OpenCv基础（C++）
1.图像读取与显示
#include<opencv2/opencv.hpp>
using namespace cv;Mat src = imread("C:/Users/16385/Desktop/new/photo/1.jpg");//读取图像
Mat src = imread("C:/Users/16385/Desktop/new/photo/1.jpg",IMREAD_GRAYSCALE);
//将读取的图像转为灰色imshow("输入图像", src);//展示图像
2.色彩空间转换
Mat gray, hsv;cvtColor(image, hsv, COLOR_BGR2HSV);//将image的RGB色彩空间转换为hsv色彩空间
cvtColor(image, gray, COLOR_BGR2GRAY);imwrite("D:/hsv.png", hsv);//保存图片到D盘
imwrite("D:/gray.png", gray);hsv色彩空间
inrange 检查输入图像中的每个像素，如果像素值位于指定的范围内，则在输出图像（或掩码）中对应位置的像素被设置为白色（或者说是255），否则被设置为黑色（0）
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", mask);Mat redback=Mat::zeros(image.size(),image.type());redback = Scalar(40, 40, 200);bitwise_not(mask, mask);imshow("mask", mask);image.copyTo(redback, mask);//copyTO的行为//只在mask为白色的区域，将image的像素复制到redback。//黑色区域保持不变（即保留红色背景）。imshow("roi区域提取", redback);
}3.创建空白图像
这种初始化通常用于：创建空白图像，初始化临时处理矩阵，准备存储三通道数据（如彩色图像）
Mat m3 = Mat::zeros(Size(512, 512), CV_8UC3);//表示每个元素是 8 位无符号整数（0-255），并且每个像素有 3 个通道 8U C3m3 = Scalar(0, 0, 255);//设置图像的色彩值std::cout << "width: " << m3.cols << "height: " << m3.rows << "channel: " << m3.channels() << std::endl;//std::cout << m3 << std::endl;imshow("图像", m3);
1，2，3可以看作是一个像素点
4.拷贝图像
Mat m4;
m3.copyTo(m4);//深拷贝（再创建一份空间，存储）Mat m4=m3.clone();//深拷贝Mat m4=m3;//浅拷贝（使用原来的空间，后续更改m4，m3也会更改）5.图像的读写
//for循环读写int w = image.cols;int h = image.rows;int dims = image.channels();for (int row = 0; row < h; 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 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];}}}imshow("像素图像",image);//指针遍历，速度快for (int row = 0; row < h; row++){uchar* current = image.ptr<uchar>(row);//获取当前行指针for (int col = 0; col < w; col++){if (dims == 1)//灰度图像{int pv = *current;*current++ = 255 - pv;//先索引，指针再++}if (dims == 3)//彩色图像{*current++ = 255 - *current;*current++ = 255 - *current;*current++ = 255 - *current;}}}6.图像像素的运算操作
Mat dst = Mat::zeros(image.size(),image.type());Mat m = Mat::zeros(image.size(), image.type());m = Scalar(5, 5, 5);//运算后超过255，值会变成255，反之少于0，为0add(image, m, dst);subtract(image, m, dst);multiply(image, m, dst);divide(image, m, dst);imshow("运算图像", dst);7.滑块控件动态改变图像亮度
static void on_track(int b, void* userdata)
{Mat image = *((Mat*)userdata);//先将void*指针强制转化为Mat*，再解引用得到image对象Mat dst = Mat::zeros(image.size(), image.type());Mat m = Mat::zeros(image.size(), image.type());m = Scalar(b, b, b);add(image, m, dst);imshow("亮度调整", dst);
}
void QuickDemo::tracking_bar_demo(Mat& image)//引用传递是浅拷贝
{namedWindow("亮度调整", WINDOW_AUTOSIZE);//创建窗口//src = image;int lightness = 50;int max_value = 100;createTrackbar("Value Bar:", "亮度调整", &lightness, max_value, on_track,(void*)(&image));//强制将&image转换为void*指针on_track(50, &image);
}8.键盘响应操作
void QuickDemo::key_demo(Mat& image)
{Mat dst = Mat::zeros(image.size(), image.type());while (true){int c = waitKey(0);if (c == 27) break;if (c == 49)//键盘按下了数字1{std::cout << "press the number 1" << std::endl;cvtColor(image, dst, COLOR_BGR2GRAY);}if (c == 50)//键盘按下了数字2{std::cout << "press the number 2" << std::endl;cvtColor(image, dst, COLOR_BGR2HSV);}if (c == 51)//键盘按下了数字3{std::cout << "press the number 3" << std::endl;dst = Scalar(50, 50, 50);add(image, dst, dst);}imshow("图像", dst);}
}9.颜色表操作colormap，applyColorMap()
