opencv标准视差图不起作用

Question

我似乎无法使用标准的 opencv 函数 (stereoBM) 获得任何类型的深度图像。

我试过了：

Mat disp, disp8;

StereoBM *sbm = StereoBM::create(16, 2);
sbm->setDisp12MaxDiff(1);
sbm->setSpeckleRange(8);
sbm->setSpeckleWindowSize(0);
sbm->setUniquenessRatio(0);
sbm->setTextureThreshold(507);
sbm->setMinDisparity(-39);
sbm->setPreFilterCap(61);
sbm->setPreFilterSize(5);
sbm->compute(imgLeft, imgRight, disp);
normalize(disp, disp8, 0, 255, CV_MINMAX, CV_8U);

cv::imshow("disp", disp8);

它可以编译但会处理大量错误。不确定我是否使用了抽象类？

谢谢

Answer 1

我的功能在这里工作。希望对你有帮助。

cv::Mat Arritmic::DepthMap(cv::Mat &imageL, cv::Mat &imageR)
{

    /// DISPARITY MAP AND DEPTH MAP
    cv::Mat left_for_matcher, right_for_matcher;
    cv::Mat left_disp,right_disp;
    cv::Mat filtered_disp;
    cv::Mat conf_map =  cv::Mat(imageL.rows, imageL.cols, CV_8U);
    conf_map =  cv::Scalar(255);
    cv::Rect ROI;
    int max_disp = 16; // n*16
    int wsize = 15;




   // Perform matching and create the filter instance
   /* I am using StereoBM for faster processing. If speed is not critical, 
   though, StereoSGBM would provide better quality.
   The filter instance is created by providing the StereoMatcher instance
   that we intend to use. Another matcher instance is returned by the
   createRightMatcher function. These two matcher instances are then used
   to compute disparity maps both for the left and right views, that are
   required by the filter. */

    cv::Ptr<cv::ximgproc::DisparityWLSFilter> wls_filter;

    cv::Ptr<cv::StereoBM >left_matcher = cv::StereoBM::create(max_disp,wsize);
    wls_filter = cv::ximgproc::createDisparityWLSFilter(left_matcher);
    cv::Ptr<cv::StereoMatcher> right_matcher = cv::ximgproc::createRightMatcher(left_matcher);
    cv::cvtColor(imageL,  left_for_matcher,  cv::COLOR_BGR2GRAY);
    cv::cvtColor(imageR, right_for_matcher, cv::COLOR_BGR2GRAY);


    left_matcher-> compute(left_for_matcher, right_for_matcher,left_disp);
    right_matcher->compute(right_for_matcher,left_for_matcher, right_disp);

    // Perform filtering
    /* Disparity maps computed by the respective matcher instances, as
    well as the source left view are passed to the filter. Note that we
    are using the original non-downscaled view to guide the filtering 
    process. 
    The disparity map is automatically upscaled in an edge-aware fashion
    to match the original view resolution. The result is stored in
    filtered_disp. */


    double lambda = 6000.0;   // hardcode
    double sigma = 2.0;       // hardcode

    //! [filtering]
    wls_filter->setLambda(lambda);
    wls_filter->setSigmaColor(sigma);

    wls_filter->filter(left_disp, imageL, filtered_disp, right_disp);

    //! [filtering]
    conf_map = wls_filter->getConfidenceMap();

    // Get the ROI that was used in the last filter call:
    ROI = wls_filter->getROI();

      cv::Mat raw_disp_vis;
    cv::ximgproc::getDisparityVis(left_disp,raw_disp_vis, 21.0);
    cv::Mat filtered_disp_vis;
    cv::ximgproc::getDisparityVis(filtered_disp,filtered_disp_vis, 15.0);


    return raw_disp_vis;  // rerturning de depth map image.
}