官方在线帮助文档:OpenCV: OpenCV modules
1. 视频文件摄像头使用
VideoCapture capture("mv.mp4");// 0 是读取摄像头
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67#include<opencv2/opencv.hpp> #include<iostream> #include<vector> using namespace cv; using namespace std; class demo { public: // 图像色彩空间转换 void 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("hsv.png", hsv); //imwrite("gray.png", gray); } // 视频操作 // 视频文件摄像头使用 void video_demo(Mat& image) { VideoCapture capture("mv.mp4");// 0 是读取摄像头 Mat frame; while (true) { capture.read(frame); if (frame.empty()) {// 检测是否检测到人 break; } imshow("frame", frame); // 读取了灰度的视频和HSV的视频 colorSpace_Demo(frame); //flip(frame, frame, 1);// 翻转 // 做事情 int c = waitKey(10); if (c == 27) { break; } } // release capture.release(); } }; int main(int argc, char** argv) { Mat src = imread("迪丽热巴.png",IMREAD_UNCHANGED); // Mat是一种特殊的数据类型格式,是一种二维数组,用来存储图片的数据 // namedWindow("输出窗口", WINDOW_FREERATIO); // 只有imshow无法调整图片显示窗口的大小,通过namedWindow调整窗口的大小 if (src.empty()) { cout << "没有找到你的图片" << endl; return -1; } imshow("输出窗口", src); demo d; d.video_demo(src); waitKey(0);// 设置图片显示时间 destroyAllWindows();// 释放所有窗口 return 0; }
2. 视频处理与保存
- SD 标清
- HD 高清
- UHD 超清
写入的还是原视频,上限是2g。
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79#include<opencv2/opencv.hpp> #include<iostream> #include<vector> using namespace cv; using namespace std; class demo { public: // 图像色彩空间转换 void 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("hsv.png", hsv); //imwrite("gray.png", gray); } // 视频操作 // 视频文件摄像头使用 // 视频处理与保存 void video_demo(Mat& image) { VideoCapture capture("mv.mp4");// 0 是读取摄像头 Mat frame; 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);// 帧数 int fps = capture.get(CAP_PROP_FPS); cout << "frame-width:" << frame_width << endl; cout << "frame-height:" << frame_height << endl; cout << "FPS:" << fps << endl; cout << "Number of Frames:" << count << endl; VideoWriter writer("D:/桌面/test.mp4", capture.get(CAP_PROP_FOURCC), fps, Size(frame_width, frame_height), true); while (true) { capture.read(frame); if (frame.empty()) {// 检测是否检测到人 break; } imshow("frame", frame); // 读取了灰度的视频和HSV的视频 colorSpace_Demo(frame); writer.write(frame); //flip(frame, frame, 1);// 翻转 // 做事情 int c = waitKey(10); if (c == 27) { break; } } // release capture.release(); writer.release(); } }; int main(int argc, char** argv) { Mat src = imread("迪丽热巴.png",IMREAD_UNCHANGED); // Mat是一种特殊的数据类型格式,是一种二维数组,用来存储图片的数据 // namedWindow("输出窗口", WINDOW_FREERATIO); // 只有imshow无法调整图片显示窗口的大小,通过namedWindow调整窗口的大小 if (src.empty()) { cout << "没有找到你的图片" << endl; return -1; } imshow("输出窗口", src); demo d; d.video_demo(src); waitKey(0);// 设置图片显示时间 destroyAllWindows();// 释放所有窗口 return 0; }
从头开始的所有代码
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614#include<opencv2/opencv.hpp> #include<iostream> #include<vector> using namespace cv; using namespace std; // 鼠标响应 Point sp(-1, -1); Point ep(-1, -1); // 设置一个临时图层,确保显示最后一个图片 Mat temp; class demo { public: // 图像色彩空间转换 void 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("hsv.png", hsv); //imwrite("gray.png", gray); } // 图像对象的创建与赋值 void mat_creation_demo(Mat& image) { Mat m1, m2; m1 = image.clone(); image.copyTo(m2); // 创建空白图像 Mat m3 = Mat::zeros(Size(400, 400), CV_8UC3); // 8位无符号字符1个通道的数据,改成3之后在表示像素点的时候的像素值都有三个,表示有三个通道 m3 = 127;// 只赋值一个值的时候默认是第一个通道 