OpenCv图像处理基础例子(C++版)

一、创建图像

void QuickDemo::mat_creation_demo(Mat &image) {
	//克隆，复制
    Mat m1, m2;
    m1 = image.clone();
    image.copyTo(m2);
    // 创建空白图像
    Mat m3 = Mat::zeros(Size(88, 88), CV_8UC3);//大小8*8像素，8位3通道
    m3 = Scalar(0, 0, 255);//上色，三通道分别为B,G,R，取值范围均在0~255
    std::cout << "width: " << m3.cols << " height: " << m3.rows << " channels: "<

二、像素读写

void QuickDemo::pixel_visit_demo(Mat &image) {
    int w = image.cols;//获取图像宽度
    int h = image.rows;//获取图像高度
    int dims = image.channels();//获取图像通道数

    /*
    //图像颜色反转
    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(row, col);
                image.at(row, col) = 255 - pv;
            }
            if (dims == 3) { // 彩色图像
                Vec3b bgr = image.at(row, col);
                image.at(row, col)[0] = 255 - bgr[0];
                image.at(row, col)[1] = 255 - bgr[1];
                image.at(row, col)[2] = 255 - bgr[2];
            }
        }
    }
    */

    for (int row = 0; row < h; row++) {
    	uchar* current_row = image.ptr(row);
    	for (int col = 0; col < w; 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);
}

三、算术操作

加：add()        add(image, m, dst);

减：subtract()        subtract(image, m, dst);

乘：multiply()        multiply(image, m, dst);

除：divide()        divide(image, m, dst);

限制值的范围：saturate_cast        saturate_cast(a + b);//将a+b限制在uchar之内，结果大于255，则为255；小于0，则为0

void QuickDemo::operators_demo(Mat &image) {
    Mat dst = Mat::zeros(image.size(), image.type());
    Mat m = Mat::zeros(image.size(), image.type());
    m = Scalar(5, 5, 5);

    // 加法
    /*
    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++) {
            Vec3b p1 = image.at(row, col);
            Vec3b p2 = m.at(row, col);
            dst.at(row, col)[0] = saturate_cast(p1[0] + p2[0]);
            dst.at(row, col)[1] = saturate_cast(p1[1] + p2[1]);
            dst.at(row, col)[2] = saturate_cast(p1[2] + p2[2]);
        }
    }
    */
    divide(image, m, dst);

    imshow("加法操作", dst);
}

四、滚动条

static void on_lightness(int b, void* userdata) {//亮度回调函数
    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) {//对比度回调函数
	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 QuickDemo::tracking_bar_demo(Mat &image) {
	namedWindow("亮度与对比度调整", WINDOW_AUTOSIZE);
	int lightness = 50;
	int max_value = 100;
	int contrast_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", &contrast_value, 200, on_contrast, (void*)(&image));
	on_lightness(50, &image);
}

五、键盘响应

void QuickDemo::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
            std::cout << "you enter key # 1 "<< std::endl;
            cvtColor(image, dst, COLOR_BGR2GRAY);
        }
        if (c == 50) { // Key #2
            std::cout << "you enter key # 2 " << std::endl;
            cvtColor(image, dst, COLOR_BGR2HSV);
        }
        if (c == 51) { // Key #3
            std::cout << "you enter key # 3 " << std::endl;
            dst = Scalar(50, 50, 50);
            add(image, dst, dst);
        }
        imshow("键盘响应", dst);
    }
}

六、颜色表操作

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;
    int index = 0;
    while (true) {
        int c = waitKey(500);
        if (c == 27) { // 退出
            break;
        }
        applyColorMap(image, dst, colormap[index%19]);
        index++;
        imshow("颜色风格", dst);
    }
}

七、像素逻辑操作

与：bitwise_and(m1,m2,dst);

或：bitwise_or(m1,m2,dst);

非：bitwise_not(image,dst); 图像取反操作，dst = ~image;

异或：bitwise_xor(m1,m2,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);
    rectangle(m1, Rect(100, 100, 80, 80), Scalar(255, 255, 0), -1, LINE_8, 0);//画出一个矩形（名，位置及大小，颜色，线宽（>0绘制，<0填充），？，？）
    rectangle(m2, Rect(150, 150, 80, 80), Scalar(0, 255, 255), -1, LINE_8, 0);
    imshow("m1", m1);
    imshow("m2", m2);
    Mat dst;
    bitwise_xor(m1, m2, dst);//逻辑操作函数
    imshow("像素位操作", dst);
}

