void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h)
{
vector<KeyPoint> kp1, kp2;
Ptr<SURF> m_surf = SURF::create(1000);
Mat descriptors_1, descriptors_2;
m_surf->detectAndCompute(im1,Mat(),kp1,descriptors_1);
m_surf->detectAndCompute(im2,Mat(),kp2,descriptors_2);
cout<<"*********"<<endl;
BFMatcher matcher(NORM_L2);
cout<<"++++++++++"<<endl;
Mat img_matches;
vector<vector<DMatch>> matches;
vector<DMatch> bestMatches;
matcher.knnMatch(descriptors_1, descriptors_2, matches, 2);
std::cout << "matches size:" << (int)matches.size() << endl;
for (int i = 0; i < (int)matches.size(); i++)
{
if (matches[i][0].distance < 0.8 * matches[i][1].distance)
{
bestMatches.push_back(matches[i][0]);
}
}
float angel1 = 0;
float angel2 = 0;
float oneToMore = 0;
int k = 0;
struct keypointFlag{
DMatch match;
int flag;
};
keypointFlag initFlag[bestMatches.size()];
for(int i=0;i<bestMatches.size();i++)
{
initFlag[i].match=bestMatches[i];
initFlag[i].flag=0;
}
vector<DMatch>goodMatches;
for(int i = 0;i<bestMatches.size();i++)
{
k = 0;
for(int j = 0;j<bestMatches.size();j++)
{
if(i!=j&&initFlag[i].flag != 2)
{
oneToMore = sqrt(pow(kp1[initFlag[i].match.queryIdx].pt.x-kp1[initFlag[j].match.queryIdx].pt.x,2)
+pow(kp1[initFlag[i].match.queryIdx].pt.y-kp1[initFlag[j].match.queryIdx].pt.y,2));
angel1 = atan((kp1[initFlag[i].match.queryIdx].pt.y-kp2[initFlag[i].match.trainIdx].pt.y)
/(kp1[initFlag[i].match.queryIdx].pt.x-kp2[initFlag[i].match.trainIdx].pt.x));
angel2 = atan((kp1[initFlag[j].match.queryIdx].pt.y-kp2[initFlag[j].match.trainIdx].pt.y)
/(kp1[initFlag[j].match.queryIdx].pt.x-kp2[initFlag[j].match.trainIdx].pt.x));
if(oneToMore<70&&abs(angel1-angel2)<0.1)
{
initFlag[j].flag = 1;
cout<<abs(angel1-angel2)<<endl;
}
}
}
for(int j = 0;j<bestMatches.size();j++)
{
if(i!=j)
{
if(initFlag[j].flag == 1)
{
k++;
}
}
}
cout<<"k: "<<k<<endl;
if(k>4)
{
goodMatches.push_back(initFlag[i].match);
initFlag[i].flag = 2;
for(int j = 0;j<bestMatches.size();j++)
{
if(initFlag[j].flag == 1)
{
int flagTmp = 1;
for(int m = 0;m < goodMatches.size();m++)
{
if(kp1[initFlag[j].match.queryIdx].pt.x == kp1[goodMatches[m].queryIdx].pt.x
&& kp1[initFlag[j].match.queryIdx].pt.y == kp1[goodMatches[m].queryIdx].pt.y)
{
flagTmp = 0;
break;
}
}
if(flagTmp == 1)
{
goodMatches.push_back(initFlag[j].match);
initFlag[j].flag = 0;
}
}
}
}
for(int j = 0;j<bestMatches.size();j++)
{
if(initFlag[j].flag == 1)
initFlag[j].flag = 0;
}
}
cout<<"goodMatches size: "<<goodMatches.size()<<endl;
// for(int i = 0;i<bestMatches.size();i++)
// {
// angel = atan((kp1[bestMatches[i].queryIdx].pt.x-kp2[bestMatches[i].trainIdx].pt.x)/(kp1[bestMatches[i].queryIdx].pt.y-kp2[bestMatches[i].trainIdx].pt.y));
// cout<<angel<<endl;
// }
drawMatches(im1, kp1, im2, kp2, goodMatches, img_matches, Scalar::all(-1), Scalar::all(-1),
vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
imshow("Matches", img_matches);
imwrite("../match.jpg", img_matches);
std::cout << "the size of bestmatches:" << goodMatches.size() << endl;
// Extract location of good matches
int ptCount = (int)goodMatches.size();
Mat p1(ptCount, 2, CV_32F);
Mat p2(ptCount, 2, CV_32F);
Point2f pt;
for (int i = 0; i < ptCount; i++)
{
pt = kp1[goodMatches[i].queryIdx].pt;
p1.at<float>(i, 0) = pt.x;
p1.at<float>(i, 1) = pt.y;
pt = kp2[goodMatches[i].trainIdx].pt;
p2.at<float>(i, 0) = pt.x;
p2.at<float>(i, 1) = pt.y;
}
int InlinerCount = ptCount;
cout << "InlinerCount: " << InlinerCount << endl;
vector<Point2f> m_LeftInlier;
vector<Point2f> m_RightInlier;
vector<DMatch> m_InlierMatches;
m_InlierMatches.resize(InlinerCount);
m_LeftInlier.resize(InlinerCount);
m_RightInlier.resize(InlinerCount);
InlinerCount = 0;
for (int i = 0; i < ptCount; i++)
{
m_LeftInlier[InlinerCount].x = p1.at<float>(i, 0);
