opencv - 获取深度图

Question

我无法从差异中获得正常的深度图。这是我的代码:

#include "opencv2/core/core.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/contrib/contrib.hpp"
#include <cstdio>
#include <iostream>
#include <fstream>

using namespace cv;
using namespace std;

ofstream out("points.txt");

int main()
{
    Mat img1, img2;
    img1 = imread("images/im7rect.bmp");
    img2 = imread("images/im8rect.bmp");

    //resize(img1, img1, Size(320, 280));
    //resize(img2, img2, Size(320, 280));

    Mat g1,g2, disp, disp8;
    cvtColor(img1, g1, CV_BGR2GRAY);
    cvtColor(img2, g2, CV_BGR2GRAY);

    int sadSize = 3;
    StereoSGBM sbm;
    sbm.SADWindowSize = sadSize;
    sbm.numberOfDisparities = 144;//144; 128
    sbm.preFilterCap = 10; //63
    sbm.minDisparity = 0; //-39; 0
    sbm.uniquenessRatio = 10;
    sbm.speckleWindowSize = 100;
    sbm.speckleRange = 32;
    sbm.disp12MaxDiff = 1;
    sbm.fullDP = true;
    sbm.P1 = sadSize*sadSize*4;
    sbm.P2 = sadSize*sadSize*32;
    sbm(g1, g2, disp);

    normalize(disp, disp8, 0, 255, CV_MINMAX, CV_8U);

    Mat dispSGBMscale; 
    disp.convertTo(dispSGBMscale,CV_32F, 1./16); 

    imshow("image", img1);

    imshow("disparity", disp8);

    Mat Q;
    FileStorage fs("Q.txt", FileStorage::READ);
    fs["Q"] >> Q;
    fs.release();

    Mat points, points1;
    //reprojectImageTo3D(disp, points, Q, true);
    reprojectImageTo3D(disp, points, Q, false, CV_32F);
    imshow("points", points);

    ofstream point_cloud_file;
    point_cloud_file.open ("point_cloud.xyz");
    for(int i = 0; i < points.rows; i++) {
        for(int j = 0; j < points.cols; j++) {
            Vec3f point = points.at<Vec3f>(i,j);
            if(point[2] < 10) {
                point_cloud_file << point[0] << " " << point[1] << " " << point[2]
                    << " " << static_cast<unsigned>(img1.at<uchar>(i,j)) << " " << static_cast<unsigned>(img1.at<uchar>(i,j)) << " " << static_cast<unsigned>(img1.at<uchar>(i,j)) << endl;
            }
        }
    }
    point_cloud_file.close(); 

    waitKey(0);

    return 0;
}

我的图片是:

视差图:

我得到了这样的点云:

Q 相等:[ 1., 0., 0., -3.2883545303344727e+02, 0., 1., 0., -2.3697290992736816e+02, 0., 0., 0., 5.4497170185417110e+02, 0., 0., -1.4446083962336606e-02, 0.]

我尝试了很多其他的东西。我尝试了不同的图像，但没有人能够获得正常的深度图。

我做错了什么？我应该使用 reprojectImageTo3D 还是使用其他方法代替它？可视化深度图的最佳方法是什么？ (我试过 point_cloud 库)或者您能否向我提供包含数据集和校准信息的工作示例，我可以运行它并获取深度图。或者如何从 middlebury 立体数据库 ( http://vision.middlebury.edu/stereo/data/ ) 获取 depth_map，我认为没有足够的校准信息。

编辑:现在我得到像:

当然更好了，但还是不正常。

编辑2:我试过你说的:

Mat disp;
disp = imread("disparity-image.pgm", CV_LOAD_IMAGE_GRAYSCALE);

Mat disp64; 
disp.convertTo(disp64,CV_64F, 1.0/16.0); 
imshow("disp", disp);

我在 cv::minMaxIdx(...) 行得到了这个结果:

当我评论这一行时:

Ps:另外请你告诉我如何只知道以像素为单位的基线和焦距来计算深度。

Answer 1

我对OpenCV的reprojectImageTo3D()做了一个简单的比较和我自己的(见下文)，并运行测试以获取正确的视差和 Q 矩阵。

// Reproject image to 3D
void customReproject(const cv::Mat& disparity, const cv::Mat& Q, cv::Mat& out3D)
{
    CV_Assert(disparity.type() == CV_32F && !disparity.empty());
    CV_Assert(Q.type() == CV_32F && Q.cols == 4 && Q.rows == 4);

    // 3-channel matrix for containing the reprojected 3D world coordinates
    out3D = cv::Mat::zeros(disparity.size(), CV_32FC3);

    // Getting the interesting parameters from Q, everything else is zero or one
    float Q03 = Q.at<float>(0, 3);
    float Q13 = Q.at<float>(1, 3);
    float Q23 = Q.at<float>(2, 3);
    float Q32 = Q.at<float>(3, 2);
    float Q33 = Q.at<float>(3, 3);

    // Transforming a single-channel disparity map to a 3-channel image representing a 3D surface
    for (int i = 0; i < disparity.rows; i++)
    {
        const float* disp_ptr = disparity.ptr<float>(i);
        cv::Vec3f* out3D_ptr = out3D.ptr<cv::Vec3f>(i);

        for (int j = 0; j < disparity.cols; j++)
        {
            const float pw = 1.0f / (disp_ptr[j] * Q32 + Q33);

            cv::Vec3f& point = out3D_ptr[j];
            point[0] = (static_cast<float>(j)+Q03) * pw;
            point[1] = (static_cast<float>(i)+Q13) * pw;
            point[2] = Q23 * pw;
        }
    }
}

这两种方法产生的结果几乎相同，我认为它们都是正确的。你能在你的视差图和Q矩阵上试试吗？您可以在我的 GitHub 上安装我的测试环境.

注意 1:还要注意不要将视差缩放两倍(注释掉行 disparity.convertTo(disparity, CV_32F, 1.0/16.0); 如果您disparity 也被缩放。)

注意 2:它是用 OpenCV 3.0 构建的，您可能需要更改包含。