undistort

Computes an undistortion table using locally-weighted homographies.
The method used is described in:

Locally-weighted Homographies for Calibration of Imaging Systems

Pradeep Ranganathan and Edwin Olson

Quickstart with Docker

Use the provided Dockerfile to build a docker image and run this code
inside it.

docker build -t undistort .
docker run --rm -it \
    undistort /code/undistort.py --scale=1.5 /code/examples/example.png

The command above writes the output files to the container’s internal
filesystem, which is not easily accessible from the host. One way to
work around this is to mount the host examples folder into the
container.

docker run --rm -it                       \
    -v $PWD/examples/:/code/examples/     \
    undistort /code/undistort.py --scale=1.5 /code/examples/example.png

This command runs the undistortion script on the examples/example.png
and produces the undistortion table examples/example.png.table and
rectified image examples/example.fixed.png.

The produced .table file can be used to undistort other images using
the examples/render_undistorted.py script.

Notes

The file examples/example.png is an example of an ideal image for
estimating distortion. It provides features that can cover the entire
image. The non-parametric nature of the distortion model makes it
data-intensive — the model is accurate near observed data points. Hence
an image with detections that covers the entire image produces better
models.

The file examples/bad_example.png is an example of an image that might
not produce a good distortion model.