/// <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); } } } } }