官方引例——色彩目标跟踪

简介

OpenCV官方提供了多种接口的示例文件，本文在D:\Program Files (x86)\OpenCV249\opencv\sources\samples路径下，对应自己的电脑类似，有android、gpu、MacOSX等：

居然还有Python：

物体颜色追踪

在D:\Program Files (x86)\OpenCV249\opencv\sources\samples路径下有camshiftdemo.cpp。CamShift:Continuously Adaptive Mean-Shifts是对MeanShift算法的改进

#include "opencv2/video/tracking.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>#include <ctype.h>using namespace cv;using namespace std;Mat image;bool backprojMode = false;bool selectObject = false;int trackObject = 0;bool showHist = true;Point origin;Rect selection;int vmin = 10, vmax = 256, smin = 30;//--------------------------------【onMouse( )回调函数】------------------------------------//      描述：鼠标操作回调//-------------------------------------------------------------------------------------------------static void onMouse(int event, int x, int y, int, void*){    if (selectObject)    {        selection.x = MIN(x, origin.x);        selection.y = MIN(y, origin.y);        selection.width = std::abs(x - origin.x);        selection.height = std::abs(y - origin.y);        selection &= Rect(0, 0, image.cols, image.rows);    }    switch (event)    {    case CV_EVENT_LBUTTONDOWN:        origin = Point(x, y);        selection = Rect(x, y, 0, 0);        selectObject = true;        break;    case CV_EVENT_LBUTTONUP:        selectObject = false;        if (selection.width > 0 && selection.height > 0)            trackObject = -1;        break;    }}//--------------------------------【help( )函数】----------------------------------------------//      描述：输出帮助信息//-------------------------------------------------------------------------------------------------static void help(){    cout << "\nThis is a demo that shows mean-shift based tracking\n"        "You select a color objects such as your face and it tracks it.\n"        "This reads from video camera (0 by default, or the camera number the user enters\n"        "Usage: \n"        "   ./camshiftdemo [camera number]\n";    cout << "\n\nHot keys: \n"        "\tESC - quit the program\n"        "\tc - stop the tracking\n"        "\tb - switch to/from backprojection view\n"        "\th - show/hide object histogram\n"        "\tp - pause video\n"        "To initialize tracking, select the object with mouse\n";}const char* keys ={    "{1|  | 0 | camera number}"};int main(int argc, const char** argv){    help();    VideoCapture cap;    Rect trackWindow;    int hsize = 16;    float hranges[] = { 0, 180 };    const float* phranges = hranges;    CommandLineParser parser(argc, argv, keys);    int camNum = parser.get<int>("1");    cap.open(camNum);    if (!cap.isOpened())    {        help();        cout << "***Could not initialize capturing...***\n";        cout << "Current parameter's value: \n";        parser.printParams();        return -1;    }    namedWindow("Histogram", 0);    namedWindow("CamShift Demo", 0);    setMouseCallback("CamShift Demo", onMouse, 0);    createTrackbar("Vmin", "CamShift Demo", &vmin, 256, 0);    createTrackbar("Vmax", "CamShift Demo", &vmax, 256, 0);    createTrackbar("Smin", "CamShift Demo", &smin, 256, 0);    Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj;    bool paused = false;    for (;;)    {        if (!paused)        {            cap >> frame;            if (frame.empty())                break;        }        frame.copyTo(image);        if (!paused)        {            cvtColor(image, hsv, COLOR_BGR2HSV);            if (trackObject)            {                int _vmin = vmin, _vmax = vmax;                inRange(hsv, Scalar(0, smin, MIN(_vmin, _vmax)),                    Scalar(180, 256, MAX(_vmin, _vmax)), mask);                int ch[] = { 0, 0 };                hue.create(hsv.size(), hsv.depth());                mixChannels(&hsv, 1, &hue, 1, ch, 1);                if (trackObject < 0)                {                    Mat roi(hue, selection), maskroi(mask, selection);                    calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);                    normalize(hist, hist, 0, 255, CV_MINMAX);                    trackWindow = selection;                    trackObject = 1;                    histimg = Scalar::all(0);                    int binW = histimg.cols / hsize;                    Mat buf(1, hsize, CV_8UC3);                    for (int i = 0; i < hsize; i++)                        buf.at<Vec3b>(i) = Vec3b(saturate_cast<uchar>(i*180. / hsize), 255, 255);                    cvtColor(buf, buf, CV_HSV2BGR);                    for (int i = 0; i < hsize; i++)                    {                        int val = saturate_cast<int>(hist.at<float>(i)*histimg.rows / 255);                        rectangle(histimg, Point(i*binW, histimg.rows),                            Point((i + 1)*binW, histimg.rows - val),                            Scalar(buf.at<Vec3b>(i)), -1, 8);                    }                }                calcBackProject(&hue, 1, 0, hist, backproj, &phranges);                backproj &= mask;                RotatedRect trackBox = CamShift(backproj, trackWindow,                    TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1));                if (trackWindow.area() <= 1)                {                    int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5) / 6;                    trackWindow = Rect(trackWindow.x - r, trackWindow.y - r,                        trackWindow.x + r, trackWindow.y + r) &                        Rect(0, 0, cols, rows);                }                if (backprojMode)                    cvtColor(backproj, image, COLOR_GRAY2BGR);                ellipse(image, trackBox, Scalar(0, 0, 255), 3, CV_AA);            }        }        else if (trackObject < 0)            paused = false;        if (selectObject && selection.width > 0 && selection.height > 0)        {            Mat roi(image, selection);            bitwise_not(roi, roi);        }        imshow("CamShift Demo", image);        imshow("Histogram", histimg);        char c = (char)waitKey(10);        if (c == 27)            break;        switch (c)        {        case 'b':            backprojMode = !backprojMode;            break;        case 'c':            trackObject = 0;            histimg = Scalar::all(0);            break;        case 'h':            showHist = !showHist;            if (!showHist)                destroyWindow("Histogram");            else                namedWindow("Histogram", 1);            break;        case 'p':            paused = !paused;            break;        default:            ;        }    }    return 0;}

