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authorNaushir Patuck <naush@raspberrypi.com>2020-05-03 16:49:53 +0100
committerLaurent Pinchart <laurent.pinchart@ideasonboard.com>2020-05-11 23:54:45 +0300
commitc01cfe14f5540ba96b458088185ac7ae90bb3534 (patch)
treef9112e0195de83ea1b20cf81cb62144cd50174f9
parent0db2c8dc75e466e7648dc1b95380495c6a126349 (diff)
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>
Diffstat
-rwxr-xr-xutils/raspberrypi/ctt/ctt.py823
-rw-r--r--utils/raspberrypi/ctt/ctt_alsc.py297
-rw-r--r--utils/raspberrypi/ctt/ctt_awb.py374
-rw-r--r--utils/raspberrypi/ctt/ctt_ccm.py221
-rw-r--r--utils/raspberrypi/ctt/ctt_config_example.json16
-rw-r--r--utils/raspberrypi/ctt/ctt_geq.py179
-rw-r--r--utils/raspberrypi/ctt/ctt_image_load.py428
-rw-r--r--utils/raspberrypi/ctt/ctt_lux.py58
-rw-r--r--utils/raspberrypi/ctt/ctt_macbeth_locator.py748
-rw-r--r--utils/raspberrypi/ctt/ctt_noise.py123
-rw-r--r--utils/raspberrypi/ctt/ctt_pretty_print_json.py70
-rw-r--r--utils/raspberrypi/ctt/ctt_ransac.py69
-rw-r--r--utils/raspberrypi/ctt/ctt_ref.pgm5
-rw-r--r--utils/raspberrypi/ctt/ctt_tools.py141
14 files changed, 3552 insertions, 0 deletions
diff --git a/utils/raspberrypi/ctt/ctt.py b/utils/raspberrypi/ctt/ctt.py
new file mode 100755
index 00000000..5fe22e14
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt.py
@@ -0,0 +1,823 @@
+#!/usr/bin/env python3
+#
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt.py - 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 *
+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('([0-9]+)[kK](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$',string)
+ lux = re.search('([0-9]+)[lL](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$',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]
+ },
+ "sport": {
+ "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 (not "rpi.alsc" 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 (not "rpi.alsc" 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 ouput 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
+ """
+ l = len(noise_out)
+ noise_out = np.mean(noise_out[l//4:1+3*l//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
+ """
+ jstring = json.dumps(self.json,sort_keys=False)
+ """
+ make it pretty :)
+ """
+ pretty_print_json(jstring,self.jf)
+
+ """
+ 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 == None:
+ self.log += '\nNo configuration file input... using default options'
+ elif config == False:
+ self.log += '\nWARNING: Invalid configuration file path...'
+ self.log += ' using default options'
+ elif config == True:
+ self.log += '\nWARNING: Invalid syntax in configuration file...'
+ self.log += ' using default options'
+ else:
+ self.log += '\nConfiguration file: ' + config
+ if log_output == 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 == 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 != 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 == 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):
+ 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:
+ print('\nERROR: No usable macbeth chart images found')
+ self.log += '\nERROR: No usable macbeth chart 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):
+ """
+ check input files are jsons
+ """
+ if json_output[-5:] != '.json':
+ raise ArgError('\n\nError: Output must be a json file!')
+ if config != 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)
+ 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():
+ 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 == 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
new file mode 100644
index 00000000..fe1cff65
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_alsc.py
@@ -0,0 +1,297 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_alsc.py - camera tuning tool for ALSC (auto lens shading correction)
+
+from ctt_image_load import *
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from mpl_toolkits.mplot3d import Axes3D
+
+"""
+preform alsc calibration on a set of images
+"""
+def alsc_all(Cam,do_alsc_colour,plot):
+ imgs_alsc = Cam.imgs_alsc
+ """
+ create list of colour temperatures and associated calibration tables
+ """
+ list_col = []
+ list_cr = []
+ list_cb = []
+ list_cg = []
+ for Img in imgs_alsc:
+ col,cr,cb,cg,size = alsc(Cam,Img,do_alsc_colour,plot)
+ list_col.append(col)
+ list_cr.append(cr)
+ list_cb.append(cb)
+ list_cg.append(cg)
+ Cam.log += '\n'
+ Cam.log += '\nFinished processing images'
+ w,h,dx,dy = size
+ Cam.log += '\nChannel dimensions: w = {} h = {}'.format(int(w),int(h))
+ Cam.log += '\n16x12 grid rectangle size: w = {} h = {}'.format(dx,dy)
+
+ """
+ convert to numpy array for data manipulation
+ """
+ list_col = np.array(list_col)
+ list_cr = np.array(list_cr)
+ list_cb = np.array(list_cb)
+ list_cg = np.array(list_cg)
+
+ cal_cr_list = []
+ cal_cb_list = []
+
+ """
+ only do colour calculations if required
+ """
+ if do_alsc_colour:
+ Cam.log += '\nALSC colour tables'
+ for ct in sorted(set(list_col)):
+ Cam.log += '\nColour temperature: {} K'.format(ct)
+ """
+ average tables for the same colour temperature
+ """
+ indices = np.where(list_col == ct)
+ ct = int(ct)
+ t_r = np.mean(list_cr[indices],axis=0)
+ t_b = np.mean(list_cb[indices],axis=0)
+ """
+ force numbers to be stored to 3dp.... :(
+ """
+ t_r = np.where((100*t_r)%1<=0.05, t_r+0.001,t_r)
+ t_b = np.where((100*t_b)%1<=0.05, t_b+0.001,t_b)
+ t_r = np.where((100*t_r)%1>=0.95, t_r-0.001,t_r)
+ 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_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 = round(b_cen/4,3)
+ Cam.log += '\nRed table corners: {}'.format(r_corners)
+ Cam.log += '\nRed table centre: {}'.format(r_cen)
+ Cam.log += '\nBlue table corners: {}'.format(b_corners)
+ Cam.log += '\nBlue table centre: {}'.format(b_cen)
+ cr_dict = {
+ 'ct':ct,
+ 'table':list(t_r)
+ }
+ cb_dict = {
+ 'ct':ct,
+ 'table':list(t_b)
+ }
+ cal_cr_list.append(cr_dict)
+ cal_cb_list.append(cb_dict)
+ Cam.log += '\n'
+ else:
+ cal_cr_list,cal_cb_list = None,None
+
+ """
+ average all values for luminance shading and return one table for all temperatures
+ """
+ lum_lut = np.mean(list_cg,axis=0)
+ lum_lut = np.where((100*lum_lut)%1<=0.05,lum_lut+0.001,lum_lut)
+ lum_lut = np.where((100*lum_lut)%1>=0.95,lum_lut-0.001,lum_lut)
+ lum_lut = list(np.round(lum_lut,3))
+
+ """
+ calculate average corner for lsc gain calculation further on
+ """
+ corners = (lum_lut[0],lum_lut[15],lum_lut[-1],lum_lut[-16])
+ Cam.log += '\nLuminance table corners: {}'.format(corners)
+ l_cen = lum_lut[5*16+7]+lum_lut[5*16+8]+lum_lut[6*16+7]+lum_lut[6*16+8]
+ l_cen = round(l_cen/4,3)
+ Cam.log += '\nLuminance table centre: {}'.format(l_cen)
+ av_corn = np.sum(corners)/4
+
+ return cal_cr_list,cal_cb_list,lum_lut,av_corn
+
+
+"""
+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):
+ Cam.log += '\nProcessing image: ' + Img.name
+ """
+ get channel in correct order
+ """
+ channels = [Img.channels[i] for i in Img.order]
+ """
+ calculate size of single rectangle.
+ -(-(w-1)//32) is a ceiling division. w-1 is to deal robustly with the case
+ 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))
+ """
+ average the green channels into one
+ """
+ av_ch_g = np.mean((channels[1:2]),axis = 0)
+ if do_alsc_colour:
+ """
+ obtain 16x12 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
+ """
+ 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')
+ """
+ median blur to remove peaks and save as float 64
+ """
+ cr = cv2.medianBlur(cr,3).astype('float64')
+ cb = cv2.medianBlur(cb,3).astype('float64')
+ cg = cv2.medianBlur(cg,3).astype('float64')
+ cg = cg/np.min(cg)
+
+ """
+ debugging code showing 2D surface plot of vignetting. Quite useful for
+ for sanity check
+ """
+ if plot:
+ hf = plt.figure(figsize=(8,8))
+ ha = hf.add_subplot(311, projection='3d')
+ """
+ note Y is plotted as -Y so plot has same axes as image
+ """
+ X,Y = np.meshgrid(range(16),range(12))
+ 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')
+ hb.plot_surface(X,-Y,cb,cmap=cm.coolwarm,linewidth=0)
+ hb.set_title('cb')
+ hc = hf.add_subplot(313, projection='3d')
+ hc.plot_surface(X,-Y,cg,cmap=cm.coolwarm,linewidth=0)
+ hc.set_title('g')
+ # print(Img.str)
+ plt.show()
+
+ return Img.col,cr.flatten(),cb.flatten(),cg.flatten(),(w,h,dx,dy)
+
+ else:
+ """
+ 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')
+ 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))
+ 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()
+
+ return Img.col,None,None,cg.flatten(),(w,h,dx,dy)
+
+
+"""
+Compresses channel down to a 16x12 grid
+"""
+def get_16x12_grid(chan,dx,dy):
+ 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):
+ 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:]))
+ """
+ return as np.array, ready for further manipulation
+ """
+ return np.array(grid)
+
+"""
+obtains sigmas for red and blue, effectively a measure of the 'error'
+"""
+def get_sigma(Cam,cal_cr_list,cal_cb_list):
+ Cam.log += '\nCalculating sigmas'
+ """
+ provided colour alsc tables were generated for two different colour
+ temperatures sigma is calculated by comparing two calibration temperatures
+ adjacent in colour space
+ """
+ """
+ create list of colour temperatures
+ """
+ cts = [cal['ct'] for cal in cal_cr_list]
+ # print(cts)
+ """
+ calculate sigmas for each adjacent cts and return worst one
+ """
+ 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']))
+ 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])
+
+ """
+ return maximum sigmas, not necessarily from the same colour temperature
+ interval
+ """
+ sigma_r = max(sigma_rs) if sigma_rs else 0.005
+ sigma_b = max(sigma_bs) if sigma_bs else 0.005
+ Cam.log += '\nMaximum sigmas: Red = {} Blue = {}'.format(sigma_r, sigma_b)
+
+
+ # print(sigma_rs,sigma_bs)
+ # print(sigma_r,sigma_b)
+ return sigma_r,sigma_b
+
+"""
+calculate sigma from two adjacent gain tables
+"""
+def calc_sigma(g1,g2):
+ """
+ reshape into 16x12 matrix
+ """
+ g1 = np.reshape(g1,(12,16))
+ g2 = np.reshape(g2,(12,16))
+ """
+ apply gains to gain table
+ """
+ gg = g1/g2
+ if np.mean(gg) < 1:
+ gg = 1/gg
+ """
+ for each internal patch, compute average difference between it and its 4
+ neighbours, then append to list
+ """
+ diffs = []
+ for i in range(10):
+ for j in range(14):
+ """
+ note indexing is incremented by 1 since all patches on borders are
+ not counted
+ """
+ diff = np.abs(gg[i+1][j+1]-gg[i][j+1])
+ diff += np.abs(gg[i+1][j+1]-gg[i+2][j+1])
+ diff += np.abs(gg[i+1][j+1]-gg[i+1][j])
+ diff += np.abs(gg[i+1][j+1]-gg[i+1][j+2])
+ diffs.append(diff/4)
+
+ """
+ return mean difference
+ """
+ mean_diff = np.mean(diffs)
+ return(np.round(mean_diff,5))
diff --git a/utils/raspberrypi/ctt/ctt_awb.py b/utils/raspberrypi/ctt/ctt_awb.py
new file mode 100644
index 00000000..d3130612
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_awb.py
@@ -0,0 +1,374 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_awb.py - camera tuning tool for AWB
+
+from ctt_image_load import *
+import matplotlib.pyplot as plt
+from bisect import bisect_left
+from scipy.optimize import fmin
+
+"""
+obtain piecewise linear approximation for colour curve
+"""
+def awb(Cam,cal_cr_list,cal_cb_list,plot):
+ imgs = Cam.imgs
+ """
+ condense alsc calibration tables into one dictionary
+ """
+ if cal_cr_list == None:
+ colour_cals = None
+ else:
+ colour_cals = {}
+ for cr,cb in zip(cal_cr_list,cal_cb_list):
+ cr_tab = cr['table']
+ cb_tab = cb['table']
+ """
+ normalise tables so min value is 1
+ """
+ cr_tab= cr_tab/np.min(cr_tab)
+ cb_tab= cb_tab/np.min(cb_tab)
+ colour_cals[cr['ct']] = [cr_tab,cb_tab]
+ """
+ obtain data from greyscale macbeth patches
+ """
+ rb_raw = []
+ rbs_hat = []
+ for Img in imgs:
+ Cam.log += '\nProcessing '+Img.name
+ """
+ get greyscale patches with alsc applied if alsc enabled.
