OpenCV课程C++实例代码实现

这篇文章主要是为了保存一下之前学习的opencv代码，其次是希望能帮助到其他人。

B站网课链接贾志刚老师43讲课程感觉有点没用免费课程干货太少，简单的介绍OpenCV常见函数的用法，并没有深入讲解源码。可能是免费课程的原因,建议看一下源码就行。目前在看C++的OpenCV课程推荐有夏曹俊老师的课程。这个应该是教育机构的推销课程相对其他OpenCV相比还是比较优秀的。

一、什么是OpenCV

OpenCV的全称是Open Source Computer Vision Library，是一个跨平台的计算机视觉处理开源软件库，是由Intel公司俄罗斯团队发起并参与和维护，支持与计算机视觉和机器学习相关的众多算法，以BSD许可证授权发行，可以在商业和研究领域中免费使用。OpenCV可用于开发实时的图像处理、计算机视觉以及模式识别程序，该程序库也可以使用英特尔公司的IPP进行加速处理。

二、OpenCV官方下载链接

Opencv官网地址为官方地址，网站提供了各个版本源码、发行版的下载地址及说明文档，其中Windows的下载包是一个自解压程序，解压之后有两个文件夹，其中sources文件夹里包含源码，build中包含编译好的库文件，以供Windows下编译opencv使用。
GitHub上的地址为Git地址，该网址下有多个opencv相关的仓库，其中opencv仓库中包含了opencv的源码。

三、Visual Studio配置OpenCV环境

之前写过一篇这方面的配置方法跳转接口

综合比较了几篇CSDN其他大佬的配置环境的文章比较推荐这一篇OpenCV配置

四、代码实现功能目录

void colorSpace_demo(Mat& image); //图像色彩空间转换
   void mat_creation_demo(Mat& image);           //图像对象的创建与赋值
   void pixel_visit_demo(Mat& image); //图像像素的读写操作
   void operators_demo(Mat& image); //图像像素的算术操作
   void tracking_bar_demo(Mat& image); //滚动条调节图像亮度
   void key_demo(Mat& image); //滚动条参数传递
   void color_style_demo(Mat& image); //键盘响应操作
   void bitwise_demo(Mat& image);   //自带颜色表操作
   void channels_demo(Mat& image); //图像像素逻辑操作
   void inrange_demo(Mat& image); //通道分离与合并
   void pixel_statistic_demo(Mat& image);               //图像色彩空间转换
   void drawing_demo(Mat& image);                       //图像几何图形绘制
   void random_drawing_demo(Mat& image); //随机数与随机颜色
   void polyline_drawing_demo(Mat& image);           //多边形天聪与绘制
   void mouse_drawing_demo(Mat& image);             //鼠标操作与响应
   void norm_demo(Mat& image); //图像像素转化
   void resize_demo(Mat& image); //图像放缩与插值
   void flip_demo(Mat& image); //图像翻转
   void rotate_demo(Mat& image); //图像旋转
   void video_demo(Mat& image); //视频文件使用
   void videokeep_demo(Mat& image);                   //视频处理与保存
   void showHistogram_demo(Mat& image); //图像直方图
   void histogram_2d_demo(Mat& image);               //二维直方图
   void hisogram_eq_demo(Mat& image);                 //直方图均衡化
   void blur_demo(Mat& image); //图像卷积操作
   void gaussian_blur_demo(Mat& image);               //高斯模糊
   void bifilter_demo(Mat& image); //高斯双边模糊

