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>
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
# SPDX-License-Identifier: GPL-2.0-or-later
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#
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# Copyright (C) 2024, Ideas On Board
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#
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# AWB module for tuning rkisp1
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from .awb import AWB
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class AWBRkISP1(AWB):
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hr_name = 'AWB (RkISP1)'
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out_name = 'Awb'
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def validate_config(self, config: dict) -> bool:
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return True
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def process(self, config: dict, images: list, outputs: dict) -> dict:
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if not 'awb' in config['general']:
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raise ValueError('AWB configuration missing')
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awb_config = config['general']['awb']
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algorithm = awb_config['algorithm']
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output = {'algorithm': algorithm}
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data = self.do_calculation(images)
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if algorithm == 'grey':
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output['colourGains'] = data['colourGains']
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elif algorithm == 'bayes':
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output['AwbMode'] = awb_config['AwbMode']
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output['priors'] = awb_config['priors']
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output.update(data)
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else:
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raise ValueError(f"Unknown AWB algorithm {output['algorithm']}")
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return output
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