opencv学习系列：实例练习（含多个实例）

//-----------------------------------（续10月份）------------------------------------- //int main(int argc,char* argv[])和int main(int argc, char **argv)的使用方法int   main(int   argc,   char*   argv[])     {     int   i;     for   (i   =   0;   i<argc;   i++)     cout<<argv[i]<<endl;     cin>>i;     return   0;     }     在DOS执行时敲入     F:\MYDOCU~1\TEMPCODE\D1\DEBUG\D1.EXE   aaaa   bbb   ccc   ddd     输出如下：     F:\MYDOCU~1\TEMPCODE\D1\DEBUG\D1.EXE     aaaa     bbb     ccc     ddd  //-----------------------------------OpenCV学习------------------------------------- //  程序名称:OSTU算法选自适应阈值//  2016年10月 Created by孙立波（Visual Studio 2013+OpenCV2.4.9）#include <opencv2\opencv.hpp> #include <iostream>#include <string> #include <cassert>using namespace cv;using namespace std;////隐藏控制台窗口//#pragma comment(linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"")int otsu(IplImage *image){    assert(NULL != image);//若执行该函数，则终止程序运行    int width = image->width;    int height = image->height;    int x = 0, y = 0;    int pixelCount[256];    float pixelPro[256];    int i, j, pixelSum = width * height, threshold = 0;    uchar* data = (uchar*)image->imageData;    //初始化      for (i = 0; i < 256; i++)    {        pixelCount[i] = 0;        pixelPro[i] = 0;    }    //统计灰度级中每个像素在整幅图像中的个数      for (i = y; i < height; i++)    {        for (j = x; j<width;j++)        pixelCount[data[i * image->widthStep + j]]++;    }    //计算每个像素在整幅图像中的比例      for (i = 0; i < 256; i++)    {        pixelPro[i] = (float)(pixelCount[i]) / (float)(pixelSum);    }    //经典ostu算法,得到前景和背景的分割      //遍历灰度级[0,255],计算出方差最大的灰度值,为最佳阈值      float w0, w1, u0tmp, u1tmp, u0, u1, u, deltaTmp, deltaMax = 0;    for (i = 0; i < 256; i++)//i为阈值    {        w0 = w1 = u0tmp = u1tmp = u0 = u1 = u = deltaTmp = 0;        for (j = 0; j < 256; j++)//遍历阈值数组当i=0，1，2，3...255        {            if (j <= i) //背景部分              {                //以i为阈值分类，第一类总的概率                  w0 += pixelPro[j];                u0tmp += j * pixelPro[j];            }            else       //前景部分              {                //以i为阈值分类，第二类总的概率                  w1 += pixelPro[j];                u1tmp += j * pixelPro[j];            }        }        u0 = u0tmp / w0;        //第一类的平均灰度          u1 = u1tmp / w1;        //第二类的平均灰度          u = u0tmp + u1tmp;      //整幅图像的平均灰度          //计算类间方差          deltaTmp = w0 * (u0 - u)*(u0 - u) + w1 * (u1 - u)*(u1 - u);        //找出最大类间方差以及对应的阈值          if (deltaTmp > deltaMax)        {            deltaMax = deltaTmp;            threshold = i;        }    }    //返回最佳阈值;      return threshold;}int main(int argc, char* argv[]){    IplImage* srcImage = cvLoadImage("F:\\workplace\\opencv_training\\test2.png", 0);    assert(NULL != srcImage);    cvNamedWindow("src");    cvShowImage("src", srcImage);    IplImage* biImage = cvCreateImage(cvGetSize(srcImage), 8, 1);    //计算最佳阈值      int threshold = otsu(srcImage);    //对图像二值化      cvThreshold(srcImage, biImage, threshold, 255, CV_THRESH_BINARY);    cvNamedWindow("binary");    cvShowImage("binary", biImage);    cvWaitKey(0);    cvReleaseImage(&srcImage);    cvReleaseImage(&biImage);    cvDestroyWindow("src");    cvDestroyWindow("binary");    return 0;}//************************************************************************************************//-----------------------------------OpenCV2---标定焊接图像------------------------------------- //  程序名称:OpenCV标定焊接图像，该程序根据DOS提示，一步步实现标定的自动化，求取内参//  所用IDE版本：        Visual Studio 2013    //  开发所用OpenCV版本：        2.4.9    //  2016年10月 Created by 孙立波    //包含程序所依赖的头文件:为方便起见把经常用的头文件都写在这里（前三必须包含），也可以用#include "opencv.hpp"包含下面所有头文件#include <opencv2\opencv.hpp>   #include <iostream>#include <string> #include <vector>#include <iomanip>#include <fstream>using namespace cv;using namespace std;//隐藏控制台窗口//#pragma comment(linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"")void main(){    /************************************************************************    读取21张图像，存入内存F:\biaoding文件中    *************************************************************************/    cout << "开始提取21张标定板图像………………" << endl;    int imageCount = 21;    int key;    int count1=0;    for (int i=0; i != imageCount; i++)    {        cout << "Frame#" << i + 1 << "..." << endl;        std::stringstream StrStm;        //为了将i转化为字符型，用StrStm做中介        string imageFileName;        StrStm << i + 1;        StrStm >> imageFileName;        imageFileName += ".jpg";        key = waitKey();        cout << "按Enter开始抽取图像，进入后可按q或者ESC键重新抽取图像，若按Enter键表明这帧图像被存入文件夹中" << endl;        if (key == 13)        {            int flag = 1;            while (flag)            {                Mat image0;//抽取到的临时图像数据并显示出来                imshow("显示抓取图像", image0);//显示是否符合要求                int key2 = waitKey();                if (key2 == 13)                {                    cout << "提取标定板图像成功！………………" << endl;                    std::stringstream str;                    str << "F:\\biaoding\\img" << std::setw(2) << std::setfill('0') << i + 1 << ".jpg";                    std::cout<<"提取的图像保存路径及文件名" << str.str() << endl;                    imwrite(str.str(), image0);                    flag = 0;                    count1 += 1;//已经得到的标定图像计数总数                }                else                   if (key2 == 113 || key2 == 27)                        cout << "这次提取的标定板图像不成功！