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_HSV,COLORMAP_PINK,COLORMAP_HOT,COLORMAP_PARULA,COLORMAP_MAGMA,COLORMAP_INFERNO,COLORMAP_PLASMA,COLORMAP_VIRIDIS,COLORMAP_CIVIDIS,COLORMAP_TWILIGHT,COLORMAP_TWILIGHT_SHIFTED,COLORMAP_TURBO,COLORMAP_DEEPGREEN};Mat dst;int index = 0;while (true){int c = waitKey(1000);if (c == 27) break;applyColorMap(image, dst, colormap[index]);index=(++index)%22;imshow("hh", dst);}
}10.图像像素运算（bitwise）
void QuickDemo::bitwise_demo(Mat& image)
{Mat m1 = Mat::zeros(Size(255, 255), CV_8UC3);Mat m2 = Mat::zeros(Size(255, 255), CV_8UC3);rectangle(m1, Rect(100, 100, 80, 80), Scalar(0, 255, 255),-1);rectangle(m2, Rect(150, 150, 80, 80), Scalar(255, 255, 0),-1);imshow("m1", m1);imshow("m2", m2);Mat dst=m1^m2;//等价于bitwise_xor(m1, m2, dst);//bitwise_or(m1, m2, dst);//bitwise_and(m1, m2, dst);//bitwise_not(image, dst);imshow("hh", dst);}11.通道分离、合并、混合
void QuickDemo::channels_demo(Mat& image)
{std::vector<Mat> mv;split(image, mv);//分离通道//imshow("blue", mv[0]);//opencv设置单通道只显示灰度图(黑白)//imshow("green", mv[1]);//imshow("red", mv[2]);Mat dst;mv[1] = 0;//只显示两个通道的颜色merge(mv, dst);imshow("merge", dst);int from_to[] = { 0,2,1,1,2,0 };/* src颜色通道0复制到dst颜色通道2src颜色通道1复制到dst颜色通道1src颜色通道2复制到dst颜色通道0*/mixChannels(image, dst, from_to, 3);//通道混合imshow("mix", dst);
}12.图像像素最大值，最小值，平均值，标准差（minMaxLoc，meanStdDev）
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 < 3; i++){minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc);//这里需要使用mv的单通道std::cout << "i:" << i << "minv= " << minv << "maxv= " << maxv << std::endl;}Mat mean, stddev;meanStdDev(image, mean, stddev);std::cout << "mean: " << mean << std::endl;std::cout << "stddev: " << stddev << std::endl;
}13.图像几何形状绘制
void QuickDemo::drawing_demo(Mat& image)
{rectangle(image, Rect(100, 100, 250, 300), Scalar(0, 0, 255),1, LINE_AA,0);circle(image, Point(100, 100), 50, Scalar(255, 0, 0),-1);line(image, Point(100, 100), Point(200, 200), Scalar(0, 255, 0),5,8,0);ellipse(image, RotatedRect(Point(200, 200), Size(50, 100), 90),Scalar(0,0,255),1, LINE_AA);Mat dst;dst = Mat::zeros(image.size(), image.type());dst = Scalar(0, 255, 0);addWeighted(image, 0.7, dst, 0.3,50,dst);//addWeighted() 函数用于实现两个图像的加权融合（线性混合）imshow("draw", dst);
}14.随机数
// 方法1：使用默认种子（系统时间）
cv::RNG rng; // 等价于 cv::RNG rng(time(NULL));// 方法2：指定种子（生成可重复的随机序列）
cv::RNG rng(12345); // 固定种子，每次运行生成相同的随机数
//指定种子后，假如第一次随机的是生成的随机数序列：85 22 91 14 67 //重置种子后再次生成：85 22 91 14 67 void QuickDemo::random_drawing()
{Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);RNG rng(12345);//创建对象，12345是固定种子while (true){int c = waitKey(100);if (c == 27) break;int x1 = rng.uniform(0, canvas.cols);int y1 = rng.uniform(0, canvas.rows);int x2 = rng.uniform(0, canvas.cols);int y2 = rng.uniform(0, canvas.rows);//canvas = Scalar(0, 0, 0);可以每次使窗口变为黑色，每次窗口只显示一条线line(canvas, Point(x1, y1), Point(x2, y2), Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)));imshow("hh", canvas);}
}15.多边形填充与绘制
polylines();//绘制折线或轮廓
fillPoly();//填充多边形
drawContours();//绘制 / 填充轮廓void QuickDemo::polyline_drawing_demo()
{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);polylines(canvas, pts, true, Scalar(255, 0, 0), 3, LINE_AA, 0);fillPoly(canvas, pts, Scalar(0, 255, 0));std::vector<std::vector<Point>> contours;contours.push_back(pts);drawContours(canvas, contours, -1, Scalar(0, 255, 0),-1);imshow("hh", canvas);
