OpenCV官方例程引导与赏识-2

　　在安装好的OpenCV的文件夹下，有相关的文件。具体位置看各人的安装路径，但大体上路径如下：***opencvsourcessamplescpp。

　　如“彩色目标跟踪”：Camshift

　　“光流”：optical flow

　　“点跟踪”：lkdemo

　　“人脸识别”：objectDetection

　　“支持向量机引导”：CvSVM::train

　　

　　在上一级目录中可以发现，除了CPP之外，还有其他语言的，如java，Python等。

　　

 2.1  camshiftdemo

#include "opencv2/core/utility.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/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;

// User draws box around object to track. This triggers CAMShift to start tracking
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 EVENT_LBUTTONDOWN:
        origin = Point(x, y);
        selection = Rect(x, y, 0, 0);
        selectObject = true;
        break;
    case EVENT_LBUTTONUP:
        selectObject = false;
        if (selection.width > 0 && selection.height > 0)
            trackObject = -1;   // Set up CAMShift properties in main() loop
        break;
    }
}

string hot_keys =
"

Hot keys: 
"
"	ESC - quit the program
"
"	c - stop the tracking
"
"	b - switch to/from backprojection view
"
"	h - show/hide object histogram
"
"	p - pause video
"
"To initialize tracking, select the object with mouse
";

static void help(const char** argv)
{
    cout << "
This is a demo that shows mean-shift based tracking
"
        "You select a color objects such as your face and it tracks it.
"
        "This reads from video camera (0 by default, or the camera number the user enters
"
        "Usage: 
	";
    cout << argv[0] << " [camera number]
";
    cout << hot_keys;
}

const char* keys =
{
    "{help h | | show help message}{@camera_number| 0 | camera number}"
};

int main(int argc, const char** argv)
{
    VideoCapture cap;
    Rect trackWindow;
    int hsize = 16;
    float hranges[] = { 0,180 };
    const float* phranges = hranges;
    CommandLineParser parser(argc, argv, keys);
    if (parser.has("help"))
    {
        help(argv);
        return 0;
    }
    int camNum = parser.get<int>(0);
    cap.open(camNum);

    if (!cap.isOpened())
    {
        help(argv);
        cout << "***Could not initialize capturing...***
";
        cout << "Current parameter's value: 
";
        parser.printMessage();
        return -1;
    }
    cout << hot_keys;
    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)
                {
                    // Object has been selected by user, set up CAMShift search properties once
                    Mat roi(hue, selection), maskroi(mask, selection);
                    calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);
                    normalize(hist, hist, 0, 255, NORM_MINMAX);

                    trackWindow = selection;
                    trackObject = 1; // Don't set up again, unless user selects new ROI

                    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, COLOR_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);
                    }
                }

                // Perform CAMShift
                calcBackProject(&hue, 1, 0, hist, backproj, &phranges);
                backproj &= mask;
                RotatedRect trackBox = CamShift(backproj, trackWindow,
                    TermCriteria(TermCriteria::EPS | TermCriteria::COUNT, 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, LINE_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;
}

运行示例：

 1.2  opticalFlow

//---------------------------------【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0
vector<float> err;


//-----------------------------------【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");

    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)
{

    //此句代码的OpenCV3版为：
    cvtColor(frame, gray, COLOR_BGR2GRAY);
    //此句代码的OpenCV2版为：
    //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);
}

运行示例

2.3 lkdemo

#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"

#include <iostream>
#include <ctype.h>

using namespace cv;
using namespace std;

static void help()
{
    // print a welcome message, and the OpenCV version
    cout << "
This is a demo of Lukas-Kanade optical flow lkdemo(),
"
        "Using OpenCV version " << CV_VERSION << endl;
    cout << "
It uses camera by default, but you can provide a path to video as an argument.
";
    cout << "
Hot keys: 
"
        "	ESC - quit the program
"
        "	r - auto-initialize tracking
"
        "	c - delete all the points
"
        "	n - switch the "night" mode on/off
"
        "To add/remove a feature point click it
" << endl;
}

