学习OpenCV——SVM
2015-10-11 22:12
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OpenCV开发SVM算法是基于LibSVM软件包开发的,LibSVM是台湾大学林智仁(Lin Chih-Jen)等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包。用OpenCV使用SVM算法的大概流程是
CvSVM::C_SVC C-SVC
CvSVM::NU_SVC v-SVC
CvSVM::ONE_CLASS 一类SVM
CvSVM::EPS_SVR e-SVR
CvSVM::NU_SVR v-SVR
成员变量kernel_type表示核函数的类型:
CvSVM::LINEAR 线性:u‘v
CvSVM::POLY 多项式:(r*u'v + coef0)^degree
CvSVM::RBF RBF函数:exp(-r|u-v|^2)
CvSVM::SIGMOID sigmoid函数:tanh(r*u'v + coef0)
成员变量degree针对多项式核函数degree的设置,gamma针对多项式/rbf/sigmoid核函数的设置,coef0针对多项式/sigmoid核函数的设置,Cvalue为损失函数,在C-SVC、e-SVR、v-SVR中有效,nu设置v-SVC、一类SVM和v-SVR参数,p为设置e-SVR中损失函数的值,class_weightsC_SVC的权重,term_crit为SVM训练过程的终止条件。其中默认值degree = 0,gamma = 1,coef0 = 0,Cvalue = 1,nu = 0,p
= 0,class_weights = 0
实现代码如下:运行步骤
[cpp] view
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// step 1:
float labels[4] = {1.0, -1.0, -1.0, -1.0};
Mat labelsMat(3, 1, CV_32FC1, labels);
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(3, 2, CV_32FC1, trainingData);
// step 2:
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
// step 3:
CvSVM SVM;
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
// step 4:
Vec3b green(0, 255, 0), blue(255, 0, 0);
for (int i=0; i<image.rows; i++)
{
for (int j=0; j<image.cols; j++)
{
Mat sampleMat = (Mat_<float>(1,2) << i,j);
float response = SVM.predict(sampleMat);
if (fabs(response-1.0) < 0.0001)
{
image.at<Vec3b>(j, i) = green;
}
else if (fabs(response+1.0) < 0.001)
{
image.at<Vec3b>(j, i) = blue;
}
}
}
// step 5:
int c = SVM.get_support_vector_count();
for (int i=0; i<c; i++)
{
const float* v = SVM.get_support_vector(i);
}
实验代码1:颜色分类
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//利用SVM解决2维空间向量的3级分类问题
#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <ML.H>
#include <TIME.H>
#include <CTYPE.H>
#include <IOSTREAM>
using namespace std;
int main(int argc, char **argv)
{
int size = 400; //图像的长度和宽度
const int s = 1000; //试验点个数(可更改!!)
