1.圖像讀取與顯示

#include<opencv2/opencv.hpp>
using namespace cv;Mat src = imread("C:/Users/16385/Desktop/new/photo/1.jpg");//讀取圖像
Mat src = imread("C:/Users/16385/Desktop/new/photo/1.jpg",IMREAD_GRAYSCALE);
//將讀取的圖像轉為灰色imshow("輸入圖像", src);//展示圖像

2.色彩空間轉換

Mat gray, hsv;cvtColor(image, hsv, COLOR_BGR2HSV);//將image的RGB色彩空間轉換為hsv色彩空間
cvtColor(image, gray, COLOR_BGR2GRAY);imwrite("D:/hsv.png", hsv);//保存圖片到D盤
imwrite("D:/gray.png", gray);

hsv色彩空間??

inrange?檢查輸入圖像中的每個像素，如果像素值位于指定的范圍內，則在輸出圖像（或掩碼）中對應位置的像素被設置為白色（或者說是255），否則被設置為黑色（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的行為//只在mask為白色的區域，將image的像素復制到redback。//黑色區域保持不變（即保留紅色背景）。imshow("roi區域提取", redback);
}

3.創建空白圖像

這種初始化通常用于：創建空白圖像，初始化臨時處理矩陣，準備存儲三通道數據（如彩色圖像）

	Mat m3 = Mat::zeros(Size(512, 512), CV_8UC3);//表示每個元素是 8 位無符號整數（0-255），并且每個像素有 3 個通道 8U C3m3 = Scalar(0, 0, 255);//設置圖像的色彩值std::cout << "width: " << m3.cols << "height: " << m3.rows << "channel: " << m3.channels() << std::endl;//std::cout << m3 << std::endl;imshow("圖像", m3);

?1，2，3可以看作是一個像素點

4.拷貝圖像

Mat m4;
m3.copyTo(m4);//深拷貝（再創建一份空間，存儲）Mat m4=m3.clone();//深拷貝Mat m4=m3;//淺拷貝（使用原來的空間，后續更改m4，m3也會更改）

?5.圖像的讀寫

//for循環讀寫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)//灰度圖像{int pv = image.at<uchar>(row, col);image.at<uchar>(row, col) = 255 - pv;}if (dims == 3)//彩色圖像{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("像素圖像",image);//指針遍歷，速度快for (int row = 0; row < h; row++){uchar* current = image.ptr<uchar>(row);//獲取當前行指針for (int col = 0; col < w; col++){if (dims == 1)//灰度圖像{int pv = *current;*current++ = 255 - pv;//先索引，指針再++}if (dims == 3)//彩色圖像{*current++ = 255 - *current;*current++ = 255 - *current;*current++ = 255 - *current;}}}

6.圖像像素的運算操作

	Mat dst = Mat::zeros(image.size(),image.type());Mat m = Mat::zeros(image.size(), image.type());m = Scalar(5, 5, 5);//運算后超過255，值會變成255，反之少于0，為0add(image, m, dst);subtract(image, m, dst);multiply(image, m, dst);divide(image, m, dst);imshow("運算圖像", dst);

7.滑塊控件動態改變圖像亮度

static void on_track(int b, void* userdata)
{Mat image = *((Mat*)userdata);//先將void*指針強制轉化為Mat*，再解引用得到image對象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("亮度調整", dst);
}
void QuickDemo::tracking_bar_demo(Mat& image)//引用傳遞是淺拷貝
{namedWindow("亮度調整", WINDOW_AUTOSIZE);//創建窗口//src = image;int lightness = 50;int max_value = 100;createTrackbar("Value Bar:", "亮度調整", &lightness, max_value, on_track,(void*)(&image));//強制將&image轉換為void*指針on_track(50, &image);
}

8.鍵盤響應操作

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)//鍵盤按下了數字1{std::cout << "press the number 1" << std::endl;cvtColor(image, dst, COLOR_BGR2GRAY);}if (c == 50)//鍵盤按下了數字2{std::cout << "press the number 2" << std::endl;cvtColor(image, dst, COLOR_BGR2HSV);}if (c == 51)//鍵盤按下了數字3{std::cout << "press the number 3" << std::endl;dst = Scalar(50, 50, 50);add(image, dst, dst);}imshow("圖像", dst);}
}

9.顏色表操作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.圖像像素運算（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;//等價于bitwise_xor(m1, m2, dst);//bitwise_or(m1, m2, dst);//bitwise_and(m1, m2, dst);//bitwise_not(image, dst);imshow("hh", dst);}

11.通道分離、合并、混合

void QuickDemo::channels_demo(Mat& image)
{std::vector<Mat> mv;split(image, mv);//分離通道//imshow("blue", mv[0]);//opencv設置單通道只顯示灰度圖(黑白)//imshow("green", mv[1]);//imshow("red", mv[2]);Mat dst;mv[1] = 0;//只顯示兩個通道的顏色merge(mv, dst);imshow("merge", dst);int from_to[] = { 0,2,1,1,2,0 };/*  src顏色通道0復制到dst顏色通道2src顏色通道1復制到dst顏色通道1src顏色通道2復制到dst顏色通道0*/mixChannels(image, dst, from_to, 3);//通道混合imshow("mix", dst);
}

