C4: Real-time pedestrian detection——C4实时行人检测算法
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2024-03-16 16:49:04
...
http://cs.nju.edu.cn/wujx/projects/C4/C4.htm
Jianxin Wu实现的快速行人检测方法。
Real-Time Human Detection Using Contour Cues：
http://c2inet.sce.ntu.edu.sg/Jianxin/paper/ICRA_final.pdf
C4能够达到比现有人体检测算法更高的速度。在640*480的20fps视频上，使用单核2.8GHz处理器，现有的最快系统(保证有较低的虚警率和较高的检测率)能达到大约10fps，但它是使用了GPU的并行处理。C4在小分辨率的图片上速度更快，在480*360分辨率的视频上速度达到36.3fps，在320*240分辨率大小的视频上能达到109fps。
不需要显式地构造Hlin的特征向量并不是使得C4如此快的唯一原因。在INRIA数据集上进行的测试表明，第一级线性分类器Hlin是一个强大的分类器，可以过滤大约99.43%的图片区域，只有不到0.6%的图片区域需要HIK核分类器Hhik的处理。C4处理INRIA测试集中的所有图片用了27.1秒，而HOG检测器[1]需要2167.5秒(所以C4相对于HOG大约有80倍的加速)。
下面是仅仅使用CPU运行的代码：
#include <iostream>
#include <fstream>
#include <algorithm>
#include <numeric>
#include <cassert>
#include <string>
#include <cmath>
#include <vector>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#define USE_DOUBLE
#ifdef USE_DOUBLE
typedef double REAL;
#else
typedef float REAL;
#endif
template<class T> class Array2dC;
template<class T>
class Array2d
{
public:
int nrow;
int ncol;
T** p;
public:
Array2d():nrow(0),ncol(0),p(NULL) { }
Array2d(const int nrow,const int ncol):nrow(0),ncol(0),p(NULL)
{
Create(nrow,ncol);
}
Array2d(const Array2d<T>& source);
virtual ~Array2d()
{
Clear();
}
Array2d<T>& operator=(const Array2d<T>& source);
void Create(const int _nrow,const int _ncol);
void Swap(Array2d<T>& array2);
void Clear();
void Zero(const T t = 0);
};
template<class T>
class Array2dC
{
public:
int nrow;
int ncol;
T** p;
T* buf;
public:
Array2dC():nrow(0),ncol(0),p(NULL),buf(NULL) {}
Array2dC(const int nrow,const int ncol):nrow(0),ncol(0),p(NULL),buf(NULL)
{
Create(nrow,ncol);
}
Array2dC(const Array2dC<T>& source);
virtual ~Array2dC()
{
Clear();
}
Array2dC<T>& operator=(const Array2dC<T>& source);
void Create(const int _nrow,const int _ncol);
void Swap(Array2dC<T>& array2);
void Zero(const T t = 0);
void Clear();
};
template<class T>
Array2d<T>::Array2d(const Array2d<T>& source):nrow(0),ncol(0),p(NULL)
{
if(source.p!=NULL)
{
Create(source.nrow,source.ncol);
for(int i=0; i<nrow; i++) std::copy(source.p[i],source.p[i]+ncol,p[i]);
}
}
template<class T>
Array2d<T>& Array2d<T>::operator=(const Array2d<T>& source)
{
if(source.p!=NULL)
{
Create(source.nrow,source.ncol);
for(int i=0; i<nrow; i++) std::copy(source.p[i],source.p[i]+ncol,p[i]);
}
else
Clear();
return *this;
}
template<class T>
void Array2d<T>::Create(const int _nrow,const int _ncol)
{
assert(_nrow>0 && _ncol>0);
Clear();
nrow = _nrow;
ncol = _ncol;
p = new T*[nrow];
assert(p!=NULL);
for(int i=0; i<nrow; i++)
{
p[i] = new T[ncol];
assert(p[i]!=NULL);
}
}
template<class T>
void Array2d<T>::Swap(Array2d<T>& array2)
{
std::swap(nrow,array2.nrow);
std::swap(ncol,array2.ncol);
std::swap(p,array2.p);
}
template<class T>
void Array2d<T>::Zero(const T t)
{
if(nrow>0)
{
for(int i=0; i<nrow; i++) std::fill(p[i],p[i]+ncol,t);
}
}
template<class T>
void Array2d<T>::Clear()
{
for(int i=0; i<nrow; i++)
{
delete[] p[i];
p[i] = NULL;
}
delete[] p;
p = NULL;
nrow = ncol = 0;
}
template<class T>
Array2dC<T>::Array2dC(const Array2dC<T>& source):nrow(0),ncol(0),p(NULL),buf(NULL)
{
if(source.buf!=NULL)
{
Create(source.nrow,source.ncol);
std::copy(source.buf,source.buf+nrow*ncol,buf);
}
}
template<class T>
Array2dC<T>& Array2dC<T>::operator=(const Array2dC<T>& source)
{
if(source.buf!=NULL)
{