重新提取！！！！………………" << endl;             }         }    }       if (count1 == 21)    {        cout << "***********************………………" << endl;        cout << "***********************………………" << endl;        cout << "下面开始标定图像...................." << endl;        count1 = 0;    }    /************************************************************************    读取每一幅图像，从中提取出角点，然后对角点进行亚像素精确化    *************************************************************************/    cout << "开始提取角点………………" << endl;    double time0 = static_cast<double>(getTickCount());    ofstream fout("F:\\biaoding\\caliberation_result.txt");  /**    保存定标结果的文件     **/    Size image_size;                          /****    图像的尺寸      ****/    Size board_size = Size(9, 6);             /****    定标板上每行、列的角点数       ****/    vector<Point2f> corners;                  /****    缓存每幅图像上检测到的角点     ****/    vector<vector<Point2f>>  corners_Seq;     /****    保存检测到的所有角点           ****/    vector<Mat>  image_Seq;    int count = 0;    int image_count = 21;    for (int i = 0; i != image_count; i++)    {        cout << "Frame #" << i + 1 << "..." << endl;        std::stringstream str;        str << "F:\\biaoding\\img" << std::setw(2) << std::setfill('0') << i + 1 << ".jpg";        std::cout << str.str() << std::endl;        Mat image = cv::imread(str.str());        image_size = image.size();        //image_size = Size(image.cols , image.rows);        /* 提取角点 */        int channel = 3;        Mat imageGray;        if (channel == image.channels() )            cvtColor(image, imageGray, CV_RGB2GRAY);        else            imageGray=image;        bool patternfound = findChessboardCorners(image, board_size, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE +            CALIB_CB_FAST_CHECK);        if (!patternfound)        {            cout << "can not find chessboard corners!\n";            continue;            exit(1);        }        else        {            /*            亚像素精确化 :迭代过程的终止条件可以是最大迭代次数CV_TERMCRIT_ITER类型，或者是设定的精度CV_TERMCRIT_EPS类型(或者是两            者的组合)。终止条件的设置在极大程度上影响最终得到的亚像素值的精度。在此，指定为0.10，则求得的亚像素级精度为像素的十分            之一            */            cornerSubPix(imageGray, corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));            //Size(11, 11)为搜索窗口的一半尺寸，Size(-1, -1)死区的一半尺寸，死区为不对搜索区的中央位置做求和运算的区域。/            //它是用来避免自相关矩阵出现的某些可能的奇异性。当值为（-1，-1）表示没有死区。            //TermCriteria为求角点的迭代过程的终止条件。即角点位置的确定，要么迭代数大于某个设定值，或者是精确懂达到某个设定值。            //criteria可以是最大迭代数目，或者是设定的精确度，也可以是它们的组合。            /* 绘制检测到的角点并保存 */            Mat imageTemp = image.clone();            for (int j = 0; j < corners.size(); j++)            {                circle(imageTemp, corners[j], 10, Scalar(0, 0, 255), 2, 8, 0);            }            string imageFileName1;            std::stringstream StrStm;            StrStm << i + 1;            StrStm >> imageFileName1;            imageFileName1 += "_corner.jpg";            //保存提取角点的图像            imwrite("F:\\biaoding\\imageFileName1", imageTemp);            cout << "Frame corner#" << i + 1 << "...end" << endl;            count = count + corners.size();//记录这一副保存到coners容器中的角点总数            //将该角点压入角点序列堆栈            corners_Seq.push_back(corners);//将所有图像的coners全部压入一个序列的堆栈，即元素为vector容器的堆栈        }        //将处理过的图像压入源图像堆栈        image_Seq.push_back(image);    }    cout << "角点提取完成！下一步摄像机定标\n";    /************************************************************************    摄像机定标    *************************************************************************/    cout << "开始定标………………" << endl;    Size square_size = Size(80, 80);                                      /**** 实际测量得到的定标板上每个棋盘格的大小，单位为像素 ****/    vector<vector<Point3f>>  object_Points;                               /**** 保存定标板上角点的三维坐标   ****/    Mat image_points = Mat(1, count, CV_32FC2, Scalar::all(0));          /***** 保存提取的所有角点1*序列图像的角点总数   *****/    vector<int>  point_counts;                                           /***** 每幅图像中角点的数量 ****/    Mat intrinsic_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0));          /***** 摄像机内参数矩阵    ****/    Mat distortion_coeffs = Mat(1, 4, CV_32FC1, Scalar::all(0));         /* 摄像机的4个畸变系数：k1,k2,p1,p2 */    vector<cv::Mat> rotation_vectors;                                    /* 图像序列图像的旋转向量，每一行代表一个旋转向量 */    vector<cv::Mat> translation_vectors;                                 /* 每幅图像的平移向量，每一行代表一个平移向量 */    /* 初始化定标板上角点的三维坐标，此用的是像素坐标 */    for (int t = 0; t<image_count; t++)    {        vector<Point3f> tempPointSet;//存储每幅图像的像素坐标        for (int i = 0; i<board_size.height; i++)        {            for (int j = 0; j<board_size.width; j++)            {                /* 假设定标板放在世界坐标系中z=0的平面上 */                Point3f tempPoint;                //在这里，board_size中棋盘格子为单位长度，当square_size存储的像素边长为边长为80，代表width、height为80                tempPoint.x = i*square_size.width;                tempPoint.y = j*square_size.height;                tempPoint.z = 0;                tempPointSet.push_back(tempPoint);            }        }        object_Points.push_back(tempPointSet);//存储图像序列中每幅图像的像素坐标    }    /* 初始化每幅图像中的角点数量，这里我们假设每幅图像中都可以看到完整的定标板 */    for (int i = 0; i< image_count; i++)    {        point_counts.push_back(board_size.width*board_size.height);    }    /* 开始定标 */    calibrateCamera(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors, 0);    cout << "定标完成！