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深度的计算公式如下:
\[z = \frac{f \cdot b}{d}\]其中$f$ 为相机的焦距,$b$为相机的基线,$d$为左右视图的视差。得到像素的深度后,我们通过相机投影的逆过程得到像素的三维坐标,其中:
\[x = \frac{u - c_x}{f_x} \cdot z y = \frac{v - c_y}{f_y} \cdot z z = z\]最后,我们用pangolin将结果可视化,使用的c++库有:Eigen,pangolin,opencv, 核心实现代码如下:
#include <opencv2/opencv.hpp>
#include <string>
#include <Eigen/Core>
#include <pangolin/pangolin.h>
#include <unistd.h>
using namespace std;
using namespace Eigen;
// 文件路径,如果不对,请调整
string left_file = "../left.png";
string right_file = "../right.png";
string disparity_file = "../disparity.png";
// 在panglin中画图,已写好,无需调整
void showPointCloud(const vector<Vector4d, Eigen::aligned_allocator<Vector4d>> &pointcloud);
int main(int argc, char **argv) {
// 内参
double fx = 718.856, fy = 718.856, cx = 607.1928, cy = 185.2157;
// 间距
double d = 0.573;
// 读取图像
cv::Mat left = cv::imread(left_file, 0);
cv::Mat right = cv::imread(right_file, 0);
cv::Mat disparity = cv::imread(disparity_file, 0); // disparty 为CV_8U,单位为像素
if(left.data == nullptr && right.data == nullptr && disparity.data == nullptr)
{
cerr << "文件" << "不存在" << endl;
return 1;
}
// 生成点云
vector<Vector4d, Eigen::aligned_allocator<Vector4d>> pointcloud;
double z = 0;
// TODO 根据双目模型计算点云
// 如果你的机器慢,请把后面的v++和u++改成v+=2, u+=2
for (int v = 0; v < left.rows; v++)
for (int u = 0; u < left.cols; u++) {
Vector4d point(0, 0, 0, left.at<uchar>(v, u) / 255.0); // 前三维为xyz,第四维为颜色
// start your code here (~6 lines)
// 根据双目模型计算 point 的位置
unsigned int disp = disparity.ptr<unsigned short >(v)[u];
if (disp == 0)
{
cout << "disp == 0" << endl;
}
z = (fx * d * 1000) / disp;
point[0] = (u - cx) / fx * z;
point[1] = (v - cy) / fy * z;
point[2] = z;
// end your code here
pointcloud.push_back(point);
}
// 画出点云
showPointCloud(pointcloud);
return 0;
}
void showPointCloud(const vector<Vector4d, Eigen::aligned_allocator<Vector4d>> &pointcloud) {
if (pointcloud.empty()) {
cerr << "Point cloud is empty!" << endl;
return;
}
pangolin::CreateWindowAndBind("Point Cloud Viewer", 1024, 768);
glEnable(GL_DEPTH_TEST);
glEnable(GL_BLEND);
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
pangolin::OpenGlRenderState s_cam(
pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),
pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)
);
pangolin::View &d_cam = pangolin::CreateDisplay()
.SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)
.SetHandler(new pangolin::Handler3D(s_cam));
while (pangolin::ShouldQuit() == false) {
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
d_cam.Activate(s_cam);
glClearColor(1.0f, 1.0f, 1.0f, 1.0f);
glPointSize(2);
glBegin(GL_POINTS);
for (auto &p: pointcloud) {
glColor3f(p[3], p[3], p[3]);
glVertex3d(p[0], p[1], p[2]);
}
glEnd();
pangolin::FinishFrame();
usleep(5000); // sleep 5 ms
}
return;
}
结果如下: