utils: raspberrypi: ctt: Adapt tuning tool for both VC4 and PiSP

The old ctt.py and alsc_only.py scripts are removed.

Instead of ctt.py use ctt_vc4.py or ctt_pisp.py, depending on your
target platform.

Instead of alsc_only.py use alsc_vc4.py or alsc_pisp.py, again
according to your platform.

Signed-off-by: David Plowman <david.plowman@raspberrypi.com>
Reviewed-by: Naushir Patuck <naush@raspberrypi.com>
Tested-by: Naushir Patuck <naush@raspberrypi.com>
Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>

1 files changed, 0 insertions, 837 deletions

diff --git a/utils/raspberrypi/ctt/ctt.py b/utils/raspberrypi/ctt/ctt.py
deleted file mode 100755
index bbe960b0..00000000
--- a/utils/raspberrypi/ctt/ctt.py
+++ /dev/null
@@ -1,837 +0,0 @@
-#!/usr/bin/env python3
-#
-# SPDX-License-Identifier: BSD-2-Clause
-#
-# Copyright (C) 2019, Raspberry Pi Ltd
-#
-# camera tuning tool
-
-import os
-import sys
-from ctt_image_load import *
-from ctt_ccm import *
-from ctt_awb import *
-from ctt_alsc import *
-from ctt_lux import *
-from ctt_noise import *
-from ctt_geq import *
-from ctt_pretty_print_json import pretty_print
-import random
-import json
-import re
-
-"""
-This file houses the camera object, which is used to perform the calibrations.
-The camera object houses all the calibration images as attributes in two lists:
-    - imgs (macbeth charts)
-    - imgs_alsc (alsc correction images)
-Various calibrations are methods of the camera object, and the output is stored
-in a dictionary called self.json.
-Once all the caibration has been completed, the Camera.json is written into a
-json file.
-The camera object initialises its json dictionary by reading from a pre-written
-blank json file. This has been done to avoid reproducing the entire json file
-in the code here, thereby avoiding unecessary clutter.
-"""
-
-
-"""
-Get the colour and lux values from the strings of each inidvidual image
-"""
-def get_col_lux(string):
-    """
-    Extract colour and lux values from filename
-    """
-    col = re.search(r'([0-9]+)[kK](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string)
-    lux = re.search(r'([0-9]+)[lL](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string)
-    try:
-        col = col.group(1)
-    except AttributeError:
-        """
-        Catch error if images labelled incorrectly and pass reasonable defaults
-        """
-        return None, None
-    try:
-        lux = lux.group(1)
-    except AttributeError:
-        """
-        Catch error if images labelled incorrectly and pass reasonable defaults
-        Still returns colour if that has been found.
-        """
-        return col, None
-    return int(col), int(lux)
-
-
-"""
-Camera object that is the backbone of the tuning tool.
-Input is the desired path of the output json.