+ 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)
+ """
+ calculate ratio of r,b to g
+ """
+ r_g = np.mean(r_patchs/g_patchs)
+ b_g = np.mean(b_patchs/g_patchs)
+ Cam.log += '\n r : {:.4f} b : {:.4f}'.format(r_g,b_g)
+ """
+ The curve tends to be better behaved in so-called hatspace.
+ R,B,G represent the individual channels. The colour curve is plotted in
+ r,b space, where:
+ r = R/G
+ b = B/G
+ This will be referred to as dehatspace... (sorry)
+ Hatspace is defined as:
+ r_hat = R/(R+B+G)
+ b_hat = B/(R+B+G)
+ To convert from dehatspace to hastpace (hat operation):
+ r_hat = r/(1+r+b)
+ b_hat = b/(1+r+b)
+ To convert from hatspace to dehatspace (dehat operation):
+ r = r_hat/(1-r_hat-b_hat)
+ b = b_hat/(1-r_hat-b_hat)
+ Proof is left as an excercise to the reader...
+ Throughout the code, r and b are sometimes referred to as r_g and b_g
+ as a reminder that they are ratios
+ """
+ r_g_hat = r_g/(1+r_g+b_g)
+ b_g_hat = b_g/(1+r_g+b_g)
+ Cam.log += '\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat,b_g_hat)
+ rbs_hat.append((r_g_hat,b_g_hat,Img.col))
+ rb_raw.append((r_g,b_g))
+ Cam.log += '\n'
+
+ Cam.log += '\nFinished processing images'
+ """
+ sort all lits simultaneously by r_hat
+ """
+ rbs_zip = list(zip(rbs_hat,rb_raw))
+ rbs_zip.sort(key=lambda x:x[0][0])
+ rbs_hat,rb_raw = list(zip(*rbs_zip))
+ """
+ unzip tuples ready for processing
+ """
+ rbs_hat = list(zip(*rbs_hat))
+ rb_raw = list(zip(*rb_raw))
+ """
+ fit quadratic fit to r_g hat and b_g_hat
+ """
+ a,b,c = np.polyfit(rbs_hat[0],rbs_hat[1],2)
+ Cam.log += '\nFit quadratic curve in hatspace'
+ """
+ the algorithm now approximates the shortest distance from each point to the
+ curve in dehatspace. Since the fit is done in hatspace, it is easier to
+ find the actual shortest distance in hatspace and use the projection back
+ into dehatspace as an overestimate.
+ The distance will be used for two things:
+ 1) In the case that colour temperature does not strictly decrease with
+ increasing r/g, the closest point to the line will be chosen out of an
+ increasing pair of colours.
+
+ 2) To calculate transverse negative an dpositive, the maximum positive
+ and negative distance from the line are chosen. This benefits from the
+ overestimate as the transverse pos/neg are upper bound values.
+ """
+ """
+ define fit function
+ """
+ def f(x):
+ return a*x**2 + b*x + c
+ """
+ iterate over points (R,B are x and y coordinates of points) and calculate
+ distance to line in dehatspace
+ """
+ dists = []
+ for i, (R,B) in enumerate(zip(rbs_hat[0],rbs_hat[1])):
+ """
+ define function to minimise as square distance between datapoint and
+ point on curve. Squaring is monotonic so minimising radius squared is
+ equivalent to minimising radius
+ """
+ def f_min(x):
+ y = f(x)
+ return((x-R)**2+(y-B)**2)
+ """
+ perform optimisation with scipy.optmisie.fmin
+ """
+ x_hat = fmin(f_min,R,disp=0)[0]
+ y_hat = f(x_hat)
+ """
+ dehat
+ """
+ x = x_hat/(1-x_hat-y_hat)
+ y = y_hat/(1-x_hat-y_hat)
+ rr = R/(1-R-B)
+ bb = B/(1-R-B)
+ """
+ calculate euclidean distance in dehatspace
+ """
+ dist = ((x-rr)**2+(y-bb)**2)**0.5
+ """
+ return negative if point is below the fit curve
+ """
+ if (x+y) > (rr+bb):
+ dist *= -1
+ dists.append(dist)
+ Cam.log += '\nFound closest point on fit line to each point in dehatspace'
+ """
+ calculate wiggle factors in awb. 10% added since this is an upper bound
+ """
+ transverse_neg = - np.min(dists) * 1.1
+ transverse_pos = np.max(dists) * 1.1
+ Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos)
+ Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg)
+ """
+ set minimum transverse wiggles to 0.1 .
+ Wiggle factors dictate how far off of the curve the algorithm searches. 0.1
+ is a suitable minimum that gives better results for lighting conditions not
+ within calibration dataset. Anything less will generalise poorly.
+ """
+ if transverse_pos < 0.01:
+ transverse_pos = 0.01
+ Cam.log += '\nForced transverse pos to 0.01'
+ if transverse_neg < 0.01:
+ transverse_neg = 0.01
+ Cam.log += '\nForced transverse neg to 0.01'
+
+ """
+ generate new b_hat values at each r_hat according to fit
+ """
+ r_hat_fit = np.array(rbs_hat[0])
+ b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c
+ """
+ transform from hatspace to dehatspace
+ """
+ r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit)
+ b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit)
+ c_fit = np.round(rbs_hat[2],0)
+ """
+ round to 4dp
+ """
+ r_fit = np.where((1000*r_fit)%1<=0.05,r_fit+0.0001,r_fit)
+ r_fit = np.where((1000*r_fit)%1>=0.95,r_fit-0.0001,r_fit)
+ b_fit = np.where((1000*b_fit)%1<=0.05,b_fit+0.0001,b_fit)
+ b_fit = np.where((1000*b_fit)%1>=0.95,b_fit-0.0001,b_fit)
+ r_fit = np.round(r_fit,4)
+ b_fit = np.round(b_fit,4)
+ """
+ The following code ensures that colour temperature decreases with
+ increasing r/g
+ """
+ """
+ iterate backwards over list for easier indexing
+ """
+ i = len(c_fit) - 1
+ while i > 0 :
+ if c_fit[i] > c_fit[i-1]:
+ Cam.log += '\nColour temperature increase found\n'
+ Cam.log += '{} K at r = {} to '.format(c_fit[i-1],r_fit[i-1])
+ Cam.log += '{} K at r = {}'.format(c_fit[i],r_fit[i])
+ """
+ if colour temperature increases then discard point furthest from
+ the transformed fit (dehatspace)
+ """
+ error_1 = abs(dists[i-1])
+ error_2 = abs(dists[i])
+ Cam.log += '\nDistances from fit:\n'
+ Cam.log += '{} K : {:.5f} , '.format(c_fit[i],error_1)
+ Cam.log += '{} K : {:.5f}'.format(c_fit[i-1],error_2)
+ """
+ find bad index
+ note that in python false = 0 and true = 1
+ """
+ bad = i - (error_1<error_2)
+ Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad])
+ Cam.log += 'it is furthest from fit'
+ """
+ delete bad point
+ """
+ r_fit = np.delete(r_fit,bad)
+ b_fit = np.delete(b_fit,bad)
+ c_fit = np.delete(c_fit,bad).astype(np.uint16)
+ """
+ note that if a point has been discarded then the length has decreased
+ by one, meaning that decreasing the index by one will reassess the kept
+ point against the next point. It is therefore possible, in theory, for
+ two adjacent points to be discarded, although probably rare
+ """
+ i -= 1
+
+ """
+ return formatted ct curve, ordered by increasing colour temperature
+ """
+ ct_curve = list(np.array(list(zip(b_fit,r_fit,c_fit))).flatten())[::-1]
+ Cam.log += '\nFinal CT curve:'
+ for i in range(len(ct_curve)//3):
+ j = 3*i
+ Cam.log += '\n ct: {} '.format(ct_curve[j])
+ Cam.log += ' r: {} '.format(ct_curve[j+1])
+ Cam.log += ' b: {} '.format(ct_curve[j+2])
+
+ """
+ plotting code for debug
+ """
+ if plot:
+ x = np.linspace(np.min(rbs_hat[0]),np.max(rbs_hat[0]),100)
+ y = a*x**2 + b*x + c
+ plt.subplot(2,1,1)
+ plt.title('hatspace')
+ plt.plot(rbs_hat[0],rbs_hat[1],ls='--',color='blue')
+ plt.plot(x,y,color='green',ls='-')
+ plt.scatter(rbs_hat[0],rbs_hat[1],color='red')
+ for i, ct in enumerate(rbs_hat[2]):
+ plt.annotate(str(ct),(rbs_hat[0][i],rbs_hat[1][i]))
+ plt.xlabel('$\hat{r}$')
+ plt.ylabel('$\hat{b}$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.grid()
+ plt.subplot(2,1,2)
+ plt.title('dehatspace - indoors?')
+ plt.plot(r_fit,b_fit,color='blue')
+ plt.scatter(rb_raw[0],rb_raw[1],color='green')
+ plt.scatter(r_fit,b_fit,color='red')
+ for i,ct in enumerate(c_fit):
+ plt.annotate(str(ct),(r_fit[i],b_fit[i]))
+ plt.xlabel('$r$')
+ plt.ylabel('$b$')
+ """
+ optional set axes equal to shortest distance so line really does
+ looks perpendicular and everybody is happy
+ """
+ # ax = plt.gca()
+ # ax.set_aspect('equal')
+ plt.subplots_adjust(hspace=0.5)
+ plt.grid()
+ plt.show()
+ """
+ end of plotting code
+ """
+ return(ct_curve,np.round(transverse_pos,5),np.round(transverse_neg,5))
+
+"""
+obtain greyscale patches and perform alsc colour correction
+"""
+def get_alsc_patches(Img,colour_cals,grey=True):
+ """
+ get patch centre coordinates, image colour and the actual
+ patches for each channel,remembering to subtract blacklevel
+ If grey then only greyscale patches considered
+ """
+ if grey:
+ cen_coords = Img.cen_coords[3::4]
+ col = Img.col
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0][3::4] - Img.blacklevel_16
+ b_patchs = patches[3][3::4] - Img.blacklevel_16
+ """
+ note two green channels are averages
+ """
+ g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16
+ else:
+ cen_coords = Img.cen_coords
+ col = Img.col
+ patches = [np.array(Img.patches[i]) for i in Img.order]
+ r_patchs = patches[0] - Img.blacklevel_16
+ b_patchs = patches[3] - Img.blacklevel_16
+ g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16
+
+ if colour_cals == None:
+ return r_patchs,b_patchs,g_patchs
+ """
+ find where image colour fits in alsc colour calibration tables
+ """
+ cts = list(colour_cals.keys())
+ pos = bisect_left(cts,col)
+ """
+ if img colour is below minimum or above maximum alsc calibration colour, simply
+ pick extreme closest to img colour
+ """
+ if pos%(len(cts)) == 0:
+ """
+ this works because -0 = 0 = first and -1 = last index
+ """
+ col_tabs = np.array(colour_cals[cts[-pos//len(cts)]])
+ """
+ else, perform linear interpolation between existing alsc colour
+ calibration tables
+ """
+ else:
+ bef = cts[pos-1]
+ aft = cts[pos]
+ da = col-bef
+ db = aft-col
+ 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))
+ """
+ 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))
+ """
+ make list of pairs of gains for each patch by selecting the correct value
+ in alsc colour calibration table
+ """
+ patch_gains = []
+ for cen in cen_coords:
+ x,y = cen[0]//dx, cen[1]//dy
+ # We could probably do with some better spatial interpolation here?
