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 21:14:06 浏览: 30
根据给出的双目相机参数和左相机拍摄的两个物体的二维坐标,可以通过三角化方法求出两个物体的三维坐标。具体步骤如下:
1. 构建左右相机的投影矩阵P_left和P_right。其中,左相机的投影矩阵为[P_left|0],右相机的投影矩阵为[P_right|0],其中0表示一个3x1的零向量。
```
P_left = left_camera_matrix @ np.hstack((np.eye(3), np.zeros((3, 1))))
P_right = right_camera_matrix @ np.hstack((rotation_matrix, translation_vector))
```
2. 使用cv2.triangulatePoints函数对左右相机拍摄的两个物体的二维坐标进行三角化,得到它们在相机坐标系下的三维坐标。
```
points_3d_homogeneous = cv2.triangulatePoints(P_left, P_right,
np.expand_dims(np.array([670, 252, 1]), axis=1),
np.expand_dims(np.array([744, 326, 1]), axis=1))
points_3d = points_3d_homogeneous[:3] / points_3d_homogeneous[3]
```
3. 计算两个物体的三维空间距离。
```
distance = np.linalg.norm(points_3d[:, 0] - points_3d[:, 1])
```
最终,两个物体的三维坐标为points_3d,它们之间的距离为distance。