特征描述

目標

在本教程中，我們將涉及:

• 使用?DescriptorExtractor?接口來尋找關鍵點對應的特征向量. 特別地:
  • 使用?SurfDescriptorExtractor?以及它的函數?compute?來完成特定的計算.
  • 使用?BruteForceMatcher?來匹配特征向量。
  • 使用函數?drawMatches?來繪制檢測到的匹配點.

理論

代碼

這個教程代碼如下所示. 你還可以?從這里下載到源代碼

#include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp"using namespace cv;void readme();/** @function main */ int main( int argc, char** argv ) {if( argc != 3 ){ return -1; }Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );if( !img_1.data || !img_2.data ){ return -1; }//-- Step 1: Detect the keypoints using SURF Detectorint minHessian = 400;SurfFeatureDetector detector( minHessian );std::vector<KeyPoint> keypoints_1, keypoints_2;detector.detect( img_1, keypoints_1 );detector.detect( img_2, keypoints_2 );//-- Step 2: Calculate descriptors (feature vectors)SurfDescriptorExtractor extractor;Mat descriptors_1, descriptors_2;extractor.compute( img_1, keypoints_1, descriptors_1 );extractor.compute( img_2, keypoints_2, descriptors_2 );//-- Step 3: Matching descriptor vectors with a brute force matcherBruteForceMatcher< L2<float> > matcher;std::vector< DMatch > matches;matcher.match( descriptors_1, descriptors_2, matches );//-- Draw matchesMat img_matches;drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );//-- Show detected matchesimshow("Matches", img_matches );waitKey(0);return 0;}/** @function readme */void readme(){ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

解釋

結果

這是使用BruteForce 匹配兩張圖的結果:

使用FLANN進行特征點匹配

目標

在本教程中我們將涉及以下內容:

• 使用?FlannBasedMatcher?接口以及函數?FLANN?實現快速高效匹配(?快速最近鄰逼近搜索函數庫(Fast Approximate Nearest Neighbor Search Library)?)

理論

代碼

這個教程的源代碼如下所示。你還可以從?以下鏈接下載得到源代碼

#include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp"using namespace cv;void readme();/** @function main */ int main( int argc, char** argv ) {if( argc != 3 ){ readme(); return -1; }Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );if( !img_1.data || !img_2.data ){ std::cout<< " --(!) Error reading images " << std::endl; return -1; }//-- Step 1: Detect the keypoints using SURF Detectorint minHessian = 400;SurfFeatureDetector detector( minHessian );std::vector<KeyPoint> keypoints_1, keypoints_2;detector.detect( img_1, keypoints_1 );detector.detect( img_2, keypoints_2 );//-- Step 2: Calculate descriptors (feature vectors)SurfDescriptorExtractor extractor;Mat descriptors_1, descriptors_2;extractor.compute( img_1, keypoints_1, descriptors_1 );extractor.compute( img_2, keypoints_2, descriptors_2 );//-- Step 3: Matching descriptor vectors using FLANN matcherFlannBasedMatcher matcher;std::vector< DMatch > matches;matcher.match( descriptors_1, descriptors_2, matches );double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor( int i = 0; i < descriptors_1.rows; i++ ){ double dist = matches[i].distance;if( dist < min_dist ) min_dist = dist;if( dist > max_dist ) max_dist = dist;}printf("-- Max dist : %f \n", max_dist );printf("-- Min dist : %f \n", min_dist );//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )//-- PS.- radiusMatch can also be used here.std::vector< DMatch > good_matches;for( int i = 0; i < descriptors_1.rows; i++ ){ if( matches[i].distance < 2*min_dist ){ good_matches.push_back( matches[i]); }}//-- Draw only "good" matchesMat img_matches;drawMatches( img_1, keypoints_1, img_2, keypoints_2,good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );//-- Show detected matchesimshow( "Good Matches", img_matches );for( int i = 0; i < good_matches.size(); i++ ){ printf( "-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }waitKey(0);return 0;}/** @function readme */void readme(){ std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }

解釋

結果

這里是第一張圖特征點檢測結果:

此外我們通過控制臺輸出FLANN匹配關鍵點結果:

使用二維特征點(Features2D)和單映射(Homography)尋找已知物體

目標

在本教程中我們將涉及以下內容:

• 使用函數?findHomography?尋找匹配上的關鍵點的變換。
• 使用函數?perspectiveTransform?來映射點.

