opencv c++小笔记本（三）

深藏阁楼爱情的钟 2024-03-16 11:26 5阅读 0赞

#### opencv ####

*  一图像通道的分离和合并
 *  二.图像色彩改变
 *  三.简单形状识别
 *   *  1.灰度处理
     *  2.高斯滤波
     *  3.边缘检测
     *  4.膨胀
 *  三.像素点统计
 *  四.多边形的绘制和填充
 *  五.鼠标操作与响应（截图）
 *  六.图像的像素转换和归一化
 *  七.视频文件摄像头使用
 *  八.视频的处理与保存
 *  九.图像直方图
 *  十.卷积核

## 一图像通道的分离和合并 ##

![在这里插入图片描述][958b43439e304a20954b93ede991e6b4.png]

> **split函数,分离通道。merge函数合并通道，merge有两个版本分别对应vector的和数组的。mixChannels可以直接进行通道混合。**

![在这里插入图片描述][1652d6245ef74f68b29637d10a878963.png]

![在这里插入图片描述][5b3daca5ca024e93b4498a20b0e9989c.png]

![在这里插入图片描述][ca0caf4d877841669425a9592285869f.png]

![在这里插入图片描述][61a2f4b4ce3549adbf0e00785d8ed858.png]

## 二.图像色彩改变 ##

![在这里插入图片描述][815d073fe6654025a36613117104cd2e.png]  
![在这里插入图片描述][ada9d99b7cdb4b65b58b41b982d540cf.png]

## 三.简单形状识别 ##

[识别详解1][1]  
[识别详解2][2]

### 1.灰度处理 ###

cvtColor(img, imgGray,COLOR_BGR2GRAY, 0);

### 2.高斯滤波 ###

[传送门][Link 1]（高斯滤波）

![在这里插入图片描述][342cd8551dd74eef92fac5a6a297c517.png]

### 3.边缘检测 ###

![在这里插入图片描述][008956f6ff2045c69893c7e9b15e6b7e.png]

### 4.膨胀 ###

## 三.像素点统计 ##

![在这里插入图片描述][f157b4c4a8874320b382208b99546e02.png]  
![在这里插入图片描述][c15e95a91dad47ed9fcaccd687ba86aa.png]

![在这里插入图片描述][af9a59bec6a64814bd899d059b930dfc.png]

![在这里插入图片描述][b82efd6d485f45dea3083d90ea042556.png]  
*方差*

![在这里插入图片描述][0e462566df124d95b013ea1013b14049.png]

![在这里插入图片描述][7dbe2ecdc82d4ca494a9a229c8708f7a.png]

![在这里插入图片描述][7d66b5675dc940628f96d5a2eb1164ca.png]

## 四.多边形的绘制和填充 ##

![在这里插入图片描述][9e4c5813dc664d21be43bf8e4c275abe.png]

![在这里插入图片描述][4de7525c572940afa645f72d694c5a9b.png]

![在这里插入图片描述][a2c8dea10fbe4bf88eea5b1b5ef618ef.png]

*绘制和填充*

![在这里插入图片描述][2c30c96426524d81974a8797e9e7d346.png]

![在这里插入图片描述][5f0dca577aa9464aaca77a2bdeb55671.png]

![在这里插入图片描述][c747dcd801444b4aac4f08eb7c22b7f2.png]

## 五.鼠标操作与响应（截图） ##

> **void setMousecallback(const string& winname, MouseCallback onMouse, void \* userdata=0)  
> winname:窗口的名字  
> onMouse:鼠标响应函数，回调函数。指定窗口里每次鼠标时间发生的时候，被调用的函数指针。 这个函数的 原型应该为void on\_Mouse(int event, int x, int y, int flags, void \* param);  
> userdate：传给回调函数的参数**

> **void on\_Mouse(int event, int x, int y, int flags, void \* param);**  
> **event是 CV\_EVENT\_ \* 变量之一**  
> **x和y是鼠标指针在图像坐标系的坐标（不是窗口坐标系）**  
> **flags是CV\_EVENT\_FLAG的组合， param是用户定义的传递到setMouseCallback函数调用的参数。**

*event*  
![在这里插入图片描述][10ac6cfda20846508d0b67c2eb10619a.png]

*flag*

![在这里插入图片描述][efa042d4dfb4457286ac5dbb0ced2404.png]

![在这里插入图片描述][11faa5e5305448c4afb082fed4145d0f.png]