光流Optical flow

光流Optical flow算法是运动图像分析的重要方法，因为当物体运动时，在图像对应点的亮度模式也在运动.

//---------------------------------【头文件、命名空间包含部分】----------------------------//      描述：包含程序所使用的头文件和命名空间//-------------------------------------------------------------------------------------------------#include <opencv2/video/video.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/core/core.hpp>#include <iostream>#include <cstdio>using namespace std;using namespace cv;//-----------------------------------【全局函数声明】-----------------------------------------//      描述：声明全局函数//-------------------------------------------------------------------------------------------------void tracking(Mat &frame, Mat &output);bool addNewPoints();bool acceptTrackedPoint(int i);//-----------------------------------【全局变量声明】-----------------------------------------//      描述：声明全局变量//-------------------------------------------------------------------------------------------------string window_name = "optical flow tracking";Mat gray;   // 当前图片Mat gray_prev;  // 预测图片vector<Point2f> points[2];  // point0为特征点的原来位置，point1为特征点的新位置vector<Point2f> initial;    // 初始化跟踪点的位置vector<Point2f> features;   // 检测的特征int maxCount = 500; // 检测的最大特征数double qLevel = 0.01;   // 特征检测的等级double minDist = 10.0;  // 两特征点之间的最小距离vector<uchar> status;   // 跟踪特征的状态，特征的流发现为1，否则为0vector<float> err;//--------------------------------【help( )函数】----------------------------------------------//      描述：输出帮助信息//-------------------------------------------------------------------------------------------------static void help(){    //输出欢迎信息和OpenCV版本    cout << "\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"        << "\n\n\t\t\t此为本书OpenCV2版的第9个配套示例程序\n"        << "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION        << "\n\n  ----------------------------------------------------------------------------";}//-----------------------------------【main( )函数】--------------------------------------------//      描述：控制台应用程序的入口函数，我们的程序从这里开始//-------------------------------------------------------------------------------------------------int main(){    Mat frame;    Mat result;    VideoCapture capture("1.avi");    help();    if (capture.isOpened()) // 摄像头读取文件开关    {        while (true)        {            capture >> frame;            if (!frame.empty())            {                tracking(frame, result);            }            else            {                printf(" --(!) No captured frame -- Break!");                break;            }            int c = waitKey(50);            if ((char)c == 27)            {                break;            }        }    }    return 0;}//-------------------------------------------------------------------------------------------------// function: tracking// brief: 跟踪// parameter: frame 输入的视频帧//            output 有跟踪结果的视频帧// return: void//-------------------------------------------------------------------------------------------------void tracking(Mat &frame, Mat &output){    cvtColor(frame, gray, CV_BGR2GRAY);    frame.copyTo(output);    // 添加特征点    if (addNewPoints())    {        goodFeaturesToTrack(gray, features, maxCount, qLevel, minDist);        points[0].insert(points[0].end(), features.begin(), features.end());        initial.insert(initial.end(), features.begin(), features.end());    }    if (gray_prev.empty())    {        gray.copyTo(gray_prev);    }    // l-k光流法运动估计    calcOpticalFlowPyrLK(gray_prev, gray, points[0], points[1], status, err);    // 去掉一些不好的特征点    int k = 0;    for (size_t i = 0; i<points[1].size(); i++)    {        if (acceptTrackedPoint(i))        {            initial[k] = initial[i];            points[1][k++] = points[1][i];        }    }    points[1].resize(k);    initial.resize(k);    // 显示特征点和运动轨迹    for (size_t i = 0; i<points[1].size(); i++)    {        line(output, initial[i], points[1][i], Scalar(0, 0, 255));        circle(output, points[1][i], 3, Scalar(0, 255, 0), -1);    }    // 把当前跟踪结果作为下一此参考    swap(points[1], points[0]);    swap(gray_prev, gray);    imshow(window_name, output);}//-------------------------------------------------------------------------------------------------// function: addNewPoints// brief: 检测新点是否应该被添加// parameter:// return: 是否被添加标志//-------------------------------------------------------------------------------------------------bool addNewPoints(){    return points[0].size() <= 10;}//-------------------------------------------------------------------------------------------------// function: acceptTrackedPoint// brief: 决定哪些跟踪点被接受// parameter:// return://-------------------------------------------------------------------------------------------------bool acceptTrackedPoint(int i){    return status[i] && ((abs(points[0][i].x - points[1][i].x) + abs(points[0][i].y - points[1][i].y)) > 2);}

点追踪：lkdemo

在D:\Program Files (x86)\OpenCV249\opencv\sources\samples路径下有lkdemo.cpp。程序启动后，按“r”键来启动自动点追踪，移动物体，点会跟着运动。

