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CascadeClassifier.cpp
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256 lines (235 loc) · 6.58 KB
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#include <fstream>
#include <vector>
#include <math.h>
#include <algorithm>
#include <string>
using namespace std;
#include "CascadeClassifier.h"
REAL mean_min,mean_max,sq_min,sq_max,var_min,var_max;
CascadeClassifier::CascadeClassifier():count(0),ac(NULL)
{
}
CascadeClassifier::~CascadeClassifier()
{
Clear();
}
void CascadeClassifier::Clear()
{
count = 0;
delete[] ac; ac=NULL;
}
CascadeClassifier& CascadeClassifier::operator=(const CascadeClassifier& source)
{
Clear();
count = source.count;
int max_nodes = 50;
ac = new AdaBoostClassifier[max_nodes]; ASSERT(ac!=NULL);
for(int i=0;i<count;i++) ac[i] = source.ac[i];
return *this;
}
void CascadeClassifier::ReadFromFile(ifstream& f)
{
Clear();
f>>count; f.ignore(256,'\n');
int max_nodes = 50;
ac = new AdaBoostClassifier[max_nodes]; ASSERT(ac!=NULL);
for(int i=0;i<count;i++) ac[i].ReadFromFile(f);
}
void CascadeClassifier::WriteToFile(ofstream& f) const
{
f<<count<<endl;
for(int i=0;i<count;i++) ac[i].WriteToFile(f);
}
void CascadeClassifier::LoadDefaultCascade(string& cascade_filename,string& cascade_filename_range)
{
ifstream f;
ifstream frange;
f.open(cascade_filename.c_str());
frange.open(cascade_filename_range.c_str());
frange>>mean_min>>mean_max>>sq_min>>sq_max>>var_min>>var_max;
ReadFromFile(f);
f.close();
frange.close();
}
void CascadeClassifier::DrawResults(IntImage& image,const vector<MRect>& results) const
{
int i;
unsigned int k;
int x1,x2,y1,y2;
for(k=0;k<results.size();k++)
{
y1 = (results[k].top>=0)?results[k].top:0;
y1 = (results[k].top<image.height)?results[k].top:(image.height-1);
y2 = (results[k].bottom>=0)?results[k].bottom:0;
y2 = (results[k].bottom<image.height)?results[k].bottom:(image.height-1);
x1 = (results[k].left>=0)?results[k].left:0;
x1 = (results[k].left<image.width)?results[k].left:(image.width-1);
x2 = (results[k].right>=0)?results[k].right:0;
x2 = (results[k].right<image.width)?results[k].right:(image.width-1);
for(i=y1;i<=y2;i++)
{
image.data[i][x1] = 255;
image.data[i][x2] = 255;
}
for(i=x1;i<=x2;i++)
{
image.data[y1][i] = 255;
image.data[y2][i] = 255;
}
}
}
void CascadeClassifier::ApplyOriginalSize(IntImage& original,vector<MRect>& results)
{
IntImage procface;
IntImage image,square;
REAL sq,ex,value;
int result;
MRect rect;
REAL ratio;
procface.Copy(original);
ratio = 1.0;
results.clear();
REAL paddedsize = REAL(1)/REAL((sx+1)*(sy+1));
while((procface.height>sx+1) && (procface.width>sy+1))
{
procface.CalcSquareAndIntegral(square,image);
for(int i=0,size_x=image.height-sx;i<size_x;i+=1)
for(int j=0,size_y=image.width-sy;j<size_y;j+=1)
