From c01cfe14f5540ba96b458088185ac7ae90bb3534 Mon Sep 17 00:00:00 2001 From: Naushir Patuck Date: Sun, 3 May 2020 16:49:53 +0100 Subject: 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 Acked-by: Laurent Pinchart Signed-off-by: Laurent Pinchart --- utils/raspberrypi/ctt/ctt_lux.py | 58 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 utils/raspberrypi/ctt/ctt_lux.py (limited to 'utils/raspberrypi/ctt/ctt_lux.py') 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) -- cgit v1.2.1