From d13542c28f291b88e29f1142d6dff565e772a05c Mon Sep 17 00:00:00 2001 From: David Plowman Date: Thu, 6 Jun 2024 11:15:07 +0100 Subject: utils: raspberrypi: ctt: Adapt tuning tool for both VC4 and PiSP The old ctt.py and alsc_only.py scripts are removed. Instead of ctt.py use ctt_vc4.py or ctt_pisp.py, depending on your target platform. Instead of alsc_only.py use alsc_vc4.py or alsc_pisp.py, again according to your platform. Signed-off-by: David Plowman Reviewed-by: Naushir Patuck Tested-by: Naushir Patuck Acked-by: Kieran Bingham Signed-off-by: Kieran Bingham --- utils/raspberrypi/ctt/alsc_only.py | 34 - utils/raspberrypi/ctt/alsc_pisp.py | 37 ++ utils/raspberrypi/ctt/alsc_vc4.py | 37 ++ utils/raspberrypi/ctt/ctt.py | 837 ------------------------- utils/raspberrypi/ctt/ctt_alsc.py | 75 +-- utils/raspberrypi/ctt/ctt_awb.py | 11 +- utils/raspberrypi/ctt/ctt_ccm.py | 6 +- utils/raspberrypi/ctt/ctt_pisp.py | 233 +++++++ utils/raspberrypi/ctt/ctt_pretty_print_json.py | 7 +- utils/raspberrypi/ctt/ctt_run.py | 703 +++++++++++++++++++++ utils/raspberrypi/ctt/ctt_vc4.py | 157 +++++ 11 files changed, 1220 insertions(+), 917 deletions(-) delete mode 100755 utils/raspberrypi/ctt/alsc_only.py create mode 100755 utils/raspberrypi/ctt/alsc_pisp.py create mode 100755 utils/raspberrypi/ctt/alsc_vc4.py delete mode 100755 utils/raspberrypi/ctt/ctt.py create mode 100755 utils/raspberrypi/ctt/ctt_pisp.py create mode 100755 utils/raspberrypi/ctt/ctt_run.py create mode 100755 utils/raspberrypi/ctt/ctt_vc4.py (limited to 'utils') diff --git a/utils/raspberrypi/ctt/alsc_only.py b/utils/raspberrypi/ctt/alsc_only.py deleted file mode 100755 index 092aa40e..00000000 --- a/utils/raspberrypi/ctt/alsc_only.py +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-License-Identifier: BSD-2-Clause -# -# Copyright (C) 2022, Raspberry Pi (Trading) Limited -# -# alsc tuning tool - -from ctt import * - - -if __name__ == '__main__': - """ - initialise calibration - """ - if len(sys.argv) == 1: - print(""" - Pisp Camera Tuning Tool version 1.0 - - Required Arguments: - '-i' : Calibration image directory. - '-o' : Name of output json file. - - Optional Arguments: - '-c' : Config file for the CTT. If not passed, default parameters used. - '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. - """) - quit(0) - else: - """ - parse input arguments - """ - json_output, directory, config, log_output = parse_input() - run_ctt(json_output, directory, config, log_output, alsc_only=True) diff --git a/utils/raspberrypi/ctt/alsc_pisp.py b/utils/raspberrypi/ctt/alsc_pisp.py new file mode 100755 index 00000000..499aecd1 --- /dev/null +++ b/utils/raspberrypi/ctt/alsc_pisp.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2022, Raspberry Pi (Trading) Limited +# +# alsc_only.py - alsc tuning tool + +import sys + +from ctt_pisp import json_template, grid_size, target +from ctt_run import run_ctt +from ctt_tools import parse_input + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + PiSP Lens Shading Camera Tuning Tool version 1.0 + + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-c' : Config file for the CTT. If not passed, default parameters used. + '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. + """) + quit(0) + else: + """ + parse input arguments + """ + json_output, directory, config, log_output = parse_input() + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target, alsc_only=True) diff --git a/utils/raspberrypi/ctt/alsc_vc4.py b/utils/raspberrypi/ctt/alsc_vc4.py new file mode 100755 index 00000000..caf6a174 --- /dev/null +++ b/utils/raspberrypi/ctt/alsc_vc4.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2022, Raspberry Pi (Trading) Limited +# +# alsc tuning tool + +import sys + +from ctt_vc4 import json_template, grid_size, target +from ctt_run import run_ctt +from ctt_tools import parse_input + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + VC4 Lens Shading Camera Tuning Tool version 1.0 + + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-c' : Config file for the CTT. If not passed, default parameters used. + '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. + """) + quit(0) + else: + """ + parse input arguments + """ + json_output, directory, config, log_output = parse_input() + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target, alsc_only=True) diff --git a/utils/raspberrypi/ctt/ctt.py b/utils/raspberrypi/ctt/ctt.py deleted file mode 100755 index bbe960b0..00000000 --- a/utils/raspberrypi/ctt/ctt.py +++ /dev/null @@ -1,837 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-License-Identifier: BSD-2-Clause -# -# Copyright (C) 2019, Raspberry Pi Ltd -# -# camera tuning tool - -import os -import sys -from ctt_image_load import * -from ctt_ccm import * -from ctt_awb import * -from ctt_alsc import * -from ctt_lux import * -from ctt_noise import * -from ctt_geq import * -from ctt_pretty_print_json import pretty_print -import random -import json -import re - -""" -This file houses the camera object, which is used to perform the calibrations. -The camera object houses all the calibration images as attributes in two lists: - - imgs (macbeth charts) - - imgs_alsc (alsc correction images) -Various calibrations are methods of the camera object, and the output is stored -in a dictionary called self.json. -Once all the caibration has been completed, the Camera.json is written into a -json file. -The camera object initialises its json dictionary by reading from a pre-written -blank json file. This has been done to avoid reproducing the entire json file -in the code here, thereby avoiding unecessary clutter. -""" - - -""" -Get the colour and lux values from the strings of each inidvidual image -""" -def get_col_lux(string): - """ - Extract colour and lux values from filename - """ - col = re.search(r'([0-9]+)[kK](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) - lux = re.search(r'([0-9]+)[lL](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) - try: - col = col.group(1) - except AttributeError: - """ - Catch error if images labelled incorrectly and pass reasonable defaults - """ - return None, None - try: - lux = lux.group(1) - except AttributeError: - """ - Catch error if images labelled incorrectly and pass reasonable defaults - Still returns colour if that has been found. - """ - return col, None - return int(col), int(lux) - - -""" -Camera object that is the backbone of the tuning tool. -Input is the desired path of the output json. -""" -class Camera: - def __init__(self, jfile): - self.path = os.path.dirname(os.path.expanduser(__file__)) + '/' - if self.path == '/': - self.path = '' - self.imgs = [] - self.imgs_alsc = [] - self.log = 'Log created : ' + time.asctime(time.localtime(time.time())) - self.log_separator = '\n'+'-'*70+'\n' - self.jf = jfile - """ - initial json dict populated by uncalibrated values - """ - self.json = { - "rpi.black_level": { - "black_level": 4096 - }, - "rpi.dpc": { - }, - "rpi.lux": { - "reference_shutter_speed": 10000, - "reference_gain": 1, - "reference_aperture": 1.0 - }, - "rpi.noise": { - }, - "rpi.geq": { - }, - "rpi.sdn": { - }, - "rpi.awb": { - "priors": [ - {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]}, - {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]}, - {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]} - ], - "modes": { - "auto": {"lo": 2500, "hi": 8000}, - "incandescent": {"lo": 2500, "hi": 3000}, - "tungsten": {"lo": 3000, "hi": 3500}, - "fluorescent": {"lo": 4000, "hi": 4700}, - "indoor": {"lo": 3000, "hi": 5000}, - "daylight": {"lo": 5500, "hi": 6500}, - "cloudy": {"lo": 7000, "hi": 8600} - }, - "bayes": 1 - }, - "rpi.agc": { - "metering_modes": { - "centre-weighted": { - "weights": [3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0] - }, - "spot": { - "weights": [2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - }, - "matrix": { - "weights": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - } - }, - "exposure_modes": { - "normal": { - "shutter": [100, 10000, 30000, 60000, 120000], - "gain": [1.0, 2.0, 4.0, 6.0, 6.0] - }, - "short": { - "shutter": [100, 5000, 10000, 20000, 120000], - "gain": [1.0, 2.0, 4.0, 6.0, 6.0] - } - }, - "constraint_modes": { - "normal": [ - {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]} - ], - "highlight": [ - {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]}, - {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]} - ] - }, - "y_target": [0, 0.16, 1000, 0.165, 10000, 0.17] - }, - "rpi.alsc": { - 'omega': 1.3, - 'n_iter': 100, - 'luminance_strength': 0.7, - }, - "rpi.contrast": { - "ce_enable": 1, - "gamma_curve": [ - 0, 0, - 1024, 5040, - 2048, 9338, - 3072, 12356, - 4096, 15312, - 5120, 18051, - 6144, 20790, - 7168, 23193, - 8192, 25744, - 9216, 27942, - 10240, 30035, - 11264, 32005, - 12288, 33975, - 13312, 35815, - 14336, 37600, - 15360, 39168, - 16384, 40642, - 18432, 43379, - 20480, 45749, - 22528, 47753, - 24576, 49621, - 26624, 51253, - 28672, 52698, - 30720, 53796, - 32768, 54876, - 36864, 57012, - 40960, 58656, - 45056, 59954, - 49152, 61183, - 53248, 62355, - 57344, 63419, - 61440, 64476, - 65535, 65535 - ] - }, - "rpi.ccm": { - }, - "rpi.sharpen": { - } - } - - """ - Perform colour correction calibrations by comparing macbeth patch colours - to standard macbeth chart colours. - """ - def ccm_cal(self, do_alsc_colour): - if 'rpi.ccm' in self.disable: - return 1 - print('\nStarting CCM calibration') - self.log_new_sec('CCM') - """ - if image is greyscale then CCm makes no sense - """ - if self.grey: - print('\nERROR: Can\'t do CCM on greyscale image!') - self.log += '\nERROR: Cannot perform CCM calibration ' - self.log += 'on greyscale image!