OpenCV C++ 创建可重用的关键点和描述符集，用于拼接多个图像
OpenCV C++ create reusable set of keypoints and descriptors for stitching multiple images
我创建了一个可以将多张图像拼接在一起的程序，现在正在寻求提高它的效率。根据拼接图像的大小，最终它会变得很大并且包含太多关键点，以至于机器 运行 无法分配内存。为了弥补这一点，我的目标是存储所有找到的关键点和描述符，这样我就不需要在主拼接图像中再次找到它们，只需要在正在拼接的新图像中找到它们。我在 python 中有这个过程，但在 C++ 中没有同样的运气。
为此，我需要在关键点上执行 perspectiveTransform()，然后将它们从 vector<keypoint> 转换为 vector<point2f>，然后再转换回 vector<keypoint>。我已经能够做到这一点并且可以确认它有效（选择跟随）。 我不确定是否需要对描述符进行相同的处理（目前我已经这样做了，但要么错误要么无效）。
问题：当我 运行 时，关键点和描述符似乎不起作用，我抛出一个我创建的错误："Not enough matches found" 即使我知道至少关键点正在制作它函数的方式。
这是关键点和描述符转换的代码。该代码首先计算要应用于第一个图像的 warpPerspective，因为单应性将仅扭曲第二个图像。 codd 的其余部分处理关键点和描述符。
tuple<Mat, vector<KeyPoint>, Mat> stitchMatches(Mat image1,Mat image2, Mat homography, vector<KeyPoint> kp1, vector<KeyPoint> kp2 , Mat desc1, Mat desc2){
Mat result, destination, descriptors_updated;
vector<Point2f> fourPoint;
vector<KeyPoint> keypoints_updated;
//-Get the four corners of the first image (master)
fourPoint.push_back(Point2f (0,0));
fourPoint.push_back(Point2f (image1.size().width,0));
fourPoint.push_back(Point2f (0, image1.size().height));
fourPoint.push_back(Point2f (image1.size().width, image1.size().height));
//perspectiveTransform(Mat(fourPoint), destination, homography);
//- Get points used to determine Htr
double min_x, min_y, tam_x, tam_y;
float min_x1, min_x2, min_y1, min_y2, max_x1, max_x2, max_y1, max_y2;
min_x1 = min(fourPoint.at(0).x, fourPoint.at(1).x);
min_x2 = min(fourPoint.at(2).x, fourPoint.at(3).x);
min_y1 = min(fourPoint.at(0).y, fourPoint.at(1).y);
min_y2 = min(fourPoint.at(2).y, fourPoint.at(3).y);
max_x1 = max(fourPoint.at(0).x, fourPoint.at(1).x);
max_x2 = max(fourPoint.at(2).x, fourPoint.at(3).x);
max_y1 = max(fourPoint.at(0).y, fourPoint.at(1).y);
max_y2 = max(fourPoint.at(2).y, fourPoint.at(3).y);
min_x = min(min_x1, min_x2);
min_y = min(min_y1, min_y2);
tam_x = max(max_x1, max_x2);
tam_y = max(max_y1, max_y2);
//- Htr use to map image one to result in line with the alredy warped image 1
Mat Htr = Mat::eye(3,3,CV_64F);
if (min_x < 0){
tam_x = image2.size().width - min_x;
Htr.at<double>(0,2)= -min_x;
}
if (min_y < 0){
tam_y = image2.size().height - min_y;
Htr.at<double>(1,2)= -min_y;
}
result = Mat(Size(tam_x*2,tam_y*2), CV_8UC3,cv::Scalar(0,0,0));
warpPerspective(image2, result, Htr, result.size(), INTER_LINEAR, BORDER_TRANSPARENT, 0);
warpPerspective(image1, result, (Htr*homography), result.size(), INTER_LINEAR, BORDER_TRANSPARENT,0);
//-- Variables to hold the keypoints at the respective stages
vector<Point2f> kp1Local,kp2Local;
vector<KeyPoint> kp1updated, kp2updated;
//Localize the keypoints to allow for perspective change
KeyPoint::convert(kp1, kp1Local);
KeyPoint::convert(kp2, kp2Local);
//perform persepctive transform on the keypoints of type vector<point2f>
perspectiveTransform(kp1Local, kp1Local, (Htr));
perspectiveTransform(kp2Local, kp2Local, (Htr*homography));
//convert keypoints back to type vector<keypoint>
for( size_t i = 0; i < kp1Local.size(); i++ ) {
kp1updated.push_back(KeyPoint(kp1Local[i], 1.f));
}
for( size_t i = 0; i < kp2Local.size(); i++ ) {
