import numpy as np dimiandian = np.loadtxt('C:\dimiandian\dimiandianzuobiao.txt') #内参矩阵 K 和畸变系数 D #内参矩阵K包含了相机的焦距、光心等参数、畸变系数D描述了相机镜头的畸变情况 K = np.array([[97981909.8521,0,914.5],[0,97956658.9519,64.5],[0,0,1]]) D = np.array([0,0,-0,-0,0]) #外参矩阵R和T R是旋转矩阵 R = np.array([[1,-0,0],[-0,1,0],[-0,-0,1]]) T = np.array([-4.3939,-2.891,589187.672]) extrinsic = np.hstack((R, T.reshape(3, 1))) # 投影矩阵 P P = K.dot(np.hstack((R, T.reshape(-1, 1)))) np.set_printoptions(precision=4, suppress=True) print(P) doc_dimiandian = np.hstack((dimiandian, np.ones((dimiandian.shape[0], 1)))) camera_point =extrinsic.dot(doc_dimiandian.T) pixel_point =P.dot(camera_point) uv = pixel_point[:2, :] / pixel_point[2, :] print(uv.T)这段代码做了什么工作

这段代码实现了相机坐标系下三维物体点云到像素坐标系下的映射。具体实现步骤如下：

1. 从文件中读取点云坐标信息，保存在dimiandian中。

2. 定义内参矩阵K和畸变系数D，用于描述相机的内部参数和畸变情况。

3. 定义外参矩阵R和T，用于描述相机的位置和朝向。

4. 计算投影矩阵P，将点云坐标从相机坐标系映射到像素坐标系。

5. 将点云坐标转换成齐次坐标，再将其乘以外参矩阵extrinsic，得到相机坐标系下的坐标。

6. 将相机坐标系下的坐标乘以投影矩阵P，得到像素坐标系下的坐标。

7. 将像素坐标系下的坐标除以最后一维，得到归一化坐标。

8. 输出像素坐标系下的坐标。

相关问题

相机标定python代码,并保存相机内参矩阵和外参矩阵为.txt文件，将保存的内参矩阵和外参矩阵用以实现矫正

以下是使用Python OpenCV库进行相机标定的代码示例，并将相机内参矩阵和外参矩阵保存为.txt文件，并使用保存的内参矩阵和外参矩阵实现相机矫正：

import numpy as np
import cv2
import glob

# 设置棋盘格的行列数
rows = 6
cols = 9

# 准备棋盘格角点的空列表
objpoints = []
imgpoints = []

# 获取棋盘格角点的世界坐标系坐标
objp = np.zeros((rows*cols,3), np.float32)
objp[:,:2] = np.mgrid[0:cols,0:rows].T.reshape(-1,2)

# 获取所有棋盘格图片的路径
images = glob.glob('path/to/images/*.jpg')

# 遍历所有图片
for fname in images:
    # 读取图片
    img = cv2.imread(fname)
    # 将图片转为灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 寻找棋盘格角点
    ret, corners = cv2.findChessboardCorners(gray, (cols, rows), None)

    # 如果找到了棋盘格角点
    if ret == True:
        # 将棋盘格角点添加到列表中
        objpoints.append(objp)
        imgpoints.append(corners)

# 进行相机标定
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

# 打印相机内参矩阵
print("Camera matrix:")
print(mtx)

# 打印畸变系数
print("Distortion coefficients:")
print(dist)

# 保存相机内参矩阵和外参矩阵
np.savetxt('path/to/camera_matrix.txt', mtx)
np.savetxt('path/to/distortion_coefficients.txt', dist)

# 读取相机内参矩阵和外参矩阵
mtx = np.loadtxt('path/to/camera_matrix.txt')
dist = np.loadtxt('path/to/distortion_coefficients.txt')

# 读取需要矫正的图片
img = cv2.imread('path/to/image.jpg')

# 进行相机矫正
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)

# 显示矫正前后的图片
cv2.imshow('Original Image', img)
cv2.imshow('Undistorted Image', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`path/to/images/*.jpg`需要替换为存储棋盘格图片的路径，`path/to/camera_matrix.txt`和`path/to/distortion_coefficients.txt`需要替换为保存相机内参矩阵和畸变系数的文件路径，`path/to/image.jpg`需要替换为需要矫正的图片的路径。