Point2f point;
bool addRemovePt = false;

static void onMouse(int event, int x, int y, int /*flags*/, void* /*param*/)
{
    if (event == EVENT_LBUTTONDOWN)
    {
        point = Point2f((float)x, (float)y);
        addRemovePt = true;
    }
}

int main(int argc, char** argv)
{
    VideoCapture cap;
    TermCriteria termcrit(TermCriteria::COUNT | TermCriteria::EPS, 20, 0.03);
    Size subPixWinSize(10, 10), winSize(31, 31);

    const int MAX_COUNT = 500;
    bool needToInit = false;
    bool nightMode = false;

    help();
    cv::CommandLineParser parser(argc, argv, "{@input|0|}");
    string input = parser.get<string>("@input");

    if (input.size() == 1 && isdigit(input[0]))
        cap.open(input[0] - '0');
    else
        cap.open(input);

    if (!cap.isOpened())
    {
        cout << "Could not initialize capturing...
";
        return 0;
    }

    namedWindow("LK Demo", 1);
    setMouseCallback("LK Demo", onMouse, 0);

    Mat gray, prevGray, image, frame;
    vector<Point2f> points[2];

    for (;;)
    {
        cap >> frame;
        if (frame.empty())
            break;

        frame.copyTo(image);
        cvtColor(image, gray, COLOR_BGR2GRAY);

        if (nightMode)
            image = Scalar::all(0);

        if (needToInit)
        {
            // automatic initialization
            goodFeaturesToTrack(gray, points[1], MAX_COUNT, 0.01, 10, Mat(), 3, 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, Size(-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;
}

运行示例

 2.4 ObjectDetection

 //---------------------------------【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);


//-----------------------------------【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
"); return -1; };
    if (!eyes_cascade.load(eyes_cascade_name)) { printf("--(!)Error loading
"); return -1; };

    //-- 2. 读取视频
    capture.open(0);
   
    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(10);
            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
    //此句代码的OpenCV2版为：
   //face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
    //此句代码的OpenCV3版为：
    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_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
        //此句代码的OpenCV2版为：
       // eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
        //此句代码的OpenCV3版为：
        eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CASCADE_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);
}

运行示例：（已打码！）

 2.5  train_svmgd

#include "opencv2/core.hpp"
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/ml.hpp"

using namespace cv;
using namespace cv::ml;


struct Data
{
    Mat img;
    Mat samples;          //Set of train samples. Contains points on image
    Mat responses;        //Set of responses for train samples

    Data()
    {
        const int WIDTH = 841;
        const int HEIGHT = 594;
        img = Mat::zeros(HEIGHT, WIDTH, CV_8UC3);
        imshow("Train svmsgd", img);
    }
};

//Train with SVMSGD algorithm
//(samples, responses) is a train set
//weights is a required vector for decision function of SVMSGD algorithm
bool doTrain(const Mat samples, const Mat responses, Mat& weights, float& shift);

//function finds two points for drawing line (wx = 0)
bool findPointsForLine(const Mat& weights, float shift, Point points[], int width, int height);

// function finds cross point of line (wx = 0) and segment ( (y = HEIGHT, 0 <= x <= WIDTH) or (x = WIDTH, 0 <= y <= HEIGHT) )
bool findCrossPointWithBorders(const Mat& weights, float shift, const std::pair<Point, Point>& segment, Point& crossPoint);

//segments' initialization ( (y = HEIGHT, 0 <= x <= WIDTH) and (x = WIDTH, 0 <= y <= HEIGHT) )
void fillSegments(std::vector<std::pair<Point, Point> >& segments, int width, int height);

//redraw points' set and line (wx = 0)
void redraw(Data data, const Point points[2]);

//add point in train set, train SVMSGD algorithm and draw results on image
void addPointRetrainAndRedraw(Data& data, int x, int y, int response);


bool doTrain(const Mat samples, const Mat responses, Mat& weights, float& shift)
{
    cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();

    cv::Ptr<TrainData> trainData = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
    svmsgd->train(trainData);

    if (svmsgd->isTrained())
    {
        weights = svmsgd->getWeights();
        shift = svmsgd->getShift();

        return true;
    }
    return false;
}

void fillSegments(std::vector<std::pair<Point, Point> >& segments, int width, int height)
{
    std::pair<Point, Point> currentSegment;

    currentSegment.first = Point(width, 0);
    currentSegment.second = Point(width, height);
    segments.push_back(currentSegment);

