如何在libsvm训练代码中输入样本图像
How to input sample images in libsvm train code
我已经下载了用于对象检测的libsvm代码。我在使用train-svm代码时遇到了问题。我无法正确输入示例文件。任何人请帮助我如何输入正面和负面图像。这是列车代码。
#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 SVMn"
" 3 -- epsilon-SVR (regression)n"
" 4 -- nu-SVR (regression)n"
"-t kernel_type : set type of kernel function (default 2)n"
" 0 -- linear: u'*vn"
" 1 -- polynomial: (gamma*u'*v + coef0)^degreen"
" 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 moden"
"-q : quiet mode (no outputs)n"
);
exit(1);
}
void exit_input_error(int line_num)
{
fprintf(stderr,"Wrong input format at line %dn", 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,¶m);
if(error_msg)
{
fprintf(stderr,"ERROR: %sn",error_msg);
exit(1);
}
if(cross_validation)
{
do_cross_validation();
}
else
{
model = svm_train(&prob,¶m);
if(svm_save_model(model_file_name,model))
{
fprintf(stderr, "can't save model to file %sn", model_file_name);
exit(1);
}
svm_free_and_destroy_model(&model);
}
svm_destroy_param(¶m);
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,¶m,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 = %gn",total_error/prob.l);
printf("Cross Validation Squared correlation coefficient = %gn",
((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 >= 2n");
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: -%cn", 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 %sn",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," tn");
if(label == NULL) // empty line
exit_input_error(i+1);
prob.y[i] = strtod(label,&endptr);
if(endptr == label || *endptr != ' ')
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 != ' ' || 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 != ' ' && !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_numbern");
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 rangen");
exit(1);
}
}
fclose(fp);
}
更新
我可以用这个代码转换成数字表示吗?
#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 Recognitionn";
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%sn",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%sn",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;
}
我可以从您的代码中看到,您正在混合OpenCV和LIBSVM。
基本上,您可以按照以下方式之一进行操作。就我个人而言,我建议只使用OpenCV。
OpenCV
OpenCV是一个非常强大的库,用于处理图像。因此,他们实现了自己的机器学习算法,包括SVM。
正如这里以一种非常好的方式描述的那样,通过OpenCV对图像进行分类非常容易,因为算法为此使用了一个通用接口。
LIBSVM
LIBSVM是一个独立的库,用于各种形式的SVM分类(例如多类、两类、带概率估计等)。如果你这样做,你必须执行以下步骤才能成功分类:
- 想想你想区分多少不同的类(例如+/-)
- 也许可以预处理你的图像(过滤器,…)
- 使用特征选择方法(例如:相互信息)从图像中提取所谓的"特征"。这些方法会告诉你,由于我们遵循基本假设,并不是图像中的每个像素都很重要,所以哪些点对给定的类来说是重要的
- 根据提取的特征,您可以将图像转换为矢量表示
-
根据LIBSVM数据格式将其写入文件:
标签feature_id1:特征值1特征_id2:特征值2
+1 1:0.53265 2:0.5232
-1 1:0.78543 2:0.64326
-
根据"svm_train"的说明继续操作。分类将是2.)4.)5.)和运行"svm_predict"的组合。
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