\nCCM aborted!' - del self.json['rpi.ccm'] - return 0 - a = time.time() - """ - Check if alsc tables have been generated, if not then do ccm without - alsc - """ - if ("rpi.alsc" not in self.disable) and do_alsc_colour: - """ - case where ALSC colour has been done, so no errors should be - expected... - """ - try: - cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] - cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] - self.log += '\nALSC tables found successfully' - except KeyError: - cal_cr_list, cal_cb_list = None, None - print('WARNING! No ALSC tables found for CCM!') - print('Performing CCM calibrations without ALSC correction...') - self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' - self.log += 'performed without ALSC correction...' - else: - """ - case where config options result in CCM done without ALSC colour tables - """ - cal_cr_list, cal_cb_list = None, None - self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' - self.log += 'performed without ALSC correction...' - - """ - Do CCM calibration - """ - try: - ccms = ccm(self, cal_cr_list, cal_cb_list) - except ArithmeticError: - print('ERROR: Matrix is singular!\nTake new pictures and try again...') - self.log += '\nERROR: Singular matrix encountered during fit!' - self.log += '\nCCM aborted!' - return 1 - """ - Write output to json - """ - self.json['rpi.ccm']['ccms'] = ccms - self.log += '\nCCM calibration written to json file' - print('Finished CCM calibration') - - """ - Auto white balance calibration produces a colour curve for - various colour temperatures, as well as providing a maximum 'wiggle room' - distance from this curve (transverse_neg/pos). - """ - def awb_cal(self, greyworld, do_alsc_colour): - if 'rpi.awb' in self.disable: - return 1 - print('\nStarting AWB calibration') - self.log_new_sec('AWB') - """ - if image is greyscale then AWB makes no sense - """ - if self.grey: - print('\nERROR: Can\'t do AWB on greyscale image!') - self.log += '\nERROR: Cannot perform AWB calibration ' - self.log += 'on greyscale image!\nAWB aborted!' - del self.json['rpi.awb'] - return 0 - """ - optional set greyworld (e.g. for noir cameras) - """ - if greyworld: - self.json['rpi.awb']['bayes'] = 0 - self.log += '\nGreyworld set' - """ - Check if alsc tables have been generated, if not then do awb without - alsc correction - """ - if ("rpi.alsc" not in self.disable) and do_alsc_colour: - try: - cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] - cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] - self.log += '\nALSC tables found successfully' - except KeyError: - cal_cr_list, cal_cb_list = None, None - print('ERROR, no ALSC calibrations found for AWB') - print('Performing AWB without ALSC tables') - self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' - self.log += 'performed without ALSC correction...' - else: - cal_cr_list, cal_cb_list = None, None - self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' - self.log += 'performed without ALSC correction...' - """ - call calibration function - """ - plot = "rpi.awb" in self.plot - awb_out = awb(self, cal_cr_list, cal_cb_list, plot) - ct_curve, transverse_neg, transverse_pos = awb_out - """ - write output to json - """ - self.json['rpi.awb']['ct_curve'] = ct_curve - self.json['rpi.awb']['sensitivity_r'] = 1.0 - self.json['rpi.awb']['sensitivity_b'] = 1.0 - self.json['rpi.awb']['transverse_pos'] = transverse_pos - self.json['rpi.awb']['transverse_neg'] = transverse_neg - self.log += '\nAWB calibration written to json file' - print('Finished AWB calibration') - - """ - Auto lens shading correction completely mitigates the effects of lens shading for ech - colour channel seperately, and then partially corrects for vignetting. - The extent of the correction depends on the 'luminance_strength' parameter. - """ - def alsc_cal(self, luminance_strength, do_alsc_colour): - if 'rpi.alsc' in self.disable: - return 1 - print('\nStarting ALSC calibration') - self.log_new_sec('ALSC') - """ - check if alsc images have been taken - """ - if len(self.imgs_alsc) == 0: - print('\nError:\nNo alsc calibration images found') - self.log += '\nERROR: No ALSC calibration images found!' - self.log += '\nALSC calibration aborted!' - return 1 - self.json['rpi.alsc']['luminance_strength'] = luminance_strength - if self.grey and do_alsc_colour: - print('Greyscale camera so only luminance_lut calculated') - do_alsc_colour = False - self.log += '\nWARNING: ALSC colour correction cannot be done on ' - self.log += 'greyscale image!\nALSC colour corrections forced off!' - """ - call calibration function - """ - plot = "rpi.alsc" in self.plot - alsc_out = alsc_all(self, do_alsc_colour, plot) - cal_cr_list, cal_cb_list, luminance_lut, av_corn = alsc_out - """ - write output to json and finish if not do_alsc_colour - """ - if not do_alsc_colour: - self.json['rpi.alsc']['luminance_lut'] = luminance_lut - self.json['rpi.alsc']['n_iter'] = 0 - self.log += '\nALSC calibrations written to json file' - self.log += '\nNo colour calibrations performed' - print('Finished ALSC calibrations') - return 1 - - self.json['rpi.alsc']['calibrations_Cr'] = cal_cr_list - self.json['rpi.alsc']['calibrations_Cb'] = cal_cb_list - self.json['rpi.alsc']['luminance_lut'] = luminance_lut - self.log += '\nALSC colour and luminance tables written to json file' - - """ - The sigmas determine the strength of the adaptive algorithm, that - cleans up any lens shading that has slipped through the alsc. These are - determined by measuring a 'worst-case' difference between two alsc tables - that are adjacent in colour space. If, however, only one colour - temperature has been provided, then this difference can not be computed - as only one table is available. - To determine the sigmas you would have to estimate the error of an alsc - table with only the image it was taken on as a check. To avoid circularity, - dfault exaggerated sigmas are used, which can result in too much alsc and - is therefore not advised. - In general, just take another alsc picture at another colour temperature! - """ - - if len(self.imgs_alsc) == 1: - self.json['rpi.alsc']['sigma'] = 0.005 - self.json['rpi.alsc']['sigma_Cb'] = 0.005 - print('\nWarning:\nOnly one alsc calibration found' - '\nStandard sigmas used for adaptive algorithm.') - print('Finished ALSC calibrations') - self.log += '\nWARNING: Only one colour temperature found in ' - self.log += 'calibration images.\nStandard sigmas used for adaptive ' - self.log += 'algorithm!' - return 1 - - """ - obtain worst-case scenario residual sigmas - """ - sigma_r, sigma_b = get_sigma(self, cal_cr_list, cal_cb_list) - """ - write output to json - """ - self.json['rpi.alsc']['sigma'] = np.round(sigma_r, 5) - self.json['rpi.alsc']['sigma_Cb'] = np.round(sigma_b, 5) - self.log += '\nCalibrated sigmas written to json file' - print('Finished ALSC calibrations') - - """ - Green equalisation fixes problems caused by discrepancies in green - channels. This is done by measuring the effect on macbeth chart patches, - which ideally would have the same green values throughout. - An upper bound linear model is fit, fixing a threshold for the green - differences that are corrected. - """ - def geq_cal(self): - if 'rpi.geq' in self.disable: - return 1 - print('\nStarting GEQ calibrations') - self.log_new_sec('GEQ') - """ - perform calibration - """ - plot = 'rpi.geq' in self.plot - slope, offset = geq_fit(self, plot) - """ - write output to json - """ - self.json['rpi.geq']['offset'] = offset - self.json['rpi.geq']['slope'] = slope - self.log += '\nGEQ calibrations written to json file' - print('Finished GEQ calibrations') - - """ - Lux calibrations allow the lux level of a scene to be estimated by a ratio - calculation. Lux values are used in the pipeline for algorithms such as AGC - and AWB - """ - def lux_cal(self): - if 'rpi.lux' in self.disable: - return 1 - print('\nStarting LUX calibrations') - self.log_new_sec('LUX') - """ - The lux calibration is done on a single image. For best effects, the - image with lux level closest to 1000 is chosen. - """ - luxes = [Img.lux for Img in self.imgs] - argmax = luxes.index(min(luxes, key=lambda l: abs(1000-l))) - Img = self.imgs[argmax] - self.log += '\nLux found closest to 