我正在尝试使用OpenCV拍摄立体图像对...即同一主体的左图像和右图像...然后在不知道相机任何属性的情况下校正它们进行旋转和平移。一旦图像被纠正，我应该能够将它们显示给用户。
到目前为止，我已经从OpenCV示例目录中合并了两个演示程序，目前非常糟糕......我将清理代码并在我开始工作时更好地安排它...它似乎正在工作，但是当我尝试显示结果时，程序崩溃并出现调试错误。在命令窗口中，它在文件中的未知函数中显示“OpenCV错误：断言失败（scn == 1＆amp;＆amp;（dcn == 3 || dcn == 4）”........ \ opencv \ modules \ imgproc \ src \ color.cpp，第2453行“
注释掉代码的各个部分以显示结果只会导致不同的OpenCV错误。这是我的代码。如果有人能帮助我，我会永远爱你。
#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;
}
主要焦点应该是围绕doIteration方法，但我已将其余部分放在那里，以便您可以准确地看到发生了什么。
答案 0 :(得分：0)
也许为时已晚;） 我没看清你的代码。但在我看来，你忘了将图像转换成灰色风格。