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Diffstat (limited to 'utils/raspberrypi/ctt')
23 files changed, 5495 insertions, 0 deletions
diff --git a/utils/raspberrypi/ctt/alsc_only.py b/utils/raspberrypi/ctt/alsc_only.py new file mode 100755 index 00000000..a521c4ad --- /dev/null +++ b/utils/raspberrypi/ctt/alsc_only.py @@ -0,0 +1,42 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2022, Raspberry Pi Ltd +# +# alsc tuning tool + +import sys + +from ctt import * +from ctt_tools import parse_input + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + PiSP Lens Shading Camera Tuning Tool version 1.0 + + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-t' : Target platform - 'pisp' or 'vc4'. Default 'vc4' + '-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, target = parse_input() + if target == 'pisp': + from ctt_pisp import json_template, grid_size + elif target == 'vc4': + from ctt_vc4 import json_template, grid_size + + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target, alsc_only=True) diff --git a/utils/raspberrypi/ctt/cac_only.py b/utils/raspberrypi/ctt/cac_only.py new file mode 100644 index 00000000..1c0a8193 --- /dev/null +++ b/utils/raspberrypi/ctt/cac_only.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2023, Raspberry Pi (Trading) Ltd. +# +# cac_only.py - cac tuning tool + + +# This file allows you to tune only the chromatic aberration correction +# Specify any number of files in the command line args, and it shall iterate through +# and generate an averaged cac table from all the input images, which you can then +# input into your tuning file. + +# Takes .dng files produced by the camera modules of the dots grid and calculates the chromatic abberation of each dot. +# Then takes each dot, and works out where it was in the image, and uses that to output a tables of the shifts +# across the whole image. + +from PIL import Image +import numpy as np +import rawpy +import sys +import getopt + +from ctt_cac import * + + +def cac(filelist, output_filepath, plot_results=False): + np.set_printoptions(precision=3) + np.set_printoptions(suppress=True) + + # Create arrays to hold all the dots data and their colour offsets + red_shift = [] # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]] + blue_shift = [] + # Iterate through the files + # Multiple files is reccomended to average out the lens aberration through rotations + for file in filelist: + print("\n Processing file " + str(file)) + # Read the raw RGB values from the .dng file + with rawpy.imread(file) as raw: + rgb = raw.postprocess() + sizes = (raw.sizes) + + image_size = [sizes[2], sizes[3]] # Image size, X, Y + # Create a colour copy of the RGB values to use later in the calibration + imout = Image.new(mode="RGB", size=image_size) + rgb_image = np.array(imout) + # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily + rgb.reshape((image_size[0], image_size[1], 3)) + rgb_image = rgb + + # Pass the RGB image through to the dots locating program + # Returns an array of the dots (colour rectangles around the dots), and an array of their locations + print("Finding dots") + dots, dots_locations = find_dots_locations(rgb_image) + + # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out + # by how far the chromatic aberration has shifted each channel + print('Dots found: ' + str(len(dots))) + + for dot, dot_location in zip(dots, dots_locations): + if len(dot) > 0: + if (dot_location[0] > 0) and (dot_location[1] > 0): + ret = analyse_dot(dot, dot_location) + red_shift.append(ret[0]) + blue_shift.append(ret[1]) + + # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix + # for the CAC block to handle and then store these as a .json file to be added to the camera + # tuning file + print("\nCreating output grid") + rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size) + + print("CAC correction complete!") + + # The json format that we then paste into the tuning file (manually) + sample = ''' + { + "rpi.cac" : + { + "strength": 1.0, + "lut_rx" : [ + rx_vals + ], + "lut_ry" : [ + ry_vals + ], + "lut_bx" : [ + bx_vals + ], + "lut_by" : [ + by_vals + ] + } + } + ''' + + # Below, may look incorrect, however, the PiSP (standard) dimensions are flipped in comparison to + # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts, + # and the PiSP block asks for the values it should shift (hence the * -1, to convert from colour shift to a pixel shift) + sample = sample.replace("rx_vals", pprint_array(ry * -1)) + sample = sample.replace("ry_vals", pprint_array(rx * -1)) + sample = sample.replace("bx_vals", pprint_array(by * -1)) + sample = sample.replace("by_vals", pprint_array(bx * -1)) + print("Successfully converted to JSON") + f = open(str(output_filepath), "w+") + f.write(sample) + f.close() + print("Successfully written to json file") + ''' + If you wish to see a plot of the colour channel shifts, add the -p or --plots option + Can be a quick way of validating if the data/dots you've got are good, or if you need to + change some parameters/take some better images + ''' + if plot_results: + plot_shifts(red_shift, blue_shift) + + +if __name__ == "__main__": + argv = sys.argv + # Detect the input and output file paths + arg_output = "output.json" + arg_help = "{0} -i <input> -o <output> -p <plot results>".format(argv[0]) + opts, args = getopt.getopt(argv[1:], "hi:o:p", ["help", "input=", "output=", "plot"]) + + output_location = 0 + input_location = 0 + filelist = [] + plot_results = False + for i in range(len(argv)): + if ("-h") in argv[i]: + print(arg_help) # print the help message + sys.exit(2) + if "-o" in argv[i]: + output_location = i + if ".dng" in argv[i]: + filelist.append(argv[i]) + if "-p" in argv[i]: + plot_results = True + + arg_output = argv[output_location + 1] + cac(filelist, arg_output, plot_results) diff --git a/utils/raspberrypi/ctt/colors.py b/utils/raspberrypi/ctt/colors.py new file mode 100644 index 00000000..cb4d236b --- /dev/null +++ b/utils/raspberrypi/ctt/colors.py @@ -0,0 +1,30 @@ +# Program to convert from RGB to LAB color space +def RGB_to_LAB(RGB): # where RGB is a 1x3 array. e.g RGB = [100, 255, 230] + num = 0 + XYZ = [0, 0, 0] + # converted all the three R, G, B to X, Y, Z + X = RGB[0] * 0.4124 + RGB[1] * 0.3576 + RGB[2] * 0.1805 + Y = RGB[0] * 0.2126 + RGB[1] * 0.7152 + RGB[2] * 0.0722 + Z = RGB[0] * 0.0193 + RGB[1] * 0.1192 + RGB[2] * 0.9505 + + XYZ[0] = X / 255 * 100 + XYZ[1] = Y / 255 * 100 # XYZ Must be in range 0 -> 100, so scale down from 255 + XYZ[2] = Z / 255 * 100 + XYZ[0] = XYZ[0] / 95.047 # ref_X = 95.047 Observer= 2°, Illuminant= D65 + XYZ[1] = XYZ[1] / 100.0 # ref_Y = 100.000 + XYZ[2] = XYZ[2] / 108.883 # ref_Z = 108.883 + num = 0 + for value in XYZ: + if value > 0.008856: + value = value ** (0.3333333333333333) + else: + value = (7.787 * value) + (16 / 116) + XYZ[num] = value + num = num + 1 + + # L, A, B, values calculated below + L = (116 * XYZ[1]) - 16 + a = 500 * (XYZ[0] - XYZ[1]) + b = 200 * (XYZ[1] - XYZ[2]) + + return [L, a, b] diff --git a/utils/raspberrypi/ctt/convert_tuning.py b/utils/raspberrypi/ctt/convert_tuning.py new file mode 100755 index 00000000..83cf69d4 --- /dev/null +++ b/utils/raspberrypi/ctt/convert_tuning.py @@ -0,0 +1,120 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Script to convert version 1.0 Raspberry Pi camera tuning files to version 2.0. +# +# Copyright 2022 Raspberry Pi Ltd + +import argparse +import json +import numpy as np +import sys + +from ctt_pretty_print_json import pretty_print +from ctt_pisp import grid_size as grid_size_pisp +from ctt_pisp import json_template as json_template_pisp +from ctt_vc4 import grid_size as grid_size_vc4 +from ctt_vc4 import json_template as json_template_vc4 + + +def interp_2d(in_ls, src_w, src_h, dst_w, dst_h): + + out_ls = np.zeros((dst_h, dst_w)) + for i in range(src_h): + out_ls[i] = np.interp(np.linspace(0, dst_w - 1, dst_w), + np.linspace(0, dst_w - 1, src_w), + in_ls[i]) + for i in range(dst_w): + out_ls[:,i] = np.interp(np.linspace(0, dst_h - 1, dst_h), + np.linspace(0, dst_h - 1, src_h), + out_ls[:src_h, i]) + return out_ls + + +def convert_target(in_json: dict, target: str): + + src_w, src_h = grid_size_pisp if target == 'vc4' else grid_size_vc4 + dst_w, dst_h = grid_size_vc4 if target == 'vc4' else grid_size_pisp + json_template = json_template_vc4 if target == 'vc4' else json_template_pisp + + # ALSC grid sizes + alsc = next(algo for algo in in_json['algorithms'] if 'rpi.alsc' in algo)['rpi.alsc'] + for colour in ['calibrations_Cr', 'calibrations_Cb']: + if colour not in alsc: + continue + for temperature in alsc[colour]: + in_ls = np.reshape(temperature['table'], (src_h, src_w)) + out_ls = interp_2d(in_ls, src_w, src_h, dst_w, dst_h) + temperature['table'] = np.round(out_ls.flatten(), 3).tolist() + + if 'luminance_lut' in alsc: + in_ls = np.reshape(alsc['luminance_lut'], (src_h, src_w)) + out_ls = interp_2d(in_ls, src_w, src_h, dst_w, dst_h) + alsc['luminance_lut'] = np.round(out_ls.flatten(), 3).tolist() + + # Denoise blocks + for i, algo in enumerate(in_json['algorithms']): + if list(algo.keys())[0] == 'rpi.sdn': + in_json['algorithms'][i] = {'rpi.denoise': json_template['rpi.sdn'] if target == 'vc4' else json_template['rpi.denoise']} + break + + # AGC mode weights + agc = next(algo for algo in in_json['algorithms'] if 'rpi.agc' in algo)['rpi.agc'] + if 'channels' in agc: + for i, channel in enumerate(agc['channels']): + target_agc_metering = json_template['rpi.agc']['channels'][i]['metering_modes'] + for mode, v in channel['metering_modes'].items(): + v['weights'] = target_agc_metering[mode]['weights'] + else: + for mode, v in agc["metering_modes"].items(): + target_agc_metering = json_template['rpi.agc']['channels'][0]['metering_modes'] + v['weights'] = target_agc_metering[mode]['weights'] + + # HDR + if target == 'pisp': + for i, algo in enumerate(in_json['algorithms']): + if list(algo.keys())[0] == 'rpi.hdr': + in_json['algorithms'][i] = {'rpi.hdr': json_template['rpi.hdr']} + + return in_json + + +def convert_v2(in_json: dict, target: str) -> str: + + if 'version' in in_json.keys() and in_json['version'] == 1.0: + converted = { + 'version': 2.0, + 'target': target, + 'algorithms': [{algo: config} for algo, config in in_json.items()] + } + else: + converted = in_json + + # Convert between vc4 <-> pisp targets. This is a best effort thing. + if converted['target'] != target: + converted = convert_target(converted, target) + converted['target'] = target + + grid_size = grid_size_vc4[0] if target == 'vc4' else grid_size_pisp[0] + return pretty_print(converted, custom_elems={'table': grid_size, 'luminance_lut': grid_size}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description= + 'Convert the format of the Raspberry Pi camera tuning file from v1.0 to v2.0 and/or the vc4 <-> pisp targets.