[EmguCV] C # Implementation of HOG and SVM,

[EmguCV] C # Implementation of HOG and SVM,

 

About the alpha and rock vectors in SVM

EmguCV encapsulation is more thorough. in C #, the two intermediate variables cannot be obtained through reload like C ++.
// Inherit from the CvSVM class, because the decision_func parameter of the trained SVM must be used to generate the detection sub-parameters used in setSVMDetector, // However, by viewing the CvSVM source code, we can see that the decision_func parameter is a protected type variable and cannot be accessed directly. You can only access class MySVM: public CvSVM {public: // obtain the double * get_alpha_vector () {return this-> decision_func-> alpha;} from the alpha array in the decision-making function of SVM. // obtain the ROV parameter in the decision-making function of SVM, that is, the offset float get_rov () {return this-> decision_func-> ROV ;}};
See the C ++ example: training SVM classifier for HOG Pedestrian detection.Http://blog.csdn.net/pb09013037/article/details/41256945)
 

To get these two variables for custom HOG detection, you can think of the following methods:

1. C # Read the generated XML file

After the classifier is trained, it is generally saved for direct prediction.
SVM svm = new SVM();bool trained = svm.Train(my_train.sampleFeatureMat, my_train.sampleLabelMat, null, null, p);svm.Save(@"../HOG_SVM.xml");
Here is my C # Method for Extracting SVM parameters:
(It is only used to extract XML files whose training target is 1 and-1. If the type is greater than 2, there will be more than one arrays of rock and alpha, which need to be further combined)
using System;using System.Text;using System.Xml;using System.IO;namespace HOG_SVM{    class GetData    {        public double[] alpha;        public double rho;        XmlDocument doc;                StreamReader sr;        int sv_count;        string alpha_str;        public GetData()        {            doc = new XmlDocument();            doc.Load(Form1.LOAD_PATH);            XmlNode nodes = doc.DocumentElement;            get_rho(nodes);            getAlpha_str(nodes);            getSv_count(nodes);            getAlpha();         }        public void get_rho(XmlNode nodes)        {            if (nodes.HasChildNodes)            {                foreach (XmlNode node in nodes.ChildNodes)                {                    if (nodes.Name == "rho")                    {                        rho = Double.Parse(nodes.InnerText);                        return;                    }                    get_rho(node);                }            }        }        public void getAlpha_str(XmlNode nodes)        {            if (nodes.HasChildNodes)            {                foreach (XmlNode node 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(XmlNode nodes)        {            if (nodes.HasChildNodes)            {                foreach (XmlNode node 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 # There are many ways to read XML. You can also use Linq to operate xml. You can also refer to the following link:
C # Read the xml classifier trained by svm generated by opencv:Http://blog.csdn.net/yeyang911/article/details/12905153
 

 

2. Use other C # SVM Libraries

The question about Parameter Extraction and custom HOG Detector was found on the Internet.
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 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 isstd::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]);
 
   
 
  
 
 
From:Http://stackoverflow.com/questions/15339657/training-custom-svm-to-use-with-hogdescriptor-in-opencv
 
The LIBSVM Library mentioned in this answer is a good alternative. You should be able to directly obtain the intermediate values without parsing XML.
You can download it on the author's homepage.LIBSVMLibrary:Http://www.csie.ntu.edu.tw /~ Cjlin/libsvm/# csharp
 

 

3. Other Related Links

 
 

  
  
In the previous days, [OpenCV] pedestrian detection Learning Based on HOG and SVM (Principle summary ):
 
 
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/