12.圖像像素最大值，最小值，平均值，標準差（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);//這里需要使用mv的單通道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.圖像幾何形狀繪制

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() 函數用于實現兩個圖像的加權融合（線性混合）imshow("draw", dst);
}

14.隨機數

// 方法1：使用默認種子（系統時間）
cv::RNG rng;  // 等價于 cv::RNG rng(time(NULL));// 方法2：指定種子（生成可重復的隨機序列）
cv::RNG rng(12345);  // 固定種子，每次運行生成相同的隨機數
//指定種子后，假如第一次隨機的是生成的隨機數序列：85 22 91 14 67 //重置種子后再次生成：85 22 91 14 67 

void QuickDemo::random_drawing()
{Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);RNG rng(12345);//創建對象，12345是固定種子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);可以每次使窗口變為黑色，每次窗口只顯示一條線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.多邊形填充與繪制

polylines();//繪制折線或輪廓
fillPoly();//填充多邊形
drawContours();//繪制 / 填充輪廓

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.鼠標操作與響應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));//從image提取出box區域rectangle(image, box, Scalar(0, 255, 0));imshow("鼠標繪制", 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);//覆蓋原來的圖像rectangle(image, box, Scalar(0, 255, 0));imshow("鼠標繪制", image);}}}
}void QuickDemo::mouse_drawing_demo(Mat& image)
{namedWindow("鼠標繪制", WINDOW_AUTOSIZE);setMouseCallback("鼠標繪制", on_draw, (void*)(&image));imshow("鼠標繪制", image);temp = image.clone();
}

17.圖像像素類型轉換與歸一化(normalize)

void QuickDemo::normal_demo(Mat& image)
{Mat dst;image.convertTo(image, CV_32F);//將圖像轉化為32位的浮點數
//這一步是必要的，因為歸一化到 [0,1] 范圍需要浮點數支持normalize(image, dst, 1, 0, NORM_MINMAX);imshow("hh", dst);
//OpenCV 的imshow函數在顯示浮點數圖像時，默認將 [0,1] 范圍映射到 [0,255] 顯示
}

18.圖像縮放與插值（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.圖像翻轉flip

void QuickDemo::filp_demo(Mat& image)
{Mat dst1,dst2,dst3;flip(image, dst1, 0);//0：沿 X 軸（水平）翻轉。flip(image, dst2, 1);//	正數（如 1）：沿 Y 軸（垂直）翻轉。flip(image, dst3, -1);//負數（如 - 1）：同時沿 X 軸和 Y 軸翻轉。imshow("hh1", dst1);imshow("hh2", dst2);imshow("hh3", dst3);
}

20.圖像旋轉

Mat getRotationMatrix2D(Point2f center, double angle, double scale);

• center：旋轉中心點（圖像坐標系中的坐標，例如Point2f(x, y)）。
• angle：旋轉角度（單位：度），正值表示逆時針旋轉，負值表示順時針旋轉。
• scale：縮放因子（例如1.0表示保持原大小，0.5表示縮小一半）。

void warpAffine(InputArray src, OutputArray dst, InputArray M, Size dsize, int flags=INTER_LINEAR, int borderMode=BORDER_CONSTANT, const Scalar& borderValue=Scalar());

• src：輸入圖像。
• dst：輸出圖像，大小由?dsize?指定，類型與輸入圖像相同。
• M：2×3 的變換矩陣（通常由?getRotationMatrix2D?或自定義方法生成）。

[ cosθ  -sinθ  0 ]//M旋轉時的矩陣
[ sinθ   cosθ  0 ]

• dsize：輸出圖像的大小（Size(width, height)）。
• flags：插值方法，常用選項：
  • INTER_LINEAR：雙線性插值（默認）。
  • INTER_NEAREST：最近鄰插值。
  • INTER_CUBIC：雙三次插值（質量更高，但速度較慢）。
• borderMode：邊界填充模式，常用?BORDER_CONSTANT（用常量填充）。
• borderValue：當?borderMode?為?BORDER_CONSTANT?時的填充值（默認黑色）。

旋轉后的寬和高?的計算

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;//新圖像的尺寸int nh = sin * w + cos * h;M.at<double>(0, 2) += (nw / 2 - w / 2);//x軸偏移量M.at<double>(1, 2) += (nh / 2 - h / 2);warpAffine(image, dst, M, Size(nw, nh));imshow("hh", dst);
}

?21.視頻文件攝像頭的使用（VideoCapture）

void QuickDemo::vedio_demo()
{VideoCapture v(0);//讀取的使本地攝像頭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);//獲得v的相關信息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);//保存視頻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.圖像直方圖calcHist

calcHist