//---------------------------------【头文件、命名空间包含部分】----------------------------//      描述：包含程序所使用的头文件和命名空间//-------------------------------------------------------------------------------------------------#include "opencv2/video/tracking.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>#include <ctype.h>using namespace cv;using namespace std;//--------------------------------【help( )函数】----------------------------------------------//      描述：输出帮助信息//-------------------------------------------------------------------------------------------------static void help(){    //输出欢迎信息和OpenCV版本    cout << "\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"        << "\n\n\t\t\t此为本书OpenCV2版的第10个配套示例程序\n"        << "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION        << "\n\n  ----------------------------------------------------------------------------";    cout << "\n\n\t该Demo演示了 Lukas-Kanade基于光流的lkdemo\n";    cout << "\n\t程序默认从摄像头读入视频，可以按需改为从视频文件读入图像\n";    cout << "\n\t操作说明: \n"        "\t\t通过点击在图像中添加/删除特征点\n"        "\t\tESC - 退出程序\n"        "\t\tr -自动进行追踪\n"        "\t\tc - 删除所有点\n"        "\t\tn - 开/光-夜晚模式\n" << endl;}Point2f point;bool addRemovePt = false;//--------------------------------【onMouse( )回调函数】------------------------------------//      描述：鼠标操作回调//-------------------------------------------------------------------------------------------------static void onMouse(int event, int x, int y, int /*flags*/, void* /*param*/){    if (event == CV_EVENT_LBUTTONDOWN)    {        point = Point2f((float)x, (float)y);        addRemovePt = true;    }}//-----------------------------------【main( )函数】--------------------------------------------//      描述：控制台应用程序的入口函数，我们的程序从这里开始//-------------------------------------------------------------------------------------------------int main(int argc, char** argv){    help();    VideoCapture cap;    TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);    Size subPixWinSize(10, 10), winSize(31, 31);    const int MAX_COUNT = 500;    bool needToInit = false;    bool nightMode = false;    cap.open(0);    if (!cap.isOpened())    {        cout << "Could not initialize capturing...\n";        return 0;    }    namedWindow("LK Demo", 1);    setMouseCallback("LK Demo", onMouse, 0);    Mat gray, prevGray, image;    vector<Point2f> points[2];    for (;;)    {        Mat frame;        cap >> frame;        if (frame.empty())            break;        frame.copyTo(image);        cvtColor(image, gray, COLOR_BGR2GRAY);        if (nightMode)            image = Scalar::all(0);        if (needToInit)        {            // 自动初始化            goodFeaturesToTrack(gray, points[1], MAX_COUNT, 0.01, 10, Mat(), 3, 0, 0.04);            cornerSubPix(gray, points[1], subPixWinSize, Size(-1, -1), termcrit);            addRemovePt = false;        }        else if (!points[0].empty())        {            vector<uchar> status;            vector<float> err;            if (prevGray.empty())                gray.copyTo(prevGray);            calcOpticalFlowPyrLK(prevGray, gray, points[0], points[1], status, err, winSize,                3, termcrit, 0, 0.001);            size_t i, k;            for (i = k = 0; i < points[1].size(); i++)            {                if (addRemovePt)                {                    if (norm(point - points[1][i]) <= 5)                    {                        addRemovePt = false;                        continue;                    }                }                if (!status[i])                    continue;                points[1][k++] = points[1][i];                circle(image, points[1][i], 3, Scalar(0, 255, 0), -1, 8);            }            points[1].resize(k);        }        if (addRemovePt && points[1].size() < (size_t)MAX_COUNT)        {            vector<Point2f> tmp;            tmp.push_back(point);            cornerSubPix(gray, tmp, winSize, cvSize(-1, -1), termcrit);            points[1].push_back(tmp[0]);            addRemovePt = false;        }        needToInit = false;        imshow("LK Demo", image);        char c = (char)waitKey(10);        if (c == 27)            break;        switch (c)        {        case 'r':            needToInit = true;            break;        case 'c':            points[0].clear();            points[1].clear();            break;        case 'n':            nightMode = !nightMode;            break;        }        std::swap(points[1], points[0]);        cv::swap(prevGray, gray);    }    return 0;}

人脸识别

此示例位于D:\Program Files (x86)\OpenCV249\opencv\sources\samples\cpp\tutorial_code\objectDetection中的objectDetection.cpp和objectDetection2.cpp.需要额外主意的是，要将D:\Program Files (x86)\OpenCV249\opencv\sources\data\haarcascades下的haarcascade_eye_tree_eyeglasses.xml、haarcascade_frontalface_alt.xml放在源文件CPP同一文件夹里才能够运行。