m_LeftInlier[InlinerCount].y = p1.at<float>(i, 1);
m_RightInlier[InlinerCount].x = p2.at<float>(i, 0);
m_RightInlier[InlinerCount].y = p2.at<float>(i, 1);
m_InlierMatches[InlinerCount].queryIdx = InlinerCount;
m_InlierMatches[InlinerCount].trainIdx = InlinerCount;
InlinerCount++;
cout << "InlinerCount:" << InlinerCount << endl;
}
vector<KeyPoint> key1;
vector<KeyPoint> key2;
KeyPoint::convert(m_LeftInlier, key1);
KeyPoint::convert(m_RightInlier, key2);
Mat OutImage;
drawMatches(im1, key1, im2, key2, m_InlierMatches, OutImage, CV_RGB(255, 0, 0), CV_RGB(0, 0, 255));
imwrite("final.jpg", OutImage);
Mat R = estimateRigidTransform(m_LeftInlier, m_RightInlier, true);
//Mat R = estimateAffine2D(m_LeftInlier, m_RightInlier);
cout << R << endl;
double s = sqrt((R.at<double>(0, 0)) * (R.at<double>(0, 0)) + (R.at <double>(1, 0)) * (R.at <double>(1, 0)));
cout << "s: " << s << endl;
cv::Mat H = cv::Mat(3, 3, R.type());
H.at< double>(0, 0) = R.at<double>(0, 0) / s;
H.at< double>(0, 1) = R.at <double>(0, 1) / s;
H.at< double>(0, 2) = R.at <double>(0, 2);//
H.at< double>(1, 0) = R.at <double>(1, 0) / s;
H.at< double>(1, 1) = R.at <double>(1, 1) / s;
H.at< double>(1, 2) = R.at <double>(1, 2);//
H.at< double>(2, 0) = 0.0;
H.at< double>(2, 1) = 0.0;
H.at<double>(2, 2) = 1.0;
cout << H << endl;
normalize(H, H, 1, 0, NORM_L2, -1);
cout << H << endl;
vector<Point2f> obj_corners(4);
obj_corners[0] = Point(0, 0);
obj_corners[1] = Point(im1.cols, 0);
obj_corners[2] = Point(im1.cols, im1.rows);
obj_corners[3] = Point(0, im1.rows);
vector<Point2f> scene_corners(4);
perspectiveTransform(obj_corners, scene_corners, H);
cout << "scene_corners" << scene_corners << endl;
int width1 = max(abs(scene_corners[0].x - scene_corners[2].x),
abs(scene_corners[1].x - scene_corners[3].x));
int height1 = max(abs(scene_corners[0].y - scene_corners[2].y),
abs(scene_corners[1].y - scene_corners[3].y));
float origin_x = 0, origin_y = 0;
if (scene_corners[0].x < 0 || scene_corners[1].x < 0
|| scene_corners[2].x < 0 || scene_corners[3].x < 0)
origin_x += min(min(scene_corners[0].x, scene_corners[2].x),
min(scene_corners[1].x, scene_corners[3].x));
else origin_x += min(min(scene_corners[0].x, scene_corners[2].x),
min(scene_corners[1].x, scene_corners[3].x));
if (scene_corners[0].y < 0 || scene_corners[1].y < 0
|| scene_corners[2].y < 0 || scene_corners[3].y < 0)
origin_y += min(min(scene_corners[0].y, scene_corners[2].y),
min(scene_corners[1].y, scene_corners[3].y));
else origin_y += min(min(scene_corners[0].y, scene_corners[2].y),
min(scene_corners[1].y, scene_corners[3].y));
Mat imageturn = Mat::zeros(width1, height1, im1.type());
for (int i = 0; i < 4; i++)
{
scene_corners[i].x -= origin_x;
scene_corners[i].y -= origin_y;
}
Mat H1 = getPerspectiveTransform(obj_corners, scene_corners);
normalize(H1, H1, 1, 0, NORM_L2, -1);
vector<Point2f> warp_m_leftInlier;
vector<KeyPoint> L_key;
perspectiveTransform(m_LeftInlier, warp_m_leftInlier, H1);
KeyPoint::convert(warp_m_leftInlier, L_key);
warpPerspective(im1, im1Reg, H1, Size(width1, height1), INTER_NEAREST + 8, BORDER_CONSTANT, Scalar::all(255));
imwrite("imageturn.jpg", im1Reg);
Mat OutImage1;
drawMatches(im1Reg, L_key, im2, key2, m_InlierMatches, OutImage1, CV_RGB(255, 0, 0), CV_RGB(0, 0, 255));
imwrite("OutImage1.jpg", OutImage1);
// // Find homography
// h = findHomography( points1, points2, RANSAC );
//
// // Use homography to warp image
// warpPerspective(im1, im1Reg, h, im2.size());
}
基本思想，使用surf进行特征点检测，使用暴力匹配进行粗匹配，对粗匹配结果采取密度分布与斜率约等的思想，即任意选取一点为定点，计算其周围像素距离小于70的点的斜率与选定点的斜率是否约等，如果是，将其标记为疑似最优点，当选定点周围能找到大于4个这样的点即认为其这一组为相匹配的最优点，放入新建的向量里，一次检查没给点，直至选取所有最优点，经反复实验不同的图片其准确率可达100%。
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