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）

#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