Gets an runtime error when running the following code error: OpenCV Error: Bad argument (The input arrays should be 2D or 3D point sets)

Question

错误：错误：cv::findHomography 中的错误参数（输入数组应为 2D 或 3D 点集），文件 C:\opencv\source\opencv-3.3.0\modules\calib3d\src\fundam.cpp，行341

我是 c++ 和 opencv3.3 的新手。以下是我正在尝试开发的用于拼接 2 张图像的程序。我有类似的 python 代码，我正在尝试将其转换为 C++。出现运行错误，请帮助..

#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>

using namespace std;
using namespace cv;

Mat DetectAndDescribe(Mat image) {
    //create the greyscale image
    Mat grayImage;
    cv::cvtColor(image, grayImage, COLOR_BGR2GRAY);

    //create the keypoint detector and descriptor as "feature" using SIFT
    Ptr<Feature2D> feature = xfeatures2d::SIFT::create();

    //create a matrix of keypoints using feature
    vector<KeyPoint> keypoints;
    feature->detect(grayImage, keypoints);

    //create a maatrix of descriptors using feature and keypoints
    Mat descriptor;
    feature->compute(grayImage, keypoints, descriptor);

    return (keypoints, descriptor);
}

Mat matchKeypoints(Mat imageA, Mat imageB, vector<KeyPoint> keypointA, vector<KeyPoint> keypointB, Mat featuresA, Mat featuresB, float ratio, double repojThresh){
    //create a vector of vector to hold raw matches
    vector<vector<DMatch>> rawMatches;

    //create a vector of DMatches to hold good matches
    vector<DMatch> goodMatches;

    //create two vector points to hold the points where the lines will be drawn
    vector<Point2f> pointsA;
    vector<Point2f> pointsB;

    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
    matcher->knnMatch(featuresA, featuresB, rawMatches, 2);

    goodMatches.reserve(rawMatches.size());

/*  double tresholdDist = ratio * sqrt(double(imageA.size().height*imageA.size().height + imageA.size().width*imageA.size().width));

    for (size_t i = 0; i < rawMatches.size(); ++i)
    {
        for (int j = 0; j < rawMatches[i].size(); j++)
        {
            Point2f from = keypointA[rawMatches[i][j].queryIdx].pt;
            Point2f to = keypointB[rawMatches[i][j].trainIdx].pt;

            //calculate local distance for each possible match
            double dist = sqrt((from.x - to.x) * (from.x - to.x) + (from.y - to.y) * (from.y - to.y));

            //save as best match if local distance is in specified area and on same height
            if (dist < tresholdDist && abs(from.y - to.y)<2)
            {
                goodMatches.push_back(rawMatches[i][j]);
                j = rawMatches[i].size();
            }
        }
    }
*/
    for (size_t i = 0; i < rawMatches.size(); i++)
    {
        if ((rawMatches[i].size()==2) && (rawMatches[i][0].distance < (rawMatches[i][1].distance*ratio)))
        {
            goodMatches.push_back(rawMatches[i][0]);
        }
    }

    cv::KeyPoint::convert(keypointA, pointsA);
    cv::KeyPoint::convert(keypointB, pointsB);

    if (goodMatches.size() > 4) {
        Mat homographyM = findHomography(pointsA, pointsB, RANSAC, repojThresh);
        return(goodMatches, homographyM);
    }
}

Mat drawMatches(Mat imageA, Mat imageB, vector<KeyPoint> keypointsA, vector<KeyPoint> keypointsB, vector<DMatch> matches) {
    //initialize the output visualization image
    float hA = imageA.size().height;
    float wA = imageA.size().width;

    float hB = imageB.size().height;
    float wB = imageB.size().width;

    Mat resultImage = Mat(fmax(hA, hB), wA + wB, 3, "uint8");