-"""
-class Camera:
-    def __init__(self, jfile):
-        self.path = os.path.dirname(os.path.expanduser(__file__)) + '/'
-        if self.path == '/':
-            self.path = ''
-        self.imgs = []
-        self.imgs_alsc = []
-        self.log = 'Log created : ' + time.asctime(time.localtime(time.time()))
-        self.log_separator = '\n'+'-'*70+'\n'
-        self.jf = jfile
-        """
-        initial json dict populated by uncalibrated values
-        """
-        self.json = {
-            "rpi.black_level": {
-                "black_level": 4096
-            },
-            "rpi.dpc": {
-            },
-            "rpi.lux": {
-                "reference_shutter_speed": 10000,
-                "reference_gain": 1,
-                "reference_aperture": 1.0
-            },
-            "rpi.noise": {
-            },
-            "rpi.geq": {
-            },
-            "rpi.sdn": {
-            },
-            "rpi.awb": {
-                "priors": [
-                    {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]},
-                    {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]},
-                    {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]}
-                ],
-                "modes": {
-                    "auto": {"lo": 2500, "hi": 8000},
-                    "incandescent": {"lo": 2500, "hi": 3000},
-                    "tungsten": {"lo": 3000, "hi": 3500},
-                    "fluorescent": {"lo": 4000, "hi": 4700},
-                    "indoor": {"lo": 3000, "hi": 5000},
-                    "daylight": {"lo": 5500, "hi": 6500},
-                    "cloudy": {"lo": 7000, "hi": 8600}
-                },
-                "bayes": 1
-            },
-            "rpi.agc": {
-                "metering_modes": {
-                    "centre-weighted": {
-                        "weights": [3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0]
-                    },
-                    "spot": {
-                        "weights": [2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-                    },
-                    "matrix": {
-                        "weights": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
-                    }
-                },
-                "exposure_modes": {
-                    "normal": {
-                        "shutter": [100, 10000, 30000, 60000, 120000],
-                        "gain": [1.0, 2.0, 4.0, 6.0, 6.0]
-                    },
-                    "short": {
-                        "shutter": [100, 5000, 10000, 20000, 120000],
-                        "gain": [1.0, 2.0, 4.0, 6.0, 6.0]
-                    }
-                },
-                "constraint_modes": {
-                    "normal": [
-                        {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]}
-                    ],
-                    "highlight": [
-                        {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]},
-                        {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]}
-                    ]
-                },
-                "y_target": [0, 0.16, 1000, 0.165, 10000, 0.17]
-            },
-            "rpi.alsc": {
-                'omega': 1.3,
-                'n_iter': 100,
-                'luminance_strength': 0.7,
-            },
-            "rpi.contrast": {
-                "ce_enable": 1,
-                "gamma_curve": [
-                    0,     0,
-                    1024,  5040,
-                    2048,  9338,
-                    3072,  12356,
-                    4096,  15312,
-                    5120,  18051,
-                    6144,  20790,
-                    7168,  23193,
-                    8192,  25744,
-                    9216,  27942,
-                    10240, 30035,
-                    11264, 32005,
-                    12288, 33975,
-                    13312, 35815,
-                    14336, 37600,
-                    15360, 39168,
-                    16384, 40642,
-                    18432, 43379,
-                    20480, 45749,
-                    22528, 47753,
-                    24576, 49621,
-                    26624, 51253,
-                    28672, 52698,
-                    30720, 53796,
-                    32768, 54876,
-                    36864, 57012,
-                    40960, 58656,
-                    45056, 59954,
-                    49152, 61183,
-                    53248, 62355,
-                    57344, 63419,
-                    61440, 64476,
-                    65535, 65535
-                ]
-            },
-            "rpi.ccm": {
-            },
-            "rpi.sharpen": {
-            }
-        }
-
-    """
-    Perform colour correction calibrations by comparing macbeth patch colours
-    to standard macbeth chart colours.
-    """
-    def ccm_cal(self, do_alsc_colour):
-        if 'rpi.ccm' in self.disable:
-            return 1
-        print('\nStarting CCM calibration')
-        self.log_new_sec('CCM')
-        """
-        if image is greyscale then CCm makes no sense
-        """
-        if self.grey:
-            print('\nERROR: Can\'t do CCM on greyscale image!')
-            self.log += '\nERROR: Cannot perform CCM calibration '
-            self.log += 'on greyscale image!\nCCM aborted!'
-            del self.json['rpi.ccm']
-            return 0
-        a = time.time()
-        """
-        Check if alsc tables have been generated, if not then do ccm without
-        alsc
-        """
-        if ("rpi.alsc" not in self.disable) and do_alsc_colour:
-            """
-            case where ALSC colour has been done, so no errors should be
-            expected...
