opencv学习系列：实例练习，含多个工程实例

/*-----------------------------------------------------------------------------------------------------------*///****16.opencv来采集USB摄像头图像，显示彩色、灰度图像时，可录制彩色和灰度图像和截灰度图****///*-----------------------------------------------------------------------------------------------------------*///注意：录制的时候只能选择不同的编结码器！！！//操作：按“Esc”退出，“c”截图，“v”开始录制视频#include<opencv2/opencv.hpp>#include <iomanip>using namespace std;using namespace cv;int main(){    VideoCapture cap(4);//实验室的摄像头端口    VideoWriter wri;//录制彩色图像，并保存到libo_output    VideoWriter wri2;//录制彩色图像，并保存到libo_output    //获得帧的宽高并在控制台显示    int frameWidth, frameHeight;    frameWidth = static_cast<int>(cap.get(CV_CAP_PROP_FRAME_WIDTH));    frameHeight = static_cast<int>(cap.get(CV_CAP_PROP_FRAME_HEIGHT));    //cout << "总帧数:" << cap.get(CV_CAP_PROP_FRAME_COUNT)<<endl;//若读入为视频文件，可以输出视频文件总帧数    cout << "帧宽：" << frameWidth << "像素" << endl;    cout << "帧高：" << frameHeight << "像素" << endl;    Size frameSize(frameWidth, frameHeight); //Size(x,y)型，或用Size frameSize = Size((int)cap.get(CV_CAP_PROP_FRAME_WIDTH),(int)cap.get(CV_CAP_PROP_FRAME_HEIGHT));    //将从设备或文件获得的帧写入指定的视频文件中    string outFile = "../libo_output/outputColor.avi";    string outFile2 = "../libo_output/outputGray.avi";    //打开要写入的视频文件，准备写入！编解码方式参数设置为-1，表示代码运行时会弹出对话框，手动选择编解码方式    //当读出帧率为0时，可改为wri.open(outFile, -1, 25.0, frameSize, true);25.0对应的形参影响生产的文件的播放速度      wri.open(outFile, -1, 10.0, frameSize, true);    wri2.open(outFile2, -1, 10.0, frameSize, false);    //wri.open(outFile, -1, frameRate, frameSize, true);//true代表彩色输出！    int count = 0;    if (!wri.isOpened())    {        cout << "写视频对象问件预打开操作失败" << endl;        return -2;    }    if (!wri2.isOpened())    {        cout << "写视频对象问件预打开操作失败" << endl;        return -3;    }    Mat frame, grayImage;    int i = 0;    bool flag = false;    while (waitKey(30) != 27)    {        if (!cap.read(frame))//尝试读取下一帧            break;//检验确实读到了图像数据到Mat对象的数据缓冲区，或用if(!frame.empty())        cvtColor(frame, grayImage, CV_BGR2GRAY);//grayImage为单通道灰度图像        imshow("【摄像头彩色图】", frame);        imshow("【摄像头灰度图】", grayImage);        char key = cvWaitKey(1);        if (key == 'c')        {            cout << "提取图像成功！………………" << endl;            std::stringstream str;            str << "F:\\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(), grayImage);//            imshow("截取图像显示", grayImage);            i = i + 1;        }        if (key == 'v')        {            flag = true;            cout << "开始录制视频" << endl;        }        if (key == 's')        {            flag = false;            cout << "停止视频录制" << endl;        }        if (flag == true)        {            //写入此帧到定义的视频文件            wri << frame;//或者wri.write(frame);写彩色视频帧到文件            wri2 << grayImage;//或者wri.write(frame);写灰度视频帧到文件            count++;        }    }    cout << "写入输出的视频文件总帧数:" << count << endl;    //释放对象    waitKey(0);    return 0;}/*-----------------------------------------------------------------------------------------------------------*///****17. opencv2采集免驱UVC设备图像-截图单通道灰度图像****///*-----------------------------------------------------------------------------------------------------------*/#include<opencv2/opencv.hpp>#include <iomanip>using namespace std;using namespace cv;int main(){    VideoCapture capture(0);    Mat frame, grayImage;    int i = 0;    while (waitKey(30) != 27)    {        capture >> frame;        cvtColor(frame, grayImage, CV_BGR2GRAY);//grayImage为单通道灰度图像        imshow("【摄像头彩色图】", frame);        imshow("【摄像头灰度图】", grayImage);        char key = cvWaitKey(1);        if (key == 'c')        {            cout << "提取图像成功！