{
ex = image.data[i+sx][j+sy]+image.data[i][j]-image.data[i+sx][j]-image.data[i][j+sy];
if(ex<mean_min || ex>mean_max) continue;
sq = square.data[i+sx][j+sy]+square.data[i][j]-square.data[i+sx][j]-square.data[i][j+sy];
if(sq<sq_min) continue;
ex *= paddedsize;
ex = ex * ex;
sq *= paddedsize;
sq = sq - ex;
ASSERT(sq>=0);
if(sq>0) sq = sqrt(sq); else sq = 1.0;
if(sq<var_min) continue;
result = 1;
for(int k=0;k<count;k++)
{
value = 0.0;
for(int t=0,size=ac[k].count;t<size;t++)
{
REAL f1 = 0;
REAL** p = image.data + i;
SimpleClassifier& s = ac[k].scs[t];
switch(s.type)
{
case 0:
f1 = p[s.x1][j+s.y3] - p[s.x1][j+s.y1] + p[s.x3][j+s.y3] - p[s.x3][j+s.y1]
+ REAL(2)*(p[s.x2][j+s.y1] - p[s.x2][j+s.y3]);
break;
case 1:
f1 = p[s.x3][j+s.y1] + p[s.x3][j+s.y3] - p[s.x1][j+s.y1] - p[s.x1][j+s.y3]
+ REAL(2)*(p[s.x1][j+s.y2] - p[s.x3][j+s.y2]);
break;
case 2:
f1 = p[s.x1][j+s.y1] - p[s.x1][j+s.y3] + p[s.x4][j+s.y3] - p[s.x4][j+s.y1]
+ REAL(3)*(p[s.x2][j+s.y3] - p[s.x2][j+s.y1] + p[s.x3][j+s.y1] - p[s.x3][j+s.y3]);
break;
case 3:
f1 = p[s.x1][j+s.y1] - p[s.x1][j+s.y4] + p[s.x3][j+s.y4] - p[s.x3][j+s.y1]
+ REAL(3)*(p[s.x3][j+s.y2] - p[s.x3][j+s.y3] + p[s.x1][j+s.y3] - p[s.x1][j+s.y2]);
break;
case 4:
f1 = p[s.x1][j+s.y1] + p[s.x1][j+s.y3] + p[s.x3][j+s.y1] + p[s.x3][j+s.y3]
- REAL(2)*(p[s.x2][j+s.y1] + p[s.x2][j+s.y3] + p[s.x1][j+s.y2] + p[s.x3][j+s.y2])
+ REAL(4)*p[s.x2][j+s.y2];
break;
default:
;
}
if(s.parity!=0)
if(f1<sq*s.thresh)
value += ac[k].alphas[t];
else ;
else
if(f1>=sq*s.thresh)
value += ac[k].alphas[t];
else ;
}
if(value<ac[k].thresh)
{
result = 0;
break;
}
}
if(result!=0)
{
const REAL r = 1.0/ratio;
rect.left = (long)(j*r);rect.top = (long)(i*r);
rect.right = (long)((j+sy)*r);rect.bottom = (long)((i+sx)*r);
results.push_back(rect);
}
}
ratio = ratio * REAL(0.8);
procface.Resize(image,REAL(0.8));
SwapIntImage(procface,image);
}
//total_fp += results.size();
PostProcess(results,2);
PostProcess(results,0);
DrawResults(original,results);
// original.Save(filename+"_result.JPG");
}
inline int SizeOfRect(const MRect& rect)
{
return rect.Height()*rect.Width();
}
void PostProcess(vector<MRect>& result,const int combine_min)
{
vector<MRect> res1;
vector<MRect> resmax;
vector<int> res2;
bool yet;
MRect rectInter;
for(unsigned int i=0,size_i=result.size();i<size_i;i++)
{
yet = false;
MRect& result_i = result[i];
for(unsigned int j=0,size_r=res1.size();j<size_r;j++)
{
MRect& resmax_j = resmax[j];
if(rectInter.IntersectRect(result_i,resmax_j))
{
if(
SizeOfRect(rectInter)>0.6*SizeOfRect(result_i)
&& SizeOfRect(rectInter)>0.6*SizeOfRect(resmax_j)
)
{
MRect& res1_j = res1[j];
resmax_j.UnionRect(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];
MRect& 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]);
}