【opencv】鱼眼图像畸变校正——双经度法

5月23日更新代码

参考：基于双经度模型的鱼眼图像畸变矫正方法_魏利胜

注意：原文中公式14，15，17，18，22，23有误，均未考虑符号问题。双经度法适用与视场接近180°以及小于180°情况

#define Q_R 311
#define X0 392
#define Y0 282

IplImage* Double( IplImage* img)
{
float R = Q_R;
IplImage* imgCalib = cvCreateImage( cvSize( 2*R, 2*R ),IPL_DEPTH_8U, 3);        //创建校正后图像大小
float sita = 0;
float fi = 0;
float tempu = 0;
float tempv = 0;
float x0 = X0 ;//扩展边界后圆心发生偏移
float y0 = Y0 ;
float flag1 =1;
float flag2 =1;
CvPoint2D32f xy;
CvPoint2D32f uv;

for (int i = 0; i < imgCalib->width; i++)
{
for (int j = 0; j < imgCalib->height; j++)
{
sita = PI - (PI/2/R)*i;
fi = PI - (PI/2/R)*j;
tempu = tan(fi)*tan(fi) + 1 + (tan(fi)*tan(fi))/(tan(sita)*tan(sita));
tempv = tan(sita)*tan(sita) + 1 + (tan(sita)*tan(sita))/(tan(fi)*tan(fi));
if (fi >PI/2)    //文中公式22，23 ，17，18有符号错误
flag1 = 1;
else
flag1 = -1;

if (sita <PI/2)
flag2 = 1;
else
flag2 = -1;
uv.x = flag1*R/sqrt(tempu) + x0;//u-fi 下正上负
uv.y = flag2*R/sqrt(tempv) + y0;//v-sita 右正左负

int iu = (int)uv.x;
int iv = (int)uv.y;
//!此处取的是左上点，为了双线性方便
CvPoint3D32f f1;
CvPoint3D32f f2;
if (iu>=0&&iv>=0&&iu<img->width&&iv<img->height)
{
//双线性插值法
f1.x = ((uchar*)(img->imageData + img->widthStep*iv))[iu*3] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*iv))[(iu+1)*3] * (1-abs(uv.x-(iu+1)));

f1.y = ((uchar*)(img->imageData + img->widthStep*iv))[iu*3+1] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*iv))[(iu+1)*3+1] * (1-abs(uv.x-(iu+1)));

f1.z = ((uchar*)(img->imageData + img->widthStep*iv))[iu*3+2] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*iv))[(iu+1)*3+2] * (1-abs(uv.x-(iu+1)));

f2.x = ((uchar*)(img->imageData + img->widthStep*(iv+1)))[iu*3] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*(iv+1)))[(iu+1)*3] * (1-abs(uv.x-(iu+1)));

f2.y = ((uchar*)(img->imageData + img->widthStep*(iv+1)))[iu*3+1] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*(iv+1)))[(iu+1)*3+1] * (1-abs(uv.x-(iu+1)));

f2.z = ((uchar*)(img->imageData + img->widthStep*(iv+1)))[iu*3+2] * (1-abs(uv.x-iu))+
((uchar*)(img->imageData + img->widthStep*(iv+1)))[(iu+1)*3+2] * (1-abs(uv.x-(iu+1)));

((uchar*)(imgCalib->imageData + imgCalib->widthStep*j))[i*3] = f1.x*(1-abs(uv.y-iv))+f2.x*(abs(uv.y-iv));
((uchar*)(imgCalib->imageData + imgCalib->widthStep*j))[i*3+1] = f1.y*(1-abs(uv.y-iv))+f2.y*(abs(uv.y-iv));
((uchar*)(imgCalib->imageData + imgCalib->widthStep*j))[i*3+2] = f1.z*(1-abs(uv.y-iv))+f2.z*(abs(uv.y-iv));
}
}
}
return imgCalib;
}

可以看到在四个极点图像拉伸现象非常严重。