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OpenCV fisheye calibration and undistortion
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 17 19:59:33 2018
@author: mesut
"""
import yaml
import cv2
assert cv2.__version__[0] == '3', 'The fisheye module requires opencv version >= 3.0.0'
import numpy as np
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('images/*.png')
for fname in images:
img = cv2.imread(fname)
if _img_shape == None:
_img_shape = img.shape[:2]
else:
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
ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
imgpoints.append(corners)
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, _, _, _, _ = \
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("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()) + ")")
DIM=_img_shape[::-1]
balance=1
dim2=None
dim3=None
img = cv2.imread("db/2.png")
dim1 = img.shape[:2][::-1] #dim1 is the dimension of input image to un-distort
assert dim1[0]/dim1[1] == DIM[0]/DIM[1], "Image to undistort needs to have same aspect ratio as the ones used in calibration"
if not dim2:
dim2 = dim1
if not dim3:
dim3 = dim1
scaled_K = K * dim1[0] / DIM[0] # The values of K is to scale with image dimension.
scaled_K[2][2] = 1.0 # Except that K[2][2] is always 1.0
# This is how scaled_K, dim2 and balance are used to determine the final K used to un-distort image. OpenCV document failed to make this clear!
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D, dim2, np.eye(3), balance=balance)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3), new_K, dim3, cv2.CV_16SC2)
undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
data = {'dim1': dim1,
'dim2':dim2,
'dim3': dim3,
'K': np.asarray(K).tolist(),
'D':np.asarray(D).tolist(),
'new_K':np.asarray(new_K).tolist(),
'scaled_K':np.asarray(scaled_K).tolist(),
'balance':balance}
import json
with open("fisheye_calibration_data.json", "w") as f:
json.dump(data, f)
cv2.imshow("undistorted", undistorted_img)
img2 = cv2.imread("2.png")
cv2.imshow("none undistorted", img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
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