我想从图像中找到一些东西。
就像人脸检测，但没有检测到人脸，我想检测其他东西。
所以我用SURF算法找到关键点，用FLANN算法匹配关键点。
但我怎么知道图像是否匹配？
我觉得如果源图的Key Points分布和模板图的Key Points分布需要非常相似，那么两者就匹配了，但是怎么办呢？
int main( int argc, char** argv )
{
std::string templateStr = "D:\\template2.jpg";
std::string srcString = "D:\\IMG_0284.jpg";
Mat img_1 = imread(templateStr, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread(srcString, CV_LOAD_IMAGE_GRAYSCALE );
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 500;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> keypoints_1, keypoints_2;
detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );
//show keypoint,only test
Mat img_11 = imread(templateStr, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_21 = imread(srcString, CV_LOAD_IMAGE_GRAYSCALE );
drawKeypoints (img_11, keypoints_1, img_11, cv::Scalar::all(0), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
drawKeypoints (img_21, keypoints_2, img_21, cv::Scalar::all(0), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
cv::namedWindow ("img_11");
cv::imshow ("img_11",img_11);
cv::namedWindow ("img_21");
cv::imshow ("img_21",img_21);
cv::waitKey (0);
//-- 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 matcher
FlannBasedMatcher matcher;
std::vector<DMatch> matches;
tt = (double)cvGetTickCount();
matcher.match( descriptors_1, descriptors_2, matches );
double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for( 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 < 3*min_dist )
{
good_matches.push_back( matches[i]);
}
}
//-- Draw only "good" matches
Mat 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 );
//now I have two group keypoint,keypoints_1 and keypoints_2,and they is match.
//keypoints_1 is tmeplate image`s keypoints,
//keypoints_2 is source image`s keypoints,
//so I how to compare distribution of keypoints_1 and keypoints_2?
//if the two group keypoint`s distribute is very similarity,I will think the two image is match
return 0;
我用的是OpenCV2.4.9,VS 2010
最佳答案
您可以尝试下面的代码。也许这会对您有所帮助。
#include <opencv2/nonfree/nonfree.hpp> #include <iostream> #include <dirent.h> #include <ctime> #include <stdio.h> using namespace cv; using namespace std; int main(int argc, const char *argv[]) { double ratio = 0.9; Mat image1 = imread("Image1_path"); Mat image2 = cv::imread("Image2_path"); Ptr<FeatureDetector> detector; Ptr<DescriptorExtractor> extractor; // TODO default is 500 keypoints..but we can change detector = FeatureDetector::create("ORB"); extractor = DescriptorExtractor::create("ORB"); vector<KeyPoint> keypoints1, keypoints2; detector->detect(image1, keypoints1); detector->detect(image2, keypoints2); cout << "# keypoints of image1 :" << keypoints1.size() << endl; cout << "# keypoints of image2 :" << keypoints2.size() << endl; Mat descriptors1,descriptors2; extractor->compute(image1,keypoints1,descriptors1); extractor->compute(image2,keypoints2,descriptors2); cout << "Descriptors size :" << descriptors1.cols << ":"<< descriptors1.rows << endl; vector< vector<DMatch> > matches12, matches21; Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming"); matcher->knnMatch( descriptors1, descriptors2, matches12, 2); matcher->knnMatch( descriptors2, descriptors1, matches21, 2); //BFMatcher bfmatcher(NORM_L2, true); //vector<DMatch> matches; //bfmatcher.match(descriptors1, descriptors2, matches); double max_dist = 0; double min_dist = 100; for( int i = 0; i < descriptors1.rows; i++) { double dist = matches12[i].data()->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); cout << "Matches1-2:" << matches12.size() << endl; cout << "Matches2-1:" << matches21.size() << endl; std::vector<DMatch> good_matches1, good_matches2; for(int i=0; i < matches12.size(); i++) { if(matches12[i][0].distance < ratio * matches12[i][1].distance) good_matches1.push_back(matches12[i][0]); } for(int i=0; i < matches21.size(); i++) { if(matches21[i][0].distance < ratio * matches21[i][1].distance) good_matches2.push_back(matches21[i][0]); } cout << "Good matches1:" << good_matches1.size() << endl; cout << "Good matches2:" << good_matches2.size() << endl; // Symmetric Test std::vector<DMatch> better_matches; for(int i=0; i<good_matches1.size(); i++) { for(int j=0; j<good_matches2.size(); j++) { if(good_matches1[i].queryIdx == good_matches2[j].trainIdx && good_matches2[j].queryIdx == good_matches1[i].trainIdx) { better_matches.push_back(DMatch(good_matches1[i].queryIdx, good_matches1[i].trainIdx, good_matches1[i].distance)); break; } } } cout << "Better matches:" << better_matches.size() << endl; // show it on an image Mat output; drawMatches(image1, keypoints1, image2, keypoints2, better_matches, output); imshow("Matches result",output); waitKey(0); return 0; }为了找到良好的匹配，您可以采用阈值，例如 Better
matches.size() > threshold然后将其视为良好图像。
关于c++ - 如何在 OpenCV 中比较两组关键点，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/35194681/