C++ BoundingBox::br方法代码示例

void TLD::bbPredict(const vector<cv::Point2f>& points1,const vector<cv::Point2f>& points2,
                    const BoundingBox& bb1,BoundingBox& bb2)    {
  int npoints = (int)points1.size();
  vector<float> xoff(npoints);
  vector<float> yoff(npoints);
  printf("tracked points : %d\n",npoints);
  for (int i=0;i<npoints;i++){
      xoff[i]=points2[i].x-points1[i].x;
      yoff[i]=points2[i].y-points1[i].y;
  }
  float dx = median(xoff);
  float dy = median(yoff);
  float s;
  if (npoints>1){
      vector<float> d;
      d.reserve(npoints*(npoints-1)/2);
      for (int i=0;i<npoints;i++){
          for (int j=i+1;j<npoints;j++){
              d.push_back(norm(points2[i]-points2[j])/norm(points1[i]-points1[j]));
          }
      }
      s = median(d);
  }
  else {
      s = 1.0;
  }
  float s1 = 0.5*(s-1)*bb1.width;
  float s2 = 0.5*(s-1)*bb1.height;
  printf("s= %f s1= %f s2= %f \n",s,s1,s2);
  bb2.x = round( bb1.x + dx -s1);
  bb2.y = round( bb1.y + dy -s2);
  bb2.width = round(bb1.width*s);
  bb2.height = round(bb1.height*s);
  printf("predicted bb: %d %d %d %d\n",bb2.x,bb2.y,bb2.br().x,bb2.br().y);
}

void Detector::generateScanGrids(const BoundingBox& initBoundingBox)
{
	vector<Size> gridSizes;
	Size bbSize = initBoundingBox.size();
	double scale = 1.0;
	while (bbSize.width <= frame.cols && bbSize.height <= frame.rows)
	{
		gridSizes.push_back(bbSize);

		scale *= 1.2;
		bbSize = Size((int)(initBoundingBox.width * scale + 0.5), (int)(initBoundingBox.height * scale + 0.5));
	}

	scale = 1.0 / STEP_S;
	bbSize = Size((int)(initBoundingBox.width * scale + 0.5), (int)(initBoundingBox.height * scale + 0.5));
	//while (bbSize.width * bbSize.height >= MIN_BB_AREA)
	while (bbSize.width >= MIN_BB_SIDE && bbSize.height >= MIN_BB_SIDE)
	{
		gridSizes.push_back(bbSize);
		scale /= 1.2;
		bbSize = Size((int)(initBoundingBox.width * scale + 0.5), (int)(initBoundingBox.height * scale + 0.5));
	}

	//int stepH = (int)(STEP_H * frame.cols + 0.5);
	//int stepV = (int)(STEP_V * frame.rows + 0.5);

	BoundingBox patch;
	for (vector<Size>::const_iterator scaleIt = gridSizes.begin(); scaleIt != gridSizes.end(); ++scaleIt)
	{
		patch.width = scaleIt->width;
		patch.height = scaleIt->height;

		int minSide = min(scaleIt->width, scaleIt->height);
		//int stepH = (int)(STEP_H * scaleIt->width + 0.5);
		//int stepV = (int)(STEP_V * scaleIt->height + 0.5);
		int stepH = (int)(STEP_H * minSide + 0.5);
		int stepV = (int)(STEP_V * minSide + 0.5);

#ifdef DEBUG
		assert(stepH >= 1 && stepV >= 1);
#endif

		for (patch.y = 0; patch.br().y < frame.rows; patch.y += stepV)
		{
			for (patch.x = 0; patch.br().x < frame.cols; patch.x += stepH)
			{
				patch.refreshOverlap(initBoundingBox);
				scanGrids.push_back(patch);
			}
		}
	}
}

//利用剩下的不到一半的跟踪点输入来预测bounding box在当前帧的位置和大小
void TLD::bbPredict(const vector<cv::Point2f>& points1,const vector<cv::Point2f>& points2,
                    const BoundingBox& bb1,BoundingBox& bb2)    {
  int npoints = (int)points1.size();
  vector<float> xoff(npoints);//位移  
  vector<float> yoff(npoints);
  printf("tracked points : %d\n",npoints);
  for (int i=0;i<npoints;i++){//计算每个特征点在两帧之间的位移 
      xoff[i]=points2[i].x-points1[i].x;
      yoff[i]=points2[i].y-points1[i].y;
  }

  float dx = median(xoff);//计算位移的中值 
  float dy = median(yoff);
  float s;

  //计算bounding box尺度scale的变化
  //通过计算当前特征点相互间的距离与上一帧特征点相互间的距离的
  //比值，以比值的中值作为尺度的变化因子
  if (npoints>1){
      vector<float> d;
      d.reserve(npoints*(npoints-1)/2);
      for (int i=0;i<npoints;i++){
          for (int j=i+1;j<npoints;j++){
              d.push_back(norm(points2[i]-points2[j])/norm(points1[i]-points1[j]));
          }
      }
      s = median(d);
  }
  else {
      s = 1.0;
  }

  float s1 = 0.5*(s-1)*bb1.width;
  float s2 = 0.5*(s-1)*bb1.height;
  printf("s= %f s1= %f s2= %f \n",s,s1,s2);
  
  //得到当前bounding box的位置与大小信息  
  //当前box的x坐标 = 前一帧box的x坐标 + 全部特征点位移的中值 - 当前box宽的一半  
  bb2.x = round( bb1.x + dx -s1);
  bb2.y = round( bb1.y + dy -s2);
  bb2.width = round(bb1.width*s);
  bb2.height = round(bb1.height*s);
  printf("predicted bb: %d %d %d %d\n",bb2.x,bb2.y,bb2.br().x,bb2.br().y);
}

double PatchVariance::computeVariance(const BoundingBox& patch) const
{
    double sum = integralImg.at<double>(patch.br())
                 - integralImg.at<double>(patch.br().y, patch.x)
                 - integralImg.at<double>(patch.y, patch.br().x)
                 + integralImg.at<double>(patch.tl());
    double sumSq = sqIntegralImg.at<double>(patch.br())
                   - sqIntegralImg.at<double>(patch.br().y, patch.x)
                   - sqIntegralImg.at<double>(patch.y, patch.br().x)
                   + sqIntegralImg.at<double>(patch.tl());
    return sumSq / patch.area() - pow(sum / patch.area(), 2);
}