Color Difference Calculations

def delta_e_cie1976(color1, color2):
    """
    Calculate CIE 1976 Delta E difference.
    
    Parameters:
    - color1: First color object (Lab, Luv, or other color space)
    - color2: Second color object (same type as color1)
    
    Returns:
    float: Delta E difference value (typically 0-100+)
    
    Notes:
    - Simple Euclidean distance in Lab space
    - Values < 1: Not perceptible by human eye
    - Values 1-2: Perceptible through close observation
    - Values 2-10: Perceptible at a glance
    - Values > 10: Colors appear as different colors
    """

def delta_e_cie1994(color1, color2, K_L=1, K_C=1, K_H=1, K_1=0.045, K_2=0.015):
    """
    Calculate CIE 1994 Delta E difference.
    
    Parameters:
    - color1: First color object (Lab color recommended)
    - color2: Second color object (same type as color1)
    - K_L: Lightness weighting factor (default: 1)
    - K_C: Chroma weighting factor (default: 1)
    - K_H: Hue weighting factor (default: 1)
    - K_1: First parametric factor (default: 0.045)
    - K_2: Second parametric factor (default: 0.015)
    
    Returns:
    float: Delta E difference value
    
    Notes:
    - Better perceptual uniformity than CIE 1976
    - Separate weighting for lightness, chroma, and hue
    - Standard conditions: K_L=K_C=K_H=1
    - Textiles: K_L=2, K_C=K_H=1
    """

def delta_e_cie2000(color1, color2, Kl=1, Kc=1, Kh=1):
    """
    Calculate CIE 2000 Delta E difference.
    
    Parameters:
    - color1: First color object (Lab color recommended)
    - color2: Second color object (same type as color1)
    - Kl: Lightness weighting factor (default: 1)
    - Kc: Chroma weighting factor (default: 1)
    - Kh: Hue weighting factor (default: 1)
    
    Returns:
    float: Delta E difference value
    
    Notes:
    - Most perceptually uniform Delta E formula
    - Addresses issues with CIE 1994 in blue region
    - Industry standard for color quality control
    - Recommended for most applications
    - Values < 1: Not perceptible
    - Values 1-2: Just perceptible
    - Values 2-5: Perceptible
    - Values > 5: Well perceptible
    """

def delta_e_cmc(color1, color2, pl=2, pc=1):
    """
    Calculate CMC Delta E difference.
    
    Parameters:
    - color1: First color object (Lab color recommended)
    - color2: Second color object (same type as color1)
    - pl: Lightness tolerance factor (default: 2)
    - pc: Chroma tolerance factor (default: 1)
    
    Returns:
    float: Delta E difference value
    
    Notes:
    - Designed for textile industry applications
    - Standard acceptability: pl=2, pc=1 (CMC 2:1)
    - Perceptibility threshold: pl=1, pc=1 (CMC 1:1)
    - Good performance in neutral colors
    """

from colormath.color_objects import LabColor
from colormath.color_diff import delta_e_cie2000

# Create two Lab colors
color1 = LabColor(lab_l=50.0, lab_a=20.0, lab_b=-30.0)
color2 = LabColor(lab_l=52.0, lab_a=18.0, lab_b=-28.0)

# Calculate Delta E 2000 (recommended)
diff = delta_e_cie2000(color1, color2)
print(f"Delta E 2000: {diff:.2f}")

from colormath.color_objects import LabColor
from colormath.color_diff import (
    delta_e_cie1976, delta_e_cie1994, 
    delta_e_cie2000, delta_e_cmc
)

color1 = LabColor(lab_l=50, lab_a=0, lab_b=0)
color2 = LabColor(lab_l=52, lab_a=2, lab_b=-1)

# Compare all methods
diff_1976 = delta_e_cie1976(color1, color2)
diff_1994 = delta_e_cie1994(color1, color2)
diff_2000 = delta_e_cie2000(color1, color2)
diff_cmc = delta_e_cmc(color1, color2)

print(f"CIE 1976: {diff_1976:.2f}")
print(f"CIE 1994: {diff_1994:.2f}")
print(f"CIE 2000: {diff_2000:.2f}")
print(f"CMC:      {diff_cmc:.2f}")

from colormath.color_objects import sRGBColor, LabColor
from colormath.color_conversions import convert_color
from colormath.color_diff import delta_e_cie2000

# Create RGB colors
rgb1 = sRGBColor(rgb_r=0.8, rgb_g=0.2, rgb_b=0.1)
rgb2 = sRGBColor(rgb_r=0.85, rgb_g=0.15, rgb_b=0.12)

# Convert to Lab for Delta E calculation
lab1 = convert_color(rgb1, LabColor)
lab2 = convert_color(rgb2, LabColor)

# Calculate difference
diff = delta_e_cie2000(lab1, lab2)
print(f"RGB color difference: {diff:.2f}")

from colormath.color_objects import LabColor
from colormath.color_diff import delta_e_cie1994, delta_e_cmc

color_standard = LabColor(lab_l=50, lab_a=10, lab_b=-5)
color_sample = LabColor(lab_l=51, lab_a=12, lab_b=-3)

# Graphic arts (standard conditions)
diff_graphics = delta_e_cie1994(color_standard, color_sample)

# Textiles (K_L=2 for textile industry)
diff_textiles = delta_e_cie1994(color_standard, color_sample, K_L=2)

# CMC for acceptability (2:1 ratio)
diff_cmc_accept = delta_e_cmc(color_standard, color_sample, pl=2, pc=1)

# CMC for perceptibility (1:1 ratio)
diff_cmc_percept = delta_e_cmc(color_standard, color_sample, pl=1, pc=1)

print(f"Graphics:      {diff_graphics:.2f}")
print(f"Textiles:      {diff_textiles:.2f}")
print(f"CMC Accept:    {diff_cmc_accept:.2f}")
print(f"CMC Percept:   {diff_cmc_percept:.2f}")

from colormath.color_objects import LabColor
from colormath.color_diff import delta_e_cie2000

# Reference color
reference = LabColor(lab_l=50, lab_a=0, lab_b=0)

# Sample colors to compare
samples = [
    LabColor(lab_l=52, lab_a=2, lab_b=-1),
    LabColor(lab_l=48, lab_a=-1, lab_b=3),
    LabColor(lab_l=50, lab_a=5, lab_b=-2),
    LabColor(lab_l=53, lab_a=-3, lab_b=4)
]

# Calculate differences
differences = [delta_e_cie2000(reference, sample) for sample in samples]

# Analyze results
for i, diff in enumerate(differences):
    status = "PASS" if diff < 2.0 else "FAIL"
    print(f"Sample {i+1}: ΔE = {diff:.2f} [{status}]")

from colormath.color_objects import LabColor
from colormath.color_diff import delta_e_cie2000

def assess_color_quality(standard_color, test_color, tolerance=2.0):
    """
    Assess color quality against standard.
    
    Parameters:
    - standard_color: Reference color
    - test_color: Color to test
    - tolerance: Maximum acceptable Delta E
    
    Returns:
    dict: Quality assessment results
    """
    diff = delta_e_cie2000(standard_color, test_color)
    
    if diff <= tolerance * 0.5:
        grade = "Excellent"
    elif diff <= tolerance:
        grade = "Acceptable"
    else:
        grade = "Reject"
    
    return {
        'delta_e': diff,
        'grade': grade,
        'pass': diff <= tolerance,
        'tolerance': tolerance
    }

# Example usage
standard = LabColor(lab_l=50, lab_a=10, lab_b=-10)
test_sample = LabColor(lab_l=51, lab_a=11, lab_b=-9)

result = assess_color_quality(standard, test_sample)
print(f"Grade: {result['grade']}")
print(f"Delta E: {result['delta_e']:.2f}")
print(f"Pass: {result['pass']}")