+ col_gains = (col_tabs[0][y][x],col_tabs[1][y][x])
+ patch_gains.append(col_gains)
+
+ """
+ multiply the r and b channels in each patch by the respective gain, finally
+ performing the alsc colour correction
+ """
+ for i,gains in enumerate(patch_gains):
+ r_patchs[i] = r_patchs[i] * gains[0]
+ b_patchs[i] = b_patchs[i] * gains[1]
+
+ """
+ return greyscale patches, g channel and correct r,b channels
+ """
+ return r_patchs,b_patchs,g_patchs
diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py
new file mode 100644
index 00000000..8200a27f
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_ccm.py
@@ -0,0 +1,221 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_ccm.py - camera tuning tool for CCM (colour correction matrix)
+
+from ctt_image_load import *
+from ctt_awb import get_alsc_patches
+
+"""
+takes 8-bit macbeth chart values, degammas and returns 16 bit
+"""
+def degamma(x):
+ x = x / ((2**8)-1)
+ x = np.where(x < 0.04045, x/12.92, ((x+0.055)/1.055)**2.4)
+ x = x * ((2**16)-1)
+ return x
+
+"""
+FInds colour correction matrices for list of images
+"""
+def ccm(Cam,cal_cr_list,cal_cb_list):
+ imgs = Cam.imgs
+ """
+ standard macbeth chart colour values
+ """
+ m_rgb = np.array([ # these are in sRGB
+ [116, 81, 67], # dark skin
+ [199, 147, 129], # light skin
+ [91, 122, 156], # blue sky
+ [90, 108, 64], # foliage
+ [130, 128, 176], # blue flower
+ [92, 190, 172], # bluish green
+ [224, 124, 47], # orange
+ [68, 91,170], # purplish blue
+ [198, 82, 97], # moderate red
+ [94, 58, 106], # purple
+ [159, 189, 63], # yellow green
+ [230, 162, 39], # orange yellow
+ [35, 63, 147], # blue
+ [67, 149, 74], # green
+ [180, 49, 57], # red
+ [238, 198, 20], # yellow
+ [193, 84, 151], # magenta
+ [0, 136, 170], # cyan (goes out of gamut)
+ [245, 245, 243], # white 9.5
+ [200, 202, 202], # neutral 8
+ [161, 163, 163], # neutral 6.5
+ [121, 121, 122], # neutral 5
+ [82, 84, 86], # neutral 3.5
+ [49, 49, 51] # black 2
+ ])
+
+ """
+ convert reference colours from srgb to rgb
+ """
+ m_srgb = degamma(m_rgb)
+ """
+ reorder reference values to match how patches are ordered
+ """
+ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24,3))
+
+ """
+ reformat alsc correction tables or set colour_cals to None if alsc is
+ deactivated
+ """
+ if cal_cr_list == None:
+ colour_cals = None
+ else:
+ colour_cals = {}
+ for cr,cb in zip(cal_cr_list,cal_cb_list):
+ cr_tab = cr['table']
+ cb_tab = cb['table']
+ """
+ normalise tables so min value is 1
+ """
+ cr_tab= cr_tab/np.min(cr_tab)
+ cb_tab= cb_tab/np.min(cb_tab)
+ colour_cals[cr['ct']] = [cr_tab,cb_tab]
+
+ """
+ for each image, perform awb and alsc corrections.
+ Then calculate the colour correction matrix for that image, recording the
+ ccm and the colour tempertaure.
+ """
+ ccm_tab = {}
+ for Img in imgs:
+ Cam.log += '\nProcessing image: ' + Img.name
+ """
+ get macbeth patches with alsc applied if alsc enabled.
+ 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)
+ """
+ do awb
+ Note: awb is done by measuring the macbeth chart in the image, rather
+ than from the awb calibration. This is done so the awb will be perfect
+ and the ccm matrices will be more accurate.
+ """
+ r_greys,b_greys,g_greys = r[3::4],b[3::4],g[3::4]
+ r_g = np.mean(r_greys/g_greys)
+ b_g = np.mean(b_greys/g_greys)
+ r = r / r_g
+ b = b / b_g
+
+ """
+ normalise brightness wrt reference macbeth colours and then average
+ each channel for each patch
+ """
+ gain = np.mean(m_srgb)/np.mean((r,g,b))
+ Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain)
+ r = np.mean(gain*r,axis=1)
+ b = np.mean(gain*b,axis=1)
+ g = np.mean(gain*g,axis=1)
+
+ """
+ calculate ccm matrix
+ """
+ ccm = do_ccm(r,g,b,m_srgb)
+
+ """
+ if a ccm has already been calculated for that temperature then don't
+ overwrite but save both. They will then be averaged later on
+ """
+ if Img.col in ccm_tab.keys():
+ ccm_tab[Img.col].append(ccm)
+ else:
+ ccm_tab[Img.col] = [ccm]
+ Cam.log += '\n'
+
+ Cam.log += '\nFinished processing images'
+ """
+ average any ccms that share a colour temperature
+ """
+ for k,v in ccm_tab.items():
+ tab = np.mean(v,axis=0)
+ tab = np.where((10000*tab)%1<=0.05,tab+0.00001,tab)
+ tab = np.where((10000*tab)%1>=0.95,tab-0.00001,tab)
+ ccm_tab[k] = list(np.round(tab,5))
+ Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k)
+
+ """
+ return all ccms with respective colour temperature in the correct format,
+ sorted by their colour temperature
+ """
+ sorted_ccms = sorted(ccm_tab.items(),key=lambda kv: kv[0])
+ ccms = []
+ for i in sorted_ccms:
+ ccms.append({
+ 'ct' : i[0],
+ 'ccm' : i[1]
+ })
+ return ccms
+
+"""
+calculates the ccm for an individual image.
+ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3
+matrix, each row must add up to 1 in order to conserve greyness, simplifying
+calculation.
+Should you want to fit them in another space (e.g. LAB) we wish you the best of
+luck and send us the code when you are done! :-)
+"""
+def do_ccm(r,g,b,m_srgb):
+ rb = r-b
+ gb = g-b
+ rb_2s = (rb*rb)
+ rb_gbs = (rb*gb)
+ gb_2s = (gb*gb)
+
+ r_rbs = ( rb * (m_srgb[...,0] - b) )
+ r_gbs = ( gb * (m_srgb[...,0] - b) )
+ g_rbs = ( rb * (m_srgb[...,1] - b) )
+ g_gbs = ( gb * (m_srgb[...,1] - b) )
+ b_rbs = ( rb * (m_srgb[...,2] - b) )
+ b_gbs = ( gb * (m_srgb[...,2] - b) )
+
+ """
+ Obtain least squares fit
+ """
+ rb_2 = np.sum(rb_2s)
+ gb_2 = np.sum(gb_2s)
+ rb_gb = np.sum(rb_gbs)
+ r_rb = np.sum(r_rbs)
+ r_gb = np.sum(r_gbs)
+ g_rb = np.sum(g_rbs)
+ g_gb = np.sum(g_gbs)
+ b_rb = np.sum(b_rbs)
+ b_gb = np.sum(b_gbs)
+
+ det = rb_2*gb_2 - rb_gb*rb_gb
+
+ """
+ Raise error if matrix is singular...
+ This shouldn't really happen with real data but if it does just take new
+ pictures and try again, not much else to be done unfortunately...
+ """
+ if det < 0.001:
+ raise ArithmeticError
+
+ r_a = (gb_2*r_rb - rb_gb*r_gb)/det
+ r_b = (rb_2*r_gb - rb_gb*r_rb)/det
+ """
+ Last row can be calculated by knowing the sum must be 1
+ """
+ r_c = 1 - r_a - r_b
+
+ g_a = (gb_2*g_rb - rb_gb*g_gb)/det
+ g_b = (rb_2*g_gb - rb_gb*g_rb)/det
+ g_c = 1 - g_a - g_b
+
+ b_a = (gb_2*b_rb - rb_gb*b_gb)/det
+ b_b = (rb_2*b_gb - rb_gb*b_rb)/det
+ b_c = 1 - b_a - b_b
+
+ """
+ format ccm
+ """
+ ccm = [r_a,r_b,r_c,g_a,g_b,g_c,b_a,b_b,b_c]
+
+ return ccm
diff --git a/utils/raspberrypi/ctt/ctt_config_example.json b/utils/raspberrypi/ctt/ctt_config_example.json
new file mode 100644
index 00000000..c7f90761
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_config_example.json
@@ -0,0 +1,16 @@
+{
+ "disable": [],
+ "plot": [],
+ "alsc": {
+ "do_alsc_colour": 1,
+ "luminance_strength": 0.5
+ },
+ "awb": {
+ "greyworld": 0
+ },
+ "blacklevel": -1,
+ "macbeth": {
+ "small": 0,
+ "show": 0
+ }
+} \ No newline at end of file
diff --git a/utils/raspberrypi/ctt/ctt_geq.py b/utils/raspberrypi/ctt/ctt_geq.py
new file mode 100644
index 00000000..dd798f4a
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_geq.py
@@ -0,0 +1,179 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_geq.py - camera tuning tool for GEQ (green equalisation)
+
+from ctt_tools import *
+import matplotlib.pyplot as plt
+import scipy.optimize as optimize
+
+"""
+Uses green differences in macbeth patches to fit green equalisation threshold
+model. Ideally, all macbeth chart centres would fall below the threshold as
+these should be corrected by geq.
+"""
+def geq_fit(Cam,plot):
+ imgs = Cam.imgs
+ """
+ green equalisation to mitigate mazing.
+ Fits geq model by looking at difference
+ between greens in macbeth patches
+ """
+ geqs = np.array([ geq(Cam,Img)*Img.againQ8_norm for Img in imgs ])
+ Cam.log += '\nProcessed all images'
+ geqs = geqs.reshape((-1,2))
+ """
+ data is sorted by green difference and top half is selected since higher
+ green difference data define the decision boundary.
+ """
+ geqs = np.array(sorted(geqs,key = lambda r:np.abs((r[1]-r[0])/r[0])))
+
+ length = len(geqs)
+ g0 = geqs[length//2:,0]
+ g1 = geqs[length//2:,1]
+ gdiff = np.abs(g0-g1)
+ """
+ find linear fit by minimising asymmetric least square errors
+ in order to cover most of the macbeth images.
+ the philosophy here is that every macbeth patch should fall within the
+ threshold, hence the upper bound approach
+ """
+ def f(params):
+ m,c = params
+ a = gdiff - (m*g0+c)
+ """
+ asymmetric square error returns:
+ 1.95 * a**2 if a is positive
+ 0.05 * a**2 if a is negative
+ """
+ return(np.sum(a**2+0.95*np.abs(a)*a))
+
+ initial_guess = [0.01,500]
+ """
+ Nelder-Mead is usually not the most desirable optimisation method
+ but has been chosen here due to its robustness to undifferentiability
+ (is that a word?)
+ """
+ result = optimize.minimize(f,initial_guess,method='Nelder-Mead')
+ """
+ need to check if the fit worked correectly
+ """
+ if result.success:
+ slope,offset = result.x
+ Cam.log += '\nFit result: slope = {:.5f} '.format(slope)
+ Cam.log += 'offset = {}'.format(int(offset))
+ """
+ optional plotting code
+ """
+ if plot:
+ x = np.linspace(max(g0)*1.1,100)
+ y = slope*x + offset
+ plt.title('GEQ Asymmetric \'Upper Bound\' Fit')
+ plt.plot(x,y,color='red',ls='--',label='fit')
+ plt.scatter(g0,gdiff,color='b',label='data')
+ plt.ylabel('Difference in green channels')
+ plt.xlabel('Green value')
+
+ """
+ This upper bound asymmetric gives correct order of magnitude values.
+ The pipeline approximates a 1st derivative of a gaussian with some
+ linear piecewise functions, introducing arbitrary cutoffs. For
+ pessimistic geq, the model parameters have been increased by a
+ scaling factor/constant.
+
+ Feel free to tune these or edit the json files directly if you
+ belive there are still mazing effects left (threshold too low) or if you
+ think it is being overcorrected (threshold too high).
+ We have gone for a one size fits most approach that will produce
+ acceptable results in most applications.
+ """
+ slope *= 1.5
+ offset += 201
+ Cam.log += '\nFit after correction factors: slope = {:.5f}'.format(slope)
+ Cam.log += ' offset = {}'.format(int(offset))
+ """
+ clamp offset at 0 due to pipeline considerations
+ """
+ if offset < 0:
+ Cam.log += '\nOffset raised to 0'
+ offset = 0
+ """
+ optional plotting code
+ """
+ if plot:
+ y2 = slope*x + offset
+ plt.plot(x,y2,color='green',ls='--',label='scaled fit')
+ plt.grid()
+ plt.legend()
+ plt.show()
+
+ """
+ the case where for some reason the fit didn't work correctly
+
+ Transpose data and then least squares linear fit. Transposing data
+ makes it robust to many patches where green difference is the same
+ since they only contribute to one error minimisation, instead of dragging
+ the entire linear fit down.
+ """
+
+ else:
+ print('\nError! Couldn\'t fit asymmetric lest squares')
+ print(result.message)
+ Cam.log += '\nWARNING: Asymmetric least squares fit failed! '
+ Cam.log += 'Standard fit used could possibly lead to worse results'
+ fit = np.polyfit(gdiff,g0,1)
+ offset,slope = -fit[1]/fit[0],1/fit[0]
+ Cam.log += '\nFit result: slope = {:.5f} '.format(slope)
+ Cam.log += 'offset = {}'.format(int(offset))
+ """
+ optional plotting code
+ """
+ if plot:
+ x = np.linspace(max(g0)*1.1,100)
+ y = slope*x + offset
+ plt.title('GEQ Linear Fit')
+ plt.plot(x,y,color='red',ls='--',label='fit')
+ plt.scatter(g0,gdiff,color='b',label='data')
+ plt.ylabel('Difference in green channels')
+ plt.xlabel('Green value')
+ """
+ Scaling factors (see previous justification)
+ The model here will not be an upper bound so scaling factors have
+ been increased.
+ This method of deriving geq model parameters is extremely arbitrary
+ and undesirable.
