使用OpenCV进行RaspiCam鱼眼校准 [英] RaspiCam fisheye calibration with OpenCV

问题描述

我正在尝试使用OpenCV校准RaspiCam鱼眼镜头相机.我正在使用Python示例代码，并且棋盘行和列号也正确，但是以某种方式我无法获得成功的结果.我已经在下面测试了很多照片，您可以看到它们.我的源代码: https://github.com/jagracar/OpenCV-python-tests/blob/master/OpenCV-tutorials/cameraCalibration/cameraCalibration.py

I am trying to calibrate RaspiCam Fisheye lens camera with OpenCV. I am using Python example code and the cheesboard row and column numbers are also correct but somehow I can not get a successful result. I have tested with a lso much of photos below you can see them. My source code: https://github.com/jagracar/OpenCV-python-tests/blob/master/OpenCV-tutorials/cameraCalibration/cameraCalibration.py

我的国际象棋棋盘的行和列:行= 9，列= 6

my chess board rows and columns: rows = 9, cols = 6

但没有获得成功的结果

我的解决方案

https://gist.github.com/mesutpiskin/0412c44bae399adf1f48007f22bdd22d

推荐答案

从opencv 3开始，引入了fisheye模块，该模块很好地管理了鱼眼镜头镜片的校准. (至少对于那些不熟悉校准过程背后的数学的人.)

Starting with opencv 3, the fisheye module was introduced, which manages the calibration for fisheye type lenses quite well. (At least for those who are not familiar with the mathematics behind the calibration process.)

# Checkboard dimensions
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)
img_shape = img.shape[:2]

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)
###

# calculate K & D
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))

现在您有了 K 和 D ，您可以取消失真:

And now that you have K and D, you can undistort:

img = # your image to undistort
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)

这应该有效！

更新

如果要查看图像的隐藏部分(例如，上图中黄色框之外的部分)，请在校准后进行以下操作:

If you want to see the hidden parts of the image (for example the portion outside the yellow box in the above image), after the calibration, you need this:

img = cv2.imread(img_path)
img_dim = img.shape[:2][::-1]  

DIM = # dimension of the images used for calibration

scaled_K = K * img_dim[0] / DIM[0]  
scaled_K[2][2] = 1.0  
new_K = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(scaled_K, D,
    img_dim, np.eye(3), balance=balance)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(scaled_K, D, np.eye(3),
    new_K, img_dim, cv2.CV_16SC2)
undist_image = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR,
    borderMode=cv2.BORDER_CONSTANT)

现在，通过更改balance值，您应该减小或增大最终图像的大小(与上图相比，实际上是黄色矩形).

Now, by varying the balance value you should decrease or increase the size of the final immage (compared to the image above, practically the yellow rectangle).

通过OpenCV API: balance:在最小焦距和最大焦距之间的范围内设置新的焦距.余额在[0，1]范围内.

From OpenCV API: balance: Sets the new focal length in range between the min focal length and the max focal length. Balance is in range of [0, 1].

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