五、源代码

quickopencv.h

#pragma once
#include <opencv2/opencv.hpp>
#include <iostream>using namespace std;
using namespace cv;class QuickDemo {public:void colorSpace_demo(Mat& image);                    //图像色彩空间转换void mat_creation_demo(Mat& image);                 //图像对象的创建与赋值void pixel_visit_demo(Mat& image);                      //图像像素的读写操作void operators_demo(Mat& image);                     //图像像素的算术操作void tracking_bar_demo(Mat& image);                  //滚动条调节图像亮度void key_demo(Mat& image);                           //滚动条参数传递void color_style_demo(Mat& image);                     //键盘响应操作void bitwise_demo(Mat& image);                          //自带颜色表操作void channels_demo(Mat& image);                    //图像像素逻辑操作void inrange_demo(Mat& image);                        //通道分离与合并void pixel_statistic_demo(Mat& image);                 //图像色彩空间转换void drawing_demo(Mat& image);                        //图像几何图形绘制void random_drawing_demo(Mat& image);                 //随机数与随机颜色void polyline_drawing_demo(Mat& image);           //多边形天聪与绘制void mouse_drawing_demo(Mat& image);                  //鼠标操作与响应void norm_demo(Mat& image);                        //图像像素转化void resize_demo(Mat& image);                       //图像放缩与插值void flip_demo(Mat& image);                        //图像翻转void rotate_demo(Mat& image);                         //图像旋转void video_demo(Mat& image);                          //视频文件使用void videokeep_demo(Mat& image);                    //视频处理与保存void showHistogram_demo(Mat& image);               //图像直方图void histogram_2d_demo(Mat& image);                  //二维直方图void hisogram_eq_demo(Mat& image);                   //直方图均衡化void blur_demo(Mat& image);                         //图像卷积操作void gaussian_blur_demo(Mat& image);                //高斯模糊void bifilter_demo(Mat& image);                      //高斯双边模糊          };