+ """
+ slope *= 2.5
+ offset += 301
+ Cam.log += '\nFit after correction factors: slope = {:.5f}'.format(slope)
+ Cam.log += ' offset = {}'.format(int(offset))
+
+ if offset < 0:
+ Cam.log += '\nOffset raised to 0'
+ offset = 0
+
+ """
+ optional plotting code
+ """
+ if plot:
+ y2 = slope*x + offset
+ plt.plot(x,y2,color='green',ls='--',label='scaled fit')
+ plt.legend()
+ plt.grid()
+ plt.show()
+
+ return round(slope,5),int(offset)
+
+""""
+Return green channels of macbeth patches
+returns g0,g1 where
+> g0 is green next to red
+> g1 is green next to blue
+"""
+def geq(Cam,Img):
+ Cam.log += '\nProcessing image {}'.format(Img.name)
+ patches = [Img.patches[i] for i in Img.order][1:3]
+ g_patches = np.array([(np.mean(patches[0][i]),np.mean(patches[1][i])) for i in range(24)])
+ Cam.log += '\n'
+ return(g_patches)
diff --git a/utils/raspberrypi/ctt/ctt_image_load.py b/utils/raspberrypi/ctt/ctt_image_load.py
new file mode 100644
index 00000000..dd7adb16
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_image_load.py
@@ -0,0 +1,428 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019-2020, Raspberry Pi (Trading) Limited
+#
+# ctt_image_load.py - camera tuning tool image loading
+
+from ctt_tools import *
+from ctt_macbeth_locator import *
+import json
+import pyexiv2 as pyexif
+import rawpy as raw
+
+
+"""
+Image class load image from raw data and extracts metadata.
+
+Once image is extracted from data, it finds 24 16x16 patches for each
+channel, centred at the macbeth chart squares
+"""
+class Image:
+ def __init__(self,buf):
+ self.buf = buf
+ self.patches = None
+ self.saturated = False
+
+ '''
+ obtain metadata from buffer
+ '''
+ def get_meta(self):
+ self.ver = ba_to_b(self.buf[4:5])
+ self.w = ba_to_b(self.buf[0xd0:0xd2])
+ self.h = ba_to_b(self.buf[0xd2:0xd4])
+ self.pad = ba_to_b(self.buf[0xd4:0xd6])
+ self.fmt = self.buf[0xf5]
+ self.sigbits = 2*self.fmt + 4
+ self.pattern = self.buf[0xf4]
+ self.exposure = ba_to_b(self.buf[0x90:0x94])
+ self.againQ8 = ba_to_b(self.buf[0x94:0x96])
+ self.againQ8_norm = self.againQ8/256
+ camName = self.buf[0x10:0x10+128]
+ camName_end = camName.find(0x00)
+ self.camName = self.buf[0x10:0x10+128][:camName_end].decode()
+
+ """
+ Channel order depending on bayer pattern
+ """
+ bayer_case = {
+ 0 : (0,1,2,3), #red
+ 1 : (2,0,3,1), #green next to red
+ 2 : (3,2,1,0), #green next to blue
+ 3 : (1,0,3,2), #blue
+ 128 : (0,1,2,3) #arbitrary order for greyscale casw
+ }
+ self.order = bayer_case[self.pattern]
+
+ '''
+ manual blacklevel - not robust
+ '''
+ if 'ov5647' in self.camName:
+ self.blacklevel = 16
+ else:
+ self.blacklevel = 64
+ self.blacklevel_16 = self.blacklevel << (6)
+ return 1
+
+ '''
+ print metadata for debug
+ '''
+ def print_meta(self):
+ print('\nData:')
+ print(' ver = {}'.format(self.ver))
+ print(' w = {}'.format(self.w))
+ print(' h = {}'.format(self.h))
+ print(' pad = {}'.format(self.pad))
+ print(' fmt = {}'.format(self.fmt))
+ print(' sigbits = {}'.format(self.sigbits))
+ print(' pattern = {}'.format(self.pattern))
+ print(' exposure = {}'.format(self.exposure))
+ print(' againQ8 = {}'.format(self.againQ8))
+ print(' againQ8_norm = {}'.format(self.againQ8_norm))
+ print(' camName = {}'.format(self.camName))
+ print(' blacklevel = {}'.format(self.blacklevel))
+ print(' blacklevel_16 = {}'.format(self.blacklevel_16))
+
+ return 1
+
+ """
+ get image from raw scanline data
+ """
+ def get_image(self,raw):
+ self.dptr = []
+ """
+ check if data is 10 or 12 bits
+ """
+ if self.sigbits == 10:
+ """
+ calc length of scanline
+ """
+ lin_len = ((((((self.w+self.pad+3)>>2)) * 5)+31)>>5) * 32
+ """
+ stack scan lines into matrix
+ """
+ raw = np.array(raw).reshape(-1,lin_len).astype(np.int64)[:self.h,...]
+ """
+ separate 5 bits in each package, stopping when w is satisfied
+ """
+ ba0 = raw[...,0:5*((self.w+3)>>2):5]
+ ba1 = raw[...,1:5*((self.w+3)>>2):5]
+ ba2 = raw[...,2:5*((self.w+3)>>2):5]
+ ba3 = raw[...,3:5*((self.w+3)>>2):5]
+ ba4 = raw[...,4:5*((self.w+3)>>2):5]
+ """
+ assemble 10 bit numbers
+ """
+ ch0 = np.left_shift((np.left_shift(ba0,2) + (ba4%4)),6)
+ ch1 = np.left_shift((np.left_shift(ba1,2) + (np.right_shift(ba4,2)%4)),6)
+ ch2 = np.left_shift((np.left_shift(ba2,2) + (np.right_shift(ba4,4)%4)),6)
+ ch3 = np.left_shift((np.left_shift(ba3,2) + (np.right_shift(ba4,6)%4)),6)
+ """
+ interleave bits
+ """
+ mat = np.empty((self.h,self.w),dtype=ch0.dtype)
+
+ mat[...,0::4] = ch0
+ mat[...,1::4] = ch1
+ mat[...,2::4] = ch2
+ mat[...,3::4] = ch3
+
+ """
+ There is som eleaking memory somewhere in the code. This code here
+ seemed to make things good enough that the code would run for
+ reasonable numbers of images, however this is techincally just a
+ workaround. (sorry)
+ """
+ ba0,ba1,ba2,ba3,ba4 = None,None,None,None,None
+ del ba0,ba1,ba2,ba3,ba4
+ ch0,ch1,ch2,ch3 = None,None,None,None
+ del ch0,ch1,ch2,ch3
+
+ """
+ same as before but 12 bit case
+ """
+ elif self.sigbits == 12:
+ lin_len = ((((((self.w+self.pad+1)>>1)) * 3)+31)>>5) * 32
+ raw = np.array(raw).reshape(-1,lin_len).astype(np.int64)[:self.h,...]
+ ba0 = raw[...,0:3*((self.w+1)>>1):3]
+ ba1 = raw[...,1:3*((self.w+1)>>1):3]
+ ba2 = raw[...,2:3*((self.w+1)>>1):3]
+ ch0 = np.left_shift((np.left_shift(ba0,4) + ba2%16),4)
+ ch1 = np.left_shift((np.left_shift(ba1,4) + (np.right_shift(ba2,4))%16),4)
+ mat = np.empty((self.h,self.w),dtype=ch0.dtype)
+ mat[...,0::2] = ch0
+ mat[...,1::2] = ch1
+
+ else:
+ """
+ data is neither 10 nor 12 or incorrect data
+ """
+ print('ERROR: wrong bit format, only 10 or 12 bit supported')
+ return 0
+
+ """
+ separate bayer channels
+ """
+ c0 = mat[0::2,0::2]
+ c1 = mat[0::2,1::2]
+ c2 = mat[1::2,0::2]
+ c3 = mat[1::2,1::2]
+ self.channels = [c0,c1,c2,c3]
+ return 1
+
+ """
+ obtain 16x16 patch centred at macbeth square centre for each channel
+ """
+ def get_patches(self,cen_coords,size=16):
+ """
+ obtain channel widths and heights
+ """
+ ch_w,ch_h = self.w,self.h
+ cen_coords = list(np.array((cen_coords[0])).astype(np.int32))
+ self.cen_coords = cen_coords
+ """
+ squares are ordered by stacking macbeth chart columns from
+ left to right. Some useful patch indices:
+ white = 3
+ black = 23
+ 'reds' = 9,10
+ 'blues' = 2,5,8,20,22
+ 'greens' = 6,12,17
+ greyscale = 3,7,11,15,19,23
+ """
+ all_patches = []
+ for ch in self.channels:
+ ch_patches = []
+ for cen in cen_coords:
+ '''
+ macbeth centre is placed at top left of central 2x2 patch
+ to account for rounding
+ Patch pixels are sorted by pixel brightness so spatial
+ information is lost.
+ '''
+ patch = ch[cen[1]-7:cen[1]+9,cen[0]-7:cen[0]+9].flatten()
+ patch.sort()
+ if patch[-5] == (2**self.sigbits-1)*2**(16-self.sigbits):
+ self.saturated = True
+ ch_patches.append(patch)
+ # print('\nNew Patch\n')
+ all_patches.append(ch_patches)
+ # print('\n\nNew Channel\n\n')
+ self.patches = all_patches
+ return 1
+
+def brcm_load_image(Cam, im_str):
+ """
+ Load image where raw data and metadata is in the BRCM format
+ """
+ try:
+ """
+ create byte array
+ """
+ with open(im_str,'rb') as image:
+ f = image.read()
+ b = bytearray(f)
+ """
+ return error if incorrect image address
+ """
+ except FileNotFoundError:
+ print('\nERROR:\nInvalid image address')
+ Cam.log += '\nWARNING: Invalid image address'
+ return 0
+
+ """
+ return error if problem reading file
+ """
+ if f == None:
+ print('\nERROR:\nProblem reading file')
+ Cam.log += '\nWARNING: Problem readin file'
+ return 0
+
+ # print('\nLooking for EOI and BRCM header')
+ """
+ find end of image followed by BRCM header by turning
+ bytearray into hex string and string matching with regexp
+ """
+ start = -1
+ match = bytearray(b'\xff\xd9@BRCM')
+ match_str = binascii.hexlify(match)
+ b_str = binascii.hexlify(b)
+ """
+ note index is divided by two to go from string to hex
+ """
+ indices = [m.start()//2 for m in re.finditer(match_str,b_str)]
+ # print(indices)
+ try:
+ start = indices[0] + 3
+ except IndexError:
+ print('\nERROR:\nNo Broadcom header found')
+ Cam.log += '\nWARNING: No Broadcom header found!'
+ return 0
+ """
+ extract data after header
+ """
+ # print('\nExtracting data after header')
+ buf = b[start:start+32768]
+ Img = Image(buf)
+ Img.str = im_str
+ # print('Data found successfully')
+
+ """
+ obtain metadata
+ """
+ # print('\nReading metadata')
+ Img.get_meta()
+ Cam.log += '\nExposure : {} us'.format(Img.exposure)
+ Cam.log += '\nNormalised gain : {}'.format(Img.againQ8_norm)
+ # print('Metadata read successfully')
+
+ """
+ obtain raw image data
+ """
+ # print('\nObtaining raw image data')
+ raw = b[start+32768:]
+ Img.get_image(raw)
+ """
+ delete raw to stop memory errors
+ """
+ raw = None
+ del raw
+ # print('Raw image data obtained successfully')
+
+ return Img
+
+def dng_load_image(Cam, im_str):
+ try:
+ Img = Image(None)
+
+ # RawPy doesn't load all the image tags that we need, so we use py3exiv2
+ metadata = pyexif.ImageMetadata(im_str)
+ metadata.read()
+
+ Img.ver = 100 # random value
+ Img.w = metadata['Exif.SubImage1.ImageWidth'].value
+ Img.pad = 0
+ Img.h = metadata['Exif.SubImage1.ImageLength'].value
+ white = metadata['Exif.SubImage1.WhiteLevel'].value
+ Img.sigbits = int(white).bit_length()
+ Img.fmt = (Img.sigbits - 4) // 2
+ Img.exposure = int(metadata['Exif.Photo.ExposureTime'].value*1000000)
+ Img.againQ8 = metadata['Exif.Photo.ISOSpeedRatings'].value*256/100
+ Img.againQ8_norm = Img.againQ8 / 256
+ Img.camName = metadata['Exif.Image.Model'].value
+ Img.blacklevel = int(metadata['Exif.SubImage1.BlackLevel'].value[0])
+ Img.blacklevel_16 = Img.blacklevel << (16 - Img.sigbits)
+ bayer_case = {
+ '0 1 1 2': (0, (0, 1, 2, 3)),
+ '1 2 0 1': (1, (2, 0, 3, 1)),
+ '2 1 1 0': (2, (3, 2, 1, 0)),
+ '1 0 2 1': (3, (1, 0, 3, 2))
+ }
+ cfa_pattern = metadata['Exif.SubImage1.CFAPattern'].value
+ Img.pattern = bayer_case[cfa_pattern][0]
+ Img.order = bayer_case[cfa_pattern][1]
+
+ # Now use RawPy tp get the raw Bayer pixels
+ raw_im = raw.imread(im_str)
+ raw_data = raw_im.raw_image
+ shift = 16 - Img.sigbits
+ c0 = np.left_shift(raw_data[0::2,0::2].astype(np.int64), shift)
+ c1 = np.left_shift(raw_data[0::2,1::2].astype(np.int64), shift)
+ c2 = np.left_shift(raw_data[1::2,0::2].astype(np.int64), shift)
+ c3 = np.left_shift(raw_data[1::2,1::2].astype(np.int64), shift)
+ Img.channels = [c0, c1, c2, c3]
+
+ except:
+ print("\nERROR: failed to load DNG file", im_str)
+ print("Either file does not exist or is incompatible")
+ Cam.log += '\nERROR: DNG file does not exist or is incompatible'
+ raise
+
+ return Img
+
+
+'''
+load image from file location and perform calibration
+check correct filetype
+
+mac boolean is true if image is expected to contain macbeth chart and false
+if not (alsc images don't have macbeth charts)
+'''
+def load_image(Cam,im_str,mac_config=None,show=False,mac=True,show_meta=False):
+ """
+ check image is correct filetype
+ """
+ if '.jpg' in im_str or '.jpeg' in im_str or '.brcm' in im_str or '.dng' in im_str:
+ if '.dng' in im_str:
+ Img = dng_load_image(Cam, im_str)
+ else:
+ Img = brcm_load_image(Cam, im_str)
+ if show_meta:
+ Img.print_meta()
+
+ if mac:
+ """
+ find macbeth centres, discarding images that are too dark or light
+ """
+ av_chan = (np.mean(np.array(Img.channels),axis=0)/(2**16))
+ av_val = np.mean(av_chan)
+ # print(av_val)
+ if av_val < Img.blacklevel_16/(2**16)+1/64:
+ macbeth = None
+ print('\nError: Image too dark!')
