libcamera: utils: Raspberry Pi Camera Tuning Tool

Initial implementation of the Raspberry Pi (BCM2835) Camera Tuning Tool.

All code is licensed under the BSD-2-Clause terms.
Copyright (c) 2019-2020 Raspberry Pi Trading Ltd.

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>

1 files changed, 58 insertions, 0 deletions

diff --git a/utils/raspberrypi/ctt/ctt_lux.py b/utils/raspberrypi/ctt/ctt_lux.py
new file mode 100644
index 00000000..8a16d346
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_lux.py
@@ -0,0 +1,58 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_lux.py - camera tuning tool for lux level
+
+from ctt_tools import *
+"""
+Find lux values from metadata and calculate Y
+"""
+def lux(Cam,Img):
+    shutter_speed = Img.exposure
+    gain = Img.againQ8_norm
+    aperture = 1
+    Cam.log += '\nShutter speed = {}'.format(shutter_speed)
+    Cam.log += '\nGain = {}'.format(gain)
+    Cam.log += '\nAperture = {}'.format(aperture)
+    patches = [Img.patches[i] for i in Img.order]
+    channels = [Img.channels[i] for i in Img.order]
+    return lux_calc(Cam,Img,patches,channels),shutter_speed,gain
+
+"""
+perform lux calibration on bayer channels
+"""
+def lux_calc(Cam,Img,patches,channels):
+    """
+    find means color channels on grey patches
+    """
+    ap_r = np.mean(patches[0][3::4])
+    ap_g = (np.mean(patches[1][3::4])+np.mean(patches[2][3::4]))/2
+    ap_b = np.mean(patches[3][3::4])
+    Cam.log += '\nAverage channel values on grey patches:'
+    Cam.log += '\nRed = {:.0f} Green = {:.0f} Blue = {:.0f}'.format(ap_r,ap_b,ap_g)
+    # print(ap_r,ap_g,ap_b)
+    """
+    calculate channel gains
+    """
+    gr = ap_g/ap_r
+    gb = ap_g/ap_b
+    Cam.log += '\nChannel gains: Red = {:.3f} Blue = {:.3f}'.format(gr,gb)
+
+    """
+    find means color channels on image and scale by gain
+    note greens are averaged together (treated as one channel)
+    """
+    a_r = np.mean(channels[0])*gr
+    a_g = (np.mean(channels[1])+np.mean(channels[2]))/2
+    a_b = np.mean(channels[3])*gb
+    Cam.log += '\nAverage channel values over entire image scaled by channel gains:'
+    Cam.log += '\nRed = {:.0f} Green = {:.0f} Blue = {:.0f}'.format(a_r,a_b,a_g)
+    # print(a_r,a_g,a_b)
+    """
+    Calculate y with top row of yuv matrix
+    """
+    y = 0.299*a_r + 0.587*a_g + 0.114*a_b
+    Cam.log += '\nY value calculated: {}'.format(int(y))
+    # print(y)
+    return int(y)