\n";    /************************************************************************    对定标结果进行评价    *************************************************************************/    cout << "开始评价定标结果………………" << endl;    double total_err = 0.0;                   /* 所有图像的平均误差的总和 */    double err = 0.0;                        /* 每幅图像的平均误差 */    vector<Point2f>  image_points2;             /****   保存重新计算得到的投影点    ****/    cout << "每幅图像的定标误差：" << endl;    fout << "每幅图像的定标误差：" << endl << endl;    for (int i = 0; i<image_count; i++)    {        vector<Point3f> tempPointSet = object_Points[i];        /****    通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点     ****/        projectPoints(tempPointSet, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs, image_points2);        /* 计算新的投影点和旧的投影点之间的误差*/        vector<Point2f> tempImagePoint = corners_Seq[i];        Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);        Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);        for (size_t i = 0; i != tempImagePoint.size(); i++)        {            image_points2Mat.at<Vec2f>(0, i) = Vec2f(image_points2[i].x, image_points2[i].y);            tempImagePointMat.at<Vec2f>(0, i) = Vec2f(tempImagePoint[i].x, tempImagePoint[i].y);        }        err = norm(image_points2Mat, tempImagePointMat, NORM_L2);        total_err += err /= point_counts[i];        cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;        fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;    }    cout << "总体平均误差：" << total_err / image_count << "像素" << endl;    fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;    cout << "评价完成！" << endl;    /************************************************************************    保存定标结果    *************************************************************************/    cout << "开始保存定标结果………………" << endl;    Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */    fout << "相机内参数矩阵：" << endl;    cout << "相机内参数矩阵：" << endl;    fout << intrinsic_matrix << endl;    cout << intrinsic_matrix << endl;    fout << "畸变系数：\n";    cout << "畸变系数：\n";    fout << distortion_coeffs << endl;    cout << distortion_coeffs << endl;    for (int i = 0; i<image_count; i++)    {        fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;        fout << rotation_vectors[i] << endl;        /* 将旋转向量转换为相对应的旋转矩阵 */        Rodrigues(rotation_vectors[i], rotation_matrix);        fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;        fout << rotation_matrix << endl;        fout << "第" << i + 1 << "幅图像的平移向量：" << endl;        fout << translation_vectors[i] << endl;    }    cout << "完成保存" << endl;    fout << endl;    /************************************************************************    显示定标结果    *************************************************************************/    Mat mapx = Mat(image_size, CV_32FC1);    Mat mapy = Mat(image_size, CV_32FC1);    Mat R = Mat::eye(3, 3, CV_32F);    cout << "保存矫正图像" << endl;    for (int i = 0; i != image_count; i++)    {        cout << "Frame #" << i + 1 << "..." << endl;        //newCameraMatrix——输入的校正后的3X3摄像机矩阵（也可用cvStereoRectify()得出的3X4的左或右投影矩阵，其实系统会自动提取该        //矩阵前三列的有用部分作为输入参数）注：！！无校正变换的相机仍用求得内参矩阵        Mat newCameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));        //得到映射关系:R——输入的第一和第二相机坐标系之间的旋转矩阵,一般般无校正变换的相机，默认为单位矩阵        // opencv中，remap与undistortion都是消除畸变的函数，undistortion在设置了一些参数后调用了remap函数，二者的算法实质是一样        //的。由目标图像的坐标，找到对应的原始图像坐标，然后将其值复制到目标图像。大致思路是如此，由于图像大小和变换，需要插值或        //近似的方法，如最邻近法、线性插值等        initUndistortRectifyMap(intrinsic_matrix, distortion_coeffs, R, intrinsic_matrix, image_size, CV_32FC1, mapx, mapy);        Mat t = image_Seq[i].clone();        cv::remap(image_Seq[i], t, mapx, mapy, INTER_LINEAR);        string imageFileName;        std::stringstream StrStm;        StrStm << i + 1;        StrStm >> imageFileName;        imageFileName += "_校正图像.jpg";        imwrite("F:\\biaoding\\imageFileName", t);//*********************************把每幅校正后的图像保存到内存F:\biaoding文件中    }    cout << "保存结束" << endl;    time0 = ((double)getTickCount() - time0) / getTickFrequency();    cout << "定标及用畸变参数和内参矩阵矫正所有图像总用时:" << time0 << "秒" << endl;    waitKey(0);}***********************************************************************************************//-----------------------------------OpenCV3标定焊接图像------------------------------------- //  程序名称:OpenCV标定焊接图像，该程序根据DOS提示，一步步实现标定的自动化，求取内参,标定请在F盘创建“biaoding”用于储存采集的图像和结果//  需要设置的参数： 需要提取的的图像数imageCount = 21，每个标定板图像角点数board_size = Size(9, 6)，每个格子像素边长Size square_size = Size(80, 80);  //  过程描述：提取图像并保存，提取各图像序列角点，角点亚像素精确化，摄像机标定畸变参数和内参，定标结果评价（保存结果在F:\\biaoding\\caliberation_result.txt），最后矫正所有图像到F:\\biaoding//  所用IDE版本：        Visual Studio 2013    //  开发所用OpenCV版本：        