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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
#
# Utilities for libtuning
import cv2
import decimal
import math
import numpy as np
import os
from pathlib import Path
import re
import sys
import logging
import libtuning as lt
from libtuning.image import Image
from libtuning.macbeth import locate_macbeth
logger = logging.getLogger(__name__)
# Utility functions
def get_module_by_type_name(modules, name):
for module in modules:
if module.type == name:
return module
return None
# Private utility functions
def _list_image_files(directory):
d = Path(directory)
files = [d.joinpath(f) for f in os.listdir(d)
if re.search(r'\.(jp[e]g$)|(dng$)', f)]
files.sort()
return files
def _parse_image_filename(fn: Path):
lsc_only = False
color_temperature = None
lux = None
parts = fn.stem.split('_')
for part in parts:
if part == 'alsc':
lsc_only = True
continue
r = re.match(r'(\d+)[kK]', part)
if r:
color_temperature = int(r.group(1))
continue
r = re.match(r'(\d+)[lLuU]', part)
if r:
lux = int(r.group(1))
if color_temperature is None:
logger.error(f'The file name of "{fn.name}" does not contain a color temperature')
if lux is None and lsc_only is False:
logger.error(f'The file name of "{fn.name}" must either contain alsc or a lux level')
return color_temperature, lux, lsc_only
# \todo Implement this from check_imgs() in ctt.py
def _validate_images(images):
return True
# Public utility functions
# @brief Load images into a single list of Image instances
# @param input_dir Directory from which to load image files
# @param config Configuration dictionary
# @param load_nonlsc Whether or not to load non-lsc images
# @param load_lsc Whether or not to load lsc-only images
# @return A list of Image instances
def load_images(input_dir: str, config: dict, load_nonlsc: bool, load_lsc: bool) -> list:
files = _list_image_files(input_dir)
if len(files) == 0:
logger.error(f'No images found in {input_dir}')
return None
images = []
for f in files:
color, lux, lsc_only = _parse_image_filename(f)
if color is None:
continue
# Skip lsc image if we don't need it
if lsc_only and not load_lsc:
logger.warning(f'Skipping {f.name} as this tuner has no LSC module')
continue
# Skip non-lsc image if we don't need it
if not lsc_only and not load_nonlsc:
logger.warning(f'Skipping {f.name} as this tuner only has an LSC module')
continue
# Load image
try:
image = Image(f)
except Exception as e:
logger.error(f'Failed to load image {f.name}: {e}')
continue
# Populate simple fields
image.lsc_only = lsc_only
image.color = color
image.lux = lux
# Black level comes from the TIFF tags, but they are overridable by the
# config file.
if 'blacklevel' in config['general']:
image.blacklevel_16 = config['general']['blacklevel']
if lsc_only:
images.append(image)
continue
# Handle macbeth
macbeth = locate_macbeth(config)
if macbeth is None:
continue
images.append(image)
if not _validate_images(images):
return None
return images
"""
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
"""
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]
im_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imwrite(f'{output_filename} Generated Macbeth Chart.png', im_bgr)
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