八、通道合并与分离

void QuickDemo::channels_demo(Mat &image) {
    std::vector mv;
    split(image, mv);//分离图像
    imshow("蓝色", mv[0]);
    imshow("绿色", mv[1]);
    imshow("红色", mv[2]);//分离出三个单通道，显示为灰度图像

    Mat dst;
    mv[0] = 0;
    mv[1] = 0;
    merge(mv, dst);//合并图像
    imshow("红色", dst);

    int from_to[] = { 0, 2, 1,1, 2, 0 }; //0，2交换，1，1交换，2，0交换
    mixChannels(&image, 1, &dst, 1, from_to, 3);//通道交换函数（图片地址，图片数量，生成图片地址，图片数量，交换方式，交换通道数量）
    imshow("通道混合", dst);
}

九、色彩空间转换

void QuickDemo::inrange_demo(Mat &image) {
    Mat hsv;
    cvtColor(image, hsv, COLOR_BGR2HSV);//色彩空间转换
    Mat mask;
    inRange(hsv, Scalar(35, 43, 46), Scalar(77, 255, 255), mask);//提取图片中绿色，具体hsv色彩见最上面

    Mat redback = Mat::zeros(image.size(), image.type());
    redback = Scalar(40, 40, 200);
    bitwise_not(mask, mask);//非运算，留下出绿色外的
    imshow("mask", mask);
    image.copyTo(redback, mask);//将绿色外的图片复制到红色背景上
    imshow("roi区域提取", redback);
}

十、像素值统计

void QuickDemo::pixel_statistic_demo(Mat &image) {
    double minv, maxv;
    Point minLoc, maxLoc;
    std::vector 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 << " min value:" << minv << " max value:" << maxv << std::endl;
    }
    Mat mean, stddev;
    Mat redback = Mat::zeros(image.size(), image.type());
    redback = Scalar(40, 40, 200);
    meanStdDev(redback, mean, stddev);
    imshow("redback", redback);
    std::cout << "means:" << mean << std::endl;
    std::cout<< " stddev:" << stddev << std::endl;
}

十一、几何图形绘制

void QuickDemo::drawing_demo(Mat &image) {
    Rect rect;
    rect.x = 100;//确定x位置
    rect.y = 100;//确定y位置
    rect.width = 250;//宽
    rect.height = 300;//高
    Mat bg = Mat::zeros(image.size(), image.type());//创建Mat
    rectangle(bg, rect, Scalar(0, 0, 255), -1, 8, 0);//绘制方形
    circle(bg, Point(350, 400), 15, Scalar(255, 0, 0), -1, 8, 0);//绘制圆形（图像，圆心，半径，颜色，线宽，？，？）
    line(bg, Point(100, 100), Point(350, 400), Scalar(0, 255, 0), 4, LINE_AA, 0);//绘制线（图像，点，点，颜色，宽度，？，？）
    RotatedRect rrt;
    rrt.center = Point(200, 200);//中心点
    rrt.size = Size(100, 200);//大小
    rrt.angle = 90.0;//旋转角度
    ellipse(bg, rrt, Scalar(0, 255, 255), 2, 8);//绘制椭圆（图像，位置大小，颜色，线宽，？）
    Mat dst;
    addWeighted(image, 0.7, bg, 0.3, 0, dst);
    imshow("绘制演示", bg);
}

十二、随机数与随机颜色

void QuickDemo::random_drawing() {
    Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);
    int w = canvas.cols;
    int h = canvas.rows;
    RNG rng(12345);//产生随机数函数
    while (true) {
        int c = waitKey(10);
        if (c == 27) { // 退出
            break;
        }
        int x1 = rng.uniform(0, w);//产生(0,w)的随机数
        int y1 = rng.uniform(0, h);//产生(0,h)的随机数
        int x2 = rng.uniform(0, w);//产生(0,w)的随机数
        int y2 = rng.uniform(0, h);//产生(0,h)的随机数
        int b = rng.uniform(0, 255);//产生(0,255)的随机数
        int g = rng.uniform(0, 255);//产生(0,255)的随机数
        int r = rng.uniform(0, 255);//产生(0,255)的随机数
        // canvas = Scalar(0, 0, 0);
        line(canvas, Point(x1, y1), Point(x2, y2), Scalar(b, g, r), 1, LINE_AA, 0);
        imshow("随机绘制演示", canvas);
    }
}

