60d60c1367
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>
54 lines
No EOL
1.2 KiB
YAML
54 lines
No EOL
1.2 KiB
YAML
general:
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disable: []
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plot: []
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alsc:
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do_alsc_colour: 1
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luminance_strength: 0.5
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awb:
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# Algorithm can either be 'grey' or 'bayes'
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algorithm: bayes
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# Priors is only used for the bayes algorithm. They are defined in
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# logarithmic space. A good staring point is:
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# - lux: 0
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# ct: [ 2000, 3000, 13000 ]
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# probability: [ 1.0, 0.0, 0.0 ]
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# - lux: 800
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# ct: [ 2000, 6000, 13000 ]
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# probability: [ 0.0, 2.0, 2.0 ]
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# - lux: 1500
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# ct: [ 2000, 4000, 6000, 6500, 7000, 13000 ]
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# probability: [ 0.0, 1.0, 6.0, 7.0, 1.0, 1.0 ]
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priors:
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- lux: 0
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ct: [ 2000, 13000 ]
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probability: [ 0.0, 0.0 ]
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AwbMode:
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AwbAuto:
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lo: 2500
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hi: 8000
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AwbIncandescent:
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lo: 2500
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hi: 3000
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AwbTungsten:
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lo: 3000
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hi: 3500
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AwbFluorescent:
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lo: 4000
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hi: 4700
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AwbIndoor:
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lo: 3000
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hi: 5000
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AwbDaylight:
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lo: 5500
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hi: 6500
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AwbCloudy:
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lo: 6500
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hi: 8000
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# One custom mode can be defined if needed
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#AwbCustom:
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# lo: 2000
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# hi: 1300
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macbeth:
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small: 1
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show: 0
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# blacklevel: 32 |