Create(source.nrow,source.ncol);
std::copy(source.buf,source.buf+nrow*ncol,buf);
}
else
Clear();
return *this;
}
template<class T>
void Array2dC<T>::Create(const int _nrow,const int _ncol)
{
assert(_nrow>0 && _ncol>0);
if(nrow==_nrow && ncol==_ncol) return;
Clear();
nrow = _nrow;
ncol = _ncol;
buf = new T[nrow*ncol];
assert(buf!=NULL);
p = new T*[nrow];
assert(p!=NULL);
for(int i=0; i<nrow; i++) p[i] = buf + i * ncol;
}
template<class T>
void Array2dC<T>::Swap(Array2dC<T>& array2)
{
std::swap(nrow,array2.nrow);
std::swap(ncol,array2.ncol);
std::swap(p,array2.p);
std::swap(buf,array2.buf);
}
template<class T>
void Array2dC<T>::Zero(const T t)
{
if(nrow>0) std::fill(buf,buf+nrow*ncol,t);
}
template<class T>
void Array2dC<T>::Clear()
{
delete[] buf;
buf = NULL;
delete[] p;
p = NULL;
nrow = ncol = 0;
}
/*****************************************/
// IntImage.h
/*****************************************/
template<class T>
class IntImage:public Array2dC<T>
{
private:
IntImage(const IntImage<T> &source) { } // prohibit copy constructor
public:
IntImage():variance(0.0),label(-1) { }
virtual ~IntImage()
{
Clear();
}
virtual void Clear(void);
inline void SetSize(const int h, const int w);
bool Load(cv::Mat img, const char channel = 'I');
void Save(const std::string& filename) const;
void Swap(IntImage<T>& image2);
void CalcIntegralImageInPlace(void);
void Resize(IntImage<T> &result,const REAL ratio) const;
void Resize(IntImage<T>& result,const int height,const int width) const;
IntImage<T>& operator=(const IntImage<T>& source);
void Sobel(IntImage<REAL>& result,const bool useSqrt,const bool normalize);
public:
using Array2dC<T>::nrow;
using Array2dC<T>::ncol;
using Array2dC<T>::buf;
using Array2dC<T>::p;
REAL variance;
int label;
};
template<class T>
void IntImage<T>::Clear(void)
{
Array2dC<T>::Clear();
variance = 0.0;
label = -1;
}
template<class T>
bool IntImage<T>::Load(cv::Mat img, const char channel)
{
if (img.empty()) return false;
if (channel == 'R' || channel == 'G' || channel == 'B')
{
int c;
if (channel == 'B') c = 0;
else if (channel == 'G') c = 1;
else c = 2; // OpenCV is 'BGR' ordering
cv::Mat planes[3];
split(img, planes);
img = planes[c];
}
else // use gray scale for all others
{
cv::cvtColor(img, img, cv::COLOR_BGR2GRAY);
}
SetSize(img.rows, img.cols);
for(int i=0,ih=img.rows,iw=img.cols; i<ih; i++)
{
T* pdata = p[i];
unsigned char* pimg = reinterpret_cast<unsigned char*>(img.data+img.step*i);
for(int j=0; j<iw; j++) pdata[j] = pimg[j];
}
return true;
}
template<class T>
void IntImage<T>::Save(const std::string& filename) const
{
IplImage* img;
img = cvCreateImage(cvSize(ncol,nrow),IPL_DEPTH_8U,1);
for(int i=0,ih=img->height,iw=img->width; i<ih; i++)
{
T* pdata = p[i];
unsigned char* pimg = reinterpret_cast<unsigned char*>(img->imageData+img->widthStep*i);
for(int j=0; j<iw; j++) pimg[j] = (unsigned char)pdata[j];
}
cvSaveImage(filename.c_str(),img);
cvReleaseImage(&img);
}
template<class T>
void IntImage<T>::SetSize(const int h,const int w)
{
if((h == nrow) && (w == ncol)) return;
Clear();
Array2dC<T>::Create(h,w);
}
template<class T>
IntImage<T>& IntImage<T>::operator=(const IntImage<T>& source)
{
if(&source==this) return *this;
SetSize(source.nrow,source.ncol);
std::copy(source.buf,source.buf+nrow*ncol,buf);
label = source.label;
variance = source.variance;
return *this;
}
template<class T>