2.4.9    //  2016年10月 Created by 孙立波    //包含程序所依赖的头文件:为方便起见把经常用的头文件都写在这里（前三必须包含），也可以用#include "opencv.hpp"包含下面所有头文件#include <opencv2\opencv.hpp>   #include <iostream>#include <string> #include <vector>#include <iomanip>#include <fstream>using namespace cv;using namespace std;//隐藏控制台窗口//#pragma comment(linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"")void main(){    /************************************************************************    读取21张图像，存入内存F:\biaoding文件中    *************************************************************************/    cout << "开始提取21张标定板图像………………" << endl;    int imageCount = 21;    int key;    int count1=0;    for (int i=0; i != imageCount; i++)    {        cout << "Frame#" << i + 1 << "..." << endl;        std::stringstream StrStm;        //为了将i转化为字符型，用StrStm做中介        string imageFileName;        StrStm << i + 1;        StrStm >> imageFileName;        imageFileName += ".jpg";        key = waitKey();        cout << "按Enter开始抽取图像，进入后可按q或者ESC键重新抽取图像，若按Enter键表明这帧图像被存入文件夹中" << endl;        if (key == 13)        {            int flag = 1;            while (flag)            {                Mat image0;//抽取到的临时图像数据并显示出来                imshow("显示抓取图像", image0);//显示是否符合要求                int key2 = waitKey();                if (key2 == 13)                {                    cout << "提取标定板图像成功！………………" << endl;                    std::stringstream str;                    str << "F:\\biaoding\\img" << std::setw(2) << std::setfill('0') << i + 1 << ".jpg";                    std::cout<<"提取的图像保存路径及文件名" << str.str() << endl;                    imwrite(str.str(), image0);                    flag = 0;                    count1 += 1;//已经得到的标定图像计数总数                }                else                   if (key2 == 113 || key2 == 27)                        cout << "这次提取的标定板图像不成功！重新提取！！！！………………" << endl;             }         }    }       if (count1 == imageCount)    {        cout << "***********************………………" << endl;        cout << "***********************………………" << endl;        cout << "下面开始标定图像...................." << endl;        count1 = 0;    }    /************************************************************************    读取每一幅图像，从中提取出角点，然后对角点进行亚像素精确化    *************************************************************************/    cout << "开始提取角点………………" << endl;    double time0 = static_cast<double>(getTickCount());//记录定标参数求取和将所有图像矫正用的总时间    ofstream fout("F:\\biaoding\\caliberation_result.txt");  /**    保存定标结果的文件     **/    Size image_size;                          /****    图像的尺寸      ****/    Size board_size = Size(9, 6);             /****    定标板上每行、列的角点数       ****/    vector<Point2f> corners;                  /****    缓存每幅图像上检测到的角点     ****/    vector<vector<Point2f>>  corners_Seq;     /****    保存检测到的所有角点           ****/    vector<Mat>  image_Seq;    int count = 0;    int image_count = imageCount;    for (int i = 0; i != image_count; i++)    {        cout << "Frame #" << i + 1 << "..." << endl;        std::stringstream str;        str << "F:\\biaoding\\img" << std::setw(2) << std::setfill('0') << i + 1 << ".jpg";        std::cout << str.str() << std::endl;        Mat image = cv::imread(str.str());        image_size = image.size();        //image_size = Size(image.cols , image.rows);        /* 提取角点 */        int channel = 3;        Mat imageGray;        if (channel == image.channels() )            cvtColor(image, imageGray, CV_RGB2GRAY);        else            imageGray=image;        bool patternfound = findChessboardCorners(image, board_size, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE +            CALIB_CB_FAST_CHECK);        if (!patternfound)        {            cout << "can not find chessboard corners!\n";            continue;            exit(1);        }        else        {            /*            亚像素精确化 :迭代过程的终止条件可以是最大迭代次数CV_TERMCRIT_ITER类型，或者是设定的精度CV_TERMCRIT_EPS类型(或者是两            者的组合)。终止条件的设置在极大程度上影响最终得到的亚像素值的精度。在此，指定为0.10，则求得的亚像素级精度为像素的十分            之一            */            cornerSubPix(imageGray, corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));            //Size(11, 11)为搜索窗口的一半尺寸，Size(-1, -1)死区的一半尺寸，死区为不对搜索区的中央位置做求和运算的区域。/            //它是用来避免自相关矩阵出现的某些可能的奇异性。当值为（-1，-1）表示没有死区。            //TermCriteria为求角点的迭代过程的终止条件。即角点位置的确定，要么迭代数大于某个设定值，或者是精确懂达到某个设定值。            //criteria可以是最大迭代数目，或者是设定的精确度，也可以是它们的组合。            /* 绘制检测到的角点并保存 */            Mat imageTemp = image.clone();            for (int j = 0; j < corners.size(); j++)            {                circle(imageTemp, corners[j], 10, Scalar(0, 0, 255), 2, 8, 0);            }            string imageFileName1;            std::stringstream StrStm;            StrStm << i + 1;            StrStm >> imageFileName1;            imageFileName1 += "_corner.jpg";            //保存提取角点的图像            imwrite("F:\\biaoding\\imageFileName1", imageTemp);            cout << "Frame corner#" << i + 1 << "...end" << endl;            count = count + corners.size();//记录这一副保存到coners容器中的角点总数            //将该角点压入角点序列堆栈            corners_Seq.push_back(corners);//将所有图像的coners全部压入一个序列的堆栈，即元素为vector容器的堆栈        }        //将处理过的图像压入源图像堆栈        image_Seq.push_back(image);    }    cout << "角点提取完成！