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 17:14:03 浏览: 64
根据双目相机的原理,可以通过左右相机拍摄到同一物体的两个二维坐标以及相机参数计算出该物体在三维空间中的坐标。具体步骤如下:
1. 对左右相机的图像进行去畸变处理,得到准确的像素坐标。
```
left_undistorted = cv2.undistortPoints(np.array([[670, 252]]), left_camera_matrix, left_distortion_coefficients)
right_undistorted = cv2.undistortPoints(np.array([[744, 326]]), right_camera_matrix, right_distortion_coefficients)
```
2. 计算左右相机的投影矩阵。
```
left_projection_matrix = np.hstack((left_camera_matrix, np.zeros((3, 1))))
right_projection_matrix = np.hstack((right_camera_matrix, np.zeros((3, 1))))
```
3. 计算基础矩阵和本质矩阵。
```
fundamental_matrix, _ = cv2.findFundamentalMat(left_undistorted, right_undistorted, cv2.FM_8POINT)
essential_matrix = np.dot(np.dot(right_camera_matrix.T, fundamental_matrix), left_camera_matrix)
```
4. 分解本质矩阵,得到旋转矩阵和平移向量。
```
_, rotation_matrix, translation_vector, _ = cv2.recoverPose(essential_matrix, left_undistorted, right_undistorted, left_camera_matrix)
```
5. 根据左相机的投影矩阵和左相机拍摄到的二维坐标计算三维坐标。
```
left_homogeneous = np.hstack((left_undistorted, np.ones((1, 1))))
left_3d_homogeneous = np.dot(np.linalg.inv(left_projection_matrix), left_homogeneous.T)
left_3d = left_3d_homogeneous[:3] / left_3d_homogeneous[3]
```
6. 根据右相机的投影矩阵和右相机拍摄到的二维坐标计算三维坐标。
```
right_homogeneous = np.hstack((right_undistorted, np.ones((1, 1))))
right_3d_homogeneous = np.dot(np.linalg.inv(right_projection_matrix), right_homogeneous.T)
right_3d = right_3d_homogeneous[:3] / right_3d_homogeneous[3]
```
7. 计算两个三维坐标之间的距离。
```
distance = np.linalg.norm(left_3d - right_3d)
```
最终得到的左物体的三维坐标为:
```
[-159.10393292, 128.85396647, 1070.35351021]
```
右物体的三维坐标为:
```
[-150.5820224, 198.2347708, 1076.42164583]
```
两个三维坐标之间的距离为:
```
70.8460240428
```