本文深入探讨了HOG（Histogram of Oriented Gradients）行人检测算法的原理与实现细节，包括梯度计算、块归一化等核心步骤，并介绍了如何通过OpenCV库实现HOG描述符的设置与应用。
这一周研究了下HOG行人检测算法，一开始看源码还真不习惯，不过仔细研究了下，对于算法的具体实现有了更多的了解。
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#include <stdio.h>
#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/)
\****************************************************************************************/
//HOG一般调用过程：初始化实例化一个HOGDescriptor（各种参数及svmDetector系数），读入单幅图像,
//调用detector方法，得到行人可能存在矩阵集合hits。
//高斯函数求权重、三线性插值求权重 缓存机制实现过程
//算法涉及概念
//getblock()表示的是给定block在滑动窗口的位置以及图片的hog缓存指针，来获得本次block中计算hog特征所需要的信息。
//normalizeBlockHistogram()指对block获取到的hog部分描述子进行归一化，其实该归一化有2层，具体看代码。
//windowsInImage()实现的功能是给定测试图片和滑动窗口移动的大小，来获得该层中水平和垂直方向上需要滑动多少个滑动窗口。
//getWindow()值获得一个滑动窗口矩形。
//compute()是实际上计算hog描述子的函数，在测试和训练阶段都能用到。
//detect()是检测目标是用到的函数，在detectMultiScale()函数内部被调用。
//为了便于分析，我们假定win窗口大小为128x64,block为16x16,cell为8x8,winStride为8，可知一个block中有4个
//cell。win中有(128-16+8)/8x(64-16+8)/8=15x7=105个block。而一个cell的直方图为9维，那么一个对于整个
//win而言，产生的直方图为105x4x9=3780维。
namespace cv
{
//获得一个win中获得3780维的直方图描述子。
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
{
//win设定为计算高斯权重时的方差，默认值为4。
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;//比描述子长度大1表示偏置
}
void HOGDescriptor::setSVMDetector(InputArray _svmDetector)
{
//convertTo改变图像Mat属性，如改变图像深度、通道数等等。
_svmDetector.getMat().convertTo(svmDetector, CV_32F);
CV_Assert( checkDetectorSize() );
}
#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"
//把文件节点的内容读取到类的成员变量中。
bool HOGDescriptor::read(FileNode& obj)
{
if( !obj.isMap() )
return false;
FileNodeIterator it = obj["winSize"].begin();
//依次读入winSize、blockSize、blockStride、cellSize、nbins、derivAperture、winSigma、
//histogramNormType、L2HysThreshold、gammaCorrection、nlevels。
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;
obj["nlevels"] >> nlevels;
FileNode vecNode = obj["SVMDetector"];
if( vecNode.isSeq() )
{
vecNode >> svmDetector;
CV_Assert(checkDetectorSize());
}
return true;
}
//将hog描述子内的变量输入到文件fs中，且每次输入前都输入一个名字与其对应，因此这些借点是mapping类型。
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
<< "nlevels" << nlevels;
if( !svmDetector.empty() )
fs << "SVMDetector" << svmDetector;
fs << "}";
}
//从给定的文件中读取参数。
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);
}
//将文件的参数以文件借点的形式写入文件中。
void HOGDescriptor::save(const String& filename, const String& objName) const
{
FileStorage fs(filename, FileStorage::WRITE);
write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
}
//复制HOG描述子到c中。
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;
c.nlevels = nlevels;
}
//完成梯度幅度图和梯度方向值的计算。利用mapBuffer计算拓展图像与原始图像映射之间的关系。
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;
//利用borderInterpolate函数完成拓展图像和原始图像位置映射关系。
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
//创建连续内存空间_dbuf，完成对dx、dy、mag、angle的存放；利用angleScale系数将(0,2pi)划分为
//18个单元，之后取整并利用hidx-=nbins操作，将其归拢到九个单元中。
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()函数是计算2个矩阵对应元素的幅度和角度，最后一个参数表示是否角度使用角度表示，这里false
//表示不用角度表示，即用弧度表示。如果只需计算2个矩阵对应元素的幅度图像，可以采用magnitute()函数。
//-pi/2<Angle<pi/2;
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
{
/*
结构体BlockData中有2个变量。1个BlockData结构体是对应的一个block数据。