1000: {} lx'.format(Img.lux) - self.log += '\nImage used: ' + Img.name - if Img.lux < 50: - self.log += '\nWARNING: Low lux could cause inaccurate calibrations!' - """ - do calibration - """ - lux_out, shutter_speed, gain = lux(self, Img) - """ - write output to json - """ - self.json['rpi.lux']['reference_shutter_speed'] = shutter_speed - self.json['rpi.lux']['reference_gain'] = gain - self.json['rpi.lux']['reference_lux'] = Img.lux - self.json['rpi.lux']['reference_Y'] = lux_out - self.log += '\nLUX calibrations written to json file' - print('Finished LUX calibrations') - - """ - Noise alibration attempts to describe the noise profile of the sensor. The - calibration is run on macbeth images and the final output is taken as the average - """ - def noise_cal(self): - if 'rpi.noise' in self.disable: - return 1 - print('\nStarting NOISE calibrations') - self.log_new_sec('NOISE') - """ - run calibration on all images and sort by slope. - """ - plot = "rpi.noise" in self.plot - noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key=lambda x: x[0]) - self.log += '\nFinished processing images' - """ - take the average of the interquartile - """ - length = len(noise_out) - noise_out = np.mean(noise_out[length//4:1+3*length//4], axis=0) - self.log += '\nAverage noise profile: constant = {} '.format(int(noise_out[1])) - self.log += 'slope = {:.3f}'.format(noise_out[0]) - """ - write to json - """ - self.json['rpi.noise']['reference_constant'] = int(noise_out[1]) - self.json['rpi.noise']['reference_slope'] = round(noise_out[0], 3) - self.log += '\nNOISE calibrations written to json' - print('Finished NOISE calibrations') - - """ - Removes json entries that are turned off - """ - def json_remove(self, disable): - self.log_new_sec('Disabling Options', cal=False) - if len(self.disable) == 0: - self.log += '\nNothing disabled!' - return 1 - for key in disable: - try: - del self.json[key] - self.log += '\nDisabled: ' + key - except KeyError: - self.log += '\nERROR: ' + key + ' not found!' - """ - writes the json dictionary to the raw json file then make pretty - """ - def write_json(self): - """ - Write json dictionary to file using our version 2 format - """ - - out_json = { - "version": 2.0, - 'target': 'bcm2835', - "algorithms": [{name: data} for name, data in self.json.items()], - } - - with open(self.jf, 'w') as f: - f.write(pretty_print(out_json)) - - """ - add a new section to the log file - """ - def log_new_sec(self, section, cal=True): - self.log += '\n'+self.log_separator - self.log += section - if cal: - self.log += ' Calibration' - self.log += self.log_separator - - """ - write script arguments to log file - """ - def log_user_input(self, json_output, directory, config, log_output): - self.log_new_sec('User Arguments', cal=False) - self.log += '\nJson file output: ' + json_output - self.log += '\nCalibration images directory: ' + directory - if config is None: - self.log += '\nNo configuration file input... using default options' - elif config is False: - self.log += '\nWARNING: Invalid configuration file path...' - self.log += ' using default options' - elif config is True: - self.log += '\nWARNING: Invalid syntax in configuration file...' - self.log += ' using default options' - else: - self.log += '\nConfiguration file: ' + config - if log_output is None: - self.log += '\nNo log file path input... using default: ctt_log.txt' - else: - self.log += '\nLog file output: ' + log_output - - # if log_output - - """ - write log file - """ - def write_log(self, filename): - if filename is None: - filename = 'ctt_log.txt' - self.log += '\n' + self.log_separator - with open(filename, 'w') as logfile: - logfile.write(self.log) - - """ - Add all images from directory, pass into relevant list of images and - extrace lux and temperature values. - """ - def add_imgs(self, directory, mac_config, blacklevel=-1): - self.log_new_sec('Image Loading', cal=False) - img_suc_msg = 'Image loaded successfully!' - print('\n\nLoading images from '+directory) - self.log += '\nDirectory: ' + directory - """ - get list of files - """ - filename_list = get_photos(directory) - print("Files found: {}".format(len(filename_list))) - self.log += '\nFiles found: {}'.format(len(filename_list)) - """ - iterate over files - """ - filename_list.sort() - for filename in filename_list: - address = directory + filename - print('\nLoading image: '+filename) - self.log += '\n\nImage: ' + filename - """ - obtain colour and lux value - """ - col, lux = get_col_lux(filename) - """ - Check if image is an alsc calibration image - """ - if 'alsc' in filename: - Img = load_image(self, address, mac=False) - self.log += '\nIdentified as an ALSC image' - """ - check if imagae data has been successfully unpacked - """ - if Img == 0: - print('\nDISCARDED') - self.log += '\nImage discarded!' - continue - """ - check that image colour temperature has been successfuly obtained - """ - elif col is not None: - """ - if successful, append to list and continue to next image - """ - Img.col = col - Img.name = filename - self.log += '\nColour temperature: {} K'.format(col) - self.imgs_alsc.append(Img) - if blacklevel != -1: - Img.blacklevel_16 = blacklevel - print(img_suc_msg) - continue - else: - print('Error! No colour temperature found!') - self.log += '\nWARNING: Error reading colour temperature' - self.log += '\nImage discarded!' - print('DISCARDED') - else: - self.log += '\nIdentified as macbeth chart image' - """ - if image isn't an alsc correction then it must have a lux and a - colour temperature value to be useful - """ - if lux is None: - print('DISCARDED') - self.log += '\nWARNING: Error reading lux value' - self.log += '\nImage discarded!' - continue - Img = load_image(self, address, mac_config) - """ - check that image data has been successfuly unpacked - """ - if Img == 0: - print('DISCARDED') - self.log += '\nImage discarded!' - continue - else: - """ - if successful, append to list and continue to next image - """ - Img.col, Img.lux = col, lux - Img.name = filename - self.log += '\nColour temperature: {} K'.format(col) - self.log += '\nLux value: {} lx'.format(lux) - if blacklevel != -1: - Img.blacklevel_16 = blacklevel - print(img_suc_msg) - self.imgs.append(Img) - - print('\nFinished loading images') - - """ - Check that usable images have been found - Possible errors include: - - no macbeth chart - - incorrect filename/extension - - images from different cameras - """ - def check_imgs(self, macbeth=True): - self.log += '\n\nImages found:' - self.log += '\nMacbeth : {}'.format(len(self.imgs)) - self.log += '\nALSC : {} '.format(len(self.imgs_alsc)) - self.log += '\n\nCamera metadata' - """ - check usable images found - """ - if len(self.imgs) == 0 and macbeth: - print('\nERROR: No usable macbeth chart images found') - self.log += '\nERROR: No usable macbeth chart images found' - return 0 - elif len(self.imgs) == 0 and len(self.imgs_alsc) == 0: - print('\nERROR: No usable images found') - self.log += '\nERROR: No usable images found' - return 0 - """ - Double check that every image has come from the same camera... - """ - all_imgs = self.imgs + self.imgs_alsc - camNames = list(set([Img.camName for Img in all_imgs])) - patterns = list(set([Img.pattern for Img in all_imgs])) - sigbitss = list(set([Img.sigbits for Img in all_imgs])) - blacklevels = list(set([Img.blacklevel_16 for Img in all_imgs])) - sizes = list(set([(Img.w, Img.h) for Img in all_imgs])) - - if len(camNames) == 1 and len(patterns) == 1 and len(sigbitss) == 1 and \ - len(blacklevels) == 1 and len(sizes) == 1: - self.grey = (patterns[0] == 128) - self.blacklevel_16 = blacklevels[0] - self.log += '\nName: {}'.format(camNames[0]) - self.log += '\nBayer pattern case: {}'.format(patterns[0]) - if self.grey: - self.log += '\nGreyscale camera identified' - self.log += '\nSignificant bits: {}'.format(sigbitss[0]) - self.log += '\nBlacklevel: {}'.format(blacklevels[0]) - self.log += '\nImage size: w = {} h = {}'.format(sizes[0][0], sizes[0][1]) - return 1 - else: - print('\nERROR: Images from different cameras') - self.log += '\nERROR: Images are from different cameras' - return 0 - - -def run_ctt(json_output, directory, config, log_output, alsc_only=False): - """ - check input files are jsons - """ - if json_output[-5:] != '.json': - raise ArgError('\n\nError: Output must be a json file!') - if config is not None: - """ - check if config file is actually a json - """ - if config[-5:] != '.json': - raise ArgError('\n\nError: Config file must be a json file!') - """ - read configurations - """ - try: - with open(config, 'r') as config_json: - configs = json.load(config_json) - except FileNotFoundError: - configs = {} - config = False - except json.decoder.JSONDecodeError: - configs = {} - config = True - - else: - configs = {} - """ - load configurations from config file, if not given then set default - """ - disable = get_config(configs, "disable", [], 'list') - plot = get_config(configs, "plot", [], 