-            """
-            try:
-                cal_cr_list = self.json['rpi.alsc']['calibrations_Cr']
-                cal_cb_list = self.json['rpi.alsc']['calibrations_Cb']
-                self.log += '\nALSC tables found successfully'
-            except KeyError:
-                cal_cr_list, cal_cb_list = None, None
-                print('WARNING! No ALSC tables found for CCM!')
-                print('Performing CCM calibrations without ALSC correction...')
-                self.log += '\nWARNING: No ALSC tables found.\nCCM calibration '
-                self.log += 'performed without ALSC correction...'
-        else:
-            """
-            case where config options result in CCM done without ALSC colour tables
-            """
-            cal_cr_list, cal_cb_list = None, None
-            self.log += '\nWARNING: No ALSC tables found.\nCCM calibration '
-            self.log += 'performed without ALSC correction...'
-
-        """
-        Do CCM calibration
-        """
-        try:
-            ccms = ccm(self, cal_cr_list, cal_cb_list)
-        except ArithmeticError:
-            print('ERROR: Matrix is singular!\nTake new pictures and try again...')
-            self.log += '\nERROR: Singular matrix encountered during fit!'
-            self.log += '\nCCM aborted!'
-            return 1
-        """
-        Write output to json
-        """
-        self.json['rpi.ccm']['ccms'] = ccms
-        self.log += '\nCCM calibration written to json file'
-        print('Finished CCM calibration')
-
-    """
-    Auto white balance calibration produces a colour curve for
-    various colour temperatures, as well as providing a maximum 'wiggle room'
-    distance from this curve (transverse_neg/pos).
-    """
-    def awb_cal(self, greyworld, do_alsc_colour):
-        if 'rpi.awb' in self.disable:
-            return 1
-        print('\nStarting AWB calibration')
-        self.log_new_sec('AWB')
-        """
-        if image is greyscale then AWB makes no sense
-        """
-        if self.grey:
-            print('\nERROR: Can\'t do AWB on greyscale image!')
-            self.log += '\nERROR: Cannot perform AWB calibration '
-            self.log += 'on greyscale image!\nAWB aborted!'
-            del self.json['rpi.awb']
-            return 0
-        """
-        optional set greyworld (e.g. for noir cameras)
-        """
-        if greyworld:
-            self.json['rpi.awb']['bayes'] = 0
-            self.log += '\nGreyworld set'
-        """
-        Check if alsc tables have been generated, if not then do awb without
-        alsc correction
-        """
-        if ("rpi.alsc" not in self.disable) and do_alsc_colour:
-            try:
-                cal_cr_list = self.json['rpi.alsc']['calibrations_Cr']
-                cal_cb_list = self.json['rpi.alsc']['calibrations_Cb']
-                self.log += '\nALSC tables found successfully'
-            except KeyError:
-                cal_cr_list, cal_cb_list = None, None
-                print('ERROR, no ALSC calibrations found for AWB')
-                print('Performing AWB without ALSC tables')
-                self.log += '\nWARNING: No ALSC tables found.\nAWB calibration '
-                self.log += 'performed without ALSC correction...'
-        else:
-            cal_cr_list, cal_cb_list = None, None
-            self.log += '\nWARNING: No ALSC tables found.\nAWB calibration '
-            self.log += 'performed without ALSC correction...'
-        """
-        call calibration function
-        """
-        plot = "rpi.awb" in self.plot
-        awb_out = awb(self, cal_cr_list, cal_cb_list, plot)
-        ct_curve, transverse_neg, transverse_pos = awb_out
-        """
-        write output to json
-        """
-        self.json['rpi.awb']['ct_curve'] = ct_curve
-        self.json['rpi.awb']['sensitivity_r'] = 1.0
-        self.json['rpi.awb']['sensitivity_b'] = 1.0
-        self.json['rpi.awb']['transverse_pos'] = transverse_pos
-        self.json['rpi.awb']['transverse_neg'] = transverse_neg
-        self.log += '\nAWB calibration written to json file'
-        print('Finished AWB calibration')
-
-    """
-    Auto lens shading correction completely mitigates the effects of lens shading for ech
-    colour channel seperately, and then partially corrects for vignetting.
