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OpenCV基础学习

• 人工智能 • 阅读 59

一：显示图像并保存

#include
#include

using namespace std;
using namespace cv;
int main()
{

    Mat src = imread("D:/images/011.jpg",IMREAD_GRAYSCALE);
    if (src.empty())
    {
        printf("could not load image");
    }

    namedWindow("输入窗口", WINDOW_FREERATIO);
    imshow("输入窗口", src);
    waitKey(0);
    imwrite("E:\\QTFiles\\zhh1913021023\\example1_zhh\\test1.png",src);
    destroyAllWindows();

    return 0;
}

二：色彩转换函数:cvtcolor

#include
#include

using namespace std;
using namespace cv;

class QuickDemo
{
    public:
        void colorSpace_Demo(Mat &imge);
};

void QuickDemo::colorSpace_Demo(Mat &image)
{
    Mat gray, hsv;
    cvtColor(image,hsv,COLOR_BGR2HSV);
    cvtColor(image,gray,COLOR_BGR2GRAY);
    imshow("HSV",hsv);
    imshow("灰度",gray);
    imwrite("D:/hsv.jpg",hsv);
    imwrite("D:/gray.jpg",gray);
}

int main()
{

    Mat src = imread("D:/images/1.jpg",IMREAD_ANYCOLOR);

    if (src.empty())
    {
        printf("could not load image");
        return -1;
    }

    namedWindow("输入窗口", WINDOW_FREERATIO);
    imshow("输入窗口", src);

    QuickDemo qd;
    qd.colorSpace_Demo(src);

    waitKey(0);
    destroyAllWindows();
    return 0;
}

三：mat对象

通过创建新的Mat对象来创建用户的特定的底色画布，创建图像的基本类型有两种一种是ones一种是zeros，ones()中的第一个参数代表图像的大小，第二个参数代表创建几维的图像，UC代表无符号字符型，数组3代表通道数。克隆和赋值的区别，克隆就是产生一个新的对象，新对象改变属性，旧对象属性不变（各自为政）。赋值是二者同体，当新属性发生改变，旧属性也发生改变（二者同体）。

void QuickDemo::mat_creation_demo(Mat &image)
{
    Mat m1, m2;
    m1 = image.clone();
    image.copyTo(m2);

    Mat m3 = Mat::ones(Size(400, 400), CV_8UC3);

    m3 = Scalar(255, 0, 0);

    Mat m4 = m3.clone();

    m4 = Scalar(0, 255, 255);
    imshow("图像3", m3);
    imshow("图像4", m4);
}

mat对象的7种创建方式，分别创建灰度和彩色图像


        Mat image1(10,10,CV_8UC1,Scalar(0,255,255));
        Mat image1_rgb(10,10,CV_8UC3,Scalar(0,255,255));

        int sz[3]={10,10,10};
        Mat image2(2,sz,CV_8UC3,Scalar::all(0));
         Mat image2_rgb(2,sz,CV_8UC1,Scalar::all(0));

        IplImage *img = cvLoadImage("d:/image/melina.jpg",CV_8UC1);
        IplImage *img1 = cvLoadImage("d:/image/melina.jpg",CV_8UC3);
           Mat image3=cvarrToMat(img);
           Mat image3_rgb=cvarrToMat(img1);

           Mat image4,image4_rgb;
           image4.create(10,10,CV_8UC1);
           image4_rgb.create(10,10,CV_8UC3);

            Mat image5 = Mat::eye(10, 10,CV_8UC1);
            Mat image5_rgb=Mat::eye(10, 10,CV_8UC3);

            Mat image6=(Mat_<unsigned char>(3,3)<<1,2,3,4,5,6,7,8,9,CV_8UC1);
            Mat image6_rgb=(Mat_<unsigned char>(3,3)<<1,2,3,4,5,6,7,8,9,CV_8UC3);

           Mat image1_row=image1.row(1).clone();
           Mat image1_row_rgb;
           image1_rgb.copyTo(image1_row_rgb);

