/// <summary>
/// 取得一个样本的特征
/// </summary>
/// <param name="bmpSource">源图像，需24位或32位真彩位图</param>
/// <param name="template">基础分类器模板</param>
/// <param name="sampletype">样本类型，1表示正样本，－1表示负样本</param>
/// <returns>返回一个样本的特征</returns>
public static SampleFeature GetSampleFeature(Bitmap bmpSource, BaseClassifierTemplate template, int sampletype)
{
SampleFeature sf = null;
Bitmap bmp = bmpSource;
if (bmp != null && (bmp.PixelFormat == PixelFormat.Format24bppRgb || bmp.PixelFormat ==
PixelFormat.Format32bppRgb || bmp.PixelFormat == PixelFormat.Format32bppArgb))
{
// 将图像调整至模板大小
if (bmp.Width != template.BmpWidth || bmp.Height != template.BmpHeight)
{
bmp = ImgOper.ResizeImage(bmpSource, template.BmpWidth, template.BmpHeight);
}
bmp = ImgOper.Grayscale(bmpSource);
int[,] igram = ImgOper.Integrogram(bmp, 1);
sf = new SampleFeature();
sf.GroupNum = template.GroupNum;
sf.FeatureNum = template.FeatureNum;
sf.SampleType = sampletype;
sf.FeatureValue = BaseClassifierTemplate.GetFeatureCodeGroup(igram, bmp.Width, bmp.Height, template, 0, 0);
}
return(sf);
}
/// <summary>
/// 用模板去检测图像中的物体
/// </summary>
/// <param name="bmpSource">源图像，需24位或32位真彩位图</param>
/// <param name="template">基础分类器模板</param>
/// <param name="fern">Fern</param>
/// <returns></returns>
public static RectangleCollection DetectObject(Bitmap bmpSource, BaseClassifierTemplate template, Fern fern)
{
RectangleCollection rc = new RectangleCollection();
Bitmap bmp = bmpSource;
if (bmpSource.Width < template.BmpWidth || bmpSource.Height < template.BmpHeight)
{
return(null);
}
if (bmp != null && (bmp.PixelFormat == PixelFormat.Format24bppRgb || bmp.PixelFormat ==
PixelFormat.Format32bppRgb || bmp.PixelFormat == PixelFormat.Format32bppArgb))
{
UInt32[] featurecode = null;
bmp = ImgOper.Grayscale(bmpSource);
int[,] igram = ImgOper.Integrogram(bmp, 1);
while (template.BmpWidth < bmp.Width && template.BmpHeight < bmp.Height)
{
for (int y = 0; y < bmp.Height - template.BmpHeight + 1; y += (template.BmpHeight / 10))
{
for (int x = 0; x < bmp.Width - template.BmpWidth + 1; x += (template.BmpWidth / 10))
{
//int posnum = 0;
//int negnum = 0;
featurecode = BaseClassifierTemplate.GetFeatureCodeGroup(igram, bmp.Width, bmp.Height, template, x, y);
double prob = 0;
for (int i = 0; i < template.GroupNum; i++)
{
prob += fern.Probability[featurecode[i]];
//prob = fern.Probability[featurecode[i]];
//if (prob > 0.5)
//{
// posnum++;
//}
//else
//{
// negnum++;
//}
}
prob = prob / template.GroupNum;
if (prob > 0.5)
//if (posnum > negnum)
{
Rectangle rect = new Rectangle(x, y, template.BmpWidth, template.BmpHeight);
rc.Add(rect);
}
}
}
template.ResizeTemplate(1.2);
}
}
return(rc);
}
/// <summary>
/// 直接计算两幅灰度图的相关系数
/// </summary>
/// <param name="bmpX"></param>
/// <param name="bmpY"></param>
/// <returns></returns>
public static double ComputeAssociationCoef(Bitmap bmpX, Bitmap bmpY)
{
int[,] igramX1 = null; //第一幅图像的标准积分图
int[,] igramX2 = null; //第一幅图像的平方积分图
int[,] igramY1 = null; //第二幅图像的标准积分图
int[,] igramY2 = null; //第二幅图像的平方积分图
int[,] igramXY1 = null; //两幅图像相乘积分图
if (bmpX != null && bmpX.PixelFormat == PixelFormat.Format8bppIndexed &&
bmpY != null && bmpY.PixelFormat == PixelFormat.Format8bppIndexed &&
bmpX.Width == bmpY.Width && bmpX.Height == bmpY.Height)
{
Rectangle rect = new Rectangle(0, 0, bmpX.Width, bmpY.Height);
igramX1 = ImgOper.Integrogram(bmpX, 1);
igramX2 = ImgOper.Integrogram(bmpX, 2);
igramY1 = ImgOper.Integrogram(bmpY, 1);
igramY2 = ImgOper.Integrogram(bmpY, 2);
igramXY1 = ImgOper.Integrogram(bmpX, bmpY, 1);