-    The extent of the correction depends on the 'luminance_strength' parameter.
-    """
-    def alsc_cal(self, luminance_strength, do_alsc_colour):
-        if 'rpi.alsc' in self.disable:
-            return 1
-        print('\nStarting ALSC calibration')
-        self.log_new_sec('ALSC')
-        """
-        check if alsc images have been taken
-        """
-        if len(self.imgs_alsc) == 0:
-            print('\nError:\nNo alsc calibration images found')
-            self.log += '\nERROR: No ALSC calibration images found!'
-            self.log += '\nALSC calibration aborted!'
-            return 1
-        self.json['rpi.alsc']['luminance_strength'] = luminance_strength
-        if self.grey and do_alsc_colour:
-            print('Greyscale camera so only luminance_lut calculated')
-            do_alsc_colour = False
-            self.log += '\nWARNING: ALSC colour correction cannot be done on '
-            self.log += 'greyscale image!\nALSC colour corrections forced off!'
-        """
-        call calibration function
-        """
-        plot = "rpi.alsc" in self.plot
-        alsc_out = alsc_all(self, do_alsc_colour, plot)
-        cal_cr_list, cal_cb_list, luminance_lut, av_corn = alsc_out
-        """
-        write output to json and finish if not do_alsc_colour
-        """
-        if not do_alsc_colour:
-            self.json['rpi.alsc']['luminance_lut'] = luminance_lut
-            self.json['rpi.alsc']['n_iter'] = 0
-            self.log += '\nALSC calibrations written to json file'
-            self.log += '\nNo colour calibrations performed'
-            print('Finished ALSC calibrations')
-            return 1
-
-        self.json['rpi.alsc']['calibrations_Cr'] = cal_cr_list
-        self.json['rpi.alsc']['calibrations_Cb'] = cal_cb_list
-        self.json['rpi.alsc']['luminance_lut'] = luminance_lut
-        self.log += '\nALSC colour and luminance tables written to json file'
-
-        """
-        The sigmas determine the strength of the adaptive algorithm, that
-        cleans up any lens shading that has slipped through the alsc. These are
-        determined by measuring a 'worst-case' difference between two alsc tables
-        that are adjacent in colour space. If, however, only one colour
-        temperature has been provided, then this difference can not be computed
-        as only one table is available.
-        To determine the sigmas you would have to estimate the error of an alsc
-        table with only the image it was taken on as a check. To avoid circularity,
-        dfault exaggerated sigmas are used, which can result in too much alsc and
-        is therefore not advised.
-        In general, just take another alsc picture at another colour temperature!
-        """
-
-        if len(self.imgs_alsc) == 1:
-            self.json['rpi.alsc']['sigma'] = 0.005
-            self.json['rpi.alsc']['sigma_Cb'] = 0.005
-            print('\nWarning:\nOnly one alsc calibration found'
-                  '\nStandard sigmas used for adaptive algorithm.')
-            print('Finished ALSC calibrations')
-            self.log += '\nWARNING: Only one colour temperature found in '
-            self.log += 'calibration images.\nStandard sigmas used for adaptive '
-            self.log += 'algorithm!'
-            return 1
-
-        """
-        obtain worst-case scenario residual sigmas
-        """
-        sigma_r, sigma_b = get_sigma(self, cal_cr_list, cal_cb_list)
-        """
-        write output to json
-        """
-        self.json['rpi.alsc']['sigma'] = np.round(sigma_r, 5)
-        self.json['rpi.alsc']['sigma_Cb'] = np.round(sigma_b, 5)
-        self.log += '\nCalibrated sigmas written to json file'
-        print('Finished ALSC calibrations')
-
-    """
-    Green equalisation fixes problems caused by discrepancies in green
-    channels. This is done by measuring the effect on macbeth chart patches,
-    which ideally would have the same green values throughout.