void IntImage<T>::Resize(IntImage<T> &result,const REAL ratio) const
{
Resize(result,int(nrow*ratio),int(ncol*ratio));
}
template<class T>
void IntImage<T>::Resize(IntImage<T>& result,const int height,const int width) const
{
assert(height>0 && width>0);
result.SetSize(height,width);
REAL ixratio = nrow*1.0/height, iyratio = ncol*1.0/width;
REAL* p_y = new REAL[result.ncol];
assert(p_y!=NULL);
int* p_y0 = new int[result.ncol];
assert(p_y0!=NULL);
for(int i=0; i<width; i++)
{
p_y[i] = i*iyratio;
p_y0[i] = (int)p_y[i];
if(p_y0[i]==ncol-1) p_y0[i]--;
p_y[i] -= p_y0[i];
}
for(int i=0; i<height; i++)
{
int x0;
REAL x;
x = i*ixratio;
x0 = (int)x;
if(x0==nrow-1) x0--;
x -= x0;
T* rp = result.p[i];
const T* px0 = p[x0];
const T* px1 = p[x0+1];
for(int j=0; j<width; j++)
{
int y0=p_y0[j];
REAL y=p_y[j],fx0,fx1;
fx0 = REAL(px0[y0] + y*(px0[y0+1]-px0[y0]));
fx1 = REAL(px1[y0] + y*(px1[y0+1]-px1[y0]));
rp[j] = T(fx0 + x*(fx1-fx0));
}
}
delete[] p_y;
p_y=NULL;
delete[] p_y0;
p_y0=NULL;
}
template<class T>
void IntImage<T>::CalcIntegralImageInPlace(void)
// We pad a zero column and a zero row, so 24*24 image will be 25*25 in size
// if the input image is not padded, the results on 1st row will be problematic
{
for(int i=1; i<ncol; i++) // process the first line
{
buf[i] += buf[i-1];
}
for(int i=1; i<nrow; i++)
{
REAL partialsum = 0;
T* curp = p[i];
T* prep = p[i-1];
for(int j=0; j<ncol; j++)
{
partialsum += REAL(curp[j]);
curp[j] = prep[j] + partialsum;
}
}
}
template<class T>
void IntImage<T>::Swap(IntImage<T>& image2)
{
Array2dC<T>::Swap(image2);
std::swap(variance,image2.variance);
std::swap(label,image2.label);
}
template<class T>
void IntImage<T>::Sobel(IntImage<REAL>& result,const bool useSqrt,const bool normalize)
{
// compute the Sobel gradient. For now, we just use the very inefficient way. Optimization can be done later
// if useSqrt = true, we compute the real Sobel gradient; otherwise, the square of it
// if normalize = true, the numbers are normalized to be in 0..255
result.Create(nrow,ncol);
for(int i=0; i<nrow; i++) result.p[i][0] = result.p[i][ncol-1] = 0;
std::fill(result.p[0],result.p[0]+ncol,0.0);
std::fill(result.p[nrow-1],result.p[nrow-1]+ncol,0.0);
for(int i=1; i<nrow-1; i++)
{
T* p1 = p[i-1];
T* p2 = p[i];
T* p3 = p[i+1];
REAL* pr = result.p[i];
for(int j=1; j<ncol-1; j++)
{
REAL gx = p1[j-1] - p1[j+1]
+ 2*(p2[j-1] - p2[j+1])
+ p3[j-1] - p3[j+1];
REAL gy = p1[j-1] - p3[j-1]
+ 2*(p1[j] - p3[j])
+ p1[j+1] - p3[j+1];
pr[j] = gx*gx+gy*gy;
}
}
if(useSqrt || normalize ) // if we want to normalize the result image, we'd better use the true Sobel gradient
for(int i=1; i<nrow-1; i++)
for(int j=1; j<ncol-1; j++)
result.p[i][j] = sqrt(result.p[i][j]);
if(normalize)
{
REAL minv = 1e20, maxv = -minv;
for(int i=1; i<nrow-1; i++)
{
for(int j=1; j<ncol-1; j++)
{
if(result.p[i][j]<minv)
minv = result.p[i][j];
else if(result.p[i][j]>maxv)
maxv = result.p[i][j];
}
}
for(int i=0; i<nrow; i++) result.p[i][0] = result.p[i][ncol-1] = minv;
for(int i=0; i<ncol; i++) result.p[0][i] = result.p[nrow-1][i] = minv;
REAL s = 255.0/(maxv-minv);
for(int i=0; i<nrow*ncol; i++) result.buf[i] = (result.buf[i]-minv)*s;
}
}
/*****************************************/
// Pedestrian.h
/*****************************************/
// my replacement for the CRect class in MS MFC -- only provides a limited number of functions