\n') + parser.add_argument('input', type=str, help='Input tuning file.') + parser.add_argument('-t', '--target', type=str, help='Target platform.', + choices=['pisp', 'vc4'], default='vc4') + parser.add_argument('output', type=str, nargs='?', + help='Output converted tuning file. If not provided, the input file will be updated in-place.', + default=None) + args = parser.parse_args() + + with open(args.input, 'r') as f: + in_json = json.load(f) + + out_json = convert_v2(in_json, args.target) + + with open(args.output if args.output is not None else args.input, 'w') as f: + f.write(out_json) diff --git a/utils/raspberrypi/ctt/ctt.py b/utils/raspberrypi/ctt/ctt.py new file mode 100755 index 00000000..96f1b5e6 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt.py @@ -0,0 +1,802 @@ +#!/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_cac 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 three lists: + - imgs (macbeth charts) + - imgs_alsc (alsc correction images) + - imgs_cac (cac correction images) +Various calibrations are methods of the camera object, and the output is stored +in a dictionary called self.json. +Once all the caibration has been completed, the Camera.json is written into a +json file. +The camera object initialises its json dictionary by reading from a pre-written +blank json file. This has been done to avoid reproducing the entire json file +in the code here, thereby avoiding unecessary clutter. +""" + + +""" +Get the colour and lux values from the strings of each inidvidual image +""" +def get_col_lux(string): + """ + Extract colour and lux values from filename + """ + col = re.search(r'([0-9]+)[kK](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) + lux = re.search(r'([0-9]+)[lL](\.(jpg|jpeg|brcm|dng)|_.*\.(jpg|jpeg|brcm|dng))$', string) + try: + col = col.group(1) + except AttributeError: + """ + Catch error if images labelled incorrectly and pass reasonable defaults + """ + return None, None + try: + lux = lux.group(1) + except AttributeError: + """ + Catch error if images labelled incorrectly and pass reasonable defaults + Still returns colour if that has been found. + """ + return col, None + return int(col), int(lux) + + +""" +Camera object that is the backbone of the tuning tool. +Input is the desired path of the output json. +""" +class Camera: + def __init__(self, jfile, json): + self.path = os.path.dirname(os.path.expanduser(__file__)) + '/' + if self.path == '/': + self.path = '' + self.imgs = [] + self.imgs_alsc = [] + self.imgs_cac = [] + self.log = 'Log created : ' + time.asctime(time.localtime(time.time())) + self.log_separator = '\n'+'-'*70+'\n' + self.jf = jfile + """ + initial json dict populated by uncalibrated values + """ + self.json = json + + """ + Perform colour correction calibrations by comparing macbeth patch colours + to standard macbeth chart colours. + """ + def ccm_cal(self, do_alsc_colour, grid_size): + if 'rpi.ccm' in self.disable: + return 1 + print('\nStarting CCM calibration') + self.log_new_sec('CCM') + """ + if image is greyscale then CCm makes no sense + """ + if self.grey: + print('\nERROR: Can\'t do CCM on greyscale image!') + self.log += '\nERROR: Cannot perform CCM calibration ' + self.log += 'on greyscale image!\nCCM aborted!' + del self.json['rpi.ccm'] + return 0 + a = time.time() + """ + Check if alsc tables have been generated, if not then do ccm without + alsc + """ + if ("rpi.alsc" not in self.disable) and do_alsc_colour: + """ + case where ALSC colour has been done, so no errors should be + expected... + """ + try: + cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] + cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] + self.log += '\nALSC tables found successfully' + except KeyError: + cal_cr_list, cal_cb_list = None, None + print('WARNING! No ALSC tables found for CCM!') + print('Performing CCM calibrations without ALSC correction...') + self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' + self.log += 'performed without ALSC correction...' + else: + """ + case where config options result in CCM done without ALSC colour tables + """ + cal_cr_list, cal_cb_list = None, None + self.log += '\nWARNING: No ALSC tables found.\nCCM calibration ' + self.log += 'performed without ALSC correction...' + + """ + Do CCM calibration + """ + try: + ccms = ccm(self, cal_cr_list, cal_cb_list, grid_size) + except ArithmeticError: + print('ERROR: Matrix is singular!\nTake new pictures and try again...') + self.log += '\nERROR: Singular matrix encountered during fit!' + self.log += '\nCCM aborted!' + return 1 + """ + Write output to json + """ + self.json['rpi.ccm']['ccms'] = ccms + self.log += '\nCCM calibration written to json file' + print('Finished CCM calibration') + + """ + Perform chromatic abberation correction using multiple dots images. + """ + def cac_cal(self, do_alsc_colour): + if 'rpi.cac' in self.disable: + return 1 + print('\nStarting CAC calibration') + self.log_new_sec('CAC') + """ + check if cac images have been taken + """ + if len(self.imgs_cac) == 0: + print('\nError:\nNo cac calibration images found') + self.log += '\nERROR: No CAC calibration images found!' + self.log += '\nCAC calibration aborted!' + return 1 + """ + if image is greyscale then CAC makes no sense + """ + if self.grey: + print('\nERROR: Can\'t do CAC on greyscale image!') + self.log += '\nERROR: Cannot perform CAC calibration ' + self.log += 'on greyscale image!\nCAC aborted!' + del self.json['rpi.cac'] + return 0 + a = time.time() + """ + Check if camera is greyscale or color. If not greyscale, then perform cac + """ + if do_alsc_colour: + """ + Here we have a color sensor. Perform cac + """ + try: + cacs = cac(self) + except ArithmeticError: + print('ERROR: Matrix is singular!\nTake new pictures and try again...') + self.log += '\nERROR: Singular matrix encountered during fit!' + self.log += '\nCAC aborted!' + return 1 + else: + """ + case where config options suggest greyscale camera. No point in doing CAC + """ + cal_cr_list, cal_cb_list = None, None + self.log += '\nWARNING: No ALSC tables found.\nCAC calibration ' + self.log += 'performed without ALSC correction...' + + """ + Write output to json + """ + self.json['rpi.cac']['cac'] = cacs + self.log += '\nCAC calibration written to json file' + print('Finished CAC calibration') + + + """ + Auto white balance calibration produces a colour curve for + various colour temperatures, as well as providing a maximum 'wiggle room' + distance from this curve (transverse_neg/pos). + """ + def awb_cal(self, greyworld, do_alsc_colour, grid_size): + if 'rpi.awb' in self.disable: + return 1 + print('\nStarting AWB calibration') + self.log_new_sec('AWB') + """ + if image is greyscale then AWB makes no sense + """ + if self.grey: + print('\nERROR: Can\'t do AWB on greyscale image!') + self.log += '\nERROR: Cannot perform AWB calibration ' + self.log += 'on greyscale image!\nAWB aborted!' + del self.json['rpi.awb'] + return 0 + """ + optional set greyworld (e.g. for noir cameras) + """ + if greyworld: + self.json['rpi.awb']['bayes'] = 0 + self.log += '\nGreyworld set' + """ + Check if alsc tables have been generated, if not then do awb without + alsc correction + """ + if ("rpi.alsc" not in self.disable) and do_alsc_colour: + try: + cal_cr_list = self.json['rpi.alsc']['calibrations_Cr'] + cal_cb_list = self.json['rpi.alsc']['calibrations_Cb'] + self.log += '\nALSC tables found successfully' + except KeyError: + cal_cr_list, cal_cb_list = None, None + print('ERROR, no ALSC calibrations found for AWB') + print('Performing AWB without ALSC tables') + self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' + self.log += 'performed without ALSC correction...' + else: + cal_cr_list, cal_cb_list = None, None + self.log += '\nWARNING: No ALSC tables found.\nAWB calibration ' + self.log += 'performed without ALSC correction...' + """ + call calibration function + """ + plot = "rpi.awb" in self.plot + awb_out = awb(self, cal_cr_list, cal_cb_list, plot, grid_size) + ct_curve, transverse_neg, transverse_pos = awb_out + """ + write output to json + """ + self.json['rpi.awb']['ct_curve'] = ct_curve + self.json['rpi.awb']['sensitivity_r'] = 1.0 + self.json['rpi.awb']['sensitivity_b'] = 1.0 + self.json['rpi.awb']['transverse_pos'] = transverse_pos + self.json['rpi.awb']['transverse_neg'] = transverse_neg + self.log += '\nAWB calibration written to json file' + print('Finished AWB calibration') + + """ + Auto lens shading correction completely mitigates the effects of lens shading for ech + colour channel seperately, and then partially corrects for vignetting. + The extent of the correction depends on the 'luminance_strength' parameter. + """ + def alsc_cal(self, luminance_strength, do_alsc_colour, grid_size, max_gain=8.0): + if 'rpi.alsc' in self.disable: + return 1 + print('\nStarting ALSC calibration') + self.log_new_sec('ALSC') + """ + check if alsc images have been taken + """ + if len(self.imgs_alsc) == 0: + print('\nError:\nNo alsc calibration images found') + self.log += '\nERROR: No ALSC calibration images found!' + self.log += '\nALSC calibration aborted!' + return 1 + self.json['rpi.alsc']['luminance_strength'] = luminance_strength + if self.grey and do_alsc_colour: + print('Greyscale camera so only luminance_lut calculated') + do_alsc_colour = False + self.log += '\nWARNING: ALSC colour correction cannot be done on ' + self.log += 'greyscale image!\nALSC colour corrections forced off!' + """ + call calibration function + """ + plot = "rpi.alsc" in self.plot + alsc_out = alsc_all(self, do_alsc_colour, plot, grid_size, max_gain=max_gain) + cal_cr_list, cal_cb_list, luminance_lut, av_corn = alsc_out + """ + write output to json and finish if not do_alsc_colour + """ + if not do_alsc_colour: + self.json['rpi.alsc']['luminance_lut'] = luminance_lut + self.json['rpi.alsc']['n_iter'] = 0 + self.log += '\nALSC calibrations written to json file' + self.log += '\nNo colour calibrations performed' + print('Finished ALSC calibrations') + return 1 + + self.json['rpi.alsc']['calibrations_Cr'] = cal_cr_list + self.json['rpi.alsc']['calibrations_Cb'] = cal_cb_list + self.json['rpi.alsc']['luminance_lut'] = luminance_lut + self.log += '\nALSC colour and luminance tables written to json file' + + """ + The sigmas determine the strength of the adaptive algorithm, that + cleans up any lens shading that has slipped through the alsc. These are + determined by measuring a 'worst-case' difference between two alsc tables + that are adjacent in colour space. If, however, only one colour + temperature has been provided, then this difference can not be computed + as only one table is available. + To determine the sigmas you would have to estimate the error of an alsc + table with only the image it was taken on as a check. To avoid circularity, + dfault exaggerated sigmas are used, which can result in too much alsc and + is therefore not advised. + In general, just take another alsc picture at another colour temperature! + """ + + if len(self.imgs_alsc) == 1: + self.json['rpi.alsc']['sigma'] = 0.005 + self.json['rpi.alsc']['sigma_Cb'] = 0.005 + print('\nWarning:\nOnly one alsc calibration found' + '\nStandard sigmas used for adaptive algorithm.') + print('Finished ALSC calibrations') + self.log += '\nWARNING: Only one colour temperature found in ' + self.log += 'calibration images.\nStandard sigmas used for adaptive ' + self.log += 'algorithm!' + return 1 + + """ + obtain worst-case scenario residual sigmas + """ + sigma_r, sigma_b = get_sigma(self, cal_cr_list, cal_cb_list, grid_size) + """ + write output to json + """ + self.json['rpi.alsc']['sigma'] = np.round(sigma_r, 5) + self.json['rpi.alsc']['sigma_Cb'] = np.round(sigma_b, 5) + self.log += '\nCalibrated sigmas written to json file' + print('Finished ALSC calibrations') + + """ + Green equalisation fixes problems caused by discrepancies in green + channels. This is done by measuring the effect on macbeth chart patches, + which ideally would have the same green values throughout. + An upper bound linear model is fit, fixing a threshold for the green + differences that are corrected. + """ + def geq_cal(self): + if 'rpi.geq' in self.disable: + return 1 + print('\nStarting GEQ calibrations') + self.log_new_sec('GEQ') + """ + perform calibration + """ + plot = 'rpi.geq' in self.plot + slope, offset = geq_fit(self, plot) + """ + write output to json + """ + self.json['rpi.geq']['offset'] = offset + self.json['rpi.geq']['slope'] = slope + self.log += '\nGEQ calibrations written to json file' + print('Finished GEQ calibrations') + + """ + Lux calibrations allow the lux level of a scene to be estimated by a ratio + calculation. Lux values are used in the pipeline for algorithms such as AGC + and AWB + """ + def lux_cal(self): + if 'rpi.lux' in self.disable: + return 1 + print('\nStarting LUX calibrations') + self.log_new_sec('LUX') + """ + The lux calibration is done on a single image. For best effects, the + image with lux level closest to 1000 is chosen. + """ + luxes = [Img.lux for Img in self.imgs] + argmax = luxes.index(min(luxes, key=lambda l: abs(1000-l))) + Img = self.imgs[argmax] + self.log += '\nLux found closest to 1000: {} lx'.format(Img.lux) + self.log += '\nImage used: ' + Img.name + if Img.lux < 50: + self.log += '\nWARNING: Low lux could cause inaccurate calibrations!' + """ + do calibration + """ + lux_out, shutter_speed, gain = lux(self, Img) + """ + write output to json + """ + self.json['rpi.lux']['reference_shutter_speed'] = shutter_speed + self.json['rpi.lux']['reference_gain'] = gain + self.json['rpi.lux']['reference_lux'] = Img.lux + self.json['rpi.lux']['reference_Y'] = lux_out + self.log += '\nLUX calibrations written to json file' + print('Finished LUX calibrations') + + """ + Noise alibration attempts to describe the noise profile of the sensor. The + calibration is run on macbeth images and the final output is taken as the average + """ + def noise_cal(self): + if 'rpi.noise' in self.disable: + return 1 + print('\nStarting NOISE calibrations') + self.log_new_sec('NOISE') + """ + run calibration on all images and sort by slope. + """ + plot = "rpi.noise" in self.plot + noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key=lambda x: x[0]) + self.log += '\nFinished processing images' + """ + take the average of the interquartile + """ + length = len(noise_out) + noise_out = np.mean(noise_out[length//4:1+3*length//4], axis=0) + self.log += '\nAverage noise profile: constant = {} '.format(int(noise_out[1])) + self.log += 'slope = {:.3f}'.format(noise_out[0]) + """ + write to json + """ + self.json['rpi.noise']['reference_constant'] = int(noise_out[1]) + self.json['rpi.noise']['reference_slope'] = round(noise_out[0], 3) + self.log += '\nNOISE calibrations written to json' + print('Finished NOISE calibrations') + + """ + Removes json entries that are turned off + """ + def json_remove(self, disable): + self.log_new_sec('Disabling Options', cal=False) + if len(self.disable) == 0: + self.log += '\nNothing disabled!' + return 1 + for key in disable: + try: + del self.json[key] + self.log += '\nDisabled: ' + key + except KeyError: + self.log += '\nERROR: ' + key + ' not found!' + """ + writes the json dictionary to the raw json file then make pretty + """ + def write_json(self, version=2.0, target='bcm2835', grid_size=(16, 12)): + """ + Write json dictionary to file using our version 2 format + """ + + out_json = { + "version": version, + 'target': target if target != 'vc4' else 'bcm2835', + "algorithms": [{name: data} for name, data in self.json.items()], + } + + with open(self.jf, 'w') as f: + f.write(pretty_print(out_json, + custom_elems={'table': grid_size[0], 'luminance_lut': grid_size[0]})) + + """ + add a new section to the log file + """ + def log_new_sec(self, section, cal=True): + self.log += '\n'+self.log_separator + self.log += section + if cal: + self.log += ' Calibration' + self.log += self.log_separator + + """ + write script arguments to log file + """ + def log_user_input(self, json_output, directory, config, log_output): + self.log_new_sec('User Arguments', cal=False) + self.log += '\nJson file output: ' + json_output + self.log += '\nCalibration images directory: ' + directory + if config is None: + self.log += '\nNo configuration file input... using default options' + elif config is False: + self.log += '\nWARNING: Invalid configuration file path...' + self.log += ' using default options' + elif config is True: + self.log += '\nWARNING: Invalid syntax in configuration file...' + self.log += ' using default options' + else: + self.log += '\nConfiguration file: ' + config + if log_output is None: + self.log += '\nNo log file path input... using default: ctt_log.txt' + else: + self.log += '\nLog file output: ' + log_output + + # if log_output + + """ + write log file + """ + def write_log(self, filename): + if filename is None: + filename = 'ctt_log.txt' + self.log += '\n' + self.log_separator + with open(filename, 'w') as logfile: + logfile.write(self.log) + + """ + Add all images from directory, pass into relevant list of images and + extrace lux and temperature values. + """ + def add_imgs(self, directory, mac_config, blacklevel=-1): + self.log_new_sec('Image Loading', cal=False) + img_suc_msg = 'Image loaded successfully!' + print('\n\nLoading images from '+directory) + self.log += '\nDirectory: ' + directory + """ + get list of files + """ + filename_list = get_photos(directory) + print("Files found: {}".format(len(filename_list))) + self.log += '\nFiles found: {}'.format(len(filename_list)) + """ + iterate over files + """ + filename_list.sort() + for filename in filename_list: + address = directory + filename + print('\nLoading image: '+filename) + self.log += '\n\nImage: ' + filename + """ + obtain colour and lux value + """ + col, lux = get_col_lux(filename) + """ + Check if image is an alsc calibration image + """ + if 'alsc' in filename: + Img = load_image(self, address, mac=False) + self.log += '\nIdentified as an ALSC image' + """ + check if imagae data has been successfully unpacked + """ + if Img == 0: + print('\nDISCARDED') + self.log += '\nImage discarded!' + continue + """ + check that image colour temperature has been successfuly obtained + """ + elif col is not None: + """ + if successful, append to list and continue to next image + """ + Img.col = col + Img.name = filename + self.log += '\nColour temperature: {} K'.format(col) + self.imgs_alsc.append(Img) + if blacklevel != -1: + Img.blacklevel_16 = blacklevel + print(img_suc_msg) + continue + else: + print('Error! No colour temperature found!') + self.log += '\nWARNING: Error reading colour temperature' + self.log += '\nImage discarded!' + print('DISCARDED') + elif 'cac' in filename: + Img = load_image(self, address, mac=False) + self.log += '\nIdentified as an CAC image' + Img.name = filename + self.log += '\nColour temperature: {} K'.format(col) + self.imgs_cac.append(Img) + if blacklevel != -1: + Img.blacklevel_16 = blacklevel + print(img_suc_msg) + continue + 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 += '\nCAC: {} '.format(len(self.imgs_cac)) + 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 and len(self.imgs_cac) == 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 + self.imgs_cac + 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 1: + self.grey = (patterns[0] == 128) + self.blacklevel_16 = blacklevels[0] + self.log += '\nName: {}'.format(camNames[0]) + self.log += '\nBayer pattern case: {}'.format(patterns[0]) + if self.grey: + self.log += '\nGreyscale camera identified' + self.log += '\nSignificant bits: {}'.format(sigbitss[0]) + self.log += '\nBlacklevel: {}'.format(blacklevels[0]) + self.log += '\nImage size: w = {} h = {}'.format(sizes[0][0], sizes[0][1]) + return 1 + else: + print('\nERROR: Images from different cameras') + self.log += '\nERROR: Images are from different cameras' + return 0 + + +def run_ctt(json_output, directory, config, log_output, json_template, grid_size, target, alsc_only=False): + """ + check input files are jsons + """ + if json_output[-5:] != '.json': + raise ArgError('\n\nError: Output must be a json file!') + if config is not None: + """ + check if config file is actually a json + """ + if config[-5:] != '.json': + raise ArgError('\n\nError: Config file must be a json file!') + """ + read configurations + """ + try: + with open(config, 'r') as config_json: + configs = json.load(config_json) + except FileNotFoundError: + configs = {} + config = False + except json.decoder.JSONDecodeError: + configs = {} + config = True + + else: + configs = {} + """ + load configurations from config file, if not given then set default + """ + disable = get_config(configs, "disable", [], 'list') + plot = get_config(configs, "plot", [], 'list') + awb_d = get_config(configs, "awb", {}, 'dict') + greyworld = get_config(awb_d, "greyworld", 0, 'bool') + alsc_d = get_config(configs, "alsc", {}, 'dict') + do_alsc_colour = get_config(alsc_d, "do_alsc_colour", 1, 'bool') + luminance_strength = get_config(alsc_d, "luminance_strength", 0.8, 'num') + lsc_max_gain = get_config(alsc_d, "max_gain", 8.0, '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) + print("Read lsc_max_gain", lsc_max_gain) + + if blacklevel < -1 or blacklevel >= 2**16: + print('\nInvalid blacklevel, defaulted to 64') + blacklevel = -1 + + if luminance_strength < 0 or luminance_strength > 1: + print('\nInvalid luminance_strength strength, defaulted to 0.5') + luminance_strength = 0.5 + + """ + sanitise directory path + """ + if directory[-1] != '/': + directory += '/' + """ + initialise tuning tool and load images + """ + try: + Cam = Camera(json_output, json=json_template) + Cam.log_user_input(json_output, directory, config, log_output) + if alsc_only: + disable = set(Cam.json.keys()).symmetric_difference({"rpi.alsc"}) + Cam.disable = disable + Cam.plot = plot + Cam.add_imgs(directory, mac_config, blacklevel) + except FileNotFoundError: + raise ArgError('\n\nError: Input image directory not found!') + + """ + preform calibrations as long as check_imgs returns True + If alsc is activated then it must be done before awb and ccm since the alsc + tables are used in awb and ccm calibrations + ccm also technically does an awb but it measures this from the macbeth + chart in the image rather than using calibration data + """ + if Cam.check_imgs(macbeth=not alsc_only): + if not alsc_only: + Cam.json['rpi.black_level']['black_level'] = Cam.blacklevel_16 + Cam.json_remove(disable) + print('\nSTARTING CALIBRATIONS') + Cam.alsc_cal(luminance_strength, do_alsc_colour, grid_size, max_gain=lsc_max_gain) + Cam.geq_cal() + Cam.lux_cal() + Cam.noise_cal() + if "rpi.cac" in json_template: + Cam.cac_cal(do_alsc_colour) + Cam.awb_cal(greyworld, do_alsc_colour, grid_size) + Cam.ccm_cal(do_alsc_colour, grid_size) + + print('\nFINISHED CALIBRATIONS') + Cam.write_json(target=target, grid_size=grid_size) + Cam.write_log(log_output) + print('\nCalibrations written to: '+json_output) + if log_output is None: + log_output = 'ctt_log.txt' + print('Log file written to: '+log_output) + pass + else: + Cam.write_log(log_output) + +if __name__ == '__main__': + """ + initialise calibration + """ + if len(sys.argv) == 1: + print(""" + PiSP Tuning Tool version 1.0 + Required Arguments: + '-i' : Calibration image directory. + '-o' : Name of output json file. + + Optional Arguments: + '-t' : Target platform - 'pisp' or 'vc4'. Default 'vc4' + '-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, target = parse_input() + if target == 'pisp': + from ctt_pisp import json_template, grid_size + elif target == 'vc4': + from ctt_vc4 import json_template, grid_size + + run_ctt(json_output, directory, config, log_output, json_template, grid_size, target) diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py new file mode 100644 index 00000000..1d94dfa5 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_alsc.py @@ -0,0 +1,308 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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, grid_size=(16, 12), max_gain=8.0): + imgs_alsc = Cam.imgs_alsc + grid_w, grid_h = grid_size + """ + 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, grid_size=grid_size, max_gain=max_gain) + 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[grid_w - 1], t_r[-1], t_r[-grid_w]) + b_corners = (t_b[0], t_b[grid_w - 1], t_b[-1], t_b[-grid_w]) + middle_pos = (grid_h // 2 - 1) * grid_w + grid_w - 1 + r_cen = t_r[middle_pos]+t_r[middle_pos + 1]+t_r[middle_pos + grid_w]+t_r[middle_pos + grid_w + 1] + r_cen = round(r_cen/4, 3) + b_cen = t_b[middle_pos]+t_b[middle_pos + 1]+t_b[middle_pos + grid_w]+t_b[middle_pos + grid_w + 1] + b_cen = round(b_cen/4, 3) + Cam.log += '\nRed table corners: {}'.format(r_corners) + Cam.log += '\nRed table centre: {}'.format(r_cen) + 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, grid_size=(16, 12), max_gain=8.0): + Cam.log += '\nProcessing image: ' + Img.name + grid_w, grid_h = grid_size + """ + 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)//grid_w)), int(-(-(h-1)//grid_h)) + """ + average the green channels into one + """ + av_ch_g = np.mean((channels[1:3]), axis=0) + if do_alsc_colour: + """ + obtain grid_w x grid_h grid of intensities for each channel and subtract black level + """ + g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16 + r = get_grid(channels[0], dx, dy, grid_size) - Img.blacklevel_16 + b = get_grid(channels[3], dx, dy, grid_size) - Img.blacklevel_16 + """ + calculate ratios as 32 bit in order to be supported by medianBlur function + """ + cr = np.reshape(g/r, (grid_h, grid_w)).astype('float32') + cb = np.reshape(g/b, (grid_h, grid_w)).astype('float32') + cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32') + """ + median blur to remove peaks and save as float 64 + """ + cr = cv2.medianBlur(cr, 3).astype('float64') + cr = cr/np.min(cr) # gain tables are easier for humans to read if the minimum is 1.0 + cb = cv2.medianBlur(cb, 3).astype('float64') + cb = cb/np.min(cb) + cg = cv2.medianBlur(cg, 3).astype('float64') + cg = cg/np.min(cg) + cg = [min(v, max_gain) for v in cg.flatten()] # never exceed the max luminance gain + + """ + 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(grid_w), range(grid_h)) + ha.plot_surface(X, -Y, cr, cmap=cm.coolwarm, linewidth=0) + ha.set_title('ALSC Plot\nImg: {}\n\ncr'.format(Img.str)) + hb = hf.add_subplot(312, projection='3d') + 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, (w, h, dx, dy) + + else: + """ + only perform calculations for luminance shading + """ + g = get_grid(av_ch_g, dx, dy, grid_size) - Img.blacklevel_16 + cg = np.reshape(1/g, (grid_h, grid_w)).astype('float32') + cg = cv2.medianBlur(cg, 3).astype('float64') + cg = cg/np.min(cg) + cg = [min(v, max_gain) for v in cg.flatten()] # never exceed the max luminance gain + + if plot: + hf = plt.figure(figssize=(8, 8)) + ha = hf.add_subplot(1, 1, 1, projection='3d') + X, Y = np.meashgrid(range(grid_w), range(grid_h)) + ha.plot_surface(X, -Y, cg, cmap=cm.coolwarm, linewidth=0) + ha.set_title('ALSC Plot (Luminance only!)\nImg: {}\n\ncg').format(Img.str) + plt.show() + + return Img.col, None, None, cg.flatten(), (w, h, dx, dy) + + +""" +Compresses channel down to a grid of the requested size +""" +def get_grid(chan, dx, dy, grid_size): + grid_w, grid_h = grid_size + grid = [] + """ + since left and bottom border will not necessarily have rectangles of + dimension dx x dy, the 32nd iteration has to be handled separately. + """ + for i in range(grid_h - 1): + for j in range(grid_w - 1): + grid.append(np.mean(chan[dy*i:dy*(1+i), dx*j:dx*(1+j)])) + grid.append(np.mean(chan[dy*i:dy*(1+i), (grid_w - 1)*dx:])) + for j in range(grid_w - 1): + grid.append(np.mean(chan[(grid_h - 1)*dy:, dx*j:dx*(1+j)])) + grid.append(np.mean(chan[(grid_h - 1)*dy:, (grid_w - 1)*dx:])) + """ + return as np.array, ready for further manipulation + """ + 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, grid_size): + 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'], grid_size)) + sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table'], grid_size)) + Cam.log += '\nColour temperature interval {} - {} K'.format(cts[i], cts[i+1]) + Cam.log += '\nSigma red: {}'.format(sigma_rs[-1]) + Cam.log += '\nSigma blue: {}'.format(sigma_bs[-1]) + + """ + 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, grid_size): + grid_w, grid_h = grid_size + """ + reshape into 16x12 matrix + """ + g1 = np.reshape(g1, (grid_h, grid_w)) + g2 = np.reshape(g2, (grid_h, grid_w)) + """ + 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(grid_h - 2): + for j in range(grid_w - 2): + """ + note indexing is incremented by 1 since all patches on borders are + not counted + """ + diff = 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..4af1fe41 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_awb.py @@ -0,0 +1,377 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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, grid_size): + imgs = Cam.imgs + """ + condense alsc calibration tables into one dictionary + """ + if cal_cr_list is 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, grid_size=grid_size) + """ + 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, grid_size=(16, 12)): + """ + get patch centre coordinates, image colour and the actual + patches for each channel, remembering to subtract blacklevel + If grey then only greyscale patches considered + """ + grid_w, grid_h = grid_size + if grey: + cen_coords = Img.cen_coords[3::4] + col = Img.col + 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 is 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, grid_h, grid_w)) + """ + calculate dx, dy used to calculate alsc table + """ + w, h = Img.w/2, Img.h/2 + dx, dy = int(-(-(w-1)//grid_w)), int(-(-(h-1)//grid_h)) + """ + make list of pairs of gains for each patch by selecting the correct value + in alsc colour calibration table + """ + 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_cac.py b/utils/raspberrypi/ctt/ctt_cac.py new file mode 100644 index 00000000..5a4c5101 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_cac.py @@ -0,0 +1,228 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2023, Raspberry Pi Ltd +# +# ctt_cac.py - CAC (Chromatic Aberration Correction) tuning tool + +from PIL import Image +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import cm + +from ctt_dots_locator import find_dots_locations + + +# This is the wrapper file that creates a JSON entry for you to append +# to your camera tuning file. +# It calculates the chromatic aberration at different points throughout +# the image and uses that to produce a martix that can then be used +# in the camera tuning files to correct this aberration. + + +def pprint_array(array): + # Function to print the array in a tidier format + array = array + output = "" + for i in range(len(array)): + for j in range(len(array[0])): + output += str(round(array[i, j], 2)) + ", " + # Add the necessary indentation to the array + output += "\n " + # Cut off the end of the array (nicely formats it) + return output[:-22] + + +def plot_shifts(red_shifts, blue_shifts): + # If users want, they can pass a command line option to show the shifts on a graph + # Can be useful to check that the functions are all working, and that the sample + # images are doing the right thing + Xs = np.array(red_shifts)[:, 0] + Ys = np.array(red_shifts)[:, 1] + Zs = np.array(red_shifts)[:, 2] + Zs2 = np.array(red_shifts)[:, 3] + Zs3 = np.array(blue_shifts)[:, 2] + Zs4 = np.array(blue_shifts)[:, 3] + + fig, axs = plt.subplots(2, 2) + ax = fig.add_subplot(2, 2, 1, projection='3d') + ax.scatter(Xs, Ys, Zs, cmap=cm.jet, linewidth=0) + ax.set_title('Red X Shift') + ax = fig.add_subplot(2, 2, 2, projection='3d') + ax.scatter(Xs, Ys, Zs2, cmap=cm.jet, linewidth=0) + ax.set_title('Red Y Shift') + ax = fig.add_subplot(2, 2, 3, projection='3d') + ax.scatter(Xs, Ys, Zs3, cmap=cm.jet, linewidth=0) + ax.set_title('Blue X Shift') + ax = fig.add_subplot(2, 2, 4, projection='3d') + ax.scatter(Xs, Ys, Zs4, cmap=cm.jet, linewidth=0) + ax.set_title('Blue Y Shift') + fig.tight_layout() + plt.show() + + +def shifts_to_yaml(red_shift, blue_shift, image_dimensions, output_grid_size=9): + # Convert the shifts to a numpy array for easier handling and initialise other variables + red_shifts = np.array(red_shift) + blue_shifts = np.array(blue_shift) + # create a grid that's smaller than the output grid, which we then interpolate from to get the output values + xrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) + xbgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) + yrgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) + ybgrid = np.zeros((output_grid_size - 1, output_grid_size - 1)) + + xrsgrid = [] + xbsgrid = [] + yrsgrid = [] + ybsgrid = [] + xg = np.zeros((output_grid_size - 1, output_grid_size - 1)) + yg = np.zeros((output_grid_size - 1, output_grid_size - 1)) + + # Format the grids - numpy doesn't work for this, it wants a + # nice uniformly spaced grid, which we don't know if we have yet, hence the rather mundane setup + for x in range(output_grid_size - 1): + xrsgrid.append([]) + yrsgrid.append([]) + xbsgrid.append([]) + ybsgrid.append([]) + for y in range(output_grid_size - 1): + xrsgrid[x].append([]) + yrsgrid[x].append([]) + xbsgrid[x].append([]) + ybsgrid[x].append([]) + + image_size = (image_dimensions[0], image_dimensions[1]) + gridxsize = image_size[0] / (output_grid_size - 1) + gridysize = image_size[1] / (output_grid_size - 1) + + # Iterate through each dot, and it's shift values and put these into the correct grid location + for red_shift in red_shifts: + xgridloc = int(red_shift[0] / gridxsize) + ygridloc = int(red_shift[1] / gridysize) + xrsgrid[xgridloc][ygridloc].append(red_shift[2]) + yrsgrid[xgridloc][ygridloc].append(red_shift[3]) + + for blue_shift in blue_shifts: + xgridloc = int(blue_shift[0] / gridxsize) + ygridloc = int(blue_shift[1] / gridysize) + xbsgrid[xgridloc][ygridloc].append(blue_shift[2]) + ybsgrid[xgridloc][ygridloc].append(blue_shift[3]) + + # Now calculate the average pixel shift for each square in the grid + for x in range(output_grid_size - 1): + for y in range(output_grid_size - 1): + xrgrid[x, y] = np.mean(xrsgrid[x][y]) + yrgrid[x, y] = np.mean(yrsgrid[x][y]) + xbgrid[x, y] = np.mean(xbsgrid[x][y]) + ybgrid[x, y] = np.mean(ybsgrid[x][y]) + + # Next, we start to interpolate the central points of the grid that gets passed to the tuning file + input_grids = np.array([xrgrid, yrgrid, xbgrid, ybgrid]) + output_grids = np.zeros((4, output_grid_size, output_grid_size)) + + # Interpolate the centre of the grid + output_grids[:, 1:-1, 1:-1] = (input_grids[:, 1:, :-1] + input_grids[:, 1:, 1:] + input_grids[:, :-1, 1:] + input_grids[:, :-1, :-1]) / 4 + + # Edge cases: + output_grids[:, 1:-1, 0] = ((input_grids[:, :-1, 0] + input_grids[:, 1:, 0]) / 2 - output_grids[:, 1:-1, 1]) * 2 + output_grids[:, 1:-1, 1] + output_grids[:, 1:-1, -1] = ((input_grids[:, :-1, 7] + input_grids[:, 1:, 7]) / 2 - output_grids[:, 1:-1, -2]) * 2 + output_grids[:, 1:-1, -2] + output_grids[:, 0, 1:-1] = ((input_grids[:, 0, :-1] + input_grids[:, 0, 1:]) / 2 - output_grids[:, 1, 1:-1]) * 2 + output_grids[:, 1, 1:-1] + output_grids[:, -1, 1:-1] = ((input_grids[:, 7, :-1] + input_grids[:, 7, 1:]) / 2 - output_grids[:, -2, 1:-1]) * 2 + output_grids[:, -2, 1:-1] + + # Corner Cases: + output_grids[:, 0, 0] = (output_grids[:, 0, 1] - output_grids[:, 1, 1]) + (output_grids[:, 1, 0] - output_grids[:, 1, 1]) + output_grids[:, 1, 1] + output_grids[:, 0, -1] = (output_grids[:, 0, -2] - output_grids[:, 1, -2]) + (output_grids[:, 1, -1] - output_grids[:, 1, -2]) + output_grids[:, 1, -2] + output_grids[:, -1, 0] = (output_grids[:, -1, 1] - output_grids[:, -2, 1]) + (output_grids[:, -2, 0] - output_grids[:, -2, 1]) + output_grids[:, -2, 1] + output_grids[:, -1, -1] = (output_grids[:, -2, -1] - output_grids[:, -2, -2]) + (output_grids[:, -1, -2] - output_grids[:, -2, -2]) + output_grids[:, -2, -2] + + # Below, we swap the x and the y coordinates, and also multiply by a factor of -1 + # This is due to the PiSP (standard) dimensions being flipped in comparison to + # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts, + # and the PiSP block asks for the values it should shift by (hence the * -1, to convert from colour shift to a pixel shift) + + output_grid_yr, output_grid_xr, output_grid_yb, output_grid_xb = output_grids * -1 + return output_grid_xr, output_grid_yr, output_grid_xb, output_grid_yb + + +def analyse_dot(dot, dot_location=[0, 0]): + # Scan through the dot, calculate the centroid of each colour channel by doing: + # pixel channel brightness * distance from top left corner + # Sum these, and divide by the sum of each channel's brightnesses to get a centroid for each channel + red_channel = np.array(dot)[:, :, 0] + y_num_pixels = len(red_channel[0]) + x_num_pixels = len(red_channel) + yred_weight = np.sum(np.dot(red_channel, np.arange(y_num_pixels))) + xred_weight = np.sum(np.dot(np.arange(x_num_pixels), red_channel)) + red_sum = np.sum(red_channel) + + green_channel = np.array(dot)[:, :, 1] + ygreen_weight = np.sum(np.dot(green_channel, np.arange(y_num_pixels))) + xgreen_weight = np.sum(np.dot(np.arange(x_num_pixels), green_channel)) + green_sum = np.sum(green_channel) + + blue_channel = np.array(dot)[:, :, 2] + yblue_weight = np.sum(np.dot(blue_channel, np.arange(y_num_pixels))) + xblue_weight = np.sum(np.dot(np.arange(x_num_pixels), blue_channel)) + blue_sum = np.sum(blue_channel) + + # We return this structure. It contains 2 arrays that contain: + # the locations of the dot center, along with the channel shifts in the x and y direction: + # [ [red_center_x, red_center_y, red_x_shift, red_y_shift], [blue_center_x, blue_center_y, blue_x_shift, blue_y_shift] ] + + return [[int(dot_location[0]) + int(len(dot) / 2), int(dot_location[1]) + int(len(dot[0]) / 2), xred_weight / red_sum - xgreen_weight / green_sum, yred_weight / red_sum - ygreen_weight / green_sum], [dot_location[0] + int(len(dot) / 2), dot_location[1] + int(len(dot[0]) / 2), xblue_weight / blue_sum - xgreen_weight / green_sum, yblue_weight / blue_sum - ygreen_weight / green_sum]] + + +def cac(Cam): + filelist = Cam.imgs_cac + + Cam.log += '\nCAC analysing files: {}'.format(str(filelist)) + np.set_printoptions(precision=3) + np.set_printoptions(suppress=True) + + # Create arrays to hold all the dots data and their colour offsets + red_shift = [] # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]] + blue_shift = [] + # Iterate through the files + # Multiple files is reccomended to average out the lens aberration through rotations + for file in filelist: + Cam.log += '\nCAC processing file' + print("\n Processing file") + # Read the raw RGB values + rgb = file.rgb + image_size = [file.h, file.w] # Image size, X, Y + # Create a colour copy of the RGB values to use later in the calibration + imout = Image.new(mode="RGB", size=image_size) + rgb_image = np.array(imout) + # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily + rgb.reshape((image_size[0], image_size[1], 3)) + rgb_image = rgb + + # Pass the RGB image through to the dots locating program + # Returns an array of the dots (colour rectangles around the dots), and an array of their locations + print("Finding dots") + Cam.log += '\nFinding dots' + dots, dots_locations = find_dots_locations(rgb_image) + + # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out + # by how far the chromatic aberration has shifted each channel + Cam.log += '\nDots found: {}'.format(str(len(dots))) + print('Dots found: ' + str(len(dots))) + + for dot, dot_location in zip(dots, dots_locations): + if len(dot) > 0: + if (dot_location[0] > 0) and (dot_location[1] > 0): + ret = analyse_dot(dot, dot_location) + red_shift.append(ret[0]) + blue_shift.append(ret[1]) + + # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix + # for the CAC block to handle and then store these as a .json file to be added to the camera + # tuning file + print("\nCreating output grid") + Cam.log += '\nCreating output grid' + rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size) + + print("CAC correction complete!") + Cam.log += '\nCAC correction complete!' + + # Give the JSON dict back to the main ctt program + return {"strength": 1.0, "lut_rx": list(rx.round(2).reshape(81)), "lut_ry": list(ry.round(2).reshape(81)), "lut_bx": list(bx.round(2).reshape(81)), "lut_by": list(by.round(2).reshape(81))} diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py new file mode 100644 index 00000000..07c943a8 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_ccm.py @@ -0,0 +1,404 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# camera tuning tool for CCM (colour correction matrix) + +from ctt_image_load import * +from ctt_awb import get_alsc_patches +import colors +from scipy.optimize import minimize +from ctt_visualise import visualise_macbeth_chart +import numpy as np +""" +takes 8-bit macbeth chart values, degammas and returns 16 bit +""" + +''' +This program has many options from which to derive the color matrix from. +The first is average. This minimises the average delta E across all patches of +the macbeth chart. Testing across all cameras yeilded this as the most color +accurate and vivid. Other options are avalible however. +Maximum minimises the maximum Delta E of the patches. It iterates through till +a minimum maximum is found (so that there is +not one patch that deviates wildly.) +This yields generally good results but overall the colors are less accurate +Have a fiddle with maximum and see what you think. +The final option allows you to select the patches for which to average across. +This means that you can bias certain patches, for instance if you want the +reds to be more accurate. +''' + +matrix_selection_types = ["average", "maximum", "patches"] +typenum = 0 # select from array above, 0 = average, 1 = maximum, 2 = patches +test_patches = [1, 2, 5, 8, 9, 12, 14] + +''' +Enter patches to test for. Can also be entered twice if you +would like twice as much bias on one patch. +''' + + +def degamma(x): + x = x / ((2 ** 8) - 1) # takes 255 and scales it down to one + x = np.