+ Cam.log += '\nWARNING: Image too dark!'
+ else:
+ macbeth = find_macbeth(Cam,av_chan,mac_config)
+
+ """
+ if no macbeth found return error
+ """
+ if macbeth == None:
+ print('\nERROR: No macbeth chart found')
+ return 0
+ mac_cen_coords = macbeth[1]
+ # print('\nMacbeth centres located successfully')
+
+ """
+ obtain image patches
+ """
+ # print('\nObtaining image patches')
+ Img.get_patches(mac_cen_coords)
+ if Img.saturated:
+ print('\nERROR: Macbeth patches have saturated')
+ Cam.log += '\nWARNING: Macbeth patches have saturated!'
+ return 0
+
+ """
+ clear memory
+ """
+ Img.buf = None
+ del Img.buf
+
+ # print('Image patches obtained successfully')
+
+ """
+ optional debug
+ """
+ if show and __name__ == '__main__':
+ copy = sum(Img.channels)/2**18
+ copy = np.reshape(copy,(Img.h//2,Img.w//2)).astype(np.float64)
+ copy,_ = reshape(copy,800)
+ represent(copy)
+
+ return Img
+
+ """
+ return error if incorrect filetype
+ """
+ else:
+ # print('\nERROR:\nInvalid file extension')
+ return 0
+
+"""
+bytearray splice to number little endian
+"""
+def ba_to_b(b):
+ total = 0
+ for i in range(len(b)):
+ total += 256**i * b[i]
+ return total
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)
diff --git a/utils/raspberrypi/ctt/ctt_macbeth_locator.py b/utils/raspberrypi/ctt/ctt_macbeth_locator.py
new file mode 100644
index 00000000..583d5a69
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_macbeth_locator.py
@@ -0,0 +1,748 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_macbeth_locator.py - camera tuning tool Macbeth chart locator
+
+from ctt_ransac import *
+from ctt_tools import *
+import warnings
+
+"""
+NOTE: some custom functions have been used here to make the code more readable.
+These are defined in tools.py if they are needed for reference.
+"""
+
+"""
+Some inconsistencies between packages cause runtime warnings when running
+the clustering algorithm. This catches these warnings so they don't flood the
+output to the console
+"""
+def fxn():
+ warnings.warn("runtime",RuntimeWarning)
+
+"""
+Define the success message
+"""
+success_msg = 'Macbeth chart located successfully'
+
+def find_macbeth(Cam,img,mac_config=(0,0)):
+ small_chart,show = mac_config
+ print('Locating macbeth chart')
+ Cam.log += '\nLocating macbeth chart'
+ """
+ catch the warnings
+ """
+ warnings.simplefilter("ignore")
+ fxn()
+
+ """
+ Reference macbeth chart is created that will be correlated with the located
+ macbeth chart guess to produce a confidence value for the match.
+ """
+ ref = cv2.imread(Cam.path +'ctt_ref.pgm',flags=cv2.IMREAD_GRAYSCALE)
+ ref_w = 120
+ ref_h = 80
+ rc1 = (0,0)
+ rc2 = (0,ref_h)
+ rc3 = (ref_w,ref_h)
+ rc4 = (ref_w,0)
+ ref_corns = np.array((rc1,rc2,rc3,rc4),np.float32)
+ ref_data = (ref,ref_w,ref_h,ref_corns)
+
+ """
+ locate macbeth chart
+ """
+ cor,mac,coords,msg = get_macbeth_chart(img,ref_data)
+
+ """
+ following bits of code tries to fix common problems with simple
+ techniques.
+ If now or at any point the best correlation is of above 0.75, then
+ nothing more is tried as this is a high enough confidence to ensure
+ reliable macbeth square centre placement.
+ """
+
+ """
+ brighten image 2x
+ """
+ if cor < 0.75:
+ a = 2
+ img_br = cv2.convertScaleAbs(img,alpha=a,beta=0)
+ cor_b,mac_b,coords_b,msg_b = get_macbeth_chart(img_br,ref_data)
+ if cor_b > cor:
+ cor,mac,coords,msg = cor_b,mac_b,coords_b,msg_b
+
+ """
+ brighten image 4x
+ """
+ if cor < 0.75:
+ a = 4
+ img_br = cv2.convertScaleAbs(img,alpha=a,beta=0)
+ cor_b,mac_b,coords_b,msg_b = get_macbeth_chart(img_br,ref_data)
+ if cor_b > cor:
+ cor,mac,coords,msg = cor_b,mac_b,coords_b,msg_b
+
+ """
+ In case macbeth chart is too small, take a selection of the image and
+ attempt to locate macbeth chart within that. The scale increment is
+ root 2
+ """
+ """
+ These variables will be used to transform the found coordinates at smaller
+ scales back into the original. If ii is still -1 after this section that
+ means it was not successful
+ """
+ ii = -1
+ w_best = 0
+ h_best = 0
+ d_best = 100
+ """
+ d_best records the scale of the best match. Macbeth charts are only looked
+ for at one scale increment smaller than the current best match in order to avoid
+ unecessarily searching for macbeth charts at small scales.
+ If a macbeth chart ha already been found then set d_best to 0
+ """
+ if cor != 0:
+ d_best = 0
+
+ """
+ scale 3/2 (approx root2)
+ """
+ if cor < 0.75:
+ imgs = []
+ """
+ get size of image
+ """
+ shape = list(img.shape[:2])
+ w,h = shape
+ """
+ set dimensions of the subselection and the step along each axis between
+ selections
+ """
+ w_sel = int(2*w/3)
+ h_sel = int(2*h/3)
+ w_inc = int(w/6)
+ h_inc = int(h/6)
+ """
+ for each subselection, look for a macbeth chart
+ """
+ for i in range(3):
+ for j in range(3):
+ w_s,h_s = i*w_inc,j*h_inc
+ img_sel = img[w_s:w_s+w_sel,h_s:h_s+h_sel]
+ cor_ij,mac_ij,coords_ij,msg_ij = get_macbeth_chart(img_sel,ref_data)
+ """
+ if the correlation is better than the best then record the
+ scale and current subselection at which macbeth chart was
+ found. Also record the coordinates, macbeth chart and message.
+ """
+ if cor_ij > cor:
+ cor = cor_ij
+ mac,coords,msg = mac_ij,coords_ij,msg_ij
+ ii,jj = i,j
+ w_best,h_best = w_inc,h_inc
+ d_best = 1
+
+
+ """
+ scale 2
+ """
+ if cor < 0.75:
+ imgs = []
+ shape = list(img.shape[:2])
+ w,h = shape
+ w_sel = int(w/2)
+ h_sel = int(h/2)
+ w_inc = int(w/8)
+ h_inc = int(h/8)
+ for i in range(5):
+ for j in range(5):
+ w_s,h_s = i*w_inc,j*h_inc
+ img_sel = img[w_s:w_s+w_sel,h_s:h_s+h_sel]
+ cor_ij,mac_ij,coords_ij,msg_ij = get_macbeth_chart(img_sel,ref_data)
+ if cor_ij > cor:
+ cor = cor_ij
+ mac,coords,msg = mac_ij,coords_ij,msg_ij
+ ii,jj = i,j
+ w_best,h_best = w_inc,h_inc
+ d_best = 2
+
+ """
+ The following code checks for macbeth charts at even smaller scales. This
+ slows the code down significantly and has therefore been omitted by default,
+ however it is not unusably slow so might be useful if the macbeth chart
+ is too small to be picked up to by the current subselections.
+ Use this for macbeth charts with side lengths around 1/5 image dimensions
+ (and smaller...?) it is, however, recommended that macbeth charts take up as
+ large as possible a proportion of the image.
+ """
+
+ if small_chart:
+
+ if cor < 0.75 and d_best > 1 :
+ imgs = []
+ shape = list(img.shape[:2])
+ w,h = shape
+ w_sel = int(w/3)
+ h_sel = int(h/3)
+ w_inc = int(w/12)
+ h_inc = int(h/12)
+ for i in range(9):
+ for j in range(9):
+ w_s,h_s = i*w_inc,j*h_inc
+ img_sel = img[w_s:w_s+w_sel,h_s:h_s+h_sel]
+ cor_ij,mac_ij,coords_ij,msg_ij = get_macbeth_chart(img_sel,ref_data)
+ if cor_ij > cor:
+ cor = cor_ij
+ mac,coords,msg = mac_ij,coords_ij,msg_ij
+ ii,jj = i,j
+ w_best,h_best = w_inc,h_inc
+ d_best = 3
+
+ if cor < 0.75 and d_best > 2:
+ imgs = []
+ shape = list(img.shape[:2])
+ w,h = shape
+ w_sel = int(w/4)
+ h_sel = int(h/4)
+ w_inc = int(w/16)
+ h_inc = int(h/16)
+ for i in range(13):
+ for j in range(13):
+ w_s,h_s = i*w_inc,j*h_inc
+ img_sel = img[w_s:w_s+w_sel,h_s:h_s+h_sel]
+ cor_ij,mac_ij,coords_ij,msg_ij = get_macbeth_chart(img_sel,ref_data)
+ if cor_ij > cor:
+ cor = cor_ij
+ mac,coords,msg = mac_ij,coords_ij,msg_ij
+ ii,jj = i,j
+ w_best,h_best = w_inc,h_inc
+
+ """
+ Transform coordinates from subselection to original image
+ """
+ if ii != -1:
+ for a in range(len(coords)):
+ for b in range(len(coords[a][0])):
+ coords[a][0][b][1] += ii*w_best
+ coords[a][0][b][0] += jj*h_best
+
+ """
+ initialise coords_fit variable
+ """
+ coords_fit = None
+ # print('correlation: {}'.format(cor))
+ """
+ print error or success message
+ """
+ print(msg)
+ Cam.log += '\n' + msg
+ if msg == success_msg:
+ coords_fit = coords
+ Cam.log += '\nMacbeth chart vertices:\n'
+ Cam.log += '{}'.format(2*np.round(coords_fit[0][0]),0)
+ """
+ if correlation is lower than 0.75 there may be a risk of macbeth chart
+ corners not having been located properly. It might be worth running
+ with show set to true to check where the macbeth chart centres have
+ been located.
+ """
+ print('Confidence: {:.3f}'.format(cor))
+ Cam.log += '\nConfidence: {:.3f}'.format(cor)
+ if cor < 0.75:
+ print('Caution: Low confidence guess!')
+ Cam.log += 'WARNING: Low confidence guess!'
+ # cv2.imshow('MacBeth',mac)
+ # represent(mac,'MacBeth chart')
+
+ """
+ extract data from coords_fit and plot on original image
+ """
+ if show and coords_fit != None:
+ copy = img.copy()
+ verts = coords_fit[0][0]
+ cents = coords_fit[1][0]
+
+ """
+ draw circles at vertices of macbeth chart
+ """
+ for vert in verts:
+ p = tuple(np.round(vert).astype(np.int32))
+ cv2.circle(copy,p,10,1,-1)
+ """
+ draw circles at centres of squares
+ """
+ for i in range(len(cents)):
+ cent = cents[i]
+ p = tuple(np.round(cent).astype(np.int32))
+ """
+ draw black circle on white square, white circle on black square an
+ grey circle everywhere else.
+ """
+ if i == 3:
+ cv2.circle(copy,p,8,0,-1)
+ elif i == 23:
+ cv2.circle(copy,p,8,1,-1)
+ else:
+ cv2.circle(copy,p,8,0.5,-1)
+ copy,_ = reshape(copy,400)
+ represent(copy)
+
+ return(coords_fit)
+
+def get_macbeth_chart(img,ref_data):
+ """
+ function returns coordinates of macbeth chart vertices and square centres,
+ along with an error/success message for debugging purposes. Additionally,
+ it scores the match with a confidence value.