………………" << endl;            std::stringstream str;            str << "F:\\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(), grayImage);//            i = i + 1;        }    }    return 0;}/*-----------------------------------------------------------------------------------------------------------*///****18. opencv1.0采集免驱UVC设备图像-截图单通道灰度图像  ****///*-----------------------------------------------------------------------------------------------------------*/#include <opencv2/opencv.hpp>#include <iomanip>//setfill,setw,setbase,setprecision等等,I/O流控制头文件,就像C里面的格式化输出一样，setw( n ) 设域宽为n个字符，setfill( 'c' ) 设填充字符为c。//#include <opencv2/opencv.hpp>包含以下：//#include <stdio.h> //#include <iostream>//#include <sstream>//继承自<iostream>可用stringstream str;using namespace std;using namespace cv;int main(){    IplImage* colorImg = NULL;    IplImage* grayImg = NULL;    int i = 0;    CvCapture* pCapture = cvCreateCameraCapture(0);//初始化摄像头，参数可以用0      if (NULL == pCapture)    {        fprintf(stderr, "Can't init Camera!\n");        return -1;    }    //cvSetCaptureProperty(cam, CV_CAP_PROP_FRAME_WIDTH, 640);//设置图像属性 宽和高      //cvSetCaptureProperty(cam, CV_CAP_PROP_FRAME_HEIGHT, 480);    cvNamedWindow("colorTest", CV_WINDOW_AUTOSIZE);    cvNamedWindow("grayTest", CV_WINDOW_AUTOSIZE);    int frameWidth, frameHeight;    frameWidth = static_cast<int>(cvGetCaptureProperty(pCapture, CV_CAP_PROP_FRAME_WIDTH));    frameHeight = static_cast<int>(cvGetCaptureProperty(pCapture, CV_CAP_PROP_FRAME_HEIGHT));    cout << "设备默认输出帧宽：" << frameWidth << "像素" << endl;    cout << "设备默认输出帧高：" << frameHeight << "像素" << endl;    //double frameRate = cvGetCaptureProperty(pCapture, CV_CAP_PROP_FPS);//视频帧率    //cout << "视频帧率:" << frameRate << "fps" << endl;    grayImg = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1);//注意：不分配确定通道和大小，cvCvtColor()会出错！    while (1)    {        colorImg = cvQueryFrame(pCapture);//获取下一帧        if (!colorImg)        {            fprintf(stderr, "Can't get a frame\n");            return -2;        }        cvCvtColor(colorImg, grayImg, CV_BGR2GRAY);        cvShowImage("colorTest", colorImg);        cvShowImage("grayTest", grayImg);        char key = cvWaitKey(33);        if (key == 27)            break;        if (key == 'c')        {            cout << "提取图像成功！………………" << endl;            std::stringstream str;            str << "F:\\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;//循环一次自动析构            Mat cameraPicture;            //读入图像            Mat frame(colorImg, false);//将C的IplImage结构转化为Mat结构，变量用于存储当前帧图像,false表示共用数据缓冲区            cvtColor(frame, cameraPicture, CV_BGR2GRAY);            imwrite(str.str(), cameraPicture);//保存的是从硬件得到的源格式图像            imshow("截取图像显示", cameraPicture);            i = i + 1;        }    }    cvWaitKey(0);    cvReleaseImage(&colorImg);    cvReleaseImage(&grayImg);    cvDestroyWindow("colorTest");    cvDestroyWindow("grayTest");    return 0;}/*-----------------------------------------------------------------------------------------------------------*///****19. 高度差检测测试1,这个程序有乱****///*-----------------------------------------------------------------------------------------------------------*/#include<opencv2/opencv.hpp>#include <iomanip>#include <stdlib.h>#include <math.h>#include <time.h>#include <vector>using namespace std;using namespace cv;//Filter2D只对单通道进行滤波操作，如果对多通道进行滤波，可以先用Split将图像分解到单通道分别操作void prewitt(Mat &src, Mat &dst){    //定义prewitt算子的模板      dst.create(src.rows, src.cols, src.type());    Mat Kernelx, Kernely;    Kernely = (Mat_<double>(3, 3) << 1, 1, 1, 0, 0, 0, -1, -1, -1);    Kernelx = (Mat_<double>(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);   //建立内核    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    filter2D(src, grad_x, CV_16S, Kernelx, Point(-1, -1));    filter2D(src, grad_y, CV_16S, Kernely, Point(-1, -1));    convertScaleAbs(grad_x, abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);}//Sobel核中已经融合进去了高斯平滑！