Camera calibration for vision engineers. Maximally powerful, minimally complex.

One job: fit camera models and verify the results. OpenCV models when they work, spline-based distortion when they don't.

Why lensboy

Even for standard OpenCV models, lensboy gives you better calibrations than raw cv2.calibrateCamera (see model comparison notebook):

• Automatic outlier filtering removes bad detections
• Target warp estimation compensates for non-flat calibration boards

For cheap or wide-angle lenses where OpenCV's distortion model isn't enough, lensboy offers spline-based models that can capture arbitrary distortion patterns.

Lensboy also offers analysis tools to verify your calibration is actually good.

Quick example

import lensboy as lb

target_points, frames = lb.extract_frames_from_charuco(board, imgs)

result = lb.calibrate_camera(
    target_points, frames,
    camera_model_config=lb.OpenCVConfig(
        image_height=h, image_width=w, initial_focal_length=1000,
    ),
)

result.optimized_camera_model.save("camera.json")

Swap the config for a spline model — same API, more flexible:

result = lb.calibrate_camera(
    target_points, frames,
    camera_model_config=lb.PinholeSplinedConfig(
        image_height=h, image_width=w, initial_focal_length=1000,
    ),
)

Analysis tools

Plots for residuals, distortion, detection coverage, and more. See the example notebooks.

Install

Full install, with analysis and plotting:

pip install lensboy[analysis]

Minimal install, for loading and using models:

pip install lensboy

Getting started

See the quickstart notebook.

Spline models

Spline models use B-spline grids instead of polynomial coefficients, so they can fit lenses that OpenCV's model can't. This approach is inspired by mrcal.

The calibrated model converts to a pinhole model with undistortion maps, so you can use it with any standard pinhole pipeline:

pinhole = spline_model.get_pinhole_model()
undistorted = pinhole.undistort(image)