使用opencv StereoBM的不良视差图

Question

我正在尝试使用StereoBM来获取两个图像的视差图.我尝试了一些示例代码和图像.他们工作正常.然而,当我尝试自己的图像时,我的地图非常糟糕,非常嘈杂.

我的StereoBM参数

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;

我的问题是

1. 关于我的图片的任何问题？
2. 没有校准相机可以获得良好的视差图吗？我是否需要在StereoBM之前纠正图像

谢谢.

这是我纠正图像的代码

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);

经过校正的图像和视差图在这里

Answer 1

1. 您的图片没有特别的问题.但是,如果计算时间不重要,我建议您使用更大的分辨率.此外,如果可能的话,您最好使用未压缩的图像格式.

2. 您校准立体相机以纠正立体声图像.哟确实需要纠正图片,但也可以在没有校准相机的情况下纠正它们.如果您只有很少的图片需要处理,您可以通过移动或旋转图像在Photoshop等中进行,以便匹配点在同一条线上.如果您要处理的图片数量较多,则可以像在代码中尝试一样进行处理.

我没有详细介绍你的代码,但我想你应该检查匹配点是否在同一行.

在你的样本图片中实际情况就是如此,使用StereoSGMB而不是StereoBM我得到了一些更好但仍然非常嘈杂的结果.

这是一个参数调整,以在StereoSGMB中获得良好的结果.另请注意,后面的块的结果比前面的对象好得多,因为块具有纹理表面.

这是我使用的参数:

    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);