}16.鼠标操作与响应setMouseCallback
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 << 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);temp.copyTo(image);imshow("ROI", image(box));//从image提取出box区域rectangle(image, box, Scalar(0, 255, 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);//覆盖原来的图像rectangle(image, box, Scalar(0, 255, 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();
}17.图像像素类型转换与归一化(normalize)
void QuickDemo::normal_demo(Mat& image)
{Mat dst;image.convertTo(image, CV_32F);//将图像转化为32位的浮点数
//这一步是必要的，因为归一化到 [0,1] 范围需要浮点数支持normalize(image, dst, 1, 0, NORM_MINMAX);imshow("hh", dst);
//OpenCV 的imshow函数在显示浮点数图像时，默认将 [0,1] 范围映射到 [0,255] 显示
}18.图像缩放与插值（resize）
resize
void QuickDemo::resize_demo(Mat& image)
{Mat dst1,dst2;int w = image.rows;int h = image.cols;resize(image, dst1, Size(w / 2, h / 2), 0, 0,INTER_LINEAR);imshow("hh", dst1);resize(image, dst2, Size(w * 1.2, h * 1.2), 0, 0, INTER_LINEAR);imshow("hh1", dst2);
}19.图像翻转flip
void QuickDemo::filp_demo(Mat& image)
{Mat dst1,dst2,dst3;flip(image, dst1, 0);//0：沿 X 轴（水平）翻转。flip(image, dst2, 1);// 正数（如 1）：沿 Y 轴（垂直）翻转。flip(image, dst3, -1);//负数（如 - 1）：同时沿 X 轴和 Y 轴翻转。imshow("hh1", dst1);imshow("hh2", dst2);imshow("hh3", dst3);
}20.图像旋转
Mat getRotationMatrix2D(Point2f center, double angle, double scale);center：旋转中心点（图像坐标系中的坐标，例如Point2f(x, y)）。angle：旋转角度（单位：度），正值表示逆时针旋转，负值表示顺时针旋转。scale：缩放因子（例如1.0表示保持原大小，0.5表示缩小一半）。
void warpAffine(InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar());src：输入图像。dst：输出图像，大小由dsize指定，类型与输入图像相同。M：2×3 的变换矩阵（通常由getRotationMatrix2D或自定义方法生成）。
[ cosθ -sinθ 0 ]//M旋转时的矩阵
[ sinθ cosθ 0 ]dsize：输出图像的大小（Size(width, height)）。flags：插值方法，常用选项：INTER_LINEAR：双线性插值（默认）。INTER_NEAREST：最近邻插值。INTER_CUBIC：双三次插值（质量更高，但速度较慢）。
borderMode：边界填充模式，常用BORDER_CONSTANT（用常量填充）。borderValue：当borderMode为BORDER_CONSTANT时的填充值（默认黑色）。
旋转后的宽和高 的计算
void QuickDemo::rotato_demo(Mat& image)
{Mat M, dst;int w = image.cols;int h = image.rows;M = getRotationMatrix2D(Point2f(w / 2, h / 2), 45, 1);double cos =abs(M.at<double>(0, 0));double sin = abs(M.at<double>(0, 1));int nw = cos * w + sin * h;//新图像的尺寸int nh = sin * w + cos * h;M.at<double>(0, 2) += (nw / 2 - w / 2);//x轴偏移量M.at<double>(1, 2) += (nh / 2 - h / 2);warpAffine(image, dst, M, Size(nw, nh));imshow("hh", dst);
}21.视频文件摄像头的使用（VideoCapture）
void QuickDemo::vedio_demo()
{VideoCapture v(0);//读取的使本地摄像头Mat frame;while (true){v.read(frame);if (frame.empty()) break;imshow("hh", frame);if (waitKey(1) == 27) break;}v.release();
}void QuickDemo::vedio_demo()
{VideoCapture v("C:/Users/16385/Desktop/new/photo/2.mp4");int frame_width = v.get(CAP_PROP_FRAME_WIDTH);//获得v的相关信息int frame_height = v.get(CAP_PROP_FRAME_HEIGHT);int count = v.get(CAP_PROP_FRAME_COUNT);double fps = v.get(CAP_PROP_FPS);std::cout << "frame width:" << frame_width << std::endl;std::cout << "frame height:" << frame_height << std::endl;std::cout << "FPS:" << fps << std::endl;std::cout << "Number of Frames:" << count << std::endl;VideoWriter writer("D:/test.mp4", v.get(CAP_PROP_FOURCC), fps, Size(frame_width, frame_height), true);//保存视频Mat frame;while (true){v.read(frame);if (frame.empty()) break;imshow("hh", frame);writer.write(frame);if (waitKey(1) == 27) break;}v.release();writer.release();
}22.图像直方图calcHist
calcHist