学习OpenCV——SVM 手写数字检测

转自http://blog.csdn.net/firefight/article/details/6452188

是MNIST手写数字图片库：http://code.google.com/p/supplement-of-the-mnist-database-of-handwritten-digits/downloads/list

其他方法：http://blog.csdn.net/onezeros/article/details/5672192

使用OPENCV训练手写数字识别分类器

1，下载训练数据和测试数据文件，这里用的是MNIST手写数字图片库，其中训练数据库中为60000个，测试数据库中为10000个
2，创建训练数据和测试数据文件读取函数，注意字节顺序为大端
3，确定字符特征方式为最简单的8×8网格内的字符点数

4，创建SVM，训练并读取，结果如下
1000个训练样本，测试数据正确率80.21%（并没有体现SVM小样本高准确率的特性啊）
10000个训练样本，测试数据正确率95.45%
60000个训练样本，测试数据正确率97.67%

5，编写手写输入的GUI程序，并进行验证，效果还可以接受。

以下为主要代码，以供参考

（类似的也实现了随机树分类器，比较发现在相同的样本数情况下，SVM准确率略高）

[cpp] view plaincopyprint?

#include "stdafx.h"
#include <fstream>
#include "opencv2/opencv.hpp"
#include <vector>
using namespace std;
using namespace cv;
#define SHOW_PROCESS 0
#define ON_STUDY 0
class NumTrainData
{
public:
NumTrainData()
{
memset(data, 0, sizeof(data));
result = -1;
}
public:
float data[64];
int result;
};
vector<NumTrainData> buffer;
int featureLen = 64;
void swapBuffer(char* buf)
{
char temp;
temp = *(buf);
*buf = *(buf+3);
*(buf+3) = temp;
temp = *(buf+1);
*(buf+1) = *(buf+2);
*(buf+2) = temp;
}
void GetROI(Mat& src, Mat& dst)
{
int left, right, top, bottom;
left = src.cols;
right = 0;
top = src.rows;
bottom = 0;
//Get valid area
for(int i=0; i<src.rows; i++)
{
for(int j=0; j<src.cols; j++)
{
if(src.at<uchar>(i, j) > 0)
{
if(j<left) left = j;
if(j>right) right = j;
if(i<top) top = i;
if(i>bottom) bottom = i;
}
}
}
//Point center;
//center.x = (left + right) / 2;
//center.y = (top + bottom) / 2;
int width = right - left;
int height = bottom - top;
int len = (width < height) ? height : width;
//Create a squre
dst = Mat::zeros(len, len, CV_8UC1);
//Copy valid data to squre center
Rect dstRect((len - width)/2, (len - height)/2, width, height);
Rect srcRect(left, top, width, height);
Mat dstROI = dst(dstRect);
Mat srcROI = src(srcRect);
srcROI.copyTo(dstROI);
}
int ReadTrainData(int maxCount)
{
//Open image and label file
const char fileName[] = "../res/train-images.idx3-ubyte";
const char labelFileName[] = "../res/train-labels.idx1-ubyte";
ifstream lab_ifs(labelFileName, ios_base::binary);
ifstream ifs(fileName, ios_base::binary);
if( ifs.fail() == true )
return -1;
if( lab_ifs.fail() == true )
return -1;
//Read train data number and image rows / cols
char magicNum[4], ccount[4], crows[4], ccols[4];
ifs.read(magicNum, sizeof(magicNum));
ifs.read(ccount, sizeof(ccount));
ifs.read(crows, sizeof(crows));
ifs.read(ccols, sizeof(ccols));
int count, rows, cols;
swapBuffer(ccount);
swapBuffer(crows);
swapBuffer(ccols);
memcpy(&count, ccount, sizeof(count));
memcpy(&rows, crows, sizeof(rows));
memcpy(&cols, ccols, sizeof(cols));
//Just skip label header
lab_ifs.read(magicNum, sizeof(magicNum));
lab_ifs.read(ccount, sizeof(ccount));
//Create source and show image matrix
Mat src = Mat::zeros(rows, cols, CV_8UC1);
Mat temp = Mat::zeros(8, 8, CV_8UC1);
Mat img, dst;
char label = 0;
Scalar templateColor(255, 0, 255 );
NumTrainData rtd;
//int loop = 1000;
int total = 0;
while(!ifs.eof())
{
if(total >= count)
break;
total++;
cout << total << endl;
//Read label
lab_ifs.read(&label, 1);
label = label + '0';
//Read source data
ifs.read((char*)src.data, rows * cols);
GetROI(src, dst);
#if(SHOW_PROCESS)
//Too small to watch
img = Mat::zeros(dst.rows*10, dst.cols*10, CV_8UC1);
resize(dst, img, img.size());
stringstream ss;
ss << "Number " << label;
