如何使用 C++ 在 OpenCV 3.0 中使用 SIFT？
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How do I use SIFT in OpenCV 3.0 with c++?
提问by leinaD_natipaC
I have OpenCV 3.0, and I have compiled & installed it with the opencv_contrib module so that's not a problem. Unfortunately the examples from previous versions do not work with the current one, and so although this question has already been askedmore than onceI would like a more current example that I can actually work with. Even the official examplesdon't work in this version (feature detection works but not other feature examples) and they use SURF anyway.
我有 OpenCV 3.0，我已经用 opencv_contrib 模块编译并安装了它，所以这不是问题。不幸的是，以前版本中的示例不适用于当前版本，因此尽管已经不止一次地问过这个问题，但我想要一个我可以实际使用的更当前的示例。甚至官方示例在此版本中也不起作用（特征检测有效，但其他特征示例无效），无论如何他们都使用 SURF。
So, how do I use OpenCV SIFT on C++? I want to grab the keypoints in two images and match them, similar to this example, but even just getting the points and descriptors would be enough help. Help!
那么，如何在 C++ 上使用 OpenCV SIFT？我想抓取两个图像中的关键点并匹配它们，类似于这个例子，但即使只是获取点和描述符也足够了。帮助！
回答by berak
- get the opencv_contrib repo
- take your time with the readme there, add it to your mainopencv cmake settings
- rerun cmake /make / install in the main opencv repo
- 获取opencv_contrib 仓库
- 花点时间阅读那里的自述文件，将其添加到您的主要opencv cmake 设置中
- 在主 opencv 存储库中重新运行 cmake /make / install
then:
然后：
#include "opencv2/xfeatures2d.hpp"
//
// now, you can no more create an instance on the 'stack', like in the tutorial
// (yea, noticed for a fix/pr).
// you will have to use cv::Ptr all the way down:
//
cv::Ptr<Feature2D> f2d = xfeatures2d::SIFT::create();
//cv::Ptr<Feature2D> f2d = xfeatures2d::SURF::create();
//cv::Ptr<Feature2D> f2d = ORB::create();
// you get the picture, i hope..
//-- Step 1: Detect the keypoints:
std::vector<KeyPoint> keypoints_1, keypoints_2;
f2d->detect( img_1, keypoints_1 );
f2d->detect( img_2, keypoints_2 );
//-- Step 2: Calculate descriptors (feature vectors)
Mat descriptors_1, descriptors_2;
f2d->compute( img_1, keypoints_1, descriptors_1 );
f2d->compute( img_2, keypoints_2, descriptors_2 );
//-- Step 3: Matching descriptor vectors using BFMatcher :
BFMatcher matcher;
std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );
also, don't forget to link opencv_xfeatures2d !
另外，不要忘记链接 opencv_xfeatures2d ！
回答by Okan Barut
There are useful answers, but I'll add my version (for OpenCV 3.X) just in case the above ones aren't clear (tested and tried):
有一些有用的答案，但我会添加我的版本（对于 OpenCV 3.X），以防万一上述内容不清楚（经过测试和尝试）：
- Clone opencv from https://github.com/opencv/opencvto home dir
- Clone opencv_contrib from https://github.com/opencv/opencv_contribto home dir
- Inside opencv, create a folder named
build - Use this CMake command, to activate non-free modules:
cmake -DOPENCV_EXTRA_MODULES_PATH=/home/YOURUSERNAME/opencv_contrib/modules -DOPENCV_ENABLE_NONFREE:BOOL=ON ..(Please notice that we showed where the contrib modules resides and also activated the nonfree modules) - Do
makeandmake installafterwards
- 将 opencv 从https://github.com/opencv/opencv克隆到主目录
- 将 opencv_contrib 从https://github.com/opencv/opencv_contrib克隆到主目录
- 在 opencv 中，创建一个名为的文件夹
build - 使用此命令的CMake，激活非游离模块：
cmake -DOPENCV_EXTRA_MODULES_PATH=/home/YOURUSERNAME/opencv_contrib/modules -DOPENCV_ENABLE_NONFREE:BOOL=ON ..（请注意，我们发现其中的contrib模块所在，也激活了非免费模块） - 做
make和make install之后
The above steps should work out for OpenCV 3.X
上述步骤应该适用于 OpenCV 3.X
After that, you may run the below code using g++ with the appropriate flags:
之后，您可以使用带有适当标志的 g++ 运行以下代码：
g++ -std=c++11 main.cpp `pkg-config --libs --cflags opencv` -lutil -lboost_iostreams -lboost_system -lboost_filesystem -lopencv_xfeatures2d -o surftestexecutable
The important thing not to forget is to link the xfeatures2D library with -lopencv_xfeatures2das shown on the command. And the main.cppfile is:
重要的是不要忘记将 xfeatures2D 库与-lopencv_xfeatures2d链接，如命令所示。该main.cpp文件是：
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "opencv2/xfeatures2d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"
using namespace cv;
using namespace std;
int main(int argc, const char* argv[])
{
const cv::Mat input = cv::imread("surf_test_input_image.png", 0); //Load as grayscale
Ptr< cv::xfeatures2d::SURF> surf = xfeatures2d::SURF::create();
std::vector<cv::KeyPoint> keypoints;
surf->detect(input, keypoints);
// Add results to image and save.
cv::Mat output;
cv::drawKeypoints(input, keypoints, output);
cv::imwrite("surf_result.jpg", output);
return 0;
}
This should create and save an image with surf keypoints.
这应该创建并保存带有冲浪关键点的图像。
