<p>{1}介绍了一个很好的使用cd3型光学镜片进行校准的模块。(至少对于那些不熟悉校准过程背后的数学原理的人来说。)</p>
<pre><code># 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))
</code></pre>
<p>现在你有了<strong>K</strong>和<strong>D</strong>,你可以不失真:</p>
^{pr2}$
<p>这应该行得通!在</p>
<p><strong>更新</strong><a href="https://i.stack.imgur.com/tcnYe.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tcnYe.png" alt="enter image description here"/></a></p>
<p>如果要查看图像的隐藏部分(例如上图中黄色框外的部分),则在校准后,您需要:</p>
<pre><code>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)
</code></pre>
<p>现在,通过改变<code>balance</code>值,您应该减小或增加最终immage的大小(与上面的图像相比,实际上是黄色矩形)。在</p>
<p>来自OpenCV API:
<code>balance</code>:设置最小焦距和最大焦距之间的新焦距。余额在[0,1]范围内。在</p>