十三、多边形绘制与填充

void QuickDemo::polyline_drawing_demo() {
    Mat canvas = Mat::zeros(Size(512, 512), CV_8UC3);//新建画布
    int w = canvas.cols;//获取宽
    int h = canvas.rows;//获取高
    Point p1(100, 100);
    Point p2(300, 150);
    Point p3(300, 350);
    Point p4(250, 450);
    Point p5(50, 450);
    std::vector pts;
    pts.push_back(p1);
    pts.push_back(p2);
    pts.push_back(p3);
    pts.push_back(p3);
    pts.push_back(p4);
    pts.push_back(p5);
    //fillPoly(canvas, pts, Scalar(255, 0, 255), 8, 0);//填充图形
    // polylines(canvas, pts, true, Scalar(0, 255, 0), -1, 8, 0);//绘制边框
    std::vector> contours;//构造多个点集的集合
    contours.push_back(pts);
    drawContours(canvas, contours, 0, Scalar(0, 0, 255), -1, 8);//绘制/填充多边形（图像，点集，绘制第几个(全部:-1)，颜色，线宽，？）
    imshow("绘制多边形", canvas);
}

十四、鼠标操作与响应

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);
				temp.copyTo(image);
				imshow("ROI区域", image(box));
				rectangle(image, box, Scalar(0, 0, 255), 2, 8, 0);
				imshow("鼠标绘制", image);
				// ready for next drawing
				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();
}

十五、图像像素类型转换与归一化

四种归一化：

NORM_MINMAX;（最常用）

NORM_INF;

NORM_L1;

NORM_L2;

void QuickDemo::norm_demo(Mat &image) {
    Mat dst;
    std::cout << image.type() << std::endl;
    image.convertTo(image, CV_32F);//数据类型转换，转换为浮点数类型
    std::cout << image.type() << std::endl;
    normalize(image, dst, 1.0, 0, NORM_MINMAX);//归一化函数
    std::cout << dst.type() << std::endl;
    imshow("图像数据归一化", dst);
    // CV_8UC3, CV_32FC3
}

十六、图像放缩与插值

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);//图像缩放（输入图像，输出图像，缩放大小，拉伸x轴(0-1)，拉伸y轴(0-1)，插值方法(线性插值)）	**放缩大小和拉伸二选其一**
    imshow("zoomout", zoomout);//缩小
    resize(image, zoomout, Size(w*1.5, h*1.5), 0, 0, INTER_LINEAR);
    imshow("zoomin", zoomin);//放大
}

十七、图像翻转

void QuickDemo::flip_demo(Mat &image) {
    Mat dst;
    // flip(image, dst, 0); // 上下翻转，x轴对称
    // flip(image, dst, 1); // 左右翻转，y轴对称
    flip(image, dst, -1); // 180°旋转，中心对称
    imshow("图像翻转", dst);
}

十八、图像旋转

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);//获取图像数组（原来图像中心位置，旋转角度，缩放倍数）
    //计算新的宽高
    double cos = abs(M.at(0, 0));
    double sin = abs(M.at(0, 1));
    int nw = cos*w + sin*h;//新宽
    int nh = sin*w + cos*h;//新高
    M.at(0, 2) += (nw / 2 - w / 2);//新中心位置
    M.at(1,2) += (nh / 2 - h / 2);//新中心位置
    warpAffine(image, dst, M, Size(nw, nh), INTER_LINEAR, 0, Scalar(255, 255, 0));//旋转图片（输入，输出，图像数组，输出大小，插值方法，？，颜色）
    imshow("旋转演示", dst);
}

