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OpenCV를 사용하여 동일한 제목의 왼쪽 및 오른쪽 이미지 인 스테레오 이미지 쌍을 얻으려고합니다. 회전 및 번역을 위해 수정합니다. 카메라의 속성을 알지 못합니다. 이미지가 수정되면 사용자에게 표시 할 수 있어야합니다.OpenCV 스테레오 이미지 쌍 보정 ... 결과 표시
지금까지 OpenCV 샘플 디렉토리의 두 가지 데모 프로그램을 병합했습니다. 당분간 심하게 ... 코드를 정리하고 작동시킬 때 더 정중하게 정렬합니다. 작동하고있는 것 같습니다. , 그러나 결과를 표시하려고하면 프로그램이 디버그 오류와 충돌합니다. 명령 창에서 "OpenCV 오류 : 어설 션이 실패했습니다 (scn == 1 & & (dcn == 3 || dcn == 4)) 파일의 알 수없는 함수에서 ... \ opencv \ modules \ imgproc \ src \ color.cpp, line 2453 "
결과를 표시하는 코드의 여러 부분을 주석 처리하면 OpenCV 오류가 달라집니다. 여기 내 코드가있다. 누군가가 도울 수 있다면 나는 너를 영원히 사랑할 것이다.
#include "stdafx.h"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include <iostream>
using namespace cv;
using namespace std;
void help(char** argv)
{
cout << "\nThis program demonstrates keypoint finding and matching between 2 images using features2d framework.\n"
<< "Example of usage:\n"
<< argv[0] << " [detectorType] [descriptorType] [image1] [image2] [ransacReprojThreshold]\n"
<< "\n"
<< "Matches are filtered using homography matrix if ransacReprojThreshold>=0\n"
<< "Example:\n"
<< "./descriptor_extractor_matcher SURF SURF cola1.jpg cola2.jpg 3\n"
<< "\n"
<< "Possible detectorType values: see in documentation on createFeatureDetector().\n"
<< "Possible descriptorType values: see in documentation on createDescriptorExtractor().\n" << endl;
}
const string winName = "rectified";
void crossCheckMatching(Ptr<DescriptorMatcher>& descriptorMatcher,
const Mat& descriptors1, const Mat& descriptors2,
vector<DMatch>& filteredMatches12, int knn=1)
{
filteredMatches12.clear();
vector<vector<DMatch> > matches12, matches21;
descriptorMatcher->knnMatch(descriptors1, descriptors2, matches12, knn);
descriptorMatcher->knnMatch(descriptors2, descriptors1, matches21, knn);
for(size_t m = 0; m < matches12.size(); m++)
{
bool findCrossCheck = false;
for(size_t fk = 0; fk < matches12[m].size(); fk++)
{
DMatch forward = matches12[m][fk];
for(size_t bk = 0; bk < matches21[forward.trainIdx].size(); bk++)
{
DMatch backward = matches21[forward.trainIdx][bk];
if(backward.trainIdx == forward.queryIdx)
{
filteredMatches12.push_back(forward);
findCrossCheck = true;
break;
}
}
if(findCrossCheck) break;
}
}
}
void doIteration(const Mat& leftImg, Mat& rightImg,
vector<KeyPoint>& keypoints1, const Mat& descriptors1,
Ptr<FeatureDetector>& detector, Ptr<DescriptorExtractor>& descriptorExtractor,
Ptr<DescriptorMatcher>& descriptorMatcher,
double ransacReprojThreshold)
{
assert(!leftImg.empty());
Mat H12;
assert(!rightImg.empty()/* && rightImg.cols==leftImg.cols && rightImg.rows==leftImg.rows*/);
cout << endl << "< Extracting keypoints from second image..." << endl;
vector<KeyPoint> keypoints2;
detector->detect(rightImg, keypoints2);
cout << keypoints2.size() << " points" << endl << ">" << endl;
cout << "< Computing descriptors for keypoints from second image..." << endl;
Mat descriptors2;
descriptorExtractor->compute(rightImg, keypoints2, descriptors2);
cout << ">" << endl;
cout << "< Matching descriptors..." << endl;
vector<DMatch> filteredMatches;
crossCheckMatching(descriptorMatcher, descriptors1, descriptors2, filteredMatches, 1);
cout << ">" << endl;
vector<int> queryIdxs(filteredMatches.size()), trainIdxs(filteredMatches.size());
for(size_t i = 0; i < filteredMatches.size(); i++)
{
queryIdxs[i] = filteredMatches[i].queryIdx;