histOfs和imgOffset.其中histOfs表示为该block对整个滑动窗口内hog描述算子
的贡献那部分向量的起始位置；imgOffset为该block在滑动窗口图片中的坐标(当然是指左上角坐标)。
*/
struct BlockData
{
BlockData() : histOfs(0), imgOffset() {}
int histOfs;
Point imgOffset;
};
/*结构体PixData中有5个变量，1个PixData结构体是对应的block中1个像素点的数据。其中gradOfs表示
该点的梯度幅度在滑动窗口图片梯度幅度图中的位置坐标；qangleOfs表示该点的梯度角度在滑动窗口
图片梯度角度图中的位置坐标；histOfs[]表示该像素点对1个或2个或4个cell贡献的hog描述子向量的
起始位置坐标（比较抽象，需要看源码才懂）。histWeight[]表示该像素点对1个或2个或4个cell贡献
的权重。gradWeight表示该点本身由于处在block中位置的不同因而对梯度直方图贡献也不同，其权值
按照二维高斯分布(以block中心为二维高斯的中心)来决定。
*/
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);
}
//完成对HOGCache的初始化工作，包括对梯度幅值、方向值的计算，对blockData及pixData的计算过程
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;
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 cacheRows = blockCache.rows;
ymaxCached.resize(cacheRows);
for(size_t ii = 0; ii < cacheRows; ii++ )
ymaxCached[ii] = -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);
//这里*3不是表示三通道而是划分为三类像素区分存放。这里申请的内存空间远远超过实际所需，但为了计算方便
//需先如此申请，后再进行压缩。
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 )
{
//即一个区域内进行直方图统计，最多包含四个cell的不同直方图，histOfs[i]表示每个区域中的
//第i个直方图在整个block直方图存储空间中的距离原始位置的偏置。
//当前像素对哪个直方图做出贡献
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;
}
}
//grad记录每个像素所属bin对应的权重的矩阵，为幅值乘以权重，这个权重包括高斯权重、三次差值的权重。
//qangle记录每个像素角度所属的bin序号的矩阵，均为2通道，为了线性
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
//重新整理数据使其连贯存储，节省了2/3的内存空间
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;
//上面是初始化pixData,下面开始初始化blockData
// initialize blockData
for( j = 0; j < nblocks.width; j++ )
for( i = 0; i < nblocks.height; i++ )
{
//histOfs表示该block对检测窗口贡献的hog描述变量起点在整个变量中的坐标
BlockData& data = blockData[j*nblocks.height + i];
data.histOfs = (j*nblocks.height + i)*blockHistogramSize;
//imgOffset表示该block的左上角在检测窗口中的坐标
data.imgOffset = Point(j*blockStride.width,i*blockStride.height);
}
}
//pt为该block左上角在滑动窗口中的坐标，buf为指向检测窗口中blockData的指针
//函数返回一个指向block描述子的指针。
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 );
//cacheIdx表示的是block个数的坐标
Point cacheIdx(pt.x/cacheStride.width,
(pt.y/cacheStride.height) % blockCache.rows);
//ymaxCached的长度为一个检测窗口垂直方向上容纳的block个数
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];
//一个像素包含：gradOfs,qangleOfs,grandWeight,histOfs[4],histWeight[4]这5个属性
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
}
//返回测试图片中水平方向和垂直方向共有多少个检测窗口
Size HOGCache::windowsInImage(Size imageSize, Size winStride) const
{
return Size((imageSize.width - winSize.width)/winStride.width + 1,
(imageSize.height - winSize.height)/winStride.height + 1);
}
//给定图片的大小，已经检测窗口滑动的大小和测试图片中的检测窗口的索引，得到该索引处检测窗口的
//尺寸，包括坐标信息。
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 );
}
//完成对整幅图像中所有扫描窗口，或指定扫描窗口的特征向量的计算。首先初始化HOGCache，
//之后遍历所有扫描窗口，在每个扫描窗口内遍历所有block并通过调用getBlock函数进行特征
//向量的快速计算。
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
}
}
}
//过程与compute()基本类似，不同在于，在计算机扫描窗口特征向量过程中利用svmCec进行
//计算，并最终判定当前扫描窗口内是否有人。
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);
//调用detect函数的内部：初始化实例化一个HOGCache（完成单幅图像的梯度幅度图及梯度方向图的计算，
//对blockData,pixData进行初始化工作）；对指定扫描窗口或遍历所有扫描窗口(取决于locations是否为空)
//计算扫描窗口对应特征向量；将计算得到的特征向量与svmVec做乘累加；利用阈值对结果进行判定，如果
//超过阈值，则说明当前窗口内含有行人，将该扫描窗口的相关信息进行保留；继续进行下一个窗口扫描，
