Question

我是立体相机的新手（或许是opencv的新手）。但是，我需要做我的FYP，需要立体相机来测量所选对象的距离。

获得准确的视差图有哪些步骤？据我所知，我们需要校准以获得内在和外部参数，不失真，然后纠正它，然后只计算视差映射。

我已完成校准以获得内在和外在价值。然后我试图纠正它，我得到了这个RMS value_and_reprojection_error_value.jpg。问题是，视差映射可以接受什么值？

之后，我开始使用Block Matching进行视差映射。这是我的代码 `

int main(void)
{

   VideoCapture camLeft(0);
   VideoCapture camRight(2);

   camLeft.set(CV_CAP_PROP_FRAME_WIDTH, 500);
   camLeft.set(CV_CAP_PROP_FRAME_HEIGHT, 500);
   camRight.set(CV_CAP_PROP_FRAME_WIDTH, 500);
   camRight.set(CV_CAP_PROP_FRAME_HEIGHT, 500);



   if (!camLeft.isOpened() || !camRight.isOpened()) {
      cout << "Error: Stereo Cameras not found or there is some problem       connecting them. Please check your cameras.\n";
      exit(-1);
     }


//Read intrinsice parameters
string intrinsic_filepath = "C:/Users/Jerry/Documents/Visual Studio 2015/Projects/OctStereoCalibration/OctStereoCalibration/intrinsics.yml";
FileStorage fs(intrinsic_filepath, FileStorage::READ);
if (!fs.isOpened())
{
    printf("Failed to open intrinsics.yml");
    return -1;
}
Mat M1, D1, M2, D2;
fs["M1"] >> M1;
fs["D1"] >> D1;
fs["M2"] >> M2;
fs["D2"] >> D2;

//Read Extrinsic Parameters
string extrinsic_filepath = "C:/Users/Jerry/Documents/Visual Studio 2015/Projects/OctStereoCalibration/OctStereoCalibration/extrinsics.yml";
fs.open(extrinsic_filepath, FileStorage::READ);
if (!fs.isOpened())
{
    printf("Failed to open extrinsics");
    return -1;
}

Mat R, T, R1, P1, R2, P2;
fs["R"] >> R;
fs["T"] >> T;

Mat frame1, frame2, gray1, gray2, copyImageLeft, copyImageRight;
int counter = 0;

camLeft >> frame1;
camRight >> frame2;

Size img_size = frame1.size();
Rect roi1, roi2;
Mat Q;

stereoRectify(M1, D1, M2, D2, img_size, R, T, R1, R2, P1, P2, Q, CALIB_ZERO_DISPARITY, -1, img_size, &roi1, &roi2);

Mat map11, map12, map21, map22;
initUndistortRectifyMap(M1, D1, R1, P1, img_size, CV_16SC2, map11, map12);
initUndistortRectifyMap(M2, D2, R2, P2, img_size, CV_16SC2, map21, map22);



while (1) {
    createTrackbars();
    on_trackbar(0, 0);

    bm->setROI1(roi1);
    bm->setROI2(roi2);
    bm->setPreFilterCap(PreFilterCap);
    bm->setPreFilterSize(PrefilterSize);
    bm->setBlockSize(SADWindowSize);
    bm->setMinDisparity(MinDisparity);  //0
    bm->setNumDisparities(numberOfDisparities);
    bm->setTextureThreshold(TextureThreshold);
    bm->setUniquenessRatio(UniquenessRatio);
    bm->setSpeckleWindowSize(SpeckleWindowSize);
    bm->setSpeckleRange(SpeckleRange);
    bm->setDisp12MaxDiff(Disp12MaxDiff);    //1

    camLeft >> frame1;
    camRight >> frame2;
    if ((frame1.rows != frame2.rows) || (frame1.cols != frame2.cols)) {
        cout << "Error: Images from both cameras are not of some size. Please check the size of each camera.\n";
        exit(-1);
    }
    //frame1.copyTo(copyImageLeft);
    //frame2.copyTo(copyImageRight);
    imshow("Cam1", frame1);
    imshow("Cam2", frame2);

    /************************* STEREO ***********************/

    cvtColor(frame1, gray1, CV_RGB2GRAY);
    cvtColor(frame2, gray2, CV_RGB2GRAY);

    int64 t = getTickCount();

    Mat img1r, img2r;
    remap(gray1, img1r, map11, map12, INTER_LINEAR);
    remap(gray2, img2r, map21, map22, INTER_LINEAR);

    Mat disp, disp8;
    Mat XYZ;
    bm->compute(img1r, img2r, disp);
    t = getTickCount() - t;
    printf("Time elapsed: %fms\n", t * 1000 / getTickFrequency());

    disp.convertTo(disp8, CV_8U, 255 / (numberOfDisparities*16.));
    //normalize(disp, disp8, 0, 255, CV_MINMAX, CV_8U);
    imshow("disparity", disp8);

    //reprojectImageTo3D(disp8, XYZ, Q, false, CV_32F);

    char keyBoardInput = (char)waitKey(50);
    if (keyBoardInput == 'q' || keyBoardInput == 'Q') {
        break;
        return(0);
    }
}

} `

我的BM参数是：

int PreFilterCap = 31; int PrefilterSize = 21; int SADWindowSize = 33; int MinDisparity = 0; int numberOfDisparities = 48; int TextureThreshold = 29; int UniquenessRatio = 15; int SpeckleWindowSize = 32; int SpeckleRange = 32; int Disp12MaxDiff = 0; 我得到的差异图是： Disparity map

如何获得更好的视差映射质量？感谢

Answer 1

您可以尝试更改参数窗口大小的值和差异的数量。对于不同的深度，不同的值集合起作用。
第二种选择是使用SGBM而不是sbm，我所知道的第三种方法是使用高斯金字塔来计算左右相机低分辨率的视差，然后用一些简单的逻辑融合它们。低水平视差图的计算有一个好处，就是当你沿着金字塔水平向下钻孔将开始填充，从而更好地制作最终的视差图。

不准确的实时视差图。

1 个答案: