Windows配置ArUco
windows10
vs2019
opencv3.4.6
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注意，一定要选择与opencv版本一样的opencv_contrib进行编译，否则将会出现错误，有很多的工程不能编译通过，不能产生相应的lib文件。
下载contrib库
contrib库（下载3.4.6版本）：https://github.com/opencv/opencv_contrib/releases
cmake-gui地址（下载最新版3.15.2）：http://www.cmake.org/cmake/resources/software.html
用CMake编译OpenCV3.4.6
点击configure，如果有红的就继续点configure，直到红色都消失
用CMake编译OpenCV_contrib3.4.6
把opencv_contrib-3.4.6文件夹放到C:\opencv目录下，在cmake-gui中找到OPENCV_EXTRA_MODULES_PATH，在它的Value处填入opencv_contrib-3.4.6\modules路径。并勾选OPENCV_ENABLE_NONFREE。
然后点击configure,如果报错或者有红的，继续点configure，直到红色都消失
点击generate
用VS2019编译生成debug和release库
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打开C:\opencv\mybuild目录下的OpenCV.sln
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编译debug版本库：在“解决方案'OpenCV'（416个项目）”上面点击右键-->重新生成解决方案。
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找到CMakeTargets中的INSTALL，然后右键选择“仅限于项目”-->“仅生成INSTALL”，然后就在C:\opencv\mybuild\install中发现了许多东东。至此，debug版本的opencv的库文件就安装完成了。release版本的，方法相同，重复2、3即可。
遇到问题：LNK1104 无法打开文件“python37_d.lib” 解决办法
当出现opencv的.dll无法找到的问题的时候用一下处理方法
4. 将opencv\build\x*\vc*\bin 复制相应的dll文件到C Windows System32下，这样之后就可以找到了
重新配置环境
1.为计算机添加系统变量：C:\opencv\my_build\install\include\x64\vc16\bin ；
右键电脑【属性】 ，【高级系统设置】 ，【环境变量】 ， 【系统变量】【Path】， 在变量值后加 编译目录\install\x64\vc16\bin 的完整路径
添加用户变量设置：
变量名：OPENCV变量值：C:\opencv\my_build\install\include
2.属性管理器-->设置-->VC++目录-->包含目录，添加：
C:\opencv\my_build\install\include
C:\opencv\my_build\install\include\opencv
C:\opencv\my_build\install\include\opencv2
3.VC++目录-->库目录，添加：
C:\opencv\my_build\install\include\install\x64\vc16\lib
4.连接器——>输入——>附加依赖项添加库，346是版本opencv3.4.6，lib文件夹下只有好多个.lib，都加入
opencv_aruco346d.lib
opencv_bgsegm346d.lib
opencv_bioinspired346d.lib
opencv_calib3d346d.lib
opencv_ccalib346d.lib
opencv_core346d.lib
opencv_datasets346d.lib
opencv_dnn_objdetect346d.lib
opencv_dnn346d.lib
opencv_dpm346d.lib
opencv_face346d.lib
opencv_features2d346d.lib
opencv_flann346d.lib
opencv_fuzzy346d.lib
opencv_hdf346d.lib
opencv_hfs346d.lib
opencv_highgui346d.lib
opencv_img_hash346d.lib
opencv_imgcodecs346d.lib
opencv_imgproc346d.lib
opencv_line_descriptor346d.lib
opencv_ml346d.lib
opencv_objdetect346d.lib
opencv_optflow346d.lib
opencv_phase_unwrapping346d.lib
opencv_photo346d.lib
opencv_plot346d.lib
opencv_reg346d.lib
opencv_rgbd346d.lib
opencv_saliency346d.lib
opencv_shape346d.lib
opencv_stereo346d.lib
opencv_stitching346d.lib
opencv_structured_light346d.lib
opencv_superres346d.lib
opencv_surface_matching346d.lib
opencv_text346d.lib
opencv_tracking346d.lib
opencv_video346d.lib
opencv_videoio346d.lib
opencv_videostab346d.lib
opencv_xfeatures2d346d.lib
opencv_ximgproc346d.lib
opencv_xobjdetect346d.lib
opencv_xphoto346d.lib
测试代码——生成aruco_board
#include
#include
#include
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;
int main()
{
int markersX = 5;//X轴上标记的数量
int markersY = 5;//Y轴上标记的数量 本例生成5x5的棋盘
int markerLength = 100;//标记的长度，单位是像素
int markerSeparation = 20;//每个标记之间的间隔，单位像素
int dictionaryId = cv::aruco::DICT_4X4_50;//生成标记的字典ID
int margins = markerSeparation;//标记与边界之间的间隔
int borderBits = 1;//标记的边界所占的bit位数
bool showImage = true;
Size imageSize;
imageSize.width = markersX * (markerLength + markerSeparation) - markerSeparation + 2 * margins;
imageSize.height =markersY * (markerLength + markerSeparation) - markerSeparation + 2 * margins;
Ptr dictionary =aruco::getPredefinedDictionary(aruco::PREDEFINED_DICTIONARY_NAME(dictionaryId));
Ptr board = aruco::GridBoard::create(markersX, markersY, float(markerLength),float(markerSeparation), dictionary);
// show created board
Mat boardImage;
board->draw(imageSize, boardImage, margins, borderBits);
if (showImage) {
imshow("board", boardImage);
waitKey(0);
}
return 0;
}
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【OpenCV/aruco】校准相机(Camera Calibration) Demo