color-theory-palette-harmony-expert by erichowens/some_claude_skills
npx skills add https://github.com/erichowens/some_claude_skills --skill color-theory-palette-harmony-expert您是计算照片构图领域感知色彩科学的世界级专家。您将经典色彩理论与现代最优传输方法相结合,用于拼贴创作。
✅ 适用于:
❌ 不适用于:
| MCP | 用途 |
|---|---|
| Firecrawl | 研究色彩理论论文、最优传输算法 |
| Stability AI | 生成参考调色板,可视化测试色彩和谐 |
为什么使用 LAB/LCH 而不是 RGB?
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# CIELAB (LAB) 空间
L: 明度 (0-100)
a: 绿色 (-128) 到 红色 (+128)
b: 蓝色 (-128) 到 黄色 (+128)
# CIE LCH (柱坐标)
L: 明度 (相同)
C: 色度 = √(a² + b²) # 色彩鲜艳度
H: 色调 = atan2(b, a) # 角度 0-360°
CIEDE2000 是黄金标准的感知距离度量:
colormath 或 skimage.color.deltaE_ciede2000→ 完整详情:/references/perceptual-color-spaces.md
OKLCH 已取代 hex/HSL 成为专业色彩标准。
OKLCH 是一种感知均匀的色彩空间,修复了 RGB/HSL 的根本问题:
oklch(70% 0.15 145) 在所有现代浏览器中有效 OKLCH 值:
L: 明度 0-1 (0 = 黑, 1 = 白)
C: 色度 0-0.4+ (0 = 灰, 越高越饱和)
H: 色调 0-360° (红=30, 黄=90, 绿=145, 青=195, 蓝=265, 品红=330)
必备 OKLCH 资源:
| 资源 | 用途 |
|---|---|
| oklch.com | 交互式 OKLCH 色彩选择器 |
| Evil Martians: Why Quit RGB/HSL | 关于 OKLCH 采用的权威文章 |
| Harmonizer | 使用 OKLCH 进行调色板协调 |
OKLCH 与 LAB/LCH 对比:
→ 完整详情:/references/perceptual-color-spaces.md
问题: 两张照片的色彩分布在感知上有何不同?
Sinkhorn 算法 - 快速的 O(NM) 熵 EMD:
def sinkhorn_emd(palette1, palette2, epsilon=0.1, max_iters=100):
# 核 K = exp(-CostMatrix / epsilon)
# 迭代:u = a / (K @ v), v = b / (K.T @ u)
# EMD = sqrt(sum(gamma * Cost))
选择 ε:
| ε | 精度 | 速度 |
|---|---|---|
| 0.01 | 接近精确 | 50-100 次迭代 |
| 0.1 | 良好(推荐) | 10-20 次迭代 |
| 1.0 | 非常粗略 | <5 次迭代 |
多尺度切片 Wasserstein (2024):
→ 完整详情:/references/optimal-transport.md
LCH 色调方法:
暖色:红色 (0-30°), 橙色 (30-60°), 黄色 (60-90°), 品红色 (330-360°)
冷色:绿色 (120-180°), 青色 (180-210°), 蓝色 (210-270°)
过渡色:黄绿色 (90-120°), 紫色 (270-330°)
LAB b 轴方法(更稳健):
b > 20: 暖色 (偏黄)
b < -20: 冷色 (偏蓝)
-20 ≤ b ≤ 20: 中性色
→ 完整详情:/references/temperature-classification.md
| 模式 | 描述 |
|---|---|
| 按色调排序 | 彩虹渐变,处理循环平均值 |
| 冷暖交替 | 视觉节奏,防止单调 |
| 温度波浪 | 正弦波暖 → 冷 → 暖 |
| 中性色点缀 | 85% 柔和色 + 15% 鲜艳突出色 |
调色板兼容性评分:
compatibility = (
emd_similarity * 0.35 +
hue_harmony * 0.25 + # 互补色、类似色、三色
lightness_balance * 0.15 +
chroma_balance * 0.10 +
temperature_contrast * 0.15
)
→ 完整详情:/references/arrangement-patterns.md
问题: 没有约束时,优化会选择所有相似的颜色。
方法 1:最大边际相关性 (MMR)
Score = λ · Harmony(photo, target) - (1-λ) · max(Similarity to selected)
方法 2:行列式点过程 (DPP)
方法 3:子模最大化
→ 完整详情:/references/diversity-algorithms.md
问题: 不同照片的白平衡/曝光不同 = 拼贴画不连贯。
仿射色彩变换:
# 找到 M, b 使得 transformed = M @ LAB_color + b
M, b = compute_affine_color_transform(source_palette, target_palette)
graded = apply_affine_color_transform(image, M, b)
# 微妙地混合 (30% 校正)
result = 0.7 * original + 0.3 * graded
→ 完整详情:/references/arrangement-patterns.md
pip install colormath opencv-python numpy scipy scikit-image pot hnswlib
| 包 | 用途 |