int i, j, sv_num;
IplImage *img;
CvSVM svm = CvSVM(); //★★★
CvSVMParams param;
CvTermCriteria criteria;//停止迭代的标准
CvRNG rng = cvRNG(time(NULL));
CvPoint pts[s]; //定义1000个点
float data[s*2]; //点的坐标
int res[s]; //点的所属类
CvMat data_mat, res_mat;
CvScalar rcolor;
const float *support;
// (1)图像区域的确保和初始化
img= cvCreateImage(cvSize(size, size), IPL_DEPTH_8U, 3);
cvZero(img);
//确保画像区域,并清0(用黑色作初始化处理)。
// (2)学习数据的生成
for (i= 0; i< s; i++) {
pts[i].x= cvRandInt(&rng) % size; //用随机整数赋值
pts[i].y= cvRandInt(&rng) % size;
if (pts[i].y> 50 * cos(pts[i].x* CV_PI/ 100) + 200) {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(255, 0, 0));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(255, 0, 0));
res[i] = 1;
}
else {
if (pts[i].x> 200) {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(0, 255, 0));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(0, 255, 0));
res[i] = 2;
}
else {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(0, 0, 255));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(0, 0, 255));
res[i] = 3;
}
}
}
//生成2维随机训练数据,并将其值放在CvPoint数据类型的数组pts[ ]中。
// (3)学习数据的显示
cvNamedWindow("SVM", CV_WINDOW_AUTOSIZE);
cvShowImage("SVM", img);
cvWaitKey(0);
// (4)学习参数的生成
for (i= 0; i< s; i++) {
data[i* 2] = float (pts[i].x) / size;
data[i* 2 + 1] = float (pts[i].y) / size;
}
cvInitMatHeader(&data_mat, s, 2, CV_32FC1, data);
cvInitMatHeader(&res_mat, s, 1, CV_32SC1, res);
criteria= cvTermCriteria(CV_TERMCRIT_EPS, 1000, FLT_EPSILON);
param= CvSVMParams (CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, criteria);
/*
SVM种类:CvSVM::C_SVC
Kernel的种类:CvSVM::RBF
degree:10.0(此次不使用)
gamma:8.0
coef0:1.0(此次不使用)
C:10.0
nu:0.5(此次不使用)
p:0.1(此次不使用)
然后对训练数据正规化处理,并放在CvMat型的数组里。
*/
//☆☆☆☆☆☆☆☆☆(5)SVM学习☆☆☆☆☆☆☆☆☆☆☆☆
svm.train(&data_mat, &res_mat, NULL, NULL, param);//☆
//☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆
// (6)学习结果的绘图
for (i= 0; i< size; i++) {
for (j= 0; j< size; j++) {
CvMat m;
float ret = 0.0;
float a[] = { float (j) / size, float (i) / size };
cvInitMatHeader(&m, 1, 2, CV_32FC1, a);
ret= svm.predict(&m);
switch ((int) ret) {
case 1:
rcolor= CV_RGB(100, 0, 0);
break;
case 2:
rcolor= CV_RGB(0, 100, 0);
break;
case 3:
rcolor= CV_RGB(0, 0, 100);
break;
}
cvSet2D(img, i, j, rcolor);
}
}
//为了显示学习结果,通过输入图像区域的所有像素(特征向量)并进行分类。然后对输入像素用所属等级的颜色绘图。
// (7)训练数据的再绘制
for (i= 0; i< s; i++) {
CvScalar rcolor;
switch (res[i]) {
case 1:
rcolor= CV_RGB(255, 0, 0);
break;
case 2:
rcolor= CV_RGB(0, 255, 0);
break;
case 3:
rcolor= CV_RGB(0, 0, 255);
break;
}
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), rcolor);
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), rcolor);
}
//将训练数据在结果图像上重复的绘制出来。
// (8)支持向量的绘制
sv_num= svm.get_support_vector_count();
for (i= 0; i< sv_num; i++) {
support = svm.get_support_vector(i);
cvCircle(img, cvPoint((int) (support[0] * size), (int) (support[1] * size)), 5, CV_RGB(200, 200, 200));
}
//用白色的圆圈对支持向量作标记。
// (9)图像的显示
cvNamedWindow("SVM", CV_WINDOW_AUTOSIZE);
cvShowImage("SVM", img);
cvWaitKey(0);
cvDestroyWindow("SVM");
cvReleaseImage(&img);
return 0;
//显示实际处理结果的图像,直到某个键被按下为止。
}
实验代码2:用MIT人脸库检测,效果实在不好,检测结果全是人脸或者全都不是人脸。原因应该是图像检测没有做好应该用HoG等特征首先检测,在进行分类训练,不特征不明显,肯定分类效果并不好。
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//////////////////////////////////////////////////////////////////////////
// File Name: pjSVM.cpp
// Author: easyfov(easyfov@gmail.com)
// Company: Lida Optical and Electronic Co.,Ltd.