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4) + x = x * ((2 ** 16) - 1) # takes one and scales up to 65535, 16 bit color + return x + + +def gamma(x): + # Take 3 long array of color values and gamma them + return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x] + + +""" +FInds colour correction matrices for list of images +""" + + +def ccm(Cam, cal_cr_list, cal_cb_list, grid_size): + global matrix_selection_types, typenum + imgs = Cam.imgs + """ + standard macbeth chart colour values + """ + m_rgb = np.array([ # these are in RGB + [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) # now in 16 bit color. + + # Produce array of LAB values for ideal color chart + m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb] + + """ + 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)) + m_lab = np.array([m_lab[i::6] for i in range(6)]).reshape((24, 3)) + m_rgb = np.array([m_rgb[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 is 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, grid_size=grid_size) + """ + 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 + """ + # ==== All of below should in sRGB ===## + sumde = 0 + ccm = do_ccm(r, g, b, m_srgb) + # This is the initial guess that our optimisation code works with. + original_ccm = ccm + r1 = ccm[0] + r2 = ccm[1] + g1 = ccm[3] + g2 = ccm[4] + b1 = ccm[6] + b2 = ccm[7] + ''' + COLOR MATRIX LOOKS AS BELOW + R1 R2 R3 Rval Outr + G1 G2 G3 * Gval = G + B1 B2 B3 Bval B + Will be optimising 6 elements and working out the third element using 1-r1-r2 = r3 + ''' + + x0 = [r1, r2, g1, g2, b1, b2] + ''' + We use our old CCM as the initial guess for the program to find the + optimised matrix + ''' + result = minimize(guess, x0, args=(r, g, b, m_lab), tol=0.01) + ''' + This produces a color matrix which has the lowest delta E possible, + based off the input data. Note it is impossible for this to reach + zero since the input data is imperfect + ''' + + Cam.log += ("\n \n Optimised Matrix Below: \n \n") + [r1, r2, g1, g2, b1, b2] = result.x + # The new, optimised color correction matrix values + optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)] + + # This is the optimised Color Matrix (preserving greys by summing rows up to 1) + Cam.log += str(optimised_ccm) + Cam.log += "\n Old Color Correction Matrix Below \n" + Cam.log += str(ccm) + + formatted_ccm = np.array(original_ccm).reshape((3, 3)) + + ''' + below is a whole load of code that then applies the latest color + matrix, and returns LAB values for color. This can then be used + to calculate the final delta E + ''' + optimised_ccm_rgb = [] # Original Color Corrected Matrix RGB / LAB + optimised_ccm_lab = [] + + formatted_optimised_ccm = np.array(optimised_ccm).reshape((3, 3)) + after_gamma_rgb = [] + after_gamma_lab = [] + + for RGB in zip(r, g, b): + ccm_applied_rgb = np.dot(formatted_ccm, (np.array(RGB) / 256)) + optimised_ccm_rgb.append(gamma(ccm_applied_rgb)) + optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb)) + + optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, np.array(RGB) / 256) + after_gamma_rgb.append(gamma(optimised_ccm_applied_rgb)) + after_gamma_lab.append(colors.RGB_to_LAB(optimised_ccm_applied_rgb)) + ''' + Gamma After RGB / LAB - not used in calculations, only used for visualisation + We now want to spit out some data that shows + how the optimisation has improved the color matrices + ''' + Cam.log += "Here are the Improvements" + + # CALCULATE WORST CASE delta e + old_worst_delta_e = 0 + before_average = transform_and_evaluate(formatted_ccm, r, g, b, m_lab) + new_worst_delta_e = 0 + after_average = transform_and_evaluate(formatted_optimised_ccm, r, g, b, m_lab) + for i in range(24): + old_delta_e = deltae(optimised_ccm_lab[i], m_lab[i]) # Current Old Delta E + new_delta_e = deltae(after_gamma_lab[i], m_lab[i]) # Current New Delta E + if old_delta_e > old_worst_delta_e: + old_worst_delta_e = old_delta_e + if new_delta_e > new_worst_delta_e: + new_worst_delta_e = new_delta_e + + Cam.log += "Before color correction matrix was optimised, we got an average delta E of " + str(before_average) + " and a maximum delta E of " + str(old_worst_delta_e) + Cam.log += "After color correction matrix was optimised, we got an average delta E of " + str(after_average) + " and a maximum delta E of " + str(new_worst_delta_e) + + visualise_macbeth_chart(m_rgb, optimised_ccm_rgb, after_gamma_rgb, str(Img.col) + str(matrix_selection_types[typenum])) + ''' + The program will also save some visualisations of improvements. + Very pretty to look at. Top rectangle is ideal, Left square is + before optimisation, right square is after. + ''' + + """ + if a ccm has already been calculated for that temperature then don't + overwrite but save both. They will then be averaged later on + """ # Now going to use optimised color matrix, optimised_ccm + if Img.col in ccm_tab.keys(): + ccm_tab[Img.col].append(optimised_ccm) + else: + ccm_tab[Img.col] = [optimised_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 + + +def guess(x0, r, g, b, m_lab): # provides a method of numerical feedback for the optimisation code + [r1, r2, g1, g2, b1, b2] = x0 + ccm = np.array([r1, r2, (1 - r1 - r2), + g1, g2, (1 - g1 - g2), + b1, b2, (1 - b1 - b2)]).reshape((3, 3)) # format the matrix correctly + return transform_and_evaluate(ccm, r, g, b, m_lab) + + +def transform_and_evaluate(ccm, r, g, b, m_lab): # Transforms colors to LAB and applies the correction matrix + # create list of matrix changed colors + realrgb = [] + for RGB in zip(r, g, b): + rgb_post_ccm = np.dot(ccm, np.array(RGB) / 256) # This is RGB values after the color correction matrix has been applied + realrgb.append(colors.RGB_to_LAB(rgb_post_ccm)) + # now compare that with m_lab and return numeric result, averaged for each patch + return (sumde(realrgb, m_lab) / 24) # returns an average result of delta E + + +def sumde(listA, listB): + global typenum, test_patches + sumde = 0 + maxde = 0 + patchde = [] # Create array of the delta E values for each patch. useful for optimisation of certain patches + for listA_item, listB_item in zip(listA, listB): + if maxde < (deltae(listA_item, listB_item)): + maxde = deltae(listA_item, listB_item) + patchde.append(deltae(listA_item, listB_item)) + sumde += deltae(listA_item, listB_item) + ''' + The different options specified at the start allow for + the maximum to be returned, average or specific patches + ''' + if typenum == 0: + return sumde + if typenum == 1: + return maxde + if typenum == 2: + output = sum([patchde[test_patch] for test_patch in test_patches]) + # Selects only certain patches and returns the output for them + return output + + +""" +calculates the ccm for an individual image. +ccms are calculated 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. +The initial CCM is calculated in RGB, and then optimised in LAB color space +This simplifies the initial calculation but then gets us the accuracy of +using LAB color space. +""" + + +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 + + +def deltae(colorA, colorB): + return ((colorA[0] - colorB[0]) ** 2 + (colorA[1] - colorB[1]) ** 2 + (colorA[2] - colorB[2]) ** 2) ** 0.5 + # return ((colorA[1]-colorB[1]) * * 2 + (colorA[2]-colorB[2]) * * 2) * * 0.5 + # UNCOMMENT IF YOU WANT TO NEGLECT LUMINANCE FROM CALCULATION OF DELTA E diff --git a/utils/raspberrypi/ctt/ctt_config_example.json b/utils/raspberrypi/ctt/ctt_config_example.json new file mode 100644 index 00000000..1105862c --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_config_example.json @@ -0,0 +1,17 @@ +{ + "disable": [], + "plot": [], + "alsc": { + "do_alsc_colour": 1, + "luminance_strength": 0.8, + "max_gain": 8.0 + }, + "awb": { + "greyworld": 0 + }, + "blacklevel": -1, + "macbeth": { + "small": 0, + "show": 0 + } +} diff --git a/utils/raspberrypi/ctt/ctt_dots_locator.py b/utils/raspberrypi/ctt/ctt_dots_locator.py new file mode 100644 index 00000000..4945c04b --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_dots_locator.py @@ -0,0 +1,118 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2023, Raspberry Pi Ltd +# +# find_dots.py - Used by CAC algorithm to convert image to set of dots + +''' +This file takes the black and white version of the image, along with +the color version. It then located the black dots on the image by +thresholding dark pixels. +In a rather fun way, the algorithm bounces around the thresholded area in a random path +We then use the maximum and minimum of these paths to determine the dot shape and size +This info is then used to return colored dots and locations back to the main file +''' + +import numpy as np +import random +from PIL import Image, ImageEnhance, ImageFilter + + +def find_dots_locations(rgb_image, color_threshold=100, dots_edge_avoid=75, image_edge_avoid=10, search_path_length=500, grid_scan_step_size=10, logfile=open("log.txt", "a+")): + # Initialise some starting variables + pixels = Image.fromarray(rgb_image) + pixels = pixels.convert("L") + enhancer = ImageEnhance.Contrast(pixels) + im_output = enhancer.enhance(1.4) + # We smooth it slightly to make it easier for the dot recognition program to locate the dots + im_output = im_output.filter(ImageFilter.GaussianBlur(radius=2)) + bw_image = np.array(im_output) + + location = [0, 0] + dots = [] + dots_location = [] + # the program takes away the edges - we don't want a dot that is half a circle, the + # centroids would all be wrong + for x in range(dots_edge_avoid, len(bw_image) - dots_edge_avoid, grid_scan_step_size): + for y in range(dots_edge_avoid, len(bw_image[0]) - dots_edge_avoid, grid_scan_step_size): + location = [x, y] + scrap_dot = False # A variable used to make sure that this is a valid dot + if (bw_image[location[0], location[1]] < color_threshold) and not (scrap_dot): + heading = "south" # Define a starting direction to move in + coords = [] + for i in range(search_path_length): # Creates a path of length `search_path_length`. This turns out to always be enough to work out the rough shape of the dot. + # Now make sure that the thresholded area doesn't come within 10 pixels of the edge of the image, ensures we capture all the CA + if ((image_edge_avoid < location[0] < len(bw_image) - image_edge_avoid) and (image_edge_avoid < location[1] < len(bw_image[0]) - image_edge_avoid)) and not (scrap_dot): + if heading == "south": + if bw_image[location[0] + 1, location[1]] < color_threshold: + # Here, notice it does not go south, but actually goes southeast + # This is crucial in ensuring that we make our way around the majority of the dot + location[0] = location[0] + 1 + location[1] = location[1] + 1 + heading = "south" + else: + # This happens when we reach a thresholded edge. We now randomly change direction and keep searching + dir = random.randint(1, 2) + if dir == 1: + heading = "west" + if dir == 2: + heading = "east" + + if heading == "east": + if bw_image[location[0], location[1] + 1] < color_threshold: + location[1] = location[1] + 1 + heading = "east" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "north" + if dir == 2: + heading = "south" + + if heading == "west": + if bw_image[location[0], location[1] - 1] < color_threshold: + location[1] = location[1] - 1 + heading = "west" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "north" + if dir == 2: + heading = "south" + + if heading == "north": + if bw_image[location[0] - 1, location[1]] < color_threshold: + location[0] = location[0] - 1 + heading = "north" + else: + dir = random.randint(1, 2) + if dir == 1: + heading = "west" + if dir == 2: + heading = "east" + # Log where our particle travels across the dot + coords.append([location[0], location[1]]) + else: + scrap_dot = True # We just don't have enough space around the dot, discard this one, and move on + if not scrap_dot: + # get the size of the dot surrounding the dot + x_coords = np.array(coords)[:, 0] + y_coords = np.array(coords)[:, 1] + hsquaresize = max(list(x_coords)) - min(list(x_coords)) + vsquaresize = max(list(y_coords)) - min(list(y_coords)) + # Create the bounding coordinates of the rectangle surrounding the dot + # Program uses the dotsize + half of the dotsize to ensure we get all that color fringing + extra_space_factor = 0.45 + top_left_x = (min(list(x_coords)) - int(hsquaresize * extra_space_factor)) + btm_right_x = max(list(x_coords)) + int(hsquaresize * extra_space_factor) + top_left_y = (min(list(y_coords)) - int(vsquaresize * extra_space_factor)) + btm_right_y = max(list(y_coords)) + int(vsquaresize * extra_space_factor) + # Overwrite the area of the dot to ensure we don't use it again + bw_image[top_left_x:btm_right_x, top_left_y:btm_right_y] = 255 + # Add the color version of the dot to the list to send off, along with some coordinates. + dots.append(rgb_image[top_left_x:btm_right_x, top_left_y:btm_right_y]) + dots_location.append([top_left_x, top_left_y]) + else: + # Dot was too close to the image border to be useable + pass + return dots, dots_location diff --git a/utils/raspberrypi/ctt/ctt_geq.py b/utils/raspberrypi/ctt/ctt_geq.py new file mode 100644 index 00000000..5a91ebb4 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_geq.py @@ -0,0 +1,181 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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..531de328 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_image_load.py @@ -0,0 +1,455 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019-2020, Raspberry Pi Ltd +# +# 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 is 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 + """ + The DNG and TIFF/EP specifications use different IFDs to store the raw + image data and the Exif tags. DNG stores them in a SubIFD and in an Exif + IFD respectively (named "SubImage1" and "Photo" by pyexiv2), while + TIFF/EP stores them both in IFD0 (name "Image"). Both are used in "DNG" + files, with libcamera-apps following the DNG recommendation and + applications based on picamera2 following TIFF/EP. + + This code detects which tags are being used, and therefore extracts the + correct values. + """ + try: + Img.w = metadata['Exif.SubImage1.ImageWidth'].value + subimage = "SubImage1" + photo = "Photo" + except KeyError: + Img.w = metadata['Exif.Image.ImageWidth'].value + subimage = "Image" + photo = "Image" + Img.pad = 0 + Img.h = metadata[f'Exif.{subimage}.ImageLength'].value + white = metadata[f'Exif.{subimage}.WhiteLevel'].value + Img.sigbits = int(white).bit_length() + Img.fmt = (Img.sigbits - 4) // 2 + Img.exposure = int(metadata[f'Exif.{photo}.ExposureTime'].value * 1000000) + Img.againQ8 = metadata[f'Exif.{photo}.ISOSpeedRatings'].value * 256 / 100 + Img.againQ8_norm = Img.againQ8 / 256 + Img.camName = metadata['Exif.Image.Model'].value + Img.blacklevel = int(metadata[f'Exif.{subimage}.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[f'Exif.{subimage}.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] + Img.rgb = raw_im.postprocess() + + except Exception: + 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) + """ + handle errors smoothly if loading image failed + """ + if Img == 0: + return 0 + 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 is 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..46be1512 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_lux.py @@ -0,0 +1,61 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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..f22dbf31 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_macbeth_locator.py @@ -0,0 +1,757 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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) + + # Keep a list that will include this and any brightened up versions of + # the image for reuse. + all_images = [img] + + """ + 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) + all_images.append(img_br) + 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) + all_images.append(img_br) + 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 + loop over this and any brightened up images that we made to increase the + likelihood of success + """ + for img_br in all_images: + for i in range(3): + for j in range(3): + w_s, h_s = i*w_inc, j*h_inc + img_sel = img_br[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) + # Again, loop over any brightened up images as well + for img_br in all_images: + for i in range(5): + for j in range(5): + w_s, h_s = i*w_inc, j*h_inc + img_sel = img_br[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' + str(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 is not 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 four squares need to have voted for a centre in + order for a transform to be found + """ + if len(sq_cents) < 4: + raise MacbethError( + '\nWARNING: No macbeth chart found!' + '\nNot enough squares found' + '\nPossible problems:\n' + '- Macbeth chart is occluded\n' + '- Macbeth chart is too dark or 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..0b18d83f --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_noise.py @@ -0,0 +1,123 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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_pisp.py b/utils/raspberrypi/ctt/ctt_pisp.py new file mode 100755 index 00000000..a59b053c --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_pisp.py @@ -0,0 +1,805 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# ctt_pisp.py - camera tuning tool data for PiSP platforms + + +json_template = { + "rpi.black_level": { + "black_level": 4096 + }, + "rpi.lux": { + "reference_shutter_speed": 10000, + "reference_gain": 1, + "reference_aperture": 1.0 + }, + "rpi.dpc": { + "strength": 1 + }, + "rpi.noise": { + }, + "rpi.geq": { + }, + "rpi.denoise": + { + "normal": + { + "sdn": + { + "deviation": 1.6, + "strength": 0.5, + "deviation2": 3.2, + "deviation_no_tdn": 3.2, + "strength_no_tdn": 0.75 + }, + "cdn": + { + "deviation": 200, + "strength": 0.3 + }, + "tdn": + { + "deviation": 0.8, + "threshold": 0.05 + } + }, + "hdr": + { + "sdn": + { + "deviation": 1.6, + "strength": 0.5, + "deviation2": 3.2, + "deviation_no_tdn": 3.2, + "strength_no_tdn": 0.75 + }, + "cdn": + { + "deviation": 200, + "strength": 0.3 + }, + "tdn": + { + "deviation": 1.3, + "threshold": 0.1 + } + }, + "night": + { + "sdn": + { + "deviation": 1.6, + "strength": 0.5, + "deviation2": 3.2, + "deviation_no_tdn": 3.2, + "strength_no_tdn": 0.75 + }, + "cdn": + { + "deviation": 200, + "strength": 0.3 + }, + "tdn": + { + "deviation": 1.3, + "threshold": 0.1 + } + } + }, + "rpi.awb": { + "priors": [ + {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]}, + {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]}, + {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]} + ], + "modes": { + "auto": {"lo": 2500, "hi": 7700}, + "incandescent": {"lo": 2500, "hi": 3000}, + "tungsten": {"lo": 3000, "hi": 3500}, + "fluorescent": {"lo": 4000, "hi": 4700}, + "indoor": {"lo": 3000, "hi": 5000}, + "daylight": {"lo": 5500, "hi": 6500}, + "cloudy": {"lo": 7000, "hi": 8000} + }, + "bayes": 1 + 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0.8 + ] + } + ], + "shadows": [ + { + "bound": "LOWER", + "q_lo": 0.98, + "q_hi": 1.0, + "y_target": + [ + 0, 0.5, + 1000, 0.5 + ] + } + ] + }, + "y_target": + [ + 0, 0.16, + 1000, 0.16, + 10000, 0.17 + ] + } + ] + }, + "rpi.alsc": { + 'omega': 1.3, + 'n_iter': 100, + 'luminance_strength': 0.8, + }, + "rpi.contrast": { + "ce_enable": 1, + "gamma_curve": [ + 0, 0, + 1024, 5040, + 2048, 9338, + 3072, 12356, + 4096, 15312, + 5120, 18051, + 6144, 20790, + 7168, 23193, + 8192, 25744, + 9216, 27942, + 10240, 30035, + 11264, 32005, + 12288, 33975, + 13312, 35815, + 14336, 37600, + 15360, 39168, + 16384, 40642, + 18432, 43379, + 20480, 45749, + 22528, 47753, + 24576, 49621, + 26624, 51253, + 28672, 52698, + 30720, 53796, + 32768, 54876, + 36864, 57012, + 40960, 58656, + 45056, 59954, + 49152, 61183, + 53248, 62355, + 57344, 63419, + 61440, 64476, + 65535, 65535 + ] + }, + "rpi.ccm": { + }, + "rpi.cac": { + }, + "rpi.sharpen": { + "threshold": 0.25, + "limit": 1.0, + "strength": 1.0 + }, + "rpi.hdr": + { + "Off": + { + "cadence": [ 0 ] + }, + "MultiExposureUnmerged": + { + "cadence": [ 1, 2 ], + "channel_map": { "short": 1, "long": 2 } + }, + "SingleExposure": + { + "cadence": [1], + "channel_map": { "short": 1 }, + "spatial_gain": 2.0, + "tonemap_enable": 1 + }, + "MultiExposure": + { + "cadence": [1, 2], + "channel_map": { "short": 1, "long": 2 }, + "stitch_enable": 1, + "spatial_gain": 2.0, + "tonemap_enable": 1 + }, + "Night": + { + "cadence": [ 3 ], + "channel_map": { "night": 3 }, + "tonemap_enable": 1, + "tonemap": + [ + 0, 0, + 5000, 20000, + 10000, 30000, + 20000, 47000, + 30000, 55000, + 65535, 65535 + ] + } + } +} + +grid_size = (32, 32) diff --git a/utils/raspberrypi/ctt/ctt_pretty_print_json.py b/utils/raspberrypi/ctt/ctt_pretty_print_json.py new file mode 100755 index 00000000..a4cae62d --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_pretty_print_json.