为什么校准相机时的失真系数会发生变化？

Question

我在上面放了一个带有棋盘的计算机显示器的iPhone视频。在视频中，我没有更改任何相机设置，只需将手机移动。

从视频中，我保存了两个屏幕截图，其中充分查看了网格：

我使用下面的代码对两个图像进行了校准，并且得到了两组非常不同的失真系数...如果完全相同的相机，为什么它们会有所不同？

import cv2
import numpy as np
import os
import glob
 
 
# Define the dimensions of checkerboard
CHECKERBOARD = (15,22)
 
 
# stop the iteration when specified
# accuracy, epsilon, is reached or
# specified number of iterations are completed.
criteria = (cv2.TERM_CRITERIA_EPS +
            cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
 
 
# Vector for 3D points
threedpoints = []
 
# Vector for 2D points
twodpoints = []
 
 
#  3D points real world coordinates
objectp3d = np.zeros((1, CHECKERBOARD[0]
                      * CHECKERBOARD[1],
                      3), np.float32)
objectp3d[0, :, :2] = np.mgrid[0:CHECKERBOARD[0],
                               0:CHECKERBOARD[1]].T.reshape(-1, 2)

prev_img_shape = None
 
 
# Extracting path of individual image stored
# in a given directory. Since no path is
# specified, it will take current directory
# jpg files alone
images = glob.glob('*.jpg')
print(images)

for filename in images:
    image = cv2.imread(filename)
    grayColor = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
    # Find the chess board corners
    # If desired number of corners are
    # found in the image then ret = true
    ret, corners = cv2.findChessboardCorners(
                    grayColor, CHECKERBOARD,
                    cv2.CALIB_CB_ADAPTIVE_THRESH
                    + cv2.CALIB_CB_FAST_CHECK +
                    cv2.CALIB_CB_NORMALIZE_IMAGE)
    
    print("return: " + ret.__str__())

    # If desired number of corners can be detected then,
    # refine the pixel coordinates and display
    # them on the images of checker board
    if ret == True:
        threedpoints.append(objectp3d)
 
        # Refining pixel coordinates
        # for given 2d points. 
        corners2 = cv2.cornerSubPix(
            grayColor, corners, (11, 11), (-1, -1), criteria)
 
        twodpoints.append(corners2)
 
        # Draw and display the corners
        image = cv2.drawChessboardCorners(image,
                                          CHECKERBOARD,
                                          corners2, ret)
 
    cv2.imshow('img', image)
    cv2.waitKey(0)
 
cv2.destroyAllWindows()
 
h, w = image.shape[:2]
 
 
# Perform camera calibration by
# passing the value of above found out 3D points (threedpoints)
# and its corresponding pixel coordinates of the
# detected corners (twodpoints)
ret, matrix, distortion, r_vecs, t_vecs = cv2.calibrateCamera(
    threedpoints, twodpoints, grayColor.shape[::-1], None, None)
 
 
# Displaying required output
print(" Camera matrix:")
print(matrix)
 
print("\n Distortion coefficient:")
print(distortion)

图像1和2的失真系数分别：

 Distortion coefficient:
[[ 1.15092474e-01  2.51065895e+00  2.16077891e-03  4.76654910e-03
  -3.40419245e+01]]

 Distortion coefficient:
[[ 2.50995880e-01 -6.98047707e+00  1.14468356e-03 -1.10525114e-02
   1.43212364e+02]]