Calibrate fisheye lens using OpenCV — part 1

When you are using a fisheye (>160 degree field-of-view) lens, the ‘classic’ way in OpenCV to calibrate lens may not work for you. Even if you carefully follow steps in OpenCV document, you may end up with ‘undistorted’ images like these:

If you find yourself in similar situtation, read on.

Since version 3.0 OpenCV has included a package cv2.fisheye that decently handles fisheye lens calibration. However that module does not have easy-to-follow tutorials for readers who are not a mathematician.

That’s why I wrote this tutorial. My goal is to get your fisheye lens calibrated even if you don’t have any prior experience in OpenCV (or optical science for that matter).

At 30000 feet, calibrating your lens involves 2 steps.

  1. Help OpenCV find 2 parameters intrinsic to your lens. OpenCV call them K and D. You don’t really need to know what K and D are except they are numpy arrays.

Find K and D

  • Download the checkerboard pattern and print it on a paper (letter or A4 size). You also want to attach the paper to a hard, flat surface such as a piece of cardboard. The key here: straight lines need to be straight.
  • Save these pictures in a folder in JPG format.
import cv2
assert cv2.__version__[0] == '3', 'The fisheye module requires opencv version >= 3.0.0'
import numpy as np
import os
import glob
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)
_img_shape = None
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('*.jpg')for fname in images:
img = cv2.imread(fname)
if _img_shape == None:
_img_shape = img.shape[:2]
assert _img_shape == img.shape[:2], "All images must share the same size."
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chess board corners
# If found, add object points, image points (after refining them)
if ret == True:
N_OK = len(objpoints)
K = np.zeros((3, 3))
D = np.zeros((4, 1))
rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
rms, _, _, _, _ = \
print("Found " + str(N_OK) + " valid images for calibration")
print("DIM=" + str(_img_shape[::-1]))
print("K=np.array(" + str(K.tolist()) + ")")
print("D=np.array(" + str(D.tolist()) + ")")
  • Run python If everything goes smoothly the script will print out something like this:
Found 36 images for calibration
DIM=(1600, 1200)
K=np.array([[781.3524863867165, 0.0, 794.7118000552183], [0.0, 779.5071163774452, 561.3314451453386], [0.0, 0.0, 1.0]])
D=np.array([[-0.042595202508066574], [0.031307765215775184], [-0.04104704724832258], [0.015343014605793324]])

Un-distort images

Once you obtain K and D, follow the steps here to un-distort images if:

  • The images you need to undistort are the same dimension as the ones captured during calibration.

Your life will be a lot easier if this is the case. Otherwise you need to follow part 2 of the tutorial.

Create fileundistort.pywith the following python code:

# You should replace these 3 lines with the output in calibration step
def undistort(img_path): img = cv2.imread(img_path)
h,w = img.shape[:2]
map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K, DIM, cv2.CV_16SC2)
undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
cv2.imshow("undistorted", undistorted_img)
if __name__ == '__main__':
for p in sys.argv[1:]:

Now run python file_to_undistort.jpg and that’s it.


If you look closely, you may notice a problem: a significant chunk in the original image gets cropped out in the process. For instance, the orange RC car to the left side of the image only has half a wheel kept in the undistorted image. As a matter of fact about 30% of the pixels in original image get lost. Ouch!

If you want to have a way to get back those lost pixels, continue to part 2 of this tutorial.