一：棋盘校准参数说明(内参)

棋盘校准的方法及代码很多，参见其他连接

1：内参矩阵

2：畸变系数

针对鱼眼相机此处是4个参数，在其校准代码中也可以知道，其通常的定义如下：

       data.camera_mat = np.eye(3, 3)data.dist_coeff = np.zeros((4, 1))

二：基于生成的参数D和K进行调整

直接上代码，

import os
import numpy as np
import cv2
#下面的值是来自棋盘格校准后的内参矩阵K
initial_camera_matrix = np.array([[389.3455401867884, 0.0, 630.3678577531273],[0.0, 388.5686773894828, 361.167452606629],[0.0, 0.0, 1.0]])camera_mat = np.array([[302.116935,0.0,521.926531],[  0.0, 358.512097, 363.652721],[  0.0, 0.0,     1.0      ]])#下面的值是来自棋盘格校准后的D
dist_coeff = np.array([[0.05648235312901486],[-0.024826520405491565],[-0.002416551582982325],[0.0010672440368159684]])def adjust_parameters(x):
# 获取当前滑动条的值fx = cv2.getTrackbarPos('fx', 'Parameters')/ 100.0 fy = cv2.getTrackbarPos('fy', 'Parameters')/ 100.0 cx = cv2.getTrackbarPos('cx', 'Parameters')/ 100.0 cy = cv2.getTrackbarPos('cy', 'Parameters')/ 100.0 k1 = cv2.getTrackbarPos('k1', 'Parameters') / 2000000  # 调整范围以便于滑动条控制k2 = cv2.getTrackbarPos('k2', 'Parameters') / 2000000p1 = cv2.getTrackbarPos('p1', 'Parameters') / 2000000p2 = cv2.getTrackbarPos('p2', 'Parameters') / 2000000# 更新相机矩阵和畸变系数new_camera_matrix = np.array([[fx, 0.0, cx],[0.0, fy, cy],[0.0, 0.0, 1.0]])new_distortion_coeff = np.array([[k1], [-k2], [-p1], [p2]])map1, map2 = cv2.fisheye.initUndistortRectifyMap(initial_camera_matrix, new_distortion_coeff, np.eye(3, 3), new_camera_matrix, (args.width * args.sizescale, args.height * args.sizescale), cv2.CV_16SC2) img = np.load("./calibrate_img/img_cam5.npy")print(new_camera_matrix)print(new_distortion_coeff)undistort_img = cv2.remap(img, map1, map2, cv2.INTER_LINEAR)undistort_img=cv2.resize(undistort_img,(img.shape[1],img.shape[0]))cv2.imshow("undistort_img",undistort_img)cv2.imshow("src_img",img)def main():cv2.namedWindow('Parameters')# 添加相机矩阵的滑动条cv2.createTrackbar('fx', 'Parameters', int(camera_mat[0, 0]* 10000), 90*100000, adjust_parameters)cv2.createTrackbar('fy', 'Parameters', int(camera_mat[1, 1]* 10000), 90*100000, adjust_parameters)cv2.createTrackbar('cx', 'Parameters', int(camera_mat[0, 2]* 10000), 90*1000000, adjust_parameters)cv2.createTrackbar('cy', 'Parameters', int(camera_mat[1, 2]* 10000), 90*100000, adjust_parameters)# 添加畸变系数的滑动条cv2.createTrackbar('k1', 'Parameters', int(dist_coeff[0, 0] * 100000), 4000000, adjust_parameters)cv2.createTrackbar('k2', 'Parameters', int(dist_coeff[1, 0] * 100000), 4000000, adjust_parameters)cv2.createTrackbar('p1', 'Parameters', int(dist_coeff[2, 0] * 100000), 4000000, adjust_parameters)cv2.createTrackbar('p2', 'Parameters', int(dist_coeff[3, 0] * 100000), 4000000, adjust_parameters)while True:key = cv2.waitKey(1) & 0xFFif key == ord('q'):breakcv2.destroyAllWindows()  if __name__ == '__main__':main()