//http://apps.hi.baidu.com/share/detail/32719017
//////////////////////////////////////////////////////////////////////////
#include <cv.h>
#include <highgui.h>
#include <ml.h>
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
using namespace std;
#define WIDTH 20
#define HEIGHT 20
int main( /*int argc, char** argv*/ )
{
vector<string> img_path;
vector<int> img_catg;
int nLine = 0;
string buf;
ifstream svm_data( "E:/SVM_DATA.txt" );
while( svm_data )
{
if( getline( svm_data, buf ) )
{
nLine ++;
if( nLine % 2 == 0 )
{
img_catg.push_back( atoi( buf.c_str() ) );//atoi将字符串转换成整型,标志(0,1)
}
else
{
img_path.push_back( buf );//图像路径
}
}
}
svm_data.close();//关闭文件
CvMat *data_mat, *res_mat;
int nImgNum = nLine / 2; //读入样本数量
////样本矩阵,nImgNum:横坐标是样本数量, WIDTH * HEIGHT:样本特征向量,即图像大小
data_mat = cvCreateMat( nImgNum, WIDTH * HEIGHT, CV_32FC1 );
cvSetZero( data_mat );
//类型矩阵,存储每个样本的类型标志
res_mat = cvCreateMat( nImgNum, 1, CV_32FC1 );
cvSetZero( res_mat );
IplImage *srcImg, *sampleImg;
float b;
DWORD n;
for( string::size_type i = 0; i != img_path.size(); i++ )
{
srcImg = cvLoadImage( img_path[i].c_str(), CV_LOAD_IMAGE_GRAYSCALE );
if( srcImg == NULL )
{
cout<<" can not load the image: "<<img_path[i].c_str()<<endl;
continue;
}
cout<<" processing "<<img_path[i].c_str()<<endl;
sampleImg = cvCreateImage( cvSize( WIDTH, HEIGHT ), IPL_DEPTH_8U, 1 );//样本大小(WIDTH, HEIGHT)
cvResize( srcImg, sampleImg );//改变图像大小
cvSmooth( sampleImg, sampleImg ); //降噪
//生成训练数据
n = 0;
for( int ii = 0; ii < sampleImg->height; ii++ )
{
for( int jj = 0; jj < sampleImg->width; jj++, n++ )
{
b = (float)((int)((uchar)( sampleImg->imageData + sampleImg->widthStep * ii + jj )) / 255.0 );
cvmSet( data_mat, (int)i, n, b );
}
}
cvmSet( res_mat, i, 0, img_catg[i] );
cout<<" end processing "<<img_path[i].c_str()<<" "<<img_catg[i]<<endl;
}
CvSVM svm = CvSVM();
CvSVMParams param;
CvTermCriteria criteria;
criteria = cvTermCriteria( CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
param = CvSVMParams( CvSVM::C_SVC, CvSVM::RBF, 10.0, 0.09, 1.0, 10.0, 0.5, 1.0, NULL, criteria );
/*
SVM种类:CvSVM::C_SVC
Kernel的种类:CvSVM::RBF
degree:10.0(此次不使用)
gamma:8.0
coef0:1.0(此次不使用)
C:10.0
nu:0.5(此次不使用)
p:0.1(此次不使用)
然后对训练数据正规化处理,并放在CvMat型的数组里。
*/
//☆☆☆☆☆☆☆☆☆(5)SVM学习☆☆☆☆☆☆☆☆☆☆☆☆
svm.train( data_mat, res_mat, NULL, NULL, param );
//☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆
svm.save( "SVM_DATA.xml" );
//检测样本
IplImage *tst, *tst_tmp;
vector<string> img_tst_path;
ifstream img_tst( "E:/SVM_TEST.txt" );
while( img_tst )
{
if( getline( img_tst, buf ) )
{
img_tst_path.push_back( buf );