核只可以为1 3 5 或7，其中1代表3*1的内核void sobelImage(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    Sobel(src, grad_x, CV_16S, 1,0,3,1,0, BORDER_DEFAULT);    //求Y方向梯度    Sobel(src, grad_y, CV_16S, 0, 1, 3, 1, 0, BORDER_DEFAULT);    convertScaleAbs(grad_x, abs_grad_x);    imshow("X方向Sobel效果图", abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    imshow("Y方向Sobel效果图", abs_grad_y);    //合并梯度，用加权近似    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    imshow("合并梯度后的Sobel效果图", dst);}//Scharr滤波器基本同Sobel，但是比Sobel算子更精确计算图像差分梯度，但是核只能为3*3void scharrImage(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    Scharr(src, grad_x, CV_16S, 1, 0, 1, 0, BORDER_DEFAULT);    //求Y方向梯度    Scharr(src, grad_y, CV_16S, 0, 1, 1, 0, BORDER_DEFAULT);    convertScaleAbs(grad_x, abs_grad_x);    imshow("X方向Scharr效果图", abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    imshow("Y方向Scharr效果图", abs_grad_y);    //合并梯度，用加权近似    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    imshow("合并梯度后的Scharr效果图", dst);}//laplace二阶微分边缘检测，输入须为单通道8位灰度图像void laplaceImage(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat graydst;    //    Laplacian(src, graydst, CV_16S, 3, 1, 0, BORDER_DEFAULT);    //计算绝对值，并将结果转换成8位    convertScaleAbs(graydst, dst);    imshow("图像Laplace变换检测边缘效果图", dst);}cv::Mat thinImage(const cv::Mat & src, const int maxIterations = -1){    assert(src.type() == CV_8UC1);    cv::Mat dst;    int width = src.cols;    int height = src.rows;    src.copyTo(dst);    int count = 0;  //记录迭代次数      while (true)    {        count++;        if (maxIterations != -1 && count > maxIterations) //限制次数并且迭代次数到达              break;        std::vector<uchar *> mFlag; //用于标记需要删除的点          //对点标记          for (int i = 0; i < height; ++i)        {            uchar * p = dst.ptr<uchar>(i);            for (int j = 0; j < width; ++j)            {                //如果满足四个条件，进行标记                  //  p9 p2 p3                  //  p8 p1 p4                  //  p7 p6 p5                  uchar p1 = p[j];                if (p1 != 1) continue;                uchar p4 = (j == width - 1) ? 0 : *(p + j + 1);                uchar p8 = (j == 0) ? 0 : *(p + j - 1);                uchar p2 = (i == 0) ? 0 : *(p - dst.step + j);                uchar p3 = (i == 0 || j == width - 1) ? 0 : *(p - dst.step + j + 1);                uchar p9 = (i == 0 || j == 0) ? 0 : *(p - dst.step + j - 1);                uchar p6 = (i == height - 1) ? 0 : *(p + dst.step + j);                uchar p5 = (i == height - 1 || j == width - 1) ? 0 : *(p + dst.step + j + 1);                uchar p7 = (i == height - 1 || j == 0) ? 0 : *(p + dst.step + j - 1);                if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2 && (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6)                {                    int ap = 0;                    if (p2 == 0 && p3 == 1) ++ap;                    if (p3 == 0 && p4 == 1) ++ap;                    if (p4 == 0 && p5 == 1) ++ap;                    if (p5 == 0 && p6 == 1) ++ap;                    if (p6 == 0 && p7 == 1) ++ap;                    if (p7 == 0 && p8 == 1) ++ap;                    if (p8 == 0 && p9 == 1) ++ap;                    if (p9 == 0 && p2 == 1) ++ap;                    if (ap == 1 && p2 * p4 * p6 == 0 && p4 * p6 * p8 == 0)                    {                        //标记                          mFlag.push_back(p + j);                    }                }            }        }        //将标记的点删除          for (std::vector<uchar *>::iterator i = mFlag.begin(); i != mFlag.end(); ++i)        {            **i = 0;        }        //直到没有点满足，算法结束          if (mFlag.empty())        {            break;        }        else        {            mFlag.clear();//将mFlag清空          }        //对点标记          for (int i = 0; i < height; ++i)        {            uchar * p = dst.ptr<uchar>(i);            for (int j = 0; j < width; ++j)            {                //如果满足四个条件，进行标记                  //  p9 p2 p3                  //  p8 p1 p4                  //  p7 p6 p5                  uchar p1 = p[j];                if (p1 != 1) continue;                uchar p4 = (j == width - 1) ? 