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），请直接告诉我三维坐标和距离

根据双目相机原理，可以通过左右相机拍摄到同一物体的对应像素点坐标，计算出该物体在相机坐标系下的三维坐标。具体步骤如下：

1. 将左右相机的二维像素坐标转换为归一化坐标系下的二维坐标：

left_normalized = np.linalg.inv(left_camera_matrix) @ np.array([[670], [252], [1]])
right_normalized = np.linalg.inv(right_camera_matrix) @ np.array([[744], [326], [1]])

2. 根据左右相机的归一化坐标系下的二维坐标