四：图像像素读写

数组方式

void QuickDemo::pixel_visit_demo(Mat &image)
{
    int dims = image.channels();
    int h = image.rows;
    int w = image.cols;
    for (int row = 0; row < h; row++)
    {
        for (int col = 0; col < w; col++)
        {
            if (dims == 1)
            {
                int pv = image.at<uchar>(row, col);
                image.at<uchar>(row, col) = 255 - pv;

            }
            if (dims == 3)
            {
                Vec3b bgr = image.at<Vec3b>(row, col);
                image.at<Vec3b>(row, col)[0] = 255 - bgr[0];
                image.at<Vec3b>(row, col)[1] = 255 - bgr[1];
                image.at<Vec3b>(row, col)[2] = 255 - bgr[2];
            }
        }
    }
    namedWindow("像素读写演示", WINDOW_FREERATIO);
    imshow("像素读写演示", image);
}

指针方式

void QuickDemo::pixel_visit_demo(Mat &image)
{
    int dims = image.channels();
    int h = image.rows;
    int w = image.cols;
    for (int row = 0; row < h; row++)
    {
        uchar *current_row = image.ptr<uchar>(row);

        for (int col = 0; col < w; col++)
        {
            if (dims == 1)
            {
                int pv = *current_row;
                    *current_row++ = 255 - pv;

            }
            if (dims == 3)
            {
                *current_row++ = 255 - *current_row;
                *current_row++ = 255 - *current_row;
                *current_row++ = 255 - *current_row;
            }
        }
    }
    namedWindow("像素读写演示", WINDOW_FREERATIO);
    imshow("像素读写演示", image);

}

五：图像像素操作：加减乘除

void QuickDemo::operators_demo(Mat &image)
{
    Mat dst = Mat::zeros(image.size(), image.type());
    Mat m = Mat::zeros(image.size(), image.type());
    dst = image - Scalar(50, 50, 50);
    m = Scalar(50, 50, 50);
    multiply(image,m,dst);
    imshow("乘法操作", dst);
    add(image, m, dst);
    imshow("加法操作", dst);
    subtract(image, m, dst);
    imshow("减法操作", dst);
    divide(image, m, dst);
    namedWindow("加法操作", WINDOW_FREERATIO);
    imshow("加法操作", dst);

    int dims = image.channels();
    int h = image.rows;
    int w = image.cols;
    for (int row = 0; row < h; row++)
    {
        for (int col = 0; col < w; col++)
        {
                Vec3b p1 = image.at<Vec3b>(row, col);
                Vec3b p2 = m.at<Vec3b>(row, col);
                dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(p1[0] + p2[0]);
                dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(p1[1] + p2[1]);
                dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(p1[2] + p2[2]);
        }
    }
    imshow("加法操作", dst);
}

六：滚动条调节图片亮度

Mat  src, dst, m;
int lightness = 50;
static void on_track(int ,void*)
{
    m = Scalar(lightness,lightness,lightness);
    subtract(src, m, dst);
    imshow("亮度调整", dst);
}
void QuickDemo::tracking_bar_demo(Mat &image)
{
    namedWindow("亮度调整",WINDOW_AUTOSIZE);
    dst = Mat::zeros(image.size(), image.type());
    m = Mat::zeros(image.size(), image.type());
    src = image;
    int max_value = 100;
    createTrackbar("Value Bar:", "亮度调整", &lightness, max_value,on_track);
    on_track(50, 0);
}

可传参数

static void on_lightness(int b ,void* userdata)
{
    Mat image = *((Mat*)userdata);
    Mat dst = Mat::zeros(image.size(), image.type());
    Mat m = Mat::zeros(image.size(), image.type());
    m = Scalar(b,b,b);
    addWeighted(image,1.0,m,0,b,dst);
    imshow("亮度&对比度调整", dst);
}
static void on_contrast(int b, void* userdata)
{
    Mat image = *((Mat*)userdata);
    Mat dst = Mat::zeros(image.size(), image.type());
    Mat m = Mat::zeros(image.size(), image.type());
    double contrast = b / 100.0;
    addWeighted(image, contrast, m, 0.0, 0, dst);
    imshow("亮度&对比度调整", dst);
}
void QuickDemo::tracking_bar_demo(Mat &image)
{
    namedWindow("亮度&对比度调整",WINDOW_AUTOSIZE);
    int lightness = 50;
    int max_value = 100;
    int contrast_value = 100;
    createTrackbar("Value Bar:", "亮度&对比度调整", &lightness, max_value, on_lightness,(void*)(&image));
    createTrackbar("Contrast Bar:", "亮度&对比度调整", &contrast_value, 200, on_contrast, (void*)(&image));
    on_lightness(50, &image);
}