class CRect
{
public:
double left;
double top;
double right;
double bottom;
public:
CRect()
{
Clear();
}
~CRect()
{
Clear();
}
public:
bool Empty() const
{
return (left >= right) || (top >= bottom);
}
void Clear()
{
left = right = top = bottom = 0;
}
double Size() const
{
if(Empty())
return 0;
else
return (bottom-top)*(right-left);
}
// Intersect and Union of two rectangles, both function should be able to run when &result==this
bool Intersect(CRect& result,const CRect& rect2) const;
bool Union(CRect& result,const CRect& rect2) const;
};
class NodeDetector
{
public:
enum NodeType { CD_LIN, CD_HIK, LINEAR, HISTOGRAM };
public:
int type; // linear or histogram?
Array2dC<double> classifier;
double thresh;
int featurelength;
int upper_bound;
int index;
std::string filename;
public:
NodeDetector(const NodeType _type,const int _featurelength,const int _upper_bound,const int _index,const char* _filename)
{
Load(_type,_featurelength,_upper_bound,_index,_filename);
minvalue = DBL_MAX;
maxvalue = -minvalue;
}
~NodeDetector()
{
}
void Load(const NodeType _type,const int _featurelength,const int _upper_bound,const int _index,const char* _filename);
bool Classify(int* f);
private:
double minvalue;
double maxvalue;
public:
void SetValues(const double v)
{
if(v>maxvalue) maxvalue = v;
if(v<minvalue) minvalue = v;
}
};
class CascadeDetector
{
public:
int size;
int length;
NodeDetector** nodes;
public:
public:
CascadeDetector()
: size(20), length(0)
{
nodes = new NodeDetector*[size];
}
~CascadeDetector()
{
for(int i=0; i<length; i++) delete nodes[i];
delete[] nodes;
}
void AddNode(const NodeDetector::NodeType _type,const int _featurelength,const int _upper_bound,const char* _filename);
};
class DetectionScanner // who does the dirty jobs
{
public:
int height,width;
int xdiv,ydiv;
int baseflength;
double ratio;
CascadeDetector* cascade;
public:
DetectionScanner()
: height(0), width(0), xdiv(0), ydiv(0), baseflength(0), ratio(0.0), cascade(NULL), integrals(NULL)
{
}
DetectionScanner(const int _height,const int _width,const int _xdiv,const int _ydiv,
const int _baseflength,const double _ratio)
:height(_height),width(_width),xdiv(_xdiv),ydiv(_ydiv),
baseflength(_baseflength),ratio(_ratio),cascade(NULL),integrals(NULL)
{
}
~DetectionScanner()
{
delete cascade;
delete[] integrals;
}
public:
void LoadDetector(std::vector<NodeDetector::NodeType>& types,std::vector<int>& upper_bounds,std::vector<std::string>& filenames);
private:
IntImage<double>* integrals;
IntImage<double> image,sobel;
IntImage<int> ct;
Array2dC<int> hist;
IntImage<double> scores;
void InitImage(IntImage<double>& original);
void InitIntegralImages(const int stepsize);
void ResizeImage();
public:
int Scan(IntImage<double>& original,std::vector<CRect>& results,const int stepsize,const int round,std::ofstream* out,const int upper_bound);
int FastScan(IntImage<double>& original,std::vector<CRect>& results,const int stepsize);
int FeatureLength() const
{
return (xdiv-1)*(ydiv-1)*baseflength;
}
};
void RunFiles();
/*****************************************/
// Pedestrian_ICRA.cpp
/*****************************************/
const int HUMAN_height = 108;
const int HUMAN_width = 36;
const int HUMAN_xdiv = 9;
const int HUMAN_ydiv = 4;
static const int EXT = 1;
// The detector
DetectionScanner scanner(HUMAN_height,HUMAN_width,HUMAN_xdiv,HUMAN_ydiv,256,0.8);
