Opencv 3 SVM培训 [英] Opencv 3 SVM training

问题描述

您可能知道，OpenCV 3中有许多事情发生了改变（与openCV2或旧的第一版本相比）。

在过去，训练SVM将使用：

  CvSVMParams params; 
 params.svm_type = CvSVM :: C_SVC; 
 params.kernel_type = CvSVM :: POLY; 
 params.gamma = 3; 
 
 CvSVM svm; 
 svm.train（training_mat，labels，Mat（），Mat（），params）; 
  

在第三版API中，没有 CvSVMParams 或 CvSVM 。令人惊讶的是，有有关SVM的文档页面，但它告诉一切，但不知道如何真正使用它（至少我不能让它出来）。
此外，看起来没有人在互联网上使用SVC从OpenCV的3.0。

目前，我只能得到以下：

  ml :: SVM.Params params; 
 params.svmType = ml :: SVM :: C_SVC; 
 params.kernelType = ml :: SVM :: POLY; 
 params.gamma = 3;你可以向我提供信息，如何重写实际的培训openCV 3？
  
解决方案
与opencv3.0，它是绝对不同，但不难：
  Ptr< ml :: SVM> svm = ml :: SVM :: create（）; 
 // edit：params struct被删除，
 //我们使用setter / getter now：
 svm-> setType（ml :: SVM :: C_SVC）; 
 svm-> setKernel（ml :: SVM :: POLY）; 
 svm-> setGamma（3）; 
 
 Mat trainData; //每行一个特征
 Mat标签; 
 svm-> train（trainData，ml :: ROW_SAMPLE，labels）; 
 // ... 
 Mat查询; // input，1channel，1 row（apply reshape（1,1）if nessecary）
 Mat res; // output 
 svm-> predict（query，res）; 
  
 
As you may know, many things changed in OpenCV 3 (in comparision to the openCV2 or the old first version).

In the old days, to train SVM one would use:
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::POLY;
params.gamma = 3;

CvSVM svm;
svm.train(training_mat, labels, Mat(), Mat(), params);
In the third version of API, there is no CvSVMParams nor CvSVM. Surprisingly, there is a documentation page about SVM, but it tells everything, but not how to really use it (at least I cannot make it out).
Moreover, it looks like no one in the Internet uses SVM from OpenCV's 3.0.

Currently, I only managed to get the following:
ml::SVM.Params params;
params.svmType = ml::SVM::C_SVC;
params.kernelType = ml::SVM::POLY;
params.gamma = 3;
Can you please provide me with information, how to rewrite the actual training to openCV 3?
解决方案
with opencv3.0, it's definitely different , but not difficult:
Ptr<ml::SVM> svm = ml::SVM::create();
// edit: the params struct got removed,
// we use setter/getter now:
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::POLY);
svm->setGamma(3); 

Mat trainData; // one row per feature
Mat labels;    
svm->train( trainData , ml::ROW_SAMPLE , labels );
// ...
Mat query; // input, 1channel, 1 row (apply reshape(1,1) if nessecary)
Mat res;   // output
svm->predict(query, res);


                        
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