攝像頭標定實施

一.標定流程

在opencv中提供了一組函數用于實現相機的標定，標定返回的值包括：相機內參矩陣(fx fy xc yc)、相機外參矩陣(R t)以及畸變矩陣。

標定的步驟如下：

1. 準備棋盤格，棋盤格圖片可以自行打印，以下使用107方格的棋盤格，交點則為96,棋盤格的大小1mm,即 gridsize=1

2. 拍照，拍照的原則是多角度，根據理論至少要兩種角度的拍照，實際中通常會拍20張左右；

3. 使用opencv提供的角點檢測函數findChessboardCorners找到棋盤格中的角點，并將每幅圖片的角點值存放到list中，同時將棋盤格的角點的三維坐標存放到另一個list。

4. 使用calibrateCamera函數獲取內存矩陣、畸變矩陣、旋轉矩陣以及轉移矩陣。

5.使用undistort函數將畸變的圖像進行校正并查看校正后的圖片效果。

二．README.md：

calibrate camera 相機校正，使用opencv自帶的函數庫，計算出如下幾個參數。

內參矩陣： 3*3

In [22]: mtx

Out[22]:

array([[1.16022336e+03, 0.00000000e+00, 6.68285471e+02],

[0.00000000e+00, 1.15738493e+03, 3.89459697e+02],

[0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])

畸變矩陣： 1*5

In [25]: dist

Out[25]: array([[-0.25129777, 0.02823272, -0.00053603, 0.00037274,
-0.08995589]])

旋轉矩陣:

In [27]: rvecs

Out[27]: 18*(3*1)

[array([[ 0.03558055], [-0.03112721], [-0.00755535]]),

array([[ 0.63788424], [-0.04903354], [ 0.01762295]]),

array([[-0.44908256], [-0.06512295], [-0.01916963]]),

array([[ 0.01780734], [ 0.0209126 ], [-0.00558506]]),

array([[0.02198169], [0.6367404 ], [0.00977959]]),

array([[ 0.03046199], [-0.7040381 ], [-0.01932221]]),

array([[-0.19237824], [-0.75952006], [ 0.1201012 ]]),

array([[ 0.51440228], [-0.2194547 ], [ 0.02910641]]),

array([[0.03761499], [0.45929723], [0.00663988]]),

array([[0.03691831], [0.64815823], [0.01041448]]),

array([[-0.3272451 ], [ 0.65900314], [-0.41478724]]),

array([[ 0.05770817], [-0.51997066], [-0.00538347]]),

array([[-0.01886995], [-0.48934854], [ 0.01885913]]),

array([[ 0.04012555], [-0.46639335], [-0.05743551]]),

array([[ 0.18608573], [-0.05068572], [-0.00117477]]),

array([[ 0.22181091], [-0.06412907], [ 0.0115335 ]]),

array([[0.0882598 ], [0.38487441], [0.05529661]]),

array([[-0.01748482], [ 0.38362373], [-0.00271536]])]

平移向量: 18*(3*1)

In [33]: tvecs

Out[33]:

[array([[-4.21904478], [-2.32362579], [ 8.49747635]]),

array([[-3.81963279], [-1.62195346], [ 7.98860175]]),

array([[-4.39150219], [-3.07999134], [10.75041784]]),

array([[-4.94259067], [-3.93663095], [30.57685167]]),

array([[-9.62311687], [-3.36509195], [32.2423649 ]]),

array([[ 0.73000489], [-2.96584094], [19.6837078 ]]),

array([[-0.85448692], [-4.63545431], [21.80683115]]),

array([[-2.09590548], [-0.77674132], [19.65246328]]),

array([[-16.99384696],[ -3.57759924],[ 32.14998811]]),

array([[-0.19382096], [-3.52948313], [21.95184873]]),

array([[-6.04109212], [-1.6349801 ], [26.75950346]]),

array([[ 5.40125149], [-4.50757377], [20.8880559 ]]),

array([[ 4.51225567], [-1.52138071], [20.08076553]]),

array([[ 4.91858056], [-5.1101675 ], [19.89170706]]),

array([[-3.63023758], [-4.17313449], [17.87154811]]),

array([[-3.96462648], [-1.36057071], [17.1048456 ]]),

array([[-13.02067108],[ -5.65276501],[ 23.81054552]]),

array([[-13.4309631 ],[ -0.55047404],[ 24.62701854]])]

通過計算后的參數生成未畸變的圖片image.jpg

三．calibrate.py

#!/usr/bin/env python3

-- coding: utf-8 --

“”"

Created on Wed Oct 16 08:45:25 2019

@author: hmeng

“”"

import numpy as np

import cv2

objp_dict = {

1: (9, 5),

2: (9, 6),

3: (9, 6),

4: (9, 6),

5: (9, 6),

6: (9, 6),

7: (9, 6),

8: (9, 6),

9: (9, 6),

10: (9, 6),

11: (9, 6),

12: (9, 6),

13: (9, 6),

14: (9, 6),

15: (9, 6),

16: (9, 6),

18: (9, 6),

17: (9, 6),

19: (9, 6),

20: (9, 6),

}

objp_list = []

corners_list = []

for k in objp_dict:

nx, ny = objp_dict[k]

Prepare object points, like (0,0,0), (1,0,0), (2,0,0) …,(6,5,0)

objp = np.zeros((nx*ny,3), np.float32)

objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)

Make a list of calibration images

fname = ‘camera_cal/calibration%s.jpg’ % str(k)

img = cv2.imread(fname)

Convert to grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Find the chessboard corners

ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

If found, save & draw corners

if ret == True:

Save object points and corresponding corners

objp_list.append(objp)

corners_list.append(corners)

Draw and display the corners

#cv2.drawChessboardCorners(img, (nx, ny), corners, ret)

#plt.imshow(img)

#plt.show()

#print(‘Found corners for %s’ % fname)

else:

print(‘Warning: ret = %s for %s’ % (ret, fname))

img = cv2.imread(‘camera_cal/calibration1.jpg’)

img_size = (img.shape[1], img.shape[0])

‘’’

mtx :

dist:

‘’’

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objp_list, corners_list,
img_size,None,None)

dst = cv2.undistort(img, mtx, dist, None, mtx)

com_img = np.hstack((img, dst))

cv2.namedWindow(‘image’, cv2.WINDOW_NORMAL)

cv2.imshow(‘image’, com_img)

cv2.waitKey(0)

cv2.destroyAllWindows()

總結

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