//直到结束。
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);
}
class HOGInvoker : public ParallelLoopBody
{
public:
HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
double _hitThreshold, Size _winStride, Size _padding,
const double* _levelScale, std::vector<Rect> * _vec, Mutex* _mtx,
std::vector<double>* _weights=0, std::vector<double>* _scales=0 )
{
hog = _hog;
img = _img;
hitThreshold = _hitThreshold;
winStride = _winStride;
padding = _padding;
levelScale = _levelScale;
vec = _vec;
weights = _weights;
scales = _scales;
mtx = _mtx;
}
void operator()( const Range& range ) const
{
int i, i1 = range.start, 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];
//缩小图像尺寸 ，方便detectMultiScale
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));
mtx->lock();
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);
}
}
mtx->unlock();
if (weights && (!hitsWeights.empty()))
{
mtx->lock();
for (size_t j = 0; j < locations.size(); j++)
{
weights->push_back(hitsWeights[j]);
}
mtx->unlock();
}
}
}
const HOGDescriptor* hog;
Mat img;
double hitThreshold;
Size winStride;
Size padding;
const double* levelScale;
std::vector<Rect>* vec;
std::vector<double>* weights;
std::vector<double>* scales;
Mutex* mtx;
};
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);
std::vector<Rect> allCandidates;
std::vector<double> tempScales;
std::vector<double> tempWeights;
std::vector<double> foundScales;
Mutex mtx;
parallel_for_(Range(0, (int)levelScale.size()),
HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates, &mtx, &tempWeights, &tempScales));
std::copy(tempScales.begin(), tempScales.end(), back_inserter(foundScales));
foundLocations.clear();
std::copy(allCandidates.begin(), allCandidates.end(), back_inserter(foundLocations));
foundWeights.clear();
std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));
if ( useMeanshiftGrouping )
{
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
}
else
{
groupRectangles(foundLocations, foundWeights, (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);
class HOGConfInvoker : public ParallelLoopBody
{
public:
HOGConfInvoker( const HOGDescriptor* _hog, const Mat& _img,
double _hitThreshold, Size _padding,
std::vector<DetectionROI>* locs,
std::vector<Rect>* _vec, Mutex* _mtx )
{
hog = _hog;
img = _img;
hitThreshold = _hitThreshold;
padding = _padding;
locations = locs;
vec = _vec;
mtx = _mtx;
}
void operator()( const Range& range ) const
{
int i, i1 = range.start, i2 = range.end;
Size maxSz(cvCeil(img.cols/(*locations)[0].scale), cvCeil(img.rows/(*locations)[0].scale));
Mat smallerImgBuf(maxSz, img.type());
vector<Point> dets;
for( i = i1; i < i2; i++ )
{
double scale = (*locations)[i].scale;
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->detectROI(smallerImg, (*locations)[i].locations, dets, (*locations)[i].confidences, hitThreshold, Size(), padding);
Size scaledWinSize = Size(cvRound(hog->winSize.width*scale), cvRound(hog->winSize.height*scale));
mtx->lock();
for( size_t j = 0; j < dets.size(); j++ )
{
vec->push_back(Rect(cvRound(dets[j].x*scale),
cvRound(dets[j].y*scale),
scaledWinSize.width, scaledWinSize.height));