kp2updated.push_back(KeyPoint(kp2Local[i], 1.f));
}
//Add to master of list of keypoints to be passed along during next iteration of image
keypoints_updated.reserve(kp1updated.size() + kp2updated.size());
keypoints_updated.insert(keypoints_updated.end(),kp1updated.begin(),kp1updated.end());
keypoints_updated.insert(keypoints_updated.end(),kp2updated.begin(),kp2updated.end());
//WarpPerspective of decriptors to match that of the images and cooresponding keypoints
Mat desc1New, desc2New;
warpPerspective(desc2, desc2New, Htr, result.size(), INTER_LINEAR, BORDER_TRANSPARENT, 0);
warpPerspective(desc1, desc1New, (Htr*homography), result.size(), INTER_LINEAR, BORDER_TRANSPARENT,0);
//create a new Mat including the descriports from desc1 and desc2
descriptors_updated.push_back(desc1New);
descriptors_updated.push_back(desc2New);
//------------TEST TO see if keypoints have moved
Mat img_keypoints;
drawKeypoints( result, keypoints_updated, img_keypoints, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
imshow("Keypoints 1", img_keypoints );
waitKey();
destroyAllWindows();
return {result, keypoints_updated, descriptors_updated};
}
以下代码是我的主拼接程序，它执行实际的拼接。
tuple<Mat,vector<KeyPoint>,Mat> stitch(Mat img1,Mat img2 ,vector<KeyPoint> keypoints, Mat descriptors, String featureDetection,String featureExtractor,String keypointsMatcher,String showMatches){
Mat desc, desc1, desc2, homography, result, croppedResult,descriptors_updated;
std::vector<KeyPoint> keypoints_updated, kp1, kp2;
std::vector<DMatch> matches;
//-Base Case[2]
if (keypoints.empty()){
//-Detect Keypoints and their descriptors
tie(kp1,desc1) = KeyPointDescriptor(img1, featureDetection,featureExtractor);
tie(kp2,desc2) = KeyPointDescriptor(img2, featureDetection,featureExtractor);
//Find matches and calculated homography based on keypoints and descriptors
std::tie(matches,homography) = matchFeatures(kp1, desc1,kp2, desc2, keypointsMatcher);
//draw matches if requested
if(showMatches == "true"){
drawMatchedImages( img1, kp1, img2, kp2, matches);
}
//stitch the images and update the keypoint and descriptors
std::tie(result,keypoints_updated,descriptors_updated) = stitchMatches(img1, img2, homography,kp1,kp2,desc1,desc2);
//crop function using created cropping function
croppedResult = crop(result);
return {croppedResult,keypoints_updated,descriptors_updated};
}
//base case[3:n]
else{
//Get keypoints and descriptors of new image and add to respective lists
tie(kp2,desc2) = KeyPointDescriptor(img2, featureDetection,featureExtractor);
//find matches and determine homography
std::tie(matches,homography) = matchFeatures(keypoints_updated,descriptors_updated,kp2,desc2, keypointsMatcher);
//draw matches if requested
if(showMatches == "true")
drawMatchedImages( img1, keypoints, img2, kp2, matches);
//stitch the images and update the keypoint and descriptors
tie(result,keypoints_updated,descriptors_updated) = stitchMatches(img1, img2, homography,keypoints,kp2,descriptors,desc2);
//crop function using created cropping function
croppedResult = crop(result);
return {croppedResult,keypoints_updated,descriptors_updated};