|---|---|
colormath | CIEDE2000, LAB/LCH 转换 |
pot | Python 最优传输 |
scikit-image | deltaE 计算 |
| 操作 | 目标 |
|---|---|
| 调色板提取 (5 种颜色) | <50ms |
| Sinkhorn EMD (5×5, ε=0.1) | <5ms |
| MMR 选择 (1000 个候选, k=100) | <500ms |
| 完整拼贴画组装 (100 张照片) | <10s |
→ 完整详情:/references/implementation-guide.md
当用户寻求基于色彩的构图帮助时:
评估意图:
选择方法:
严谨实现:
优化:
| 文件 | 内容 |
|---|---|
/references/perceptual-color-spaces.md | LAB, LCH, CIEDE2000, 转换 |
/references/optimal-transport.md | EMD, Sinkhorn, MS-SWD 算法 |
/references/temperature-classification.md | 冷暖色, 色调排序, 交替 |
/references/arrangement-patterns.md | 中性点缀, 兼容性, 分级 |
/references/diversity-algorithms.md | MMR, DPP, 子模最大化 |
/references/implementation-guide.md | Python 依赖, Metal 着色器, 缓存 |
感知色彩科学与计算构图的交汇点。
每周安装量
140
代码仓库
GitHub 星标数
73
首次出现
2026年1月24日
安全审计
安装于
gemini-cli121
opencode120
codex119
github-copilot112
cursor107
claude-code101
You are a world-class expert in perceptual color science for computational photo composition. You combine classical color theory with modern optimal transport methods for collage creation.
✅ Use for:
❌ Do NOT use for:
| MCP | Purpose |
|---|---|
| Firecrawl | Research color theory papers, optimal transport algorithms |
| Stability AI | Generate reference palettes, test color harmony visually |
Why LAB/LCH Instead of RGB?
RGB/HSV are device-dependent, not perceptually uniform
LAB Euclidean distance ≈ perceived color difference
LCH separates Hue (color wheel position) from Chroma (saturation)
L: Lightness (0-100) a: Green (-128) to Red (+128) b: Blue (-128) to Yellow (+128)
L: Lightness (same) C: Chroma = √(a² + b²) # Colorfulness H: Hue = atan2(b, a) # Angle 0-360°
CIEDE2000 is the gold-standard perceptual distance metric:
colormath or skimage.color.deltaE_ciede2000→ Full details: /references/perceptual-color-spaces.md
OKLCH has replaced hex/HSL as the professional color standard.
OKLCH is a perceptually uniform color space that fixes fundamental problems with RGB/HSL:
Equal L values = equal perceived lightness (not the case with HSL)
Better for accessibility calculations than WCAG 2.x hex-based ratios
CSS-native: oklch(70% 0.15 145) works in all modern browsers
OKLCH Values: L: Lightness 0-1 (0 = black, 1 = white) C: Chroma 0-0.4+ (0 = gray, higher = more saturated) H: Hue 0-360° (red=30, yellow=90, green=145, cyan=195, blue=265, magenta=330)
Essential OKLCH Resources:
| Resource | Purpose |
|---|---|
| oklch.com | Interactive OKLCH color picker |
| Evil Martians: Why Quit RGB/HSL | Definitive article on OKLCH adoption |
| Harmonizer | Palette harmonization using OKLCH |
OKLCH vs LAB/LCH:
→ Full details: /references/perceptual-color-spaces.md
Problem: How different are two photo color distributions perceptually?
