1. Calibrate the yml code generated by the camera

2. Checkerboard download address

3. Precautions

Don't want to talk nonsense, go directly to the code

The environment is opencv-python==4.7 version, python==3.9 version, the checkerboard is 9 rows and 6 columns, and the checkerboard grid is 15mm

1. Calibrate the yml code generated by the camera

import cv2
import numpy as np
import glob
import yaml

# 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
w = 8  # 10 - 1
h = 5  # 7  - 1
# 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 * 15  # 18.1 mm

# 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pic文件夹下所有的jpg图像
images = glob.glob('./images/*.png')  # 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(200)
cv2.destroyAllWindows()
# %% 标定
print('正在计算')
print(u, v)
# 标定
ret, mtx, dist, rvecs, tvecs = \
    cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

# 写入文件
###写入文件路径###
file_path = ("./标定文件.yaml")
###写入文件路径###

# 存取标定数据
mtx_yaml = mtx.tolist()
dist_yaml = dist.tolist()
camera_u, camera_v = u, v
data = {"information": "Camera calibration parameters", "camera_matrix": mtx_yaml, "dist_coeff": dist_yaml,
        "camera_u": camera_u, "camera_v": camera_v}
with open(file_path, "w") as file:
    yaml.dump(data, file)

print("ret:", ret)
print("mtx:\n", mtx)  # 内参数矩阵
print("dist畸变值:\n", dist)  # 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
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
print('newcameramtx外参', newcameramtx)
print("写入文件名称:", file_path)

2. Checkerboard download address

The checkerboard is in A4 printable pdf format, just download it directly. The checkerboard is 9 rows and 6 columns, and the grid size is a small grid of 15mm*15mm.

https://download.csdn.net/download/sunnyrainflower/87873947

If you don't want a checkerboard of this size, you can find tools for generating checkerboards in other formats in my "3D Scanning" column.

3. Precautions

Note that the setting of the row and column data of the calibration board in the program is 1 less than that on the board

Camera + checkerboard opencv-python calibration generates yaml files

1. Calibrate the yml code generated by the camera

2. Checkerboard download address

3. Precautions

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