- Version 1.0.1-272-ga0864af

mrcal-graft-models - Combines the intrinsics of one cameramodel with the extrinsics of another

```
$ mrcal-graft-models
intrinsics.cameramodel
extrinsics.cameramodel
> joint.cameramodel
Merged intrinsics from 'intrinsics.cameramodel' with extrinsics from
'exrinsics.cameramodel'
```

This tool combines intrinsics and extrinsics from different sources into a single model. The output is written to standard output.

We combine the intrinsics from one model with the extrinsics of another. A common use case is a system where the intrinsics are calibrated prior to moving the cameras to their final location, and then computing the extrinsics separately after the cameras are moved.

If we have computed such a combined model, and we decide to recompute the intrinsics afterwards, we can graft the new intrinsics to the previous extrinsics. However, this won't be a drop-in replacement for the previous model, since the intrinsics come with an implied geometric transformation, which will be different in the new intrinsics. If the "extrinsics" models contains the old intrinsics, then this tool is able to compute the relative implied transformation, and to apply it to the extrinsics. As a result, on average, the projection of any world point ends up at the same pixel coordinate as before.

The implied transformation logic is controlled by a number of commandline arguments, same ones as used by the mrcal-show-projection-diff tool. The only difference in options is that THIS tool uses --radius 0 by default, so we do not compute or apply the implied transformation unless asked. Pass --radius with a non-zero argument to compute and apply the implied transformation.

```
intrinsics Input camera model for the intrinsics. If "-" is given,
we read standard input. Both the intrinsics and
extrinsics sources may not be "-"
extrinsics Input camera model for the extrinsics. If "-" is given,
we read standard input. Both the intrinsics and
extrinsics sources may not be "-"
```

```
-h, --help show this help message and exit
--gridn GRIDN GRIDN Used if we're computing the implied-by-the-intrinsics
transformation. How densely we should sample the
imager. By default we use a 60x40 grid
--distance DISTANCE Used if we're computing the implied-by-the-intrinsics
transformation. By default we compute the implied
transformation for points infinitely far away from the
camera. If we want to look closer in, the desired
observation distance can be given in this argument. We
can also fit multiple distances at the same time by
passing them here in a comma-separated, whitespace-less
list
--where WHERE WHERE Used if we're computing the implied-by-the-intrinsics
transformation. Center of the region of interest used
for the transformatoin fit. It is usually impossible
for the models to match everywhere, but focusing on a
particular area can work better. The implied
transformation will be fit to match as large as
possible an area centered on this argument. If omitted,
we will focus on the center of the imager
--radius RADIUS Used if we're computing the implied-by-the-intrinsics
transformation. Radius of the region of interest. If
==0, we do NOT fit an implied transformation at all. If
omitted or <0, we use a "reasonable" value: the whole
imager if we're using uncertainties, or
min(width,height)/6 if --no-uncertainties. To fit with
data across the whole imager in either case, pass in a
very large radius
--no-uncertainties Used if we're computing the implied-by-the-intrinsics
transformation. By default we use the uncertainties in
the model to weigh the fit. This will focus the fit on
the confident region in the models without --where or
--radius. The computation will run faster with --no-
uncertainties, but the default --where and --radius may
need to be adjusted
```

https://www.github.com/dkogan/mrcal

Dima Kogan, `<dima@secretsauce.net>`

Copyright (c) 2017-2020 California Institute of Technology ("Caltech"). U.S. Government sponsorship acknowledged. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); You may obtain a copy of the License at

` http://www.apache.org/licenses/LICENSE-2.0`