quickdemo.cpp

#include "quickopencv.h"void QuickDemo::colorSpace_demo(Mat& image)
{Mat gray, hsv;cvtColor(image, hsv, COLOR_BGR2HSV);//HSV 三通道  H0~180  S0~255   V0~255   V是亮度cvtColor(image, gray, COLOR_BGR2GRAY);imshow("HSV", hsv);imshow("hh", gray);imwrite("D:\\系统默认\\桌面\\OpenCV\\OpenCV_S02\\hsv.jpg",hsv);imwrite("D:\\系统默认\\桌面\\OpenCV\\OpenCV_S02\\gray.jpg",gray);
}void QuickDemo::mat_creation_demo(Mat& image)
{Mat m1, m2;m1 = image.clone();image.copyTo(m2);//创建空白图像Mat m3 = Mat::ones(Size(8, 8), CV_8UC3); //8位无符号的单通道数据cout << "width: " << m3.cols << "height:  " << m3.rows << "channels  " << m3.channels() << endl;m3 = Scalar(0, 0,255);//Mat m4 = m3;//clone 克隆 而直接等于就会改变两个，随便用 “= ”会重构Mat m4 = m3.clone();m4 = Scalar(0, 255, 255);imshow("m3", m3);imshow("m4", m4);
}
void QuickDemo::pixel_visit_demo(Mat& image)
{int w = image.cols;int h = image.rows;int dims = image.channels(); //通道for (int row = 0; row < h; row++){uchar* current_row = image.ptr<uchar>(row);for (int col = 0; col < w; col++){if (dims == 1)  //灰度图像{int pv = image.at<uchar>(row, col);image.at<uchar>(row, col) = 255 - pv;} if (dims == 3)  //彩色图像{//Vec3b 一次性获取是三个值Vec3b bgr = image.at<Vec3b>(row, col);image.at<Vec3b>(row, col)[0] = 255 - bgr[0]-34;image.at<Vec3b>(row, col)[1] = 255 - bgr[1]-34;image.at<Vec3b>(row, col)[2] = 255 - bgr[2]-34;}}}imshow("像素读写", image);
}
void QuickDemo::operators_demo(Mat& image)
{Mat dst = Mat::zeros(image.size(), image.type());;//创建图像全是0Mat m = Mat::zeros(image.size(), image.type());m=Scalar(50, 50, 50);//opencv中两个图像相乘与专有函数multiply//dst = image * m;//multiply(image, m, dst);//加法//int w = image.cols;//int h = image.rows;//int dims = image.channels(); //通道//for (int row = 0; row < h; row++)//{//  for (int col = 0; col < w; col++)//   {//         //Vec3b 一次性获取是三个值//         Vec3b p1 = image.at<Vec3b>(row, col);//          Vec3b p2 = m.at<Vec3b>(row, col);//          //saturate_cast<uchar>（）类型数值限定uchar------>0~255//            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);//add(image, m, dst);imshow("减法操作",dst);//subtract(image, m, dst);imshow("乘法操作",dst);//multiply(image, m, dst);imshow("除法操作",dst);//divide(image, m, dst);
}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);//图像融合 1:数组通常为图片，2.第一个数组的权重3.第二的数组图片//，4，第二过数组的权重，5一个加到权重总和的标量，6：输出数组，7：可选深度addWeighted(image, 1.0, m, 0.0, b, dst);imshow("亮度与对比度调节", 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;//图像融合addWeighted(image,contrast,m,0.0,0,dst);imshow("亮度与对比度调节", dst);
}void QuickDemo::tracking_bar_demo(Mat& image)
{namedWindow("亮度与对比度调节",WINDOW_AUTOSIZE);int lightness = 50;int max_value = 100;//回调参数错:1:滑动栏名称，2：吸附窗口名称，3.滑动条初始值，指针，4.最大值，5.回调函数，6.void*类型指针 //向回调函数传输数据createTrackbar("Value Bar：","亮度与对比度调节", &lightness, max_value, on_lightness,(void*)(&image));createTrackbar("Contrast Bar：", "亮度与对比度调节", &max_value, 200, on_contrast, (void*)(&image));
} 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){cout << "you key #1" << endl;cvtColor(image, dst, COLOR_BGR2GRAY);}if (c == 50){cout << "you key #2" << endl;cvtColor(image, dst, COLOR_BGR2HSV);}if (c == 51){cvtColor(image, dst, COLOR_BGR2HSV); //不加会卡死cout << "you key #3" << endl;dst = Scalar(50, 50, 50);add(image, dst, dst);}imshow("键盘响应", dst);}
}
void  QuickDemo::color_style_demo(Mat& image)
{int colormap[] = {COLORMAP_AUTUMN,COLORMAP_BONE,COLORMAP_JET,COLORMAP_WINTER,COLORMAP_RAINBOW,COLORMAP_OCEAN,COLORMAP_SUMMER,COLORMAP_SPRING,COLORMAP_COOL,COLORMAP_PINK,COLORMAP_HOT,COLORMAP_PARULA,COLORMAP_MAGMA,COLORMAP_INFERNO,COLORMAP_PLASMA,COLORMAP_VIRIDIS,COLORMAP_CIVIDIS,COLORMAP_TWILIGHT,COLORMAP_TWILIGHT_SHIFTED,};Mat dst = Mat::zeros(image.size(), image.type());int index = 0;while (true){char c = waitKey(2000);if (c == 27){break;}//滤镜函数1输入图像，2.输出图像，3.颜色类型applyColorMap(image, dst, colormap[index % 19]);index++;imshow("颜色style", dst);}
