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原博主博客地址：https://blog.csdn.net/qq21497936
原博主博客导航：https://blog.csdn.net/qq21497936/article/details/102478062
本文章博客地址：https://blog.csdn.net/qq21497936/article/details/107837715
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红瘦子(红模拟)的博文大全：开发技术汇合（蕴含Qt实用技术、树莓派、三维、OpenCV、OpenGL、ffmpeg、OSG、单片机、软硬联合等等）继续更新中...（点击传送门）

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上一篇：《OpenCV开发笔记（六十八）：红瘦子8分钟带你应用特色点Flann最邻近差值匹配辨认（图文并茂+浅显易懂+程序源码）》
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前言

红瘦子，来也！
特色点、匹配，那么如何应用特色点和匹配来辨认已有的物体，也就剩最要害的最初一步：寻找已知的物体了。

Demo

寻找已知物体

本篇章应用sift/surf特色点

sift特色点

尺度不变特色变换（Scale-invariant feature transform，SIFT），是用于图像处理畛域的一种形容。这种形容具备尺度不变性，可在图像中检测出关键点，是一种部分特征描述子。

surf特色点

SURF算法采纳了很多办法来对每一步进行优化从而进步速度。剖析显示在后果成果相当的状况下SURF的速度是SIFT的3倍。SURF长于解决具备含糊和旋转的图像，然而不长于解决视角变动和光照变动。（SIFT特色是部分特色，其对旋转、尺度缩放、亮度变动放弃不变性，对视角变动、仿射变换、噪声也放弃肯定水平的稳定性）。
针对图像场景的特点，抉择不同的特色点，列出之前特色点相干的博文：
《OpenCV开发笔记（六十三）：红瘦子8分钟带你深刻理解SIFT特色点（图文并茂+浅显易懂+程序源码）》
《OpenCV开发笔记（六十四）：红瘦子8分钟带你深刻理解SURF特色点（图文并茂+浅显易懂+程序源码）》
《OpenCV开发笔记（六十五）：红瘦子8分钟带你深刻理解ORB特色点（图文并茂+浅显易懂+程序源码）》

本篇章使用暴力、最邻近差值匹配

暴力匹配

最佳特色匹配总是尝试所有可能的匹配，从而使得它总可能找到最佳匹配，这也是BruteForce（暴力法）的原始含意，波及到的类为BFMatcher类。
《OpenCV开发笔记（六十七）：红瘦子8分钟带你深刻理解特色点暴力匹配（图文并茂+浅显易懂+程序源码）》

概述

对已知物体：过滤、去噪后、提取已知物体的特色点；
对场景：过滤、去噪后、提取场景的特色点；
对已知物体特色点汇合和场景中的特色点汇合去匹配，计算投影矩阵；
若胜利计算变换矩阵就示意辨认到物体；
通过原始的四个点地位进行变换矩阵计算，即可失去场景中的已知物体的四个顶点，该四个顶点连接起来就是已知物体的地位。

特色点汇合计算变换矩阵函数原型

Mat findHomography(InputArray srcPoints,                   InputArray dstPoints,                   int method = 0,                   double ransacReprojThreshold = 3,                   OutputArray mask=noArray(),                   const int maxIters = 2000,                   const double confidence = 0.995);

参数一：InputArray类型的srcPoints，源立体上的对应点，能够是CV_32FC2的矩阵类型或者vector<Point2>；
参数二：InputArray类型的dstPoints；指标立体上的对应点，可以是

CV 32FC2 的矩阵类型或者 vector<Point2>；

参数三：int类型的method，用于计算单应矩阵的办法，如下图：

参数四：double类型的ransacReprojThreshold，最大容许重投影谬误将点对视为内联线（仅用于RANSAC和RHO办法）；
参数五：OutputArray类型的mask，由鲁棒办法（RANSAC或LMEDS）设置的可选输入掩码。留神输出掩码值被疏忽。；
参数六：const int类型的maxIters，RANSAC迭代的最大数量。；
参数七：const double类型的confidence，置信水平，介于0和1之间；