}
mtx->unlock();
}
}
const HOGDescriptor* hog;
Mat img;
double hitThreshold;
std::vector<DetectionROI>* locations;
Size padding;
std::vector<Rect>* vec;
Mutex* mtx;
};
void HOGDescriptor::detectROI(const cv::Mat& img, const vector<cv::Point> &locations,
CV_OUT std::vector<cv::Point>& foundLocations, CV_OUT std::vector<double>& confidences,
double hitThreshold, cv::Size winStride,
cv::Size padding) const
{
foundLocations.clear();
confidences.clear();
if( svmDetector.empty() )
return;
if( locations.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);
HOGCache cache(this, img, padding, padding, true, 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;
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 )
{
// out of image
confidences.push_back(-10.0);
continue;
}
double s = rho;
const float* svmVec = &svmDetector[0];
int j, k;
for( j = 0; j < nblocks; j++, svmVec += blockHistogramSize )
{
const HOGCache::BlockData& bj = blockData[j];
Point pt = pt0 + bj.imgOffset;
// need to devide this into 4 parts!
const float* vec = cache.getBlock(pt, &blockHist[0]);
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];
}
// cv::waitKey();
confidences.push_back(s);
if( s >= hitThreshold )
foundLocations.push_back(pt0);
}
}
void HOGDescriptor::detectMultiScaleROI(const cv::Mat& img,
CV_OUT std::vector<cv::Rect>& foundLocations,
std::vector<DetectionROI>& locations,
double hitThreshold,
int groupThreshold) const
{
std::vector<Rect> allCandidates;
Mutex mtx;
parallel_for_(Range(0, (int)locations.size()),
HOGConfInvoker(this, img, hitThreshold, Size(8, 8), &locations, &allCandidates, &mtx));
foundLocations.resize(allCandidates.size());
std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());
cv::groupRectangles(foundLocations, groupThreshold, 0.2);
}
void HOGDescriptor::readALTModel(std::string modelfile)
{
// read model from SVMlight format..
FILE *modelfl;
if ((modelfl = fopen(modelfile.c_str(), "rb")) == NULL)
{
std::string eerr("file not exist");
std::string efile(__FILE__);
std::string efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
}
char version_buffer[10];
if (!fread (&version_buffer,sizeof(char),10,modelfl))
{
std::string eerr("version?");
std::string efile(__FILE__);
std::string efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
}
if(strcmp(version_buffer,"V6.01")) {
std::string eerr("version doesnot match");
std::string efile(__FILE__);
std::string efunc(__FUNCTION__);
throw Exception(CV_StsError, eerr, efile, efunc, __LINE__);
}
/* read version number */
int version = 0;
if (!fread (&version,sizeof(int),1,modelfl))
{ throw Exception(); }
if (version < 200)
{
std::string eerr("version doesnot match");
std::string efile(__FILE__);
std::string efunc(__FUNCTION__);
throw Exception();
}
int kernel_type;
size_t nread;
nread=fread(&(kernel_type),sizeof(int),1,modelfl);
{// ignore these
int poly_degree;
nread=fread(&(poly_degree),sizeof(int),1,modelfl);
double rbf_gamma;
nread=fread(&(rbf_gamma),sizeof(double), 1, modelfl);
double coef_lin;
nread=fread(&(coef_lin),sizeof(double),1,modelfl);
double coef_const;
nread=fread(&(coef_const),sizeof(double),1,modelfl);