}
void QuickDemo::bitwise_demo(Mat& image)
{Mat m1 = Mat::zeros(Size(256,256),CV_8UC3);Mat m2 = Mat::zeros(Size(256,256),CV_8UC3);// 矩形函数1.图像，2.矩形顶点，3.对角线定点 3.线条颜色，4.线条粗细 ,5.线条类型,6.坐标小数点位数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;//图像位运算函数，1，图片1,2.图片2,3.得到的图片//bitwise_or  bitwise_andbitwise_xor(m1, m2, dst);imshow("像素位操作",dst);
}
void QuickDemo::channels_demo(Mat& image)
{vector<Mat> mv;split(image, mv);imshow("蓝色风格", mv[0]);imshow("绿色风格", mv[1]);imshow("红色风格", mv[2]);Mat dst;mv[1] = 0;mv[2] = 0;//合并数据merge(mv, dst);imshow("蓝色", dst);int from_to[] = { 0,2,1,1,2,0 };//1.输入对象，2.输入对象个数，3.输出对象, 4.输出对象个数,5.拷贝构造mixChannels(&image,1, &dst, 1,from_to,3);imshow("通道混合", dst);
}
void QuickDemo::inrange_demo(Mat& image)
{Mat hsv;cvtColor(image, hsv, COLOR_BGR2HSV);Mat mask;//二值化HSV 1·处理图像，2·下界数组，3·上边界数组，4·输出图像inRange(hsv, Scalar(35, 43, 46), Scalar(77, 255, 25), mask);imshow("mask", mask);Mat redback = Mat::zeros(image.size(), image.type());redback = Scalar(40, 40, 200);bitwise_not(mask, mask);image.copyTo(redback, mask);
}
void QuickDemo::pixel_statistic_demo(Mat& image)
{double minv, maxv;Point minLoc, maxLoc;vector<Mat> mv;split(image, mv);cout << endl;for (int i = 0; i <mv.size(); i++){//功能：从一个矩阵中找出全局的最大值和最小值。/* 参数1：InputArray类型的src，输入单通道数组（图像）。参数2：double* 类型的minVal，返回最小值的指针。若无须返回，此值置为NULL。参数3：double* 类型的maxVal，返回最大值的指针。若无须返回，此值置为NULL。参数4：Point* 类型的minLoc，返回最小位置的指针（二维情况下）。若无须返回，此值置为NULL。参数5：Point* 类型的maxLoc，返回最大位置的指针（二维情况下）。若无须返回，此值置为NULL。参数6：InputArray类型的mask，用于选择子阵列的可选掩膜。*/minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc, Mat());cout <<"No.channels:" <<i<< "  " << "min value:" << minv << "  "<<"max value:" << maxv << endl;}Mat mean, stddev;// 参数      说明   用于：平均值和标准方差统计//src        输入数组（图像）1 - 4通道都可以//mean  均值//stddev  标准差//mask   用于选择子阵列的可选掩码meanStdDev(image, mean, stddev);cout << "means:" <<endl<< mean << endl;cout <<"stddev:" <<endl<< stddev << endl;}void  QuickDemo::drawing_demo(Mat& image)
{Rect rect;rect.x = 170;rect.y = 100;rect.width = 100;rect.height = 120;Mat bg = Mat::zeros(image.size(), image.type());/*  参数     说明img           Mat类型 输入输出图像rec         Rect类型 绘制好的矩形框(左上角坐标和长宽已确定)color        用Scalar函数绘制颜色thickness  矩形框填充方式 thickness > 0为矩形框， thickness < 0为填充 默认为1lineType   矩形框的线条类型 默认为8shift      点坐标小数数位 默认为0*/rectangle(image, rect, Scalar(0, 0, 255), -1, 8, 0);//  参数        说明//img      Mat类型 输入输出图像//center   画圆的圆心坐标//radius 圆的半径//color 用Scalar函数绘制圆颜色//thickness   如果是正数，表示组成圆的线条的粗细程度。否则，表示圆是否被填充//lineType 线条类型 默认为8//shift        圆心坐标点和半径值的小数点位数circle(image, Point(300, 450),20, Scalar(255, 0, 0), -1, 8, 0);/*    参数  说明img       Mat类型 输入输出图像ptr1    point类型 线段起点ptr2    point类型 线段终点color   用Scalar函数绘制圆颜色thickness 如果是正数，表示组成圆的线条的粗细程度。否则，表示圆是否被填充lineType   线条类型 默认为8shift  坐标点的小数点位数*/line(bg, Point(100, 100), Point(350, 400), Scalar(0, 255, 0), 2, 8, 0);RotatedRect rrt;rrt.center = Point(200, 200);rrt.size = Size(100, 200);rrt.angle = 90.0;/*   参数    说明img       Mat类型 输入输出图像box     RotatedRect类型 旋转好的外接矩阵内嵌椭圆（包括外接矩形的中心，变长和旋转角度）color 用Scalar函数绘制圆颜色thickness 如果是正数，表示组成圆的线条的粗细程度。否则，表示圆是否被填充lineType   线条类型 默认为8*/ellipse(bg, rrt, Scalar(0,255,255), 2, 8);Mat dst;addWeighted(image, 0.7, bg, 0.3, 0,dst);imshow("绘制演示",dst);
}
void  QuickDemo::random_drawing_demo(Mat& image)
{Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);RNG rng(12345);int w = canvas.cols;int h = canvas.rows;while (true){int c = waitKey(10);if (c == 27){break;}int x1 = rng.uniform(0, w);int y1 = rng.uniform(0, h);int x2 = rng.uniform(0, w);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), 1, LINE_AA, 0);imshow("显示",canvas);}}
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(70, 400);vector<Point> pts;pts.push_back(p1);pts.push_back(p2);pts.push_back(p3);pts.push_back(p4);pts.push_back(p5);/*fillPoly(img,ppt,npt,1,Scalar(255,255,255),lineType);1、多边形将被画到img上2、多边形的顶点集为ppt3、绘制的多边形顶点数目为npt4、要绘制的多边形数量为15、多边形的颜色定义为Scarlar(255, 255, 255)，即RGB的值为白色*/fillPoly(canvas, pts, Scalar(255, 0, 0),8,0);/*  void polylines(Mat& img, const Point** pts, const int* npts, int ncontours, bool isClosed, const Scalar& color, int thickness=1, int lineType=8, int shift=0 );        参数            说明img           作为画布的矩阵pts             折线顶点数组npts      折线顶点个数ncontours     待绘制折线数isClosed  是否是闭合折线(多边形)color       折线的颜色thickness  折线粗细lineType    线段类型shift       缩放比例(0是不缩放, 4是1 / 4)*/polylines(canvas, pts, true, Scalar(0, 255, 0), 2,LINE_AA, 0);vector<vector<Point>> contours;contours.push_back(pts);/*void drawContours(InputOutputArray image, InputArrayOfArrays contours, int contourIdx,const Scalar & color, int thickness = 1, int lineType = 8, InputArray hierarchy = noArray(), int maxLevel = INT_MAX, Point offset = Point())函数参数详解：第一个参数image表示目标图像，第二个参数contours表示输入的轮廓组，每一组轮廓由点vector构成，第三个参数contourIdx指明画第几个轮廓，如果该参数为负值，则画全部轮廓，第四个参数color为轮廓的颜色，第五个参数thickness为轮廓的线宽，如果为负值或CV_FILLED表示填充轮廓内部，第六个参数lineType为线型，第七个参数为轮廓结构信息，第八个参数为maxLevel*/drawContours(canvas, contours, -1, Scalar(0, 255, 0), 2);imshow("多边形绘制", canvas);
}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;cout << "start point:" << sp << 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);rectangle(image,box, Scalar(0, 0, 255), 2, 8, 0);imshow("鼠标绘制", image);imshow("ROI区域", temp(box));//为下一次画图准备 更新操作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:: QuickDemo::mouse_drawing_demo(Mat& image)
{namedWindow("鼠标绘制", WINDOW_AUTOSIZE);namedWindow("ROI区域", WINDOW_NORMAL);setMouseCallback("鼠标绘制",on_draw,(void*)(&image));setMouseCallback("ROI区域", on_draw, (void*)(&image));imshow("鼠标绘制", image);temp = image.clone();   imshow("ROI区域", temp);
}
void  QuickDemo::norm_demo(Mat& image)
{Mat dst;cout << image.type() << endl;image.convertTo(dst,CV_32F);cout << dst.type() << endl;//归一化函数normalize(image, dst, 1.0, 0, NORM_MINMAX);cout << dst.type() << endl;imshow("图像数据归一化",dst);
}
void QuickDemo::resize_demo(Mat& image)
{Mat zoomin = Mat::zeros(image.size(), image.type()); Mat zoomax = Mat::zeros(image.size(), image.type());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, zoomax, Size(w*1.5, h*1.5), 0, 0, INTER_LINEAR);imshow("zoomax", zoomax);
}
void QuickDemo::flip_demo(Mat& image)
{Mat dst;/*     参数 src 输入矩阵.参数 dst 输出矩阵，和输入矩阵一样大小。参数 flipCode 一个标志，决定怎么翻转矩阵; 0 是围绕着x轴翻转，正直是围绕着y轴翻转，负值是围绕着两个轴一起翻转。 around both axes.参考 transpose, repeat, completeSym*/flip(image, dst, 0);imshow("图像翻转",dst);
}
void QuickDemo::rotate_demo(Mat& image)
{Mat dst, M;int w = image.cols;int h = image.rows;M = getRotationMatrix2D(Point2f(w/2, h/2), 45, 1.0);double cos = abs(M.at<double>(0, 0));double sin = abs(M.at<double>(0, 1));double nw = cos * w + sin * h;double 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(0,255,0));imshow("旋转演示", dst);
}
void QuickDemo::video_demo(Mat& image)
{VideoCapture capture(0);Mat frame;while (true){capture.read(frame);flip(frame, frame,1);if (frame.empty()){break;}imshow("frame", frame);int c = waitKey(10);if (c == 27){break;}}
