Iam尝试使用ORB探测器/描述符和Flann或暴力强制匹配器在更大的目标图像（包含3个面的allimg.jpg）中识别源图像（c1.jpg-面部）。 c1.jpg是通过裁剪/复制从allimg.jpg创建的。 ORB检测器/描述符正常工作返回检测器/描述符正确但Flann或暴力强制匹配器为目标提供不正确的匹配结果。结果当我进一步尝试使用findHomography（）时，它显示不正确的结果，将源映射到某处否则在目的地而不是目的地的正确面孔（allimg）。 虽然没有显示下面的代码，但是在Knnmatch之后，我在匹配后在c1.jpg和allimag.jpg上绘制了一个边界矩形并显示了图像。我发现源边界矩形是正确的但是allimag的边界矩形非常非常大而且包括源脸。它应该刚刚在目的地找到了源脸。 我使用opencv 3.0。 有人遇到过这样的问题吗？是否有其他匹配器可以准确地找到目的地中的源图像（面部或任何东西）？
我已经给出了下面的代码和图片（由链接给出）：
#include <opencv2/core/core.hpp>
#include <opencv2\opencv.hpp>
#include <opencv2/features2d/features2d.hpp>
using namespace std;
using namespace cv;
const double nn_match_ratio = 0.80f; // Nearest-neighbour matching ratio
const double ransac_thresh = 2.5f; // RANSAC inlier threshold
const int bb_min_inliers = 100; // Minimal number of inliers to draw BBox
Mat img1;
Mat img2;
bool refineMatchesWithHomography(const vector<cv::KeyPoint>& queryKeypoints,
const vector<cv::KeyPoint>& trainKeypoints,
float reprojectionThreshold,
vector<cv::DMatch>& matches,
Mat& homography )
{
const int minNumberMatchesAllowed = 4;
if (matches.size() <minNumberMatchesAllowed)
return false;
// Prepare data for cv::findHomography
vector<cv::Point2f> queryPoints(matches.size());
std::vector<cv::Point2f> trainPoints(matches.size());
for (size_t i = 0; i <matches.size(); i++)
{
queryPoints[i] = queryKeypoints[matches[i].queryIdx].pt;
trainPoints[i] = trainKeypoints[matches[i].trainIdx].pt;
}
// Find homography matrix and get inliers mask
std::vector<unsigned char> inliersMask(matches.size());
homography = findHomography(queryPoints,
trainPoints,
CV_FM_RANSAC,
reprojectionThreshold,
inliersMask);
vector<cv::DMatch> inliers;
for (size_t i=0; i<inliersMask.size(); i++)
{
if (inliersMask[i])
inliers.push_back(matches[i]);
}
matches.swap(inliers);
Mat homoShow;
drawMatches (img1,queryKeypoints,img2,trainKeypoints,matches,homoShow,
Scalar::all(-1),CV_RGB(255,255,255), Mat(), 2);
imshow("homoShow",homoShow);
waitKey(100000);
return matches.size() > minNumberMatchesAllowed;
}
int main()
{
//Stats stats;
vector<String> fileName;
fileName.push_back("D:\\pmn\\c1.jpg");
fileName.push_back("D:\\pmn\\allimg.jpg");
img1 = imread(fileName[0], CV_LOAD_IMAGE_COLOR);
img2 = imread(fileName[1], CV_LOAD_IMAGE_COLOR);
if (img1.rows*img1.cols <= 0)
{
cout << "Image " << fileName[0] << " is empty or cannot be found\n";
return(0);
}
if (img2.rows*img2.cols <= 0)
{
cout << "Image " << fileName[1] << " is empty or cannot be found\n";
return(0);
}
// keypoint for img1 and img2
vector<KeyPoint> keyImg1, keyImg2;
// Descriptor for img1 and img2
Mat descImg1, descImg2;
Ptr<Feature2D> porb = ORB::create(500,1.2f,8,0,0,2,0,14);
porb->detect(img2, keyImg2, Mat());
// and compute their descriptors with method compute
porb->compute(img2, keyImg2, descImg2);
// We can detect keypoint with detect method
porb->detect(img1, keyImg1,Mat());
// and compute their descriptors with method compute
porb->compute(img1, keyImg1, descImg1);
//FLANN parameters
// Ptr<flann::IndexParams> indexParams =
makePtr<flann::LshIndexParams> (6, 12, 1);
// Ptr<flann::SearchParams> searchParams = makePtr<flann::SearchParams>
(50);
String itMatcher = "BruteForce-L1";
Ptr<DescriptorMatcher>
matdescriptorMatchercher(newcv::BFMatcher(cv::NORM_HAMMING, false));
vector<vector<DMatch> > matches,bestMatches;
vector<DMatch> m;
matdescriptorMatchercher->knnMatch(descImg1, descImg2, matches,2);
const float minRatio = 0.95f;//1.f / 1.5f;
for (int i = 0; i<matches.size(); i++)
{
if(matches[i].size()>1)
{
DMatch& bestMatch = matches[i][0];
DMatch& betterMatch = matches[i][1];
float distanceRatio = bestMatch.distance / betterMatch.distance;
if (distanceRatio <minRatio)
{
bestMatches.push_back(matches[i]);
m.push_back(bestMatch);
}
}
}
Mat homo;
float homographyReprojectionThreshold = 1.0;
bool homographyFound = refineMatchesWithHomography(
keyImg1,keyImg2,homographyReprojectionThreshold,m,homo);
return 0;
}
[c1.jpg][1]
[allimg.jpg][2]
[1]: http://i.stack.imgur.com/Uuy3o.jpg
[2]: http://i.stack.imgur.com/Kwne7.jpg
答案 0 :(得分：0)