string text = ss.str();
putText(img, text, Point(10, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
//imshow("img", img);
#endif
rtd.result = label;
resize(dst, temp, temp.size());
//threshold(temp, temp, 10, 1, CV_THRESH_BINARY);
for(int i = 0; i<8; i++)
{
for(int j = 0; j<8; j++)
{
rtd.data[ i*8 + j] = temp.at<uchar>(i, j);
}
}
buffer.push_back(rtd);
//if(waitKey(0)==27) //ESC to quit
// break;
maxCount--;
if(maxCount == 0)
break;
}
ifs.close();
lab_ifs.close();
return 0;
}
void newRtStudy(vector<NumTrainData>& trainData)
{
int testCount = trainData.size();
Mat data = Mat::zeros(testCount, featureLen, CV_32FC1);
Mat res = Mat::zeros(testCount, 1, CV_32SC1);
for (int i= 0; i< testCount; i++)
{
NumTrainData td = trainData.at(i);
memcpy(data.data + i*featureLen*sizeof(float), td.data, featureLen*sizeof(float));
res.at<unsigned int>(i, 0) = td.result;
}
/START RT TRAINNING//
CvRTrees forest;
CvMat* var_importance = 0;
forest.train( data, CV_ROW_SAMPLE, res, Mat(), Mat(), Mat(), Mat(),
CvRTParams(10,10,0,false,15,0,true,4,100,0.01f,CV_TERMCRIT_ITER));
forest.save( "new_rtrees.xml" );
}
int newRtPredict()
{
CvRTrees forest;
forest.load( "new_rtrees.xml" );
const char fileName[] = "../res/t10k-images.idx3-ubyte";
const char labelFileName[] = "../res/t10k-labels.idx1-ubyte";
ifstream lab_ifs(labelFileName, ios_base::binary);
ifstream ifs(fileName, ios_base::binary);
if( ifs.fail() == true )
return -1;
if( lab_ifs.fail() == true )
return -1;
char magicNum[4], ccount[4], crows[4], ccols[4];
ifs.read(magicNum, sizeof(magicNum));
ifs.read(ccount, sizeof(ccount));
ifs.read(crows, sizeof(crows));
ifs.read(ccols, sizeof(ccols));
int count, rows, cols;
swapBuffer(ccount);
swapBuffer(crows);
swapBuffer(ccols);
memcpy(&count, ccount, sizeof(count));
memcpy(&rows, crows, sizeof(rows));
memcpy(&cols, ccols, sizeof(cols));
Mat src = Mat::zeros(rows, cols, CV_8UC1);
Mat temp = Mat::zeros(8, 8, CV_8UC1);
Mat m = Mat::zeros(1, featureLen, CV_32FC1);
Mat img, dst;
//Just skip label header
lab_ifs.read(magicNum, sizeof(magicNum));
lab_ifs.read(ccount, sizeof(ccount));
char label = 0;
Scalar templateColor(255, 0, 0);
NumTrainData rtd;
int right = 0, error = 0, total = 0;
int right_1 = 0, error_1 = 0, right_2 = 0, error_2 = 0;
while(ifs.good())
{
//Read label
lab_ifs.read(&label, 1);
label = label + '0';
//Read data
ifs.read((char*)src.data, rows * cols);
GetROI(src, dst);
//Too small to watch
img = Mat::zeros(dst.rows*30, dst.cols*30, CV_8UC3);
resize(dst, img, img.size());
rtd.result = label;
resize(dst, temp, temp.size());
//threshold(temp, temp, 10, 1, CV_THRESH_BINARY);
for(int i = 0; i<8; i++)
{
for(int j = 0; j<8; j++)
{
m.at<float>(0,j + i*8) = temp.at<uchar>(i, j);
}
}
if(total >= count)
break;
char ret = (char)forest.predict(m);
if(ret == label)
{
right++;
if(total <= 5000)
right_1++;
else
right_2++;
}
else
{
error++;
if(total <= 5000)
error_1++;
else
error_2++;
}
total++;
#if(SHOW_PROCESS)
stringstream ss;
ss << "Number " << label << ", predict " << ret;
string text = ss.str();
putText(img, text, Point(10, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
if(waitKey(0)==27) //ESC to quit
break;
#endif
}
ifs.close();
lab_ifs.close();
stringstream ss;
ss << "Total " << total << ", right " << right <<", error " << error;
string text = ss.str();
putText(img, text, Point(50, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
waitKey(0);
return 0;
}
void newSvmStudy(vector<NumTrainData>& trainData)
{
int testCount = trainData.size();
Mat m = Mat::zeros(1, featureLen, CV_32FC1);
Mat data = Mat::zeros(testCount, featureLen, CV_32FC1);
Mat res = Mat::zeros(testCount, 1, CV_32SC1);
for (int i= 0; i< testCount; i++)
{
NumTrainData td = trainData.at(i);
memcpy(m.data, td.data, featureLen*sizeof(float));
normalize(m, m);
memcpy(data.data + i*featureLen*sizeof(float), m.data, featureLen*sizeof(float));
res.at<unsigned int>(i, 0) = td.result;
}
/START SVM TRAINNING//
CvSVM svm = CvSVM();
CvSVMParams param;
CvTermCriteria criteria;
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.train(data, res, Mat(), Mat(), param);
svm.save( "SVM_DATA.xml" );
}
int newSvmPredict()
{
CvSVM svm = CvSVM();
svm.load( "SVM_DATA.xml" );
const char fileName[] = "../res/t10k-images.idx3-ubyte";
const char labelFileName[] = "../res/t10k-labels.idx1-ubyte";
ifstream lab_ifs(labelFileName, ios_base::binary);
ifstream ifs(fileName, ios_base::binary);
if( ifs.fail() == true )
return -1;
if( lab_ifs.fail() == true )
return -1;
char magicNum[4], ccount[4], crows[4], ccols[4];
ifs.read(magicNum, sizeof(magicNum));
ifs.read(ccount, sizeof(ccount));
ifs.read(crows, sizeof(crows));
ifs.read(ccols, sizeof(ccols));
int count, rows, cols;
swapBuffer(ccount);
swapBuffer(crows);
swapBuffer(ccols);
memcpy(&count, ccount, sizeof(count));
memcpy(&rows, crows, sizeof(rows));
memcpy(&cols, ccols, sizeof(cols));
Mat src = Mat::zeros(rows, cols, CV_8UC1);
Mat temp = Mat::zeros(8, 8, CV_8UC1);
Mat m = Mat::zeros(1, featureLen, CV_32FC1);
Mat img, dst;
//Just skip label header
lab_ifs.read(magicNum, sizeof(magicNum));
lab_ifs.read(ccount, sizeof(ccount));
char label = 0;
Scalar templateColor(255, 0, 0);
NumTrainData rtd;
int right = 0, error = 0, total = 0;
int right_1 = 0, error_1 = 0, right_2 = 0, error_2 = 0;
while(ifs.good())
{
//Read label
lab_ifs.read(&label, 1);
label = label + '0';
//Read data
ifs.read((char*)src.data, rows * cols);
GetROI(src, dst);
//Too small to watch
img = Mat::zeros(dst.rows*30, dst.cols*30, CV_8UC3);
resize(dst, img, img.size());
rtd.result = label;
resize(dst, temp, temp.size());
//threshold(temp, temp, 10, 1, CV_THRESH_BINARY);
for(int i = 0; i<8; i++)
{
for(int j = 0; j<8; j++)
{
m.at<float>(0,j + i*8) = temp.at<uchar>(i, j);
}
}
if(total >= count)
break;
normalize(m, m);
char ret = (char)svm.predict(m);
if(ret == label)
{
right++;
if(total <= 5000)
right_1++;
else
right_2++;
}
else
{
error++;
if(total <= 5000)
error_1++;
else
error_2++;
}
total++;
#if(SHOW_PROCESS)
stringstream ss;
ss << "Number " << label << ", predict " << ret;
string text = ss.str();
putText(img, text, Point(10, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
if(waitKey(0)==27) //ESC to quit
break;
#endif
}
ifs.close();
lab_ifs.close();
stringstream ss;
ss << "Total " << total << ", right " << right <<", error " << error;
string text = ss.str();
putText(img, text, Point(50, 50), FONT_HERSHEY_SIMPLEX, 1.0, templateColor);
imshow("img", img);
waitKey(0);
return 0;
}
int main( int argc, char *argv[] )
{
#if(ON_STUDY)
int maxCount = 60000;
ReadTrainData(maxCount);
//newRtStudy(buffer);
newSvmStudy(buffer);
#else
//newRtPredict();
newSvmPredict();
#endif
return 0;
}
//from: http://blog.csdn.net/yangtrees/article/details/7458466

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