// ---------------------------------------------------------------------
// Helper functions
// compute the Sobel image "ct" from "original"
void ComputeCT(IntImage<double>& original,IntImage<int>& ct)
{
ct.Create(original.nrow,original.ncol);
for(int i=2; i<original.nrow-2; i++)
{
double* p1 = original.p[i-1];
double* p2 = original.p[i];
double* p3 = original.p[i+1];
int* ctp = ct.p[i];
for(int j=2; j<original.ncol-2; j++)
{
int index = 0;
if(p2[j]<=p1[j-1]) index += 0x80;
if(p2[j]<=p1[j]) index += 0x40;
if(p2[j]<=p1[j+1]) index += 0x20;
if(p2[j]<=p2[j-1]) index += 0x10;
if(p2[j]<=p2[j+1]) index += 0x08;
if(p2[j]<=p3[j-1]) index += 0x04;
if(p2[j]<=p3[j]) index += 0x02;
if(p2[j]<=p3[j+1]) index ++;
ctp[j] = index;
}
}
}
// Load SVM models -- linear SVM trained using LIBLINEAR
double UseSVM_CD_FastEvaluationStructure(const char* modelfile,const int m,Array2dC<double>& result)
{
std::ifstream in(modelfile);
if(in.good()==false)
{
std::cout<<"SVM model "<<modelfile<<" can not be loaded."<<std::endl;
exit(-1);
}
std::string buffer;
std::getline(in,buffer); // first line
std::getline(in,buffer); // second line
std::getline(in,buffer); // third line
in>>buffer;
assert(buffer=="nr_feature");
int num_dim;
in>>num_dim;
assert(num_dim>0 && num_dim==m);
std::getline(in,buffer); // end of line 4
in>>buffer;
assert(buffer=="bias");
int bias;
in>>bias;
std::getline(in,buffer); //end of line 5;
in>>buffer;
assert(buffer=="w");
std::getline(in,buffer); //end of line 6
result.Create(1,num_dim);
for(int i=0; i<num_dim; i++) in>>result.buf[i];
double rho = 0;
if(bias>=0) in>>rho;
in.close();
return rho;
}
// Load SVM models -- Histogram Intersectin Kernel SVM trained by libHIK
double UseSVM_CD_FastEvaluationStructure(const char* modelfile, const int m, const int upper_bound, Array2dC<double>& result)
{
std::ifstream fs(modelfile, std::fstream::binary);
if( !fs.is_open() )
{
std::cout << "SVM model " << modelfile << " can not be loaded." << std::endl;
exit(-1);
}
// Header
int rows, cols, type, channels;
fs.read((char*)&rows, sizeof(int)); // rows
fs.read((char*)&cols, sizeof(int)); // cols
fs.read((char*)&type, sizeof(int)); // type
fs.read((char*)&channels, sizeof(int)); // channels
// Data
cv::Mat mat(rows, cols, type);
fs.read((char*)mat.data, CV_ELEM_SIZE(type) * rows * cols);
int num_dim = m;
result.Create(num_dim, upper_bound);
for(int i=0; i<num_dim; i++)
for (int j = 0; j < upper_bound; j++)
{
result.p[i][j]= mat.at<double>(i, j);
}
return -0.00455891;
}
// find the intersection of "this" and "rect2", and put into "result"
bool CRect::Intersect(CRect& result,const CRect& rect2) const
{
if( Empty() || rect2.Empty() ||
left >= rect2.right || rect2.left >= right ||
top >= rect2.bottom || rect2.top >= bottom )
{
result.Clear();
return false;
}
result.left = std::max( left, rect2.left );
result.right = std::min( right, rect2.right );
result.top = std::max( top, rect2.top );
result.bottom = std::min( bottom, rect2.bottom );
return true;
}
// find the union of "this" and "rect2", and put into "result"
bool CRect::Union(CRect& result,const CRect& rect2) const
{
if(Empty())
{
if(rect2.Empty())
{
result.Clear();
return false;
}
else
result = rect2;
}
else
{
if(rect2.Empty())
result = *this;
else
{
result.left = std::min( left, rect2.left );
result.right = std::max( right, rect2.right );
result.top = std::min( top, rect2.top );