十九、视频文件处理与保存

void QuickDemo::video_demo(Mat &image) {
    VideoCapture capture("D:/images/video/example_dsh.mp4");//初始化对象
    int frame_width = capture.get(CAP_PROP_FRAME_WIDTH);//获取每帧宽度
    int frame_height = capture.get(CAP_PROP_FRAME_HEIGHT);//获取每帧高度
    int count = capture.get(CAP_PROP_FRAME_COUNT);//获取总帧数
    double fps = capture.get(CAP_PROP_FPS);//获取帧率
    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", capture.get(CAP_PROP_FOURCC), fps, Size(frame_width, frame_height), true);//保持视频（视频名，视频编码格式(直接获取如该代码)，帧率，视频宽高，？）
    Mat frame;
    while (true) {
        capture.read(frame);
        flip(frame, frame, 1);
        if (frame.empty()) {
            break;
        }
        imshow("frame", frame);
        colorSpace_Demo(frame);
        writer.write(frame);//保持视频
        // TODO: do something....
        int c = waitKey(1);
        if (c == 27) { // 退出
            break;
        }
    }

    // release
    capture.release();
    writer.release();
}

二十、图像直方图

void QuickDemo::histogram_demo(Mat &image) {
    // 三通道分离
    std::vector 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);//计算直方图（图像，图像张数，通道，蒙版，输出，维度(1维)，？，范围？）
    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(i - 1))),
            Point(bin_w*(i), hist_h - cvRound(b_hist.at(i))), Scalar(255, 0, 0), 2, 8, 0);
        line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(g_hist.at(i - 1))),
            Point(bin_w*(i), hist_h - cvRound(g_hist.at(i))), Scalar(0, 255, 0), 2, 8, 0);
        line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(r_hist.at(i - 1))),
            Point(bin_w*(i), hist_h - cvRound(r_hist.at(i))), Scalar(0, 0, 255), 2, 8, 0);
    }
    // 显示直方图
    namedWindow("Histogram Demo", WINDOW_AUTOSIZE);
    imshow("Histogram Demo", histImage);
}

二十一、二维直方图

void QuickDemo::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(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 Histogram", hist2d_image);
    imwrite("D:/hist_2d.png", hist2d_image);
}

二十二、直方图均衡化

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("直方图均衡化演示", dst);
}

二十三、图像卷积操作

void QuickDemo::blur_demo(Mat &image) {
Mat dst;
blur(image, dst, Size(15, 15), Point(-1, -1));
imshow("图像模糊", dst);
}

二十四、高斯模糊

void QuickDemo::gaussian_blur_demo(Mat &image) {
    Mat dst;
    GaussianBlur(image, dst, Size(0, 0), 15);//（输入，输出，卷积核大小，σ参数）	**size和σ只需要一个，一般设置σ**
    imshow("高斯模糊", dst);
}

二十五、高斯双边模糊

void QuickDemo::bifilter_demo(Mat &image) {
    Mat dst;
    bilateralFilter(image, dst, 0, 100, 10);//（输入，输出）
    imshow("双边模糊", dst);
}

二十六、人脸识别实例

*注意更改自己文件位置

void QuickDemo::face_detection_demo() {
    std::string root_dir = "D:/opencv-4.4.0/opencv/sources/samples/dnn/face_detector/";
    dnn::Net net = dnn::readNetFromTensorflow(root_dir+ "opencv_face_detector_uint8.pb", root_dir+"opencv_face_detector.pbtxt");
    VideoCapture capture("D:/images/video/example_dsh.mp4");
    Mat frame;
    while (true) {
        capture.read(frame);
        if (frame.empty()) {
            break;
        }
        Mat blob = dnn::blobFromImage(frame, 1.0, Size(300, 300), Scalar(104, 177, 123), false, false);
        net.setInput(blob);// NCHW
        Mat probs = net.forward(); // 
        Mat detectionMat(probs.size[2], probs.size[3], CV_32F, probs.ptr());
        // 解析结果
        for (int i = 0; i < detectionMat.rows; i++) {
            float confidence = detectionMat.at(i, 2);
            if (confidence > 0.5) {
                int x1 = static_cast(detectionMat.at(i, 3)*frame.cols);
                int y1 = static_cast(detectionMat.at(i, 4)*frame.rows);
                int x2 = static_cast(detectionMat.at(i, 5)*frame.cols);
                int y2 = static_cast(detectionMat.at(i, 6)*frame.rows);
                Rect box(x1, y1, x2 - x1, y2 - y1);
                rectangle(frame, box, Scalar(0, 0, 255), 2, 8, 0);
            }
        }
        imshow("人脸检测演示", frame);
        int c = waitKey(1);
        if (c == 27) { // 退出
            break;
        }
    }
}

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注：所有代码均来自OpenCV4 C++ 快速入门视频30讲 - 系列合集