trainIdxs[i] = filteredMatches[i].trainIdx;
}
cout << "< Computing homography (RANSAC)..." << endl;
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
H12 = findHomography(Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold);
cout << ">" << endl;
//Mat drawImg;
if(!H12.empty()) // filter outliers
{
vector<char> matchesMask(filteredMatches.size(), 0);
vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);
for(size_t i1 = 0; i1 < points1.size(); i1++)
{
if(norm(points2[i1] - points1t.at<Point2f>((int)i1,0)) < 4) // inlier
matchesMask[i1] = 1;
}
/* draw inliers
drawMatches(leftImg, keypoints1, rightImg, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask, 2); */
}
Size imageSize = leftImg.size();
Mat F = findFundamentalMat(Mat(points1), Mat(points2), FM_8POINT, 0, 0);
Mat H1, H2;
stereoRectifyUncalibrated(Mat(points1), Mat(points2), F, imageSize, H1, H2, 3);
Mat cameraMatrix[2], distCoeffs[2], R1, R2, P1, P2, rmap[2][2];
cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
P1 = cameraMatrix[0];
P2 = cameraMatrix[1];
initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);
Mat canvas, img;
double sf;
int i, j, w, h;
sf = 600./MAX(imageSize.width, imageSize.height);
w = cvRound(imageSize.width*sf);
h = cvRound(imageSize.height*sf);
canvas.create(h, w*2, CV_8UC3);
for (i = 0; i < 2; i++)
{
if (i == 0)
img = leftImg;
else
img = rightImg;
Mat rimg, cimg;
remap(img, rimg, rmap[i][0], rmap[i][1], CV_INTER_LINEAR);
cvtColor(rimg, cimg, CV_GRAY2BGR);
Mat canvasPart = canvas(Rect(w*i, 0, w, h));
resize(cimg, canvasPart, canvasPart.size(), 0, 0, CV_INTER_AREA);
}
for(j = 0; j < canvas.rows; j += 16)
{
line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
}
imshow(winName, canvas);
}
int main(int argc, char** argv)
{
if(argc != 6)
{
help(argv);
return -1;
}
double ransacReprojThreshold = atof(argv[5]);
cout << "< Creating detector, descriptor extractor and descriptor matcher ..." << endl;
Ptr<FeatureDetector> detector = FeatureDetector::create(argv[1]);
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create(argv[2]);
Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create("FlannBased");
cout << ">" << endl;
if(detector.empty() || descriptorExtractor.empty() || descriptorMatcher.empty() )
{
cout << "Can not create detector or descriptor extractor or descriptor matcher of given types" << endl;
return -1;
}
cout << "< Reading the images..." << endl;
Mat leftImg = imread(argv[3]);
Mat rightImg = imread(argv[4]);
cout << ">" << endl;
if(leftImg.empty() || (rightImg.empty()))
{
cout << "Can not read images" << endl;
return -1;
}
cout << endl << "< Extracting keypoints from first image..." << endl;
vector<KeyPoint> keypoints1;
detector->detect(leftImg, keypoints1);
cout << keypoints1.size() << " points" << endl << ">" << endl;
cout << "< Computing descriptors for keypoints from first image..." << endl;
Mat descriptors1;
descriptorExtractor->compute(leftImg, keypoints1, descriptors1);
cout << ">" << endl;
namedWindow(winName, CV_WINDOW_NORMAL);
doIteration(leftImg, rightImg, keypoints1, descriptors1,
detector, descriptorExtractor, descriptorMatcher,
ransacReprojThreshold);
for(;;)
{
char c = (char)waitKey(0);
if(c == '\x1b') // esc
{
cout << "Exiting ..." << endl;
return 0;
}
}
waitKey(0);
return 0;
}
주요 초점은 아마도 doIteration 방법을 주위해야하지만, 당신이 벌어지고 정확히 볼 수 있도록 내가 거기에 나머지를 넣어했습니다.