result.bottom = std::max( bottom, rect2.bottom );
}
}
return true;
}
// A simple post-process (NMS, non-maximal suppression)
// "result" -- rectangles before merging
// -- after this function it contains rectangles after NMS
// "combine_min" -- threshold of how many detection are needed to survive
void PostProcess(std::vector<CRect>& result,const int combine_min)
{
std::vector<CRect> res1;
std::vector<CRect> resmax;
std::vector<int> res2;
bool yet;
CRect rectInter;
for(unsigned int i=0,size_i=result.size(); i<size_i; i++)
{
yet = false;
CRect& result_i = result[i];
for(unsigned int j=0,size_r=res1.size(); j<size_r; j++)
{
CRect& resmax_j = resmax[j];
if(result_i.Intersect(rectInter,resmax_j))
{
if( rectInter.Size()>0.6*result_i.Size()
&& rectInter.Size()>0.6*resmax_j.Size()
)
{
CRect& res1_j = res1[j];
resmax_j.Union(resmax_j,result_i);
res1_j.bottom += result_i.bottom;
res1_j.top += result_i.top;
res1_j.left += result_i.left;
res1_j.right += result_i.right;
res2[j]++;
yet = true;
break;
}
}
}
if(yet==false)
{
res1.push_back(result_i);
resmax.push_back(result_i);
res2.push_back(1);
}
}
for(unsigned int i=0,size=res1.size(); i<size; i++)
{
const int count = res2[i];
CRect& res1_i = res1[i];
res1_i.top /= count;
res1_i.bottom /= count;
res1_i.left /= count;
res1_i.right /= count;
}
result.clear();
for(unsigned int i=0,size=res1.size(); i<size; i++)
if(res2[i]>combine_min)
result.push_back(res1[i]);
}
// If one detection (after NMS) is inside another, remove the inside one
void RemoveCoveredRectangles(std::vector<CRect>& result)
{
std::vector<bool> covered;
covered.resize(result.size());
std::fill(covered.begin(),covered.end(),false);
CRect inter;
for(unsigned int i=0; i<result.size(); i++)
{
for(unsigned int j=i+1; j<result.size(); j++)
{
result[i].Intersect(inter,result[j]);
double isize = inter.Size();
if(isize>result[i].Size()*0.65)
covered[i] = true;
if(isize>result[j].Size()*0.65)
covered[j] = true;
}
}
std::vector<CRect> newresult;
for(unsigned int i=0; i<result.size(); i++)
if(covered[i]==false)
newresult.push_back(result[i]);
result.clear();
result.insert(result.begin(),newresult.begin(),newresult.end());
newresult.clear();
}
// End of Helper functions
// ---------------------------------------------------------------------
// ---------------------------------------------------------------------
// Functions that load the two classifiers
void LoadCascade(DetectionScanner& ds)
{
std::vector<NodeDetector::NodeType> types;
std::vector<int> upper_bounds;
std::vector<std::string> filenames;
types.push_back(NodeDetector::CD_LIN); // first node
upper_bounds.push_back(100);
filenames.push_back("combined.txt.model");
types.push_back(NodeDetector::CD_HIK); // second node
upper_bounds.push_back(353);
filenames.push_back("combined.txt.model_");
ds.LoadDetector(types,upper_bounds,filenames);
// You can adjust these parameters for different speed, accuracy etc
ds.cascade->nodes[0]->thresh += 0.8;
ds.cascade->nodes[1]->thresh -= 0.095;
}
void DetectionScanner::LoadDetector(std::vector<NodeDetector::NodeType>& types,std::vector<int>& upper_bounds,std::vector<std::string>& filenames)
{
unsigned int depth = types.size();
assert(depth>0 && depth==upper_bounds.size() && depth==filenames.size());
if(cascade)
delete cascade;
cascade = new CascadeDetector;
assert(xdiv>0 && ydiv>0);
for(unsigned int i=0; i<depth; i++)