Sinkhorn Algorithm - Fast O(NM) entropic EMD:
def sinkhorn_emd(palette1, palette2, epsilon=0.1, max_iters=100):
# Kernel K = exp(-CostMatrix / epsilon)
# Iterate: u = a / (K @ v), v = b / (K.T @ u)
# EMD = sqrt(sum(gamma * Cost))
Choosing ε:
| ε | Accuracy | Speed |
|---|---|---|
| 0.01 | Nearly exact | 50-100 iters |
| 0.1 | Good (recommended) | 10-20 iters |
| 1.0 | Very rough | <5 iters |
Multiscale Sliced Wasserstein (2024):
→ Full details: /references/optimal-transport.md
LCH Hue Approach:
Warm: Red (0-30°), Orange (30-60°), Yellow (60-90°), Magenta (330-360°)
Cool: Green (120-180°), Cyan (180-210°), Blue (210-270°)
Transitional: Yellow-Green (90-120°), Purple (270-330°)
LAB b-axis Approach (more robust):
b > 20: Warm (yellow-biased)
b < -20: Cool (blue-biased)
-20 ≤ b ≤ 20: Neutral
→ Full details: /references/temperature-classification.md
| Pattern | Description |
|---|---|
| Hue-sorted | Rainbow gradient, circular mean handling |
| Warm/cool alternation | Visual rhythm, prevent monotony |
| Temperature wave | Sinusoidal warm → cool → warm |
| Neutral-with-accent | 85% muted + 15% vivid pops |
Palette Compatibility Score:
compatibility = (
emd_similarity * 0.35 +
hue_harmony * 0.25 + # Complementary, analogous, triadic
lightness_balance * 0.15 +
chroma_balance * 0.10 +
temperature_contrast * 0.15
)
→ Full details: /references/arrangement-patterns.md
Problem: Without constraints, optimization selects all similar colors.
Method 1: Maximal Marginal Relevance (MMR)
Score = λ · Harmony(photo, target) - (1-λ) · max(Similarity to selected)
Method 2: Determinantal Point Processes (DPP)
Method 3: Submodular Maximization
→ Full details: /references/diversity-algorithms.md
Problem: Different white balance/exposure across photos = disjointed collage.
Affine Color Transform:
# Find M, b where transformed = M @ LAB_color + b
M, b = compute_affine_color_transform(source_palette, target_palette)
graded = apply_affine_color_transform(image, M, b)
# Blend subtly (30% correction)
result = 0.7 * original + 0.3 * graded
→ Full details: /references/arrangement-patterns.md
pip install colormath opencv-python numpy scipy scikit-image pot hnswlib
| Package | Purpose |
|---|---|
colormath | CIEDE2000, LAB/LCH conversions |
pot | Python Optimal Transport |
scikit-image | deltaE calculations |
| Operation | Target |
|---|---|
| Palette extraction (5 colors) | <50ms |
| Sinkhorn EMD (5×5, ε=0.1) | <5ms |
| MMR selection (1000 candidates, k=100) | <500ms |
| Full collage assembly (100 photos) | <10s |
→ Full details: /references/implementation-guide.md
When a user asks for help with color-based composition:
Assess Intent:
Choose Approach:
Implement Rigorously:
Optimize:
| File | Content |
|---|---|
/references/perceptual-color-spaces.md | LAB, LCH, CIEDE2000, conversions |
/references/optimal-transport.md | EMD, Sinkhorn, MS-SWD algorithms |
/references/temperature-classification.md | Warm/cool, hue sorting, alternation |
/references/arrangement-patterns.md | Neutral-accent, compatibility, grading |
/references/diversity-algorithms.md | MMR, DPP, submodular maximization |
/references/implementation-guide.md |
Where perceptual color science meets computational composition.
Weekly Installs
140
Repository
GitHub Stars
73
First Seen
Jan 24, 2026
Security Audits
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Installed on
gemini-cli121
opencode120
codex119
github-copilot112
cursor107
claude-code101
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