0 : *(p + j + 1);                uchar p8 = (j == 0) ? 0 : *(p + j - 1);                uchar p2 = (i == 0) ? 0 : *(p - dst.step + j);                uchar p3 = (i == 0 || j == width - 1) ? 0 : *(p - dst.step + j + 1);                uchar p9 = (i == 0 || j == 0) ? 0 : *(p - dst.step + j - 1);                uchar p6 = (i == height - 1) ? 0 : *(p + dst.step + j);                uchar p5 = (i == height - 1 || j == width - 1) ? 0 : *(p + dst.step + j + 1);                uchar p7 = (i == height - 1 || j == 0) ? 0 : *(p + dst.step + j - 1);                if ((p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) >= 2 && (p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9) <= 6)                {                    int ap = 0;                    if (p2 == 0 && p3 == 1) ++ap;                    if (p3 == 0 && p4 == 1) ++ap;                    if (p4 == 0 && p5 == 1) ++ap;                    if (p5 == 0 && p6 == 1) ++ap;                    if (p6 == 0 && p7 == 1) ++ap;                    if (p7 == 0 && p8 == 1) ++ap;                    if (p8 == 0 && p9 == 1) ++ap;                    if (p9 == 0 && p2 == 1) ++ap;                    if (ap == 1 && p2 * p4 * p8 == 0 && p2 * p6 * p8 == 0)                    {                        //标记                          mFlag.push_back(p + j);                    }                }            }        }        //将标记的点删除          for (std::vector<uchar *>::iterator i = mFlag.begin(); i != mFlag.end(); ++i)        {            **i = 0;        }        //直到没有点满足，算法结束          if (mFlag.empty())        {            break;        }        else        {            mFlag.clear();//将mFlag清空          }    }    return dst;}void main(){    Mat src = imread("../libo_resource/22.png", 0);//读入灰度图像    //srcROI = src(Range(150, 240), Range(120, 170));//只处理感兴趣区域，他们共享数据区    imshow("原图灰度图", src);    //imshow("原图灰度图的ROI", srcROI);    Mat out;    Mat dst;    //开始计算高度差检测处理一帧的时间    double time;    time = static_cast<double>(getTickCount());    int middley = src.cols / 2;    int middlex = 0;    //图像滤波增强，一般选用3*3，让滤波更明显可以选用5*5卷积核，    //medianBlur(src, src, 3);//3代表3*3模板取中值，采用中值滤波,其比较费时    //bilateralFilter(src, src,-1, 25*2, 25/2);25/2位置的值越大，代表越远的像素会相互影响，从而使更大的区域中足够相似的颜色获取相同的颜色    blur(src, src, Size(3, 3));//采用均值滤波    //GaussianBlur(src, src, Size(3, 3), 0, 0);//可以较好的保存边缘，“0，0”代表sigmaX和sigmaY参数自动由模板大小计算出，采用高斯加权均值滤波，可用Mat gauss= getGaussianKernel(9, sigma, CV_32F);获取一维模板核系数，此句为获取1*9的模板矩阵，sigma取值决定高斯加权曲线的离散程度。    //Mat kernelX = getGaussianKernel(kernelSize, sigmaX);     //Mat kernelY = getGaussianKernel(kernelSize, sigmaY);    imshow("滤波后的处理图像", src);    //边缘检测，需处理的是单通道灰度图像，滤波增强，提取边缘，以下算子都是带方向的    Mat edge;    //Canny(src,edge,3, 9 ,3);//低阈值用于边缘连接，高阈值控制什么像素值是强边缘，最后的设定值sobel孔径大小    prewitt(src, dst);    imshow("边缘检测后的图像", dst);    Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));    dilate(dst, dst, element);    imshow("膨胀后的图像", dst);    threshold(dst, dst, 30, 1, CV_THRESH_BINARY);    //图像细化    dst = thinImage(dst);    dst = dst * 255;    for (int i = 0; i < src.rows; i++)    {        for (int j = 0; j < src.cols; j++)        {            if ((j == middley) && (i != 0))            {                middlex = i;            }        }    }    cout << "焊缝中心坐标=（" << middlex << "，" << middley << "）" << endl;    time = ((double)getTickCount() - time) / getTickFrequency();    cout << "处理一帧的秒数cost time=" << time << endl;    imshow("细化后的边缘检测图像", dst);    waitKey();}/*-----------------------------------------------------------------------------------------------------------*///****20. 