cascade->AddNode(types[i],(xdiv-EXT)*(ydiv-EXT)*baseflength,upper_bounds[i],filenames[i].c_str());
hist.Create(1,baseflength*(xdiv-EXT)*(ydiv-EXT));
}
void NodeDetector::Load(const NodeType _type,const int _featurelength,const int _upper_bound,const int _index,const char* _filename)
{
type = _type;
index = _index;
filename = _filename;
featurelength = _featurelength;
upper_bound = _upper_bound;
if(type==CD_LIN)
thresh = UseSVM_CD_FastEvaluationStructure(_filename,_featurelength,classifier);
else if(type==CD_HIK)
thresh = UseSVM_CD_FastEvaluationStructure(_filename,_featurelength,upper_bound,classifier);
if(type==CD_LIN) type = LINEAR;
if(type==CD_HIK) type = HISTOGRAM;
}
void CascadeDetector::AddNode(const NodeDetector::NodeType _type,const int _featurelength,const int _upper_bound,const char* _filename)
{
if(length==size)
{
int newsize = size * 2;
NodeDetector** p = new NodeDetector*[newsize];
assert(p!=NULL);
std::copy(nodes,nodes+size,p);
size = newsize;
delete[] nodes;
nodes = p;
}
nodes[length] = new NodeDetector(_type,_featurelength,_upper_bound,length,_filename);
length++;
}
// End of functions that load the two classifiers
// ---------------------------------------------------------------------
// ---------------------------------------------------------------------
// Detection functions
// initialization -- compute the Census Tranform image for CENTRIST
void DetectionScanner::InitImage(IntImage<double>& original)
{
image = original;
image.Sobel(sobel,false,false);
ComputeCT(sobel,ct);
}
// combine the (xdiv-1)*(ydiv-1) integral images into a single one
void DetectionScanner::InitIntegralImages(const int stepsize)
{
if(cascade->nodes[0]->type!=NodeDetector::LINEAR)
return; // No need to prepare integral images
const int hd = height/xdiv*2-2;
const int wd = width/ydiv*2-2;
scores.Create(ct.nrow,ct.ncol);
scores.Zero(cascade->nodes[0]->thresh/hd/wd);
double* linearweights = cascade->nodes[0]->classifier.buf;
for(int i=0; i<xdiv-EXT; i++)
{
const int xoffset = height/xdiv*i;
for(int j=0; j<ydiv-EXT; j++)
{
const int yoffset = width/ydiv*j;
for(int x=2; x<ct.nrow-2-xoffset; x++)
{
int* ctp = ct.p[x+xoffset]+yoffset;
double* tempp = scores.p[x];
for(int y=2; y<ct.ncol-2-yoffset; y++)
tempp[y] += linearweights[ctp[y]];
}
linearweights += baseflength;
}
}
scores.CalcIntegralImageInPlace();
for(int i=2; i<ct.nrow-2-height; i+=stepsize)
{
double* p1 = scores.p[i];
double* p2 = scores.p[i+hd];
for(int j=2; j<ct.ncol-2-width; j+=stepsize)
p1[j] += (p2[j+wd] - p2[j] - p1[j+wd]);
}
}
// Resize the input image and then re-compute Sobel image etc
void DetectionScanner::ResizeImage()
{
image.Resize(sobel,ratio);
image.Swap(sobel);
image.Sobel(sobel,false,false);
ComputeCT(sobel,ct);
}
// The function that does the real detection
int DetectionScanner::FastScan(IntImage<double>& original,std::vector<CRect>& results,const int stepsize)
{
if(original.nrow<height+5 || original.ncol<width+5) return 0;
const int hd = height/xdiv;
const int wd = width/ydiv;
InitImage(original);
results.clear();
hist.Create(1,baseflength*(xdiv-EXT)*(ydiv-EXT));
NodeDetector* node = cascade->nodes[1];
double** pc = node->classifier.p;
int oheight = original.nrow, owidth = original.ncol;
CRect rect;
while(image.nrow>=height && image.ncol>=width)
{
InitIntegralImages(stepsize);
for(int i=2; i+height<image.nrow-2; i+=stepsize)
