fisheye_calibration.py

import cv2
import numpy as np
import glob
 
 
def get_K_and_D(checkerboard, imgsPath):
 
    CHECKERBOARD = checkerboard
    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 = []
    imgpoints = []
    print(imgsPath)
    images = glob.glob(imgsPath + '/*.jpg')
    print(images)
    for fname in images:
        print(fname)
        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)
        ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
        if ret == True:
            objpoints.append(objp)
            cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
            imgpoints.append(corners)
    print("==========================")
    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))
    DIM = _img_shape[::-1]
    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()) + ")")
    return DIM, K, D
 
 
def undistort(img_path,K,D,DIM,scale=0.6,imshow=False):
    img = cv2.imread(img_path)
    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 dim1[0]!=DIM[0]:
        img = cv2.resize(img,DIM,interpolation=cv2.INTER_AREA)
    Knew = K.copy()
    if scale:#change fov
        Knew[(0,1), (0,1)] = scale * Knew[(0,1), (0,1)]
    map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), Knew, DIM, cv2.CV_16SC2)
    undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
    if imshow:
        cv2.imshow("undistorted", undistorted_img)
    return undistorted_img
 
if __name__ == '__main__':
 
   #(1)开始使用图片来获取内参和畸变系数
    DIM, K, D = get_K_and_D((6,9), 'picture')
 
   #(2)利用得到内参和畸变系数畸变矫正图片进行测试
    # DIM=(1600, 1200)
    # K=np.array([[380.30264530555115, 0.0, 835.7633233422137], [0.0, 380.35608246363023, 648.7089226599809], [0.0, 0.0, 1.0]])
    # D=np.array([[-0.03261537438126976], [0.011064666775933927], [-0.02408476631916144], [0.014821224544658729]])
    # img = undistort('./picture/1.jpg',K,D,DIM)
    # cv2.imwrite('./picture/pig_checkerboard.jpg', img)