知道了双目摄像头的内参和外参，用PYTHON编写畸变校正的代码

以下是PYTHON代码实现双目摄像头畸变校正的过程：

1. 导入必要的包和库：

import cv2
import numpy as np

2. 定义内参和外参：

# 左右摄像头的内参和外参
left_intrinsic = np.array([[fx_left, 0, cx_left], [0, fy_left, cy_left], [0, 0, 1]])
left_distortion = np.array([k1_left, k2_left, p1_left, p2_left, k3_left])
left_rotation = np.array([[r11_left, r12_left, r13_left], [r21_left, r22_left, r23_left], [r31_left, r32_left, r33_left]])
left_translation = np.array([t1_left, t2_left, t3_left])

right_intrinsic = np.array([[fx_right, 0, cx_right], [0, fy_right, cy_right], [0, 0, 1]])
right_distortion = np.array([k1_right, k2_right, p1_right, p2_right, k3_right])
right_rotation = np.array([[r11_right, r12_right, r13_right], [r21_right, r22_right, r23_right], [r31_right, r32_right, r33_right]])
right_translation = np.array([t1_right, t2_right, t3_right])

3. 读取图像和标定参数：

# 读取左右摄像头的图像
left = cv2.imread('left.jpg')
right = cv2.imread('right.jpg')

# 读取双目摄像头的内参和外参
left_intrinsic = np.loadtxt('left_intrinsic.txt')
left_distortion = np.loadtxt('left_distortion.txt')
left_rotation = np.loadtxt('left_rotation.txt')
left_translation = np.loadtxt('left_translation.txt')

right_intrinsic = np.loadtxt('right_intrinsic.txt')
right_distortion = np.loadtxt('right_distortion.txt')
right_rotation = np.loadtxt('right_rotation.txt')
right_translation = np.loadtxt('right_translation.txt')

4. 进行畸变校正：

# 畸变校正
left_rectification, right_rectification, left_projection, right_projection, Q, roi_left, roi_right = cv2.stereoRectify(left_intrinsic, left_distortion, right_intrinsic, right_distortion, (640, 480), left_rotation, left_translation, right_rotation, right_translation, alpha=0)

# 生成映射矩阵
left_map1, left_map2 = cv2.initUndistortRectifyMap(left_intrinsic, left_distortion, left_rectification, left_projection, (640, 480), cv2.CV_16SC2)
right_map1, right_map2 = cv2.initUndistortRectifyMap(right_intrinsic, right_distortion, right_rectification, right_projection, (640, 480), cv2.CV_16SC2)

# 根据映射矩阵进行畸变校正
left_rectified = cv2.remap(left, left_map1, left_map2, cv2.INTER_LINEAR)
right_rectified = cv2.remap(right, right_map1, right_map2, cv2.INTER_LINEAR)

5. 对畸变校正后的图像进行显示：

# 显示畸变校正后的图像
cv2.imshow("left_rectified", left_rectified)
cv2.imshow("right_rectified", right_rectified)
cv2.waitKey(0)
cv2.destroyAllWindows()

完整代码如下：

import cv2
import numpy as np

# 左右摄像头的内参和外参
left_intrinsic = np.array([[fx_left, 0, cx_left], [0, fy_left, cy_left], [0, 0, 1]])
left_distortion = np.array([k1_left, k2_left, p1_left, p2_left, k3_left])
left_rotation = np.array([[r11_left, r12_left, r13_left], [r21_left, r22_left, r23_left], [r31_left, r32_left, r33_left]])
left_translation = np.array([t1_left, t2_left, t3_left])

right_intrinsic = np.array([[fx_right, 0, cx_right], [0, fy_right, cy_right], [0, 0, 1]])
right_distortion = np.array([k1_right, k2_right, p1_right, p2_right, k3_right])
right_rotation = np.array([[r11_right, r12_right, r13_right], [r21_right, r22_right, r23_right], [r31_right, r32_right, r33_right]])
right_translation = np.array([t1_right, t2_right, t3_right])

# 读取左右摄像头的图像
left = cv2.imread('left.jpg')
right = cv2.imread('right.jpg')

# 畸变校正
left_rectification, right_rectification, left_projection, right_projection, Q, roi_left, roi_right = cv2.stereoRectify(left_intrinsic, left_distortion, right_intrinsic, right_distortion, (640, 480), left_rotation, left_translation, right_rotation, right_translation, alpha=0)

# 生成映射矩阵
left_map1, left_map2 = cv2.initUndistortRectifyMap(left_intrinsic, left_distortion, left_rectification, left_projection, (640, 480), cv2.CV_16SC2)
right_map1, right_map2 = cv2.initUndistortRectifyMap(right_intrinsic, right_distortion, right_rectification, right_projection, (640, 480), cv2.CV_16SC2)

# 根据映射矩阵进行畸变校正
left_rectified = cv2.remap(left, left_map1, left_map2, cv2.INTER_LINEAR)
right_rectified = cv2.remap(right, right_map1, right_map2, cv2.INTER_LINEAR)

# 显示畸变校正后的图像
cv2.imshow("left_rectified", left_rectified)
cv2.imshow("right_rectified", right_rectified)
cv2.waitKey(0)
cv2.destroyAllWindows()