常规的SIFT算法进行图像匹配的时候,只能进行两个摄像机夹角比较小的(最大是15°),拍摄的图像进行相机的图像匹配,但是针对于相机之间的夹脚比较大的时候,上述的算法匹配就是会出现问题.为了解决上面的这个问题,使用了一种改进的算法匹配方式ASIFT算法进行匹配.具体这种算法的优点见博客论文:基于ASIFT算法特征匹配的研究算法特征匹配的研究-图像处理文档类资源-CSDN下载在进行相机图像匹配的时候，一般的情况下我们是使用的双目相机，但是二者的视角是几乎近似平行的，平时我们更多下载资源、学习资料请访问CSDN下载频道.https://download.csdn.net/download/m0_47489229/87108632配置过程出错:
一开始我是看的这两篇博客,进行的配置ASIFT算法在WIN7 64位系统下利用VS2012生成_zz420521的博客-CSDN博客_demo_asift欢迎大家交流，转载时请注明原文出处：http://blog.csdn.net/zlr289652981/article/details/63684857传统的sift算法在视角变化不大的情况下，可以获得良好的匹配效果，但如果在视角变化偏大时，其匹配效果会急剧下降，甚至找不到匹配点。由Guoshen Yu和Jean-Michel Morel提出的ASIFT算法很好的解决了在大视角变化条件下的图像https://blog.csdn.net/zz420521/article/details/63686163搭建可随意更改路径的VS工程-以ASIFT算法为例_zz420521的博客-CSDN博客在前一篇的博文中，笔者介绍了如何利用Guoshen Yu和Jean-Michel Morel提出的ASIFT算法的windows下C++版本源码来构建工程，具体见链接：http://blog.csdn.net/zz420521/article/details/63686163但实际上，虽然工程是跑通了，但是里面还包含了许多我们不熟悉的或者不需要的东西，尤其是一些路径设置的问题，对工程里面的设置
https://blog.csdn.net/zz420521/article/details/65437441?ops_request_misc=&request_id=&biz_id=102&utm_term=vs2017%E5%AE%9E%E7%8E%B0asift%E7%AE%97%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-65437441.nonecase&spm=1018.2226.3001.4187我是用的VS2017进行配置,已经配置好的是OpenCV3.6.4+contribe3.6.4,然后按照上面的步骤进行配置完成之后,发现有错误出现,当然我的需求是使用代码进行编制,在进行生成解决方案的时候会出现一个如下的错误:
这里的问题貌似是需要进行提前配置Eigen和Boost库,但是我发现这个地方是直接使用一个exe文件进行操作的,并不是我所需要的.百度也没有发现想要解决的东西.
后来查阅资料发现,就是代码形式的一般是相应的python形式的,但是在查询代码的过程之中,查到一个c++形式实现这个功能的代码,如下所示:
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/features2d.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/calib3d.hpp>
#include <iostream>
#include <iomanip>
using namespace std;
using namespace cv;
static void help(char** argv)
{
cout
<< "This is a sample usage of AffineFeature detector/extractor.\n"
<< "And this is a C++ version of samples/python/asift.py\n"
<< "Usage: " << argv[0] << "\n"
<< " [ --feature=<sift|orb|brisk> ] # Feature to use.\n"
<< " [ --flann ] # use Flann-based matcher instead of bruteforce.\n"
<< " [ --maxlines=<number(50 as default)> ] # The maximum number of lines in visualizing the matching result.\n"
<< " [ --image1=<image1(aero1.jpg as default)> ]\n"
<< " [ --image2=<image2(aero3.jpg as default)> ] # Path to images to compare."
