如何在 libsvm 训练代码中输入示例图像

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【中文标题】如何在 libsvm 训练代码中输入示例图像【英文标题】：How to input sample images in libsvm train code 【发布时间】：2016-03-14 10:12:45 【问题描述】：

我已经下载了用于对象检测的libsvm 代码。我在使用 train svm 代码时遇到问题。我无法正确输入示例文件。任何人请帮我如何输入正面和负面图像。这是火车代码。

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

void print_null(const char *s) 

void exit_with_help()

    printf(
    "Usage: svm-train [options] training_set_file [model_file]\n"
    "options:\n"
    "-s svm_type : set type of SVM (default 0)\n"
    "   0 -- C-SVC      (multi-class classification)\n"
    "   1 -- nu-SVC     (multi-class classification)\n"
    "   2 -- one-class SVM\n"
    "   3 -- epsilon-SVR    (regression)\n"
    "   4 -- nu-SVR     (regression)\n"
    "-t kernel_type : set type of kernel function (default 2)\n"
    "   0 -- linear: u'*v\n"
    "   1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
    "   2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
    "   3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
    "   4 -- precomputed kernel (kernel values in training_set_file)\n"
    "-d degree : set degree in kernel function (default 3)\n"
    "-g gamma : set gamma in kernel function (default 1/num_features)\n"
    "-r coef0 : set coef0 in kernel function (default 0)\n"
    "-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
    "-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
    "-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
    "-m cachesize : set cache memory size in MB (default 100)\n"
    "-e epsilon : set tolerance of termination criterion (default 0.001)\n"
    "-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
    "-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
    "-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
    "-v n: n-fold cross validation mode\n"
    "-q : quiet mode (no outputs)\n"
    );
    exit(1);


void exit_input_error(int line_num)

    fprintf(stderr,"Wrong input format at line %d\n", line_num);
    exit(1);


void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
void read_problem(const char *filename);
void do_cross_validation();

struct svm_parameter param;     // set by parse_command_line
struct svm_problem prob;        // set by read_problem
struct svm_model *model;
struct svm_node *x_space;
int cross_validation;
int nr_fold;

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)

    int len;

    if(fgets(line,max_line_len,input) == NULL)
        return NULL;

    while(strrchr(line,'\n') == NULL)
    
        max_line_len *= 2;
        line = (char *) realloc(line,max_line_len);
        len = (int) strlen(line);
        if(fgets(line+len,max_line_len-len,input) == NULL)
            break;
    
    return line;


int main(int argc, char **argv)

    char input_file_name[1024];
    char model_file_name[1024];
    const char *error_msg;

    parse_command_line(argc, argv, input_file_name, model_file_name);
    read_problem(input_file_name);
    error_msg = svm_check_parameter(&prob,&param);

    if(error_msg)
    
        fprintf(stderr,"ERROR: %s\n",error_msg);
        exit(1);
    

    if(cross_validation)
    
        do_cross_validation();
    
    else
    
        model = svm_train(&prob,&param);
        if(svm_save_model(model_file_name,model))
        
            fprintf(stderr, "can't save model to file %s\n", model_file_name);
            exit(1);
        
        svm_free_and_destroy_model(&model);
    
    svm_destroy_param(&param);
    free(prob.y);
    free(prob.x);
    free(x_space);
    free(line);

    return 0;


void do_cross_validation()

    int i;
    int total_correct = 0;
    double total_error = 0;
    double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
    double *target = Malloc(double,prob.l);

    svm_cross_validation(&prob,&param,nr_fold,target);
    if(param.svm_type == EPSILON_SVR ||
       param.svm_type == NU_SVR)
    
        for(i=0;i<prob.l;i++)
        
            double y = prob.y[i];
            double v = target[i];
            total_error += (v-y)*(v-y);
            sumv += v;
            sumy += y;
            sumvv += v*v;
            sumyy += y*y;
            sumvy += v*y;
        
        printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
        printf("Cross Validation Squared correlation coefficient = %g\n",
            ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
            ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
            );
    
    else
    
        for(i=0;i<prob.l;i++)
            if(target[i] == prob.y[i])
                ++total_correct;
        printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
    
    free(target);


void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)

    int i;
    void (*print_func)(const char*) = NULL; // default printing to stdout

    // default values
    param.svm_type = C_SVC;
    param.kernel_type = RBF;
    param.degree = 3;
    param.gamma = 0;    // 1/num_features
    param.coef0 = 0;
    param.nu = 0.5;
    param.cache_size = 100;
    param.C = 1;
    param.eps = 1e-3;
    param.p = 0.1;
    param.shrinking = 1;
    param.probability = 0;
    param.nr_weight = 0;
    param.weight_label = NULL;
    param.weight = NULL;
    cross_validation = 0;

