opencv-python 实现鱼眼矫正 棋盘矫正法
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转载自：https://blog.csdn.net/hpuhjl/article/details/80899931
鱼眼矫正有很多的方法， 比较常用的有：
- 棋盘标定法
- 经纬度法
opencv自带鱼眼矫正算法， 也就是第一种， 棋盘矫正法。
第一步：制作棋盘格
用A4纸打印一张棋盘格， 固定到硬纸板上， 然后用鱼眼镜头对着拍摄。 保留拍到的图片， 如下图所示：
可以从不同角度拍摄， 多保存一些。
第二步： 计算内参和矫正系数
棋盘标定法， 必须要先计算出鱼眼的内参和矫正系数， 可直接调用以下函数计算
import cv2
assert cv2.__version__[0] == '3'
import numpy as np
import os
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 = []
images = glob.glob(imgsPath + '/*.png')
for fname in images:
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)
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 # 计算内参和矫正系数
'''
# checkerboard： 棋盘格的格点数目
# imgsPath: 存放鱼眼图片的路径
'''
get_K_and_D((6,9), 'fisheyeImage')
第三步： 根据计算的K和D， 矫正鱼眼图
保存第二步计算的DIM， K， D， 利用一下程序， 矫正鱼眼图。
def undistort(img_path):
img = cv2.imread(img_path)
img = cv2.resize(img, DIM)
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)
cv2.imwrite('unfisheyeImage.png', undistorted_img)
DIM， K， D是固定不变的，因此，map1和map2也是不变的， 当你有大量的数据需要矫正时， 应当避免map1和map2的重复计算， 只需要计算remap即可。
矫正效果
后续
矫正结束， 对于上面的矫正结果， 应该适用于很多场景了， 但是矫正之后， 很多有效区域被截掉了， 能否矫正出更大的有效面积呢？ 答案当然是有的， 具体的细节， 去我的github上看项目里面的pdf吧， 这里不再介绍了。
github： https://github.com/HLearning/fisheye
def undistort(img_path,K,D,DIM,scale=0.5,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
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