a783a90dec
For the lux algorithm, reference values get calculated based on a tuning image taken at a known lux level. The reference data contains the mean Y of the image, lux level, exposure time, gain and aperture. This module calculates these values for insertion into the tuning file. 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>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# SPDX-License-Identifier: GPL-2.0-or-later
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#
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# Copyright (C) 2019, Raspberry Pi Ltd
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# Copyright (C) 2025, Ideas on Board
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#
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# Base Lux tuning module
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from ..module import Module
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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class Lux(Module):
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type = 'lux'
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hr_name = 'Lux (Base)'
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out_name = 'GenericLux'
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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 calculate_lux_reference_values(self, images):
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# The lux calibration is done on a single image. For best effects, the
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# image with lux level closest to 1000 is chosen.
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imgs = [img for img in images if img.macbeth is not None]
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lux_values = [img.lux for img in imgs]
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index = lux_values.index(min(lux_values, key=lambda l: abs(1000 - l)))
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img = imgs[index]
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logger.info(f'Selected image {img.name} for lux calibration')
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if img.lux < 50:
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logger.warning(f'A Lux level of {img.lux} is very low for proper lux calibration')
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ref_y = self.calculate_y(img)
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exposure_time = img.exposure
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gain = img.againQ8_norm
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aperture = 1
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logger.info(f'RefY:{ref_y} Exposure time:{exposure_time}µs Gain:{gain} Aperture:{aperture}')
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return {'referenceY': ref_y,
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'referenceExposureTime': exposure_time,
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'referenceAnalogueGain': gain,
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'referenceDigitalGain': 1.0,
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'referenceLux': img.lux}
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def calculate_y(self, img):
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max16Bit = 0xffff
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# Average over all grey patches.
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ap_r = np.mean(img.patches[0][3::4]) / max16Bit
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ap_g = (np.mean(img.patches[1][3::4]) + np.mean(img.patches[2][3::4])) / 2 / max16Bit
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ap_b = np.mean(img.patches[3][3::4]) / max16Bit
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logger.debug(f'Averaged grey patches: Red: {ap_r}, Green: {ap_g}, Blue: {ap_b}')
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# Calculate white balance gains.
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gr = ap_g / ap_r
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gb = ap_g / ap_b
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logger.debug(f'WB gains: Red: {gr} Blue: {gb}')
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# Calculate the mean Y value of the whole image
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a_r = np.mean(img.channels[0]) * gr
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a_g = (np.mean(img.channels[1]) + np.mean(img.channels[2])) / 2
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a_b = np.mean(img.channels[3]) * gb
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y = 0.299 * a_r + 0.587 * a_g + 0.114 * a_b
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y /= max16Bit
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return y
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