'list') - awb_d = get_config(configs, "awb", {}, 'dict') - greyworld = get_config(awb_d, "greyworld", 0, 'bool') - alsc_d = get_config(configs, "alsc", {}, 'dict') - do_alsc_colour = get_config(alsc_d, "do_alsc_colour", 1, 'bool') - luminance_strength = get_config(alsc_d, "luminance_strength", 0.5, 'num') - blacklevel = get_config(configs, "blacklevel", -1, 'num') - macbeth_d = get_config(configs, "macbeth", {}, 'dict') - mac_small = get_config(macbeth_d, "small", 0, 'bool') - mac_show = get_config(macbeth_d, "show", 0, 'bool') - mac_config = (mac_small, mac_show) - - if blacklevel < -1 or blacklevel >= 2**16: - print('\nInvalid blacklevel, defaulted to 64') - blacklevel = -1 - - if luminance_strength < 0 or luminance_strength > 1: - print('\nInvalid luminance_strength strength, defaulted to 0.5') - luminance_strength = 0.5 - - """ - sanitise directory path - """ - if directory[-1] != '/': - directory += '/' - """ - initialise tuning tool and load images - """ - try: - Cam = Camera(json_output) - Cam.log_user_input(json_output, directory, config, log_output) - if alsc_only: - disable = set(Cam.json.keys()).symmetric_difference({"rpi.alsc"}) - Cam.disable = disable - Cam.plot = plot - Cam.add_imgs(directory, mac_config, blacklevel) - except FileNotFoundError: - raise ArgError('\n\nError: Input image directory not found!') - - """ - preform calibrations as long as check_imgs returns True - If alsc is activated then it must be done before awb and ccm since the alsc - tables are used in awb and ccm calibrations - ccm also technically does an awb but it measures this from the macbeth - chart in the image rather than using calibration data - """ - if Cam.check_imgs(macbeth=not alsc_only): - if not alsc_only: - Cam.json['rpi.black_level']['black_level'] = Cam.blacklevel_16 - Cam.json_remove(disable) - print('\nSTARTING CALIBRATIONS') - Cam.alsc_cal(luminance_strength, do_alsc_colour) - Cam.geq_cal() - Cam.lux_cal() - Cam.noise_cal() - Cam.awb_cal(greyworld, do_alsc_colour) - Cam.ccm_cal(do_alsc_colour) - print('\nFINISHED CALIBRATIONS') - Cam.write_json() - Cam.write_log(log_output) - print('\nCalibrations written to: '+json_output) - if log_output is None: - log_output = 'ctt_log.txt' - print('Log file written to: '+log_output) - pass - else: - Cam.write_log(log_output) - - -if __name__ == '__main__': - """ - initialise calibration - """ - if len(sys.argv) == 1: - print(""" - Pisp Camera Tuning Tool version 1.0 - - Required Arguments: - '-i' : Calibration image directory. - '-o' : Name of output json file. - - Optional Arguments: - '-c' : Config file for the CTT. If not passed, default parameters used. - '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. - """) - quit(0) - else: - """ - parse input arguments - """ - json_output, directory, config, log_output = parse_input() - run_ctt(json_output, directory, config, log_output) diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py index b0201ac4..66ce8c14 100644 --- a/utils/raspberrypi/ctt/ctt_alsc.py +++ b/utils/raspberrypi/ctt/ctt_alsc.py @@ -13,8 +13,9 @@ from mpl_toolkits.mplot3d import Axes3D """ preform alsc calibration on a set of images """ -def alsc_all(Cam, do_alsc_colour, plot): +def alsc_all(Cam, do_alsc_colour, plot, grid_size=(16, 12)): imgs_alsc = Cam.imgs_alsc + grid_w, grid_h = grid_size """ create list of colour temperatures and associated calibration tables """ @@ -23,7 +24,7 @@ def alsc_all(Cam, do_alsc_colour, plot): list_cb = [] list_cg = [] for Img in imgs_alsc: - col, cr, cb, cg, size = alsc(Cam, Img, do_alsc_colour, plot) + col, cr, cb, cg, size = alsc(Cam, Img, do_alsc_colour, plot, grid_size=grid_size) list_col.append(col) list_cr.append(cr) list_cb.append(cb) @@ -68,11 +69,12 @@ def alsc_all(Cam, do_alsc_colour, plot): t_b = np.where((100*t_b) % 1 >= 0.95, t_b-0.001, t_b) t_r = np.round(t_r, 3) t_b = np.round(t_b, 3) - r_corners = (t_r[0], t_r[15], t_r[-1], t_r[-16]) - b_corners = (t_b[0], t_b[15], t_b[-1], t_b[-16]) - r_cen = t_r[5*16+7]+t_r[5*16+8]+t_r[6*16+7]+t_r[6*16+8] + r_corners = (t_r[0], t_r[grid_w - 1], t_r[-1], t_r[-grid_w]) + b_corners = (t_b[0], t_b[grid_w - 1], t_b[-1], t_b[-grid_w]) + middle_pos = (grid_h // 2 - 1) * grid_w + grid_w - 1 + r_cen = t_r[middle_pos]+t_r[middle_pos + 1]+t_r[middle_pos + grid_w]+t_r[middle_pos + grid_w + 1] r_cen = round(r_cen/4, 3) - b_cen = t_b[5*16+7]+t_b[5*16+8]+t_b[6*16+7]+t_b[6*16+8] + b_cen = t_b[middle_pos]+t_b[middle_pos + 1]+t_b[middle_pos + grid_w]+t_b[middle_pos + grid_w + 1] b_cen = round(b_cen/4, 3) Cam.log += '\nRed table corners: {}'.format(r_corners) Cam.log += '\nRed table centre: {}'.format(r_cen) @@ -116,8 +118,9 @@ def alsc_all(Cam, do_alsc_colour, plot): """ calculate g/r and g/b for 32x32 points arranged in a grid for a single image """ -def alsc(Cam, Img, do_alsc_colour, plot=False): +def alsc(Cam, Img, do_alsc_colour, plot=False, grid_size=(16, 12)): Cam.log += '\nProcessing image: ' + Img.name + grid_w, grid_h = grid_size """ get channel in correct order """ @@ -128,24 +131,24 @@ def alsc(Cam, Img, do_alsc_colour, plot=False): where w is a multiple of 32. """ w, h = Img.w/2, Img.h/2 - dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12)) + dx, dy = int(-(-(w-1)//grid_w)), int(-(-(h-1)//grid_h)) """ average the green channels into one """ av_ch_g = np.mean((channels[1:3]), axis=0) if do_alsc_colour: """ - obtain 16x12 grid of intensities for each channel and subtract black level + obtain grid_w x grid_h grid of intensities for each channel and subtract black level """ - g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16 - r = get_16x12_grid(channels[0], dx, dy) - Img.blacklevel_16 - b = get_16x12_grid(channels[3], dx, dy) - Img.blacklevel_16 + g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16 + r = get_grid(channels[0], dx, dy, grid_size) - Img.blacklevel_16 + b = get_grid(channels[3], dx, dy, grid_size) - Img.blacklevel_16 """ calculate ratios as 32 bit in order to be supported by medianBlur function """ - cr = np.reshape(g/r, (12, 16)).astype('float32') - cb = np.reshape(g/b, (12, 16)).astype('float32') - cg = np.reshape(1/g, (12, 16)).astype('float32') + cr = np.reshape(g/r, (grid_h, grid_w)).astype('float32') + cb = np.reshape(g/b, (grid_h, grid_w)).astype('float32') + cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32') """ median blur to remove peaks and save as float 64 """ @@ -164,7 +167,7 @@ def alsc(Cam, Img, do_alsc_colour, plot=False): """ note Y is plotted as -Y so plot has same axes as image """ - X, Y = np.meshgrid(range(16), range(12)) + X, Y = np.meshgrid(range(grid_w), range(grid_h)) ha.plot_surface(X, -Y, cr, cmap=cm.coolwarm, linewidth=0) ha.set_title('ALSC Plot\nImg: {}\n\ncr'.format(Img.str)) hb = hf.add_subplot(312, projection='3d') @@ -182,15 +185,15 @@ def alsc(Cam, Img, do_alsc_colour, plot=False): """ only perform calculations for luminance shading """ - g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16 - cg = np.reshape(1/g, (12, 16)).astype('float32') + g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16 + cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32') cg = cv2.medianBlur(cg, 3).astype('float64') cg = cg/np.min(cg) if plot: hf = plt.figure(figssize=(8, 8)) ha = hf.add_subplot(1, 1, 1, projection='3d') - X, Y = np.meashgrid(range(16), range(12)) + X, Y = np.meashgrid(range(grid_w), range(grid_h)) ha.plot_surface(X, -Y, cg, cmap=cm.coolwarm, linewidth=0) ha.set_title('ALSC Plot (Luminance only!)\nImg: {}\n\ncg').format(Img.str) plt.show() @@ -199,21 +202,22 @@ def alsc(Cam, Img, do_alsc_colour, plot=False): """ -Compresses channel down to a 16x12 grid +Compresses channel down to a grid of the requested size """ -def get_16x12_grid(chan, dx, dy): +def get_grid(chan, dx, dy, grid_size): + grid_w, grid_h = grid_size grid = [] """ since left and bottom border will not necessarily have rectangles of dimension dx x dy, the 32nd iteration has to be handled separately. """ - for i in range(11): - for j in range(15): + for i in range(grid_h - 1): + for j in range(grid_w - 1): grid.append(np.mean(chan[dy*i:dy*(1+i), dx*j:dx*(1+j)])) - grid.append(np.mean(chan[dy*i:dy*(1+i), 15*dx:])) - for j in range(15): - grid.append(np.mean(chan[11*dy:, dx*j:dx*(1+j)])) - grid.append(np.mean(chan[11*dy:, 15*dx:])) + grid.append(np.mean(chan[dy*i:dy*(1+i), (grid_w - 1)*dx:])) + for j in range(grid_w - 1): + grid.append(np.mean(chan[(grid_h - 1)*dy:, dx*j:dx*(1+j)])) + grid.append(np.mean(chan[(grid_h - 1)*dy:, (grid_w - 1)*dx:])) """ return as np.array, ready for further manipulation """ @@ -223,7 +227,7 @@ def get_16x12_grid(chan, dx, dy): """ obtains sigmas for red and blue, effectively a measure of the 'error' """ -def get_sigma(Cam, cal_cr_list, cal_cb_list): +def get_sigma(Cam, cal_cr_list, cal_cb_list, grid_size): Cam.log += '\nCalculating sigmas' """ provided colour alsc tables were generated for two different colour @@ -241,8 +245,8 @@ def get_sigma(Cam, cal_cr_list, cal_cb_list): sigma_rs = [] sigma_bs = [] for i in range(len(cts)-1): - sigma_rs.append(calc_sigma(cal_cr_list[i]['table'], cal_cr_list[i+1]['table'])) - sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table'])) + sigma_rs.append(calc_sigma(cal_cr_list[i]['table'], cal_cr_list[i+1]['table'], grid_size)) + sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table'], grid_size)) Cam.log += '\nColour temperature interval {} - {} K'.format(cts[i], cts[i+1]) Cam.log += '\nSigma red: {}'.format(sigma_rs[-1]) Cam.log += '\nSigma blue: {}'.format(sigma_bs[-1]) @@ -263,12 +267,13 @@ def get_sigma(Cam, cal_cr_list, cal_cb_list): """ calculate sigma from two adjacent gain tables """ -def calc_sigma(g1, g2): +def calc_sigma(g1, g2, grid_size): + grid_w, grid_h = grid_size """ reshape into 16x12 matrix """ - g1 = np.reshape(g1, (12, 16)) - g2 = np.reshape(g2, (12, 16)) + g1 = np.reshape(g1, (grid_h, grid_w)) + g2 = np.reshape(g2, (grid_h, grid_w)) """ apply gains to gain table """ @@ -280,8 +285,8 @@ def calc_sigma(g1, g2): neighbours, then append to list """ diffs = [] - for i in range(10): - for j in range(14): + for i in range(grid_h - 2): + for j in range(grid_w - 2): """ note indexing is incremented by 1 since all patches on borders are not counted diff --git a/utils/raspberrypi/ctt/ctt_awb.py b/utils/raspberrypi/ctt/ctt_awb.py index 5ba6f978..4af1fe41 100644 --- a/utils/raspberrypi/ctt/ctt_awb.py +++ b/utils/raspberrypi/ctt/ctt_awb.py @@ -13,7 +13,7 @@ from scipy.optimize import fmin """ obtain piecewise linear approximation for colour curve """ -def awb(Cam, cal_cr_list, cal_cb_list, plot): +def awb(Cam, cal_cr_list, cal_cb_list, plot, grid_size): imgs = Cam.imgs """ condense alsc calibration tables into one dictionary @@ -43,7 +43,7 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot): Note: if alsc is disabled then colour_cals will be set to None and the function will just return the greyscale patches """ - r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals) + r_patchs, b_patchs, g_patchs = get_alsc_patches(Img, colour_cals, grid_size=grid_size) """ calculate ratio of r, b to g """ @@ -293,12 +293,13 @@ def awb(Cam, cal_cr_list, cal_cb_list, plot): """ obtain greyscale patches and perform alsc colour correction """ -def get_alsc_patches(Img, colour_cals, grey=True): +def get_alsc_patches(Img, colour_cals, grey=True, grid_size=(16, 12)): """ get patch centre coordinates, image colour and the actual patches for each channel, remembering to subtract blacklevel If grey then only greyscale patches considered """ + grid_w, grid_h = grid_size if grey: cen_coords = Img.cen_coords[3::4] col = Img.col @@ -345,12 +346,12 @@ def get_alsc_patches(Img, colour_cals, grey=True): bef_tabs = np.array(colour_cals[bef]) aft_tabs = np.array(colour_cals[aft]) col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db) - col_tabs = np.reshape(col_tabs, (2, 12, 16)) + col_tabs = np.reshape(col_tabs, (2, grid_h, grid_w)) """ calculate dx, dy used to calculate alsc table """ w, h = Img.w/2, Img.h/2 - dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12)) + dx, dy = int(-(-(w-1)//grid_w)), int(-(-(h-1)//grid_h)) """ make list of pairs of gains for each patch by selecting the correct value in alsc colour calibration table diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py index 59753e33..07c943a8 100644 --- a/utils/raspberrypi/ctt/ctt_ccm.py +++ b/utils/raspberrypi/ctt/ctt_ccm.py @@ -56,7 +56,7 @@ FInds colour correction matrices for list of images """ -def ccm(Cam, cal_cr_list, cal_cb_list): +def ccm(Cam, cal_cr_list, cal_cb_list, grid_size): global matrix_selection_types, typenum imgs = Cam.imgs """ @@ -133,9 +133,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list): Note: if alsc is disabled then colour_cals will be set to None and no the function will simply return the macbeth patches """ - r, b, g = get_alsc_patches(Img, colour_cals, grey=False) - # 256 values for each patch of sRGB values - + r, b, g = get_alsc_patches(Img, colour_cals, grey=False, grid_size=grid_size) """ do awb Note: awb is done by measuring the macbeth chart in the image, rather diff --git a/utils/raspberrypi/ctt/ctt_pisp.py b/utils/raspberrypi/ctt/ctt_pisp.py new file mode 100755 index 00000000..f837e062 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_pisp.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# ctt_pisp.py - camera tuning tool for PiSP platforms + +import os +import sys + +from ctt_run import run_ctt +from ctt_tools import parse_input + +json_template = { + "rpi.black_level": { + "black_level": 4096 + }, + "rpi.lux": { + "reference_shutter_speed": 10000, + "reference_gain": 1, + "reference_aperture": 1.0 + }, + "rpi.dpc": { + "strength": 1 + }, + "rpi.noise": { + }, + "rpi.geq": { + }, + "rpi.denoise": + { + "sdn": + { + "deviation": 1.6, + "strength": 0.5, + "deviation2": 3.2, + "deviation_no_tdn": 3.2, + "strength_no_tdn": 0.75 + }, + "cdn": + { + "deviation": 200, + "strength": 0.3 + }, + "tdn": + { + "deviation": 0.8, + "threshold": 0.05 + } + }, + "rpi.awb": { + "priors": [ + {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]}, + {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]}, + {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]} + ], + "modes": { + "auto": {"lo": 2500, "hi": 7700}, + "incandescent": {"lo": 2500, "hi": 3000}, + "tungsten": {"lo": 3000, "hi": 3500}, + "fluorescent": {"lo": 4000, "hi": 4700}, + "indoor": {"lo": 3000, "hi": 5000}, + "daylight": {"lo": 5500, "hi": 6500}, + "cloudy": {"lo": 7000, "hi": 8000} + }, + "bayes": 1 + }, + "rpi.agc": { + "metering_modes": { + "centre-weighted": { + "weights": [ + 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, + 0, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, + 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, + 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, + 1, 1, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 1, 1, + 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, + 1, 1, 2, 2, 3, 3, 3, 4, 3, 3, 3, 2, 2, 1, 1, + 1, 1, 2, 2, 3, 3, 4, 4, 4, 3, 3, 2, 2, 1, 1, + 1, 1, 2, 2, 3, 3, 3, 4, 3, 3, 3, 2, 2, 1, 1, + 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 1, 1, + 1, 1, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 1, 1, + 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, + 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, + 0, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 0, + 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0 + ] + }, + "spot": { + "weights": [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 1, 2, 3, 2, 1, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 + ] + }, + "matrix": { + "weights": [ + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 + ] + } + }, + "exposure_modes": { + "normal": { + "shutter": [100, 10000, 30000, 60000, 66666], + "gain": [1.0, 1.5, 2.0, 4.0, 8.0] + }, + "short": { + "shutter": [100, 5000, 10000, 20000, 60000], + "gain": [1.0, 1.5, 2.0, 4.0, 8.0] + }, + "long": + { + "shutter": [ 100, 10000, 30000, 60000, 90000, 120000 ], + "gain": [ 1.0, 1.5, 2.0, 4.0, 8.0, 12.0 ] + } + }, + "constraint_modes": { + "normal": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]} + ], + "highlight": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]}, + {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]} + ] + }, + "y_target": [0, 0.16, 1000, 0.165, 10000, 0.17] + }, + "rpi.alsc": { + 'omega': 1.3, + 'n_iter': 100, + 'luminance_strength': 0.8, + }, + "rpi.contrast": { + "ce_enable": 1, + "gamma_curve": [ + 0, 0, + 1024, 5040, + 2048, 9338, + 3072, 12356, + 4096, 15312, + 5120, 18051, + 6144, 20790, + 7168, 23193, + 8192, 25744, + 9216, 27942, + 10240, 30035, + 11264, 32005, + 12288, 33975, + 13312, 35815, + 14336, 37600, + 15360, 39168, + 16384, 40642, + 18432, 43379, + 20480, 45749, + 22528, 47753, + 24576, 49621, + 26624, 51253, + 28672, 52698, + 30720, 53796, + 32768, 54876, + 36864, 57012, + 40960, 58656, + 45056, 59954, + 49152, 61183, + 53248, 62355, + 57344, 63419, + 61440, 64476, + 65535, 65535 + ] + }, + "rpi.ccm": { + }, + "rpi.sharpen": { + "threshold": 0.25, + "limit": 1.0, + "strength": 1.0 + } +} + +grid_size = (32, 32) + +target = 'pisp' + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + PiSP Camera Tuning Tool version 1.0 + + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-c' : Config file for the CTT. If not passed, default parameters used. + '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. + """) + quit(0) + else: + """ + parse input arguments + """ + json_output, directory, config, log_output = parse_input() + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target) diff --git a/utils/raspberrypi/ctt/ctt_pretty_print_json.py b/utils/raspberrypi/ctt/ctt_pretty_print_json.py index 3e3b8475..5d16b2a6 100755 --- a/utils/raspberrypi/ctt/ctt_pretty_print_json.py +++ b/utils/raspberrypi/ctt/ctt_pretty_print_json.py @@ -19,6 +19,7 @@ class Encoder(json.JSONEncoder): self.indentation_level = 0 