///// <summary>
///// 将归一化后的向量列表写入文件，按照libsvm训练样本的格式
///// </summary>
///// <param name="filepath">文件路径</param>
///// <param name="samplelabel">样本标签，1:正样本,-1:负样本</param>
//public void WriteSamepleToFile(string filepath, int samplelabel)
//{
// if (this.NormBlockVectors != null && this.NormBlockVectors.Count > 0)
// {
// FileStream fs = null;
// StreamWriter sw = null;
// try
// {
// fs = File.Open(filepath, FileMode.Append);
// sw = new StreamWriter(fs);
// sw.Write(samplelabel);
// int cnt = 0;
// foreach (object obj in this.NormBlockVectors)
// {
// double[] vec = (double[])obj;
// for (int i = 0; i < vec.Length; i++)
// {
// cnt++;
// if (vec[i] != 0)
// {
// sw.Write(" {0}:{1}", cnt, vec[i]);
// }
// }
// }
// sw.Write("\n");
// }
// catch (Exception ex)
// {
// throw new Exception("File Operation fail!" + ex.Message);
// }
// finally
// {
// if (sw != null)
// {
// sw.Close();
// sw.Dispose();
// }
// if (fs != null)
// {
// fs.Close();
// fs.Dispose();
// }
// }
// }
//}
///// <summary>
///// 预测给出的特征向量所属分类
///// </summary>
///// <param name="model">训练好的模型</param>
///// <param name="predictvec">待预测的特征向量</param>
///// <returns>返回预测值，1为正类，-1为负类</returns>
//private double PredictVecClass(svm_model model, double[] predictvec)
//{
// if (model != null && predictvec != null && predictvec.Length > 0)
// {
// svm_node[] x = new svm_node[predictvec.Length];
// for (int i = 0; i < predictvec.Length; i++)
// {
// x[i] = new svm_node();
// x[i].index = i + 1;
// x[i].value_Renamed = predictvec[i];
// }
// //DateTime dt = DateTime.Now;
// double r = svm.svm_predict(model, x);
// //double elapse = DateTime.Now.Subtract(dt).TotalMilliseconds;
// return r;
// }
// else
// {
// return 0;
// }
//}
#endregion
/// <summary>
/// 移动检测窗口匹配待检测图像，步长为一个Hog单元
/// </summary>
/// <param name="hogwindowsize">检测窗口的hog尺寸</param>
/// <param name="neg_center">负样本中心</param>
/// <param name="pos_center">正样本中心</param>
/// <param name="distancecoef">正距离系数，让检测到的目标物体稍微靠近正样本中心，排除决策边界上的点，一般取1.1到1.2之间</param>
/// <returns>返回检测结果序列</returns>
public WindowResult[] DetectImgByHogWindow(Size hogwindowsize, double[] neg_center, double[] pos_center, double distancecoef)
{
if (hogwindowsize.Width > HGram.HogSize.Width || hogwindowsize.Height > HGram.HogSize.Height ||
neg_center == null || pos_center == null)
{
return(null);
}
if (neg_center.Length != pos_center.Length)
{
return(null);
}
WindowResult[] windowresult = new WindowResult[(HGram.HogSize.Height - hogwindowsize.Height + 1) * (HGram.HogSize.Width - hogwindowsize.Width + 1)];
int cnt = 0;
for (int row = 0; row < HGram.HogSize.Height - hogwindowsize.Height + 1; row++)
{
for (int col = 0; col < HGram.HogSize.Width - hogwindowsize.Width + 1; col++)
{
double neg_distance = 0;
double pos_distance = 0;
Rectangle rect = new Rectangle(col, row, hogwindowsize.Width, hogwindowsize.Height);
double[] vec = GetHogWindowVec(rect);
if (vec.Length != pos_center.Length)
{
return(null);
}
windowresult[cnt] = new WindowResult();
for (int i = 0; i < vec.Length; i++)
{
neg_distance += Math.Abs(vec[i] - neg_center[i]);
pos_distance += Math.Abs(vec[i] - pos_center[i]);
}
windowresult[cnt].PosDistance = pos_distance;
windowresult[cnt].NegDistance = neg_distance;
if (pos_distance * distancecoef < neg_distance)
{
windowresult[cnt].label = 1;
}
else
{
windowresult[cnt].label = -1;
}
// 将HOG检测窗口转化为位图像素窗口
windowresult[cnt].ImageRegion = new Rectangle(
HGram.StartX + col * HGram.CellSize.Width,
HGram.StartY + row * HGram.CellSize.Height,
hogwindowsize.Width * HGram.CellSize.Width,
hogwindowsize.Height * HGram.CellSize.Height);
cnt++;
}
}
return(windowresult);
}
/// <summary>
/// Hog检测, 被检测到图像自动缩放到BMPLIMITSIZE容忍范围内，并在检测完后将检测框自动放大之前缩小的倍率
/// </summary>
/// <param name="bmp">位图</param>
public RectangleCollection HogDetect(Bitmap bmp)
{
RectangleCollection resultCollection = null;
if (bmp == null)
{
return(null);
}
if (NegCenter == null && PosCenter == null)
{
return(null);
}
DateTime dt = DateTime.Now;
double elapse = 0;
// 针对原图的缩放倍率
double se = 1;
if (bmp.Width > Parameter.BMPLIMITSIZE.Width || bmp.Height > Parameter.BMPLIMITSIZE.Height)
{
se = bmp.Width / (double)Parameter.BMPLIMITSIZE.Width > bmp.Height / (double)Parameter.BMPLIMITSIZE.Height ?
