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authorBen Benson <ben.benson@raspberrypi.com>2024-06-06 11:15:08 +0100
committerKieran Bingham <kieran.bingham@ideasonboard.com>2024-06-13 11:02:16 +0100
commit8bea2d5a8adf1901f49e6449a731e3fd02272b3d (patch)
tree049daee7e8d6cf6ba49010dc14902b785754bee3 /utils/raspberrypi/ctt/cac_only.py
parentd13542c28f291b88e29f1142d6dff565e772a05c (diff)
utils: raspberrypi: ctt: Added CAC support to the CTT
Added the ability to tune the chromatic aberration correction within the ctt. There are options for cac_only or to tune as part of a larger tuning process. CTT will now recognise any files that begin with "cac" as being chromatic aberration tuning files. Signed-off-by: Ben Benson <ben.benson@raspberrypi.com> Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Tested-by: Naushir Patuck <naush@raspberrypi.com> Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Diffstat (limited to 'utils/raspberrypi/ctt/cac_only.py')
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diff --git a/utils/raspberrypi/ctt/cac_only.py b/utils/raspberrypi/ctt/cac_only.py
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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)