/// <summary>
/// 训练正样本
/// </summary>
/// <param name="bmp">正样本位图</param>
public void TrainPositive(Bitmap bmp)
{
Bitmap samplebmp = null;
double neg_distance = 0;
double pos_distance = 0;
bool hasinserted = false; // 指明样本是否已插入队列
samplebmp = ImgOper.ResizeImage(bmp, Parameter.DETECT_WINDOW_SIZE.Width, Parameter.DETECT_WINDOW_SIZE.Height);
samplebmp = ImgOper.Grayscale(samplebmp);
for (double angle = (-1) * Parameter.ANGLE_BORDER; angle < Parameter.ANGLE_BORDER; angle += Parameter.ANGLE_INTERVAL)
{
Bitmap bmpclone = ImgOper.RotateImage(samplebmp, angle);
bmpclone = ImgOper.ResizeImage(bmpclone, Parameter.DETECT_WINDOW_SIZE.Width, Parameter.DETECT_WINDOW_SIZE.Height);
for (double scale = (-1) * Parameter.SCALE_BORDER; scale < Parameter.SCALE_BORDER; scale += Parameter.SCALE_INTERVAL)
{
// 往两个方向去，所以是减号
IntPoint lt = new IntPoint((int)(bmpclone.Width * scale / 2), (int)(bmpclone.Height * scale / 2));
IntPoint rt = new IntPoint(bmpclone.Width - 1 - (int)(bmpclone.Width * scale / 2), (int)(bmpclone.Height * scale / 2));
IntPoint rb = new IntPoint(bmpclone.Width - 1 - (int)(bmpclone.Width * scale / 2), bmpclone.Height - 1 - (int)(bmpclone.Height * scale / 2));
IntPoint lb = new IntPoint((int)(bmpclone.Width * scale / 2), bmpclone.Height - 1 - (int)(bmpclone.Height * scale / 2));
Bitmap scalebmp = ImgOper.QuadrilateralTransform(bmpclone, lt, rt, rb, lb);
HogGram hogGram = HogGram.GetHogFromBitmap(scalebmp, Parameter.CELL_SIZE.Width, Parameter.CELL_SIZE.Height, Parameter.PART_NUMBER);
NormBlockVectorGram blockGram = new NormBlockVectorGram(hogGram, Parameter.BLOCK_SIZE.Width, Parameter.BLOCK_SIZE.Height);
Rectangle rect = new Rectangle(0, 0, hogGram.HogSize.Width, hogGram.HogSize.Height);
double[] vect = blockGram.GetHogWindowVec(rect);
if (Dimension != 0 && vect.Length != Dimension)
{
throw new Exception("输入正样本的尺寸与其他样本尺寸不一致！");
}
ValuedBitmap vbmp = null;
if (NegCenter != null && PosCenter != null)
{
// 计算离正负中心的距离
for (int i = 0; i < vect.Length; i++)
{
neg_distance += Math.Abs(vect[i] - NegCenter[i]);
pos_distance += Math.Abs(vect[i] - PosCenter[i]);
}
// 与负样本中心重合时，说明是负样本，不能插入正样本队列
if (neg_distance == 0)
{
return;
}
// 检测到的正样本加入样本队列的第二道关，如果不够接近正样本中心，就无法加入队列
// 按照Hog检测的判定条件，正距离乘以Parameter.POS_DIST_COEF，使其避开边界
if (neg_distance < pos_distance * Parameter.POS_DIST_COEF)
{
return;
}
// 带归一化的系数，如果用pos_distance/neg_distance，值可能会溢出;
// 将pos_distance / (pos_distance + neg_distance)作为正样本的评价系数，值越小越接近正样本
vbmp = new ValuedBitmap(scalebmp, pos_distance / (pos_distance + neg_distance));
}
else
{
// 如果正或负样本库还没建立起来，则Val暂时赋值为1
vbmp = new ValuedBitmap(scalebmp, 1);
}
// 检测到的正样本加入样本队列的第三道关，与正样本评价系数的有序队列比较后，决定是否加入样本队列
hasinserted = InsertValuedBitmap(ref PosMapCollection, vbmp, Parameter.POS_LIMITED_NUMBER);
PosLength = PosMapCollection.Count;
//// 人工观察正样本插入情况
//if (hasinserted && vbmp != null)
//{
// vbmp.VBitmap.Save("Image\\pos_save\\" + poscnt + "_" + vbmp.Val + ".jpg");
// poscnt++;
//}
// 如果样本已经插入队列，说明样本比较可信，重新计算样本中心
if (hasinserted)
{
if (PosCenter == null)
{
Dimension = vect.Length;
PosCenter = new double[Dimension];
}
for (int i = 0; i < Dimension; i++)
{
PosCenter[i] = (PosCenter[i] * PosLength + vect[i]) / (PosLength + 1);
}
}
}
}
}
