Question

Mat generateDisparityMap(Mat& left, Mat& right)
{
  Mat lb, rb;
  if (left.empty() || right.empty())
    return left;
  cvtColor(left, lb, CV_BGR2GRAY);
  cvtColor(right, rb, CV_BGR2GRAY);

  const Size imsize = lb.size();
  const int32_t dims[3] = { imsize.width,imsize.height,imsize.width };

  Mat leftdpf = Mat::zeros(imsize, CV_32F);
  Mat rightdpf = Mat::zeros(imsize, CV_32F);

  Elas::parameters param(Elas::MIDDLEBURY);
  param.postprocess_only_left = true;
  Elas elas(param);

  elas.process(lb.data, rb.data, leftdpf.ptr<float>(0), rightdpf.ptr<float>
    (0), dims);


  Mat show = Mat(left.rows, left.cols, CV_8UC1, Scalar(0));
  leftdpf.convertTo(show, CV_8U, 5.);

  int   max_disp = -1;

  for (int i = 0; i < imsize.width; i++) {
    for (int j = 0; j < imsize.height; j++) {
      if (show.at<uchar>(j,i) > max_disp) 
        max_disp = show.at<uchar>(j,i);
    }
  }

  for (int i = 0; i < imsize.width; i++) {
    for (int j = 0; j < imsize.height; j++) {
      show.at<uchar>(j,i) =
      (int)max(255.0*(float)show.at<uchar>(j,i)/max_disp,0.0);
    }
  }

  //return dmap;
  return show;
  //return show;
}

请查看link中显示的代码生成的视差地图的图像。

我已经看到了LIBELAS库在线生成的结果，它们看起来很完美。我的代码运行没有任何错误，但我得到模糊的扭曲地图请让我知道我的代码的任何修改。我正在使用visual studio 2017 IDE和opencv 3.3.0贡献库。

修改我尝试使用代码来查找链接https://github.com/opencv/opencv_contrib/blob/master/modules/ximgproc/samples/disparity_filtering.cpp中给出的差异。然而，在某些领域，差异图似乎是错误的。远离相机的一些物体看起来比较近的物体更亮。我试图通过将视差值乘以校准矩阵Q来计算实际深度。计算的深度与实际测量值相差很远。我确信矩阵Q是正确的，因为我纠正的图像看起来很好。我校准的方形尺寸值也是准确的（0.05米）。我的视差图像位于给定链接https://photos.app.goo.gl/YWPc6yq7XAmUpkk62中。

这是用于根据存储在filtered_disp_vis中的滤波视差图像计算实际深度的附加代码。

fs1["Q"] >> Q;
    Mat Image;
    Mat V = Mat(4, 1, CV_64FC1);
    Mat pos = Mat(4, 1, CV_64FC1);
    vector< Point3d > points;
    //float fMaxDistance = static_cast<float>((1. / Q.at<double>(3, 2)) * Q.at<double>(2, 3));
    //filtered_disp_vis.convertTo(filtered_disp_vis, CV_64FC1, 1.0 / 16.0, 0.0);
    //imshow("filtered disparity", filtered_disp_vis);


    // outputDisparityValue is single 16-bit value from disparityMap
    // DISP_SCALE = 16
    //float fDisparity = outputDisparityValue / (float)StereoMatcher::DISP_SCALE;
    //float fDistance = fMaxDistance / fDisparity;
    reprojectImageTo3D(filtered_disp_vis, Image, Q, false, CV_32F);
    //cout << Image;
    for (int i = 0; i < filtered_disp_vis.cols; i++)
    {
    for (int j = 0; j < filtered_disp_vis.rows; j++)
    {
    int d = filtered_disp_vis.at<uchar>(j, i);
    //filtered_disp_vis.convertTo(filtered_disp_vis, CV_32F, 1.0 / 16.0, 0.0);



    //int l = img_left.at<uchar>(j, i);
    //cout << "(" << j << "," << i << ")" << "=" << d;
    //out << endl;

    // if low disparity, then ignore
    /*if (d < 2) {
    continue;
    }*/
    // V is the vector to be multiplied to Q to get
    // the 3D homogenous coordinates of the image point
    V.at<double>(0, 0) = (double)(i);
    V.at<double>(1, 0) = (double)(j);
    V.at<double>(2, 0) = (double)d;
    V.at<double>(3, 0) = 1.;
    pos = Q * V; // 3D homogeneous coordinate
    double X = pos.at<double>(0, 0) / pos.at<double>(3, 0);
    double Y = pos.at<double>(1, 0) / pos.at<double>(3, 0);
    double Z = pos.at<double>(2, 0) / pos.at<double>(3, 0);

    if (i == 446 && j == 362)
    {
    cout << "(" << j << "," << i << ")" << " =   ";

    cout << X << " " << Y << " " << Z << " " << d;
    cout << endl;
    }

    Mat point3d_cam = Mat(3, 1, CV_64FC1);
    point3d_cam.at<double>(0, 0) = X;
    point3d_cam.at<double>(1, 0) = Y;
    point3d_cam.at<double>(2, 0) = Z;
    // transform 3D point from camera frame to robot frame
    //Mat point3d_robot = XR * point3d_cam + XT;
    points.push_back(Point3d(point3d_cam));
    }

我哪里错了？我的片段的任何修改或不同的建议，以获得具有准确深度值的正确视差图将是值得赞赏的。

Answer 1

我认为这不是LIBELAS问题，而是转换问题。我不确定您生成的图像的范围。但直接将CV_32F转换为CV_8U通常不是一个好主意，您将丢失信息并且它也将取决于范围......

此外，在将值转换为8U后对值进行标准化，这可能会产生问题，因为您丢失了信息，然后最大值可能是错误的值。

如果仅显示，您可以使用OpenCV中的normalize功能。

cv::Mat show;
cv::normalize(leftdpf, show, 0, 255, cv::NORM_MINMAX, CV_8U);

这将显示一个类型为CV_8U的图像，其值已标准化以适合范围（0-255）。

使用LIBELAS和后置滤波算法的不正确视差图

1 个答案: