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# This function written in Python is not affiliated with the CIE (International Commission on Illumination),
# and is released into the public domain. It is provided "as is" without any warranty, express or implied.
# The classic CIE ΔE2000 implementation, which operates on two L*a*b* colors, and returns their difference.
# "l" ranges from 0 to 100, while "a" and "b" are unbounded and commonly clamped to the range of -128 to 127.
def ciede_2000(l_1, a_1, b_1, l_2, a_2, b_2) :
from math import pi, sqrt, atan2, sin, exp
# Working in Python with the CIEDE2000 color-difference formula.
# k_l, k_c, k_h are parametric factors to be adjusted according to
# different viewing parameters such as textures, backgrounds...
k_l = k_c = k_h = 1.0
n = (sqrt(a_1 * a_1 + b_1 * b_1) + sqrt(a_2 * a_2 + b_2 * b_2)) * 0.5
n = n * n * n * n * n * n * n
# A factor involving chroma raised to the power of 7 designed to make
# the influence of chroma on the total color difference more accurate.
n = 1.0 + 0.5 * (1.0 - sqrt(n / (n + 6103515625.0)))
# Application of the chroma correction factor.
c_1 = sqrt(a_1 * a_1 * n * n + b_1 * b_1)
c_2 = sqrt(a_2 * a_2 * n * n + b_2 * b_2)
# atan2 is preferred over atan because it accurately computes the angle of
# a point (x, y) in all quadrants, handling the signs of both coordinates.
h_1 = atan2(b_1, a_1 * n)
h_2 = atan2(b_2, a_2 * n)
h_1 += 2.0 * pi * (h_1 < 0.0)
h_2 += 2.0 * pi * (h_2 < 0.0)
n = abs(h_2 - h_1)
# Cross-implementation consistent rounding.
if pi - 1E-14 < n and n < pi + 1E-14 :
n = pi
# When the hue angles lie in different quadrants, the straightforward
# average can produce a mean that incorrectly suggests a hue angle in
# the wrong quadrant, the next lines handle this issue.
h_m = (h_1 + h_2) * 0.5
h_d = (h_2 - h_1) * 0.5
if pi < n :
h_d += pi
# 📜 Sharma’s formulation doesn’t use the next line, but the one after it,
# and these two variants differ by ±0.0003 on the final color differences.
h_m += pi
# h_m += pi if h_m < pi else -pi
p = 36.0 * h_m - 55.0 * pi
n = (c_1 + c_2) * 0.5
n = n * n * n * n * n * n * n
# The hue rotation correction term is designed to account for the
# non-linear behavior of hue differences in the blue region.
r_t = -2.0 * sqrt(n / (n + 6103515625.0)) \
* sin(pi / 3.0 * exp(p * p / (-25.0 * pi * pi)))
n = (l_1 + l_2) * 0.5
n = (n - 50.0) * (n - 50.0)
# Lightness.
l = (l_2 - l_1) / (k_l * (1.0 + 0.015 * n / sqrt(20.0 + n)))
# These coefficients adjust the impact of different harmonic
# components on the hue difference calculation.
t = 1.0 + 0.24 * sin(2.0 * h_m + pi * 0.5) \
+ 0.32 * sin(3.0 * h_m + 8.0 * pi / 15.0) \
- 0.17 * sin(h_m + pi / 3.0) \
- 0.20 * sin(4.0 * h_m + 3.0 * pi / 20.0)
n = c_1 + c_2
# Hue.
h = 2.0 * sqrt(c_1 * c_2) * sin(h_d) / (k_h * (1.0 + 0.0075 * n * t))
# Chroma.
c = (c_2 - c_1) / (k_c * (1.0 + 0.0225 * n))
# Returning the square root ensures that dE00 accurately reflects the
# geometric distance in color space, which can range from 0 to around 185.
return sqrt(l * l + h * h + c * c + c * h * r_t)
# GitHub Project : https://github.com/michel-leonard/ciede2000-color-matching
# Online Tests : https://michel-leonard.github.io/ciede2000-color-matching
# L1 = 14.1 a1 = 30.2 b1 = 2.9
# L2 = 14.4 a2 = 26.1 b2 = -2.2
# CIE ΔE00 = 3.6838696733 (Bruce Lindbloom, Netflix’s VMAF, ...)
# CIE ΔE00 = 3.6838829685 (Gaurav Sharma, OpenJDK, ...)
# Deviation between implementations ≈ 1.3e-5
# See the source code comments for easy switching between these two widely used ΔE*00 implementation variants.
###############################################
###############################################
####### #######
####### CIEDE 2000 #######
####### Testing Random Colors #######
####### #######
###############################################
###############################################
# This Python program outputs a CSV file to standard output, with its length determined by the first CLI argument.
# Each line contains seven columns :
# - Three columns for the random standard L*a*b* color
# - Three columns for the random sample L*a*b* color
# - And the seventh column for the precise Delta E 2000 color difference between the standard and sample
# The output will be correct, this can be verified :
# - With the C driver, which provides a dedicated verification feature
# - By using the JavaScript validator at https://michel-leonard.github.io/ciede2000-color-matching
import sys
import random
# Generate a random float rounded to 0, 1, or 2 decimal places
def random_lab_value(min_val, max_val):
value = random.uniform(min_val, max_val)
decimals = random.choice([0, 1, 2])
return round(value, decimals)
# Parse command-line argument for n_iterations
n_iterations = int(sys.argv[1]) if 1 < len(sys.argv) and sys.argv[1].rstrip('0123456789') == '' else 10000
n_iterations = n_iterations if 0 < n_iterations else 10000
for _ in range(n_iterations):
# Generate random LAB values with random rounding
l_1 = random_lab_value(0.0, 100.0)
a_1 = random_lab_value(-128.0, 128.0)
b_1 = random_lab_value(-128.0, 128.0)
l_2 = random_lab_value(0.0, 100.0)
a_2 = random_lab_value(-128.0, 128.0)
b_2 = random_lab_value(-128.0, 128.0)
delta_e = ciede_2000(l_1, a_1, b_1, l_2, a_2, b_2)
# Output values in CSV format
print(f'{l_1},{a_1},{b_1},{l_2},{a_2},{b_2},{delta_e}')