Hi @D-Alex,

as always, thanks for your great work in this field!

Just one question: So what i do is that i typically take images from multiple chessboard poses with a fixed chessboard size. In your images from the blog you use a large chessboard patterns and find a "dynamically" sized chessboard. How do you handle that? Dont you have to give chessboards with the same dimensions into the calibration routine? Or can you just give every chessboard its own dynamically adjusted 3D corrdinates?

Also: i see that you said that blurred images should be filtered out and you added a function estimateChessboardSharpness to estimate the sharpness for each board. Doesnt it make sense to also provide a funtion that returns the angle to the camera axis, so that the ones orthogonal are automatically removed?

Regarding "fixing couple of issues preventing a detection of the board": would you mind explaining which cases have been improved?

@tompollok for a mono camera, you can use a different coordinate system for each chessboard and feed different sized chessboards to the calibration routine as long as the object z-coordinates are zero and all boards are rectangular.

However, in the case of a stereo system, you need the same coordinate system on the target, which is solved by adding little circles to mark the origin of the board visible in all calibration images.

Concerning the orthogonal case. Little noise can completely change the orientation of the board. This is visible by a jumping overlayed pose indicator. They can only be removed after calibration because the calibration process itself will determine the pose. Usually, I try to avoid them by using a fixed target in V-configuration. The calibration process is as follows:

For each of the two poses:

• find first board and mask it
• find second board
• calculate sharpness and brightness
• discard image if unsharp or overexposed (tell the user to adjust camera)

Concerning the detection rate:

• The algorithm works by calculating a feature map with a high response value at locations where possible chessboard corners are.
• The best points are stored in a kd-tree
• Based on these points, the algorithm tries to grow multiple chessboards using cross-ratio constraints (the largest one wins).
• Here several issues were solved, leading to a wrong search area in the image for the next row/col of the chessboard preventing the growth of the chessboard under challenging scenarios such as high perspective distortion.

It looks like "Compare ABI dumps" failed because findChessboardSB has now an additional optional parameter for forwarding the actual board size in the case a larger board was found including some meta data.

What would be the best approach for fixing this and allowing an existing method to have a new optional parameter?

The required additional meta data for testing estimateChessboardSharpness:
opencv/opencv_extra#713

The test is performed for chessboards found by findChessboardCorners and findChessboardCornersSB. In the case the sharpness is unknown for a given image, the test is bypassed for the particular image.

@D-Alex Thank you for the contribution!

Do you have a chance to look on current tests failures?
On next week we are going to start preparations for the next releases, so OpenCV repo will be moved in "code freeze" stage in 2 weeks (for 1-2 weeks until final release).

@alalek - I should be able to fix all suggestions over this weekend.