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>
40 lines
968 B
Python
40 lines
968 B
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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import logging
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from ..module import Module
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from libtuning.ctt_awb import awb
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import numpy as np
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logger = logging.getLogger(__name__)
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class AWB(Module):
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type = 'awb'
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hr_name = 'AWB (Base)'
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out_name = 'GenericAWB'
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def __init__(self, *, debug: list):
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super().__init__()
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self.debug = debug
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def do_calculation(self, images):
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logger.info('Starting AWB calculation')
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imgs = [img for img in images if img.macbeth is not None]
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ct_curve, transverse_pos, transverse_neg = awb(imgs, None, None, False)
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ct_curve = np.reshape(ct_curve, (-1, 3))
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gains = [{
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'ct': int(v[0]),
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'gains': [float(1.0 / v[1]), float(1.0 / v[2])]
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} for v in ct_curve]
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return {'colourGains': gains,
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'transversePos': transverse_pos,
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'transverseNeg': transverse_neg}
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