已知双目相机标定参数，求解双目相机下物体世界坐标系的坐标，得到求解过程和python源代码

双目相机的标定参数通常包括相机内参矩阵、畸变参数、外参矩阵等。在已知这些参数的情况下，可以通过双目视差图和立体匹配的方法计算出物体在相机坐标系下的坐标，再经过坐标变换得到物体在世界坐标系下的坐标。

下面是求解双目相机下物体世界坐标系的坐标的Python源代码：

import cv2
import numpy as np

# 相机内参矩阵
K_left = np.array([[fx_left, 0, cx_left],
                   [0, fy_left, cy_left],
                   [0, 0, 1]])
K_right = np.array([[fx_right, 0, cx_right],
                    [0, fy_right, cy_right],
                    [0, 0, 1]])

# 畸变参数
dist_left = np.array([k1_left, k2_left, p1_left, p2_left, k3_left])
dist_right = np.array([k1_right, k2_right, p1_right, p2_right, k3_right])

# 外参矩阵
R = np.array([[r11, r12, r13],
              [r21, r22, r23],
              [r31, r32, r33]])
T = np.array([t1, t2, t3])

# 读取左右相机图片
img_left = cv2.imread('left.jpg')
img_right = cv2.imread('right.jpg')

# 立体校正
R_left, R_right, P_left, P_right, Q, _, _ = cv2.stereoRectify(K_left, dist_left, K_right, dist_right, (w, h), R, T)
map_left1, map_left2 = cv2.initUndistortRectifyMap(K_left, dist_left, R_left, P_left, (w, h), cv2.CV_32FC1)
map_right1, map_right2 = cv2.initUndistortRectifyMap(K_right, dist_right, R_right, P_right, (w, h), cv2.CV_32FC1)
img_left = cv2.remap(img_left, map_left1, map_left2, cv2.INTER_LINEAR)
img_right = cv2.remap(img_right, map_right1, map_right2, cv2.INTER_LINEAR)

# 立体匹配
stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
disparity = stereo.compute(img_left, img_right)

# 计算物体在相机坐标系下的坐标
points = cv2.reprojectImageTo3D(disparity, Q)
points = points.reshape(-1, 3)

# 坐标变换
points = points.dot(R.T) + T

# 保存结果
np.savetxt('points.txt', points)

其中，`fx_left`、`fy_left`、`cx_left`、`cy_left`、`k1_left`、`k2_left`、`p1_left`、`p2_left`、`k3_left`、`fx_right`、`fy_right`、`cx_right`、`cy_right`、`k1_right`、`k2_right`、`p1_right`、`p2_right`、`k3_right`、`r11`、`r12`、`r13`、`r21`、`r22`、`r23`、`r31`、`r32`、`r33`、`t1`、`t2`、`t3`等参数需要根据实际情况进行替换。同时，需要将`left.jpg`和`right.jpg`替换成实际的左右相机图片。最终求解结果保存在`points.txt`中。