    // parse options
    for(i=1;i<argc;i++)
    
        if(argv[i][0] != '-') break;
        if(++i>=argc)
            exit_with_help();
        switch(argv[i-1][1])
        
            case 's':
                param.svm_type = atoi(argv[i]);
                break;
            case 't':
                param.kernel_type = atoi(argv[i]);
                break;
            case 'd':
                param.degree = atoi(argv[i]);
                break;
            case 'g':
                param.gamma = atof(argv[i]);
                break;
            case 'r':
                param.coef0 = atof(argv[i]);
                break;
            case 'n':
                param.nu = atof(argv[i]);
                break;
            case 'm':
                param.cache_size = atof(argv[i]);
                break;
            case 'c':
                param.C = atof(argv[i]);
                break;
            case 'e':
                param.eps = atof(argv[i]);
                break;
            case 'p':
                param.p = atof(argv[i]);
                break;
            case 'h':
                param.shrinking = atoi(argv[i]);
                break;
            case 'b':
                param.probability = atoi(argv[i]);
                break;
            case 'q':
                print_func = &print_null;
                i--;
                break;
            case 'v':
                cross_validation = 1;
                nr_fold = atoi(argv[i]);
                if(nr_fold < 2)
                
                    fprintf(stderr,"n-fold cross validation: n must >= 2\n");
                    exit_with_help();
                
                break;
            case 'w':
                ++param.nr_weight;
                param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
                param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
                param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
                param.weight[param.nr_weight-1] = atof(argv[i]);
                break;
            default:
                fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
                exit_with_help();
        
    

    svm_set_print_string_function(print_func);

    // determine filenames

    if(i>=argc)
        exit_with_help();

    strcpy(input_file_name, argv[i]);

    if(i<argc-1)
        strcpy(model_file_name,argv[i+1]);
    else
    
        char *p = strrchr(argv[i],'/');
        if(p==NULL)
            p = argv[i];
        else
            ++p;
        sprintf(model_file_name,"%s.model",p);
    


// read in a problem (in svmlight format)

void read_problem(const char *filename)

    int max_index, inst_max_index, i;
    size_t elements, j;
    FILE *fp = fopen(filename,"r");
    char *endptr;
    char *idx, *val, *label;

    if(fp == NULL)
    
        fprintf(stderr,"can't open input file %s\n",filename);
        exit(1);
    

    prob.l = 0;
    elements = 0;

    max_line_len = 1024;
    line = Malloc(char,max_line_len);
    while(readline(fp)!=NULL)
    
        char *p = strtok(line," \t"); // label

        // features
        while(1)
        
            p = strtok(NULL," \t");
            if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
                break;
            ++elements;
        
        ++elements;
        ++prob.l;
    
    rewind(fp);

    prob.y = Malloc(double,prob.l);
    prob.x = Malloc(struct svm_node *,prob.l);
    x_space = Malloc(struct svm_node,elements);

    max_index = 0;
    j=0;
    for(i=0;i<prob.l;i++)
    
        inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
        readline(fp);
        prob.x[i] = &x_space[j];
        label = strtok(line," \t\n");
        if(label == NULL) // empty line
            exit_input_error(i+1);

        prob.y[i] = strtod(label,&endptr);
        if(endptr == label || *endptr != '\0')
            exit_input_error(i+1);

        while(1)
        
            idx = strtok(NULL,":");
            val = strtok(NULL," \t");

            if(val == NULL)
                break;

            errno = 0;
            x_space[j].index = (int) strtol(idx,&endptr,10);
            if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
                exit_input_error(i+1);
            else
                inst_max_index = x_space[j].index;

            errno = 0;
            x_space[j].value = strtod(val,&endptr);
            if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
                exit_input_error(i+1);

            ++j;
        

        if(inst_max_index > max_index)
            max_index = inst_max_index;
        x_space[j++].index = -1;
    

    if(param.gamma == 0 && max_index > 0)
        param.gamma = 1.0/max_index;

    if(param.kernel_type == PRECOMPUTED)
        for(i=0;i<prob.l;i++)
        
            if (prob.x[i][0].index != 0)
            
                fprintf(stderr,"Wrong input format: first column must be 0:sample_serial_number\n");
                exit(1);
            
            if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
            
                fprintf(stderr,"Wrong input format: sample_serial_number out of range\n");
                exit(1);
            