-    An upper bound linear model is fit, fixing a threshold for the green
-    differences that are corrected.
-    """
-    def geq_cal(self):
-        if 'rpi.geq' in self.disable:
-            return 1
-        print('\nStarting GEQ calibrations')
-        self.log_new_sec('GEQ')
-        """
-        perform calibration
-        """
-        plot = 'rpi.geq' in self.plot
-        slope, offset = geq_fit(self, plot)
-        """
-        write output to json
-        """
-        self.json['rpi.geq']['offset'] = offset
-        self.json['rpi.geq']['slope'] = slope
-        self.log += '\nGEQ calibrations written to json file'
-        print('Finished GEQ calibrations')
-
-    """
-    Lux calibrations allow the lux level of a scene to be estimated by a ratio
-    calculation. Lux values are used in the pipeline for algorithms such as AGC
-    and AWB
-    """
-    def lux_cal(self):
-        if 'rpi.lux' in self.disable:
-            return 1
-        print('\nStarting LUX calibrations')
-        self.log_new_sec('LUX')
-        """
-        The lux calibration is done on a single image. For best effects, the
-        image with lux level closest to 1000 is chosen.
-        """
-        luxes = [Img.lux for Img in self.imgs]
-        argmax = luxes.index(min(luxes, key=lambda l: abs(1000-l)))
-        Img = self.imgs[argmax]
-        self.log += '\nLux found closest to 1000: {} lx'.format(Img.lux)
-        self.log += '\nImage used: ' + Img.name
-        if Img.lux < 50:
-            self.log += '\nWARNING: Low lux could cause inaccurate calibrations!'
-        """
-        do calibration
-        """
-        lux_out, shutter_speed, gain = lux(self, Img)
-        """
-        write output to json
-        """
-        self.json['rpi.lux']['reference_shutter_speed'] = shutter_speed
-        self.json['rpi.lux']['reference_gain'] = gain
-        self.json['rpi.lux']['reference_lux'] = Img.lux
-        self.json['rpi.lux']['reference_Y'] = lux_out
-        self.log += '\nLUX calibrations written to json file'
-        print('Finished LUX calibrations')
-
-    """
-    Noise alibration attempts to describe the noise profile of the sensor. The
-    calibration is run on macbeth images and the final output is taken as the average
-    """
-    def noise_cal(self):
-        if 'rpi.noise' in self.disable:
-            return 1
-        print('\nStarting NOISE calibrations')
-        self.log_new_sec('NOISE')
-        """
-        run calibration on all images and sort by slope.
-        """
-        plot = "rpi.noise" in self.plot
-        noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key=lambda x: x[0])
-        self.log += '\nFinished processing images'
-        """
-        take the average of the interquartile
-        """
-        length = len(noise_out)
-        noise_out = np.mean(noise_out[length//4:1+3*length//4], axis=0)
-        self.log += '\nAverage noise profile: constant = {} '.format(int(noise_out[1]))
-        self.log += 'slope = {:.3f}'.format(noise_out[0])
-        """
-        write to json
-        """
-        self.json['rpi.noise']['reference_constant'] = int(noise_out[1])
-        self.json['rpi.noise']['reference_slope'] = round(noise_out[0], 3)
-        self.log += '\nNOISE calibrations written to json'
-        print('Finished NOISE calibrations')
-
-    """
-    Removes json entries that are turned off
-    """
-    def json_remove(self, disable):
-        self.log_new_sec('Disabling Options', cal=False)
-        if len(self.disable) == 0:
-            self.log += '\nNothing disabled!'
-            return 1
-        for key in disable:
-            try:
-                del self.json[key]
-                self.log += '\nDisabled: ' + key
-            except KeyError:
-                self.log += '\nERROR: ' + key + ' not found!'
