前言
- 文章主要给出利用棋盘格标定的代码， python 与 c 标定代码都会给出。
- 普通相机与鱼眼相机标定代码细微之处有所不同，普通相机与鱼眼相机标定代码本文都会还会。
代码
python 代码
普通相机
代码需要自行修改的地方
- 棋盘格长、宽内角点数目
- 棋盘格图片文件夹路径
import cv2
import numpy as np
import glob
# 找棋盘格角点
# 设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
criteria = (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # 阈值
# 棋盘格模板规格 内角点数目
w = 11
h = 8
# 世界坐标系中的棋盘格点,例如(0,0,0), (1,0,0), (2,0,0) ....,(8,5,0)，去掉Z坐标，记为二维矩阵
objp = np.zeros((w * h, 3), np.float32)
objp[:, :2] = np.mgrid[0:w, 0:h].T.reshape(-1, 2)
objp = objp * 35 # 35mm
# 储存棋盘格角点的世界坐标和图像坐标对
objpoints = [] # 在世界坐标系中的三维点
imgpoints = [] # 在图像平面的二维点
# 加载文件夹下所有的jpg图像
images = glob.glob('F:/aproject/camera_car/head_camera/useful/*.jpg') # 拍摄的十几张棋盘图片所在目录
i = 0
for fname in images:
img = cv2.imread(fname)
# 获取画面中心点
# 获取图像的长宽
h1, w1 = img.shape[0], img.shape[1]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
u, v = img.shape[:2]
# 找到棋盘格角点
ret, corners = cv2.findChessboardCorners(gray, (w, h), None)
# 如果找到足够点对，将其存储起来
if ret == True:
print("i:", i)
i = i 1
# 在原角点的基础上寻找亚像素角点
cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
# 追加进入世界三维点和平面二维点中
objpoints.append(objp)
imgpoints.append(corners)
# 将角点在图像上显示
cv2.drawChessboardCorners(img, (w, h), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners', img)
cv2.waitKey(100)
else:
print("标定失败", fname)
cv2.destroyAllWindows()
print('正在计算')
# 标定
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("ret标定误差:", ret) # 标定误差 0.1-0.5
print("mtx内参数矩阵:n", mtx.reshape(-1).tolist()) # 内参数矩阵
print("dist畸变值:n", dist.reshape(-1).tolist()) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs旋转（向量）外参:n", rvecs) # 旋转向量 # 外参数
print("tvecs平移（向量）外参:n", tvecs) # 平移向量 # 外参数鱼眼相机
代码语言：javascript复制import numpy as np
import cv2
import glob
# Define the chess board rows and columns
CHECKERBOARD = (11, 8)
subpix_criteria = (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC cv2.fisheye.CALIB_CHECK_COND cv2.fisheye.CALIB_FIX_SKEW
objp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
objp[0, :, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
counter = 0
images = glob.glob('F:\useful_image\*.jpg') # 拍摄的十几张棋盘图片所在目录
i = 0
for path in images:
# Load the image and convert it to gray scale
img = cv2.imread(path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,
cv2.CALIB_CB_ADAPTIVE_THRESH cv2.CALIB_CB_FAST_CHECK cv2.CALIB_CB_NORMALIZE_IMAGE)
# Make sure the chess board pattern was found in the image
if ret:
i = 1
print(i, path)
objpoints.append(objp)
cv2.cornerSubPix(gray, corners, (3, 3), (-1, -1), subpix_criteria)
imgpoints.append(corners)
cv2.drawChessboardCorners(img, (11, 8), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners', img)
cv2.waitKey(200)
else:
print("!!!!!", path)
counter = 1
N_imm = counter # number of calibration images
K = np.zeros((3, 3))
D = np.zeros((4, 1))
rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_imm)]
tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_imm)]
rms, _, _, _, _ = cv2.fisheye.calibrate(objpoints, imgpoints, gray.shape[::-1], K, D, rvecs, tvecs, calibration_flags,
(cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6))
print(K.reshape(-1).tolist(), "n", D.reshape(-1).tolist(), "n", rms)c 代码
普通相机
代码语言：javascript复制#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int main()
{
// 1. 准备标定棋盘图像
int boardWidth = 11; // 棋盘格横向内角点数量
int boardHeight = 8; // 棋盘格纵向内角点数量
float squareSize = 1.f; // 棋盘格格子的大小，单位为米,随便设置，不影响相机内参计算
Size boardSize(boardWidth, boardHeight);
vector<vector<Point3f>> objectPoints;
vector<vector<Point2f>> imagePoints;
vector<Point2f> corners;
// 2. 拍摄棋盘图像
Mat image, gray;
namedWindow("image", WINDOW_NORMAL);
vector<String> fileNames;
glob("image3/*.jpg", fileNames);
for (size_t i = 0; i < fileNames.size(); i )
{
image = imread(fileNames[i], IMREAD_COLOR);
cvtColor(image, gray, COLOR_BGR2GRAY);
// 3. 读入图像数据，并提取角点
bool found = findChessboardCorners(image, boardSize, corners, CALIB_CB_ADAPTIVE_THRESH CALIB_CB_NORMALIZE_IMAGE CALIB_CB_FAST_CHECK);
if (found)
{
cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS TermCriteria::COUNT, 30, 0.1));
drawChessboardCorners(image, boardSize, corners, found);
imshow("image", image);
waitKey();
vector<Point3f> objectCorners;
for (int j = 0; j < boardHeight; j )
{
for (int k = 0; k < boardWidth; k )
{
objectCorners.push_back(Point3f(k * squareSize, j * squareSize, 0));
}
}
objectPoints.push_back(objectCorners);
imagePoints.push_back(corners);
}
}
// 4. 标定相机
Mat cameraMatrix, distCoeffs;
vector<Mat> rvecs, tvecs;
calibrateCamera(objectPoints, imagePoints, image.size(), cameraMatrix, distCoeffs, rvecs, tvecs);
cout << "Camera matrix:" << endl << cameraMatrix << endl;
cout << "Distortion coefficients:" << endl << distCoeffs << endl;
return 0;
}校正
- 标定获得了相机内参与畸变系数，我们可以对畸变图片就行校正。关于校正代码，下一篇博客再分享。