}
}
img_tst.close();
CvMat *tst_mat = cvCreateMat( 1, WIDTH*HEIGHT, CV_32FC1 );
char line[512];
ofstream predict_txt( "SVM_PREDICT.txt" );
for( string::size_type j = 0; j != img_tst_path.size(); j++ )
{
tst = cvLoadImage( img_tst_path[j].c_str(), CV_LOAD_IMAGE_GRAYSCALE );
if( tst == NULL )
{
cout<<" can not load the image: "<<img_tst_path[j].c_str()<<endl;
continue;
}
tst_tmp = cvCreateImage( cvSize( WIDTH, HEIGHT ), IPL_DEPTH_8U, 1 );
cvResize( tst, tst_tmp );
cvSmooth( tst_tmp, tst_tmp );
n = 0;
for(int ii = 0; ii < tst_tmp->height; ii++ )
{
for(int jj = 0; jj < tst_tmp->width; jj++, n++ )
{
b = (float)(((int)((uchar)tst_tmp->imageData+tst_tmp->widthStep*ii+jj))/255.0);
cvmSet( tst_mat, 0, n, (double)b );
}
}
int ret = svm.predict( tst_mat );
sprintf( line, "%s %d\r\n", img_tst_path[j].c_str(), ret );
predict_txt<<line;
}
predict_txt.close();
cvReleaseImage( &srcImg );
cvReleaseImage( &sampleImg );
cvReleaseImage( &tst );
cvReleaseImage( &tst_tmp );
cvReleaseMat( &data_mat );
cvReleaseMat( &res_mat );
return 0;
}
其中
G:/program/pjSVM/face/1.png
0
G:/program/pjSVM/face/2.png
0
G:/program/pjSVM/face/3.png
0
G:/program/pjSVM/face/4.png
0
G:/program/pjSVM/face/5.png
0
G:/program/pjSVM/face/6.png
0
G:/program/pjSVM/face/7.png
0
G:/program/pjSVM/face/8.png
0
G:/program/pjSVM/face/9.png
0
G:/program/pjSVM/face/10.png
0
G:/program/pjSVM/face/11.png
0
G:/program/pjSVM/face/12.png
0
G:/program/pjSVM/face/13.png
0
G:/program/pjSVM/face/14.png
0
G:/program/pjSVM/face/15.png
1
G:/program/pjSVM/face/16.png
1
G:/program/pjSVM/face/17.png
1
G:/program/pjSVM/face/18.png
1
G:/program/pjSVM/face/19.png
1
G:/program/pjSVM/face/20.png
1
G:/program/pjSVM/face/21.png
1
G:/program/pjSVM/face/22.png
1
G:/program/pjSVM/face/23.png
1
G:/program/pjSVM/face/24.png
1
G:/program/pjSVM/face/25.png
1
G:/program/pjSVM/face/26.png
1
G:/program/pjSVM/face/27.png
1
G:/program/pjSVM/face/28.png
1
G:/program/pjSVM/face/29.png
1
G:/program/pjSVM/face/30.png
1
SVM_TEST.txt中内容如下:
G:/program/pjSVM/try_face/5.png
G:/program/pjSVM/try_face/9.png
G:/program/pjSVM/try_face/11.png
G:/program/pjSVM/try_face/15.png
G:/program/pjSVM/try_face/2.png
G:/program/pjSVM/try_face/30.png
G:/program/pjSVM/try_face/17.png
G:/program/pjSVM/try_face/21.png
G:/program/pjSVM/try_face/24.png
G:/program/pjSVM/try_face/27.png
PS:txt操作简单方式:http://blog.csdn.net/lytwell/article/details/6029503
1)设置训练样本集
需要两组数据,一组是数据的类别,一组是数据的向量信息。2)设置SVM参数
利用CvSVMParams类实现类内的成员变量svm_type表示SVM类型:CvSVM::C_SVC C-SVC
CvSVM::NU_SVC v-SVC
CvSVM::ONE_CLASS 一类SVM
CvSVM::EPS_SVR e-SVR
CvSVM::NU_SVR v-SVR
成员变量kernel_type表示核函数的类型:
CvSVM::LINEAR 线性:u‘v
CvSVM::POLY 多项式:(r*u'v + coef0)^degree
CvSVM::RBF RBF函数:exp(-r|u-v|^2)
CvSVM::SIGMOID sigmoid函数:tanh(r*u'v + coef0)
成员变量degree针对多项式核函数degree的设置,gamma针对多项式/rbf/sigmoid核函数的设置,coef0针对多项式/sigmoid核函数的设置,Cvalue为损失函数,在C-SVC、e-SVR、v-SVR中有效,nu设置v-SVC、一类SVM和v-SVR参数,p为设置e-SVR中损失函数的值,class_weightsC_SVC的权重,term_crit为SVM训练过程的终止条件。其中默认值degree = 0,gamma = 1,coef0 = 0,Cvalue = 1,nu = 0,p
= 0,class_weights = 0
3)训练SVM
调用CvSVM::train函数建立SVM模型,第一个参数为训练数据,第二个参数为分类结果,最后一个参数即CvSVMParams4)用这个SVM进行分类
调用函数CvSVM::predict实现分类5)获得支持向量
除了分类,也可以得到SVM的支持向量,调用函数CvSVM::get_support_vector_count获得支持向量的个数,CvSVM::get_support_vector获得对应的索引编号的支持向量。实现代码如下:运行步骤
[cpp] view
plaincopyprint?
// step 1:
float labels[4] = {1.0, -1.0, -1.0, -1.0};
Mat labelsMat(3, 1, CV_32FC1, labels);
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
Mat trainingDataMat(3, 2, CV_32FC1, trainingData);
// step 2:
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
// step 3:
CvSVM SVM;
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
// step 4:
Vec3b green(0, 255, 0), blue(255, 0, 0);
for (int i=0; i<image.rows; i++)
{
for (int j=0; j<image.cols; j++)
{
Mat sampleMat = (Mat_<float>(1,2) << i,j);
float response = SVM.predict(sampleMat);
if (fabs(response-1.0) < 0.0001)
{
image.at<Vec3b>(j, i) = green;
}
else if (fabs(response+1.0) < 0.001)
{
image.at<Vec3b>(j, i) = blue;
}
}
}
// step 5:
int c = SVM.get_support_vector_count();
for (int i=0; i<c; i++)
{
const float* v = SVM.get_support_vector(i);
}
实验代码1:颜色分类
[cpp] view
plaincopyprint?
//利用SVM解决2维空间向量的3级分类问题
#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <ML.H>
#include <TIME.H>
#include <CTYPE.H>
#include <IOSTREAM>
using namespace std;
int main(int argc, char **argv)
{
int size = 400; //图像的长度和宽度
const int s = 1000; //试验点个数(可更改!!)
int i, j, sv_num;
IplImage *img;
CvSVM svm = CvSVM(); //★★★
CvSVMParams param;
CvTermCriteria criteria;//停止迭代的标准
CvRNG rng = cvRNG(time(NULL));
CvPoint pts[s]; //定义1000个点
float data[s*2]; //点的坐标
int res[s]; //点的所属类
CvMat data_mat, res_mat;
CvScalar rcolor;
const float *support;
// (1)图像区域的确保和初始化
img= cvCreateImage(cvSize(size, size), IPL_DEPTH_8U, 3);
cvZero(img);
//确保画像区域,并清0(用黑色作初始化处理)。
// (2)学习数据的生成
for (i= 0; i< s; i++) {
pts[i].x= cvRandInt(&rng) % size; //用随机整数赋值
pts[i].y= cvRandInt(&rng) % size;
if (pts[i].y> 50 * cos(pts[i].x* CV_PI/ 100) + 200) {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(255, 0, 0));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(255, 0, 0));
res[i] = 1;
}
else {