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright 2022 Raspberry Pi Ltd +# +# Script to pretty print a Raspberry Pi tuning config JSON structure in +# version 2.0 and later formats. + +import argparse +import json +import textwrap + + +class Encoder(json.JSONEncoder): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.indentation_level = 0 + self.hard_break = 120 + self.custom_elems = { + 'weights': 15, + 'table': 16, + 'luminance_lut': 16, + 'ct_curve': 3, + 'ccm': 3, + 'lut_rx': 9, + 'lut_bx': 9, + 'lut_by': 9, + 'lut_ry': 9, + 'gamma_curve': 2, + 'y_target': 2, + 'prior': 2, + 'tonemap': 2 + } + + def encode(self, o, node_key=None): + if isinstance(o, (list, tuple)): + # Check if we are a flat list of numbers. + if not any(isinstance(el, (list, tuple, dict)) for el in o): + s = ', '.join(json.dumps(el) for el in o) + if node_key in self.custom_elems.keys(): + # Special case handling to specify number of elements in a row for tables, ccm, etc. + self.indentation_level += 1 + sl = s.split(', ') + num = self.custom_elems[node_key] + chunk = [self.indent_str + ', '.join(sl[x:x + num]) for x in range(0, len(sl), num)] + t = ',\n'.join(chunk) + self.indentation_level -= 1 + output = f'\n{self.indent_str}[\n{t}\n{self.indent_str}]' + elif len(s) > self.hard_break - len(self.indent_str): + # Break a long list with wraps. + self.indentation_level += 1 + t = textwrap.fill(s, self.hard_break, break_long_words=False, + initial_indent=self.indent_str, subsequent_indent=self.indent_str) + self.indentation_level -= 1 + output = f'\n{self.indent_str}[\n{t}\n{self.indent_str}]' + else: + # Smaller lists can remain on a single line. + output = f' [ {s} ]' + return output + else: + # Sub-structures in the list case. + self.indentation_level += 1 + output = [self.indent_str + self.encode(el) for el in o] + self.indentation_level -= 1 + output = ',\n'.join(output) + return f' [\n{output}\n{self.indent_str}]' + + elif isinstance(o, dict): + self.indentation_level += 1 + output = [] + for k, v in o.items(): + if isinstance(v, dict) and len(v) == 0: + # Empty config block special case. + output.append(self.indent_str + f'{json.dumps(k)}: {{ }}') + else: + # Only linebreak if the next node is a config block. + sep = f'\n{self.indent_str}' if isinstance(v, dict) else '' + output.append(self.indent_str + f'{json.dumps(k)}:{sep}{self.encode(v, k)}') + output = ',\n'.join(output) + self.indentation_level -= 1 + return f'{{\n{output}\n{self.indent_str}}}' + + else: + return ' ' + json.dumps(o) + + @property + def indent_str(self) -> str: + return ' ' * self.indentation_level * self.indent + + def iterencode(self, o, **kwargs): + return self.encode(o) + + +def pretty_print(in_json: dict, custom_elems={}) -> str: + + if 'version' not in in_json or \ + 'target' not in in_json or \ + 'algorithms' not in in_json or \ + in_json['version'] < 2.0: + raise RuntimeError('Incompatible JSON dictionary has been provided') + + encoder = Encoder(indent=4, sort_keys=False) + encoder.custom_elems |= custom_elems + return encoder.encode(in_json) #json.dumps(in_json, cls=Encoder, indent=4, sort_keys=False) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description= + 'Prettify a version 2.0 camera tuning config JSON file.') + parser.add_argument('-t', '--target', type=str, help='Target platform', choices=['pisp', 'vc4'], default='vc4') + parser.add_argument('input', type=str, help='Input tuning file.') + parser.add_argument('output', type=str, nargs='?', + help='Output converted tuning file. If not provided, the input file will be updated in-place.', + default=None) + args = parser.parse_args() + + with open(args.input, 'r') as f: + in_json = json.load(f) + + if args.target == 'pisp': + from ctt_pisp import grid_size + elif args.target == 'vc4': + from ctt_vc4 import grid_size + + out_json = pretty_print(in_json, custom_elems={'table': grid_size[0], 'luminance_lut': grid_size[0]}) + + with open(args.output if args.output is not None else args.input, 'w') as f: + f.write(out_json) diff --git a/utils/raspberrypi/ctt/ctt_ransac.py b/utils/raspberrypi/ctt/ctt_ransac.py new file mode 100644 index 00000000..01bba302 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_ransac.py @@ -0,0 +1,71 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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///..../.........-()))))))))))))))))))(((-,*)'(&)#($%(%"###""!%""&"&&!$" #!$ !"! 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()*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..50b01ecf --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_tools.py @@ -0,0 +1,150 @@ +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# 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 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 not isinstance(val, dict): + raise ValueError + elif ttype == 'list': + if not isinstance(val, list): + 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') + target = get_config(args_dict, '-t', "vc4", 'string') + if directory is None: + raise ArgError('\n\nERROR! No input directory given.') + if json_output is None: + raise ArgError('\n\nERROR! No output json given.') + return json_output, directory, config, log_path, target + + +""" +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 diff --git a/utils/raspberrypi/ctt/ctt_vc4.py b/utils/raspberrypi/ctt/ctt_vc4.py new file mode 100755 index 00000000..7154e110 --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_vc4.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python3 +# +# SPDX-License-Identifier: BSD-2-Clause +# +# Copyright (C) 2019, Raspberry Pi Ltd +# +# ctt_vc4.py - camera tuning tool data for VC4 platforms + + +json_template = { + "rpi.black_level": { + "black_level": 4096 + }, + "rpi.dpc": { + }, + "rpi.lux": { + "reference_shutter_speed": 10000, + "reference_gain": 1, + "reference_aperture": 1.0 + }, + "rpi.noise": { + }, + "rpi.geq": { + }, + "rpi.sdn": { + }, + "rpi.awb": { + "priors": [ + {"lux": 0, "prior": [2000, 1.0, 3000, 0.0, 13000, 0.0]}, + {"lux": 800, "prior": [2000, 0.0, 6000, 2.0, 13000, 2.0]}, + {"lux": 1500, "prior": [2000, 0.0, 4000, 1.0, 6000, 6.0, 6500, 7.0, 7000, 1.0, 13000, 1.0]} + ], + "modes": { + "auto": {"lo": 2500, "hi": 8000}, + "incandescent": {"lo": 2500, "hi": 3000}, + "tungsten": {"lo": 3000, "hi": 3500}, + "fluorescent": {"lo": 4000, "hi": 4700}, + "indoor": {"lo": 3000, "hi": 5000}, + "daylight": {"lo": 5500, "hi": 6500}, + "cloudy": {"lo": 7000, "hi": 8600} + }, + "bayes": 1 + }, + "rpi.agc": { + "metering_modes": { + "centre-weighted": { + "weights": [3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0] + }, + "spot": { + "weights": [2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + }, + "matrix": { + "weights": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] + } + }, + "exposure_modes": { + "normal": { + "shutter": [100, 10000, 30000, 60000, 120000], + "gain": [1.0, 2.0, 4.0, 6.0, 6.0] + }, + "short": { + "shutter": [100, 5000, 10000, 20000, 120000], + "gain": [1.0, 2.0, 4.0, 6.0, 6.0] + } + }, + "constraint_modes": { + "normal": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]} + ], + "highlight": [ + {"bound": "LOWER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.5, 1000, 0.5]}, + {"bound": "UPPER", "q_lo": 0.98, "q_hi": 1.0, "y_target": [0, 0.8, 1000, 0.8]} + ] + }, + "y_target": [0, 0.16, 1000, 0.165, 10000, 0.17] + }, + "rpi.alsc": { + 'omega': 1.3, + 'n_iter': 100, + 'luminance_strength': 0.7, + }, + "rpi.contrast": { + "ce_enable": 1, + "gamma_curve": [ + 0, 0, + 1024, 5040, + 2048, 9338, + 3072, 12356, + 4096, 15312, + 5120, 18051, + 6144, 20790, + 7168, 23193, + 8192, 25744, + 9216, 27942, + 10240, 30035, + 11264, 32005, + 12288, 33975, + 13312, 35815, + 14336, 37600, + 15360, 39168, + 16384, 40642, + 18432, 43379, + 20480, 45749, + 22528, 47753, + 24576, 49621, + 26624, 51253, + 28672, 52698, + 30720, 53796, + 32768, 54876, + 36864, 57012, + 40960, 58656, + 45056, 59954, + 49152, 61183, + 53248, 62355, + 57344, 63419, + 61440, 64476, + 65535, 65535 + ] + }, + "rpi.ccm": { + }, + "rpi.sharpen": { + } +} + +grid_size = (16, 12) diff --git a/utils/raspberrypi/ctt/ctt_visualise.py b/utils/raspberrypi/ctt/ctt_visualise.py new file mode 100644 index 00000000..ed2339fd --- /dev/null +++ b/utils/raspberrypi/ctt/ctt_visualise.py @@ -0,0 +1,43 @@ +""" +Some code that will save virtual macbeth charts that show the difference between optimised matrices and non optimised matrices + +The function creates an image that is 1550 by 1050 pixels wide, and fills it with patches which are 200x200 pixels in size +Each patch contains the ideal color, the color from the original matrix, and the color from the final matrix +_________________ +| | +| Ideal Color | +|_______________| +| Old | new | +| Color | Color | +|_______|_______| + +Nice way of showing how the optimisation helps change the colors and the color matricies +""" +import numpy as np +from PIL import Image + + +def visualise_macbeth_chart(macbeth_rgb, original_rgb, new_rgb, output_filename): + image = np.zeros((1050, 1550, 3), dtype=np.uint8) + colorindex = -1 + for y in range(6): + for x in range(4): # Creates 6 x 4 grid of macbeth chart + colorindex += 1 + xlocation = 50 + 250 * x # Means there is 50px of black gap between each square, more like the real macbeth chart. + ylocation = 50 + 250 * y + for g in range(200): + for i in range(100): + image[xlocation + i, ylocation + g] = macbeth_rgb[colorindex] + xlocation = 150 + 250 * x + ylocation = 50 + 250 * y + for i in range(100): + for g in range(100): + image[xlocation + i, ylocation + g] = original_rgb[colorindex] # Smaller squares below to compare the old colors with the new ones + xlocation = 150 + 250 * x + ylocation = 150 + 250 * y + for i in range(100): + for g in range(100): + image[xlocation + i, ylocation + g] = new_rgb[colorindex] + + img = Image.fromarray(image, 'RGB') + img.save(str(output_filename) + 'Generated Macbeth Chart.png') |