/*** @file ObjectDetection.cpp* @author A. Huaman ( based in the classic facedetect.cpp in samples/c )* @brief A simplified version of facedetect.cpp, show how to load a cascade classifier and how to find objects (Face + eyes) in a video stream*///---------------------------------【头文件、命名空间包含部分】----------------------------//      描述：包含程序所使用的头文件和命名空间//-------------------------------------------------------------------------------------------------#include "opencv2/objdetect/objdetect.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>using namespace std;using namespace cv;void detectAndDisplay(Mat frame);//--------------------------------【全局变量声明】----------------------------------------------//      描述：声明全局变量//-------------------------------------------------------------------------------------------------//注意，需要把"haarcascade_frontalface_alt.xml"和"haarcascade_eye_tree_eyeglasses.xml"这两个文件复制到工程路径下String face_cascade_name = "haarcascade_frontalface_alt.xml";String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";CascadeClassifier face_cascade;CascadeClassifier eyes_cascade;string window_name = "Capture - Face detection";RNG rng(12345);//--------------------------------【help( )函数】----------------------------------------------//      描述：输出帮助信息//-------------------------------------------------------------------------------------------------static void ShowHelpText(){    //输出欢迎信息和OpenCV版本    cout << "\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"        << "\n\n\t\t\t此为本书OpenCV2版的第11个配套示例程序\n"        << "\n\n\t\t\t   当前使用的OpenCV版本为：" << CV_VERSION        << "\n\n  ----------------------------------------------------------------------------";}//-----------------------------------【main( )函数】--------------------------------------------//      描述：控制台应用程序的入口函数，我们的程序从这里开始//-------------------------------------------------------------------------------------------------int main(void){    VideoCapture capture;    Mat frame;    //-- 1. 加载级联（cascades）    if (!face_cascade.load(face_cascade_name)){ printf("--(!)Error loading\n"); return -1; };    if (!eyes_cascade.load(eyes_cascade_name)){ printf("--(!)Error loading\n"); return -1; };    //-- 2. 读取视频    capture.open(0);    ShowHelpText();    if (capture.isOpened())    {        for (;;)        {            capture >> frame;            //-- 3. 对当前帧使用分类器（Apply the classifier to the frame）            if (!frame.empty())            {                detectAndDisplay(frame);            }            else            {                printf(" --(!) No captured frame -- Break!"); break;            }            int c = waitKey(1);            if ((char)c == 'c') { break; }        }    }    return 0;}void detectAndDisplay(Mat frame){    std::vector<Rect> faces;    Mat frame_gray;    cvtColor(frame, frame_gray, COLOR_BGR2GRAY);    equalizeHist(frame_gray, frame_gray);    //-- 人脸检测    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));    for (size_t i = 0; i < faces.size(); i++)    {        Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2);        ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 2, 8, 0);        Mat faceROI = frame_gray(faces[i]);        std::vector<Rect> eyes;        //-- 在脸中检测眼睛        eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));        for (size_t j = 0; j < eyes.size(); j++)        {            Point eye_center(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2);            int radius = cvRound((eyes[j].width + eyes[j].height)*0.25);            circle(frame, eye_center, radius, Scalar(255, 0, 0), 3, 8, 0);        }    }    //-- 显示最终效果图    imshow(window_name, frame);}

官方提供的objectDetection2.cpp，需要将D:\Program Files (x86)\OpenCV249\opencv\sources\data\lbpcascades下的lbpcascade_frontalface.xml放在源文件中。

/*** @file objectDetection2.cpp* @author A. Huaman ( based in the classic facedetect.cpp in samples/c )* @brief A simplified version of facedetect.cpp, show how to load a cascade classifier and how to find objects (Face + eyes) in a video stream - Using LBP here*/#include "opencv2/objdetect/objdetect.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>using namespace std;using namespace cv;/** Function Headers */void detectAndDisplay(Mat frame);/** Global variables */String face_cascade_name = "lbpcascade_frontalface.xml";String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";CascadeClassifier face_cascade;CascadeClassifier eyes_cascade;string window_name = "Capture - Face detection";RNG rng(12345);/*** @function main*/int main(void){    VideoCapture capture;    Mat frame;    //-- 1. Load the cascade    if (!face_cascade.load(face_cascade_name)){ printf("--(!)Error loading\n"); return -1; };    if (!eyes_cascade.load(eyes_cascade_name)){ printf("--(!)Error loading\n"); return -1; };    //-- 2. Read the video stream    capture.open(0);    if (capture.isOpened())    {        for (;;)        {            capture >> frame;            //-- 3. Apply the classifier to the frame            if (!frame.empty())            {                detectAndDisplay(frame);            }            else            {                printf(" --(!) No captured frame -- Break!"); break;            }            int c = waitKey(10);            if ((char)c == 'c') { break; }        }    }    return 0;}/*** @function detectAndDisplay*/void detectAndDisplay(Mat frame){    std::vector<Rect> faces;    Mat frame_gray;    cvtColor(frame, frame_gray, COLOR_BGR2GRAY);    equalizeHist(frame_gray, frame_gray);    //-- Detect faces    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0, Size(80, 80));    for (size_t i = 0; i < faces.size(); i++)    {        Mat faceROI = frame_gray(faces[i]);        std::vector<Rect> eyes;        //-- In each face, detect eyes        eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(30, 30));        if (eyes.size() == 2)        {            //-- Draw the face            Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2);            ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 0), 2, 8, 0);            for (size_t j = 0; j < eyes.size(); j++)            { //-- Draw the eyes                Point eye_center(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2);                int radius = cvRound((eyes[j].width + eyes[j].height)*0.25);                circle(frame, eye_center, radius, Scalar(255, 0, 255), 3, 8, 0);            }        }    }    //-- Show what you got    imshow(window_name, frame);}