<< endl;
}
static double timer()
{
return getTickCount() / getTickFrequency();
}
int main(int argc, char** argv)
{
vector<String> fileName;
cv::CommandLineParser parser(argc, argv,
"{help h ||}"
"{feature|brisk|}"
"{flann||}"
"{maxlines|50|}"
"{image1|aero1.jpg|}{image2|aero3.jpg|}");
if (parser.has("help"))
{
help(argv);
return 0;
}
string feature = parser.get<string>("feature");
bool useFlann = parser.has("flann");
int maxlines = parser.get<int>("maxlines");
fileName.push_back(samples::findFile(parser.get<string>("image1")));
fileName.push_back(samples::findFile(parser.get<string>("image2")));
if (!parser.check())
{
parser.printErrors();
cout << "See --help (or missing '=' between argument name and value?)" << endl;
return 1;
}
Mat img1 = imread(fileName[0], IMREAD_GRAYSCALE);
Mat img2 = imread(fileName[1], IMREAD_GRAYSCALE);
if (img1.empty())
{
cerr << "Image " << fileName[0] << " is empty or cannot be found" << endl;
return 1;
}
if (img2.empty())
{
cerr << "Image " << fileName[1] << " is empty or cannot be found" << endl;
return 1;
}
Ptr<Feature2D> backend;
Ptr<DescriptorMatcher> matcher;
if (feature == "sift")
{
backend = SIFT::create();
if (useFlann)
matcher = DescriptorMatcher::create("FlannBased");
else
matcher = DescriptorMatcher::create("BruteForce");
}
else if (feature == "orb")
{
backend = ORB::create();
if (useFlann)
matcher = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(6, 12, 1));
else
matcher = DescriptorMatcher::create("BruteForce-Hamming");
}
else if (feature == "brisk")
{
backend = BRISK::create();
if (useFlann)
matcher = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(6, 12, 1));
else
matcher = DescriptorMatcher::create("BruteForce-Hamming");
}
else
{
cerr << feature << " is not supported. See --help" << endl;
return 1;
}
cout << "extracting with " << feature << "..." << endl;
Ptr<AffineFeature> ext = AffineFeature::create(backend);
vector<KeyPoint> kp1, kp2;
Mat desc1, desc2;
ext->detectAndCompute(img1, Mat(), kp1, desc1);
ext->detectAndCompute(img2, Mat(), kp2, desc2);
cout << "img1 - " << kp1.size() << " features, "
<< "img2 - " << kp2.size() << " features"
<< endl;
cout << "matching with " << (useFlann ? "flann" : "bruteforce") << "..." << endl;
double start = timer();
// match and draw
vector< vector<DMatch> > rawMatches;
vector<Point2f> p1, p2;
vector<float> distances;
matcher->knnMatch(desc1, desc2, rawMatches, 2);
// filter_matches
for (size_t i = 0; i < rawMatches.size(); i++)
{
const vector<DMatch>& m = rawMatches[i];
if (m.size() == 2 && m[0].distance < m[1].distance * 0.75)
{
p1.push_back(kp1[m[0].queryIdx].pt);
p2.push_back(kp2[m[0].trainIdx].pt);
distances.push_back(m[0].distance);
}
}
vector<uchar> status;
vector< pair<Point2f, Point2f> > pointPairs;
Mat H = findHomography(p1, p2, status, RANSAC);
int inliers = 0;
for (size_t i = 0; i < status.size(); i++)
{
if (status[i])
{
pointPairs.push_back(make_pair(p1[i], p2[i]));
distances[inliers] = distances[i];
// CV_Assert(inliers <= (int)i);
inliers++;
}
}
distances.resize(inliers);
cout << "execution time: " << fixed << setprecision(2) << (timer() - start) * 1000 << " ms" << endl;
cout << inliers << " / " << status.size() << " inliers/matched" << endl;
cout << "visualizing..." << endl;
vector<int> indices(inliers);
cv::sortIdx(distances, indices, SORT_EVERY_ROW + SORT_ASCENDING);
// explore_match
int h1 = img1.size().height;
int w1 = img1.size().width;
int h2 = img2.size().height;
int w2 = img2.size().width;
Mat vis = Mat::zeros(max(h1, h2), w1 + w2, CV_8U);
img1.copyTo(Mat(vis, Rect(0, 0, w1, h1)));
img2.copyTo(Mat(vis, Rect(w1, 0, w2, h2)));
cvtColor(vis, vis, COLOR_GRAY2BGR);
vector<Point2f> corners(4);
corners[0] = Point2f(0, 0);
corners[1] = Point2f((float)w1, 0);
corners[2] = Point2f((float)w1, (float)h1);
corners[3] = Point2f(0, (float)h1);
vector<Point2i> icorners;
perspectiveTransform(corners, corners, H);
transform(corners, corners, Matx23f(1, 0, (float)w1, 0, 1, 0));
Mat(corners).convertTo(icorners, CV_32S);
polylines(vis, icorners, true, Scalar(255, 255, 255));
for (int i = 0; i < min(inliers, maxlines); i++)
{
int idx = indices[i];
const Point2f& pi1 = pointPairs[idx].first;
const Point2f& pi2 = pointPairs[idx].second;
circle(vis, pi1, 2, Scalar(0, 255, 0), -1);
circle(vis, pi2 + Point2f((float)w1, 0), 2, Scalar(0, 255, 0), -1);
line(vis, pi1, pi2 + Point2f((float)w1, 0), Scalar(0, 255, 0));
}
if (inliers > maxlines)
cout << "only " << maxlines << " inliers are visualized" << endl;
imshow("affine find_obj", vis);
waitKey();
cout << "done" << endl;
return 0;
}
在执行的时候,发现代码报错:
还是老样子,这里的报错原因是我的库是opencv3.6.4,但是支持这个地方的是opencv4.5.0版本以上,还必须要配置contribe,所以一切还得重新弄.