七：键盘响应

void QuickDemo::key_demo(Mat &image)
{
    Mat dst= Mat::zeros(image.size(), image.type());
    while (true)
    {
        char c = waitKey(100);
        if (c == 27) {
            break;
        }
        if (c == 49)
        {
            std::cout <<"you enter key #1" << std::endl;
            cvtColor(image, dst, COLOR_BGR2GRAY);
        }
        if (c == 50)
        {
            std::cout << "you enter key #2"  << std::endl;
            cvtColor(image, dst, COLOR_BGR2HSV);
        }
        if (c == 51)
        {
            std::cout << "you enter key #3" << std::endl;
            dst = Scalar(50, 50, 50);
            add(image,dst,dst);
        }
        imshow("键盘响应",dst);
        std::cout << c << std::endl;
    }
}

八：调用OpenCV自带颜色

void QuickDemo::color_style_demo(Mat &image)
{
    int colormap[] = {
        COLORMAP_AUTUMN ,
        COLORMAP_BONE,
        COLORMAP_CIVIDIS,
        COLORMAP_DEEPGREEN,
        COLORMAP_HOT,
        COLORMAP_HSV,
        COLORMAP_INFERNO,
        COLORMAP_JET,
        COLORMAP_MAGMA,
        COLORMAP_OCEAN,
        COLORMAP_PINK,
        COLORMAP_PARULA,
        COLORMAP_RAINBOW,
        COLORMAP_SPRING,
        COLORMAP_TWILIGHT,
        COLORMAP_TURBO,
        COLORMAP_TWILIGHT,
        COLORMAP_VIRIDIS,
        COLORMAP_TWILIGHT_SHIFTED,
        COLORMAP_WINTER
    };

    Mat dst;
    int index = 0;
    while (true)
    {
        char c = waitKey(100);
        if (c == 27) {
            break;
        }
        if (c == 49)
        {
            std::cout << "you enter key #1" << std::endl;
            imwrite("D:/gray.jpg", dst);
        }
        applyColorMap(image, dst, colormap[index%19]);
        index++;
        imshow("循环播放", dst);
    }
}

九：图像像素的逻辑操作：与或非异或

void QuickDemo::bitwise_demo(Mat &image)
{
    Mat m1 = Mat::zeros(Size(256,256),CV_8UC3);
    Mat m2 = Mat::zeros(Size(256,256),CV_8UC3);
    rectangle(m1,Rect(100,100,80,80),Scalar(255,255,0),-1,LINE_8,0);

    rectangle(m2,Rect(150,150,80,80), Scalar(0,255,255), -1, LINE_8, 0);
    imshow("m1", m1);
    imshow("m2", m2);
    Mat dst;
    bitwise_and(m1, m2, dst);
    bitwise_or(m1, m2, dst);
    bitwise_not(image, dst);
    bitwise_xor(m1, m2, dst);
    imshow("像素位操作", dst);
}

十：通道的分离和合并

void QuickDemo::channels_demo(Mat &image)
{
    std::vector<Mat>mv;
    split(image, mv);

    Mat dst;
    mv[0] = 0;
    mv[2] = 0;
    merge(mv, dst);
    imshow("蓝色", dst);
    int from_to[] = { 0,2,1,1,2,0 };

    mixChannels(&image,1,&dst,1,from_to,3);

    imshow("通道混合", dst);
}

十一：图像色彩空间转换

OpenCV基础学习 
void QuickDemo::inrange_demo(Mat &image)
{
    Mat hsv;
    cvtColor(image, hsv, COLOR_BGR2HSV);
    Mat mask;
    inRange(hsv,Scalar(35,43,46),Scalar(77,255,255),mask);

    imshow("mask",hsv);
    Mat redback = Mat::zeros(image.size(), image.type());
    redback = Scalar(40, 40, 200);
    bitwise_not(mask, mask);
    imshow("mask", mask);
    image.copyTo(redback, mask);
    imshow("roi区域提取", redback);
}