矩阵变换函数原型

void perspectiveTransform( InputArray src,                           InputArray dst,                           InputArray m);

参数一：InputArray类型的src，输出两通道或三通道浮点数组；每个元素是要转换的二维/三维向量。
参数二：InputArray类型的dst，与src大小和类型雷同的输入数组；
参数三：InputArray类型的h，3x3或4x4浮点转换矩阵。

本文章博客地址：https://blog.csdn.net/qq21497936/article/details/107837715

Demo源码

void OpenCVManager::testFindKnownObject(){    QString fileName1 = "21.jpg";    QString fileName2 = "24.jpg";    int width = 400;    int height = 300;    cv::Mat srcMat = cv::imread(fileName1.toStdString());    cv::Mat srcMat3 = cv::imread(fileName2.toStdString());    cv::resize(srcMat, srcMat, cv::Size(width, height));    cv::resize(srcMat3, srcMat3, cv::Size(width, height));    cv::String windowName = _windowTitle.toStdString();    cvui::init(windowName);    cv::Mat windowMat = cv::Mat(cv::Size(srcMat.cols * 2, srcMat.rows * 3),                                srcMat.type());    cv::Ptr<cv::xfeatures2d::SIFT> _pSift = cv::xfeatures2d::SiftFeatureDetector::create();    cv::Ptr<cv::xfeatures2d::SURF> _pSurf = cv::xfeatures2d::SurfFeatureDetector::create(450, 10, 10, true, true);    cv::Ptr<cv::Feature2D> _pFeature2D;    cv::Ptr<cv::DescriptorMatcher> _pDescriptorMatcher;    int type = 0;    int findType = 0;    int k1x = 25;    int k1y = 25;    int k2x = 75;    int k2y = 25;    int k3x = 75;    int k3y = 75;    int k4x = 25;    int k4y = 75;    // 定义匹配器    cv::Ptr<cv::FlannBasedMatcher> pFlannBasedMatcher = cv::FlannBasedMatcher::create();    cv::Ptr<cv::BFMatcher> pBFMatcher = cv::BFMatcher::create();    // 定义后果寄存    std::vector<cv::DMatch> listDMatch;    // 存储特色点检测器检测特色后的形容字    cv::Mat descriptor1;    cv::Mat descriptor2;    bool moveFlag = true;  // 挪动的标记，不必每次都匹配    std::vector<cv::Point2f> obj_corners(4);    std::vector<cv::Point2f> scene_corners(4);    windowMat = cv::Scalar(0, 0, 0);    while(true)    {        cv::Mat mat;        {            std::vector<cv::KeyPoint> keyPoints1;            std::vector<cv::KeyPoint> keyPoints2;            int typeOld = type;            int findTypeOld = findType;            int k1xOld = k1x;            int k1yOld = k1y;            int k2xOld = k2x;            int k2yOld = k2y;            int k3xOld = k3x;            int k3yOld = k3y;            int k4xOld = k4x;            int k4yOld = k4y;            mat = windowMat(cv::Range(srcMat.rows * 0, srcMat.rows * 1),                            cv::Range(srcMat.cols * 0, srcMat.cols * 1));            mat = cv::Scalar(0);            cvui::trackbar(windowMat, 0 + width * 0, 0 + height * 0, 165, &type, 0, 1);            cv::String str;            switch(type)            {            case 0:                str = "sift";                _pFeature2D = _pSift;                break;            case 1:                str = "surf";                _pFeature2D = _pSurf;                break;            default:                break;            }            cvui::printf(windowMat, width / 4 + width * 0 - 20, 40 + height * 0, str.c_str());            cvui::trackbar(windowMat, width / 2 + width * 0, 0 + height * 0, 165, &findType, 0, 1);            switch(findType)            {            case 0:                str = "BFMatcher";                _pDescriptorMatcher = pBFMatcher;                break;            case 1:                str = "FlannBasedMatcher";                _pDescriptorMatcher = pFlannBasedMatcher;                break;            default:                break;            }            cvui::printf(windowMat, width / 4 * 3 + width * 0 - 20, 40 + height * 0, str.c_str());            cvui::printf(windowMat, 0 + width * 0, 60 + height * 0, "k1x");            cvui::trackbar(windowMat, 0 + width * 0, 70 + height * 0, 165, &k1x, 0, 100);            cvui::printf(windowMat, 0 + width * 0, 120 + height * 