本文介绍cvv模块的安装和使用。 cvv，官网称之为“GUI for Interactive Visual Debugging of Computer Vision”，即交互式计算机视觉可视化debug工具，我对其很感兴趣，但是我在网上几乎没见到关于cvv的介绍，因此自行摸索了一番，有了本文。
使用cvv需要定义宏，如果不定义宏，则cvv相关函数不启动，并且开销为0
#define CVVISUAL_DEBUGMODE通过cvv调试很简单，只用以下几个函数：
showImage，添加单个图片到cvv模块debugFilter，添加两个图片到cvv模块，顾名思义，可以用于添加对图像滤波（也可以时其他处理）前后的两张图片进行对比debugDMatch，添加两张图片、特征点、匹配关系，可视化调试图像匹配finalShow，用于主函数最后，阻塞程序退出cvv界面setDebugFlag，用于使能或者失能cvv调试（通过该函数失能cvv后，cvv仍会有较小的开销，若要无开销，可以取消CVVISUAL_DEBUGMODE宏）cvv模块的API详细介绍见这里
要使用cvv，首先要安装好QT，并且下载好对应版本的opencv_contrib，在cmake时，添加opencv_contrib的module路径，并且勾选WITCH_QT，Configure一次，会出现BUILD_opencv_cvv选项，勾选它，再次Configure，Generate。后面的过程跟一般opencv配置过程相同。 note：
cvv模块在opencv3.4.1以上版本才能编译成功！ 原因见这里 ，本文用的opencv版本为4.1.0。如果你还需要sift、surf等特征提取，记得勾选nonfree。下面通过代码看一下，具体如何使用cvv进行调试。 使用的代码如下：
// system includes #include <iostream> // library includes #include <opencv2/imgproc.hpp> #include <opencv2/features2d.hpp> #include <opencv2/imgproc/types_c.h> #include <opencv2/videoio.hpp> #include <opencv2/videoio/videoio_c.h> //当调试完成，不需要改动其他任何代码，只需要注释掉CVVISUAL_DEBUGMODE即可禁用cvv模块，并且程序运行时没有任何cvv开销 #define CVVISUAL_DEBUGMODE #include <opencv2/cvv/debug_mode.hpp> #include <opencv2/cvv/show_image.hpp> #include <opencv2/cvv/filter.hpp> #include <opencv2/cvv/dmatch.hpp> #include <opencv2/cvv/final_show.hpp> using namespace std; using namespace cv; template<class T> std::string toString(const T& p_arg) { std::stringstream ss; ss << p_arg; return ss.str(); } int main(int argc, char** argv) { cv::Size* resolution = nullptr; // parser keys const char *keys = "{ help h usage ? | | show this message }" "{ width W | 0| camera resolution width. leave at 0 to use defaults }" "{ height H | 0| camera resolution height. leave at 0 to use defaults }"; CommandLineParser parser(argc, argv, keys); if (parser.has("help")) { parser.printMessage(); return 0; } int res_w = parser.get<int>("width"); int res_h = parser.get<int>("height"); // setup video capture cv::VideoCapture capture(0); if (!capture.isOpened()) { std::cout << "Could not open VideoCapture" << std::endl; return 1; } if (res_w>0 && res_h>0) { printf("Setting resolution to %dx%d\n", res_w, res_h); capture.set(CV_CAP_PROP_FRAME_WIDTH, res_w); capture.set(CV_CAP_PROP_FRAME_HEIGHT, res_h); } cv::Mat prevImgGray; std::vector<cv::KeyPoint> prevKeypoints; cv::Mat prevDescriptors; int maxFeatureCount = 500; Ptr<ORB> detector = ORB::create(maxFeatureCount); cv::BFMatcher matcher(cv::NORM_HAMMING); for (int imgId = 0; imgId < 10; imgId++) { // capture a frame cv::Mat imgRead; capture >> imgRead; printf("%d: image captured\n", imgId); std::string imgIdString{"imgRead"}; imgIdString += toString(imgId); cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str()); // convert to grayscale cv::Mat imgGray; cv::cvtColor(imgRead, imgGray, COLOR_BGR2GRAY); cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray"); // detect ORB features std::vector<cv::KeyPoint> keypoints; cv::Mat descriptors; detector->detectAndCompute(imgGray, cv::noArray(), keypoints, descriptors); printf("%d: detected %zd keypoints\n", imgId, keypoints.size()); // match them to previous image (if available) if (!prevImgGray.empty()) { std::vector<cv::DMatch> matches; matcher.match(prevDescriptors, descriptors, matches); printf("%d: all matches size=%zd\n", imgId, matches.size()); std::string allMatchIdString{"all matches "}; allMatchIdString += toString(imgId-1) + "<->" + toString(imgId); cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, allMatchIdString.c_str()); // remove worst (as defined by match distance) bestRatio quantile double bestRatio = 0.8; std::sort(matches.begin(), matches.end()); matches.resize(int(bestRatio * matches.size())); printf("%d: best matches size=%zd\n", imgId, matches.size()); std::string bestMatchIdString{"best " + toString(bestRatio) + " matches "}; bestMatchIdString += toString(imgId-1) + "<->" + toString(imgId); cvv::debugDMatch(prevImgGray, prevKeypoints, imgGray, keypoints, matches, CVVISUAL_LOCATION, bestMatchIdString.c_str()); } prevImgGray = imgGray; prevKeypoints = keypoints; prevDescriptors = descriptors; } cvv::finalShow(); return 0; }编译并运行，执行cvv::showImage(imgRead, CVVISUAL_LOCATION, imgIdString.c_str());后被阻塞。读取摄像头图像第一帧并添加到cvv中： 点Step，单步运行，执行cvv::debugFilter(imgRead, imgGray, CVVISUAL_LOCATION, "to gray");后被阻塞： 点击>>，一直执行到cvv::finalShow();为止，期间不阻塞程序： 双击 all matches 0< - >1，查看匹配图，设置Match Setting，根据匹配距离从小到大排序，仅显示前100个匹配结果：
好了，功能就演示到这里，cvv功能不仅仅如此，更多介绍详情见这里