-    """
-    writes the json dictionary to the raw json file then make pretty
-    """
-    def write_json(self):
-        """
-        Write json dictionary to file using our version 2 format
-        """
-
-        out_json = {
-            "version": 2.0,
-            'target': 'bcm2835',
-            "algorithms": [{name: data} for name, data in self.json.items()],
-        }
-
-        with open(self.jf, 'w') as f:
-            f.write(pretty_print(out_json))
-
-    """
-    add a new section to the log file
-    """
-    def log_new_sec(self, section, cal=True):
-        self.log += '\n'+self.log_separator
-        self.log += section
-        if cal:
-            self.log += ' Calibration'
-        self.log += self.log_separator
-
-    """
-    write script arguments to log file
-    """
-    def log_user_input(self, json_output, directory, config, log_output):
-        self.log_new_sec('User Arguments', cal=False)
-        self.log += '\nJson file output: ' + json_output
-        self.log += '\nCalibration images directory: ' + directory
-        if config is None:
-            self.log += '\nNo configuration file input... using default options'
-        elif config is False:
-            self.log += '\nWARNING: Invalid configuration file path...'
-            self.log += ' using default options'
-        elif config is True:
-            self.log += '\nWARNING: Invalid syntax in configuration file...'
-            self.log += ' using default options'
-        else:
-            self.log += '\nConfiguration file: ' + config
-        if log_output is None:
-            self.log += '\nNo log file path input... using default: ctt_log.txt'
-        else:
-            self.log += '\nLog file output: ' + log_output
-
-        # if log_output
-
-    """
-    write log file
-    """
-    def write_log(self, filename):
-        if filename is None:
-            filename = 'ctt_log.txt'
-        self.log += '\n' + self.log_separator
-        with open(filename, 'w') as logfile:
-            logfile.write(self.log)
-
-    """
-    Add all images from directory, pass into relevant list of images and
-    extrace lux and temperature values.
-    """
-    def add_imgs(self, directory, mac_config, blacklevel=-1):
-        self.log_new_sec('Image Loading', cal=False)
-        img_suc_msg = 'Image loaded successfully!'
-        print('\n\nLoading images from '+directory)
-        self.log += '\nDirectory: ' + directory
-        """
-        get list of files
-        """
-        filename_list = get_photos(directory)
-        print("Files found: {}".format(len(filename_list)))
-        self.log += '\nFiles found: {}'.format(len(filename_list))
-        """
-        iterate over files
-        """
-        filename_list.sort()
-        for filename in filename_list:
-            address = directory + filename
-            print('\nLoading image: '+filename)
-            self.log += '\n\nImage: ' + filename
-            """
-            obtain colour and lux value
-            """
-            col, lux = get_col_lux(filename)
-            """
-            Check if image is an alsc calibration image
-            """
-            if 'alsc' in filename:
-                Img = load_image(self, address, mac=False)
-                self.log += '\nIdentified as an ALSC image'
-                """
-                check if imagae data has been successfully unpacked
-                """
-                if Img == 0:
-                    print('\nDISCARDED')
-                    self.log += '\nImage discarded!'
-                    continue
-                    """
-                check that image colour temperature has been successfuly obtained
-                """
-                elif col is not None:
-                    """
-                    if successful, append to list and continue to next image
-                    """
-                    Img.col = col
-                    Img.name = filename
-                    self.log += '\nColour temperature: {} K'.format(col)
-                    self.imgs_alsc.append(Img)
-                    if blacklevel != -1:
-                        Img.blacklevel_16 = blacklevel
-                    print(img_suc_msg)
-                    continue
-                else:
-                    print('Error! No colour temperature found!')
-                    self.log += '\nWARNING: Error reading colour temperature'
-                    self.log += '\nImage discarded!'
