我正在尝试使用 OpenCV 拍摄立体图像对...即同一对象的左图像和右图像...然后在不知道相机的任何属性的情况下校正它们的旋转和平移。校正图像后，我应该能够将它们显示给用户。

到目前为止，我已经合并了 OpenCV 示例目录中的两个演示程序，目前情况很糟糕......我会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，程序因调试错误而崩溃。在命令窗口中，它显示“OpenCV 错误:断言失败(scn ==1 &&(dcn == 3 || dcn == 4))在文件中的未知函数........\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。如果有人可以帮助我，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;
}

主要焦点可能应该围绕 doIteration 方法，但我已将其其余部分放在那里，以便您可以准确地看到发生了什么。

最佳答案

也许为时已晚；) 我没有看你的代码。但在我看来你忘了将图像转换为灰色样式。

关于c++ - OpenCV 立体图像对校正...显示结果，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/5872657/