高度差检测测试 结果凑合能用****///*-----------------------------------------------------------------------------------------------------------*/#include<opencv2/opencv.hpp>#include <iomanip>#include <stdlib.h>#include <math.h>#include <time.h>#include <vector>using namespace std;using namespace cv;void prewittProcess(Mat &src, Mat &dst);void sobelProcess(Mat &src, Mat &dst);void scharrProcess(Mat &src, Mat &dst);void laplaceProcess(Mat &src, Mat &dst);void cannyProcess(Mat &src, Mat &dst);int main(){    //可从摄像头输入视频流或直接播放视频文件    //VideoCapture capture(0);    VideoCapture capture("../libo_resource/test.avi");    if (!capture.isOpened())    {        std::cout << "No Video Input!" << std::endl;        system("pause");        return -1;    }    //获得帧的宽高并在控制台显示    int frameWidth, frameHeight;    frameWidth = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));    frameHeight = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));    //cout << "总帧数:" << cap.get(CV_CAP_PROP_FRAME_COUNT)<<endl;//若读入为视频文件，可以输出视频文件总帧数    cout << "帧宽：" << frameWidth << "像素" << endl;    cout << "帧高：" << frameHeight << "像素" << endl;    int originalFrameSum = capture.get(CV_CAP_PROP_FRAME_COUNT);    cout << "原始视频文件的总帧数为：" << originalFrameSum << endl;    double originalFPS = capture.get(CV_CAP_PROP_FPS);    cout << "原始视频文件的帧率为：" << originalFPS << endl;    Mat frame;    Mat frameGray;    Mat grayROI;    Mat outPut;    //Mat src = imread("../libo_resource/22.png", 0);//读入灰度图像    //imshow("原图灰度图", src);    //imshow("原图灰度图的ROI", srcROI);    // 每一帧之间的延迟,即每1s处理一帧图像    int delay = 1000 / originalFPS;    bool stop(false);//循环控制标志位    // 遍历每一帧    while (!stop)    {        // 尝试读取下一帧        if (!capture.read(frame))            break;        // 先要把每帧图像转化为单通道灰度图        cv::cvtColor(frame, frameGray, CV_BGR2GRAY);        imshow("Extracted grayFrame", frameGray);        grayROI = frameGray(Range(240, 440), Range(0, 640));//只处理感兴趣区域，他们共享数据区        //imshow("Extracted grayROI", grayROI);        //图像滤波增强，一般选用3*3，让滤波更明显可以选用5*5卷积核，        blur(grayROI, grayROI, Size(5, 5));//采用均值滤波        medianBlur(grayROI, grayROI, 9);//3代表3*3模板取中值，采用中值滤波,其比较费时        //bilateralFilter(src, src,-1, 25*2, 25/2);25/2位置的值越大，代表越远的像素会相互影响，从而使更大的区域中足够相似的颜色获取相同的颜色        //GaussianBlur(grayROI, grayROI, Size(5, 5), 0, 0);//可以较好的保存边缘，“0，0”代表sigmaX和sigmaY参数自动由模板大小计算出，采用高斯加权均值滤波，可用Mat gauss= getGaussianKernel(9, sigma, CV_32F);获取一维模板核系数，此句为获取1*9的模板矩阵，sigma取值决定高斯加权曲线的离散程度。        //Mat kernelX = getGaussianKernel(kernelSize, sigmaX);         //Mat kernelY = getGaussianKernel(kernelSize, sigmaY);        imshow("平滑后的grayROI", grayROI);        //边缘检测，需处理的是单通道灰度图像，滤波增强，提取边缘，算子都是带方向的        cannyProcess(grayROI, outPut);        imshow("边缘检测后的grayROI", outPut);        // 引入延迟        if (cv::waitKey(delay) >= 0)            stop = true;    }    /*    //开始计算高度差检测处理一帧的时间    double time;    time = static_cast<double>(getTickCount());    int middley = grayROI.cols / 2;    int middlex = 0;    //threshold(outPut, outPut, 30, 1, CV_THRESH_BINARY);    for (int i = 0; i < outPut.rows; i++)    {        for (int j = 0; j < outPut.cols; j++)        {            if ((j == middley) && (i != 0))            {                middlex = i;            }        }    }    cout << "焊缝中心坐标=（" << middlex << "，" << middley << "）" << endl;    time = ((double)getTickCount() - time) / getTickFrequency();    cout << "处理一帧的秒数cost time=" << time << endl;    imshow("细化后的边缘检测图像", dst);    */    waitKey(0);    return 0;}//Filter2D只对单通道进行滤波操作，如果对多通道进行滤波，可以先用Split将图像分解到单通道分别操作void prewittProcess(Mat &src, Mat &dst){    //定义prewitt算子的模板      dst.create(src.rows, src.cols, src.type());    Mat Kernelx, Kernely;    Kernely = (Mat_<double>(3, 3) << 1, 1, 1, 0, 0, 0, -1, -1, -1);    Kernelx = (Mat_<double>(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);   //建立内核    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    filter2D(src, grad_x, CV_16S, Kernelx, Point(-1, -1));    filter2D(src, grad_y, CV_16S, Kernely, Point(-1, -1));    convertScaleAbs(grad_x, abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);}//Sobel核中已经融合进去了高斯平滑！