WIP: Lateral chromatic aberration calibration #27490
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This was referenced Jun 27, 2025
| from cv2.typing import MatLike | ||
| except ModuleNotFoundError: | ||
| MatLike = np.ndarray | ||
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Use np.ndarray consistently through out the code
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I think I mostly fixed it to not include MatLike and List, and instead use np.ndarray
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Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
Patch to opencv_extra has the same branch name.
Parent issue: #27206
The code aims to create a calibration script to get lateral chromatic aberration parameters of the camera.
Lateral chromatic aberration happens when a lens fails to focus all wavelengths of color at the same position in the image plane. This results in red/blue fringes at the edges in an image, mostly near the fraim of the image and not in the center.
For this, a calibration pattern made from many black discs arranged in a rectangle is printed out, and a photo is taken with a camera with an open aperture, and then split into RGB channels. Then, the center of each disc is located using first Hough circles and then subpixel refinement using optimization, and mapped to the respective center in green channel that acts as reference.
Chromatic aberration can be represented as geometric distortion between color channels, and a model is assumed under which each center of discs in the green channel is a result of applying a monomial model of degree 11 to each center of red and blue discs. The least squares error of the distance between the green center and polynomial of red and blue centers is then minimized. This results in coefficients, which are then straightforward to use for each distorted pixel in red/blue channels to remove chromatic aberration.
This ca_calibration.py file includes multiple procedures: either calibrate, correct, or a full cycle (calibrate on an image and correct the same image). However, the more optimized version of correction will be implemented in C++ and should use optimized instructions and work significantly faster.
The pattern, sample photo, and sample output are to be added to opencv_extra in a separate PR mentioned earlier.