我在使用配置vs2015+cmake3.24.0+opencv4.5.3+contribe4.5.3过程又出现了之前的老问题,解决方案失败,就是像下面的这个所示:
这个过程就是我是连接的外网,发现还是不行,就是需要下载很多很多的配置文件,将这些配置文件需要一个一个下载下来才可以,感觉还是比较困难的.然后就接着找博客,我发现了这一篇博客.史上最全最详细讲解 VS2015+OpenCV4.5.1+OpenCV-4.5.1_Contribute+CUDA_元宇宙MetaAI的博客-CSDN博客视频正在录制请稍等VS2015+opencv4.1+opencv4.1_contributeCmake官网CMake Warning at cmake/OpenCVDownload.cmake:202 (message)如何去掉cmake编译OpenCV时的Cmake Warning：“OpenCVGenSetupVars.cmake:54https://blog.csdn.net/CSS360/article/details/117871722?ops_request_misc=&request_id=&biz_id=102&utm_term=vs2015%20%20opencv4.5.1&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-117871722.142%5Ev66%5Econtrol,201%5Ev3%5Eadd_ask,213%5Ev2%5Et3_esquery_v2&spm=1018.2226.3001.4187
我的电脑已经是使用vs2017配置好别的环境了,所以只能是用vs2015进行配置新的,参考上面的操作步骤,里面的东西在他的百度网盘里面下载.cache文件添加进去,就可以了.
可以个锤子,还是不行.....................................................................................................................
前面的配置过程都是有问题的,就是我走过的坑.
发现要安装之前还要安装一个什么CUDA什么鬼,这具体是个什么,我现在也不知道,需要在网上进行发查找,那就接着安装...
win10 vs2015 cmake3.18.0 opencv4.5.1 opencv_contrib4.5.1 编译攻略_dragon_perfect的博客-CSDN博客_cmake编译opencv4.5.1一、前期准备：需要下载的部分有四个：OpenCV, OpenCV_contrib, CMake，VisualStudio1. 下载OpenCV and OpenCV_contrib，要求是匹配的同版本，并解压缩存储到同一文件夹下；下载OpenCV链接：https://opencv.org/releases/下载OpenCV_contrib链接 ：https://github.com/opencv/opencv_contrib/releases2. CMake的下载下载链接：ht...https://blog.csdn.net/zhulong1984/article/details/112728038https://www.baidu.com/link?url=STTSCR6UH-Zr7kAZXC8sNy3h9MSenc5VtvTHiSWMeQ30_MXa0-zrnioqai1bvwuNYjH2s4jpYwHufK5vrhn-LF0xMpJ8zL9pltVB_gINiJe&wd=&eqid=a6ef9f51000221a300000006637e2a66
https://www.baidu.com/link?url=STTSCR6UH-Zr7kAZXC8sNy3h9MSenc5VtvTHiSWMeQ30_MXa0-zrnioqai1bvwuNYjH2s4jpYwHufK5vrhn-LF0xMpJ8zL9pltVB_gINiJe&wd=&eqid=a6ef9f51000221a300000006637e2a66我在看完上面两个博客的介绍之后,发现配置之中不存在什么CUDA的配置,也就是说我没有安装CUDA,那就接着CSDN,github看不懂,智力有限.那么接下来请看下面的这个博文,进行配置CUDA配置过程.
