Fisheye Camera Calibration with OpenCV

calibration.py

import numpy as np
import cv2
import glob

# Define the chess board rows and columns
CHECKERBOARD = (6,9)
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
for path in glob.glob('datasets/*.png'):
    # 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:
        objpoints.append(objp)
        cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
        imgpoints.append(corners)
        #cv2.drawChessboardCorners(img, (rows, cols), corners, ret)
    print(str(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))

img =cv2.imread("datasets/1.png")
map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K, (340,240), cv2.CV_16SC2)
undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)

cv2.imshow('Original Image', img)
cv2.imshow('Undistort Image', undistorted_img)
cv2.waitKey(0)

what is intrinsic matrix

The intrinsic matrix, also known as the camera matrix, is a 3x3 matrix that contains the parameters of a camera's internal characteristics. It defines the relationship between the 3D world coordinates and the 2D image coordinates. The intrinsic matrix includes the focal length of the camera, the image sensor's pixel size, the principal point (the optical center of the lens), and other camera calibration parameters. The intrinsic matrix is typically computed during the camera calibration process and is used to correct the distortion in the images captured by the camera.

Hi,
Thanks for sharing. May I ask about your error (RMS) and how many images you use? Do you have any hints on how to get pictures (including tilting, different rotations angles etc.)?
Regards,
Ruben

Hi there,

Just for curiosity, is there any reason you are not using cv2.fisheye.undistortImage() rather than the combination of initUndistortRectifyMap and remap?