十二：图像像素值统计

void QuickDemo::pixel_statistic_demo(Mat &image)
{
    double minv, maxv;
    Point minLoc, maxLoc;
    std::vector<Mat>mv;
    split(image, mv);
    for (int i = 0; i < mv.size(); i++)
    {

        minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc, Mat());
        std::cout <<"No.channels:"<<i<<"minvalue:" << minv << "maxvalue:" << maxv << std::endl;
    }
    Mat mean, stddev;
    meanStdDev(image, mean, stddev);
    std::cout << "mean:" << mean << std::endl;
    std::cout << "stddev:" << stddev << std::endl;
}

十三：图像几何形状的绘制及文字的写入

void drawing_demo(Mat &image)
{
    Rect rect;
    rect.x = 400;
    rect.y = 200;
    rect.width = 100;
    rect.height = 100;
    Mat bg = Mat::zeros(image.size(),image.type());
    rectangle(bg, rect, Scalar(255, 0, 255), -1, 8, 0);

    circle(bg, Point(350, 400), 15, Scalar(0, 0, 255), 2, LINE_AA, 0);

    Mat dst;

    RotatedRect rtt;
    rtt.center = Point(200, 200);
    rtt.size = Size(100, 200);
    rtt.angle = 0.0;
    line(bg,Point(100,100),Point(350,400), Scalar(0, 0, 255), 8, LINE_AA, 0);
    ellipse(bg,rtt, Scalar(0, 0, 255), 2, 8);

    putText(bg, "hello everyone", Point(bg.cols/2-200, bg.rows/2), CV_FONT_HERSHEY_COMPLEX, 1.0, Scalar(0, 255, 0), 8, LINE_8);

    imshow("drawing",bg);
}


#include
#include
#include
using namespace std;
using namespace cv;

Mat src_bgImg;
const char *draw_window = "show windows";
void DrawLine();
void DrawRectangle();
void DrawEllipse();
void DrawCircle();
void DrawPolygon();
void DrawRandomLine();

int main()
{
            src_bgImg = Mat::ones(500,500,CV_8UC3);

            DrawLine();
            DrawRectangle();
            DrawEllipse();
            DrawCircle();
            DrawPolygon();

            putText(src_bgImg,"zhenghonghui",Point(200,200),CV_FONT_HERSHEY_COMPLEX,1.0,Scalar(255,255,0),1,8);
            namedWindow(draw_window,CV_WINDOW_AUTOSIZE);
            imshow(draw_window,src_bgImg);
            DrawRandomLine();

            waitKey(0);
            return 0;
}

void DrawLine()
{
    Point p1 = Point(100, 50);

    Point p2 = Point(300,300);
    Scalar color = Scalar(0,255,255);
    line(src_bgImg,p1,p2,color,3,LINE_8);

}

void DrawRectangle()
{
    Rect rect = Rect(150,20,100,100);
    Scalar color = Scalar(255, 0, 255);
    rectangle(src_bgImg,rect,color,3,LINE_8);
}
void DrawEllipse()
{

    Scalar color = Scalar(255, 0, 0);
    ellipse(src_bgImg,Point(src_bgImg.cols/2,src_bgImg.rows/2),Size(src_bgImg.cols/4,src_bgImg.rows/8),90,0,360,color,2,LINE_8);
}
void DrawCircle()
{
    Scalar color = Scalar(0, 255, 0);

    Point center = Point(src_bgImg.cols/2,src_bgImg.rows/2);
    circle(src_bgImg,center,80,color,2,LINE_8);