win10+cuda11.0+vs2019安装教程_离墨猫的博客-CSDN博客_cuda vs转自：https://www.jianshu.com/p/1fd15d2408bf?utm_campaign=hugo第一步：检查显卡支持的cuda版本1.第一种方法：win+R打开cmd，输入nvidia-smi，我的显卡是nvidia 2070super，支持的cuda版本是11.0图1 cmd查看显卡支持的cuda版本2.第二种方法：搜索框输入nvidia,出现nvidia控制面板，打开帮助中的系统信息，选择组件，出现cuda版本信息。第二步：安装vs2019.https://blog.csdn.net/syz201558503103/article/details/114867877?ops_request_misc=&request_id=&biz_id=102&utm_term=vs%20cuda%E5%A6%82%E4%BD%95%E5%AE%89%E8%A3%85&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-114867877.pc_v2_rank_dl_default&spm=1018.2226.3001.4449但是我看了一些实验室配的电脑的显卡,只有一个核显,没有独显,所以就是没有什么NVDIA配置,就没有什么CUDA配置.
到这个地方思路有断了,那么我可不可以不使用cuda的加速了.又开始了在vs2015上的配置过程,但是还是生成解决方案的时候,爆出了很多的错误.
实在没有办法了,只能在VS2017上进行配置了,突然发现可以,生成解决方案和install都没有爆出错误,很玄学. 然后我执行的代码把原来的修改了一下
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/features2d.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/calib3d.hpp>
#include <iostream>
#include <iomanip>
using namespace std;
using namespace cv;
static void help(char** argv)
{
cout
<< "This is a sample usage of AffineFeature detector/extractor.\n"
<< "And this is a C++ version of samples/python/asift.py\n"
<< "Usage: " << argv[0] << "\n"
<< " [ --feature=<sift|orb|brisk> ] # Feature to use.\n"
<< " [ --flann ] # use Flann-based matcher instead of bruteforce.\n"
<< " [ --maxlines=<number(50 as default)> ] # The maximum number of lines in visualizing the matching result.\n"
<< " [ --image1=<image1(aero1.jpg as default)> ]\n"
<< " [ --image2=<image2(aero3.jpg as default)> ] # Path to images to compare."
<< endl;
}
static double timer()
{
return getTickCount() / getTickFrequency();
}
int main(int argc, char** argv)
{
vector<String> fileName;
cv::CommandLineParser parser(argc, argv,
"{help h ||}"
"{feature|brisk|}"
"{flann||}"
"{maxlines|50|}"
"{image1|aero1.jpg|}{image2|aero3.jpg|}");
if (parser.has("help"))
{
help(argv);
return 0;
}
string feature = parser.get<string>("feature");
bool useFlann = parser.has("flann");
int maxlines = parser.get<int>("maxlines");
fileName.push_back(samples::findFile(parser.get<string>("image1")));
fileName.push_back(samples::findFile(parser.get<string>("image2")));
if (!parser.check())
{
parser.printErrors();
cout << "See --help (or missing '=' between argument name and value?)" << endl;
return 1;
}
Mat img1 = imread("茉莉清茶1.jpg", IMREAD_GRAYSCALE);
Mat img2 = imread("茉莉清茶2.jpg", IMREAD_GRAYSCALE);
if (img1.empty())
{
cerr << "Image1 " << " is empty or cannot be found" << endl;
return 1;
}
if (img2.empty())
{
cerr << "Image2 " << " is empty or cannot be found" << endl;
return 1;
}
Ptr<Feature2D> backend;
Ptr<DescriptorMatcher> matcher;
if (feature == "sift")
{
backend = SIFT::create();
if (useFlann)
matcher = DescriptorMatcher::create("FlannBased");
else
matcher = DescriptorMatcher::create("BruteForce");
}
else if (feature == "orb")
{
backend = ORB::create();
if (useFlann)
matcher = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(6, 12, 1));
else
matcher = DescriptorMatcher::create("BruteForce-Hamming");
}
else if (feature == "brisk")
{
backend = BRISK::create();
if (useFlann)
matcher = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(6, 12, 1));
