Configure opencv-contrib

Download the corresponding version of opencv-contrib

 

 

 

 

 

 

 CMake compilation

 

 

 

 

 

 

 

 If there are errors check the log

 

 

 Open the log file and use the address inside to download the file from the browser.

 

 After the file is downloaded, place it in the corresponding location of the file and modify the file name.

 

 

 

 

 

 

 

 

 Enter Vs compilation

 

 

 

 

 

 

 

 

 

 

About OpenCV4.0.1 + contrib configuration when running the program "cannot locate the program entry point" solution

Solution

Copy all the dll suffix files in ... \ opencv \ opencv_contrib \ x64 \ vc15 \ bin to C: \ Windows \ System32, the problem is solved.

 

 Test code

 1 #include <opencv2/opencv.hpp>
 2 #include <opencv2/xfeatures2d.hpp>
 3 //#include <opencv2/>
 4 
 5 using namespace std;
 6 using namespace cv;
 7 
 8 int main()
 9 {
10     cv::Mat imageL = cv::imread("origin_1.jpg");
11     cv::Mat imageR = cv::imread("origin_2.jpg");
12     /*imshow("1", imageL);
13     imshow("2", imageR);
14     waitKey ();
 15      return 0; * / 
16  
17  
18  
19      // Extract feature point method
 20      // SIFT 
21      cv :: Ptr <cv :: xfeatures2d :: SIFT> sift = cv :: xfeatures2d :: SIFT :: create ();
 22      // ORB
 23      // cv :: Ptr <cv :: ORB> orb = cv :: ORB :: create ();
 24      // SURF
 25      // cv :: Ptr <cv :: xfeatures2d :: SURF> surf = cv :: xfeatures2d :: SURF :: create ();
 26  
27      // Feature point 
28      std :: vector <cv :: KeyPoint> keyPointL, keyPointR;
 29      // Extract feature point 
30 separately     sift->detect(imageL, keyPointL);
31     sift->detect(imageR, keyPointR);
32 
33     //画特征点
34     cv::Mat keyPointImageL;
35     cv::Mat keyPointImageR;
36     drawKeypoints(imageL, keyPointL, keyPointImageL, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
37     drawKeypoints(imageR, keyPointR, keyPointImageR, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
38 
39     //显示窗口
40     cv::namedWindow("KeyPoints of imageL", cv :: WINDOW_AUTOSIZE);
 41      cv :: namedWindow ( " KeyPoints of imageR " , cv :: WINDOW_AUTOSIZE);
 42  
43      // Display feature points 
44      cv :: imshow ( " KeyPoints of imageL " , keyPointImageL);
 45      cv: : imshow ( " KeyPoints of imageR " , keyPointImageR);
 46  
47      // Feature point matching 
48      cv :: Mat despL, despR;
 49      // Extract feature points and calculate feature descriptor 
50      sift-> detectAndCompute (imageL, cv :: Mat (), keyPointL, despL);
 51      sift->detectAndCompute (imageR, cv :: Mat (), keyPointR, despR);
 52  
53      // Struct for DMatch: query descriptor index, train descriptor index, train image index and distance between descriptors.
 54      // int queryIdx-> is the test image The subscript of the feature point descriptor (descriptor) is also the subscript of the corresponding feature point (keypoint) of the descriptor.
55      // int trainIdx –> is the index of the feature point descriptor of the sample image, and also the index of the corresponding feature point.
56      // int imgIdx-> useful when the sample is multiple images.
57      // float distance –> represents the Euclidean distance of this pair of matched feature point descriptors (essentially vectors). The smaller the value, the more similar the two feature points. 
58      std :: vector <cv :: DMatch> matches;
 59  
60      // If the flannBased method is used, the type of the desp through orb needs to be converted first. 
61      if(despL.type ()! = CV_32F || despR.type ()! = CV_32F)
 62      {
 63          despL.convertTo (despL, CV_32F);
64          despR.convertTo (despR, CV_32F);
65      }
 66  
67      hp :: Ptr <cv :: DescriptorMatcher> matcher = cv :: DescriptorMatcher :: create ( " FlannBased " );
68      matcher-> match (despL, despR, matches);
69  
70      // 计算 特征 点 距离 的 最大值
71      double maxDist = 0 ;
72      for ( int i = 0 ; i <despL.rows; i ++ )
73     {
74         double dist = matches[i].distance;
75         if (dist > maxDist)
76             maxDist = dist;
77     }
78 
79     //挑选好的匹配点
80     std::vector< cv::DMatch > good_matches;
81     for (int i = 0; i < despL.rows; i++)
82     {
83         if (matches[i].distance < 0.5 * maxDist)
84         {
85             good_matches.push_back(matches[i]);
86         }
87     }
88 
89 
90 
91     cv::Mat imageOutput;
92     cv::drawMatches(imageL, keyPointL, imageR, keyPointR, good_matches, imageOutput);
93 
94     cv::namedWindow("picture of matching", cv::WINDOW_AUTOSIZE);
95     cv::imshow("picture of matching", imageOutput);
96     cv::waitKey(0);
97     return 0;
98 }