if (pts[i].x> 200) {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(0, 255, 0));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(0, 255, 0));
res[i] = 2;
}
else {
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), CV_RGB(0, 0, 255));
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), CV_RGB(0, 0, 255));
res[i] = 3;
}
}
}
//生成2维随机训练数据,并将其值放在CvPoint数据类型的数组pts[ ]中。
// (3)学习数据的显示
cvNamedWindow("SVM", CV_WINDOW_AUTOSIZE);
cvShowImage("SVM", img);
cvWaitKey(0);
// (4)学习参数的生成
for (i= 0; i< s; i++) {
data[i* 2] = float (pts[i].x) / size;
data[i* 2 + 1] = float (pts[i].y) / size;
}
cvInitMatHeader(&data_mat, s, 2, CV_32FC1, data);
cvInitMatHeader(&res_mat, s, 1, CV_32SC1, res);
criteria= cvTermCriteria(CV_TERMCRIT_EPS, 1000, FLT_EPSILON);
param= CvSVMParams (CvSVM::C_SVC, CvSVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, criteria);
/*
SVM种类:CvSVM::C_SVC
Kernel的种类:CvSVM::RBF
degree:10.0(此次不使用)
gamma:8.0
coef0:1.0(此次不使用)
C:10.0
nu:0.5(此次不使用)
p:0.1(此次不使用)
然后对训练数据正规化处理,并放在CvMat型的数组里。
*/
//☆☆☆☆☆☆☆☆☆(5)SVM学习☆☆☆☆☆☆☆☆☆☆☆☆
svm.train(&data_mat, &res_mat, NULL, NULL, param);//☆
//☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆
// (6)学习结果的绘图
for (i= 0; i< size; i++) {
for (j= 0; j< size; j++) {
CvMat m;
float ret = 0.0;
float a[] = { float (j) / size, float (i) / size };
cvInitMatHeader(&m, 1, 2, CV_32FC1, a);
ret= svm.predict(&m);
switch ((int) ret) {
case 1:
rcolor= CV_RGB(100, 0, 0);
break;
case 2:
rcolor= CV_RGB(0, 100, 0);
break;
case 3:
rcolor= CV_RGB(0, 0, 100);
break;
}
cvSet2D(img, i, j, rcolor);
}
}
//为了显示学习结果,通过输入图像区域的所有像素(特征向量)并进行分类。然后对输入像素用所属等级的颜色绘图。
// (7)训练数据的再绘制
for (i= 0; i< s; i++) {
CvScalar rcolor;
switch (res[i]) {
case 1:
rcolor= CV_RGB(255, 0, 0);
break;
case 2:
rcolor= CV_RGB(0, 255, 0);
break;
case 3:
rcolor= CV_RGB(0, 0, 255);
break;
}
cvLine(img, cvPoint(pts[i].x- 2, pts[i].y- 2), cvPoint(pts[i].x+ 2, pts[i].y+ 2), rcolor);
cvLine(img, cvPoint(pts[i].x+ 2, pts[i].y- 2), cvPoint(pts[i].x- 2, pts[i].y+ 2), rcolor);
}
//将训练数据在结果图像上重复的绘制出来。
// (8)支持向量的绘制
sv_num= svm.get_support_vector_count();
for (i= 0; i< sv_num; i++) {
support = svm.get_support_vector(i);
cvCircle(img, cvPoint((int) (support[0] * size), (int) (support[1] * size)), 5, CV_RGB(200, 200, 200));
}
//用白色的圆圈对支持向量作标记。
// (9)图像的显示
cvNamedWindow("SVM", CV_WINDOW_AUTOSIZE);
cvShowImage("SVM", img);
cvWaitKey(0);
cvDestroyWindow("SVM");
cvReleaseImage(&img);
return 0;
//显示实际处理结果的图像,直到某个键被按下为止。
}
实验代码2:用MIT人脸库检测,效果实在不好,检测结果全是人脸或者全都不是人脸。原因应该是图像检测没有做好应该用HoG等特征首先检测,在进行分类训练,不特征不明显,肯定分类效果并不好。
[cpp] view
plaincopyprint?
