继续坑自己，EmguCV之SVM.Train（二）

想到哪写到哪，继续。

这是续上一篇的问题，训练了SVM，然后直接通过hog.SetSVMDetector加载进来，

发现内容有点奇怪，为什么这个值就是3781，而我通过HOG.compute计算出来的矩阵只有3780？而且检测的结果基本为0？

耍了点小心机，把OpenCV中的代码抠出来，嵌到自己的XML中：（这段代码是直接写在openCV的源代码中，具体在哪个文件我也忘了，可以自行搜索）

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顺带将<var_count>3780</var_count>改成<var_count>3781</var_count>

然后调用 hog.SetSVMDetector加载进来，发现识别效果和直接调用 HOGDescriptor.GetDefaultPeopleDetector()的效果一模一样。

于是发现问题了，只能继续翻代码。在不断探索下，找到了和OpenCV大佬们和自己代码中差别的一些代码，这个注释解释了问题：

//保存HOG能识别的分类器 

于是硬生生把代码翻译了一遍，以下是代码，如果要搬到自己的程序中用还可以再优化一下逻辑：

showstatus("正在转换成HOG");                /*************************************************************************************************                 线性SVM训练完成后得到的XML文件里面，有一个数组，叫做support vector，还有一个数组，叫做alpha,有一个浮点数，叫做rho;                 将alpha矩阵同support vector相乘，注意，alpha*supportVector,将得到一个列向量。之后，再该列向量的最后添加一个元素rho。                 如此，变得到了一个分类器，利用该分类器，直接替换opencv中行人检测默认的那个分类器（cv::HOGDescriptor::setSVMDetector()），                 就可以利用你的训练样本训练出来的分类器进行行人检测了。                 ***************************************************************************************************/                int DescriptorDim = svm.GetVarCount();//特征向量的维数，即HOG描述子的维数                int supportVectorNum = svm.GetSupportVectorCount();//支持向量的个数                Matrix<float> alphaMat = new Matrix<float>(1, supportVectorNum);//alpha向量，长度等于支持向量个数                Matrix<float> supportVectorMat = new Matrix<float>(supportVectorNum, DescriptorDim);//支持向量矩阵                Matrix<float> resultMat = new Matrix<float>(1, DescriptorDim);//alpha向量乘以支持向量矩阵的结果                HOG_SVM s = new HOG_SVM();                //将支持向量的数据复制到supportVectorMat矩阵中                for (int i = 0; i < supportVectorNum; i++)                {                    float[] pSVData = svm.GetSupportVector(i);                    for (int j = 0; j < DescriptorDim; j++)                    {                        supportVectorMat[i, j] = pSVData[j];                    }                }                //将alpha向量的数据复制到alphaMat中                  double[] pAlphaData = s.alpha;//返回SVM的决策函数中的alpha向量                for (int i = 0; i < supportVectorNum; i++)                {                    alphaMat[0, i] = (float)pAlphaData[i];                }                //计算-(alphaMat * supportVectorMat),结果放到resultMat中                resultMat = -1 * alphaMat * supportVectorMat;                //得到最终的setSVMDetector(const vector<float>& detector)参数中可用的检测子                float[] myDetector = new float[DescriptorDim + 1];                //将resultMat中的数据复制到数组myDetector中                for (int i = 0; i < DescriptorDim; i++)                {                    myDetector[i] = resultMat[0, i];                }                //最后添加偏移量rho，得到检测子                myDetector[myDetector.Length - 1] = (float)s.rho;                //设置HOGDescriptor的检测子                hog.SetSVMDetector(myDetector); //保存检测子参数到文件

 //showstatus("将HOG因子保存到数据库");

                showstatus("HOG处理完毕。可以开始检测了。");

其中有一句"HOG_SVM s = new HOG_SVM()"，这个方法来自于http://www.cnblogs.com/KC-Mei/p/4553024.html中，原博主提取SVM的参数用，我在这里只用于提取alpha和rho这两个数值，代码只是堆砌出来，以后优化的时候肯定没这么乱。

所有注释都标记了，showstatus()这个方法请自行替换成你那边显示状态用的方法，指代messagebox.show()作用，或label1.Text的作用。

以及最后一段代码中，我是直接保存到数据库的，网上很多采用的是保存到txt文件，请各位自行添加相应的方法。

对SVM还不是很了解，但看到这里逻辑大概想通了一点，HOG计算的数据交给SVM进行分析，SVM进行TrainAuto()之后返回它的结果，这个结果是上一篇文章的3780的矩阵（3780来源于我的样本是64*128，但我也还不理解8192的数组交给Hog.Compute()计算后怎么就变成3780了），然后通过上述代码，将3780矩阵转换成myDetector，最后一位补齐rho偏移量，于是变成了3781，也就是HOGDescriptor可以识别的检测因子。

当然这也只是解决了（估计解决了）SVM训练结果直接加载到HOG的识别率约等于0的问题，但碰到了一个新问题：我训练的正负样本有500：3000，但用于检测时误报率依旧保持在90%以上。这个问题还在理解和寻求解决方案，如果非常难理解，到时还会有第三篇文章。

顺便说一下怎么提取样本：

通过视频流或者批量读取数据，调用HOGDescriptor.GetDefaultPeopleDetector()，

然后 hog.DetectMultiScale(frame)//frame是你的图片，格式为Image<Bgr,byte>

将得到的Rectangle[] foreach一遍，将每个Rectangle r 通过frame.Copy(r).bitmap 变成Image

然后将这个image.Save()另存为文件

因为默认训练器肯定有大量Neg_Image，不然我们为什么要自行训练呢

然后将这些文件手工筛选一下就可以丢给SVM训练了。