-                    print('DISCARDED')
-            else:
-                self.log += '\nIdentified as macbeth chart image'
-                """
-                if image isn't an alsc correction then it must have a lux and a
-                colour temperature value to be useful
-                """
-                if lux is None:
-                    print('DISCARDED')
-                    self.log += '\nWARNING: Error reading lux value'
-                    self.log += '\nImage discarded!'
-                    continue
-                Img = load_image(self, address, mac_config)
-                """
-                check that image data has been successfuly unpacked
-                """
-                if Img == 0:
-                    print('DISCARDED')
-                    self.log += '\nImage discarded!'
-                    continue
-                else:
-                    """
-                    if successful, append to list and continue to next image
-                    """
-                    Img.col, Img.lux = col, lux
-                    Img.name = filename
-                    self.log += '\nColour temperature: {} K'.format(col)
-                    self.log += '\nLux value: {} lx'.format(lux)
-                    if blacklevel != -1:
-                        Img.blacklevel_16 = blacklevel
-                    print(img_suc_msg)
-                    self.imgs.append(Img)
-
-        print('\nFinished loading images')
-
-    """
-    Check that usable images have been found
-    Possible errors include:
-        - no macbeth chart
-        - incorrect filename/extension
-        - images from different cameras
-    """
-    def check_imgs(self, macbeth=True):
-        self.log += '\n\nImages found:'
-        self.log += '\nMacbeth : {}'.format(len(self.imgs))
-        self.log += '\nALSC : {} '.format(len(self.imgs_alsc))
-        self.log += '\n\nCamera metadata'
-        """
-        check usable images found
-        """
-        if len(self.imgs) == 0 and macbeth:
-            print('\nERROR: No usable macbeth chart images found')
-            self.log += '\nERROR: No usable macbeth chart images found'
-            return 0
-        elif len(self.imgs) == 0 and len(self.imgs_alsc) == 0:
-            print('\nERROR: No usable images found')
-            self.log += '\nERROR: No usable images found'
-            return 0
-        """
-        Double check that every image has come from the same camera...
-        """
-        all_imgs = self.imgs + self.imgs_alsc
-        camNames = list(set([Img.camName for Img in all_imgs]))
-        patterns = list(set([Img.pattern for Img in all_imgs]))
-        sigbitss = list(set([Img.sigbits for Img in all_imgs]))
-        blacklevels = list(set([Img.blacklevel_16 for Img in all_imgs]))
-        sizes = list(set([(Img.w, Img.h) for Img in all_imgs]))
-
-        if len(camNames) == 1 and len(patterns) == 1 and len(sigbitss) == 1 and \
-           len(blacklevels) == 1 and len(sizes) == 1:
-            self.grey = (patterns[0] == 128)
-            self.blacklevel_16 = blacklevels[0]
-            self.log += '\nName: {}'.format(camNames[0])
-            self.log += '\nBayer pattern case: {}'.format(patterns[0])
-            if self.grey:
-                self.log += '\nGreyscale camera identified'
-            self.log += '\nSignificant bits: {}'.format(sigbitss[0])
-            self.log += '\nBlacklevel: {}'.format(blacklevels[0])
-            self.log += '\nImage size: w = {} h = {}'.format(sizes[0][0], sizes[0][1])
-            return 1
-        else:
-            print('\nERROR: Images from different cameras')
-            self.log += '\nERROR: Images are from different cameras'
-            return 0
-
-
-def run_ctt(json_output, directory, config, log_output, alsc_only=False):
-    """
-    check input files are jsons
-    """
-    if json_output[-5:] != '.json':
-        raise ArgError('\n\nError: Output must be a json file!')
-    if config is not None:
-        """
-        check if config file is actually a json
-        """
-        if config[-5:] != '.json':
-            raise ArgError('\n\nError: Config file must be a json file!')