//////////////////////////////////////////////////////////////////////////
// File Name: pjSVM.cpp
// Author: easyfov(easyfov@gmail.com)
// Company: Lida Optical and Electronic Co.,Ltd.
//http://apps.hi.baidu.com/share/detail/32719017
//////////////////////////////////////////////////////////////////////////
#include <cv.h>
#include <highgui.h>
#include <ml.h>
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
using namespace std;
#define WIDTH 20
#define HEIGHT 20
int main( /*int argc, char** argv*/ )
{
vector<string> img_path;
vector<int> img_catg;
int nLine = 0;
string buf;
ifstream svm_data( "E:/SVM_DATA.txt" );
while( svm_data )
{
if( getline( svm_data, buf ) )
{
nLine ++;
if( nLine % 2 == 0 )
{
img_catg.push_back( atoi( buf.c_str() ) );//atoi将字符串转换成整型,标志(0,1)
}
else
{
img_path.push_back( buf );//图像路径
}
}
}
svm_data.close();//关闭文件
CvMat *data_mat, *res_mat;
int nImgNum = nLine / 2; //读入样本数量
////样本矩阵,nImgNum:横坐标是样本数量, WIDTH * HEIGHT:样本特征向量,即图像大小
data_mat = cvCreateMat( nImgNum, WIDTH * HEIGHT, CV_32FC1 );
cvSetZero( data_mat );
//类型矩阵,存储每个样本的类型标志
res_mat = cvCreateMat( nImgNum, 1, CV_32FC1 );
cvSetZero( res_mat );
IplImage *srcImg, *sampleImg;
float b;
DWORD n;
for( string::size_type i = 0; i != img_path.size(); i++ )
{
srcImg = cvLoadImage( img_path[i].c_str(), CV_LOAD_IMAGE_GRAYSCALE );
if( srcImg == NULL )
{
cout<<" can not load the image: "<<img_path[i].c_str()<<endl;
continue;
}
cout<<" processing "<<img_path[i].c_str()<<endl;
sampleImg = cvCreateImage( cvSize( WIDTH, HEIGHT ), IPL_DEPTH_8U, 1 );//样本大小(WIDTH, HEIGHT)
cvResize( srcImg, sampleImg );//改变图像大小
cvSmooth( sampleImg, sampleImg ); //降噪
//生成训练数据
n = 0;
for( int ii = 0; ii < sampleImg->height; ii++ )
{
for( int jj = 0; jj < sampleImg->width; jj++, n++ )
{
b = (float)((int)((uchar)( sampleImg->imageData + sampleImg->widthStep * ii + jj )) / 255.0 );
cvmSet( data_mat, (int)i, n, b );
}
}
cvmSet( res_mat, i, 0, img_catg[i] );
cout<<" end processing "<<img_path[i].c_str()<<" "<<img_catg[i]<<endl;
}
CvSVM svm = CvSVM();
CvSVMParams param;
CvTermCriteria criteria;
criteria = cvTermCriteria( CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
param = CvSVMParams( CvSVM::C_SVC, CvSVM::RBF, 10.0, 0.09, 1.0, 10.0, 0.5, 1.0, NULL, criteria );
/*
SVM种类:CvSVM::C_SVC
Kernel的种类:CvSVM::RBF
degree:10.0(此次不使用)
gamma:8.0
coef0:1.0(此次不使用)
C:10.0
nu:0.5(此次不使用)
p:0.1(此次不使用)
然后对训练数据正规化处理,并放在CvMat型的数组里。
*/
//☆☆☆☆☆☆☆☆☆(5)SVM学习☆☆☆☆☆☆☆☆☆☆☆☆
svm.train( data_mat, res_mat, NULL, NULL, param );
//☆☆利用训练数据和确定的学习参数,进行SVM学习☆☆☆☆
svm.save( "SVM_DATA.xml" );
//检测样本
IplImage *tst, *tst_tmp;