*截取任意图形*

> **boundingRect只有一个参数即传vector，vector里是点集（不能有斜度）。minAreaRect可以有斜度。**

*简易的截图工具（按中键可以转换成三角形）ps:滑到外面会导致系统t终止可以自行修正*

//截图
    Point sp(-1, -1);
    Point ep(-1, -1);
    Point dp(-1, -1);
    
    Mat tmp;
    int ret;
    
    
    void on_Mouse(int event, int x, int y, int flags, void* param)
    {
        
    	Mat img = *(Mat*)param;
    	if (event == EVENT_MBUTTONDOWN)//按住中间
    	{
        
    		if (ret == 1) ret = 0;
    		else ret = 1;
    	}
    	if (event == EVENT_LBUTTONDOWN)//按下左键
    	{
        
    		sp.x = x;
    		sp.y = y;
    	}
    	else if (event == EVENT_LBUTTONUP)//松开左键
    	{
        
    		ep.x = x;
    		ep.y = y;
    		int dx = abs(ep.x - sp.x);
    		int dy = abs(ep.y - sp.y);
    		dp.x = sp.x;
    		dp.y = y;
    		vector<Point>p;
    		p.push_back(sp);
    		p.push_back(ep);
    		p.push_back(dp);
    		if (ret == 0)
    		{
        
    			Rect box(sp.x, sp.y, dx, dy);
    			rectangle(img, box, Scalar(0, 0, 255));
    			imshow("ROI区域", img(box));
    			imshow("鼠标绘制", img);
    		}
    		else if (ret == 1)
    		{
        
    			Rect ret = boundingRect(p);
    			polylines(img,p, 1, Scalar(0, 255, 255));
    			imshow("ROI区域", img(ret));
    			imshow("鼠标绘制", img);
    		}
    		sp.x = 0, sp.y = 0;
    		ep.x = 0, ep.y = 0;
    		dp.x = 0, dp.y = 0;
    	}
    	else if (event == EVENT_MOUSEMOVE)//移动鼠标
    	{
        
    		ep.x = x;
    		ep.y = y;
    		int dx = abs(ep.x - sp.x);
    		int dy = abs(ep.y - sp.y);
    		dp.x = sp.x;
    		dp.y = y;
    		vector<Point>p;
    		p.push_back(sp);
    		p.push_back(ep);
    		p.push_back(dp);
    		tmp.copyTo(img);
    		if (ret==0&&sp.x > 0 && sp.y > 0)
    		{
        
    			rectangle(img, Rect(sp.x, sp.y, dx, dy), Scalar(0, 0, 255));
    			imshow("鼠标绘制", img);
    		}
    		else if (ret == 1&&sp.x>0&&sp.y>0)
    		{
        
    			polylines(img, p, 1, Scalar(0, 255, 255));
    			imshow("鼠标绘制", img);
    		}
    	}
    }
    
    int main()
    {
        
    	Mat img=imread("C:/Users/ASUS/Pictures/Screenshots/建筑物1.png");
    	string p("鼠标绘制");
    	namedWindow(p);
    	setMouseCallback(p,on_Mouse,&img);
    	imshow("鼠标绘制", img);
    	tmp = img.clone();
    	//namedWindow("bin");
    	//imshow("bin", bin);
    	waitKey();
    	return 0;
    }

## 六.图像的像素转换和归一化 ##

![在这里插入图片描述][bcdfa99069fe45ada06ade4afad9d3c2.png]

![在这里插入图片描述][e015bb0d56d24a9094136c88f5649079.png]

> **一个函数convertTO,可以将图片转换成其他类型，这里归一化是将像素点调到0-1之间，所以必须先将图片转换成浮点数存储。**

![在这里插入图片描述][b44699f9bee745dd95fa5f0dd990d012.png]

## 七.视频文件摄像头使用 ##

![在这里插入图片描述][13d359cb1b8a4fc5bd7d7233f2a3b480.png]  
![在这里插入图片描述][2eab9148adf5465691972609343ec704.png]

![在这里插入图片描述][7c98eac032d84cb8988d86bb54f50446.png]

