1 files changed, 70 insertions, 0 deletions
diff --git a/utils/tuning/libtuning/modules/lux/lux.py b/utils/tuning/libtuning/modules/lux/lux.py
new file mode 100644
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+++ b/utils/tuning/libtuning/modules/lux/lux.py
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+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2019, Raspberry Pi Ltd
+# Copyright (C) 2025, Ideas on Board
+#
+# Base Lux tuning module
+
+from ..module import Module
+
+import logging
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+class Lux(Module):
+    type = 'lux'
+    hr_name = 'Lux (Base)'
+    out_name = 'GenericLux'
+
+    def __init__(self, debug: list):
+        super().__init__()
+
+        self.debug = debug
+
+    def calculate_lux_reference_values(self, images):
+        # The lux calibration is done on a single image. For best effects, the
+        # image with lux level closest to 1000 is chosen.
+        imgs = [img for img in images if img.macbeth is not None]
+        lux_values = [img.lux for img in imgs]
+        index = lux_values.index(min(lux_values, key=lambda l: abs(1000 - l)))
+        img = imgs[index]
+        logger.info(f'Selected image {img.name} for lux calibration')
+
+        if img.lux < 50:
+            logger.warning(f'A Lux level of {img.lux} is very low for proper lux calibration')
+
+        ref_y = self.calculate_y(img)
+        exposure_time = img.exposure
+        gain = img.againQ8_norm
+        aperture = 1
+        logger.info(f'RefY:{ref_y} Exposure time:{exposure_time}µs Gain:{gain} Aperture:{aperture}')
+        return {'referenceY': ref_y,
+                'referenceExposureTime': exposure_time,
+                'referenceAnalogueGain': gain,
+                'referenceDigitalGain': 1.0,
+                'referenceLux': img.lux}
+
+    def calculate_y(self, img):
+        max16Bit = 0xffff
+        # Average over all grey patches.
+        ap_r = np.mean(img.patches[0][3::4]) / max16Bit
+        ap_g = (np.mean(img.patches[1][3::4]) + np.mean(img.patches[2][3::4])) / 2 / max16Bit
+        ap_b = np.mean(img.patches[3][3::4]) / max16Bit
+        logger.debug(f'Averaged grey patches: Red: {ap_r}, Green: {ap_g}, Blue: {ap_b}')
+
+        # Calculate white balance gains.
+        gr = ap_g / ap_r
+        gb = ap_g / ap_b
+        logger.debug(f'WB gains: Red: {gr} Blue: {gb}')
+
+        # Calculate the mean Y value of the whole image
+        a_r = np.mean(img.channels[0]) * gr
+        a_g = (np.mean(img.channels[1]) + np.mean(img.channels[2])) / 2
+        a_b = np.mean(img.channels[3]) * gb
+        y = 0.299 * a_r + 0.587 * a_g + 0.114 * a_b
+        y /= max16Bit
+
+        return y
+