下一步摄像机定标\n";    /************************************************************************    摄像机定标    *************************************************************************/    cout << "开始定标………………" << endl;    Size square_size = Size(80, 80);                                      /**** 实际测量得到的定标板上每个棋盘格的大小，单位为像素 ****/    vector<vector<Point3f>>  object_Points;                               /**** 保存定标板上角点的三维坐标   ****/    Mat image_points = Mat(1, count, CV_32FC2, Scalar::all(0));          /***** 保存提取的所有角点1*序列图像的角点总数   *****/    vector<int>  point_counts;                                           /***** 每幅图像中角点的数量 ****/    Mat intrinsic_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0));          /***** 摄像机内参数矩阵    ****/    Mat distortion_coeffs = Mat(1, 4, CV_32FC1, Scalar::all(0));         /* 摄像机的4个畸变系数：k1,k2,p1,p2 */    vector<cv::Mat> rotation_vectors;                                    /* 图像序列图像的旋转向量，每一行代表一个旋转向量 */    vector<cv::Mat> translation_vectors;                                 /* 每幅图像的平移向量，每一行代表一个平移向量 */    /* 初始化定标板上角点的三维坐标，此用的是像素坐标 */    for (int t = 0; t<image_count; t++)    {        vector<Point3f> tempPointSet;//存储每幅图像的像素坐标        for (int i = 0; i<board_size.height; i++)        {            for (int j = 0; j<board_size.width; j++)            {                /* 假设定标板放在世界坐标系中z=0的平面上 */                Point3f tempPoint;                //在这里，board_size中棋盘格子为单位长度，当square_size存储的像素边长为边长为80，代表width、height为80                tempPoint.x = i*square_size.width;                tempPoint.y = j*square_size.height;                tempPoint.z = 0;                tempPointSet.push_back(tempPoint);            }        }        object_Points.push_back(tempPointSet);//存储图像序列中每幅图像的像素坐标    }    /* 初始化每幅图像中的角点数量，这里我们假设每幅图像中都可以看到完整的定标板 */    for (int i = 0; i< image_count; i++)    {        point_counts.push_back(board_size.width*board_size.height);    }    /* 开始定标 */    calibrateCamera(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors, 0);    cout << "定标完成！\n";    /************************************************************************    对定标结果进行评价    *************************************************************************/    cout << "开始评价定标结果………………" << endl;    double total_err = 0.0;                   /* 所有图像的平均误差的总和 */    double err = 0.0;                        /* 每幅图像的平均误差 */    vector<Point2f>  image_points2;             /****   保存重新计算得到的投影点    ****/    cout << "每幅图像的定标误差：" << endl;    fout << "每幅图像的定标误差：" << endl << endl;    for (int i = 0; i<image_count; i++)    {        vector<Point3f> tempPointSet = object_Points[i];        /****    通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点     ****/        projectPoints(tempPointSet, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs, image_points2);        /* 计算新的投影点和旧的投影点之间的误差*/        vector<Point2f> tempImagePoint = corners_Seq[i];        Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);        Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);        for (size_t i = 0; i != tempImagePoint.size(); i++)        {            image_points2Mat.at<Vec2f>(0, i) = Vec2f(image_points2[i].x, image_points2[i].y);            tempImagePointMat.at<Vec2f>(0, i) = Vec2f(tempImagePoint[i].x, tempImagePoint[i].y);        }        err = norm(image_points2Mat, tempImagePointMat, NORM_L2);        total_err += err /= point_counts[i];        cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;        fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;    }    cout << "总体平均误差：" << total_err / image_count << "像素" << endl;    fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;    cout << "评价完成！" << endl;    /************************************************************************    保存定标结果    *************************************************************************/    cout << "开始保存定标结果………………" << endl;    Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */    fout << "相机内参数矩阵：" << endl;    cout << "相机内参数矩阵：" << endl;    fout << intrinsic_matrix << endl;    cout << intrinsic_matrix << endl;    fout << "畸变系数：\n";    cout << "畸变系数：\n";    fout << distortion_coeffs << endl;    cout << distortion_coeffs << endl;    for (int i = 0; i<image_count; i++)    {        fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;        fout << rotation_vectors[i] << endl;        /* 将旋转向量转换为相对应的旋转矩阵 */        Rodrigues(rotation_vectors[i], rotation_matrix);        fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;        fout << rotation_matrix << endl;        fout << "第" << i + 1 << "幅图像的平移向量：" << endl;        fout << translation_vectors[i] << endl;    }    cout << "完成保存" << endl;    fout << endl;    /************************************************************************    显示定标结果    *************************************************************************/    Mat mapx = Mat(image_size, CV_32FC1);    Mat mapy = Mat(image_size, CV_32FC1);    Mat R = Mat::eye(3, 3, CV_32F);    cout << "保存矫正图像" << endl;    for (int i = 0; i != image_count; i++)    {        cout << "Frame #" << i + 1 << "..." << endl;        //newCameraMatrix——输入的校正后的3X3摄像机矩阵（也可用cvStereoRectify()得出的3X4的左或右投影矩阵，其实系统会自动提取该        //矩阵前三列的有用部分作为输入参数）注：！！