        

    fclose(fp);

更新

我可以使用此代码转换为数字表示吗？

#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <cv.h>
#include <highgui.h>
#include <cvaux.h>
#include <iostream>
#include <vector>
#include<string.h>
using namespace std;
using namespace cv;

int main ( int argc, char** argv )

    cout << "OpenCV Training SVM Automatic Number Plate Recognition\n";
    cout << "\n";

    char* path_Plates;
    char* path_NoPlates;
    int numPlates;
    int numNoPlates;
    int imageWidth=150;
    int imageHeight=150;

    //Check if user specify image to process
    if(1)
    
        numPlates= 12;
        numNoPlates= 67 ;
        path_Plates= "/home/kaushik/opencv_work/Manas6/Pics/Positive_Images/";
        path_NoPlates= "/home/kaushik/opencv_work/Manas6/Pics/Negative_Images/i";

    else
        cout << "Usage:\n" << argv[0] << " <num Plate Files> <num Non Plate Files> <path to plate folder files> <path to non plate files> \n";
        return 0;
    

    Mat classes;//(numPlates+numNoPlates, 1, CV_32FC1);
    Mat trainingData;//(numPlates+numNoPlates, imageWidth*imageHeight, CV_32FC1 );

    Mat trainingImages;
    vector<int> trainingLabels;

    for(int i=1; i<= numPlates; i++)
    

        stringstream ss(stringstream::in | stringstream::out);
        ss<<path_Plates<<i<<".jpg";
        try

            const char* a = ss.str().c_str();
            printf("\n%s\n",a);
            Mat img = imread(ss.str(), CV_LOAD_IMAGE_UNCHANGED);
            img= img.clone().reshape(1, 1);
            //imshow("Window",img);
            //cout<<ss.str();
            trainingImages.push_back(img);
            trainingLabels.push_back(1);
        
        catch(Exception e);
    

    for(int i=0; i< numNoPlates; i++)
    
        stringstream ss(stringstream::in | stringstream::out);
        ss << path_NoPlates<<i << ".jpg";
        try
        
            const char* a = ss.str().c_str();
            printf("\n%s\n",a);
            Mat img=imread(ss.str(),CV_LOAD_IMAGE_UNCHANGED);
            //imshow("Win",img);
            img= img.clone().reshape(1, 1);
            trainingImages.push_back(img);
            trainingLabels.push_back(0);
            //cout<<ss.str();
        
        catch(Exception e);
    

    Mat(trainingImages).copyTo(trainingData);
    //trainingData = trainingData.reshape(1,trainingData.rows);
    trainingData.convertTo(trainingData, CV_32FC1);
    Mat(trainingLabels).copyTo(classes);

    FileStorage fs("SVM.xml", FileStorage::WRITE);
    fs << "TrainingData" << trainingData;
    fs << "classes" << classes;
    fs.release();

    return 0;

【问题讨论】：

您是否将图像转换为数字表示（通过特征选择、转换等）？ 亲爱的 rzo，我已经用我认为可以转换为数字表示的代码转换了更新我的问题。你能查一下吗？ 【参考方案1】：

我可以从您的代码中看到，您正在混合使用 OpenCV 和 LIBSVM。

基本上你可以按照以下方式之一。我个人建议只使用 OpenCV。

OpenCV

OpenCV 是一个非常强大的图像处理库。因此，他们实现了自己的机器学习算法，包括 SVM。

正如here 所描述的那样，通过 OpenCV 对图像进行分类非常容易，因为算法为此目的使用了一个通用接口。

LIBSVM

LIBSVM 一个独立库，用于各种形式的 SVM 分类（例如多类、二类、概率估计等）。如果你这样做，你必须执行以下步骤才能成功分类：

    想想你想要区分多少个不同的类（例如 + / -） 也许预处理您的图像（过滤器，...） 使用特征选择方法从图像中提取所谓的“特征”（例如：Mutual Information）。这些方法会告诉您，哪些点对您的给定类很重要，因为我们遵循基本假设，即并非图像中的每个单个像素都很重要。 根据您提取的特征，您可以将图像转换为矢量表示。

    按照LIBSVM数据格式写入文件：

    标签 feature_id1:feature_value1 feature_id2:feature_value2

    +1 1:0.53265 2:0.5232

    -1 1:0.78543 2:0.64326

    根据其描述继续“svm_train”。分类将是 2.) 4.) 5.) 和“svm_predict”的组合。

【讨论】：

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