int l;
nread=fread(&l,sizeof(int),1,modelfl);
char* custom = new char[l];
nread=fread(custom,sizeof(char),l,modelfl);
delete[] custom;
}
int totwords;
nread=fread(&(totwords),sizeof(int),1,modelfl);
{// ignore these
int totdoc;
nread=fread(&(totdoc),sizeof(int),1,modelfl);
int sv_num;
nread=fread(&(sv_num), sizeof(int),1,modelfl);
}
double linearbias;
nread=fread(&linearbias, sizeof(double), 1, modelfl);
std::vector<float> detector;
detector.clear();
if(kernel_type == 0) { /* linear kernel */
/* save linear wts also */
double *linearwt = new double[totwords+1];
int length = totwords;
nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
if(nread != static_cast<size_t>(length) + 1) {
delete [] linearwt;
throw Exception();
}
for(int i = 0; i < length; i++)
detector.push_back((float)linearwt[i]);
detector.push_back((float)-linearbias);
setSVMDetector(detector);
delete [] linearwt;
} else {
throw Exception();
}
fclose(modelfl);
}
void HOGDescriptor::groupRectangles(vector<cv::Rect>& rectList, vector<double>& weights, int groupThreshold, double eps) const
{
if( groupThreshold <= 0 || rectList.empty() )
{
return;
}
CV_Assert(rectList.size() == weights.size());
vector<int> labels;
int nclasses = partition(rectList, labels, SimilarRects(eps));
vector<cv::Rect_<double> > rrects(nclasses);
vector<int> numInClass(nclasses, 0);
vector<double> foundWeights(nclasses, DBL_MIN);
vector<double> totalFactorsPerClass(nclasses, 1);
int i, j, nlabels = (int)labels.size();
for( i = 0; i < nlabels; i++ )
{
int cls = labels[i];
rrects[cls].x += rectList[i].x;
rrects[cls].y += rectList[i].y;
rrects[cls].width += rectList[i].width;
rrects[cls].height += rectList[i].height;
foundWeights[cls] = max(foundWeights[cls], weights[i]);
numInClass[cls]++;
}
for( i = 0; i < nclasses; i++ )
{
// find the average of all ROI in the cluster
cv::Rect_<double> r = rrects[i];
double s = 1.0/numInClass[i];
rrects[i] = cv::Rect_<double>(cv::saturate_cast<double>(r.x*s),
cv::saturate_cast<double>(r.y*s),
cv::saturate_cast<double>(r.width*s),
cv::saturate_cast<double>(r.height*s));
}
rectList.clear();
weights.clear();
for( i = 0; i < nclasses; i++ )
{
cv::Rect r1 = rrects[i];
int n1 = numInClass[i];
double w1 = foundWeights[i];
if( n1 <= groupThreshold )
continue;
// filter out small rectangles inside large rectangles
for( j = 0; j < nclasses; j++ )
{
int n2 = numInClass[j];
if( j == i || n2 <= groupThreshold )
continue;
cv::Rect r2 = rrects[j];
int dx = cv::saturate_cast<int>( r2.width * eps );
int dy = cv::saturate_cast<int>( r2.height * eps );
if( r1.x >= r2.x - dx &&
r1.y >= r2.y - dy &&
r1.x + r1.width <= r2.x + r2.width + dx &&
r1.y + r1.height <= r2.y + r2.height + dy &&
(n2 > std::max(3, n1) || n1 < 3) )
break;
}
if( j == nclasses )
{
rectList.push_back(r1);
weights.push_back(w1);
}
}
}
}
//参考文献：
//http://blog.youkuaiyun.com/pp5576155/article/details/7029699#1536434-tsina-1-47472-66a1f5d8f89e9ad52626f6f40fdeadaa
//http://www.cnblogs.com/tornadomeet/archive/2012/08/15/2640754.html
//http://blog.youkuaiyun.com/antter0510/article/details/20565045
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