Problem to remove distortion (after camera-calibration) from image

Question

The image is a single frame of a 90-minute video.

If one draws a line from the top left corner (see, left-distorted) to the right (see, right-distorted), the distortion is visible by looking at the top-line (see, middle-distorted).

The white line between the points to the left and the right should be straight but has a slight U-shape compared to the straight line in red.

Performing a camera calibration with open-cv (reference) using a sample of entire frames from calibration videos (for some examples see, indoor, outdoor-1, and outdoor-2) results in this undistorted image.

Here, the line from the top left corner (see, left-undistorted) to the right (see, right-undistorted) overcorrects the distortion as the difference between the red-line and the white has become an inverse U shape (see, middle-undistorted).

I took the recording with an iPhone 11, and I am using python 3.8.8 and open-cv 4.5.3.

I followed the advice I could find at StackOverflow (and through the most popular search results), but using any checkerboard (variation of size, camera angle, distance, and setting) does not correct distortion correction. I fail to understand why.

UPDATE:

Based on below conversation the calibration footage needs to

have a focus, similar to the reference scenario (in my case, objects are far away),
the board should cover a minimum of say 20% of the image.

Here the video that I used (focus is fixed on objects being far away) to extract a sample of 50 frames to calculate the camera calibration:

SQUARE_SIZE = 0.012
CHECKERBOARD_WIDTH = 22
CHECKERBOARD_HEIGHT = 15

objpoints = []
imgpoints = []

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
val = cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE
SAMPLE_SIZE = 50

objp = np.zeros((CHECKERBOARD_HEIGHT * CHECKERBOARD_WIDTH, 3), np.float32)
objp[:, :2] = np.mgrid[0:CHECKERBOARD_WIDTH, 0:CHECKERBOARD_HEIGHT.T.reshape(-1, 2)
objp = objp * SQUARE_SIZE

# for loop going over the sample of images
img = cv2.imread('path to image')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (CHECKERBOARD_WIDTH[i], CHECKERBOARD_HEIGHT[i]), val)
if ret == True:
    objpoints.append(objp)
    # refining pixel coordinates for given 2d points.
    corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)
    imgpoints.append(corners2)

# Calculate camera calibration
img = cv2.imread('path to reference image')
h,w = img.shape[:2]
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints,
                                                   imgpoints,
                                                   (w,h),
                                                   None,
                                                   None)                                          

newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))

# Undistort
mapx,mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (w,h), 5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
cv2.imwrite('path to output image', dst)

The result is unfortunately not correct as shown by the deviation of the red and white line at the top of the pitch.

Problem to remove distortion (after camera-calibration) from image

0 Answers