else
matcher = DescriptorMatcher::create("BruteForce-Hamming");
}
else
{
cerr << feature << " is not supported. See --help" << endl;
return 1;
}
cout << "extracting with " << feature << "..." << endl;
Ptr<AffineFeature> ext = AffineFeature::create(backend);
vector<KeyPoint> kp1, kp2;
Mat desc1, desc2;
ext->detectAndCompute(img1, Mat(), kp1, desc1);
ext->detectAndCompute(img2, Mat(), kp2, desc2);
cout << "img1 - " << kp1.size() << " features, "
<< "img2 - " << kp2.size() << " features"
<< endl;
cout << "matching with " << (useFlann ? "flann" : "bruteforce") << "..." << endl;
double start = timer();
// match and draw
vector< vector<DMatch> > rawMatches;
vector<Point2f> p1, p2;
vector<float> distances;
matcher->knnMatch(desc1, desc2, rawMatches, 2);
// filter_matches
for (size_t i = 0; i < rawMatches.size(); i++)
{
const vector<DMatch>& m = rawMatches[i];
if (m.size() == 2 && m[0].distance < m[1].distance * 0.75)
{
p1.push_back(kp1[m[0].queryIdx].pt);
p2.push_back(kp2[m[0].trainIdx].pt);
distances.push_back(m[0].distance);
}
}
vector<uchar> status;
vector< pair<Point2f, Point2f> > pointPairs;
Mat H = findHomography(p1, p2, status, RANSAC);
int inliers = 0;
for (size_t i = 0; i < status.size(); i++)
{
if (status[i])
{
pointPairs.push_back(make_pair(p1[i], p2[i]));
distances[inliers] = distances[i];
// CV_Assert(inliers <= (int)i);
inliers++;
}
}
distances.resize(inliers);
cout << "execution time: " << fixed << setprecision(2) << (timer() - start) * 1000 << " ms" << endl;
cout << inliers << " / " << status.size() << " inliers/matched" << endl;
cout << "visualizing..." << endl;
vector<int> indices(inliers);
cv::sortIdx(distances, indices, SORT_EVERY_ROW + SORT_ASCENDING);
// explore_match
int h1 = img1.size().height;
int w1 = img1.size().width;
int h2 = img2.size().height;
int w2 = img2.size().width;
Mat vis = Mat::zeros(max(h1, h2), w1 + w2, CV_8U);
img1.copyTo(Mat(vis, Rect(0, 0, w1, h1)));
img2.copyTo(Mat(vis, Rect(w1, 0, w2, h2)));
cvtColor(vis, vis, COLOR_GRAY2BGR);
vector<Point2f> corners(4);
corners[0] = Point2f(0, 0);
corners[1] = Point2f((float)w1, 0);
corners[2] = Point2f((float)w1, (float)h1);
corners[3] = Point2f(0, (float)h1);
vector<Point2i> icorners;
perspectiveTransform(corners, corners, H);
transform(corners, corners, Matx23f(1, 0, (float)w1, 0, 1, 0));
Mat(corners).convertTo(icorners, CV_32S);
polylines(vis, icorners, true, Scalar(255, 255, 255));
for (int i = 0; i < min(inliers, maxlines); i++)
{
int idx = indices[i];
const Point2f& pi1 = pointPairs[idx].first;
const Point2f& pi2 = pointPairs[idx].second;
circle(vis, pi1, 2, Scalar(0, 255, 0), -1);
circle(vis, pi2 + Point2f((float)w1, 0), 2, Scalar(0, 255, 0), -1);
line(vis, pi1, pi2 + Point2f((float)w1, 0), Scalar(0, 255, 0));
}
if (inliers > maxlines)
cout << "only " << maxlines << " inliers are visualized" << endl;
imshow("affine find_obj", vis);
waitKey();
cout << "done" << endl;
return 0;
}
貌似是没有配置相应的cuda加速,然后代码就是一直处于卡顿的状态.
因为ASIFT本来就是比较慢的,那只能慢慢等啦.反正我的CPU已经跑疯了. 
然后这个过程使用的VS2017+cmake+opencv4.5.1+contribe4.5.1(cuda没有配置),我就放在下面的百度网盘连接里了.
链接：https://pan.baidu.com/s/1hPGGiupQW-1khpiCe4QHGw?pwd=1111
提取码：1111