return(ComputeAssociationCoef(igramX1, igramX2, igramY1, igramY2, igramXY1, rect));
}
return(0);
}
/// <summary>
/// 用训练好的模型测试已标记样本的类别
/// </summary>
/// <param name="sample">已标记样本图像</param>
/// <param name="template">基础分类器模板</param>
/// <param name="fern">Fern</param>
/// <returns>返回测试结果，类别</returns>
public static int DetectSample(Bitmap sample, BaseClassifierTemplate template, Fern fern)
{
Bitmap bmp = null;
if (sample.Width != template.BmpWidth || sample.Height != template.BmpHeight)
{
bmp = ImgOper.ResizeImage(sample, template.BmpWidth, template.BmpHeight);
}
else
{
bmp = sample;
}
if (bmp != null && (bmp.PixelFormat == PixelFormat.Format24bppRgb || bmp.PixelFormat ==
PixelFormat.Format32bppRgb || bmp.PixelFormat == PixelFormat.Format32bppArgb))
{
bmp = ImgOper.Grayscale(bmp);
int[,] igram = ImgOper.Integrogram(bmp, 1);
UInt32[] featurecode = BaseClassifierTemplate.GetFeatureCodeGroup(igram, bmp.Width, bmp.Height, template, 0, 0);
double prob = 0;
for (int i = 0; i < template.GroupNum; i++)
{
prob += fern.Probability[featurecode[i]];
}
prob = prob / template.GroupNum;
if (prob > 0.5)
{
return(1);
}
else
{
return(-1);
}
}
return(0);
}
/// <summary>
/// 计算最近正样本距离系数，按照距离而不是相关系数，这样效率高，系数越小，检测对象越接近目标
/// </summary>
/// <param name="rect">检测目标区域</param>
/// <param name="bmp">整幅位图</param>
/// <returns>最近距离系数， double.MaxValue表示计算异常或没计算</returns>
public double MinDistance(RectangleF rect, Bitmap bmp)
{
double mindistance = double.MaxValue; // 返回值
double dist = double.MaxValue;
double minposdist = double.MaxValue;
double minnegdist = double.MaxValue;
if (rect == Rectangle.Empty)
{
return(mindistance);
}
DateTime dt = DateTime.Now;
double elapse = 0;
Bitmap patch = ImgOper.CutImage(bmp, (int)rect.X, (int)rect.Y, (int)rect.Width, (int)rect.Height);
patch = ImgOper.ResizeImage(patch, Parameter.DETECT_WINDOW_SIZE.Width, Parameter.DETECT_WINDOW_SIZE.Height);
patch = ImgOper.Grayscale(patch);
int[,] patchgram = ImgOper.Integrogram(patch, 1);
byte[] patchdata = ImgOper.GetGraybmpData(patch);
double patchmean = (double)patchgram[patch.Height - 1, patch.Width - 1] / (double)(patch.Width * patch.Height);
double[] patchdatad = new double[patchdata.Length];
for (int i = 0; i < patchdata.Length; i++)
{
patchdatad[i] = patchdata[i] - patchmean;
}
foreach (ValuedBitmap posbmp in PosMapCollection)
{
int[,] posgram = ImgOper.Integrogram(posbmp.VBitmap, 1);
byte[] posdata = ImgOper.GetGraybmpData(posbmp.VBitmap);
double posmean = (double)posgram[posbmp.VBitmap.Height - 1, posbmp.VBitmap.Width - 1] / (double)(posbmp.VBitmap.Width * posbmp.VBitmap.Height);
double[] posdatad = new double[posdata.Length];
for (int i = 0; i < posdata.Length; i++)
{
posdatad[i] = posdata[i] - posmean;
}
dist = ImgStatCompute.ComputeDistance(patchdatad, posdatad);
if (dist < minposdist)
{
minposdist = dist;
}
}
foreach (ValuedBitmap negbmp in NegMapCollection)
{
int[,] neggram = ImgOper.Integrogram(negbmp.VBitmap, 1);
byte[] negdata = ImgOper.GetGraybmpData(negbmp.VBitmap);
double negmean = (double)neggram[negbmp.VBitmap.Height - 1, negbmp.VBitmap.Width - 1] / (double)(negbmp.VBitmap.Width * negbmp.VBitmap.Height);
double[] negdatad = new double[negdata.Length];
for (int i = 0; i < negdata.Length; i++)
{
negdatad[i] = negdata[i] - negmean;
}
dist = ImgStatCompute.ComputeDistance(patchdatad, negdatad);
if (dist < minnegdist)
{
minnegdist = dist;
}
}
if (minnegdist != 0 || minposdist != 0)
{
// 带归一化的系数，如果用minposdist/minnegdist，值可能会溢出
mindistance = minposdist / (minposdist + minnegdist);
}
elapse = DateTime.Now.Subtract(dt).TotalMilliseconds;
return(mindistance);
}
