The original code used to specify the probabilities in log space and
scaled for the RaspberryPi hardware with 192 AWB measurement points.
This is reasonable as the whole algorithm makes use of unitless numbers
to prefer some colour temperatures based on a lux level. These numbers
are then hand tuned with the specific device in mind.
This has two shortcomings:
1. The linear interpolation of PWLs in log space is mathematically
incorrect. The outcome might still be ok, as both spaces (log and
linear) are monotonic, but it is still not "right".
2. Having unitless numbers gets more error prone when we try to
harmonize the behavior over multiple platforms.
Change the algorithm to interpret the numbers as being in linear space.
This makes the interpolation mathematically correct at the expense of a
few log operations.
To account for that change, update the numbers in the tuning example
file with the linear counterparts scaled to one AWB zone measurement.
Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
Reviewed-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Daniel Scally <dan.scally@ideasonboard.com>
To support the bayesian AWB algorithm in libtuning, the necessary data
needs to be collected and written to the tuning file.
Extend libtuning to calculate and output that additional data.
Prior probabilities and AwbModes are manually specified and not
calculated in the tuning process. Add sample values from the RaspberryPi
tuning files to the example config file.
Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
Reviewed-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
At the moment this just reads the yaml file and returns it verbatim.
This needs to evolve further in the near future.
Signed-off-by: Stefan Klug <stefan.klug@ideasonboard.com>
Reviewed-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
