/// <summary>
/// 将ValuePointCollection按照val值从小到大排序
/// </summary>
/// <param name="vpCollection">ValuePointCollection</param>
/// <returns></returns>
public static ValuePointCollection SortValuePointCollection(ValuePointCollection vpCollection)
{
if (vpCollection == null || vpCollection.Count == 0)
{
return(new ValuePointCollection());
}
int min;
for (int i = 0; i < vpCollection.Count - 1; i++)
{
min = i;
for (int j = i + 1; j < vpCollection.Count; j++)
{
if (((ValuePoint)vpCollection[j]).Val < ((ValuePoint)vpCollection[min]).Val)
{
min = j;
}
}
ValuePoint t = (ValuePoint)vpCollection[min];
vpCollection[min] = vpCollection[i];
vpCollection[i] = t;
}
return(vpCollection);
}
/// <summary>
/// 根据两幅图像金字塔和已选定的初始点，计算整体位移，返回位移值和缩放系数
/// </summary>
/// <param name="firstlayercollection">第一幅图像金字塔</param>
/// <param name="secondlayercollection">第二幅图像金字塔</param>
/// <param name="ipoints">第一幅图像中选中的初始点</param>
/// <param name="wx">计算每个点时，积分邻域横向半径，此值寻找初始点时只需要取1，跟踪时要取的大一些</param>
/// <param name="wy">计算每个点时，积分邻域纵向半径，此值寻找初始点时只需要取1，跟踪时要取的大一些</param>
/// <param name="reversebias">反向跟踪时，输出点和原来点之间的距离差阈值（像素值），该差值超过阈值被认为不是好的跟踪点，当被删除，建议取1</param>
/// <param name="medianbias">每个点的位移与中值位移之间的最大距离(像素值)，超过这个距离的点认为是跟踪失败的，建议取10</param>
/// <param name="scalestep">缩放台阶，缩放系数的变化刻度，如变化差小于0.2的视为不变，大于0.2小于0.4的视为0.2，以此类推</param>
/// <param name="firstlayerpoints">第一幅图像的最终输出点</param>
/// <param name="secondlayerpoints">第二幅图像的最终输出点</param>
/// <returns>位移值和缩放系数</returns>
public float[] ComputerDisplacement(LayerImageCollection firstlayercollection, LayerImageCollection secondlayercollection, ArrayList ipoints,
float reversebias, // 这个参数不好控制，导致跟踪非常不稳定
int wx, int wy, float medianbias, float scalestep,
ref ArrayList firstlayerpoints, ref ArrayList secondlayerpoints)
{
if (ipoints == null || secondlayercollection == null || firstlayercollection == null)
{
return(null);
}
// 输出结果，前两个维度表示位移，第三个维度表示区域缩放系数
float[] outvector = new float[3];
firstlayerpoints = new ArrayList();
secondlayerpoints = new ArrayList();
ArrayList jpoints = new ArrayList();
ValuePointCollection fbPointsArray = new ValuePointCollection(); // 用于计算Forward-Backward的集合，ValuePoint类型的
ValuePoint vp = null;
foreach (PointF p in ipoints)
{
PointF jpoint = TrackPoint(firstlayercollection, secondlayercollection, p, wx, wy); // 正跟踪，第一幅图跟踪到第二幅图
PointF ipoint;
if (jpoint.X >= 0 && jpoint.Y >= 0)
{
vp = new ValuePoint();
vp.Ptf1 = p; // 第一幅图的点加入队列
vp.Ptf2 = jpoint; // 第二幅图的对应点加入队列
fbPointsArray.Add(vp);
