/// <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>
/// 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);
}