    currentSegment.first = Point(0, height);
    currentSegment.second = Point(width, height);
    segments.push_back(currentSegment);

    currentSegment.first = Point(0, 0);
    currentSegment.second = Point(width, 0);
    segments.push_back(currentSegment);

    currentSegment.first = Point(0, 0);
    currentSegment.second = Point(0, height);
    segments.push_back(currentSegment);
}


bool findCrossPointWithBorders(const Mat& weights, float shift, const std::pair<Point, Point>& segment, Point& crossPoint)
{
    int x = 0;
    int y = 0;
    int xMin = std::min(segment.first.x, segment.second.x);
    int xMax = std::max(segment.first.x, segment.second.x);
    int yMin = std::min(segment.first.y, segment.second.y);
    int yMax = std::max(segment.first.y, segment.second.y);

    CV_Assert(weights.type() == CV_32FC1);
    CV_Assert(xMin == xMax || yMin == yMax);

    if (xMin == xMax && weights.at<float>(1) != 0)
    {
        x = xMin;
        y = static_cast<int>(std::floor(-(weights.at<float>(0) * x + shift) / weights.at<float>(1)));
        if (y >= yMin && y <= yMax)
        {
            crossPoint.x = x;
            crossPoint.y = y;
            return true;
        }
    }
    else if (yMin == yMax && weights.at<float>(0) != 0)
    {
        y = yMin;
        x = static_cast<int>(std::floor(-(weights.at<float>(1) * y + shift) / weights.at<float>(0)));
        if (x >= xMin && x <= xMax)
        {
            crossPoint.x = x;
            crossPoint.y = y;
            return true;
        }
    }
    return false;
}

bool findPointsForLine(const Mat& weights, float shift, Point points[2], int width, int height)
{
    if (weights.empty())
    {
        return false;
    }

    int foundPointsCount = 0;
    std::vector<std::pair<Point, Point> > segments;
    fillSegments(segments, width, height);

    for (uint i = 0; i < segments.size(); i++)
    {
        if (findCrossPointWithBorders(weights, shift, segments[i], points[foundPointsCount]))
            foundPointsCount++;
        if (foundPointsCount >= 2)
            break;
    }

    return true;
}

void redraw(Data data, const Point points[2])
{
    data.img.setTo(0);
    Point center;
    int radius = 3;
    Scalar color;
    CV_Assert((data.samples.type() == CV_32FC1) && (data.responses.type() == CV_32FC1));
    for (int i = 0; i < data.samples.rows; i++)
    {
        center.x = static_cast<int>(data.samples.at<float>(i, 0));
        center.y = static_cast<int>(data.samples.at<float>(i, 1));
        color = (data.responses.at<float>(i) > 0) ? Scalar(128, 128, 0) : Scalar(0, 128, 128);
        circle(data.img, center, radius, color, 5);
    }
    line(data.img, points[0], points[1], cv::Scalar(1, 255, 1));

    imshow("Train svmsgd", data.img);
}

void addPointRetrainAndRedraw(Data& data, int x, int y, int response)
{
    Mat currentSample(1, 2, CV_32FC1);

    currentSample.at<float>(0, 0) = (float)x;
    currentSample.at<float>(0, 1) = (float)y;
    data.samples.push_back(currentSample);
    data.responses.push_back(static_cast<float>(response));

    Mat weights(1, 2, CV_32FC1);
    float shift = 0;

    if (doTrain(data.samples, data.responses, weights, shift))
    {
        Point points[2];
        findPointsForLine(weights, shift, points, data.img.cols, data.img.rows);

        redraw(data, points);
    }
}


static void onMouse(int event, int x, int y, int, void* pData)
{
    Data& data = *(Data*)pData;

    switch (event)
    {
    case EVENT_LBUTTONUP:
        addPointRetrainAndRedraw(data, x, y, 1);
        break;

    case EVENT_RBUTTONDOWN:
        addPointRetrainAndRedraw(data, x, y, -1);
        break;
    }

}

int main()
{
    Data data;

    setMouseCallback("Train svmsgd", onMouse, &data);
    waitKey();

    return 0;
}

运行实例

右键黄点，左键蓝点

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参考文献

[1]  OpenCV4的官方实例.

[2]  毛星云.OpenCV3编程入门[M].电子工业出版社.北京.2015.2.