}
}
最后是要转换到拼接图像上的关键点图像。非常感谢任何帮助！
经过梳理之后，我恰好发现我有一次使用了错误的变量！:)
我创建了一个可以将多张图像拼接在一起的程序，现在正在寻求提高它的效率。根据拼接图像的大小，最终它会变得很大并且包含太多关键点，以至于机器 运行 无法分配内存。为了弥补这一点，我的目标是存储所有找到的关键点和描述符，这样我就不需要在主拼接图像中再次找到它们，只需要在正在拼接的新图像中找到它们。我在 python 中有这个过程，但在 C++ 中没有同样的运气。
为此，我需要在关键点上执行 perspectiveTransform()，然后将它们从 vector<keypoint> 转换为 vector<point2f>，然后再转换回 vector<keypoint>。我已经能够做到这一点并且可以确认它有效（选择跟随）。 我不确定是否需要对描述符进行相同的处理（目前我已经这样做了，但要么错误要么无效）。
问题：当我 运行 时，关键点和描述符似乎不起作用，我抛出一个我创建的错误："Not enough matches found" 即使我知道至少关键点正在制作它函数的方式。
这是关键点和描述符转换的代码。该代码首先计算要应用于第一个图像的 warpPerspective，因为单应性将仅扭曲第二个图像。 codd 的其余部分处理关键点和描述符。
tuple<Mat, vector<KeyPoint>, Mat> stitchMatches(Mat image1,Mat image2, Mat homography, vector<KeyPoint> kp1, vector<KeyPoint> kp2 , Mat desc1, Mat desc2){
Mat result, destination, descriptors_updated;
vector<Point2f> fourPoint;
vector<KeyPoint> keypoints_updated;
//-Get the four corners of the first image (master)
fourPoint.push_back(Point2f (0,0));
fourPoint.push_back(Point2f (image1.size().width,0));
fourPoint.push_back(Point2f (0, image1.size().height));
fourPoint.push_back(Point2f (image1.size().width, image1.size().height));
//perspectiveTransform(Mat(fourPoint), destination, homography);
//- Get points used to determine Htr
double min_x, min_y, tam_x, tam_y;
float min_x1, min_x2, min_y1, min_y2, max_x1, max_x2, max_y1, max_y2;
min_x1 = min(fourPoint.at(0).x, fourPoint.at(1).x);
min_x2 = min(fourPoint.at(2).x, fourPoint.at(3).x);
min_y1 = min(fourPoint.at(0).y, fourPoint.at(1).y);
min_y2 = min(fourPoint.at(2).y, fourPoint.at(3).y);
max_x1 = max(fourPoint.at(0).x, fourPoint.at(1).x);
max_x2 = max(fourPoint.at(2).x, fourPoint.at(3).x);
max_y1 = max(fourPoint.at(0).y, fourPoint.at(1).y);
max_y2 = max(fourPoint.at(2).y, fourPoint.at(3).y);
min_x = min(min_x1, min_x2);
min_y = min(min_y1, min_y2);
tam_x = max(max_x1, max_x2);
tam_y = max(max_y1, max_y2);
//- Htr use to map image one to result in line with the alredy warped image 1
Mat Htr = Mat::eye(3,3,CV_64F);
if (min_x < 0){
tam_x = image2.size().width - min_x;
Htr.at<double>(0,2)= -min_x;
}
if (min_y < 0){
tam_y = image2.size().height - min_y;
Htr.at<double>(1,2)= -min_y;
}
result = Mat(Size(tam_x*2,tam_y*2), CV_8UC3,cv::Scalar(0,0,0));
warpPerspective(image2, result, Htr, result.size(), INTER_LINEAR, BORDER_TRANSPARENT, 0);
warpPerspective(image1, result, (Htr*homography), result.size(), INTER_LINEAR, BORDER_TRANSPARENT,0);
//-- Variables to hold the keypoints at the respective stages
vector<Point2f> kp1Local,kp2Local;
vector<KeyPoint> kp1updated, kp2updated;
//Localize the keypoints to allow for perspective change
KeyPoint::convert(kp1, kp1Local);
KeyPoint::convert(kp2, kp2Local);
//perform persepctive transform on the keypoints of type vector<point2f>
perspectiveTransform(kp1Local, kp1Local, (Htr));
perspectiveTransform(kp2Local, kp2Local, (Htr*homography));
//convert keypoints back to type vector<keypoint>
for( size_t i = 0; i < kp1Local.size(); i++ ) {
kp1updated.push_back(KeyPoint(kp1Local[i], 1.f));
}
for( size_t i = 0; i < kp2Local.size(); i++ ) {
kp2updated.push_back(KeyPoint(kp2Local[i], 1.f));
}
