.073270f, 0.013263f,
0.175323f, 0.012082f, 0.093993f, 0.015282f,
0.105854f, 0.107990f, 0.077798f, -0.096166f,
-0.079607f, 0.177820f, 0.142392f, 0.033337f,
-0.078100f, -0.081616f, -0.046993f, 0.139459f,
0.020272f, -0.123161f, 0.175269f, 0.105217f,
0.057328f, 0.080909f, -0.012612f, -0.097081f,
0.082060f, -0.096716f, -0.063921f, 0.201884f,
0.128166f, -0.035051f, -0.032227f, -0.068139f,
-0.115915f, 0.095080f, -0.086007f, -0.067543f,
0.030776f, 0.032712f, 0.088937f, 0.054336f,
-0.039329f, -0.114022f, 0.171672f, -0.112321f,
-0.217646f, 0.065186f, 0.060223f, 0.192174f,
0.055580f, -0.131107f, -0.144338f, 0.056730f,
-0.034707f, -0.081616f, -0.135298f, -0.000614f,
0.087189f, 0.014614f, 0.067709f, 0.107689f,
0.225780f, 0.084361f, -0.008544f, 0.051649f,
-0.048369f, -0.037739f, -0.060710f, 0.002654f,
0.016935f, 0.085563f, -0.015961f, -0.019265f,
0.111788f, 0.062376f, 0.202019f, 0.047713f,
0.042261f, 0.069716f, 0.242913f, 0.021052f,
-0.072812f, -0.155920f, -0.026436f, 0.035621f,
-0.079300f, -0.028787f, -0.048329f, 0.084718f,
-0.060565f, -0.083750f, -0.164075f, -0.040742f,
-0.086219f, 0.015271f, -0.005204f, -0.016038f,
0.045816f, -0.050433f, -0.077652f, 0.117109f,
0.009611f, -0.009045f, -0.008634f, -0.055373f,
-0.085968f, 0.028527f, -0.054736f, -0.168089f,
0.175839f, 0.071205f, -0.023603f, 0.037907f,
-0.004561f, -0.022634f, 0.123831f, 0.094469f,
-0.072920f, -0.133642f, -0.014032f, -0.142754f,
-0.026999f, -0.199409f, 0.013268f, 0.226989f,
0.048650f, -0.170988f, -0.050141f, 0.007880f,
0.061880f, 0.019078f, -0.043578f, -0.038139f,
0.134814f, 0.054097f, -0.081670f, 0.176838f,
0.047920f, -0.038176f, 0.050406f, -0.107181f,
-0.036279f, 0.027060f, 0.081594f, -0.002820f,
0.090507f, -0.033338f, -0.059571f, 0.013404f,
-0.099860f, 0.073371f, 0.342805f, 0.098305f,
-0.150910f, -0.020822f, -0.056960f, 0.046262f,
-0.043413f, -0.149405f, -0.129105f, -0.010899f,
-0.014229f, -0.179949f, -0.113044f, -0.049468f,
-0.065513f, 0.090269f, -0.011919f, 0.087846f,
0.095796f, 0.146127f, 0.101599f, 0.078066f,
-0.084348f, -0.100002f, -0.020134f, -0.050169f,
0.062122f, 0.014640f, 0.019143f, 0.036543f,
0.180924f, -0.013976f, -0.066768f, -0.001090f,
-0.070419f, -0.004839f, -0.001504f, 0.034483f,
-0.044954f, -0.050336f, -0.088638f, -0.174782f,
-0.116082f, -0.205507f, 0.015587f, -0.042839f,
-0.096879f, -0.144097f, -0.050268f, -0.196796f,
0.109639f, 0.271411f, 0.173732f, 0.108070f,
0.156437f, 0.124255f, 0.097242f, 0.238693f,
0.083941f, 0.109105f, 0.223940f, 0.267188f,
0.027385f, 0.025819f, 0.125070f, 0.093738f,
0.040353f, 0.038645f, -0.012730f, 0.144063f,
0.052931f, -0.009138f, 0.084193f, 0.160272f,
-0.041366f, 0.011951f, -0.121446f, -0.106713f,
-0.047566f, 0.047984f, -0.255224f, -0.076116f,
0.098685f, -0.150845f, -0.171513f, -0.156590f,
0.058331f, 0.187493f, 0.413018f, 0.554265f,
0.372242f, 0.237943f, 0.124571f, 0.110829f,
0.010322f, -0.174477f, -0.067627f, -0.001979f,
0.142913f, 0.040597f, 0.019907f, 0.025963f,
-0.043585f, -0.120732f, 0.099937f, 0.091059f,
0.247307f, 0.204226f, -0.042753f, -0.068580f,
-0.119002f, 0.026722f, 0.034853f, -0.060934f,
-0.025054f, -0.093026f, -0.035372f, -0.233209f,
-0.049869f, -0.039151f, -0.022279f, -0.065380f,
-9.063785f};
return vector<float>(detector, detector + sizeof(detector)/sizeof(detector[0]));
}
}