OpenCV 3.4相机标定出现异常主点问题求助

Hey there, let's work through why your calibration results (especially that wonky focal length) are off, starting with those 4 failed chessboard detections—bad input data is almost always the culprit here.

First: Fix the Failed findChessboardCorners Detections #

Those 4 images where corners weren't detected are a critical issue. Even if you skip them, the remaining 24 might not be enough if they're poorly distributed. Let's address this first:

• Check the problematic images: Are the chessboards fully in frame? Blurry? Low contrast? If they're out of focus or cut off, toss them and replace with 4 new shots that cover different angles, distances, and positions (tilted, close-up, far away, edge of the frame).
• Tweak detection parameters: Modify the findChessboardCorners call in your calibration.cpp to handle tricky lighting or low contrast:

  bool found = findChessboardCorners(gray, boardSize, corners,
      CALIB_CB_ADAPTIVE_THRESH | CALIB_CB_NORMALIZE_IMAGE | CALIB_CB_FAST_CHECK);
  

  These flags help the algorithm adapt to varying lighting conditions and speed up checks.
• Manual fallback: If auto-detection still fails, you can manually mark corners (tedious, but doable with OpenCV's drawing tools) or use higher-res versions of those images if available.

Double-Check Chessboard & Input Configuration #

It's easy to mix up these parameters, which directly skews focal length calculations:

• Inner corner count vs square count: You mentioned a 9×6 chessboard (squares). Remember, boardSize in the code should be the number of inner corners, not squares. So 9×6 squares mean Size(8,5) inner corners. If you used Size(9,6) by mistake, the 3.45mm square size will be scaled incorrectly, leading to totally wrong focal length values.
• Square size consistency: Make sure the squareSize variable is set correctly (3.45 if using mm, 0.00345 if using meters—just be consistent; calibration uses relative units, but mixing units breaks scaling).
• Validate imgList.xml: Ensure all 28 image paths are correct, no duplicates, and no missing files. A broken path can silently throw off the calibration dataset.

Improve Your Calibration Image Coverage #

Even with 24 valid images, if they're all taken from the same angle/distance, calibration will be unstable:

• Make sure your shots cover:
  • Close and far distances from the camera
  • Left, right, top, and bottom edges of the frame
  • Different tilts (slight angles, portrait/landscape orientations)
• Avoid clustering most images in the center of the frame—this leads to poor estimation of focal length and distortion parameters.

Adjust Calibration Flags #

The default calibrateCamera flags might not be optimal for your setup. Try these to get more stable results:

double rms = calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs, tvecs,
    CALIB_FIX_K4 | CALIB_FIX_K5);

Most consumer cameras don't need higher-order distortion terms (k4, k5), so fixing them reduces noise in the calibration output. If you suspect significant lens distortion, you can add CALIB_USE_INTRINSIC_GUESS only if you have a rough, reliable estimate of the focal length.

Validate the Output #

• Check the RMS reprojection error: The value returned by calibrateCamera should ideally be below 1 pixel. If it's above 2, your calibration is unreliable. A high RMS paired with abnormal focal length confirms bad input data or misconfigured parameters.
• Inspect the camera matrix: Open camera.yml and look at the focal length values (fx, fy). For a typical webcam, these are usually in the range of 500-1500 (if using pixel units). If yours is way outside this range, go back and double-check the board size and square size parameters.

Once you fix the detection issues, verify your configuration, and ensure your image set covers the full field of view, your calibration results should start making sense. If you still hit problems, share the camera matrix from camera.yml and the RMS error—those details will help narrow things down further.

内容的提问来源于stack exchange，提问作者Remus Pop