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#!/usr/bin/env python3
#
# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2023, Raspberry Pi (Trading) Limited
#
# 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 YAML")
f = open(str(output_filepath), "w+")
f.write(sample)
f.close()
print("Successfully written to yaml 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]
logfile = open("log.txt", "a+")
cac(filelist, arg_output, plot_results, logfile)
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