//Add to master of list of keypoints to be passed along during next iteration of image
keypoints_updated.reserve(kp1updated.size() + kp2updated.size());
keypoints_updated.insert(keypoints_updated.end(),kp1updated.begin(),kp1updated.end());
keypoints_updated.insert(keypoints_updated.end(),kp2updated.begin(),kp2updated.end());
//WarpPerspective of decriptors to match that of the images and cooresponding keypoints
Mat desc1New, desc2New;
warpPerspective(desc2, desc2New, Htr, result.size(), INTER_LINEAR, BORDER_TRANSPARENT, 0);
warpPerspective(desc1, desc1New, (Htr*homography), result.size(), INTER_LINEAR, BORDER_TRANSPARENT,0);
//create a new Mat including the descriports from desc1 and desc2
descriptors_updated.push_back(desc1New);
descriptors_updated.push_back(desc2New);
//------------TEST TO see if keypoints have moved
Mat img_keypoints;
drawKeypoints( result, keypoints_updated, img_keypoints, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
imshow("Keypoints 1", img_keypoints );
waitKey();
destroyAllWindows();
return {result, keypoints_updated, descriptors_updated};
}
以下代码是我的主拼接程序，它执行实际的拼接。
tuple<Mat,vector<KeyPoint>,Mat> stitch(Mat img1,Mat img2 ,vector<KeyPoint> keypoints, Mat descriptors, String featureDetection,String featureExtractor,String keypointsMatcher,String showMatches){
Mat desc, desc1, desc2, homography, result, croppedResult,descriptors_updated;
std::vector<KeyPoint> keypoints_updated, kp1, kp2;
std::vector<DMatch> matches;
//-Base Case[2]
if (keypoints.empty()){
//-Detect Keypoints and their descriptors
tie(kp1,desc1) = KeyPointDescriptor(img1, featureDetection,featureExtractor);
tie(kp2,desc2) = KeyPointDescriptor(img2, featureDetection,featureExtractor);
//Find matches and calculated homography based on keypoints and descriptors
std::tie(matches,homography) = matchFeatures(kp1, desc1,kp2, desc2, keypointsMatcher);
//draw matches if requested
if(showMatches == "true"){
drawMatchedImages( img1, kp1, img2, kp2, matches);
}
//stitch the images and update the keypoint and descriptors
std::tie(result,keypoints_updated,descriptors_updated) = stitchMatches(img1, img2, homography,kp1,kp2,desc1,desc2);
//crop function using created cropping function
croppedResult = crop(result);
return {croppedResult,keypoints_updated,descriptors_updated};
}
//base case[3:n]
else{
//Get keypoints and descriptors of new image and add to respective lists
tie(kp2,desc2) = KeyPointDescriptor(img2, featureDetection,featureExtractor);
//find matches and determine homography
std::tie(matches,homography) = matchFeatures(keypoints_updated,descriptors_updated,kp2,desc2, keypointsMatcher);
//draw matches if requested
if(showMatches == "true")
drawMatchedImages( img1, keypoints, img2, kp2, matches);
//stitch the images and update the keypoint and descriptors
tie(result,keypoints_updated,descriptors_updated) = stitchMatches(img1, img2, homography,keypoints,kp2,descriptors,desc2);
//crop function using created cropping function
croppedResult = crop(result);
return {croppedResult,keypoints_updated,descriptors_updated};
}
}
最后是要转换到拼接图像上的关键点图像。非常感谢任何帮助！
经过梳理之后，我恰好发现我有一次使用了错误的变量！:)