bmp.Width / (double)Parameter.BMPLIMITSIZE.Width : bmp.Height / (double)Parameter.BMPLIMITSIZE.Height;
bmp = ImgOper.ResizeImage(bmp, (int)(bmp.Width / se), (int)(bmp.Height / se));
}
bmp = ImgOper.Grayscale(bmp);
//bmp = ImgOper.GaussianConvolution(bmp, GAUSSIAN_SIGMA, GAUSSIAN_SIZE); // 高斯卷积，使得图像平滑
// 所有层的检测结果
ArrayList resultlayers = new ArrayList();
// 初始缩放因子
double scalecoef = 1.0;
Bitmap scalebmp = null;
int newwidth = (int)(bmp.Width / scalecoef);
int newheight = (int)(bmp.Height / scalecoef);
// 每层最小距离点的集合
ArrayList idx_layermindistance = new ArrayList();
int cnt = 0;
do
{
scalebmp = ImgOper.ResizeImage(bmp, newwidth, newheight);
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);
DetectResultLayer detectlayer = new DetectResultLayer();
// !!!!!!检测窗口的像素尺寸必须能被cell尺寸整除!!!!!!像素尺寸除以hog尺寸就是检测窗口的尺寸
detectlayer.DetectResult = blockGram.DetectImgByHogWindow(
new Size(Parameter.DETECT_WINDOW_SIZE.Width / Parameter.CELL_SIZE.Width, Parameter.DETECT_WINDOW_SIZE.Height / Parameter.CELL_SIZE.Height),
NegCenter, PosCenter, Parameter.POS_DIST_COEF);
if (detectlayer.DetectResult == null)
{
return(null);
}
detectlayer.ScaleCoef = scalecoef;
resultlayers.Add(detectlayer); // 本层检测结果加入队列
scalecoef *= Parameter.SCALE_COEF; // 逐次缩小图像
newwidth = (int)(bmp.Width / scalecoef);
newheight = (int)(bmp.Height / scalecoef);
cnt++;
} while (newwidth > 2 * Parameter.DETECT_WINDOW_SIZE.Width && newheight > 2 * Parameter.DETECT_WINDOW_SIZE.Height);
elapse = DateTime.Now.Subtract(dt).TotalSeconds;
// 框出所有可能的物体
WindowResult[] wr = null;
Rectangle rect;
double mindist = -1;
WindowResult min_obj = null;
double min_scalecoef = 1;
resultCollection = new RectangleCollection();
foreach (DetectResultLayer layer in resultlayers)
{
wr = layer.DetectResult;
for (int i = 0; i < wr.Length; i++)
{
if (wr[i].label == 1)
{
if (mindist == -1 || mindist > wr[i].PosDistance)
{
mindist = wr[i].PosDistance;
min_obj = wr[i];
min_scalecoef = layer.ScaleCoef;
}
rect = new Rectangle((int)(wr[i].ImageRegion.X * layer.ScaleCoef * se),
(int)(wr[i].ImageRegion.Y * layer.ScaleCoef * se),
(int)(wr[i].ImageRegion.Width * layer.ScaleCoef * se),
(int)(wr[i].ImageRegion.Height * layer.ScaleCoef * se));
resultCollection.Add(rect);
}
}
}
//rect = new Rectangle((int)(min_obj.ImageRegion.X * min_scalecoef * se),
// (int)(min_obj.ImageRegion.Y * min_scalecoef * se),
// (int)(min_obj.ImageRegion.Width * min_scalecoef * se),
// (int)(min_obj.ImageRegion.Height * min_scalecoef * se));
//resultCollection.Add(rect);
return(resultCollection);
}