// 这段代码导致跟踪极不稳定
ipoint = TrackPoint(secondlayercollection, firstlayercollection, jpoint, wx, wy); // 反跟踪，第二幅图跟踪到第一幅图
vp.Val = (float)Math.Sqrt((p.X - ipoint.X) * (p.X - ipoint.X) + (p.Y - ipoint.Y) * (p.Y - ipoint.Y)); // 反向跟踪后产生的点与原来点的距离
if (vp.Val < reversebias)
{
fbPointsArray.Add(vp);
}
}
}
if (fbPointsArray.Count == 0)
{
return(null);
}
//// 这段代码不是很合适
//fbPointsArray = ValuePoint.SortValuePointCollection(fbPointsArray);
//// 根据Forward-Backward值，去掉一半的不稳定点
//int arraycont = fbPointsArray.Count;
//for (int i = arraycont / 2; i < arraycont; i++)
//{
// fbPointsArray.RemoveAt(arraycont / 2);
//} // Forward-Backward计算完成
// 将fbPointsArray用于中值位移计算
for (int i = 0; i < fbPointsArray.Count; i++)
{
vp = fbPointsArray[i];
// 求每一点的位移值，不作开方减小计算量，不影响结果
vp.Val = (vp.Ptf2.X - vp.Ptf1.X) * (vp.Ptf2.X - vp.Ptf1.X) + (vp.Ptf2.Y - vp.Ptf1.Y) * (vp.Ptf2.Y - vp.Ptf1.Y);
}
fbPointsArray = ValuePoint.SortValuePointCollection(fbPointsArray);
ValuePoint median_vp = fbPointsArray[fbPointsArray.Count / 2];
outvector[0] = median_vp.Ptf2.X - median_vp.Ptf1.X;
outvector[1] = median_vp.Ptf2.Y - median_vp.Ptf1.Y; // 中值位移计算完毕
// 去掉位移偏离中值位移过大的点
ValuePointCollection tmpCollection = new ValuePointCollection();
foreach (ValuePoint vpoint in fbPointsArray)
{
float dist = (float)Math.Sqrt(Math.Pow(vpoint.Ptf2.X - vpoint.Ptf1.X - median_vp.Ptf2.X + median_vp.Ptf1.X, 2) +
Math.Pow(vpoint.Ptf2.Y - vpoint.Ptf1.Y - median_vp.Ptf2.Y + median_vp.Ptf1.Y, 2));
if (dist > medianbias) // 超过中值偏移的点删掉
{
tmpCollection.Add(vpoint);
}
else // 没超过中值位移的点加入图像金字塔
{
firstlayerpoints.Add(vpoint.Ptf1);
secondlayerpoints.Add(vpoint.Ptf2);
}
}
foreach (ValuePoint vpoint in tmpCollection)
{
fbPointsArray.Remove(vpoint);
}
// 计算区域的缩放值
float[] scales = new float[fbPointsArray.Count - 1];
for (int i = 0; i < fbPointsArray.Count - 1; i++)
{
ValuePoint v1 = fbPointsArray[i];
ValuePoint v2 = fbPointsArray[i + 1];
if ((v2.Ptf1.X - v1.Ptf1.X) * (v2.Ptf1.X - v1.Ptf1.X) + (v2.Ptf1.Y - v1.Ptf1.Y) * (v2.Ptf1.Y - v1.Ptf1.Y) == 0)
{
scales[i] = 1;
}
else
{
scales[i] = (float)(Math.Sqrt((v2.Ptf2.X - v1.Ptf2.X) * (v2.Ptf2.X - v1.Ptf2.X) + (v2.Ptf2.Y - v1.Ptf2.Y) * (v2.Ptf2.Y - v1.Ptf2.Y)) /
Math.Sqrt((v2.Ptf1.X - v1.Ptf1.X) * (v2.Ptf1.X - v1.Ptf1.X) + (v2.Ptf1.Y - v1.Ptf1.Y) * (v2.Ptf1.Y - v1.Ptf1.Y)));
}
}
outvector[2] = GetMedian(scales);
if (outvector[2] < 0)
{
outvector[2] = 1;
}
else
{
outvector[2] = 1 + (int)((outvector[2] - 1) / scalestep) * scalestep;
}
return(outvector);
}