+
+ Brief explanation of the macbeth chart locating algorithm:
+ - Find rectangles within image
+ - Take rectangles within percentage offset of median perimeter. The
+ assumption is that these will be the macbeth squares
+ - For each potential square, find the 24 possible macbeth centre locations
+ that would produce a square in that location
+ - Find clusters of potential macbeth chart centres to find the potential
+ macbeth centres with the most votes, i.e. the most likely ones
+ - For each potential macbeth centre, use the centres of the squares that
+ voted for it to find macbeth chart corners
+ - For each set of corners, transform the possible match into normalised
+ space and correlate with a reference chart to evaluate the match
+ - Select the highest correlation as the macbeth chart match, returning the
+ correlation as the confidence score
+ """
+
+ """
+ get reference macbeth chart data
+ """
+ (ref,ref_w,ref_h,ref_corns) = ref_data
+
+ """
+ the code will raise and catch a MacbethError in case of a problem, trying
+ to give some likely reasons why the problem occred, hence the try/except
+ """
+ try:
+ """
+ obtain image, convert to grayscale and normalise
+ """
+ src = img
+ src,factor = reshape(src,200)
+ original=src.copy()
+ a = 125/np.average(src)
+ src_norm = cv2.convertScaleAbs(src,alpha=a,beta=0)
+ """
+ This code checks if there are seperate colour channels. In the past the
+ macbeth locator ran on jpgs and this makes it robust to different
+ filetypes. Note that running it on a jpg has 4x the pixels of the
+ average bayer channel so coordinates must be doubled.
+
+ This is best done in img_load.py in the get_patches method. The
+ coordinates and image width,height must be divided by two if the
+ macbeth locator has been run on a demosaicked image.
+ """
+ if len(src_norm.shape) == 3:
+ src_bw = cv2.cvtColor(src_norm,cv2.COLOR_BGR2GRAY)
+ else:
+ src_bw = src_norm
+ original_bw = src_bw.copy()
+ """
+ obtain image edges
+ """
+ sigma=2
+ src_bw = cv2.GaussianBlur(src_bw,(0,0),sigma)
+ t1,t2 = 50,100
+ edges = cv2.Canny(src_bw,t1,t2)
+ """
+ dilate edges to prevent self-intersections in contours
+ """
+ k_size = 2
+ kernel = np.ones((k_size,k_size))
+ its = 1
+ edges = cv2.dilate(edges,kernel,iterations=its)
+ """
+ find Contours in image
+ """
+ conts,_ = cv2.findContours(edges,
+ cv2.RETR_TREE,
+ cv2.CHAIN_APPROX_NONE)
+ if len(conts) == 0:
+ raise MacbethError(
+ '\nWARNING: No macbeth chart found!'
+ '\nNo contours found in image\n'
+ 'Possible problems:\n'
+ '- Macbeth chart is too dark or bright\n'
+ '- Macbeth chart is occluded\n'
+ )
+ """
+ find quadrilateral contours
+ """
+ epsilon = 0.07
+ conts_per = []
+ for i in range(len(conts)):
+ per = cv2.arcLength(conts[i],True)
+ poly = cv2.approxPolyDP(conts[i],
+ epsilon*per,True)
+ if len(poly) == 4 and cv2.isContourConvex(poly):
+ conts_per.append((poly,per))
+
+ if len(conts_per) == 0:
+ raise MacbethError(
+ '\nWARNING: No macbeth chart found!'
+ '\nNo quadrilateral contours found'
+ '\nPossible problems:\n'
+ '- Macbeth chart is too dark or bright\n'
+ '- Macbeth chart is occluded\n'
+ '- Macbeth chart is out of camera plane\n'
+ )
+
+ """
+ sort contours by perimeter and get perimeters within percent of median
+ """
+ conts_per = sorted(conts_per,key=lambda x:x[1])
+ med_per = conts_per[int(len(conts_per)/2)][1]
+ side = med_per/4
+ perc = 0.1
+ med_low,med_high = med_per*(1-perc),med_per*(1+perc)
+ squares = []
+ for i in conts_per:
+ if med_low <= i[1] and med_high >= i[1]:
+ squares.append(i[0])
+
+ """
+ obtain coordinates of nomralised macbeth and squares
+ """
+ square_verts,mac_norm = get_square_verts(0.06)
+ """
+ for each square guess, find 24 possible macbeth chart centres
+ """
+ mac_mids = []
+ squares_raw = []
+ for i in range(len(squares)):
+ square = squares[i]
+ squares_raw.append(square)
+ """
+ convert quads to rotated rectangles. This is required as the
+ 'squares' are usually quite irregular quadrilaterls, so performing
+ a transform would result in exaggerated warping and inaccurate
+ macbeth chart centre placement
+ """
+ rect = cv2.minAreaRect(square)
+ square = cv2.boxPoints(rect).astype(np.float32)
+ """
+ reorder vertices to prevent 'hourglass shape'
+ """
+ square = sorted(square,key=lambda x:x[0])
+ square_1 = sorted(square[:2],key=lambda x:x[1])
+ square_2 = sorted(square[2:],key=lambda x:-x[1])
+ square = np.array(np.concatenate((square_1,square_2)),np.float32)
+ square = np.reshape(square,(4,2)).astype(np.float32)
+ squares[i] = square
+ """
+ find 24 possible macbeth chart centres by trasnforming normalised
+ macbeth square vertices onto candidate square vertices found in image
+ """
+ for j in range(len(square_verts)):
+ verts = square_verts[j]
+ p_mat = cv2.getPerspectiveTransform(verts,square)
+ mac_guess = cv2.perspectiveTransform(mac_norm,p_mat)
+ mac_guess = np.round(mac_guess).astype(np.int32)
+ """
+ keep only if candidate macbeth is within image border
+ (deprecated)
+ """
+ in_border = True
+ # for p in mac_guess[0]:
+ # pptest = cv2.pointPolygonTest(
+ # img_con,
+ # tuple(p),
+ # False
+ # )
+ # if pptest == -1:
+ # in_border = False
+ # break
+
+ if in_border:
+ mac_mid = np.mean(mac_guess,
+ axis=1)
+ mac_mids.append([mac_mid,(i,j)])
+
+ if len(mac_mids) == 0:
+ raise MacbethError(
+ '\nWARNING: No macbeth chart found!'
+ '\nNo possible macbeth charts found within image'
+ '\nPossible problems:\n'
+ '- Part of the macbeth chart is outside the image\n'
+ '- Quadrilaterals in image background\n'
+ )
+
+ """
+ reshape data
+ """
+ for i in range(len(mac_mids)):
+ mac_mids[i][0] = mac_mids[i][0][0]
+
+ """
+ find where midpoints cluster to identify most likely macbeth centres
+ """
+ clustering = cluster.AgglomerativeClustering(
+ n_clusters=None,
+ compute_full_tree = True,
+ distance_threshold = side*2
+ )
+ mac_mids_list = [x[0] for x in mac_mids]
+
+ if len(mac_mids_list)==1:
+ """
+ special case of only one valid centre found (probably not needed)
+ """
+ clus_list = []
+ clus_list.append([mac_mids,len(mac_mids)])
+
+ else:
+ clustering.fit(mac_mids_list)
+ # try:
+ # clustering.fit(mac_mids_list)
+ # except RuntimeWarning as error:
+ # return(0,None,None,error)
+
+ """
+ create list of all clusters
+ """
+ clus_list = []
+ if clustering.n_clusters_ >1:
+ for i in range(clustering.labels_.max()+1):
+ indices = [j for j,x in enumerate(clustering.labels_) if x == i]
+ clus = []
+ for index in indices:
+ clus.append(mac_mids[index])
+ clus_list.append([clus,len(clus)])
+ clus_list.sort(key=lambda x:-x[1])
+
+ elif clustering.n_clusters_ == 1:
+ """
+ special case of only one cluster found
+ """
+ # print('only 1 cluster')
+ clus_list.append([mac_mids,len(mac_mids)])
+ else:
+ raise MacbethError(
+ '\nWARNING: No macebth chart found!'
+ '\nNo clusters found'
+ '\nPossible problems:\n'
+ '- NA\n'
+ )
+
+ """
+ keep only clusters with enough votes
+ """
+ clus_len_max = clus_list[0][1]
+ clus_tol= 0.7
+ for i in range(len(clus_list)):
+ if clus_list[i][1] < clus_len_max * clus_tol:
+ clus_list = clus_list[:i]
+ break
+ cent = np.mean(clus_list[i][0],axis=0)[0]
+ clus_list[i].append(cent)
+
+ """
+ represent most popular cluster centroids
+ """
+ # copy = original_bw.copy()
+ # copy = cv2.cvtColor(copy,cv2.COLOR_GRAY2RGB)
+ # copy = cv2.resize(copy,None,fx=2,fy=2)
+ # for clus in clus_list:
+ # centroid = tuple(2*np.round(clus[2]).astype(np.int32))
+ # cv2.circle(copy,centroid,7,(255,0,0),-1)
+ # cv2.circle(copy,centroid,2,(0,0,255),-1)
+ # represent(copy)
+
+ """
+ get centres of each normalised square
+ """
+ reference = get_square_centres(0.06)
+
+ """
+ for each possible macbeth chart, transform image into
+ normalised space and find correlation with reference
+ """
+ max_cor = 0
+ best_map = None
+ best_fit = None
+ best_cen_fit = None
+ best_ref_mat = None
+
+ for clus in clus_list:
+ clus = clus[0]
+ sq_cents = []
+ ref_cents = []
+ i_list = [p[1][0] for p in clus]
+ for point in clus:
+ i,j = point[1]
+ """
+ remove any square that voted for two different points within
+ the same cluster. This causes the same point in the image to be
+ mapped to two different reference square centres, resulting in
+ a very distorted perspective transform since cv2.findHomography
+ simply minimises error.
+ This phenomenon is not particularly likely to occur due to the
+ enforced distance threshold in the clustering fit but it is
+ best to keep this in just in case.
+ """
+ if i_list.count(i) == 1:
+ square = squares_raw[i]
+ sq_cent = np.mean(square,axis=0)
+ ref_cent = reference[j]
+ sq_cents.append(sq_cent)
+ ref_cents.append(ref_cent)
+
+ """
+ At least three squares need to have voted for a centre in
+ order for a transform to be found
+ """
+ if len(sq_cents) < 3:
+ raise MacbethError(
+ '\nWARNING: No macbeth chart found!'