}
void DrawPolygon()
{
    Scalar color = Scalar(255, 255, 0);
    Point pts[1][5];
    pts[0][0] = Point(100,100);
    pts[0][1] = Point(100, 200);
    pts[0][2] = Point(200, 200);
    pts[0][3] = Point(200, 100);
    pts[0][4] = Point(100, 100);
    const Point *ppts[] = {pts[0]};
    int npt[] = {5};
    fillPoly(src_bgImg,ppts,npt,1,color,8);
}
void DrawRandomLine()
{
    RNG rng(12345);
    Point pt1;
    Point pt2;
    Mat bg_imgs = Mat::zeros(src_bgImg.size(),src_bgImg.type());
    namedWindow("randow_window",CV_WINDOW_AUTOSIZE);
    for (int i = 0; i < 20000; i++)
    {
        pt1.x = rng.uniform(0, src_bgImg.cols);
        pt2.x = rng.uniform(0, src_bgImg.cols);
        pt1.y = rng.uniform(0, src_bgImg.cols);
        pt2.y = rng.uniform(0, src_bgImg.cols);
        Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
        if (waitKey(100)==27)
        {
            break;
        }
        line(bg_imgs, pt1, pt2, color, 1, 8);
        imshow("randow_window", bg_imgs);
    }
}

十四：随机数和随机颜色

void QuickDemo::random_drawing()
{
    Mat canvas = Mat::zeros(Size(512,512), CV_8UC3);
    int w = canvas.cols;
    int h = canvas.rows;
    RNG rng(12345);
    while (true)
    {
        int c = waitKey(10);
        if (c == 27)
        {
            break;
        }
        int x1 = rng.uniform(0,canvas.cols);
        int y1 = rng.uniform(0, h);
        int x2 = rng.uniform(0, canvas.cols);
        int y2 = rng.uniform(0, h);
        int b  = rng.uniform(0, 255);
        int g  = rng.uniform(0, 255);
        int r  = rng.uniform(0, 255);
        canvas = Scalar(0,0,0);
        line(canvas, Point(x1, y1), Point(x2, y2), Scalar(b,g,r), 8, LINE_AA,0);
        imshow("随机绘制演示", canvas);
    }
}

十五：多边形填充和绘制

第一种方式，通过标记各个点，然后存储到容器中，之后对容器中的点进行操作。填充多边形调用fillPoly，绘制多边形调用polylines。第二种方式，使用一个API接口绘制。通过一个容器中的存储的点组成的另一个容器。

void QuickDemo::polyline_drawing_demo(Mat &image)
{
    Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);
    Point p1(100, 100);
    Point p2(350, 100);
    Point p3(450, 280);
    Point p4(320, 450);
    Point p5(80, 400);
    std::vector<Point>pts;
    pts.push_back(p1);

    pts.push_back(p2);
    pts.push_back(p3);
    pts.push_back(p4);
    pts.push_back(p5);

    std::vector<std::vector<Point>>contours;
    contours.push_back(pts);
    drawContours(canvas,contours,-1, Scalar(0, 0, 255),-1);

    imshow("多边形绘制", canvas);
}

十六：鼠标操作和响应


Point sp(-1, -1);
Point ep(-1, -1);
Mat temp;
static void on_draw(int event,int x,int y,int flags,void *userdata)
{
    Mat image = *((Mat*)userdata);
    if(event == EVENT_LBUTTONDOWN)
    {
        sp.x = x;
        sp.y = y;
        std::cout << "start point" <<sp<< std::endl;
    }
    else if (event == EVENT_LBUTTONUP)
    {
        ep.x = x;
        ep.y = y;
        int dx = ep.x - sp.x;
        int dy = ep.y - sp.y;
        if (dx > 0 && dy > 0)
        {
            Rect box(sp.x, sp.y, dx, dy);
            imshow("ROI区域", image(box));
            rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
            imshow("鼠标绘制", image);
            sp.x = -1;
            sp.y = -1;
        }
    }
    else if (event == EVENT_MOUSEMOVE)
    {
        if (sp.x > 0 && sp.y > 0)
        {
            ep.x = x;
            ep.y = y;
            int dx = ep.x - sp.x;
            int dy = ep.y - sp.y;
            if (dx > 0 && dy > 0)
            {
                Rect box(sp.x, sp.y, dx, dy);
                temp.copyTo(image);
                rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
                imshow("鼠标绘制", image);
            }
        }
    }
}
void QuickDemo::mouse_drawing_demo(Mat &image)
{
    namedWindow("鼠标绘制", WINDOW_AUTOSIZE);
    setMouseCallback("鼠标绘制", on_draw,(void*)(&image));

    imshow("鼠标绘制", image);
    temp = image.clone();
}

十七：图像像素类型的转换和归一化

void QuickDemo::norm_demo(Mat &image)
{
    Mat dst;
    std::cout << image.type() << std::endl;
    image.convertTo(image,CV_32F);
    std::cout << image.type() << std::endl;
    normalize(image, dst, 1.0, 0, NORM_MINMAX);
    std::cout << dst.type() << std::endl;
    imshow("图像的归一化", dst);

