Bad mismatch map using opencv StereoBM

Question

I am trying to use StereoBM to get a mismatch map of two images. I tried a few code examples and images. They work great. However, when I try to use my own images, I have a very bad card, very noisy.

my stereobm settings

sbm.state->SADWindowSize = 25;
sbm.state->numberOfDisparities = 128;
sbm.state->preFilterSize = 5;
sbm.state->preFilterCap = 61;
sbm.state->minDisparity = -39;
sbm.state->textureThreshold = 507;
sbm.state->uniquenessRatio = 0;
sbm.state->speckleWindowSize = 0;
sbm.state->speckleRange = 8;
sbm.state->disp12MaxDiff = 1;

My questions

• Any problems with my images?
• Is it possible to get a good mismatch map without calibrating the camera? Do I need to correct images before StereoBM

Thank.

Here is my image correction code

Mat img_1 = imread( "image1.jpg", CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( "image2.jpg", CV_LOAD_IMAGE_GRAYSCALE );

int minHessian = 430;
SurfFeatureDetector detector( minHessian );
std::vector<KeyPoint> keypoints_1, keypoints_2;
detector.detect( img_1, keypoints_1 );
detector.detect( img_2, keypoints_2 );

//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_1, descriptors_2;
extractor.compute( img_1, keypoints_1, descriptors_1 );
extractor.compute( img_2, keypoints_2, descriptors_2 );

//-- Step 3: Matching descriptor vectors with a brute force matcher
BFMatcher matcher(NORM_L1, true);   //BFMatcher matcher(NORM_L2);

std::vector< DMatch > matches;
matcher.match( descriptors_1, descriptors_2, matches );

double max_dist = 0; double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < matches.size(); i++ )
{ double dist = matches[i].distance;
    if( dist < min_dist ) min_dist = dist;
    if( dist > max_dist ) max_dist = dist;
}

std::vector< DMatch > good_matches;
vector<Point2f>imgpts1,imgpts2;
for( int i = 0; i < matches.size(); i++ )
{
    if( matches[i].distance <= max(4.5*min_dist, 0.02) ){
        good_matches.push_back( matches[i]);
        imgpts1.push_back(keypoints_1[matches[i].queryIdx].pt);
        imgpts2.push_back(keypoints_2[matches[i].trainIdx].pt);
    }

}

std::vector<uchar> status;
cv::Mat F = cv::findFundamentalMat(imgpts1, imgpts2, cv::FM_8POINT, 3., 0.99, status);   //FM_RANSAC

Mat H1,H2;
cv::stereoRectifyUncalibrated(imgpts1, imgpts1, F, img_1.size(), H1, H2);

cv::Mat rectified1(img_1.size(), img_1.type());
cv::warpPerspective(img_1, rectified1, H1, img_1.size());

cv::Mat rectified2(img_2.size(), img_2.type());
cv::warpPerspective(img_2, rectified2, H2, img_2.size());

StereoBM sbm;
sbm.state->SADWindowSize = 25;
sbm.state->numberOfDisparities = 128;
sbm.state->preFilterSize = 5;
sbm.state->preFilterCap = 61;
sbm.state->minDisparity = -39;
sbm.state->textureThreshold = 507;
sbm.state->uniquenessRatio = 0;
sbm.state->speckleWindowSize = 0;
sbm.state->speckleRange = 8;
sbm.state->disp12MaxDiff = 1;

Mat disp,disp8;
sbm(rectified1, rectified2, disp);

straightened images and mismatch map here

Answer 1

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StereoSGMB StereoBM. , .

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, :

    Ptr<StereoSGBM> sgbm = StereoSGBM::create(0,    //int minDisparity
                                        96,     //int numDisparities
                                        5,      //int SADWindowSize
                                        600,    //int P1 = 0
                                        2400,   //int P2 = 0
                                        20,     //int disp12MaxDiff = 0
                                        16,     //int preFilterCap = 0
                                        1,      //int uniquenessRatio = 0
                                        100,    //int speckleWindowSize = 0
                                        20,     //int speckleRange = 0
                                        true);  //bool fullDP = false

sgbm->compute(left, right, disp);

Answer 2

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