self.hard_break = 120 self.custom_elems = { + 'weights': 15, 'table': 16, 'luminance_lut': 16, 'ct_curve': 3, @@ -87,7 +88,7 @@ class Encoder(json.JSONEncoder): return self.encode(o) -def pretty_print(in_json: dict) -> str: +def pretty_print(in_json: dict, custom_elems={}) -> str: if 'version' not in in_json or \ 'target' not in in_json or \ @@ -95,7 +96,9 @@ def pretty_print(in_json: dict) -> str: in_json['version'] < 2.0: raise RuntimeError('Incompatible JSON dictionary has been provided') - return json.dumps(in_json, cls=Encoder, indent=4, sort_keys=False) + encoder = Encoder(indent=4, sort_keys=False) + encoder.custom_elems |= custom_elems + return encoder.encode(in_json) #json.dumps(in_json, cls=Encoder, indent=4, sort_keys=False) if __name__ == "__main__": diff --git a/utils/raspberrypi/ctt/ctt_run.py b/utils/raspberrypi/ctt/ctt_run.py new file mode 100755 index 00000000..0c85d7db --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_run.py @@ -0,0 +1,703 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# camera tuning tool + +import os +import sys +from ctt_image_load import * +from ctt_ccm import * +from ctt_awb import * +from ctt_alsc import * +from ctt_lux import * +from ctt_noise import * +from ctt_geq import * +from ctt_pretty_print_json import pretty_print +import random +import json +import re + +""" +This file houses the camera object, which is used to perform the calibrations. +The camera object houses all the calibration images as attributes in two lists: + - imgs (macbeth charts) + - imgs_alsc (alsc correction images) +Various calibrations are methods of the camera object, and the output is stored +in a dictionary called self.json. +Once all the caibration has been completed, the Camera.json is written into a +json file. +The camera object initialises its json dictionary by reading from a pre-written +blank json file. This has been done to avoid reproducing the entire json file +in the code here, thereby avoiding unecessary clutter. +""" + + +""" +Get the colour and lux values from the strings of each inidvidual image +""" +def get_col_lux(string): + """ + Extract colour and lux values from filename + """ + col = re.search(r'([0-9]+)[kK](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) + lux = re.search(r'([0-9]+)[lL](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) + try: + col = col.group(1) + except AttributeError: + """ + Catch error if images labelled incorrectly and pass reasonable defaults + """ + return None, None + try: + lux = lux.group(1) + except AttributeError: + """ + Catch error if images labelled incorrectly and pass reasonable defaults + Still returns colour if that has been found. + """ + return col, None + return int(col), int(lux) + + +""" +Camera object that is the backbone of the tuning tool. +Input is the desired path of the output json. +""" +class Camera: + def __init__(self, jfile, json): + self.path = os.path.dirname(os.path.expanduser(__file__)) + '/' + if self.path == '/': + self.path = '' + self.imgs = [] + self.imgs_alsc = [] + self.log = 'Log created : ' + time.asctime(time.localtime(time.time())) + self.log_separator = '\n'+'-'*70+'\n' + self.jf = jfile + """ + initial json dict populated by uncalibrated values + """ + + self.json = json + + + """ + Perform colour correction calibrations by comparing macbeth patch colours + to standard macbeth chart colours. + """ + def ccm_cal(self, do_alsc_colour, grid_size): + if 'rpi.ccm' in self.disable: + return 1 + print('\nStarting CCM calibration') + self.log_new_sec('CCM') + """ + if image is greyscale then CCm makes no sense + """ + if self.grey: + print('\nERROR: Can\'t do CCM on greyscale image!') + self.log += '\nERROR: Cannot perform CCM calibration ' + self.log += 'on greyscale image!\nCCM aborted!' + del self.json['rpi.ccm'] + return 0 + a = time.time() + """ + Check if alsc tables have been generated, if not then do ccm without + alsc + """ + if ("rpi.alsc" not in self.disable) and do_alsc_colour: + """ + case where ALSC colour has been done, so no errors should be + expected... + """ + try: + cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] + cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] + self.log += '\nALSC tables found successfully' + except KeyError: + cal_cr_list, cal_cb_list = None, None + print('WARNING! No ALSC tables found for CCM!') + print('Performing CCM calibrations without ALSC correction...') + self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' + self.log += 'performed without ALSC correction...' + else: + """ + case where config options result in CCM done without ALSC colour tables + """ + cal_cr_list, cal_cb_list = None, None + self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' + self.log += 'performed without ALSC correction...' + + """ + Do CCM calibration + """ + try: + ccms = ccm(self, cal_cr_list, cal_cb_list, grid_size) + except ArithmeticError: + print('ERROR: Matrix is singular!\nTake new pictures and try again...') + self.log += '\nERROR: Singular matrix encountered during fit!' + self.log += '\nCCM aborted!' + return 1 + """ + Write output to json + """ + self.json['rpi.ccm']['ccms'] = ccms + self.log += '\nCCM calibration written to json file' + print('Finished CCM calibration') + + """ + Auto white balance calibration produces a colour curve for + various colour temperatures, as well as providing a maximum 'wiggle room' + distance from this curve (transverse_neg/pos). + """ + def awb_cal(self, greyworld, do_alsc_colour, grid_size): + if 'rpi.awb' in self.disable: + return 1 + print('\nStarting AWB calibration') + self.log_new_sec('AWB') + """ + if image is greyscale then AWB makes no sense + """ + if self.grey: + print('\nERROR: Can\'t do AWB on greyscale image!') + self.log += '\nERROR: Cannot perform AWB calibration ' + self.log += 'on greyscale image!\nAWB aborted!' + del self.json['rpi.awb'] + return 0 + """ + optional set greyworld (e.g. for noir cameras) + """ + if greyworld: + self.json['rpi.awb']['bayes'] = 0 + self.log += '\nGreyworld set' + """ + Check if alsc tables have been generated, if not then do awb without + alsc correction + """ + if ("rpi.alsc" not in self.disable) and do_alsc_colour: + try: + cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] + cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] + self.log += '\nALSC tables found successfully' + except KeyError: + cal_cr_list, cal_cb_list = None, None + print('ERROR, no ALSC calibrations found for AWB') + print('Performing AWB without ALSC tables') + self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' + self.log += 'performed without ALSC correction...' + else: + cal_cr_list, cal_cb_list = None, None + self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' + self.log += 'performed without ALSC correction...' + """ + call calibration function + """ + plot = "rpi.awb" in self.plot + awb_out = awb(self, cal_cr_list, cal_cb_list, plot, grid_size) + ct_curve, transverse_neg, transverse_pos = awb_out + """ + write output to json + """ + self.json['rpi.awb']['ct_curve'] = ct_curve + self.json['rpi.awb']['sensitivity_r'] = 1.0 + self.json['rpi.awb']['sensitivity_b'] = 1.0 + self.json['rpi.awb']['transverse_pos'] = transverse_pos + self.json['rpi.awb']['transverse_neg'] = transverse_neg + self.log += '\nAWB calibration written to json file' + print('Finished AWB calibration') + + """ + Auto lens shading correction completely mitigates the effects of lens shading for ech + colour channel seperately, and then partially corrects for vignetting. + The extent of the correction depends on the 'luminance_strength' parameter. + """ + def alsc_cal(self, luminance_strength, do_alsc_colour, grid_size): + if 'rpi.alsc' in self.disable: + return 1 + print('\nStarting ALSC calibration') + self.log_new_sec('ALSC') + """ + check if alsc images have been taken + """ + if len(self.imgs_alsc) == 0: + print('\nError:\nNo alsc calibration images found') + self.log += '\nERROR: No ALSC calibration images found!' + self.log += '\nALSC calibration aborted!' + return 1 + self.json['rpi.alsc']['luminance_strength'] = luminance_strength + if self.grey and do_alsc_colour: + print('Greyscale camera so only luminance_lut calculated') + do_alsc_colour = False + self.log += '\nWARNING: ALSC colour correction cannot be done on ' + self.log += 'greyscale image!