{
const double* sp = scores.p[i];
for(int j=2; j+width<image.ncol-2; j+=stepsize)
{
if(sp[j]<=0) continue;
int* p = hist.buf;
hist.Zero();
for(int k=0; k<xdiv-EXT; k++)
{
for(int t=0; t<ydiv-EXT; t++)
{
for(int x=i+k*hd+1; x<i+(k+1+EXT)*hd-1; x++)
{
int* ctp = ct.p[x];
for(int y=j+t*wd+1; y<j+(t+1+EXT)*wd-1; y++)
p[ctp[y]]++;
}
p += baseflength;
}
}
double score = node->thresh;
for(int k=0; k<node->classifier.nrow; k++) score += pc[k][hist.buf[k]];
if(score>0)
{
rect.top = i*oheight/image.nrow;
rect.bottom = (i+height)*oheight/image.nrow;
rect.left = j*owidth/image.ncol;
rect.right = (j+width)*owidth/image.ncol;
results.push_back(rect);
}
}
}
ResizeImage();
}
return 0;
}
// The interface function that detects pedestrians
// "filename" -- an input image
// detect pedestrians in image "filename" and save results to "outname"
// "ds" -- the detector cascade -- pass "scanner" as this parameter
// "out" -- file stream that saves the output rectangle information
int totaltime = 0;
int DetectHuman(const char* filename,DetectionScanner& ds)
{
std::vector<CRect> results;
IntImage<double> original;
// original.Load(filename);
ds.FastScan(original,results,2);
PostProcess(results,2);
PostProcess(results,0);
RemoveCoveredRectangles(results);
cvNamedWindow("show");
{
IplImage* iplImage = NULL;
iplImage = cvLoadImage(filename);
for(unsigned int i=0; i<results.size(); i++)
cvRectangle(iplImage,cvPoint(results[i].left,results[i].top),cvPoint(results[i].right,results[i].bottom),CV_RGB(255,0,0),2);
cvShowImage("show",iplImage);
cvWaitKey();
cvReleaseImage(&iplImage);
}
return (int)results.size();
}
// End of Detection functions
// ---------------------------------------------------------------------
int main(int argc,char* argv[])
{
std::cout << "usage:" << std::endl;
std::cout << argv[0] << " <video_file>" << std::endl << std::endl;
std::cout << "keys:" << std::endl;
std::cout << "space : toggle using simple post-process (NMS, non-maximal suppression)" << std::endl;
std::cout << "0 : waits to process next frame until a key pressed" << std::endl;
std::cout << "1 : doesn't wait to process next frame" << std::endl;
std::cout << "2 : resize frames 1/2" << std::endl;
std::cout << "3 : don't resize frames" << std::endl;
std::cout << "4 : resize frames 1/4" << std::endl;
if (argc < 2)
return 0;
cv::Mat src;
cv::VideoCapture capture( argv[1] );
LoadCascade(scanner);
std::cout<<"Detectors loaded."<<std::endl;
int key = 0;
int wait_time = 1;
float fx = 1;
bool rect_organization = true;
IntImage<double> original;
while( key != 27 )
{
capture >> src;
if( src.empty() ) break;
if (fx < 1)
{
cv::resize(src, src, cv::Size(), fx, fx);
}
original.Load( src );
std::vector<CRect> results;
scanner.FastScan(original, results, 2);
if(rect_organization)
{
PostProcess(results,2);
PostProcess(results,0);
RemoveCoveredRectangles(results);
}
for(size_t i = 0; i < results.size(); i++)
{
cv::rectangle(src, cvPoint(results[i].left,results[i].top),cvPoint(results[i].right,results[i].bottom),cv::Scalar(0,255,0),2 );
}
cv::imshow("result",src);
key = cv::waitKey( wait_time );
if (key == 32)
rect_organization = !rect_organization;
if (key == 48)
wait_time = 0;
if (key == 49)
wait_time = 1;
if (key == 50)
fx = 0.5;
if (key == 51)
fx = 1;
if (key == 52)
fx = 0.25;
}
cv::waitKey();
return 0;
}
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