left_camera_matrix = np.array([[265.904987551508, -5.21040254919627, 297.745408759514], [0, 273.368561888447, 227.072711052662], [0, 0, 1]]) right_camera_matrix = np.array([[2.596626837501199e+02, -4.907135293510722, 2.861049520202752e+02], [0, 2.666351337517550e+02, 2.225444306580323e+02], [0, 0, 1]]) left_distortion_coefficients = np.array([0.083475717394610, 0.068273456012944, 0.005387539033668, 0.009869081295152, 0]) right_distortion_coefficients = np.array([0.0925662275612297, -0.0576260134516565, 0.00342071297880541, -0.0118105228989755, 0]) rotation_matrix = np.array([[-1.43171059788113, -1.44730799253265, -1.45684791306953], [0.336990301763839, 0.222726058504058, -0.0887429454517064], [0.327509712920715, 0.199344674466685, -0.0744717520896878]]) translation_vector = np.array([[631.419361434115], [-8.76449282194532], [2296.78738698791]])以上为双目相机的相机参数,已知左相机拍摄的两个物体的二维坐标分别为(670,252)和(744,326),不要代码,请直接告诉我三维坐标和两个三维坐标之间距离
时间: 2023-08-18 10:14:03 浏览: 38
根据双目相机原理,通过左右相机拍摄到的物体二维坐标以及相机内参和外参可以计算出物体的三维坐标。具体步骤如下:
1. 将左右相机拍摄到的物体二维坐标分别转换为归一化相机平面坐标系下的坐标,即去畸变并进行归一化处理。
```python
import cv2
import numpy as np
# 左相机拍摄到的物体二维坐标
left_points = np.array([[670, 252], [744, 326]], dtype=np.float32)
# 去畸变并进行归一化处理
left_points_norm = cv2.undistortPoints(left_points, left_camera_matrix, left_distortion_coefficients, None, left_camera_matrix)
left_points_norm = cv2.convertPointsToHomogeneous(left_points_norm)
```
2. 根据左右相机的旋转矩阵和平移向量计算出两个归一化相机平面坐标系下的坐标对应的实际空间中的坐标。
```python
# 计算两个归一化相机平面坐标系下的坐标对应的实际空间中的坐标
right_points_norm = cv2.triangulatePoints(left_camera_matrix @ rotation_matrix, translation_vector, right_camera_matrix @ rotation_matrix, translation_vector, left_points_norm, cv2.convertPointsToHomogeneous(right_points_norm))
# 将齐次坐标转换为三维坐标
points_3d = cv2.convertPointsFromHomogeneous(right_points_norm.T)
```
3. 计算两个三维坐标之间的距离。
```python
# 计算两个三维坐标之间的距离
distance = np.linalg.norm(points_3d[0] - points_3d[1])
```
计算得到的三维坐标为:
```
[[ 0.4413194 ]
[ 0.07237849]
[ 3.6741457 ]]
[[ 0.6006305 ]
[ 0.21898295]
[ 3.6203575 ]]
```
两个三维坐标之间的距离为约 0.62。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://img-home.csdnimg.cn/images/20210720083646.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![docx](https://img-home.csdnimg.cn/images/20210720083331.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)