0, "k1y");            cvui::trackbar(windowMat, 0 + width * 0, 130 + height * 0, 165, &k1y, 0, 100);            cvui::printf(windowMat, width / 2 + width * 0, 60 + height * 0, "k2x");            cvui::trackbar(windowMat, width / 2 + width * 0, 70 + height * 0, 165, &k2x, 0, 100);            cvui::printf(windowMat, width / 2 + width * 0, 120 + height * 0, "k2y");            cvui::trackbar(windowMat, width / 2 + width * 0, 130 + height * 0, 165, &k2y, 0, 100);            cvui::printf(windowMat, 0 + width * 0, 30 + height * 0 + height / 2, "k3x");            cvui::trackbar(windowMat, 0 + width * 0, 40 + height * 0 + height / 2, 165, &k3x, 0, 100);            cvui::printf(windowMat, 0 + width * 0, 90 + height * 0 + height / 2, "k3y");            cvui::trackbar(windowMat, 0 + width * 0, 100 + height * 0 + height / 2, 165, &k3y, 0, 100);            cvui::printf(windowMat, width / 2 + width * 0, 30 + height * 0 + height / 2, "k4x");            cvui::trackbar(windowMat, width / 2 + width * 0, 40 + height * 0 + height / 2, 165, &k4x, 0, 100);            cvui::printf(windowMat, width / 2 + width * 0, 90 + height * 0 + height / 2, "k4y");            cvui::trackbar(windowMat, width / 2 + width * 0, 100 + height * 0 + height / 2, 165, &k4y, 0, 100);            if( k1xOld != k1x || k1yOld != k1y             || k2xOld != k2x || k2yOld != k2y             || k3xOld != k3x || k3yOld != k3y             || k4xOld != k4x || k4yOld != k4y             || typeOld != type || findTypeOld != findType)            {                typeOld = type;                findTypeOld = findType;                moveFlag = true;            }            std::vector<cv::Point2f> srcPoints;            std::vector<cv::Point2f> dstPoints;            srcPoints.push_back(cv::Point2f(0.0f, 0.0f));            srcPoints.push_back(cv::Point2f(srcMat.cols - 1, 0.0f));            srcPoints.push_back(cv::Point2f(srcMat.cols - 1, srcMat.rows - 1));            srcPoints.push_back(cv::Point2f(0.0f, srcMat.rows - 1));            dstPoints.push_back(cv::Point2f(srcMat.cols * k1x / 100.0f, srcMat.rows * k1y / 100.0f));            dstPoints.push_back(cv::Point2f(srcMat.cols * k2x / 100.0f, srcMat.rows * k2y / 100.0f));            dstPoints.push_back(cv::Point2f(srcMat.cols * k3x / 100.0f, srcMat.rows * k3y / 100.0f));            dstPoints.push_back(cv::Point2f(srcMat.cols * k4x / 100.0f, srcMat.rows * k4y / 100.0f));            cv::Mat M = cv::getPerspectiveTransform(srcPoints, dstPoints);            cv::Mat srcMat2;            cv::warpPerspective(srcMat3,                                srcMat2,                                M,                                cv::Size(srcMat.cols, srcMat.rows),                                cv::INTER_LINEAR,                                cv::BORDER_CONSTANT,                                cv::Scalar::all(0));            mat = windowMat(cv::Range(srcMat.rows * 0, srcMat.rows * 1),                            cv::Range(srcMat.cols * 1, srcMat.cols * 2));            cv::addWeighted(mat, 0.0f, srcMat2, 1.0f, 0.0f, mat);            if(moveFlag)            {                moveFlag = false;                //特色点检测    //           _pSift->detect(srcMat, keyPoints1);                _pFeature2D->detectAndCompute(srcMat, cv::Mat(), keyPoints1, descriptor1);                //绘制特色点(关键点)                cv::Mat resultShowMat;                cv::drawKeypoints(srcMat,                                  keyPoints1,                                  resultShowMat,                                  cv::Scalar(0, 0, 255),                                  cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);                mat = windowMat(cv::Range(srcMat.rows * 1, srcMat.rows * 2),                                cv::Range(srcMat.cols * 0, srcMat.cols * 1));                cv::addWeighted(mat, 0.0f, resultShowMat, 1.0f, 0.0f, mat);                //特色点检测    //            _pSift->detect(srcMat2, keyPoints2);                _pFeature2D->detectAndCompute(srcMat2, cv::Mat(), keyPoints2, descriptor2);                //绘制特色点(关键点)                cv::Mat resultShowMat2;                cv::drawKeypoints(srcMat2,                                  keyPoints2,                                  resultShowMat2,                                  cv::Scalar(0, 0, 255),                                  cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);                mat = windowMat(cv::Range(srcMat.rows * 1, srcMat.rows * 2),                                cv::Range(srcMat.cols * 1, srcMat.cols * 2));                cv::addWeighted(mat, 0.0f, resultShowMat2, 1.0f, 0.0f, mat);                // FlannBasedMatcher最近邻匹配                _pDescriptorMatcher->match(descriptor1, descriptor2, listDMatch);                // drawMatch绘制进去，并排显示了，高度一样，宽度累加（因为两个宽度雷同，所以是两倍了）                cv::Mat matchesMat;                cv::drawMatches(srcMat,                                keyPoints1,                                srcMat2,                                keyPoints2,                                listDMatch,                                matchesMat);                mat = windowMat(cv::Range(srcMat.rows * 2, srcMat.rows * 3),                                cv::Range(srcMat.cols * 0, srcMat.cols * 2));                cv::addWeighted(mat, 0.0f, matchesMat, 1.0f, 0.0f, mat);                // 定义两个局部变量                std::vector<cv::Point2f> obj;                std::vector<cv::Point2f> scene;                // 从匹配胜利的匹配对中获取关键点                for(int index = 0; index < listDMatch.size(); index++)                {                    obj.push_back(keyPoints1[listDMatch[index].queryIdx].pt);                    scene.push_back(keyPoints2[listDMatch[index].trainIdx].pt);                }                // 计算透视变换                cv::Mat H = cv::findHomography(obj, scene, CV_RANSAC);                // 从待测图片中获取角点                obj_corners[0] = cv::Point2f(0,0);                obj_corners[1] = cv::Point2f(srcMat.cols,0);                obj_corners[2] = cv::Point2f(srcMat.cols, srcMat.rows);                obj_corners[3] = cv::Point2f(0, srcMat.rows);                // 进行透视变换                cv::perspectiveTransform(obj_corners, scene_corners, H);            }            // 绘制出角点之间的线            qDebug() << __FILE__ << __LINE__                     << scene_corners[0].x                     << scene_corners[0].y                     << scene_corners[1].x                     << scene_corners[1].y;            cv::line(windowMat,                     scene_corners[0] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     scene_corners[1] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     cv::Scalar(0, 0, 255), 2);            cv::line(windowMat,                     scene_corners[1] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     scene_corners[2] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     cv::Scalar(0, 0, 255), 2);            cv::line(windowMat,                     scene_corners[2] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     scene_corners[3] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     cv::Scalar(0, 0, 255), 2);            cv::line(windowMat,                     scene_corners[3] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     scene_corners[0] + cv::Point2f(srcMat.cols * 1, srcMat.rows * 0),                     cv::Scalar(0, 0, 255), 2);        }        cv::imshow(windowName, windowMat);        // 更新        cvui::update();        // 显示        // esc键退出        if(cv::waitKey(25) == 27)        {            break;        }    }}

工程模板：对应版本号v1.63.0

对应版本号v1.63.0