• 摄像头标定实施


    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()

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  • 原文地址:https://www.cnblogs.com/wujianming-110117/p/12821352.html
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