+ '\nNot enough squares found'
+ '\nPossible problems:\n'
+ '- Macbeth chart is occluded\n'
+ '- Macbeth chart is too dark of bright\n'
+ )
+
+ ref_cents = np.array(ref_cents)
+ sq_cents = np.array(sq_cents)
+ """
+ find best fit transform from normalised centres to image
+ """
+ h_mat,mask = cv2.findHomography(ref_cents,sq_cents)
+ if 'None' in str(type(h_mat)):
+ raise MacbethError(
+ '\nERROR\n'
+ )
+
+ """
+ transform normalised corners and centres into image space
+ """
+ mac_fit = cv2.perspectiveTransform(mac_norm,h_mat)
+ mac_cen_fit = cv2.perspectiveTransform(np.array([reference]),h_mat)
+ """
+ transform located corners into reference space
+ """
+ ref_mat = cv2.getPerspectiveTransform(
+ mac_fit,
+ np.array([ref_corns])
+ )
+ map_to_ref = cv2.warpPerspective(
+ original_bw,ref_mat,
+ (ref_w,ref_h)
+ )
+ """
+ normalise brigthness
+ """
+ a = 125/np.average(map_to_ref)
+ map_to_ref = cv2.convertScaleAbs(map_to_ref,alpha=a,beta=0)
+ """
+ find correlation with bw reference macbeth
+ """
+ cor = correlate(map_to_ref,ref)
+ """
+ keep only if best correlation
+ """
+ if cor > max_cor:
+ max_cor = cor
+ best_map = map_to_ref
+ best_fit = mac_fit
+ best_cen_fit = mac_cen_fit
+ best_ref_mat = ref_mat
+
+ """
+ rotate macbeth by pi and recorrelate in case macbeth chart is
+ upside-down
+ """
+ mac_fit_inv = np.array(
+ ([[mac_fit[0][2],mac_fit[0][3],
+ mac_fit[0][0],mac_fit[0][1]]])
+ )
+ mac_cen_fit_inv = np.flip(mac_cen_fit,axis=1)
+ ref_mat = cv2.getPerspectiveTransform(
+ mac_fit_inv,
+ np.array([ref_corns])
+ )
+ map_to_ref = cv2.warpPerspective(
+ original_bw,ref_mat,
+ (ref_w,ref_h)
+ )
+ a = 125/np.average(map_to_ref)
+ map_to_ref = cv2.convertScaleAbs(map_to_ref,alpha=a,beta=0)
+ cor = correlate(map_to_ref,ref)
+ if cor > max_cor:
+ max_cor = cor
+ best_map = map_to_ref
+ best_fit = mac_fit_inv
+ best_cen_fit = mac_cen_fit_inv
+ best_ref_mat = ref_mat
+
+ """
+ Check best match is above threshold
+ """
+ cor_thresh = 0.6
+ if max_cor < cor_thresh:
+ raise MacbethError(
+ '\nWARNING: Correlation too low'
+ '\nPossible problems:\n'
+ '- Bad lighting conditions\n'
+ '- Macbeth chart is occluded\n'
+ '- Background is too noisy\n'
+ '- Macbeth chart is out of camera plane\n'
+ )
+ """
+ Following code is mostly representation for debugging purposes
+ """
+
+
+ """
+ draw macbeth corners and centres on image
+ """
+ copy = original.copy()
+ copy = cv2.resize(original,None,fx=2,fy=2)
+ # print('correlation = {}'.format(round(max_cor,2)))
+ for point in best_fit[0]:
+ point = np.array(point,np.float32)
+ point = tuple(2*np.round(point).astype(np.int32))
+ cv2.circle(copy,point,4,(255,0,0),-1)
+ for point in best_cen_fit[0]:
+ point = np.array(point,np.float32)
+ point = tuple(2*np.round(point).astype(np.int32))
+ cv2.circle(copy,point,4,(0,0,255),-1)
+ copy = copy.copy()
+ cv2.circle(copy,point,4,(0,0,255),-1)
+
+ """
+ represent coloured macbeth in reference space
+ """
+ best_map_col = cv2.warpPerspective(
+ original,best_ref_mat,(ref_w,ref_h)
+ )
+ best_map_col = cv2.resize(
+ best_map_col,None,fx=4,fy=4
+ )
+ a = 125/np.average(best_map_col)
+ best_map_col_norm = cv2.convertScaleAbs(
+ best_map_col,alpha=a,beta=0
+ )
+ # cv2.imshow('Macbeth',best_map_col)
+ # represent(copy)
+
+
+ """
+ rescale coordinates to original image size
+ """
+ fit_coords = (best_fit/factor,best_cen_fit/factor)
+
+ return(max_cor,best_map_col_norm,fit_coords,success_msg)
+
+ """
+ catch macbeth errors and continue with code
+ """
+ except MacbethError as error:
+ return(0,None,None,error)
diff --git a/utils/raspberrypi/ctt/ctt_noise.py b/utils/raspberrypi/ctt/ctt_noise.py
new file mode 100644
index 00000000..b84cf0ca
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_noise.py
@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_noise.py - camera tuning tool noise calibration
+
+from ctt_image_load import *
+import matplotlib.pyplot as plt
+
+"""
+Find noise standard deviation and fit to model:
+
+ noise std = a + b*sqrt(pixel mean)
+"""
+def noise(Cam,Img,plot):
+ Cam.log += '\nProcessing image: {}'.format(Img.name)
+ stds = []
+ means = []
+ """
+ iterate through macbeth square patches
+ """
+ for ch_patches in Img.patches:
+ for patch in ch_patches:
+ """
+ renormalise patch
+ """
+ patch = np.array(patch)
+ patch = (patch-Img.blacklevel_16)/Img.againQ8_norm
+ std = np.std(patch)
+ mean = np.mean(patch)
+ stds.append(std)
+ means.append(mean)
+
+ """
+ clean data and ensure all means are above 0
+ """
+ stds = np.array(stds)
+ means = np.array(means)
+ means = np.clip(np.array(means),0,None)
+ sq_means = np.sqrt(means)
+
+
+ """
+ least squares fit model
+ """
+ fit = np.polyfit(sq_means,stds,1)
+ Cam.log += '\nBlack level = {}'.format(Img.blacklevel_16)
+ Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
+ Cam.log += ' slope = {:.3f}'.format(fit[0])
+ """
+ remove any values further than std from the fit
+
+ anomalies most likely caused by:
+ > ucharacteristically noisy white patch
+ > saturation in the white patch
+ """
+ fit_score = np.abs(stds - fit[0]*sq_means - fit[1])
+ fit_std = np.std(stds)
+ fit_score_norm = fit_score - fit_std
+ anom_ind = np.where(fit_score_norm > 1)
+ fit_score_norm.sort()
+ sq_means_clean = np.delete(sq_means,anom_ind)
+ stds_clean = np.delete(stds,anom_ind)
+ removed = len(stds) - len(stds_clean)
+ if removed != 0:
+ Cam.log += '\nIdentified and removed {} anomalies.'.format(removed)
+ Cam.log += '\nRecalculating fit'
+ """
+ recalculate fit with outliers removed
+ """
+ fit = np.polyfit(sq_means_clean,stds_clean,1)
+ Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1]))
+ Cam.log += ' slope = {:.3f}'.format(fit[0])
+
+ """
+ if fit const is < 0 then force through 0 by
+ dividing by sq_means and fitting poly order 0
+ """
+ corrected = 0
+ if fit[1] < 0:
+ corrected = 1
+ ones = np.ones(len(means))
+ y_data = stds/sq_means
+ fit2 = np.polyfit(ones,y_data,0)
+ Cam.log += '\nOffset below zero. Fit recalculated with zero offset'
+ Cam.log += '\nNoise profile: offset = 0'
+ Cam.log += ' slope = {:.3f}'.format(fit2[0])
+ # print('new fit')
+ # print(fit2)
+
+ """
+ plot fit for debug
+ """
+ if plot:
+ x = np.arange(sq_means.max()//0.88)
+ fit_plot = x*fit[0] + fit[1]
+ plt.scatter(sq_means,stds,label='data',color='blue')
+ plt.scatter(sq_means[anom_ind],stds[anom_ind],color='orange',label='anomalies')
+ plt.plot(x,fit_plot,label='fit',color='red',ls=':')
+ if fit[1] < 0:
+ fit_plot_2 = x*fit2[0]
+ plt.plot(x,fit_plot_2,label='fit 0 intercept',color='green',ls='--')
+ plt.plot(0,0)
+ plt.title('Noise Plot\nImg: {}'.format(Img.str))
+ plt.legend(loc = 'upper left')
+ plt.xlabel('Sqrt Pixel Value')
+ plt.ylabel('Noise Standard Deviation')
+ plt.grid()
+ plt.show()
+ """
+ End of plotting code
+ """
+
+ """
+ format output to include forced 0 constant
+ """
+ Cam.log += '\n'
+ if corrected:
+ fit = [fit2[0],0]
+ return fit
+
+ else:
+ return fit
diff --git a/utils/raspberrypi/ctt/ctt_pretty_print_json.py b/utils/raspberrypi/ctt/ctt_pretty_print_json.py
new file mode 100644
index 00000000..44a9311b
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_pretty_print_json.py
@@ -0,0 +1,70 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_pretty_print_json.py - camera tuning tool JSON formatter
+
+"""
+takes a collapsed json file and makes it more readable
+"""
+def process_file(string, fout, state):
+ for c in string:
+ process_char(c, fout, state)
+
+def print_newline(fout, state):
+ fout.write('\n')
+ fout.write(' '*state["indent"]*4)
+
+def process_char(c, fout, state):
+ if c == '{':
+ if not state["skipnewline"]: print_newline(fout, state)
+ fout.write(c)
+ state["indent"] += 1
+ print_newline(fout, state)
+ elif c == '}':
+ state["indent"] -= 1
+ print_newline(fout, state)
+ fout.write(c)
+ elif c == '[':
+ print_newline(fout, state)
+ fout.write(c)
+ state["indent"] += 1
+ print_newline(fout, state)
+ state["inarray"] = [True] + state["inarray"]
+ state["arraycount"] = [0] + state["arraycount"]
+ elif c == ']':
+ state["indent"] -= 1
+ print_newline(fout, state)
+ state["inarray"].pop(0)
+ state["arraycount"].pop(0)
+ fout.write(c)
+ elif c == ':':
+ fout.write(c)
+ fout.write(' ')
+ elif c == ' ':
+ pass
+ elif c == ',':
+ if not state["inarray"][0]:
+ fout.write(c)
+ fout.write(' ')
+ print_newline(fout, state)
+ else:
+ fout.write(c)
+ state["arraycount"][0] += 1
+ if state["arraycount"][0] == 16:
+ state["arraycount"][0] = 0
+ print_newline(fout, state)
+ else:
+ fout.write(' ')
+ else:
+ fout.write(c)
+ state["skipnewline"] = (c == '[')
+
+def pretty_print_json(str_in, output_filename):
+ state = {"indent": 0, "inarray": [False], "arraycount": [], "skipnewline" : True}
+ with open(output_filename, "w") as fout:
+ process_file(str_in, fout, state)
+
+
+if __name__ == '__main__':
+ pretty_print_json("../ctt/ref_json/final_imx477.json", "pretty.json")
diff --git a/utils/raspberrypi/ctt/ctt_ransac.py b/utils/raspberrypi/ctt/ctt_ransac.py
new file mode 100644
index 00000000..e7c57137
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_ransac.py
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_ransac.py - camera tuning tool RANSAC selector for Macbeth chart locator
+
+import numpy as np
+
+scale = 2
+
+"""
+constructs normalised macbeth chart corners for ransac algorithm
+"""
+def get_square_verts(c_err = 0.05,scale = scale):
+ """
+ define macbeth chart corners
+ """
+ b_bord_x,b_bord_y = scale*8.5,scale*13
+ s_bord = 6*scale
+ side = 41*scale
+ x_max = side*6 + 5*s_bord + 2*b_bord_x
+ y_max = side*4 + 3*s_bord + 2*b_bord_y
+ c1 = (0,0)
+ c2 = (0,y_max)
+ c3 = (x_max,y_max)
+ c4 = (x_max,0)
+ mac_norm = np.array((c1,c2,c3,c4),np.float32)
+ mac_norm = np.array([ mac_norm ])
+
+ square_verts = []
+ square_0 = np.array(((0,0),(0,side),
+ (side,side),(side,0)),np.float32)
+ offset_0 = np.array((b_bord_x,b_bord_y),np.float32)
+ c_off = side * c_err
