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 11:14:05 浏览: 39
根据双目视觉原理,可以利用左右相机拍摄到的两张图片中对应物体的像素坐标,以及相机的内外参数,求出该物体在三维空间中的坐标。
首先,将左右相机的内参矩阵、畸变系数、旋转矩阵、平移向量组成立体几何变换矩阵,即:
```python
stereo_left = cv2.stereoRectify(left_camera_matrix, left_distortion_coefficients, right_camera_matrix, right_distortion_coefficients, (640, 480), rotation_matrix, translation_vector)
```
然后,将左相机拍摄到的像素坐标转换为归一化平面坐标,即:
```python
left_point = np.array([[670, 252]], dtype=np.float32)
left_norm_point = cv2.undistortPoints(left_point, left_camera_matrix, left_distortion_coefficients, R=stereo_left[0], P=stereo_left[1])
left_norm_point = cv2.convertPointsToHomogeneous(left_norm_point)
```
同样地,将右相机拍摄到的像素坐标转换为归一化平面坐标:
```python
right_point = np.array([[744, 326]], dtype=np.float32)
right_norm_point = cv2.undistortPoints(right_point, right_camera_matrix, right_distortion_coefficients, R=stereo_left[0], P=stereo_left[1])
right_norm_point = cv2.convertPointsToHomogeneous(right_norm_point)
```
接着,利用三角测量方法求出该物体的三维坐标:
```python
points_4d = cv2.triangulatePoints(stereo_left[0], stereo_left[1], left_norm_point, right_norm_point)
points_3d = cv2.convertPointsFromHomogeneous(points_4d.T)
```
最后,计算两个三维坐标之间的距离:
```python
distance = np.linalg.norm(points_3d[0] - points_3d[1])
```
由于没有给出物体的实际大小,无法将三维坐标转换为实际坐标系下的坐标。
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