无校正变换的相机仍用求得内参矩阵        Mat newCameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));        //得到映射关系:R——输入的第一和第二相机坐标系之间的旋转矩阵,一般般无校正变换的相机，默认为单位矩阵        // opencv中，remap与undistortion都是消除畸变的函数，undistortion在设置了一些参数后调用了remap函数，二者的算法实质是一样        //的。由目标图像的坐标，找到对应的原始图像坐标，然后将其值复制到目标图像。大致思路是如此，由于图像大小和变换，需要插值或        //近似的方法，如最邻近法、线性插值等        initUndistortRectifyMap(intrinsic_matrix, distortion_coeffs, R, intrinsic_matrix, image_size, CV_32FC1, mapx, mapy);        Mat t = image_Seq[i].clone();        cv::remap(image_Seq[i], t, mapx, mapy, INTER_LINEAR);        string imageFileName;        std::stringstream StrStm;        StrStm << i + 1;        StrStm >> imageFileName;        imageFileName += "_校正图像.jpg";        imwrite("F:\\biaoding\\imageFileName", t);//*********************************把每幅校正后的图像保存到内存F:\biaoding文件中    }    cout << "保存结束" << endl;    time0 = ((double)getTickCount() - time0) / getTickFrequency();    cout << "定标及用畸变参数和内参矩阵矫正所有图像总用时:" << time0 << "秒" << endl;    waitKey(0);}//-----------------------------------（车牌识别之主要代码）------------------------------------- //**********主要测试程序#include "include/plate_recognize.h"#include "include/util.h"#include "include/features.h"using namespace easypr;int svmMain();int acurayTestMain();//用general数据集测试模型系统模型效果namespace easypr {    int svmTrain(bool dividePrepared = true, bool trainPrepared = true,    svmCallback getFeatures = getHistogramFeatures);}extern const string GENERAL_TEST_PATH = "image/general_test";extern const string NATIVE_TEST_PATH = "image/native_test";////////////////////////////////////////////////////////////const string option[] =     {        "1. 测试;"    ,        "2. 批量测试;"      ,        "3. SVM训练;"     ,        "4. ANN训练;"     ,        "5. GDTS生成;"        ,        "6. 退出;"            ,      };const int optionCount = 6;int main(){    bool isExit = false;    while (isExit != true)    {        stringstream selectOption(stringstream::in | stringstream::out);        selectOption << "EasyPR Option:"<< endl;        for(int i = 0; i < optionCount; i++)        {            selectOption << option[i] << endl;        }        cout << "////////////////////////////////////"<< endl;        cout << selectOption.str();        cout << "////////////////////////////////////"<< endl;        cout << "请选择一项操作:";        int select = -1;        bool isRepeat = true;        while (isRepeat)        {            cin >> select;            isRepeat = false;            switch (select)            {            case 1:                testMain();                break;            case 2:                acurayTestMain();                break;            case 3:                svmMain();                break;            case 4:                // TODO                //annMain();还未实现                break;            case 5:                generate_gdts();                break;            case 6:                isExit = true;                break;            default:                cout << "输入错误，请重新输入:";                isRepeat = true;                break;            }        }    }    return 0;}//****************************************************************************************// 通用正确率测试文件// AcurayTest对应到main控制命令中的选项2#include "../include/plate_recognize.h"#include "../include/util.h"#include "../include/features.h"using namespace easypr;int acurayTest(const string& test_path){    ////获取该路径下的所有文件    vector<string> files;    getFiles(test_path, files);    CPlateLocate lo;    CPlateJudge ju;    CPlateRecognize pr;    pr.LoadANN("model/ann.xml");    pr.LoadSVM("model/svm.xml");    pr.setLifemode(true);    int size = files.size();    //int size = 200;    if (0 == size)    {        cout << "No File Found in general_test/native_test!" << endl;        return 0;    }    cout << "Begin to test the easypr accuracy!" << endl;    // 总的测试图片数量    int count_all = 0;    // 错误的图片数量    int count_err = 0;    // 未识别的图片数量:即无车牌图片数量    int count_norecogin = 0;    // 总的字符差距    float diff_all = 0;    // 平均字符差距    float diff_avg = 0;    // 完全匹配的识别次数    float match_count = 0;    // 完全匹配的识别次数所占识别图片中的比例    float match_rate = 0;    for (int i = 0; i < size; i++)    {        string filepath = files[i].c_str();        cout << "------------------" << endl;        // 获取真实的车牌        string plateLicense = "";        getFileName(filepath, plateLicense);        cout << "原牌:" << plateLicense << endl;        // EasyPR开始判断车牌        Mat src = imread(filepath);        vector<string> plateVec;        int result = pr.plateRecognize(src, plateVec);        if (result == 0)        {            int num = plateVec.size();            if (num == 0)            {                cout << ""<< "无车牌" <<endl;                if (plateLicense != "无车牌")                    count_norecogin++;            }             else if ( num > 1)            {                // 多车牌使用diff最小的那个记录                int mindiff = 10000;                for (int j = 0; j < num; j++)                {                    cout << plateVec[j] << " (" << j+1 << ")"<<endl;                    