stereoRectifyUncalibrated() gives bad result

asked 2018-05-16 11:45:45 -0500

H M gravatar image

I am using cv::stereoRectifyUncalibrated() to rectify two images but I am getting pretty bad results including lots of shearing effects. The steps I am following:

  1. SURF to detect and match keypoints
  2. cv::findFundamentalMat() to compute fundamental matrix
  3. cv::stereoRectifyUncalibrated() to get homography matrix H1 and H2
  4. cv::warpPerspective() to get the rectified images

I want to use the rectified images for disparity. But can't use due to the bad results of rectification. My questions:

  1. Is it the fundamental matrix causing the problem?
  2. or the warpPerspective() transform responsible for this?
  3. or something else I need take care of?

Following is my code and sample images of results. I am new to opencv and appreciate any help.

    #include <iostream>
    #include <stdio.h>
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/core/affine.hpp"

using namespace cv;
using namespace cv::xfeatures2d;


int main()
{
    //Loading the stereo images
    Mat leftImage = imread("left.jpg", CV_LOAD_IMAGE_COLOR);
    Mat rightImage = imread("right.jpg", CV_LOAD_IMAGE_COLOR);

    //checking if image file succesfully opened 
    if (!leftImage.data || !rightImage.data)
    {
        std::cout << " --(!) Error reading images " << std::endl; return -1;
    }

    /*showing the input stereo images
    namedWindow("Left image original", WINDOW_FREERATIO);
    namedWindow("Right image original", WINDOW_FREERATIO);
    imshow("Left image original", leftImage);
    imshow("Right image original", rightImage);
    */

    //::::::::::::::::::::::::::::::::::::::::::::::::
    //Step 1: Detect the keypoints using SURF Detector
    int minHessian = 420;

    Ptr<SURF> detector = SURF::create(minHessian);      //here detector is a pointer which points to SURF type object
                                                        //create is also a pointer which points to SURF type object
    std::vector<KeyPoint> keypointsLeft, keypointsRight;        //vectors storing keypoints of two images

    detector->detect(leftImage, keypointsLeft);
    detector->detect(rightImage, keypointsRight);


    //::::::::::::::::::::::::::::::::
    //Step 2: Descriptors of keypoints
    Mat descriptorsLeft;
    Mat descriptorsRight;

    detector->compute(leftImage, keypointsLeft, descriptorsLeft);
    detector->compute(rightImage, keypointsRight, descriptorsRight);

    //std::cout << "descriptor matrix size: " << keypointsDescriptorsLeft.rows << " by " << keypointsDescriptorsLeft.cols << std::endl;

    //::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
    //Step 3: matching keypoints from image right and image left 
    //according to their descriptors (BruteForce, Flann based approaches)

    // Construction of the matcher
    std::vector<cv::DMatch> matches;
    static Ptr<BFMatcher> matcher = cv::BFMatcher::create();

    // Match the two image descriptors
    matcher->match(descriptorsLeft, descriptorsRight, matches);

    //std::cout << "Number of matched points: " << matches.size() << std::endl;


    //::::::::::::::::::::::::::::::::
    //Step 4: find the fundamental mat 

    // Convert 1 vector of keypoints into
    // 2 vectors of Point2f for computing F matrix
    // with cv::findFundamentalMat() function
    std::vector<int> pointIndexesLeft;          //getting index for point2f conversion
    std::vector<int> pointIndexesRight;         //getting index for point2f conversion

    for (std::vector<cv::DMatch>::const_iterator it = matches.begin(); it != matches.end(); ++it) {

        // Get the indexes of the selected matched keypoints
        pointIndexesLeft.push_back(it->queryIdx);
        pointIndexesRight.push_back(it->trainIdx);
    }

    // Convert keypoints vector into Point2f type vector
    //as needed for fundamentalMat() function
    std::vector<cv::Point2f> matchingPointsLeft, matchingPointsRight;
    cv::KeyPoint::convert(keypointsLeft, matchingPointsLeft, pointIndexesLeft);
    cv::KeyPoint::convert(keypointsRight, matchingPointsRight, pointIndexesRight);

    //creating clone Mat to draw the keypoints on
    Mat drawKeyLeft = leftImage.clone(), drawKeyRight = rightImage.clone();


    //check by drawing the points
    std::vector<cv::Point2f>::const_iterator it = matchingPointsLeft.begin();
    while (it != matchingPointsLeft.end()) {

        // draw a circle at each corner location
        cv::circle ...
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Comments

Hi, have you figure it out? I'm facing the same problem, even if my matches are excellent I got bad rectification.

HYPEREGO gravatar imageHYPEREGO ( 2019-03-05 03:42:58 -0500 )edit