m3 = Scalar(127, 12, 260);// 当确定了通道数量后可以给每个通道赋值 cout << "width:" << m3.cols << "t height:" << m3.rows << "tchannels:(通道)" << m3.channels() << endl; cout << m3 << endl; imshow("自定义图像", m3); Mat m4 = m3.clone(); m4 = Scalar(260, 12, 127); imshow("自定义图像", m3); // m4是与m3指向同一片地址,改变m4同时会改变m3 imshow("自定义图像4", m4); } // 图像像素的读写操作 void pixel_visit_demo(Mat &image) { int width = image.cols; int height = image.rows; int dims = image.channels(); for (int row = 0; row < height; row++) { for (int col = 0; col < width; 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]; } } } // 指针 /* for (int row = 0; row < height; row++) { uchar* current_row = image.ptr<uchar>(row); for (int col = 0; col < width; col++) { if (dims == 1) {// 灰度图像 int pv = *current_row; *current_row++ = 255 - pv; } if (dims == 3) {// 彩色图像 *current_row++ = 255 - *current_row; *current_row++ = 255 - *current_row; *current_row++ = 255 - *current_row; } } } */ imshow("像素读写演示", image); } // 图像像素的算术操作 void operatos_demo(Mat& image) { Mat dst = Mat::zeros(image.size(), image.type()); Mat m = Mat::zeros(image.size(), image.type()); m = Scalar(50, 50, 50); add(image, m, dst); imshow("加法操作", dst); subtract(image, m, dst); imshow("减法操作", dst); multiply(image, m, dst); imshow("乘法操作", dst); divide(image, m, dst); imshow("除法操作", dst); } // 亮度调整与对比度调整 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()); addWeighted(image, 1.0, m, 0, b, dst); imshow("亮度与对比度调整", 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("亮度与对比度调整", dst); } void tracking_bar_demo(Mat &image) { namedWindow("亮度与对比度调整", WINDOW_AUTOSIZE); int max_value = 100; int lightness = 50; int contract_value = 100; createTrackbar("Value Bar:", "亮度与对比度调整", &lightness, max_value, on_lightness,(void*)(&image)); createTrackbar("Contrast Bar:", "亮度与对比度调整", &contract_value, 200, on_contrast, (void*)(&image)); on_lightness(50, &image); } // 键盘响应操作 void key_demo(Mat& image) { Mat dst = Mat::zeros(image.size(), image.type()); while (true) { int c = waitKey(100); if (c == 27) {// 退出 break; } if (c == 49) {// Key #1 cout << "你按了1:要进行转换灰度的操作"<< endl; cvtColor(image, dst, COLOR_BGR2GRAY); } if (c == 50) {// Key #2 cout << "你按了2:要进行转换HSV的操作" << endl; cvtColor(image, dst, COLOR_BGR2HSV); } if (c == 51) {// Key #3 cout << "你按了3:要进行亮度的调节" << endl; dst = Scalar(50, 50, 50); add(image, dst, dst); } imshow("键盘响应", dst); } } // 自带颜色操作 void 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; int index = 0; while (true) { int c = waitKey(1000); if (c == 27) {// 退出 break; } applyColorMap(image, dst, colormap[index%19]); index++; imshow("颜色风格", dst); } } // 图像像素的逻辑操作 void 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); // -1 是填充,2是描边 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); imshow("像素与操作", dst); bitwise_or(m1, m2, dst); imshow("像素或操作", dst); bitwise_not(image, dst); // Mat dst = ~image;//取反 imshow("取反", dst); bitwise_xor(m1, m2, dst); imshow("异或", dst); } // 通道分离与合并 void channels_demo(Mat& image) { // 通道分离 vector<Mat> mv; split(image, mv); imshow("蓝色", mv[0]); imshow("绿色", mv[1]); imshow("红色", mv[2]); Mat dst; mv[1] = 0; mv[2] = 0; merge(mv, dst); imshow("蓝色", dst); // 同理修改其他两种通道后彰显一种通道的颜色 // 也可以只让其中一个通道的值为0 // 通道的合并 int from_to[] = { 0,2,1,1,2 }; mixChannels(&image, 1, &dst, 1, from_to, 3); imshow("通道混合", dst); } // 