核只可以为1 3 5 或7，其中1代表3*1的内核void sobelProcess(Mat &src, Mat &dst){    //dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    //Sobel(src, grad_x, CV_16S, 1, 0, 3, 1, 0, BORDER_DEFAULT);    //求Y方向梯度    Sobel(src, grad_y, CV_16S, 0, 1, 3, 1, 0, BORDER_DEFAULT);    //convertScaleAbs(grad_x, abs_grad_x);    //imshow("X方向Sobel效果图", abs_grad_x);    convertScaleAbs(grad_y, dst);    //imshow("Y方向Sobel效果图", abs_grad_y);    //合并梯度，用加权近似    //addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    //imshow("合并梯度后的Sobel效果图", dst);}//Scharr滤波器基本同Sobel，但是比Sobel算子更精确计算图像差分梯度，但是核只能为3*3void scharrProcess(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    Scharr(src, grad_x, CV_16S, 1, 0, 1, 0, BORDER_DEFAULT);    //求Y方向梯度    Scharr(src, grad_y, CV_16S, 0, 1, 1, 0, BORDER_DEFAULT);    convertScaleAbs(grad_x, abs_grad_x);    //imshow("X方向Scharr效果图", abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    //imshow("Y方向Scharr效果图", abs_grad_y);    //合并梯度，用加权近似    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    //imshow("合并梯度后的Scharr效果图", dst);}//laplace二阶微分边缘检测，输入须为单通道8位灰度图像void laplaceProcess(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat graydst;    //    Laplacian(src, graydst, CV_16S, 3, 1, 0, BORDER_DEFAULT);    //计算绝对值，并将结果转换成8位    convertScaleAbs(graydst, dst);    //imshow("图像Laplace变换检测边缘效果图", dst);}void cannyProcess(cv::Mat& img, cv::Mat& dst)//输入图像应为彩色图像！！！！！{    // 调用Canny函数,其是高斯去噪和Sobel的结合    cv::Canny(img, dst, 80, 240,3);//低阈值用于边缘连接，高阈值控制什么像素值是强边缘，最后的设定值sobel孔径大小    // 对像素进行翻转    //cv::threshold(dst, dst, 128, 255, cv::THRESH_BINARY_INV);}/*-----------------------------------------------------------------------------------------------------------*///****21. OpenCV1.0播放视频并控制位置****///*--#include <cv.h>  #include <highgui.h>  CvCapture* capture = NULL;  int pos=0;  //视频位置  void ON_Change(int n)  {      cvSetCaptureProperty(capture,CV_CAP_PROP_POS_FRAMES,n);     //设置视频走到pos位置  }  int _tmain(int argc, _TCHAR* argv[])  {      cvNamedWindow("show");  //新建一个窗口      capture  =cvCreateFileCapture("F:\\TDDOWNLOAD\\10.Little.Asians.16\\fle-10la16a.avi");//创建一个视频      int frames = (int)cvGetCaptureProperty(capture,CV_CAP_PROP_FRAME_COUNT);        //返回视频帧的总数      cvCreateTrackbar("frame","show",&pos,frames,ON_Change);//创建滚动条      IplImage * frame;//声明视频帧      while(1)      {          frame = cvQueryFrame(capture);  //获取下一帧图像          if (!frame)          {              break;          //如果不存在退出          }          pos++;              //播放一帧位置加1          //cvSetTrackbarPos("frame","show",pos);//设置进度条位置 加入此语句后视频会变卡          cvShowImage("show",frame);      //在窗口显示图像          char c = cvWaitKey(33);         //间隔33ms          if (c == 27)                    //如果按下Esc键中断            break;      }      cvReleaseCapture(&capture); //释放视频空间      cvDestroyWindow("show");    //销毁窗口  }/*-----------------------------------------------------------------------------------------------------------*///****21. 高度差检测测试 比上一个显示好一些****///*-----------------------------------------------------------------------------------------------------------*//*-----------------------------------------------------------------------------------------------------------*///****20. 