我使用本教程link成功校准了我的相机作为参考 . 这是代码：

CHESSBOARD_SIZE = (9,6)

img_dir_left = "/home/dir_left"

img_dir_right = "/home/dir_right"

criteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)

calib_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW

objp = np.zeros((1, CHESSBOARD_SIZE[0]*CHESSBOARD_SIZE[1], 3), np.float32)

objp[0,:,:2] = np.mgrid[0:CHESSBOARD_SIZE[0], 0:CHESSBOARD_SIZE[1]].T.reshape(-1, 2)

def calculate(imgDir):

imgPath = glob.glob(imgDir+'/*jpg')

img_size = None

objpoints = []

imgpoints = []

for image in sorted(imgPath):

img = cv2.imread(image)

if img_size == None:

img_size = img.shape[:2]

else:

assert img_size == img.shape[:2], "All images must share the same size."

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

ret, corners = cv2.findChessboardCorners(gray, CHESSBOARD_SIZE, cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)

if ret:

objpoints.append(objp)

cornersM = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)

imgpoints.append(cornersM)

cv2.drawChessboardCorners(img, CHESSBOARD_SIZE, cornersM, ret)

cv2.imshow(imgDir, img)

cv2.waitKey(1)

return objpoints, imgpoints, img_size, gray

def calibrate(objpoints, imgpoints, img_size, gray):

img_number = len(objpoints) #number of the pictures

K = np.zeros((3, 3))

D = np.zeros((4, 1))

rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(img_number)]

tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(img_number)]

print("rvecs", rvecs)

rms, _, _, _, _ = cv2.fisheye.calibrate(objpoints,imgpoints,gray.shape[::-1],K,D,rvecs,tvecs,calib_flags,

(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6))

print("RMS", rms)

print("Found " + str(img_number) + " valid images for calibration")

print("DIM=" + str(img_size[::-1]))

print("K=np.array(" + str(K.tolist()) + ")", )

print("D=np.array(" + str(D.tolist()) + ")\n", )

dimension = img_size[::-1]

k = K.tolist()

d = D.tolist()

return dimension , k , d

我同时拍摄了13张图像 . 图像从原始10位状态转换为8位 . 摄像机安装在7厘米基线的同一线路上 . 我的RMS_left是0.509，RMS_right是0.341 .

这是我用来从相机中取消分配帧的功能 .

def undistorted(frame, data):

data_in = data

img = frame

K, D, DIM = data_in['K'], data_in['D'], data_in['DIM']

K = np.array(K)

D = np.array(D)

map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), K, DIM, cv2.CV_16SC2)#cv2.CV_16SC2

undistorted_frame = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_TRANSPARENT, borderValue=29)

return undistorted_frame

当我尝试进行鱼眼立体声校准时，我得到了与此github link中描述的相同的错误 . 但是所有错误都有一个解决方法 .

K_left = np.zeros((3, 3))

D_left = np.zeros((4, 1))

K_right = np.zeros((3, 3))

D_right = np.zeros((4, 1))

R = np.zeros((1, 1, 3), dtype=np.float64)

T = np.zeros((1, 1, 3), dtype=np.float64)

N_OK = len(imgpoints_left)

objp = np.zeros( (CHECKERBOARD[0]*CHECKERBOARD[1], 1, 3) , np.float64)

objp[:,0, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)

objpoints = np.array([objp]*len(imgpoints_left),dtype=np.float64)

imgpoints_left = np.asarray(imgpoints_left, dtype=np.float64)

imgpoints_right = np.asarray(imgpoints_right, dtype=np.float64)

objpoints = np.reshape(objpoints, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 3))

imgpoints_left = np.reshape(imgpoints_left, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2))

imgpoints_right = np.reshape(imgpoints_right, (N_OK, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2))

(rms, K_left, D_left, K_right, D_right, R, T) = \

cv2.fisheye.stereoCalibrate(

objpoints,

imgpoints_left,

imgpoints_right,

K_left,

D_left,

K_right,

D_right,

img_size,

R,

T,

calibration_flags

)

有人可以帮我澄清这个解决方法，我是OpenCV的新手，可以完全理解它 .