}

十八：图像的放缩和差值

void QuickDemo::resize_demo(Mat &image)
{
    Mat zoomin, zoomout;
    int h = image.rows;
    int w = image.cols;
    resize(image, zoomin, Size(w/2, h/2),0,0,INTER_LINEAR);

    imshow("zoomin", zoomin);;
    resize(image, zoomout, Size(w*1.5, h*1.5), 0, 0, INTER_LINEAR);
    imshow("zoomin", zoomout);
}

十九：图像的旋转

void QuickDemo::flip_demo(Mat &image)
{
    Mat dst;
    flip(image, dst, 0);
    flip(image, dst, 1);
    flip(image, dst, -1);
    imshow("图像翻转",dst);
}

二十：图像的翻转

void QuickDemo::rotate_demo(Mat &image)
{
    Mat dst, M;
    int h = image.rows;
    int w = image.cols;
    M = getRotationMatrix2D(Point(w / 2, h / 2),45,1.0);
    double cos = abs(M.at<double>(0, 0));
    double sin = abs(M.at<double>(0, 1));
    int nw = cos * w + sin * h;
    int nh = sin * w + cos * h;
    M.at<double>(0, 2) += (nw / 2 - w / 2);
    M.at<double>(1, 2) += (nh / 2 - h / 2);

    warpAffine(image, dst, M,Size(nw,nh),INTER_LINEAR,0, Scalar(0, 0, 255));
    imshow("旋转演示", dst);
}

二十一：读取视频文件

void QuickDemo::video_demo(Mat &image)
{
    VideoCapture capture("D:/images/123.mp4");
    Mat frame;

    while (true)
    {
        capture.read(frame);

        if(frame.empty())
        {
            break;
        }
        imshow("frame", frame);
        colorSpace_Demo(frame);
        int c = waitKey(100);
        if (c == 27) {
            break;
        }
    }
    capture.release();
}

二十二：视频处理和保存

视频的属性，SD（标清）,HD（高清）,UHD（超清），蓝光。如何读取视频文件，以及读取视频文件的属性，衡量视频处理指标：FPS。保存视频时的编码格式。保存视频的实际size和create的size大小保持一致。

void QuickDemo::video_demo(Mat &image)
{
    VideoCapture capture("D:/images/123.mp4");
    int frame_width = capture.get(CAP_PROP_FRAME_WIDTH);
    int frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);
    int count = capture.get(CAP_PROP_FRAME_COUNT);

    double fps = capture.get(CAP_PROP_FPS);
    std::cout << "frame width" << frame_width << std::endl;
    std::cout << "frame height" << frame_height << std::endl;
    std::cout << "frame FPS" << fps << std::endl;
    std::cout << "frame count" << count << std::endl;
    VideoWriter writer("D:/test.mp4",capture.get(CAP_PROP_FOURCC),fps,Size(frame_width, frame_height),true);

    Mat frame;
    while (true)
    {
        capture.read(frame);

        if(frame.empty())
        {
            break;
        }
        imshow("frame", frame);
        colorSpace_Demo(frame);
        writer.write(frame);

        int c = waitKey(100);
        if (c == 27) {
            break;
        }
    }
    capture.release();
    writer.release();
}