}
void QuickDemo::videokeep_demo(Mat& image)
{VideoCapture capture("D:\\HONOR Magic-link\\Screenshot\\20220708_144725.mp4");double frame_width = capture.get(CAP_PROP_FRAME_WIDTH);double frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);double frame_count = capture.get(CAP_PROP_FRAME_COUNT);double frame_fps = capture.get(CAP_PROP_FPS);cout << " frame_fps:  " << frame_fps << endl;cout << "frame_width: "<<frame_width << endl;cout << "frame_height:"<<frame_height << endl;cout << "frame_count: "<<frame_count << endl;VideoWriter writer("D://HONOR Magic-link//Screenshot//text.mp4", capture.get(CAP_PROP_FOURCC),frame_fps,Size(frame_width,frame_height),true);Mat frame;while (true){capture.read(frame);flip(frame, frame, 1);if (frame.empty()){break;}imshow("frame", frame);colorSpace_demo(frame);writer.write(frame);int c = waitKey(10);if (c == 27){break;}}capture.release();writer.release();
}void QuickDemo::showHistogram_demo(Mat& image)
{//三通道分离vector <Mat> bgr_plane;split(image, bgr_plane);//定义参数变量const int channels[1] = { 0 };const int bins[1] = { 256 };float hranges[2] = { 0,255 };const float* ranges[1] = { hranges };Mat b_hist;Mat g_hist;Mat r_hist;//计算Blue、Green、Red通道直方图calcHist(&bgr_plane[0], 1, 0, Mat(), b_hist, 1, bins, ranges);calcHist(&bgr_plane[1], 1, 0, Mat(), g_hist, 1, bins, ranges);calcHist(&bgr_plane[2], 1, 0, Mat(), r_hist, 1, bins, ranges);//显示直方图int hist_w = 512;int hist_h = 400;int bin_w = cvRound((double)hist_w / bins[0]);Mat histImage = Mat::zeros(hist_h, hist_w, CV_8UC3);//归一化直方图数据-->将直方图的取值范围限制在画布高度中normalize(b_hist, b_hist,0, histImage.rows, NORM_MINMAX, -1, Mat());normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());//绘制直方图曲线for (int i = 1; i < bins[0]; i++){line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))),Point(bin_w * (i), hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 2, 8, 0);line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))),Point(bin_w * (i), hist_h - cvRound(g_hist.at<float>(i))), Scalar(0, 255, 0), 2, 8, 0);line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))),Point(bin_w * (i), hist_h - cvRound(r_hist.at<float>(i))), Scalar(0, 0, 255), 2, 8, 0);}//显示直方图namedWindow("Histogram Demo", WINDOW_AUTOSIZE);imshow("Histogram Demo", histImage);}
void  QuickDemo::histogram_2d_demo(Mat& image)
{Mat hsv, hs_hist;cvtColor(image, hsv, COLOR_BGR2HSV);int hbins = 30, sbins = 32;int hist_bins[] = { hbins,sbins };float h_range[] = { 0,180 };float s_range[] = { 0,256 };const float* hs_ranges[] = { h_range,s_range };int hs_channels[] = { 0,1 };calcHist(&hsv, 1, hs_channels, Mat(), hs_hist, 2, hist_bins, hs_ranges, true, false);double maxVal = 0;minMaxLoc(hs_hist, 0, &maxVal, 0, 0);int scale = 10;Mat hist2d_image = Mat::zeros(sbins * scale, hbins * scale,CV_8UC3);for (int h = 0; h < hbins; h++){for (int s = 0; s < sbins; s++){float binVal = hs_hist.at<float>(h, s);int intensity = cvRound( binVal* 255 / maxVal);rectangle(hist2d_image, Point(h * scale, s * scale),Point((h+1)*scale-1,(s+1)*scale-1),Scalar::all(intensity),-1);}}applyColorMap(hist2d_image, hist2d_image, COLORMAP_JET);imshow("H_s", hist2d_image);
}
void  QuickDemo::hisogram_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);
}void  QuickDemo::blur_demo(Mat& image)
{Mat dst;blur(image, dst, Size(3, 3), Point(-1, -1));imshow("图像模糊",dst);
}void  QuickDemo::gaussian_blur_demo(Mat& image)
{Mat dst;GaussianBlur(image, dst, Size(5, 5), 15);imshow("高斯模糊",dst);
}void  QuickDemo::bifilter_demo(Mat& image)
{Mat dst;bilateralFilter(image, dst, 0,100, 10);imshow("双边模糊", dst);
}