string colorplate = plateVec[j];                    // 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"                    vector<string> spilt_plate;                    SplitString(colorplate, spilt_plate, ":");                    int size = spilt_plate.size();                    if (size == 2)                    {                        int diff = levenshtein_distance(plateLicense, spilt_plate[size-1]);                        if (diff < mindiff)                            mindiff = diff;                    }                }                //cout << "差距:" << mindiff << "个字符" << endl;                if(mindiff == 0)                {                    // 完全匹配                    match_count++;                }                diff_all = diff_all + mindiff;            }            else            {                // 单车牌只计算一次diff                for (int j = 0; j < num; j++)                {                    cout << plateVec[j] <<endl;                    string colorplate = plateVec[j];                    // 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"                    vector<string> spilt_plate;                    SplitString(colorplate, spilt_plate, ":");                    int size = spilt_plate.size();                    if (size == 2)                    {                        int diff = levenshtein_distance(plateLicense, spilt_plate[size-1]);                        //cout << "差距:" << diff << "个字符" << endl;                        if(diff == 0)                        {                            // 完全匹配                            match_count++;                        }                        diff_all = diff_all + diff;                    }                }            }        }         else        {            cout << "错误码:" << result << endl;            count_err++;        }        count_all++;    }    cout << "------------------" << endl;    cout << "系统批量测试 end!" << endl;    cout << "------------------" << endl;    cout << endl;    cout << "统计数据如下:"  << endl;    cout << "总图片数:" << count_all << "张,  ";    //cout << "未识别图片:" << count_norecogin << "张,  ";    float count_recogin = count_all - (count_err + count_norecogin);    float count_rate  = count_recogin / count_all * 100;    //cout << "识别率:" << count_rate << "%  " << endl;    diff_avg = diff_all / count_recogin;    match_rate = match_count/ count_recogin * 100;    //cout << "平均字距:" << diff_avg << "个,  ";    cout << "完全匹配数:" << match_count << "张,  ";    cout << "完全匹配率:" << match_rate << "%  " << endl;    cout << endl;    cout << "------------------" << endl;    return 0;}//***********************************************************************************************单张图片测试程序#include "../include/plate_locate.h"#include "../include/plate_judge.h"#include "../include/chars_segment.h"#include "../include/chars_identify.h"#include "../include/plate_detect.h"#include "../include/chars_recognise.h"#include "../include/plate_recognize.h"using namespace easypr;int test_plate_locate();int test_plate_judge();int test_chars_segment();int test_chars_identify();int test_plate_detect();int test_chars_recognise();int test_plate_recognize();int testMain();//把你要测试的图片地址写在下面const string test_img = "";const string testOption[] =     {        "1. test plate_locate(车牌定位);"       /* 车牌定位 */,        "2. test plate_judge(车牌判断);"        /* 车牌判断 */,          "3. test plate_detect(车牌检测);"       /* 车牌检测（包含车牌定位与车牌判断） */,         "4. test chars_segment(字符分隔);"      /* 字符分隔 */,         "5. test chars_identify(字符鉴别);"     /* 字符鉴别 */,          "6. test chars_recognise(字符识别);"        /* 字符识别（包含字符分隔与字符鉴别） */,        "7. test plate_recognize(车牌识别);"        /* 车牌识别 */,         "8. test all(测试全部);"        /* 以上全部 */,        "9. 返回;"        /* 退出 */,    };const int testOptionCount = 9;int testMain(){    bool isExit = false;    while (isExit != true)    {        stringstream selectOption(stringstream::in | stringstream::out);        selectOption << "EasyPR Test:"<< endl;        for(int i = 0; i < testOptionCount; i++)        {            selectOption << testOption[i] << endl;        }        cout << "////////////////////////////////////"<< endl;        cout << selectOption.str();        cout << "////////////////////////////////////"<< endl;        cout << "请选择一项操作:";        int select = -1;        bool isRepeat = true;        while (isRepeat)        {            cin >> select;            isRepeat = false;            switch (select)            {            case 1:                assert (test_plate_locate() == 0);                break;            case 2:                assert (test_plate_judge() == 0);                break;            case 3:                assert (test_plate_detect() == 0);                break;            case 4:                assert (test_chars_segment() == 0);                break;            case 5:                assert (test_chars_identify() == 0);                break;            case 6:                assert (test_chars_recognise() == 0);                break;            case 7:                assert (test_plate_recognize() == 0);                break;            case 8:                