+ offset_cont = np.array(((c_off,c_off),(c_off,-c_off),
+ (-c_off,-c_off),(-c_off,c_off)),np.float32)
+ square_0 += offset_0
+ square_0 += offset_cont
+ """
+ define macbeth square corners
+ """
+ for i in range(6):
+ shift_i = np.array(((i*side,0),(i*side,0),
+ (i*side,0),(i*side,0)),np.float32)
+ shift_bord =np.array(((i*s_bord,0),(i*s_bord,0),
+ (i*s_bord,0),(i*s_bord,0)),np.float32)
+ square_i = square_0 + shift_i + shift_bord
+ for j in range(4):
+ shift_j = np.array(((0,j*side),(0,j*side),
+ (0,j*side),(0,j*side)),np.float32)
+ shift_bord = np.array(((0,j*s_bord),
+ (0,j*s_bord),(0,j*s_bord),
+ (0,j*s_bord)),np.float32)
+ square_j = square_i + shift_j + shift_bord
+ square_verts.append(square_j)
+ # print('square_verts')
+ # print(square_verts)
+ return np.array(square_verts,np.float32),mac_norm
+
+def get_square_centres(c_err = 0.05,scale=scale):
+ """
+ define macbeth square centres
+ """
+ verts,mac_norm = get_square_verts(c_err,scale=scale)
+
+ centres = np.mean(verts,axis = 1)
+ # print('centres')
+ # print(centres)
+ return np.array(centres,np.float32)
diff --git a/utils/raspberrypi/ctt/ctt_ref.pgm b/utils/raspberrypi/ctt/ctt_ref.pgm
new file mode 100644
index 00000000..9b9f4920
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_ref.pgm
@@ -0,0 +1,5 @@
+P5
+# Reference macbeth chart
+120 80
+255
+  !#!" #!"&&$#$#'"%&#+2///..../.........-()))))))))))))))))))(((-,*)'(&)#($%(%"###""!%""&"&&!$" #!$ !"! $&**" !#5.,%+,-5"0<HBAA54" %##((()*+,---.........+*)))))))))))))))-.,,--+))('((''('%'%##"!""!"!""""#!   ! %/vz:Lc,!#""%%''')**+)-../..../.-*)))))))))))))**,,)**'(''&'((&&%%##$! !!!! ! !  !  5*"-)&7(1.75Rnge`\`$ ""!"%%%'')())++--/---,-..,-.,++**))))())*)*)''%'%&%&'&%%"""""        !  !!$&$$&##(+*,,/10122126545./66402006486869650*.1.***)*+)()&((('('##)('&%%&%$$$#$%$%$ (((*))('((('('(&%V0;>>;@@>@AAAACBCB=&<<5x|64RYVTSRRRMMNLKJJLH+&0gijgdeffmmnpnkji`#3bY! 3FHHIIIHIJIIJHIII@#?=7}:5Wcbcbdcb`^^`^^_^Y,'6r'<l%2FHHIIHJJJJJJIIJI?%;>7|;8Xfeeegeccb`^aba]Z+)<r)>q#3GHIIIIJIIJJIHIJI@&5=8~;8Zgghggedbdcbda^\Z+(;y)9z"3GIIJJJJJKJJJJJJJ@'4>9|=8Zhighgeeeedeca__[/)Bv&:|#3GJJIIJKKKJJJKKJK@&6>9~<8Yghegggffihccab^\/*Cz'9$  6IKJJMMMKMKKMKKMLC&2@9<9Yghhhhijiegdcebc^0)G(7% 6JLMMNMMKMMNMMMMMD&2@:~=9Xfghhjiigdgddedc`1)M}(:¾& "8LNOONNOMONNMMNOND'3@;=:Ziiigheegegegggdc1,Q~)8%# "9NNNPPPQOOOOONNOOD'0?;=;[iigeeegghgdedgea0-P(8Ý' "#$:NNOQPPRPQPOOPQPPD*1A;;:Yfghgghgghghhdggc3.\~);¤(&%%;OQQQRSSRPQQQQSQQF)3B<=:Wfhghhhihggghfhee4/f*:ä&%%%?RSSSSSTTTTSSSTTRE)5B=@:Ygiihhiiiihihiiif72p}(9Ʃ'#%&?TUTTTUUQSTTTTTVSF*3F>A;[ghjiihiiiihihije50r)6ƫ& &#%?SVVVUUUUUTUUVVUUG*5F=A;Yhijiiijjiiiiijje81t~)5ư' '$$=OQRRQQPRSRSSSSSSG+6D@?;Wefgggggfffgeeefc41x{*5( &&&'++++,,*-,-00-0100*-SUX\]]`_ffgiooopo=;X\bedbadbca`]\]ZZ;;<::8:;9983433110/-,...1//12410/..--+)"",---,-./,,.-/-0-( &&%+/0103322011223233)(34534767::;;==:=B9;BFGEEGIKJKIJGIJCD=<:76566554111/0/1.*+00233300/00//..,+*#")(*)++,++))*++**'!!&$*w¼1-_addc`ceccdccedbb?A|B>=>?@@?====;<:;:<:11r+.( !'%*zɠ42gjmllklomooonpopmHGD>AEDEFEECEECCCDDEC46׿0:Ѿ,!!&&,|ʡ61inknnoopoppoqqrqoEEFACGFFFFFFDFDDDDDDC5709+!"%%-~ʡ42inopppppoqqqrrsrnABC?DGGGGFFFFDFFDDEDC481;+!!"#*|ʡ62imoppppqqqqrtrqtrGDH?CGGGGGGGGFFFFFFDB381<Խ, !)}ˢ63mooppqqqqqqrrtvtoDHJACHHGGHGGFFFDDGGFD293>׽, $){ˢ53jpppqprqrrrttuvuo>HJAFHHHHHGGHGGFGGFFE283:ڽ- "*{̣53loqpqsqrrrtrutsvrAHHCGHIHHHHHHGFGHGGGD5;28, +}ʡ52mqoqpqrttttttuurpFIOCEHHIHHHHGHGGFFIGF8<48ۿ, (|ʢ41krqpqqqrrtrtuvtuoEHPBHHIIIHIIHIHGHGHHE7<58* (zʡ63kpqprqqstttutrvvoFOLEHHIIHIHHHIGHGIHGF4=5<* 'zȡ62lppqrqrrrtttuttvpAGMGHIIIIHIIIHHIIJHHG4<4<+ !){Ƞ62jopqqqqqrtttutttrEHOHFIIIIIJIIIIHIHIHI7>5;, !)zƟ53lppqqrqrtttuuuutsFIRHGJIJHJKJJJIIIIIIH9>5;+  !({Ŝ41joppprqrrrutttvvrIHTHCJJJJJIJIJJIJJJIH7=5;+ (u65gjlmmmnoopnpprpqoIHOIBIJJJIJJJJIIIHHHG8929ʾ' "&,-*)-01/,0/12102-+04448789<>>??AFAD@DBCIJNRWTSUXT[WUQUOKFEBBABA?>>=<<;;67942:<<<>9999864565363&(13335422./1/-+..+ !"&$$""$"&$%'()(''*+-0124688:<>>??A>?EBCHKOLJLNOSQOXQQVMLACGHGHIGFHGDCCBB@??7432233210111.,++,++%(++)*(''%%%$$#%&$# ")0/001120024455520+-U]`addcdhefeekecYGFJRXYYVWWZWVXXVZTOBF}K7Ybccddfeg`^]^]\[Z[*)OTTPPQPOKOLLJJLIK  !1;:9:<<===;=???A@9*/FJmxyxwyzzzxyzzz{zxLO]=.-y# !!2><=;==>=<<>@@@@A9-0IKnz||{|{||{}}~}}{zLO]>..~% $2==;<>>?===>@A@AB;+1JJo{|y{||}{||}}}}}yMT_>-.}# %2<=;=<@?>==>?A@AA9+3FMlz{{y|}}}}||}|}}{MTd>-,# %1<<<;==<<=>?A?@AA:,3INo{{y{||||}|}}|~}{RTd=/-}#!$0<<<=<<==>A@@>@AA:-2HInzz{{||{{}~~}}|}zMRd=++~# "$/;<==>;===@@@@>AA:+2KHn||y|||||{}~}|}|xMSd=+,}# ! "/:<=>@<<>=@@@@@AA;-3MFs||{{{y}z}}|}|}}yMWc>,)|! !1;>?>><<>@>>=>ABB;,0LHr{|{|}|y|}}}}}zNXc?()z# $/;;<=;<>>=>>>@@BB:,1IInyz||||||{||}{~|{NVc;('}# $0:<==<;>@>>>>@ABB:,/HLlx|}y{y{|y{|}}}}yMRd>~*(y" !&3:;<<;==@@=>AABBA;-3KLqz{|||y{}|}{}|~{zRQc9w)'y" !%1<<;=>===<=@@ABBC<.5IIlz{|}~~~|}{||~}}zMUd;p)$x" $2===<==@=<>=ABBBC?/0IGkz}}{||}{||y||}zyOVc7o'&~~z"#"#/;<:<<?>;===@?AAA>07GGgwxz{yyxyzzyz{yuuHO\8v'$w~~}|||{~|{zxxxxv!"""'*+(+)*))()+,,.../0398;=<=>DCCDDCBBDHBCJMMLMPNPOJPKPSJDICCNMPONMNNOKHIFDBHE3/46433323.....*+,)( !##!!!!!$#$$#$#&"!!"(+**,,*+.//1478:<:33ACDFGGIIHIJLPKNMQFIPTTRVXVUXUUTXUSTNEGGFDEFAA>==;94877520-,))*(((('&$#!!" &%'FQPQR]dq=FQNLEznki^[YTPUOS;.%-/12322221/10//,/%#0@QQMKEH01NNQOQQOOMNNLKLJGB'&/AWOLKEF-,PQQPQPPQPOONMNNKE''0CZRMJEF,*NSQPPQOOOOMNNMKID('2D[QKIFF,*NPPPPPPNOONMMMJIF!'(2F]RLHDF+%MPPPPOOONONNMMKID)*4D^PLICF+&NPOOOPPOONMMKMKHD**6D_QJFC~F,'MPOOOOONONNKKIIIG,+7D^QIEB|E+&MONOOONNNNKMJKJHH,-8D]PIHEC,#LOOOONONNNKKKMKJF,*6CaMHIFD*%KONOMNMMKMKJJJIJE,,6B^MGHB}D+&LONOOONNMMMMKLKIA,,6A\MFIEE+&LNNMONNMMKKKKKIHF --6A[KFJCF*&LMONMNMNKKJMKJJIF **5>WKEF?}C*%KONNNJKKKMKJKJKID,*4<WMAGCxB)%HKLKKJJJKIHIHHFGC!()*qo39v|}wwwwwwrqtuspn=9^gadcfgce`dbUY[\^>;DIJDB?FEGE=7>8634.(&&(%&*&%%'+*)+*#%()''03364443233222243/-+133423333423766645789:><<<;<;<?=?;<<:78673/001113--.-+*)&&#"&$#%&""$!! ))+rbPpAD9-*******+*++)++--.//./.0/21453469:=;98<;<>=;><7766666741012.-13/-+-/(''&&&%%&$.%0()-%-#-#' #&(% )))hnYQg7(*))))*)**,--....../0/0001357666::;;>?>AA866666666656565300/20/.-*)(('((&&%)d=yoP<?FQFx;210))*RQ.0*,,5*(*))))*,**,+/.../...02/22224456468;:>BB;>;:76666666666755303033/,.-*(())('&')#)"##(+$+*#)) & 
diff --git a/utils/raspberrypi/ctt/ctt_tools.py b/utils/raspberrypi/ctt/ctt_tools.py
new file mode 100644
index 00000000..5a5cf54d
--- /dev/null
+++ b/utils/raspberrypi/ctt/ctt_tools.py
@@ -0,0 +1,141 @@
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2019, Raspberry Pi (Trading) Limited
+#
+# ctt_tools.py - camera tuning tool miscellaneous
+
+import time
+import re
+import binascii
+import os
+import cv2
+import numpy as np
+import imutils
+import sys
+import matplotlib.pyplot as plt
+from sklearn import cluster as cluster
+from sklearn.neighbors.nearest_centroid import NearestCentroid as get_centroids
+
+"""
+This file contains some useful tools, the details of which aren't important to
+understanding of the code. They ar collated here to attempt to improve code
+readability in the main files.
+"""
+
+"""
+obtain config values, unless it doesnt exist, in which case pick default
+Furthermore, it can check if the input is the correct type
+"""
+def get_config(dictt,key,default,ttype):
+ try:
+ val = dictt[key]
+ if ttype == 'string':
+ val = str(val)
+ elif ttype == 'num':
+ if 'int' not in str(type(val)):
+ if 'float' not in str(type(val)):
+ raise ValueError
+ elif ttype == 'dict':
+ if type(val) != type(dictt):
+ raise ValueError
+ elif ttype == 'list':
+ if type(val) != type([]):
+ raise ValueError
+ elif ttype == 'bool':
+ ttype = int(bool(ttype))
+ else:
+ val = dictt[key]
+ except (KeyError, ValueError):
+ val = default
+ return val
+"""
+argument parser
+"""
+def parse_input():
+ arguments = sys.argv[1:]
+ if len(arguments)%2 != 0:
+ raise ArgError('\n\nERROR! Enter value for each arguent passed.')
+ params = arguments [0::2]
+ vals = arguments [1::2]
+ args_dict = dict(zip(params,vals))
+ json_output = get_config(args_dict,'-o',None,'string')
+ directory = get_config(args_dict,'-i',None,'string')
+ config = get_config(args_dict,'-c',None,'string')
+ log_path = get_config(args_dict,'-l',None,'string')
+ if directory == None:
+ raise ArgError('\n\nERROR! No input directory given.')
+ if json_output == None:
+ raise ArgError('\n\nERROR! No output json given.')
+ return json_output,directory,config,log_path
+"""
+custom arg and macbeth error class
+"""
+class ArgError(Exception):
+ pass
+class MacbethError(Exception):
+ pass
+
+"""
+correlation function to quantify match
+"""
+def correlate(im1,im2):
+ f1 = im1.flatten()
+ f2 = im2.flatten()
+ cor = np.corrcoef(f1,f2)
+ return cor[0][1]
+
+"""
+get list of files from directory
+"""
+def get_photos(directory='photos'):
+ filename_list = []
+ for filename in os.listdir(directory):
+ if 'jp' in filename or '.dng' in filename:
+ filename_list.append(filename)
+ return filename_list
+
+"""
+display image for debugging... read at your own risk...
+"""
+def represent(img,name='image'):
+ # if type(img) == tuple or type(img) == list:
+ # for i in range(len(img)):
+ # name = 'image {}'.format(i)
+ # cv2.imshow(name,img[i])
+ # else:
+ # cv2.imshow(name,img)
+ # cv2.waitKey(0)
+ # cv2.destroyAllWindows()
+ # return 0
+ """
+ code above displays using opencv, but this doesn't catch users pressing 'x'
+ with their mouse to close the window.... therefore matplotlib is used....
+ (thanks a lot opencv)
+ """
+ grid = plt.GridSpec(22,1)
+ plt.subplot(grid[:19,0])
+ plt.imshow(img,cmap='gray')
+ plt.axis('off')
+ plt.subplot(grid[21,0])
+ plt.title('press \'q\' to continue')
+ plt.axis('off')
+ plt.show()
+
+ # f = plt.figure()
+ # ax = f.add_subplot(211)
+ # ax2 = f.add_subplot(122)
+ # ax.imshow(img,cmap='gray')
+ # ax.axis('off')
+ # ax2.set_figheight(2)
+ # ax2.title('press \'q\' to continue')
+ # ax2.axis('off')
+ # plt.show()
+
+
+"""
+reshape image to fixed width without distorting
+returns image and scale factor
+"""
+def reshape(img,width):
+ factor = width/img.shape[0]
+ return cv2.resize(img,None,fx=factor,fy=factor),factor
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-22 07:17:47 +0000