\nALSC colour corrections forced off!' + """ + call calibration function + """ + plot = "rpi.alsc" in self.plot + alsc_out = alsc_all(self, do_alsc_colour, plot, grid_size) + cal_cr_list, cal_cb_list, luminance_lut, av_corn = alsc_out + """ + write output to json and finish if not do_alsc_colour + """ + if not do_alsc_colour: + self.json['rpi.alsc']['luminance_lut'] = luminance_lut + self.json['rpi.alsc']['n_iter'] = 0 + self.log += '\nALSC calibrations written to json file' + self.log += '\nNo colour calibrations performed' + print('Finished ALSC calibrations') + return 1 + + self.json['rpi.alsc']['calibrations_Cr'] = cal_cr_list + self.json['rpi.alsc']['calibrations_Cb'] = cal_cb_list + self.json['rpi.alsc']['luminance_lut'] = luminance_lut + self.log += '\nALSC colour and luminance tables written to json file' + + """ + The sigmas determine the strength of the adaptive algorithm, that + cleans up any lens shading that has slipped through the alsc. These are + determined by measuring a 'worst-case' difference between two alsc tables + that are adjacent in colour space. If, however, only one colour + temperature has been provided, then this difference can not be computed + as only one table is available. + To determine the sigmas you would have to estimate the error of an alsc + table with only the image it was taken on as a check. To avoid circularity, + dfault exaggerated sigmas are used, which can result in too much alsc and + is therefore not advised. + In general, just take another alsc picture at another colour temperature! + """ + + if len(self.imgs_alsc) == 1: + self.json['rpi.alsc']['sigma'] = 0.005 + self.json['rpi.alsc']['sigma_Cb'] = 0.005 + print('\nWarning:\nOnly one alsc calibration found' + '\nStandard sigmas used for adaptive algorithm.') + print('Finished ALSC calibrations') + self.log += '\nWARNING: Only one colour temperature found in ' + self.log += 'calibration images.\nStandard sigmas used for adaptive ' + self.log += 'algorithm!' + return 1 + + """ + obtain worst-case scenario residual sigmas + """ + sigma_r, sigma_b = get_sigma(self, cal_cr_list, cal_cb_list, grid_size) + """ + write output to json + """ + self.json['rpi.alsc']['sigma'] = np.round(sigma_r, 5) + self.json['rpi.alsc']['sigma_Cb'] = np.round(sigma_b, 5) + self.log += '\nCalibrated sigmas written to json file' + print('Finished ALSC calibrations') + + """ + Green equalisation fixes problems caused by discrepancies in green + channels. This is done by measuring the effect on macbeth chart patches, + which ideally would have the same green values throughout. + An upper bound linear model is fit, fixing a threshold for the green + differences that are corrected. + """ + def geq_cal(self): + if 'rpi.geq' in self.disable: + return 1 + print('\nStarting GEQ calibrations') + self.log_new_sec('GEQ') + """ + perform calibration + """ + plot = 'rpi.geq' in self.plot + slope, offset = geq_fit(self, plot) + """ + write output to json + """ + self.json['rpi.geq']['offset'] = offset + self.json['rpi.geq']['slope'] = slope + self.log += '\nGEQ calibrations written to json file' + print('Finished GEQ calibrations') + + """ + Lux calibrations allow the lux level of a scene to be estimated by a ratio + calculation. Lux values are used in the pipeline for algorithms such as AGC + and AWB + """ + def lux_cal(self): + if 'rpi.lux' in self.disable: + return 1 + print('\nStarting LUX calibrations') + self.log_new_sec('LUX') + """ + The lux calibration is done on a single image. For best effects, the + image with lux level closest to 1000 is chosen. + """ + luxes = [Img.lux for Img in self.imgs] + argmax = luxes.index(min(luxes, key=lambda l: abs(1000-l))) + Img = self.imgs[argmax] + self.log += '\nLux found closest to 1000: {} lx'.format(Img.lux) + self.log += '\nImage used: ' + Img.name + if Img.lux < 50: + self.log += '\nWARNING: Low lux could cause inaccurate calibrations!' + """ + do calibration + """ + lux_out, shutter_speed, gain = lux(self, Img) + """ + write output to json + """ + self.json['rpi.lux']['reference_shutter_speed'] = shutter_speed + self.json['rpi.lux']['reference_gain'] = gain + self.json['rpi.lux']['reference_lux'] = Img.lux + self.json['rpi.lux']['reference_Y'] = lux_out + self.log += '\nLUX calibrations written to json file' + print('Finished LUX calibrations') + + """ + Noise alibration attempts to describe the noise profile of the sensor. The + calibration is run on macbeth images and the final output is taken as the average + """ + def noise_cal(self): + if 'rpi.noise' in self.disable: + return 1 + print('\nStarting NOISE calibrations') + self.log_new_sec('NOISE') + """ + run calibration on all images and sort by slope. + """ + plot = "rpi.noise" in self.plot + noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key=lambda x: x[0]) + self.log += '\nFinished processing images' + """ + take the average of the interquartile + """ + length = len(noise_out) + noise_out = np.mean(noise_out[length//4:1+3*length//4], axis=0) + self.log += '\nAverage noise profile: constant = {} '.format(int(noise_out[1])) + self.log += 'slope = {:.3f}'.format(noise_out[0]) + """ + write to json + """ + self.json['rpi.noise']['reference_constant'] = int(noise_out[1]) + self.json['rpi.noise']['reference_slope'] = round(noise_out[0], 3) + self.log += '\nNOISE calibrations written to json' + print('Finished NOISE calibrations') + + """ + Removes json entries that are turned off + """ + def json_remove(self, disable): + self.log_new_sec('Disabling Options', cal=False) + if len(self.disable) == 0: + self.log += '\nNothing disabled!' + return 1 + for key in disable: + try: + del self.json[key] + self.log += '\nDisabled: ' + key + except KeyError: + self.log += '\nERROR: ' + key + ' not found!' + """ + writes the json dictionary to the raw json file then make pretty + """ + def write_json(self, version=2.0, target='bcm2835', grid_size=(16, 12)): + """ + Write json dictionary to file using our version 2 format + """ + + out_json = { + "version": version, + 'target': target if target != 'vc4' else 'bcm2835', + "algorithms": [{name: data} for name, data in self.json.items()], + } + + with open(self.jf, 'w') as f: + f.write(pretty_print(out_json, + custom_elems={'table': grid_size[0], 'luminance_lut': grid_size[0]})) + + """ + add a new section to the log file + """ + def log_new_sec(self, section, cal=True): + self.log += '\n'+self.log_separator + self.log += section + if cal: + self.log += ' Calibration' + self.log += self.log_separator + + """ + write script arguments to log file + """ + def log_user_input(self, json_output, directory, config, log_output): + self.log_new_sec('User Arguments', cal=False) + self.log += '\nJson file output: ' + json_output + self.log += '\nCalibration images directory: ' + directory + if config is None: + self.log += '\nNo configuration file input... using default options' + elif config is False: + self.log += '\nWARNING: Invalid configuration file path...' + self.log += ' using default options' + elif config is True: + self.log += '\nWARNING: Invalid syntax in configuration file...' + self.log += ' using default options' + else: + self.log += '\nConfiguration file: ' + config + if log_output is None: + self.log += '\nNo log file path input... using default: ctt_log.txt' + else: + self.log += '\nLog file output: ' + log_output + + # if log_output + + """ + write log file + """ + def write_log(self, filename): + if filename is None: + filename = 'ctt_log.txt' + self.log += '\n' + self.log_separator + with open(filename, 'w') as logfile: + logfile.write(self.log) + + """ + Add all images from directory, pass into relevant list of images and + extrace lux and temperature values. + """ + def add_imgs(self, directory, mac_config, blacklevel=-1): + self.log_new_sec('Image Loading', cal=False) + img_suc_msg = 'Image loaded successfully!' + print('\n\nLoading images from '+directory) + self.log += '\nDirectory: ' + directory + """ + get list of files + """ + filename_list = get_photos(directory) + print("Files found: {}".format(len(filename_list))) + self.log += '\nFiles found: {}'.format(len(filename_list)) + """ + iterate over files + """ + filename_list.sort() + for filename in filename_list: + address = directory + filename + print('\nLoading image: '+filename) + self.log += '\n\nImage: ' + filename + """ + obtain colour and lux value + """ + col, lux = get_col_lux(filename) + """ + Check if image is an alsc calibration image + """ + if 'alsc' in filename: + Img = load_image(self, address, mac=False) + self.log += '\nIdentified as an ALSC image' + """ + check if imagae data has been successfully unpacked + """ + if Img == 0: + print('\nDISCARDED') + self.log += '\nImage discarded!' + continue + """ + check that image colour temperature has been successfuly obtained + """ + elif col is not None: + """ + if successful, append to list and continue to next image + """ + Img.col = col + Img.name = filename + self.log += '\nColour temperature: {} K'.format(col) + self.imgs_alsc.append(Img) + if blacklevel != -1: + Img.blacklevel_16 = blacklevel + print(img_suc_msg) + continue + else: + print('Error! No colour temperature found!') + self.log += '\nWARNING: Error reading colour temperature' + self.log += '\nImage discarded!' + print('DISCARDED') + else: + self.log += '\nIdentified as macbeth chart image' + """ + if image isn't an alsc correction then it must have a lux and a + colour temperature value to be useful + """ + if lux is None: + print('DISCARDED') + self.log += '\nWARNING: Error reading lux value' + self.log += '\nImage discarded!' + continue + Img = load_image(self, address, mac_config) + """ + check that image data has been successfuly unpacked + """ + if Img == 0: + print('DISCARDED') + self.log += '\nImage discarded!' + continue + else: + """ + if successful, append to list and continue to next image + """ + Img.col, Img.lux = col, lux + Img.name = filename + self.log += '\nColour temperature: {} K'.format(col) + self.log += '\nLux value: {} lx'.format(lux) + if blacklevel != -1: + Img.blacklevel_16 = blacklevel + print(img_suc_msg) + self.imgs.append(Img) + + print('\nFinished loading images') + + """ + Check that usable images have been found + Possible errors include: + - no macbeth chart + - incorrect filename/extension + - images from different cameras + """ + def check_imgs(self, macbeth=True): + self.log += '\n\nImages found:' + self.log += '\nMacbeth : {}'.format(len(self.imgs)) + self.log += '\nALSC : {} '.format(len(self.imgs_alsc)) + self.log += '\n\nCamera metadata' + """ + check usable images found + """ + if len(self.imgs) == 0 and macbeth: + print('\nERROR: No usable macbeth chart images found') + self.log += '\nERROR: No usable macbeth chart images found' + return 0 + elif len(self.imgs) == 0 and len(self.imgs_alsc) == 0: + print('\nERROR: No usable images found') + self.log += '\nERROR: No usable images found' + return 0 + """ + Double check that every image has come from the same camera... + """ + all_imgs = self.imgs + self.imgs_alsc + camNames = list(set([Img.camName for Img in all_imgs])) + patterns = list(set([Img.pattern for Img in all_imgs])) + sigbitss = list(set([Img.sigbits for Img in all_imgs])) + blacklevels = list(set([Img.blacklevel_16 for Img in all_imgs])) + sizes = list(set([(Img.w, Img.h) for Img in all_imgs])) + + if len(camNames) == 1 and len(patterns) == 1 and len(sigbitss) == 1 and \ + len(blacklevels) == 1 and len(sizes) == 1: + self.grey = (patterns[0] == 128) + self.blacklevel_16 = blacklevels[0] + self.log += '\nName: {}'.format(camNames[0]) + self.log += '\nBayer pattern case: {}'.format(patterns[0]) + if self.grey: + self.log += '\nGreyscale camera identified' + self.log += '\nSignificant bits: {}'.format(sigbitss[0]) + self.log += '\nBlacklevel: {}'.format(blacklevels[0]) + self.log += '\nImage size: w = {} h = {}'.format(sizes[0][0], sizes[0][1]) + return 1 + else: + print('\nERROR: Images from different cameras') + self.log += '\nERROR: Images are from different cameras' + return 0 + + +def run_ctt(json_output, directory, config, log_output, json_template, grid_size, target, alsc_only=False): + """ + check input files are jsons + """ + if json_output[-5:] != '.json': + raise ArgError('\n\nError: Output must be a json file!') + if config is not None: + """ + check if config file is actually a json + """ + if config[-5:] != '.json': + raise ArgError('\n\nError: Config file must be a json file!') + """ + read configurations + """ + try: + with open(config, 'r') as config_json: + configs = json.load(config_json) + except FileNotFoundError: + configs = {} + config = False + except json.decoder.JSONDecodeError: + configs = {} + config = True + + else: + configs = {} + """ + load configurations from config file, if not given then set default + """ + disable = get_config(configs, "disable", [], 'list') + plot = get_config(configs, "plot", [], 'list') + awb_d = get_config(configs, "awb", {}, 'dict') + greyworld = get_config(awb_d, "greyworld", 0, 'bool') + alsc_d = get_config(configs, "alsc", {}, 'dict') + do_alsc_colour = get_config(alsc_d, "do_alsc_colour", 1, 'bool') + luminance_strength = get_config(alsc_d, "luminance_strength", 0.8, 'num') + blacklevel = get_config(configs, "blacklevel", -1, 'num') + macbeth_d = get_config(configs, "macbeth", {}, 'dict') + mac_small = get_config(macbeth_d, "small", 0, 'bool') + mac_show = get_config(macbeth_d, "show", 0, 'bool') + mac_config = (mac_small, mac_show) + + if blacklevel < -1 or blacklevel >= 2**16: + print('\nInvalid blacklevel, defaulted to 64') + blacklevel = -1 + + if luminance_strength < 0 or luminance_strength > 1: + print('\nInvalid luminance_strength strength, defaulted to 0.5') + luminance_strength = 0.5 + + """ + sanitise directory path + """ + if directory[-1] != '/': + directory += '/' + """ + initialise tuning tool and load images + """ + try: + Cam = Camera(json_output, json=json_template) + Cam.log_user_input(json_output, directory, config, log_output) + if alsc_only: + disable = set(Cam.json.keys()).symmetric_difference({"rpi.alsc"}) + Cam.disable = disable + Cam.plot = plot + Cam.add_imgs(directory, mac_config, blacklevel) + except FileNotFoundError: + raise ArgError('\n\nError: Input image directory not found!') + + """ + preform calibrations as long as check_imgs returns True + If alsc is activated then it must be done before awb and ccm since the alsc + tables are used in awb and ccm calibrations + ccm also technically does an awb but it measures this from the macbeth + chart in the image rather than using calibration data + """ + if Cam.check_imgs(macbeth=not alsc_only): + if not alsc_only: + Cam.json['rpi.black_level']['black_level'] = Cam.blacklevel_16 + Cam.json_remove(disable) + print('\nSTARTING CALIBRATIONS') + Cam.alsc_cal(luminance_strength, do_alsc_colour, grid_size) + Cam.geq_cal() + Cam.lux_cal() + Cam.noise_cal() + Cam.cac_cal(do_alsc_colour) + Cam.awb_cal(greyworld, do_alsc_colour, grid_size) + Cam.ccm_cal(do_alsc_colour, grid_size) + + print('\nFINISHED CALIBRATIONS') + Cam.write_json(target=target, grid_size=grid_size) + Cam.write_log(log_output) + print('\nCalibrations written to: '+json_output) + if log_output is None: + log_output = 'ctt_log.txt' + print('Log file written to: '+log_output) + pass + else: + Cam.write_log(log_output) diff --git a/utils/raspberrypi/ctt/ctt_vc4.py b/utils/raspberrypi/ctt/ctt_vc4.py new file mode 100755 index 00000000..86acfd47 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_vc4.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# ctt_vc4.py - camera tuning tool for VC4 platforms + +import os +import sys + +from ctt_run import run_ctt +from ctt_tools import parse_input + +json_template = { + "rpi.black_level": { + "black_level": 4096 + }, + "rpi.dpc": { + }, + "rpi.lux": { + "reference_shutter_speed": 10000, + "reference_gain": 1, + "reference_aperture": 1.0 + }, + "rpi.noise": { + }, + "rpi.geq": { + }, + "rpi.sdn": { + }, + "rpi.awb": { + "priors": [ + {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]}, + {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]}, + {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]} + ], + "modes": { + "auto": {"lo": 2500, "hi": 8000}, + "incandescent": {"lo": 2500, "hi": 3000}, + "tungsten": {"lo": 3000, "hi": 3500}, + "fluorescent": {"lo": 4000, "hi": 4700}, + "indoor": {"lo": 3000, "hi": 5000}, + "daylight": {"lo": 5500, "hi": 6500}, + "cloudy": {"lo": 7000, "hi": 8600} + }, + "bayes": 1 + }, + "rpi.agc": { + "metering_modes": { + "centre-weighted": { + "weights": [3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0] + }, + "spot": { + "weights": [2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + }, + "matrix": { + "weights": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + } + }, + "exposure_modes": { + "normal": { + "shutter": [100, 10000, 30000, 60000, 120000], + "gain": [1.0, 2.0, 4.0, 6.0, 6.0] + }, + "short": { + "shutter": [100, 5000, 10000, 20000, 120000], + "gain": [1.0, 2.0, 4.0, 6.0, 6.0] + } + }, + "constraint_modes": { + "normal": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]} + ], + "highlight": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]}, + {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]} + ] + }, + "y_target": [0, 0.16, 1000, 0.165, 10000, 0.17] + }, + "rpi.alsc": { + 'omega': 1.3, + 'n_iter': 100, + 'luminance_strength': 0.7, + }, + "rpi.contrast": { + "ce_enable": 1, + "gamma_curve": [ + 0, 0, + 1024, 5040, + 2048, 9338, + 3072, 12356, + 4096, 15312, + 5120, 18051, + 6144, 20790, + 7168, 23193, + 8192, 25744, + 9216, 27942, + 10240, 30035, + 11264, 32005, + 12288, 33975, + 13312, 35815, + 14336, 37600, + 15360, 39168, + 16384, 40642, + 18432, 43379, + 20480, 45749, + 22528, 47753, + 24576, 49621, + 26624, 51253, + 28672, 52698, + 30720, 53796, + 32768, 54876, + 36864, 57012, + 40960, 58656, + 45056, 59954, + 49152, 61183, + 53248, 62355, + 57344, 63419, + 61440, 64476, + 65535, 65535 + ] + }, + "rpi.ccm": { + }, + "rpi.sharpen": { + } +} + +grid_size = (16, 12) + +target = 'bcm2835' + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + VC4 Camera Tuning Tool version 1.0 + + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-c' : Config file for the CTT. If not passed, default parameters used. + '-l' : Name of output log file. If not passed, 'ctt_log.txt' used. + """) + quit(0) + else: + """ + parse input arguments + """ + json_output, directory, config, log_output = parse_input() + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target) -- cgit v1.2.1