标签：stack ima pytho images port show none 函数 numpy
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
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#!/usr/bin/env python3 |
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# -*- coding: utf-8 -*- |
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""" |
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Created on Wed Oct 16 08:45:25 2019 |
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@author: hmeng |
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""" |
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import numpy as np |
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import cv2 |
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objp_dict = { |
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1: (9, 5), |
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2: (9, 6), |
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3: (9, 6), |
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4: (9, 6), |
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5: (9, 6), |
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6: (9, 6), |
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7: (9, 6), |
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8: (9, 6), |
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9: (9, 6), |
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10: (9, 6), |
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11: (9, 6), |
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12: (9, 6), |
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13: (9, 6), |
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14: (9, 6), |
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15: (9, 6), |
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16: (9, 6), |
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18: (9, 6), |
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17: (9, 6), |
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19: (9, 6), |
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20: (9, 6), |
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} |
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objp_list = [] |
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corners_list = [] |
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for k in objp_dict: |
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nx, ny = objp_dict[k] |
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# Prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) |
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objp = np.zeros((nx*ny,3), np.float32) |
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objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2) |
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# Make a list of calibration images |
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fname = ‘camera_cal/calibration%s.jpg‘ % str(k) |
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img = cv2.imread(fname) |
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# Convert to grayscale |
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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# Find the chessboard corners |
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ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None) |
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# If found, save & draw corners |
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if ret == True: |
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# Save object points and corresponding corners |
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objp_list.append(objp) |
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corners_list.append(corners) |
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# Draw and display the corners |
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#cv2.drawChessboardCorners(img, (nx, ny), corners, ret) |
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#plt.imshow(img) |
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#plt.show() |
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#print(‘Found corners for %s‘ % fname) |
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else: |
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print(‘Warning: ret = %s for %s‘ % (ret, fname)) |
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img = cv2.imread(‘camera_cal/calibration1.jpg‘) |
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img_size = (img.shape[1], img.shape[0]) |
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‘‘‘ |
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mtx : |
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dist: |
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‘‘‘ |
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objp_list, corners_list, img_size,None,None) |
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dst = cv2.undistort(img, mtx, dist, None, mtx) |
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com_img = np.hstack((img, dst)) |
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cv2.namedWindow(‘image‘, cv2.WINDOW_NORMAL) |
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cv2.imshow(‘image‘, com_img) |
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cv2.waitKey(0) |
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cv2.destroyAllWindows() |
标签：stack ima pytho images port show none 函数 numpy
原文地址：https://www.cnblogs.com/wujianming-110117/p/12821352.html