    //connect lines between the selected points
    Point2f pointA;
    Point2f pointB;

    for (int i=0; i<matches.size();i++) {
        pointA = Point2f(keypointsA[matches[i].queryIdx].pt.x, keypointsA[matches[i].queryIdx].pt.y);
        pointB = Point2f(keypointsA[matches[i].trainIdx].pt.x+ wA, keypointsB[matches[i].trainIdx].pt.y);

        cv::line(resultImage, pointA, pointB, (0, 255, 0), 1);
    }

    return resultImage;
}

Mat stitch(Mat imageA, Mat imageB, float ratio, double repojThresh, bool showMatches) {
    vector<KeyPoint> keypointA;
    vector<KeyPoint> keypointB;
    Mat featuresA;
    Mat featuresB;
    Mat matchFeatures;
    Mat matches;
    Mat homographyM;
    Mat result;
    float hA = imageA.size().height;
    float wA = imageA.size().width;

    float hB = imageB.size().height;
    float wB = imageB.size().width;

    (keypointA, featuresA) = DetectAndDescribe(imageA);
    (keypointB, featuresB) = DetectAndDescribe(imageB);

    (matches, homographyM) = matchKeypoints(imageA, imageB, keypointA, keypointB, featuresA, featuresB, ratio, repojThresh);

    cv::warpPerspective(imageA, result, homographyM, Size(wA+wB, hA));

    //Point a cv::Mat header at it (no allocation is done)
    Mat final(Size(imageB.cols * 2 + imageB.cols, imageA.rows * 2), CV_8UC3);

    //velikost img1
    Mat roi1(final, Rect(0, 0, imageB.cols, imageB.rows));
    Mat roi2(final, Rect(0, 0, result.cols, result.rows));
    result.copyTo(roi2);
    imageB.copyTo(roi1);


    if (showMatches) {
        Mat visiblelines = drawMatches(imageA, imageB, keypointA, keypointB, matches);
        return (result, visiblelines);
    }

    return result;
}

int main() {
    Mat imageA = cv::imread("left.jpg", 1);
    Mat imageB = cv::imread("middle.jpg", 1);

    Mat result;
    Mat visiblelines;

    (result, visiblelines) = stitch(imageB, imageA, 0.75, 4.0, true);

    cv::imshow("KeyPoint matches", visiblelines);
    cv::imshow("Resulting Image", result);

    cv::imwrite("Result.jpg", result);

    cv::waitKey(0);
    cv::destroyAllWindows();
}

Answer 1

我认为，来自 python 的您可能误解了 return 在 C/C++ 中的工作方式。看起来你的函数 DetectAndDescribe returning 只有第二个值（描述符），所以

return (keypoints, descriptor);

等同于

return descriptor;

此外

(keypointA, featuresA) = DetectAndDescribe(imageA);
(keypointB, featuresB) = DetectAndDescribe(imageB);

实际上等同于

featuresA = DetectAndDescribe(imageA);
featuresB = DetectAndDescribe(imageB);

看看comma operator在不重载的情况下在C++中的工作原理（我不相信它在OpenCV中，但如果我错了，我会被那些肯定知道的人惩罚）

一个简单的例子：

  int bb = 42;
  int cc;
  (bb, cc) = (3,5);
  std::cout << std::endl << bb << std::endl << cc;

会输出

42
5

没有

3
5

差点忘了说，对于 return 多个值，您可以通过引用传递它们并在函数内部赋值：

void DetectAndDescribe(Mat image, vector<KeyPoint>& keypoints, Mat& descriptor) {
    //create the greyscale image
    Mat grayImage;
    cv::cvtColor(image, grayImage, COLOR_BGR2GRAY);

    //create the keypoint detector and descriptor as "feature" using SIFT
    Ptr<Feature2D> feature = xfeatures2d::SIFT::create();

    //create a matrix of keypoints using feature
    feature->detect(grayImage, keypoints);

    //create a maatrix of descriptors using feature and keypoints
    feature->compute(grayImage, keypoints, descriptor);
}

并这样称呼它：

DetectAndDescribe(imageA, keypointA, featuresA);

最后，按值传递矩阵等大型结构是一种不好的做法，您也应该通过引用传递它们

void DetectAndDescribe(Mat& image, vector<KeyPoint>& keypoints, Mat& descriptor)

或者如果您不打算将它们修改为常量引用：

void DetectAndDescribe(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptor)

Gets an runtime error when running the following code error: OpenCV Error: Bad argument (The input arrays should be 2D or 3D point sets)

Gets an runtime error when running the following code error: OpenCV Error: Bad argument (The input arrays should be 2D or 3D point sets)

c++

opencv3.0