关于SVM中的alpha、rho向量
由于EmguCV封装的更加彻底，在C#中并不能跟C++一样通过重载获得这两个中间变量
//继承自CvSVM的类，因为生成setSVMDetector()中用到的检测子参数时，需要用到训练好的SVM的decision_func参数， //但通过查看CvSVM源码可知decision_func参数是protected类型变量，无法直接访问到，只能继承之后通过函数访问 class MySVM : public CvSVM { public: //获得SVM的决策函数中的alpha数组 double * get_alpha_vector() { return this->decision_func->alpha; } //获得SVM的决策函数中的rho参数,即偏移量 float get_rho() { return this->decision_func->rho; } };
见C++实例：训练SVM分类器进行HOG行人检测 http://blog.csdn.net/pb09013037/article/details/41256945）
为了获取这两个变量用于自定义HOG检测子，暂时想到的几种办法：
1、C#读取生成的
分类器训练好后一般需要进行保存，方便直接预测
SVM svm = new SVM();bool trained = svm.Train(my_train.sampleFeatureMat, my_train.sampleLabelMat, null, null, p);svm.Save(@"../HOG_SVM.");
这里给出我的C#提取SVM参数方式：
（只用于提取训练目标为1与-1两类的
using System;using System.Text;using System.using System.IO;namespace HOG_SVM{ class GetData { public double[] alpha; public double rho; int sv_count; string alpha_str; public GetData() { doc = new = doc.DocumentElement; get_rho(nodes); getAlpha_str(nodes); getSv_count(nodes); getAlpha(); } public void get_rho(if (nodes.HasChildNodes) { foreach (in nodes.ChildNodes) { if (nodes.Name == "rho") { rho = Double.Parse(nodes.InnerText); return; } get_rho(node); } } } public void getAlpha_str(if (nodes.HasChildNodes) { foreach (in nodes.ChildNodes) { if (nodes.Name == "alpha") { //sr = new StreamReader(new Stream(nodes.InnerText)); alpha_str = nodes.InnerText; return; } getAlpha_str(node); } } } public void getSv_count(if (nodes.HasChildNodes) { foreach (in nodes.ChildNodes) { if (nodes.Name == "sv_count") { sv_count = int.Parse(nodes.InnerText); return; } getSv_count(node); } } } public void getAlpha() { byte[] array = Encoding.ASCII.GetBytes(alpha_str); MemoryStream stream = new MemoryStream(array); //convert stream 2 string sr = new StreamReader(stream); alpha = new double[sv_count]; sr.ReadLine(); int i = 0; while (true) { string tmp = sr.ReadLine(); if (tmp == "") continue; string[] tmp2 = tmp.Split(' '); foreach (string ele in tmp2) { if (ele != "") { alpha[i] = double.Parse(ele); i++; } } if (i == sv_count) break; } } }}
c#读取
c# 读取opencv 生成的svm训练好的：http://blog.csdn.net/yeyang911/article/details/12905153
2、使用其他C#的SVM库
关于提取参数，自定义HOG Detector的问题，后来在网上搜到了这种方式
Training custom SVM to use with HOGDescriptor in OpenCV：
I was struggling with the same problem. Searching forums I have found, that the detector cannot be trained using CvSVM (I don't know the reason). I used LIBSVM for training the the detector. Here is the code to extract the detector for HOGDescriptor.setSVMDetector( w): For data details see LIBSVM documentation/header. I did all the training in C++, filling the LIBSVM training data from CV to LIBSVM; the code below extracts the detector vector needed for cv::HOGDescriptor. The w parameter is std::vector<float> w |
const double * const *sv_coef = model.sv_coef;const svm_node * const *SV = model.SV;int l = model.l;model.label;const svm_node* p_tmp = SV[0];int len = 0;while( p_tmp->index != -1 ){ len++; p_tmp++;}w.resize( len+1 );for( int i=0; i<l; i++){ double svcoef = sv_coef[0][i]; const svm_node* p = SV[i]; while( p->index != -1 ) { w[p->index-1] += float(svcoef * p->value); p++; }}w[len] = float(-model.rho[0]);
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来自: http://stackoverflow.com/questions/15339657/training-custom-svm-to-use-with-hogdescriptor-in-opencv
该回答提到的 LIBSVM 库就是比较好的替代手段，应该可以直接获取到这两个中间量，而不用再去解析
可以去作者主页上下载LIBSVM库：http://www.csie.ntu.edu.tw/~cjlin/libsvm/#csharp
3、其他相关链接
前些天的【OpenCV】基于HOG与SVM的行人检测学习（原理小结）:
http://www.cnblogs.com/KC-Mei/p/4534009.html
training GPU HOGDescriptor for multi scale detection：
http://answers.opencv.org/question/4351/training-gpu-hogdescriptor-for-multi-scale-detection/
原标题：【EmguCV】C#实现HOG与SVM的几个问题
关键词：C#
我知道了 