assert (test_plate_locate() == 0);                assert (test_plate_judge() == 0);                assert (test_plate_detect() == 0);                assert (test_chars_segment() == 0);                assert (test_chars_identify() == 0);                assert (test_chars_recognise() == 0);                assert (test_plate_recognize() == 0);                break;            case 9:                isExit = true;                break;            default:                cout << "输入错误，请重新输入:";                isRepeat = true;                break;            }        }    }    return 0;}int test_plate_locate(){    cout << "test_plate_locate" << endl;    Mat src = imread("image/plate_locate.jpg");    vector<Mat> resultVec;    CPlateLocate plate;    plate.setDebug(1);    plate.setLifemode(true);    int result = plate.plateLocate(src, resultVec);    if (result == 0)    {        int num = resultVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = resultVec[j];            imshow("plate_locate", resultMat);            waitKey(0);        }    }    return result;}int test_plate_judge(){    cout << "test_plate_judge" << endl;    Mat src = imread("image/plate_judge.jpg");    //可能是车牌的图块集合    vector<Mat> matVec;    //经过SVM判断后得到的图块集合    vector<Mat> resultVec;    CPlateLocate lo;    lo.setDebug(1);    lo.setLifemode(true);    int resultLo = lo.plateLocate(src, matVec);    if (0 != resultLo)        return -1;    cout << "plate_locate_img" << endl;    if (resultLo == 0)    {        int num = matVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = matVec[j];            imshow("plate_judge", resultMat);            waitKey(0);        }    }    CPlateJudge ju;    int resultJu = ju.plateJudge(matVec, resultVec);    if (0 != resultJu)        return -1;    cout << "plate_judge_img" << endl;    if (resultJu == 0)    {        int num = resultVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = resultVec[j];            imshow("plate_judge", resultMat);            waitKey(0);        }    }    return resultJu;}int test_chars_segment(){    cout << "test_chars_segment" << endl;    Mat src = imread("image/chars_segment.jpg");    vector<Mat> resultVec;    CCharsSegment plate;    int result = plate.charsSegment(src, resultVec);    if (result == 0)    {        int num = resultVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = resultVec[j];            imshow("chars_segment", resultMat);            waitKey(0);        }    }    return result;}int test_chars_identify(){    cout << "test_chars_identify" << endl;    Mat src = imread("image/chars_identify.jpg");    vector<Mat> resultVec;    CCharsSegment cs;    CCharsIdentify ci;    string plateIdentify = "";    int result = cs.charsSegment(src, resultVec);    if (result == 0)    {        int num = resultVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = resultVec[j];            bool isChinses = false;            //默认首个字符块是中文字符            if (j == 0)                isChinses = true;            string charcater = ci.charsIdentify(resultMat, isChinses);            plateIdentify = plateIdentify + charcater;        }    }    const string plateLicense = "苏E771H6";    cout << "plateLicense: " << plateLicense << endl;    cout << "plateIdentify: " << plateIdentify << endl;    if (plateLicense != plateIdentify)    {        cout << "Identify Not Correct!" << endl;        return -1;    }    cout << "Identify Correct!" << endl;    cout << "Enter 1 for coninue:";    int a = 0;    cin >> a;    return result;}int test_plate_detect(){    cout << "test_plate_detect" << endl;    //Mat src = imread("image/plate_detect.jpg");    Mat src = imread("image/baidu_image/test1.jpg");    vector<Mat> resultVec;    CPlateDetect pd;    pd.setPDLifemode(true);    int result = pd.plateDetect(src, resultVec);    if (result == 0)    {        int num = resultVec.size();        for (int j = 0; j < num; j++)        {            Mat resultMat = resultVec[j];            imshow("plate_detect", resultMat);            waitKey(0);        }    }    return result;}int test_chars_recognise(){    cout << "test_chars_recognise" << endl;    Mat src = imread("image/chars_recognise.jpg");    CCharsRecognise cr;    string charsRecognise = "";    int result = cr.charsRecognise(src, charsRecognise);    if (result == 0)    {        cout << "charsRecognise: " << charsRecognise << endl;    }    cout << "Enter 1 for coninue:";    int a = 0;    cin >> a;    return result;}int test_plate_recognize(){    cout << "test_plate_recognize" << endl;    //Mat src = imread("image/plate_locate.jpg");    Mat src = imread("image/test.jpg");    CPlateRecognize pr;    pr.LoadANN("model/ann.xml");    pr.LoadSVM("model/svm.xml");    pr.setLifemode(true);    pr.setDebug(true);    vector<string> plateVec;    int result = pr.plateRecognize(src, plateVec);    if (result == 0)    {        int num = plateVec.size();        for (int j = 0; j < num; j++)        {            cout << "plateRecognize: " << plateVec[j] << endl;                  }    }    if (result != 0)        cout << "result:" << result << endl;    cout << "Enter 1 for coninue:";    int a = 0;    cin >> a;    return result;}