摄像头的内参外参是什么

摄像头的内参是指相机的内部参数，包括焦距、主点坐标、畸变等，它们是由相机硬件本身决定的，不随着拍摄场景的变化而变化。

摄像头的外参是指相机的外部参数，包括相机的位置和朝向等，它们是相机与物体之间的相对位置和姿态关系，随着拍摄场景的变化而变化。外参决定了从三维空间到图像平面的投影关系，可以用于进行三维重建和相机位姿估计等任务。

相关问题

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()

双目摄像头知道内参和外参时，用python写一个矫正畸变的代码

可以使用OpenCV库中的函数进行矫正畸变，具体代码如下：

import cv2

# 内参矩阵
mtx = [[fx, 0, cx], [0, fy, cy], [0, 0, 1]]
# 畸变系数
dist = [k1, k2, p1, p2, k3]

# 读取图像
img = cv2.imread('image.jpg')

# 获取图像大小
h, w = img.shape[:2]

# 计算新的内参矩阵
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))

# 矫正畸变
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)

# 显示结果
cv2.imshow('original', img)
cv2.imshow('corrected', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

这样就可以对双目摄像头获取的图像进行畸变矫正了。