## 八.视频的处理与保存 ##

int main()
    {
        
        
        VideoCapture capture("C:/Users/ASUS/Videos/Captures/风景1.mp4");
        if (!capture.isOpened())//判断是否打开成功
        {
        
            cout << "open fail" << endl;
            return -1;
        }
        //获取视频属性
        int wid = capture.get(CAP_PROP_FRAME_WIDTH);//读取宽
        int hight = capture.get(CAP_PROP_FRAME_HEIGHT);//读取高
        int count = capture.get(CAP_PROP_FRAME_COUNT);//读取总帧数
        double fps = capture.get(CAP_PROP_FPS);//读取一秒多少帧
        cout << wid << "宽" << endl;
        cout << hight << "高" << endl;
        cout << count << "count" << endl;
        cout << fps << "fps" << endl;
        //保存视频
        //1.保存位置  2.保存代码段 3.fps 4.尺寸 5.颜色是否一致 
        //和下面的write搭配使用
        VideoWriter writer("C:/Users/ASUS/Videos/Captures/test.mp4",capture.get(CAP_PROP_FOURCC),fps,Size(wid,hight),true);
    
        //播放视频
        Mat fram; 
        while (1)
        {
        
            capture >> fram;//读取视频帧
            if (fram.empty())//判断是否到结尾
                break;
            imshow("fram", fram);
    
            writer.write(fram);
    
            int c = waitKey(1);//等待25ms
            if (c == 27) break;
        }
        capture.release();
        writer.release();
        return 0;
    }

## 九.图像直方图 ##

[参考博客][Link 2]

![在这里插入图片描述][d3d17f83397249fea4a5d86dd8c7e06d.png]

int main()
    {
        
        Mat img = imread("C:/Users/ASUS/Pictures/Screenshots/建筑物1.png");
        //三通道分离
        vector<Mat> bgr_plane; 
        split(img, 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, Redi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 Demo", histImage);
        waitKey();
        return 0;
    }

## 十.卷积核 ##

> **下面的卷积核就是求出3\*3方格里的均值再放入中心点，依次往后移动。这样只有最外层的格子不能被处理，对于最外层一般有丢弃和填充两种方法。这种均值操作会导致图像的信息丢失，更加模糊。**

![在这里插入图片描述][cab143e391364e57ba88ff6718eebabe.png]

![在这里插入图片描述][ea9a96b877e1404aa460c0355d0ff812.png]

![在这里插入图片描述][51e331dd3dc94f6b82e0b11513ce2d32.png]

[958b43439e304a20954b93ede991e6b4.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/c55eccee5ad844d7b84268490efd13c4.png
[1652d6245ef74f68b29637d10a878963.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/b36e4f717a974739be9f3578acc27372.png
[5b3daca5ca024e93b4498a20b0e9989c.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/f69d58fc415545d48e6d2717b053dc8c.png
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[1]: http://t.csdn.cn/k2RkZ
[2]: http://t.csdn.cn/ixYMD
[Link 1]: http://t.csdn.cn/LUFrx
[342cd8551dd74eef92fac5a6a297c517.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/fdf23d9db79d4a20817d9fdc75442c5e.png
[008956f6ff2045c69893c7e9b15e6b7e.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/646839cc34a44b389de46c0d32f0a918.png
[f157b4c4a8874320b382208b99546e02.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/66ca8586a0fb4a5192bf2a5fcc6c33fe.png
[c15e95a91dad47ed9fcaccd687ba86aa.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/6e44bc1617264d95bf801133a1549a9e.png
[af9a59bec6a64814bd899d059b930dfc.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/4e266eccb01a42cfa9cc5e104db6760b.png
[b82efd6d485f45dea3083d90ea042556.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/eb67e32e54e64c2fabaa6e1540d53bf1.png
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[a2c8dea10fbe4bf88eea5b1b5ef618ef.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/09d94d1e4e284794bb793a4d2b3fcd4b.png
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[efa042d4dfb4457286ac5dbb0ced2404.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/50c3574893d942828f720297e0295f88.png
[11faa5e5305448c4afb082fed4145d0f.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/72bfe6bd4b9f42f5af03b680f448b1be.png
[bcdfa99069fe45ada06ade4afad9d3c2.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/a572727208cd4775aa83fc9a75bf07bb.png
[e015bb0d56d24a9094136c88f5649079.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/626f1ed21f1c4ebc85242f84be3bd743.png
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[Link 2]: https://blog.csdn.net/didi_ya/article/details/113556309
[d3d17f83397249fea4a5d86dd8c7e06d.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/1056cf14bbb54749bed9da423fa364dd.png
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[ea9a96b877e1404aa460c0355d0ff812.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/5a616a8957f440c081e91227bdc53864.png
[51e331dd3dc94f6b82e0b11513ce2d32.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/16/c59e140b3980425f84e5fbcfe2ed8844.png