opencv2.3.1学习笔记（1）

今天开始学习opencv，将前一段时间maltab仿真的结果在c++下实现，这样一来可以学习c++，或者学习opencv这种开源工具的代码习惯。
我主要做的是图像的配准，我计划最终实现基于视频的全景图像拼接，慢慢来，现在只做图像配准。
今天一个代码很有意义，现在记录下来。
  1 #include <opencv2/opencv.hpp> 
  2 #include <stdio.h>
  3 
  4 using namespace std; 
  5 using namespace cv; 
  6 
  7 
  8 
  9 int main(int argc, char* argv[]) 
 10 { 
 11     Mat img1=imread("bird1.jpg");
 12     Mat img2=imread("bird2.jpg");
 13     cvtColor(img1,img1,CV_RGB2GRAY);
 14     cvtColor(img2,img2,CV_RGB2GRAY);
 15 
 16     //-- Step 1: Detect the keypoints using SURF Detector
 17     int minHessian=400;
 18     SurfFeatureDetector detector( minHessian );
 19     std::vector<KeyPoint> keypoints_1, keypoints_2;
 20     detector.detect( img1, keypoints_1 );
 21     detector.detect( img2, keypoints_2 );
 22 
 23     //-- Step 2: Calculate descriptors (feature vectors)
 24     SurfDescriptorExtractor extractor;
 25     Mat descriptors_1, descriptors_2;
 26     extractor.compute( img1, keypoints_1, descriptors_1 );
 27     extractor.compute( img2, keypoints_2, descriptors_2 );
 28 
 29     //-- Step 3: Matching descriptor vectors using FLANN matcher
 30     FlannBasedMatcher matcher;
 31 //    BruteForceMatcher<L2<float>> matcher;
 32     std::vector< DMatch > matches;
 33     matcher.match( descriptors_1, descriptors_2, matches );
 34 
 35     double max_dist = 0; 
 36     double min_dist = 100;
 37     //-- Quick calculation of max and min distances between keypoints
 38     for( int i = 0; i < descriptors_1.rows; i++ )
 39     { 
 40         double dist = matches[i].distance;
 41         if( dist < min_dist ) min_dist = dist;
 42         if( dist > max_dist ) max_dist = dist;
 43     }
 44 
 45     printf("-- Max dist : %f \n", max_dist );
 46     printf("-- Min dist : %f \n", min_dist );
 47 
 48     //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
 49     //-- PS.- radiusMatch can also be used here.
 50     std::vector< DMatch > good_matches;
 51 
 52     for( int i = 0; i < descriptors_1.rows; i++ )
 53     {
 54         if( matches[i].distance < 2*min_dist )
 55         { 
 56             good_matches.push_back( matches[i]);
 57         }
 58     }
 59 
 60     //-- Draw only "good" matches
 61     Mat img_matches;
 62     drawMatches( img1, keypoints_1, img2, keypoints_2,
 63                 good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
 64                 vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
 65 
 66 
 67     //-- Localize the object
 68     std::vector<Point2f> obj;
 69     std::vector<Point2f> scene;
 70 
 71     for( int i = 0; i < good_matches.size(); i++ )
 72     {
 73         //-- Get the keypoints from the good matches
 74         obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt );
 75         scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt );
 76     }
 77 
 78     Mat H = findHomography( obj, scene, CV_RANSAC );
 79 
 80     //-- Get the corners from the image_1 ( the object to be "detected" )
 81     std::vector<Point2f> obj_corners(4);
 82     obj_corners[0] = cvPoint(0,0); 
 83     obj_corners[1] = cvPoint( img1.cols, 0 );
 84     obj_corners[2] = cvPoint( img1.cols, img1.rows ); 
 85     obj_corners[3] = cvPoint( 0, img1.rows );
 86     std::vector<Point2f> scene_corners(4);
 87     perspectiveTransform( obj_corners, scene_corners, H);
 88 
 89     //-- register the imahe using perspective transform
 90     //-- get the size of the registerd image
 91     Mat imageturn=Mat::zeros(max(scene_corners[1].x,scene_corners[2].x),
 92         max(scene_corners[2].y,scene_corners[3].y),img1.type());
 93     warpPerspective(img1,imageturn,H,cv::Size(imageturn.rows,imageturn.cols));
 94 
 95     //-- register the image using affine transform
 96     vector<Point2f> img1corners(3);
 97     img1corners[0]=obj[0];
 98     img1corners[1]=obj[1];
 99     img1corners[2]=obj[2];
100     vector<Point2f> img2corners(3);
101     img2corners[0]=scene[0];
102     img2corners[1]=scene[1];
103     img2corners[2]=scene[2];
104     Mat affineMat=getAffineTransform(img1corners,img2corners);
105     Mat imageAffine=Mat::zeros(max(scene_corners[1].x,scene_corners[2].x),
106         max(scene_corners[2].y,scene_corners[3].y),img1.type());
107     warpAffine(img1,imageAffine,affineMat,cv::Size(imageAffine.rows,imageAffine.cols));
108 
109     //-- Draw lines between the corners (the mapped object in the scene - image_2 )
110     line( img_matches, scene_corners[0] + Point2f( img1.cols, 0), scene_corners[1] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
111     line( img_matches, scene_corners[1] + Point2f( img1.cols, 0), scene_corners[2] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
112     line( img_matches, scene_corners[2] + Point2f( img1.cols, 0), scene_corners[3] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
113     line( img_matches, scene_corners[3] + Point2f( img1.cols, 0), scene_corners[0] + Point2f( img1.cols, 0), Scalar( 0, 255, 0), 4 );
114 
115     //-- Show detected matches
116     imshow( "Good Matches & Object detection", img_matches );
117     imshow("perspective image",imageturn);
118     imshow("affine image",imageAffine);
119     //-- Show detected matches
120     //imshow( "Good Matches", img_matches );
121 
122     //for( int i = 0; i < good_matches.size(); i++ )
123     //{ 
124     //    printf( "-- Good Match [%d] Keypoint 1: %d  -- Keypoint 2: %d  \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); 
125     //}
126     waitKey();
127     return 0;
128 }