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Stereo görüntü çiftlerini almak için OpenCV kullanıyorum ... yani aynı nesnenin sol ve sağ görüntüsü ... ve sonra döndürme ve çeviri için bunları düzeltmek için kameranın özelliklerini bilmeden. Görüntüler düzeltildikten sonra bunları kullanıcıya gösterebilmeliyim.OpenCV stereo görüntü çifti düzeltmesi ... sonuçları gösteriliyor
Şu ana kadar OpenCV örnek dizininden iki demo programı birleştirdim, an için kötü ... Kodları temizlerim ve çalıştığı zaman daha güzel düzenlerim ... ve çalışıyor gibi görünüyor Ancak, sonuçları görüntülemeye çalıştığımda, program hata ayıklama hatasıyla çöküyor. Komut penceresinde "OpenCV Error: Assertion başarısız oldu" (scn == 1 & & (dcn == 3 || dcn == 4)) dosyada bilinmeyen işlevde ........ \ opencv \ modules \ imgproc \ src \ color.cpp, satır 2453 "
Sonuçları görüntülemek için kodun çeşitli bölümlerini yorumlamak, yalnızca farklı OpenCV Hataları ile sonuçlanır. İşte benim kodum. Eğer herkes yardım edebilirse seni sonsuza kadar seveceğim.
#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;
}
ana odak muhtemelen doIteration yöntemiyle civarında olmalıdır, ancak oluyor tam olarak ne görebilirsiniz Orada, kalanını koyduk.