摄像头标定实施
csdmeb 2020-05-03
摄像头标定实施
一.标定流程
在opencv中提供了一组函数用于实现相机的标定，标定返回的值包括：相机内参矩阵(fx fy xc yc)、相机外参矩阵(R t)以及畸变矩阵。
标定的步骤如下：
1. 准备棋盘格，棋盘格图片可以自行打印，以下使用10*7方格的棋盘格，交点则为9*6,棋盘格的大小1mm,即 gridsize=1
2. 拍照，拍照的原则是多角度，根据理论至少要两种角度的拍照，实际中通常会拍20张左右；
3. 使用opencv提供的角点检测函数findChessboardCorners找到棋盘格中的角点，并将每幅图片的角点值存放到list中，同时将棋盘格的角点的三维坐标存放到另一个list。
4. 使用calibrateCamera函数获取内存矩阵、畸变矩阵、旋转矩阵以及转移矩阵。
5.使用undistort函数将畸变的图像进行校正并查看校正后的图片效果。
二．README.md：
calibrate camera 相机校正，使用opencv自带的函数库，计算出如下几个参数。
内参矩阵： 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]])
畸变矩阵： 1*5
In [25]: dist
Out[25]: array([[-0.25129777, 0.02823272, -0.00053603, 0.00037274,-0.08995589]])
旋转矩阵:
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]])]
通过计算后的参数生成未畸变的图片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() |