二十三：直方图

#include
#include "opencv2/opencv.hpp"
#include

using namespace std;
using namespace cv;
Mat src, hsv_src;
Mat hue;

int bins = 12;
void Hist_And_Backprojection(int, void*);
int main()
{
   src = imread("E:/QTFiles/firsttext/fish.jpg");
   if (!src.data)
   {
       cout << "could not load image...\n";
       return -1;
   }
   namedWindow("input", CV_WINDOW_AUTOSIZE);

   cvtColor(src, hsv_src, CV_BGR2HSV);

   hue.create(hsv_src.size(), hsv_src.depth());
   int nchannels[] = { 0,0 };

   mixChannels(&hsv_src, 1, &hue, 1, nchannels, 1);

   createTrackbar("Histogram Bins：", "input", &bins, 180, Hist_And_Backprojection);
   Hist_And_Backprojection(0, 0);

   imshow("input", src);
   return 0;
}

void Hist_And_Backprojection(int, void*)
{

   float range[] = { 0,180 };
   const float *histRanges = { range };
   Mat h_hist;
   calcHist(&hue, 1, 0, Mat(), h_hist, 1, &bins, &histRanges, true, false);

   normalize(h_hist, h_hist, 0, 255, NORM_MINMAX, -1, Mat());

   Mat backProjectIamge;
   calcBackProject(&hue, 1, 0, h_hist, backProjectIamge, &histRanges, 1, true);

   namedWindow("BackProjectIamge", CV_WINDOW_AUTOSIZE);
   imshow("BackProjectIamge", backProjectIamge);

   int hist_h = 400;
   int hist_w = 400;
   int bin_w = (hist_w / bins);
   Mat histImage(hist_w, hist_h, CV_8UC3, Scalar(0, 0, 0));
   for (size_t i = 1; i < static_cast<size_t>(bins); i++)
   {
       rectangle(histImage,
          Point((i - 1)*bin_w, (hist_h - cvRound(h_hist.at<float>(i - 1)*(400 / 255)))),
          Point(i*bin_w, (hist_h - cvRound(h_hist.at<float>(i)*(400 / 255)))),
          Scalar(0, 0, 255), 2, LINE_AA);
   }
   imshow("Histogram", histImage);
   waitKey(0);

}


#include
#include
#include
using namespace cv;
using namespace std;

const char*output = "histogram iamge";

int main()
{

    Mat src, dst, dst1;
    src=imread("E:\\QTFiles\\zhhpicture\\zhh.jpg");
    if(src.empty())
    {
        cout<<"imread wrong!"<<endl;
    }

       char input[] = "input image";
       namedWindow(input, CV_WINDOW_AUTOSIZE);
       namedWindow(output, CV_WINDOW_AUTOSIZE);
       imshow(input, src);

       vector<Mat>bgr_planes;
       split(src, bgr_planes);

       int histsize = 256;
       float range[] = { 0,256 };
       const float*histRanges = { range };
       Mat b_hist, g_hist, r_hist;
       calcHist(&bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histsize, &histRanges, true, false);
       calcHist(&bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histsize, &histRanges, true, false);
       calcHist(&bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histsize, &histRanges, true, false);

       int hist_h = 400;
       int hist_w = 512;
       int bin_w = hist_w / histsize;
       Mat histImage(hist_w, hist_h, CV_8UC3, Scalar(0, 0, 0));
       normalize(b_hist, b_hist, 0, hist_h, NORM_MINMAX, -1, Mat());
       normalize(g_hist, g_hist, 0, hist_h, NORM_MINMAX, -1, Mat());
       normalize(r_hist, r_hist, 0, hist_h, NORM_MINMAX, -1, Mat());

       for (int i = 1; i < histsize; i++)
       {

           line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(b_hist.at<float>(i - 1))),
              Point((i)*bin_w, hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, CV_AA);

           line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(g_hist.at<float>(i - 1))),
              Point((i)*bin_w, hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, CV_AA);

           line(histImage, Point((i - 1)*bin_w, hist_h - cvRound(r_hist.at<float>(i - 1))),
              Point((i)*bin_w, hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, CV_AA);
       }
       imshow(output, histImage);
       waitKey(0);

       return 0;
}

二十四：直方图的均衡化

均衡化的图像只支持单通道。

void QuickDemo::histogram_eq_demo(Mat &image)
{
    Mat gray;
    cvtColor(image, gray, COLOR_BGR2GRAY);

    imshow("灰度图像", gray);
    Mat dst;
    equalizeHist(gray, dst);
    imshow("直方图均衡化", dst);
}