高度差检测测试****///*-----------------------------------------------------------------------------------------------------------*/#include<opencv2/opencv.hpp>#include <iomanip>#include <stdlib.h>#include <math.h>#include <time.h>#include <vector>#include <iostream>using namespace std;using namespace cv;void prewittProcess(Mat &src, Mat &dst);void sobelProcess(Mat &src, Mat &dst);void scharrProcess(Mat &src, Mat &dst);void laplaceProcess(Mat &src, Mat &dst);void cannyProcess(Mat &src, Mat &dst);int main(){    //可从摄像头输入视频流或直接播放视频文件    //VideoCapture capture(0);    VideoCapture capture("../libo_resource/test.avi");    if (!capture.isOpened())    {        std::cout << "No Video Input!" << std::endl;        system("pause");        return -1;    }    //获得帧的宽高并在控制台显示    int frameWidth, frameHeight;    frameWidth = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_WIDTH));    frameHeight = static_cast<int>(capture.get(CV_CAP_PROP_FRAME_HEIGHT));    //cout << "总帧数:" << cap.get(CV_CAP_PROP_FRAME_COUNT)<<endl;//若读入为视频文件，可以输出视频文件总帧数    cout << "帧宽：" << frameWidth << "像素" << endl;    cout << "帧高：" << frameHeight << "像素" << endl;    int originalFrameSum = capture.get(CV_CAP_PROP_FRAME_COUNT);    cout << "原始视频文件的总帧数为：" << originalFrameSum << endl;    double originalFPS = capture.get(CV_CAP_PROP_FPS);    cout << "原始视频文件的帧率为：" << originalFPS << endl;    Mat frame;    Mat frameGray;    Mat grayROI;    Mat outPut;    int framNumber = 0;//记录输出的第几帧图像    double time=0;    int labelX = frameWidth/2;//以原图像的宽的一半为记录数据位置    int minHeight = 100;//记录高度差的最小值，用于标定偏转阈值    int maxHeight = 0;//记录高度差的最大值，用于标定偏转阈值    int heightDiff=0;    Point point[2];//存遍历到的白点坐标值    char fpsString[30];//用于帧计数显示和显示像素高度差    namedWindow("Extracted grayFrame", 1);    namedWindow("平滑后的grayROI", 1);    namedWindow("边缘检测后的grayROI", 1);    // 每一帧之间的延迟,即每1s处理一帧图像    int delay = 1000 / originalFPS;    bool stop(false);//循环控制标志位    // 遍历每一帧    while (!stop)    {        // 尝试读取下一帧        time = 0;        time = static_cast<double>(getTickCount());        if (!capture.read(frame))            break;        // 先要把每帧图像转化为单通道灰度图        cv::cvtColor(frame, frameGray, CV_BGR2GRAY);        grayROI = frameGray(Range(240, 440), Range(0, 640));//只处理感兴趣区域，他们共享数据区        //imshow("Extracted grayROI", grayROI);        //图像滤波增强，一般选用3*3，让滤波更明显可以选用5*5卷积核，        blur(grayROI, grayROI, Size(5, 5));//采用均值滤波        medianBlur(grayROI, grayROI, 11);//3代表3*3模板取中值，采用中值滤波,其比较费时        //bilateralFilter(src, src,-1, 25*2, 25/2);25/2位置的值越大，代表越远的像素会相互影响，从而使更大的区域中足够相似的颜色获取相同的颜色        //GaussianBlur(grayROI, grayROI, Size(5, 5), 0, 0);//可以较好的保存边缘，“0，0”代表sigmaX和sigmaY参数自动由模板大小计算出，采用高斯加权均值滤波，可用Mat gauss= getGaussianKernel(9, sigma, CV_32F);获取一维模板核系数，此句为获取1*9的模板矩阵，sigma取值决定高斯加权曲线的离散程度。        //Mat kernelX = getGaussianKernel(kernelSize, sigmaX);         //Mat kernelY = getGaussianKernel(kernelSize, sigmaY);        imshow("平滑后的grayROI", grayROI);        //边缘检测，需处理的是单通道灰度图像，滤波增强，提取边缘，算子都是带方向的        cannyProcess(grayROI, outPut);              //imshow("边缘检测后的grayROI", outPut);        //从上往下找白点，并记录y坐标,算出高度差！注意at方法为at（行值，列值）        for (int i = 0; i < outPut.rows; i++)        {            if (outPut.at<uchar>(i, labelX) == 255)            {                point[0] = Point(labelX,i );                break;            }        }        for (int j = outPut.rows-1; j > 0; j--)        {            if (outPut.at<uchar>(j, labelX) == 255)            {                point[1] = Point(labelX, j);                break;            }        }        circle(outPut, point[0], 5, Scalar(255,255, 255), -1, 8);        circle(outPut, point[1], 5, Scalar(255,255, 255), -1, 8);        circle(grayROI, point[0], 5, Scalar(255, 255, 255), -1, 8);        circle(grayROI, point[1], 5, Scalar(255, 255, 255), -1, 8);        line(outPut,            point[0],            point[1],            cv::Scalar(255, 255, 255),            5,            8);        cvtColor(frameGray, frameGray, CV_GRAY2BGR);        circle(frameGray, point[0] + Point(0,240), 5, Scalar(255, 0, 255), -1, 8);        circle(frameGray, point[1]+Point(0, 240), 5, Scalar(255, 0, 255), -1, 8);        line(frameGray,            point[0] + Point(0, 240),            point[1] + Point(0, 240),            cv::Scalar(255, 0, 255),            5,            8);        framNumber++;        sprintf(fpsString, "%d heightDiff: %d", framNumber, heightDiff); // 帧计数        std::string fpsString1("frameCounter: ");//此句语句不能在循环体外！！！！！！        