支持向量机之SVM引导

线性可分时，opencv249版本

//---------------------------------【头文件、命名空间包含部分】----------------------------//      描述：包含程序所使用的头文件和命名空间//-------------------------------------------------------------------------------------------------#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/ml/ml.hpp>using namespace cv;//--------------------------------【help( )函数】----------------------------------------------//      描述：输出帮助信息//-------------------------------------------------------------------------------------------------//-----------------------------------【ShowHelpText( )函数】----------------------------------//          描述：输出一些帮助信息//----------------------------------------------------------------------------------------------void ShowHelpText(){    //输出欢迎信息和OpenCV版本    printf("\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n");    printf("\n\n\t\t\t此为本书OpenCV2版的第12个配套示例程序\n");    printf("\n\n\t\t\t   当前使用的OpenCV版本为：" CV_VERSION);    printf("\n\n  ----------------------------------------------------------------------------\n");}//-----------------------------------【main( )函数】--------------------------------------------//      描述：控制台应用程序的入口函数，我们的程序从这里开始//-------------------------------------------------------------------------------------------------int main(){    // 视觉表达数据的设置（Data for visual representation）    int width = 512, height = 512;    Mat image = Mat::zeros(height, width, CV_8UC3);    //建立训练数据（ Set up training data）    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);    ShowHelpText();    //设置支持向量机的参数（Set up SVM's parameters）    CvSVMParams params;    params.svm_type = CvSVM::C_SVC;    params.kernel_type = CvSVM::LINEAR;    params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);    // 训练支持向量机（Train the SVM）    CvSVM SVM;    SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);    Vec3b green(0, 255, 0), blue(255, 0, 0);    //显示由SVM给出的决定区域 （Show the decision regions given by the SVM）    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 (response == 1)                image.at<Vec3b>(j, i) = green;            else if (response == -1)                image.at<Vec3b>(j, i) = blue;        }    //显示训练数据 （Show the training data）    int thickness = -1;    int lineType = 8;    circle(image, Point(501, 10), 5, Scalar(0, 0, 0), thickness, lineType);    circle(image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);    circle(image, Point(10, 501), 5, Scalar(255, 255, 255), thickness, lineType);    //显示支持向量 （Show support vectors）    thickness = 2;    lineType = 8;    int c = SVM.get_support_vector_count();    for (int i = 0; i < c; ++i)    {        const float* v = SVM.get_support_vector(i);        circle(image, Point((int)v[0], (int)v[1]), 6, Scalar(128, 128, 128), thickness, lineType);    }    imwrite("result.png", image);        // 保存图像    imshow("SVM Simple Example", image); // 显示图像    waitKey(0);}

线性可分时，opencv300版本
参考OpenCv3.0+SVM的使用心得（一） - u010869312的专栏 - CSDN博客
http://blog.csdn.net/u010869312/article/details/44927721代码设计