二十五：图像的卷积操作

卷积的作用，高的往下降，低的往上升。但是会造成信息丢失。产生模糊效果。是一种线性操作，点乘，之后相加。

void QuickDemo::blur_demo(Mat &image)
{
    Mat dst;
    blur(image, dst, Size(15, 15), Point(-1, -1));

    imshow("图像卷积操作", dst);
}

二十六：模糊处理

高斯模糊
中心的数值最大，离中心距离越远，数值越小。
高斯卷积数学表达式说明：

OpenCV基础学习
OpenCV基础学习 
void QuickDemo::gaussian_blur_demo(Mat &image)
{
    Mat dst;
    GaussianBlur(image, dst, Size(5, 5), 15);
    imshow("高斯模糊", dst);

}

高斯双边模糊

void QuickDemo::bifilter_demo(Mat &image)
{
    Mat dst;
    bilateralFilter(image,dst,0,100,0);

    namedWindow("双边模糊", WINDOW_FREERATIO);
    imshow("双边模糊", dst);
}

1、在左上角加

#include
#include
#include
using namespace cv;
using namespace std;

int main()
{
    Mat image1=imread("E:\\QTFiles\\zhhpicture\\zhh.jpg");
    Mat image2=imread("E:\\QTFiles\\zhhpicture\\name.png");
    imshow("initial image",image1);

    Mat roi =image1(Rect(0,0,image2.cols,image2.rows));

    Mat mask(image2);
    image2.copyTo(roi,mask);
    imshow("image2",image2);
    imshow("mask img1",image1);
    waitKey(0);

    return 0;
}

2、在中间加

int x, y;
    x = image1.cols / 2 - image2.cols / 2;
    y = image1.rows / 2 - image2.rows / 2;
    Mat roi = image1(Rect(x, y, image2.cols, image2.rows));

二十八：Mat类型和QImage类型之间的转换

Mat->QImage

QImage cvMat2QImage(const cv::Mat&amp; mat)
{

    if(mat.type() == CV_8UC1)
    {
        QImage image(mat.cols, mat.rows, QImage::Format_Indexed8);

        image.setColorCount(256);
        for(int i = 0; i < 256; i++)
        {
            image.setColor(i, qRgb(i, i, i));
        }

        uchar *pSrc = mat.data;
        for(int row = 0; row < mat.rows; row ++)
        {
            uchar *pDest = image.scanLine(row);
            memcpy(pDest, pSrc, mat.cols);
            pSrc += mat.step;
        }
        return image;
    }

    else if(mat.type() == CV_8UC3)
    {

        const uchar *pSrc = (const uchar*)mat.data;

        QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_RGB888);
        return image.rgbSwapped();
    }
    else if(mat.type() == CV_8UC4)
    {
        qDebug() << "CV_8UC4";

        const uchar *pSrc = (const uchar*)mat.data;

        QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_ARGB32);
        return image.copy();
    }
    else
    {
        qDebug() << "ERROR: Mat could not be converted to QImage.";
        return QImage();
    }
}
cv::Mat QImage2cvMat(QImage image)
{
    cv::Mat mat;
    qDebug() << image.format();
    switch(image.format())
    {
    case QImage::Format_ARGB32:
    case QImage::Format_RGB32:
    case QImage::Format_ARGB32_Premultiplied:
        mat = cv::Mat(image.height(), image.width(), CV_8UC4, (void*)image.constBits(), image.bytesPerLine());
        break;
    case QImage::Format_RGB888:
        mat = cv::Mat(image.height(), image.width(), CV_8UC3, (void*)image.constBits(), image.bytesPerLine());
        cv::cvtColor(mat, mat, CV_BGR2RGB);
        break;
    case QImage::Format_Indexed8:
        mat = cv::Mat(image.height(), image.width(), CV_8UC1, (void*)image.constBits(), image.bytesPerLine());
        break;
    }
    return mat;
}

QImage->Mat

  image2=image2.convertToFormat(QImage::Format_RGB888);
  Mat image1=Mat(image2.height(),image2.width(),CV_8UC(3),image2.bits(),image2.bytesPerLine());

未完待续

遇到更多我会继续添加

Original: https://blog.csdn.net/qq_46338854/article/details/124474669
Author: &*Savior
Title: OpenCV基础学习

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