vector<string> img_tst_path;
ifstream img_tst( "E:/SVM_TEST.txt" );
while( img_tst )
{
if( getline( img_tst, buf ) )
{
img_tst_path.push_back( buf );
}
}
img_tst.close();
CvMat *tst_mat = cvCreateMat( 1, WIDTH*HEIGHT, CV_32FC1 );
char line[512];
ofstream predict_txt( "SVM_PREDICT.txt" );
for( string::size_type j = 0; j != img_tst_path.size(); j++ )
{
tst = cvLoadImage( img_tst_path[j].c_str(), CV_LOAD_IMAGE_GRAYSCALE );
if( tst == NULL )
{
cout<<" can not load the image: "<<img_tst_path[j].c_str()<<endl;
continue;
}
tst_tmp = cvCreateImage( cvSize( WIDTH, HEIGHT ), IPL_DEPTH_8U, 1 );
cvResize( tst, tst_tmp );
cvSmooth( tst_tmp, tst_tmp );
n = 0;
for(int ii = 0; ii < tst_tmp->height; ii++ )
{
for(int jj = 0; jj < tst_tmp->width; jj++, n++ )
{
b = (float)(((int)((uchar)tst_tmp->imageData+tst_tmp->widthStep*ii+jj))/255.0);
cvmSet( tst_mat, 0, n, (double)b );
}
}
int ret = svm.predict( tst_mat );
sprintf( line, "%s %d\r\n", img_tst_path[j].c_str(), ret );
predict_txt<<line;
}
predict_txt.close();
cvReleaseImage( &srcImg );
cvReleaseImage( &sampleImg );
cvReleaseImage( &tst );
cvReleaseImage( &tst_tmp );
cvReleaseMat( &data_mat );
cvReleaseMat( &res_mat );
return 0;
}
其中
G:/program/pjSVM/face/1.png
0
G:/program/pjSVM/face/2.png
0
G:/program/pjSVM/face/3.png
0
G:/program/pjSVM/face/4.png
0
G:/program/pjSVM/face/5.png
0
G:/program/pjSVM/face/6.png
0
G:/program/pjSVM/face/7.png
0
G:/program/pjSVM/face/8.png
0
G:/program/pjSVM/face/9.png
0
G:/program/pjSVM/face/10.png
0
G:/program/pjSVM/face/11.png
0
G:/program/pjSVM/face/12.png
0
G:/program/pjSVM/face/13.png
0
G:/program/pjSVM/face/14.png
0
G:/program/pjSVM/face/15.png
1
G:/program/pjSVM/face/16.png
1
G:/program/pjSVM/face/17.png
1
G:/program/pjSVM/face/18.png
1
G:/program/pjSVM/face/19.png
1
G:/program/pjSVM/face/20.png
1
G:/program/pjSVM/face/21.png
1
G:/program/pjSVM/face/22.png
1
G:/program/pjSVM/face/23.png
1
G:/program/pjSVM/face/24.png
1
G:/program/pjSVM/face/25.png
1
G:/program/pjSVM/face/26.png
1
G:/program/pjSVM/face/27.png
1
G:/program/pjSVM/face/28.png
1
G:/program/pjSVM/face/29.png
1
G:/program/pjSVM/face/30.png
1
SVM_TEST.txt中内容如下:
G:/program/pjSVM/try_face/5.png
G:/program/pjSVM/try_face/9.png
G:/program/pjSVM/try_face/11.png
G:/program/pjSVM/try_face/15.png
G:/program/pjSVM/try_face/2.png
G:/program/pjSVM/try_face/30.png
G:/program/pjSVM/try_face/17.png
G:/program/pjSVM/try_face/21.png
G:/program/pjSVM/try_face/24.png
G:/program/pjSVM/try_face/27.png
PS:txt操作简单方式:http://blog.csdn.net/lytwell/article/details/6029503
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