图像色彩空间转换 void inrange_demo(Mat& image) { Mat hsv; cvtColor(image, hsv, COLOR_BGR2HSV); Mat mask; inRange(hsv, Scalar(0, 43, 46), Scalar(180, 255, 255), mask); imshow("mask", mask); Mat redback = Mat::zeros(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); } // 图像像素值统计 void pixel_statistic_demo(Mat& image) { double minv, maxv; Point minLoc, maxLoc; vector<Mat> mv; split(image, mv); for (int i = 0; i < mv.size(); i++) { minMaxLoc(mv[i], &minv, &maxv, &minLoc, &maxLoc, Mat()); cout << "No.channels:" << i << "min value:" << minv << "max value:" << maxv << endl; } Mat mean, stddev; meanStdDev(image, mean, stddev); cout << "means:" << mean << endl; cout<<"stddev:" << stddev << endl; } // 图像几何形状绘制 void drawing_demo(Mat& image) { Rect rect; rect.x = 300; rect.y = 200; rect.width = 100; rect.height = 100; rectangle(image, rect, Scalar(0, 0, 255), 2, 8, 0); // 2,绘制. -1,填充 circle(image, Point(150, 250), 50, Scalar(255, 0, 0), 2, 8, 0); line(image, Point(300, 200), Point(400, 300), 2, LINE_AA, 0); // LINE_AA 边缘融合 RotatedRect rrt; rrt.center = Point(150, 150); rrt.size = Size(100, 200); rrt.angle = 90.0; ellipse(image, rrt, Scalar(0, 255, 255), 2, 8); imshow("图像演示", image); } // 随机数与随机颜色 void random_drawing() { Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3); int width = canvas.cols; int height = canvas.rows; RNG rng(12345); while (true) { int c = waitKey(100); if (c == 27) { break; } // 随机坐标 int x1 = rng.uniform(0, width); int y1 = rng.uniform(0, height); int x2 = rng.uniform(0, width); int y2 = rng.uniform(0, height); // 随机颜色 int b = rng.uniform(0, 255); int g = rng.uniform(0, 255); int r = rng.uniform(0, 255); //canvas = Scalar(0, 0, 0); // 设置每次都只花一条直线,用黑色背景覆盖每次 line(canvas, Point(x1, y1), Point(x2, y2), Scalar(b, g, r), 4, LINE_AA, 0); imshow("随机绘制演示", canvas); } } // 多边形填充与绘制 void 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); vector<Point> pts(5); pts.push_back(p1); pts.push_back(p2); pts.push_back(p3); pts.push_back(p4); pts.push_back(p5); // 绘制 //polylines(canvas, pts, true, Scalar(0, 0, 255), 2, 8, 0); // 填充 //fillPoly(canvas, pts, true, Scalar(255, 255, 0), 8, 0); vector<vector<Point>> contours; contours.push_back(pts); drawContours(canvas, contours, -1, Scalar(255, 0, 0), 2); // 2绘制形状,-1填充 imshow("多边形绘制", canvas); } // 鼠标操作与响应 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); temp.copyTo(image);// 解决ROI区域有颜色框的问题 imshow("ROI区域", image(box)); rectangle(image, box, Scalar(0, 255, 255), 2, 8, 0); imshow("鼠标绘制", image); // 每次绘制完成之后要更新数据 sp.x = -1; sp.y = -1; } cout << "end point:" << ep << endl; } 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, 255, 255), 2, 8, 0); imshow("鼠标绘制", image); } cout << "end point:" << ep << endl; } } } void 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(); } // 图像像素类型转换归一化 void norm_demo(Mat& image) { Mat dst; cout << image.type() << endl; image.convertTo(dst, CV_32F); cout << image.type() << endl; normalize(image, dst, 1.0, 0, NORM_MINMAX); cout << dst.type() << endl; imshow("图像数据归一化", dst); // CV_8UC3 转换为 CV_32FC3 } // 图像放缩与插值 void resize_demo(Mat& image) { Mat zoomin, zoomout; int height = image.rows; int width = image.cols; resize(image, zoomin, Size(width * 1.50, height * 1.50), 0, 0, INTER_LINEAR); imshow("zoomin", zoomin);// 放大 resize(image, zoomout, Size(width / 2.0, height / 2.0), 0, 0, INTER_LINEAR); imshow("zoomout", zoomout);// 缩小 } // 图像翻转 void flip_demo(Mat& image) { Mat dst; flip(image, dst, 0); imshow("图像翻转(上下)", dst);// 上下翻转 flip(image, dst, 1); imshow("图像翻转(左右)", dst);// 左右反转 flip(image, dst, -1); imshow("旋转180度", dst);// 旋转180度 } // 图像旋转 void rotate_demo(Mat& image) { Mat dst,M; int width = image.cols; int height = image.rows; M = getRotationMatrix2D(Point2f(width / 2, height / 2), 45, 1.0); double cos = abs(M.at<double>(0, 0)); double sin = abs(M.at<double>(0, 1)); int new_width = cos * width + sin * height; int new_height = sin * width + cos * height; M.at<double>(0, 2) += (new_width / 2 - width / 2); M.at<double>(1, 2) += (new_height / 2 - height / 2); warpAffine(image, dst, M, Size(new_width, new_height), INTER_LINEAR, 0, Scalar(255, 255, 0)); imshow("旋转演示", dst); } // 视频操作 // 视频文件摄像头使用 // 视频处理与保存 void video_demo(Mat& image) { VideoCapture capture("mv.mp4");// 0 是读取摄像头 Mat frame; 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);// 帧数 int fps = capture.get(CAP_PROP_FPS); cout << "frame-width:" << frame_width << endl; cout << "frame-height:" << frame_height << endl; cout << "FPS:" << fps << endl; cout << "Number of Frames:" << count << endl; VideoWriter writer("D:/桌面/test.mp4", capture.get(CAP_PROP_FOURCC), fps, Size(frame_width, frame_height), true); while (true) { capture.read(frame); if (frame.empty()) {// 检测是否检测到人 break; } imshow("frame", frame); // 读取了灰度的视频和HSV的视频 colorSpace_Demo(frame); writer.write(frame); //flip(frame, frame, 1);// 翻转 // 做事情 int c = waitKey(10); if (c == 27) { break; } } // release capture.release(); writer.release(); } // 图像直方图 void histogram_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通道的直方图 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 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 Deno", histImage); } // 二维直方图 void histogram_2d_demo(Mat& image) { // 2D直方图 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); } // 输出图像色彩转换 applyColorMap(hist2d_image, hist2d_image, COLORMAP_JET); imshow("H-S Histogram", hist2d_image); // imwrite("D:/ hist__2d.png", hist2d_image); } } // 直方图均衡化 void histogram_eq_demo(Mat& image) { Mat gray; cvtColor(image, gray, COLOR_BGR2GRAY); imshow("灰度图像", gray); Mat dst; equalizeHist(gray, dst); imshow("图像直方图均衡化演示", dst); } // 图像卷积操作 void blur_demo(Mat& image) { Mat dst; blur(image, dst, Size(23,23), Point(-1, -1)); // 支持一维卷积---->Size(15,1) imshow("图像模糊", dst); } // 高斯模糊 void gaussian_blur_demo(Mat& image) { Mat dst; GaussianBlur(image, dst, Size(0, 0), 15); // Size(0, 0)是最厉害的模糊 imshow("高斯模糊图像", dst); } // 高斯双边模糊 void bifilter_demo(Mat& image) { // 磨皮美颜 Mat dst; bilateralFilter(image, dst, 0, 100, 10); imshow("双边模糊", dst); } }; int main(int argc, char** argv) { Mat src = imread("迪丽热巴.png",IMREAD_UNCHANGED); // Mat是一种特殊的数据类型格式,是一种二维数组,用来存储图片的数据 // namedWindow("输出窗口", WINDOW_FREERATIO); // 只有imshow无法调整图片显示窗口的大小,通过namedWindow调整窗口的大小 if (src.empty()) { cout << "没有找到你的图片" << endl; return -1; } imshow("输出窗口", src); demo d; d.bifilter_demo(src); waitKey(0);// 设置图片显示时间 destroyAllWindows();// 释放所有窗口 return 0; }
最后
以上就是笨笨小霸王最近收集整理的关于OpenCV对视频的处理操作官方在线帮助文档:OpenCV: OpenCV modules从头开始的所有代码的全部内容,更多相关OpenCV对视频的处理操作官方在线帮助文档:OpenCV:内容请搜索靠谱客的其他文章。
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