理論

代碼

這個教程的源代碼如下所示。你還可以從?以下鏈接下載到源代碼

#include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/calib3d/calib3d.hpp"using namespace cv;void readme();/** @function main */ int main( int argc, char** argv ) {if( argc != 3 ){ readme(); return -1; }Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );if( !img_object.data || !img_scene.data ){ std::cout<< " --(!) Error reading images " << std::endl; return -1; }//-- Step 1: Detect the keypoints using SURF Detectorint minHessian = 400;SurfFeatureDetector detector( minHessian );std::vector<KeyPoint> keypoints_object, keypoints_scene;detector.detect( img_object, keypoints_object );detector.detect( img_scene, keypoints_scene );//-- Step 2: Calculate descriptors (feature vectors)SurfDescriptorExtractor extractor;Mat descriptors_object, descriptors_scene;extractor.compute( img_object, keypoints_object, descriptors_object );extractor.compute( img_scene, keypoints_scene, descriptors_scene );//-- Step 3: Matching descriptor vectors using FLANN matcherFlannBasedMatcher matcher;std::vector< DMatch > matches;matcher.match( descriptors_object, descriptors_scene, matches );double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor( int i = 0; i < descriptors_object.rows; i++ ){ double dist = matches[i].distance;if( dist < min_dist ) min_dist = dist;if( dist > max_dist ) max_dist = dist;}printf("-- Max dist : %f \n", max_dist );printf("-- Min dist : %f \n", min_dist );//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )std::vector< DMatch > good_matches;for( int i = 0; i < descriptors_object.rows; i++ ){ if( matches[i].distance < 3*min_dist ){ good_matches.push_back( matches[i]); }}Mat img_matches;drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );//-- Localize the objectstd::vector<Point2f> obj;std::vector<Point2f> scene;for( int i = 0; i < good_matches.size(); i++ ){//-- Get the keypoints from the good matchesobj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );}Mat H = findHomography( obj, scene, CV_RANSAC );//-- Get the corners from the image_1 ( the object to be "detected" )std::vector<Point2f> obj_corners(4);obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );std::vector<Point2f> scene_corners(4);perspectiveTransform( obj_corners, scene_corners, H);//-- Draw lines between the corners (the mapped object in the scene - image_2 )line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );//-- Show detected matchesimshow( "Good Matches & Object detection", img_matches );waitKey(0);return 0;}/** @function readme */void readme(){ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }

解釋

結果?

檢測到的目標結果 (用綠色標記出來的部分)

平面物體檢測

這個教程的目標是學習如何使用?features2d?和?calib3d?模塊來檢測場景中的已知平面物體。

測試數據: 數據圖像文件，比如 “box.png”或者“box_in_scene.png”等。

創建新的控制臺(console)項目。讀入兩個輸入圖像。

Mat img1 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE); Mat img2 = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);

檢測兩個圖像的關鍵點（尺度旋轉都不發生變化的關鍵點）。

// 對第一幅圖像進行關鍵點檢測 FastFeatureDetector detector(15); vector<KeyPoint> keypoints1; detector.detect(img1, keypoints1);... // 對第二幅圖像進行關鍵點檢測

計算每個關鍵點的描述向量(Descriptor)。

// 計算描述向量 SurfDescriptorExtractor extractor; Mat descriptors1; extractor.compute(img1, keypoints1, descriptors1);... // 計算第二幅圖像中的關鍵點對應的描述向量

計算兩幅圖像中的關鍵點對應的描述向量距離，尋找兩圖像中距離最近的描述向量對應的關鍵點，即為兩圖像中匹配上的關鍵點:

// 關鍵點描述向量匹配 BruteForceMatcher<L2<float> > matcher; vector<DMatch> matches; matcher.match(descriptors1, descriptors2, matches);

可視化結果:

// 繪制出結果 namedWindow("matches", 1); Mat img_matches; drawMatches(img1, keypoints1, img2, keypoints2, matches, img_matches); imshow("matches", img_matches); waitKey(0);

尋找兩個點集合中的單映射變換（homography transformation）:

vector<Point2f> points1, points2; // 用點填充形成矩陣(array) .... Mat H = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);

創建內匹配點集合同時繪制出匹配上的點。用perspectiveTransform函數來通過單映射來映射點:

Mat points1Projected; perspectiveTransform(Mat(points1), points1Projected, H);

用?drawMatches?來繪制內匹配點.

from: http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html#table-of-content-feature2d

總結

以上是生活随笔為你收集整理的OpenCV之feature2d 模块. 2D特征框架（2）特征描述 使用FLANN进行特征点匹配 使用二维特征点(Features2D)和单映射(Homography)寻找已知物体 平面物体检测的全部內容，希望文章能夠幫你解決所遇到的問題。

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