我正在使用鱼眼镜头相机,我想使用OpenCV对其进行校准并校正其镜筒失真。但是我一直遵循这种方法,但是会引发错误。
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.
### read images and for each image:
img = cv2.imread(fname)
print(fname)
img_shape = img.shape[:2]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2_imshow(gray)
# 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)
print(ret,corners)
# 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)
###
print(objpoints,imgpoints)
# calculate K & D
N_imm = len(objpoints)
print(N_imm) # 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)]
retval, K, D, rvecs, tvecs = 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))
error Traceback (most recent call last) in () 41 tvecs, 42 calibration_flags, ---> 43 (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)) error: OpenCV(4.1.2) /io/opencv/modules/calib3d/src/fisheye.cpp:713: error: (-215:Assertion failed) !objectPoints.empty() && !imagePoints.empty() && objectPoints.total() == imagePoints.total() in function 'calibrate'
请问有人有答案吗?预先谢谢你
答案 0 :(得分:0)
读取图像(assert img is not None
)之后和findChessboardCorners(assert ret is True
)结果之后,应该在代码中添加断言。换句话说,请确保已读取图像并找到了棋盘。