fpsString1 += fpsString; // 在"FPS:"后加入帧率数值字符串        // 将帧率信息写在输出帧上        putText(frameGray,                  // 图像矩阵            fpsString1,                 // string型文字内容            Point(labelX/5, 240),          // 文字坐标，以左下角为原点            cv::FONT_HERSHEY_SIMPLEX,   // 字体类型            1,                          // 字体大小            cv::Scalar(0, 0, 255),      // 字体颜色：黑色            4);                         // 加粗        imshow("边缘检测后的grayROI", outPut);        imshow("平滑后的grayROI", grayROI);        imshow("Extracted grayFrame", frameGray);        heightDiff = point[1].y - point[0].y;        if (heightDiff >= maxHeight)            maxHeight = heightDiff;        if (heightDiff <= minHeight)            minHeight = heightDiff;        time = ((double)getTickCount() - time) / getTickFrequency();        std::stringstream str;        str << "处理第" << framNumber << "帧图像的";        cout << str.str() << "heightDiff=" << heightDiff << "个像素" << endl;        cout << str.str() << "frameROI cost time=" << time << endl;        // 引入延迟        if (cv::waitKey(delay) >= 0)            stop = true;    }    cout << "最大高度差maxHeight=" << maxHeight << "个像素" << endl;    cout << "最大高度差minHeight=" << minHeight << "个像素" << endl;    waitKey(0);    return 0;}//Filter2D只对单通道进行滤波操作，如果对多通道进行滤波，可以先用Split将图像分解到单通道分别操作void prewittProcess(Mat &src, Mat &dst){    //定义prewitt算子的模板      dst.create(src.rows, src.cols, src.type());    Mat Kernelx, Kernely;    Kernely = (Mat_<double>(3, 3) << 1, 1, 1, 0, 0, 0, -1, -1, -1);    Kernelx = (Mat_<double>(3, 3) << -1, 0, 1, -1, 0, 1, -1, 0, 1);   //建立内核    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    filter2D(src, grad_x, CV_16S, Kernelx, Point(-1, -1));    filter2D(src, grad_y, CV_16S, Kernely, Point(-1, -1));    convertScaleAbs(grad_x, abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);}//Sobel核中已经融合进去了高斯平滑！核只可以为1 3 5 或7，其中1代表3*1的内核void sobelProcess(Mat &src, Mat &dst){    //dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    //Sobel(src, grad_x, CV_16S, 1, 0, 3, 1, 0, BORDER_DEFAULT);    //求Y方向梯度    Sobel(src, grad_y, CV_16S, 0, 1, 3, 1, 0, BORDER_DEFAULT);    //convertScaleAbs(grad_x, abs_grad_x);    //imshow("X方向Sobel效果图", abs_grad_x);    convertScaleAbs(grad_y, dst);    //imshow("Y方向Sobel效果图", abs_grad_y);    //合并梯度，用加权近似    //addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    //imshow("合并梯度后的Sobel效果图", dst);}//Scharr滤波器基本同Sobel，但是比Sobel算子更精确计算图像差分梯度，但是核只能为3*3void scharrProcess(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat grad_x, grad_y;    Mat abs_grad_x, abs_grad_y, grad;    //求X方向梯度    Scharr(src, grad_x, CV_16S, 1, 0, 1, 0, BORDER_DEFAULT);    //求Y方向梯度    Scharr(src, grad_y, CV_16S, 0, 1, 1, 0, BORDER_DEFAULT);    convertScaleAbs(grad_x, abs_grad_x);    //imshow("X方向Scharr效果图", abs_grad_x);    convertScaleAbs(grad_y, abs_grad_y);    //imshow("Y方向Scharr效果图", abs_grad_y);    //合并梯度，用加权近似    addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0, dst);    //imshow("合并梯度后的Scharr效果图", dst);}//laplace二阶微分边缘检测，输入须为单通道8位灰度图像void laplaceProcess(Mat &src, Mat &dst){    dst.create(src.rows, src.cols, src.type());    Mat graydst;    //    Laplacian(src, graydst, CV_16S, 3, 1, 0, BORDER_DEFAULT);    //计算绝对值，并将结果转换成8位    convertScaleAbs(graydst, dst);    //imshow("图像Laplace变换检测边缘效果图", dst);}void cannyProcess(cv::Mat& img, cv::Mat& dst)//输入图像应为彩色图像！！！！！{    // 调用Canny函数,其是高斯去噪和Sobel的结合    cv::Canny(img, dst, 80, 240,3);//低阈值用于边缘连接，高阈值控制什么像素值是强边缘，最后的设定值sobel孔径大小    // 对像素进行翻转    //cv::threshold(dst, dst, 128, 255, cv::THRESH_BINARY_INV);}