main.cpp

#include "quickopencv.h"
#include <fstream>
#include <string>
int main(int argc, char** argv)
{//Mat 矩阵 //imread() 读图片,参数1：图片位置（注意反斜线方向） 参数2:色域Mat src = imread("D:\\系统默认\\桌面\\OpenCV\\OpenCV_S02\\text.jpg");//判断是否找到图片位置，防止报错if (src.empty()){cout << "没找到图片" << endl;return -1;}  namedWindow("windowname",WINDOW_FREERATIO);imshow("windowname", src);QuickDemo qd;//qd.colorSpace_Demo(src);//qd.mat_creation_demo(src);//qd.pixel_visit_demo(src);//qd.operators_demo(src);//qd.tracking_bar_demo(src);//qd.key_demo(src);//qd.color_style_demo(src);//qd.bitwise_demo(src);//qd.channels_demo(src);//qd.inrange_demo(src);//qd.pixel_statistic_demo(src);//qd.drawing_demo(src);//qd.random_drawing(src);//qd.polyline_drawing_demo(src);//qd.mouse_drawing_demo(src);//qd.norm_demo(src);//qd.resize_demo(src);//qd.flip_demo(src);//qd.rotate_demo(src);//qd.video_demo(src);//qd.videokeep_demo(src);//qd.showHistogram_demo(src);//qd.histogram_2d_demo(src);//qd.hisogram_eq_demo(src);//qd.blur_demo(src);//qd.gaussian_blur_demo(src);//qd.bifilter_demo(src);waitKey(0);         //因为阻塞就会一直停留destroyAllWindows();system("cls");    return 0;
}