-        """
-        read configurations
-        """
-        try:
-            with open(config, 'r') as config_json:
-                configs = json.load(config_json)
-        except FileNotFoundError:
-            configs = {}
-            config = False
-        except json.decoder.JSONDecodeError:
-            configs = {}
-            config = True
-
-    else:
-        configs = {}
-    """
-    load configurations from config file, if not given then set default
-    """
-    disable = get_config(configs, "disable", [], 'list')
-    plot = get_config(configs, "plot", [], 'list')
-    awb_d = get_config(configs, "awb", {}, 'dict')
-    greyworld = get_config(awb_d, "greyworld", 0, 'bool')
-    alsc_d = get_config(configs, "alsc", {}, 'dict')
-    do_alsc_colour = get_config(alsc_d, "do_alsc_colour", 1, 'bool')
-    luminance_strength = get_config(alsc_d, "luminance_strength", 0.5, 'num')
-    blacklevel = get_config(configs, "blacklevel", -1, 'num')
-    macbeth_d = get_config(configs, "macbeth", {}, 'dict')
-    mac_small = get_config(macbeth_d, "small", 0, 'bool')
-    mac_show = get_config(macbeth_d, "show", 0, 'bool')
-    mac_config = (mac_small, mac_show)
-
-    if blacklevel < -1 or blacklevel >= 2**16:
-        print('\nInvalid blacklevel, defaulted to 64')
-        blacklevel = -1
-
-    if luminance_strength < 0 or luminance_strength > 1:
-        print('\nInvalid luminance_strength strength, defaulted to 0.5')
-        luminance_strength = 0.5
-
-    """
-    sanitise directory path
-    """
-    if directory[-1] != '/':
-        directory += '/'
-    """
-    initialise tuning tool and load images
-    """
-    try:
-        Cam = Camera(json_output)
-        Cam.log_user_input(json_output, directory, config, log_output)
-        if alsc_only:
-            disable = set(Cam.json.keys()).symmetric_difference({"rpi.alsc"})
-        Cam.disable = disable
-        Cam.plot = plot
-        Cam.add_imgs(directory, mac_config, blacklevel)
-    except FileNotFoundError:
-        raise ArgError('\n\nError: Input image directory not found!')
-
-    """
-    preform calibrations as long as check_imgs returns True
-    If alsc is activated then it must be done before awb and ccm since the alsc
-    tables are used in awb and ccm calibrations
-    ccm also technically does an awb but it measures this from the macbeth
-    chart in the image rather than using calibration data
-    """
-    if Cam.check_imgs(macbeth=not alsc_only):
-        if not alsc_only:
-            Cam.json['rpi.black_level']['black_level'] = Cam.blacklevel_16
-        Cam.json_remove(disable)
-        print('\nSTARTING CALIBRATIONS')
-        Cam.alsc_cal(luminance_strength, do_alsc_colour)
-        Cam.geq_cal()
-        Cam.lux_cal()
-        Cam.noise_cal()
-        Cam.awb_cal(greyworld, do_alsc_colour)
-        Cam.ccm_cal(do_alsc_colour)
-        print('\nFINISHED CALIBRATIONS')
-        Cam.write_json()
-        Cam.write_log(log_output)
-        print('\nCalibrations written to: '+json_output)
-        if log_output is None:
-            log_output = 'ctt_log.txt'
-        print('Log file written to: '+log_output)
-        pass
-    else:
-        Cam.write_log(log_output)
-
-
-if __name__ == '__main__':
-    """
-    initialise calibration
-    """
-    if len(sys.argv) == 1:
-        print("""
-    Pisp Camera Tuning Tool version 1.0
-
-    Required Arguments:
-    '-i' : Calibration image directory.
-    '-o' : Name of output json file.
-
-    Optional Arguments:
-    '-c' : Config file for the CTT. If not passed, default parameters used.
-    '-l' : Name of output log file. If not passed, 'ctt_log.txt' used.
-              """)
-        quit(0)
-    else:
-        """
-        parse input arguments
-        """
-        json_output, directory, config, log_output = parse_input()
-        run_ctt(json_output, directory, config, log_output)