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ascii-video

将视频或图片转换为彩色字符艺术,也可制作随音乐变化的动画。支持叠加歌词和文字、调整颜色与字符、组合视觉效果,并导出MP4、GIF或连续图片。

原文查看原文说明

ASCII video: convert video/audio to colored ASCII MP4/GIF.

集成工具ElevenLabs
AI 评分80分/ 100查看安装方法

适用场景

  • 将视频转换为彩色字符艺术
  • 制作随音乐变化的视觉画面
  • 制作带歌词的视频
  • 通过计算生成字符动画

中文介绍

功能

将视频或图片转换为ASCII艺术视频,用字符表现形状和明暗。支持随音乐强弱和频率变化的画面、无需素材而通过计算生成的动画,以及视频与音频响应相结合的表现形式。可显示歌词和文字,调整颜色与字符种类,组合粒子、残影及场景切换,导出MP4、GIF或PNG图片序列。

适用场景

适合制作类似终端界面的字符画面、复古风格视频、音乐可视化画面,或以文字为主角的实验作品。制作时先确定氛围、颜色和场景走向,再编写Python脚本,并通过关键时刻的静态画面检查亮度与构图。

使用示例

  • “请把这个视频转换为彩色ASCII艺术,并导出MP4。”
  • “请制作随这段音乐变化的字符动画。”
  • “请制作歌词随音频同步出现的字符艺术视频。”

注意事项

使用Python 3.10或更高版本、NumPy、Pillow和ffmpeg,音频分析使用SciPy。无需GPU,支持linux、macos和windows。语音朗读可选用ElevenLabs API。字符能否显示取决于字体,须确认字体支持所用字符。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好列出ASCII视频用途,并用表格说明各模式的输入与输出。
步骤具体性步骤、输入输出和示例是否具体
A良好说明从输入到编码的六个阶段、使用工具及视觉效果选项。
安全性发送数据、删除文件、执行命令等是否有说明
B部分不足说明执行ffmpeg及可选的外部API调用,但未提供输出位置的确认步骤。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足列出Python 3.10以上版本及所需库,但未提供安装步骤。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(10)

  • SKILL.md14.7 KB
  • README.md16.1 KB
  • references/architecture.md32.2 KB
  • references/composition.md34.1 KB
  • references/effects.md71.2 KB
  • references/inputs.md24.0 KB
  • references/optimization.md23.7 KB
  • references/scenes.md36.6 KB
  • references/shaders.md49.3 KB
  • references/troubleshooting.md12.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

ASCII Video Production Pipeline

When to use

Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.

What's inside

Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering.

Creative Standard

This is visual art. ASCII characters are the medium; cinema is the standard.

Before writing a single line of code, articulate the creative concept. What is the mood? What visual story does this tell? What makes THIS project different from every other ASCII video? The user's prompt is a starting point — interpret it with creative ambition, not literal transcription.

First-render excellence is non-negotiable. The output must be visually striking without requiring revision rounds. If something looks generic, flat, or like "AI-generated ASCII art," it is wrong — rethink the creative concept before shipping.

Go beyond the reference vocabulary. The effect catalogs, shader presets, and palette libraries in the references are a starting vocabulary. For every project, combine, modify, and invent new patterns. The catalog is a palette of paints — you write the painting.

Be proactively creative. Extend the skill's vocabulary when the project calls for it. If the references don't have what the vision demands, build it. Include at least one visual moment the user didn't ask for but will appreciate — a transition, an effect, a color choice that elevates the whole piece.

Cohesive aesthetic over technical correctness. All scenes in a video must feel connected by a unifying visual language — shared color temperature, related character palettes, consistent motion vocabulary. A technically correct video where every scene uses a random different effect is an aesthetic failure.

Dense, layered, considered. Every frame should reward viewing. Never flat black backgrounds. Always multi-grid composition. Always per-scene variation. Always intentional color.

Modes

ModeInputOutputReference
Video-to-ASCIIVideo fileASCII recreation of source footagereferences/inputs.md § Video Sampling
Audio-reactiveAudio fileGenerative visuals driven by audio featuresreferences/inputs.md § Audio Analysis
GenerativeNone (or seed params)Procedural ASCII animationreferences/effects.md
HybridVideo + audioASCII video with audio-reactive overlaysBoth input refs
Lyrics/textAudio + text/SRTTimed text with visual effectsreferences/inputs.md § Text/Lyrics
TTS narrationText quotes + TTS APINarrated testimonial/quote video with typed textreferences/inputs.md § TTS Integration

Stack

Single self-contained Python script per project. No GPU required.

LayerToolPurpose
CorePython 3.10+, NumPyMath, array ops, vectorized effects
SignalSciPyFFT, peak detection (audio modes)
ImagingPillow (PIL)Font rasterization, frame decoding, image I/O
Video I/Offmpeg (CLI)Decode input, encode output, mux audio
Parallelconcurrent.futuresN workers for batch/clip rendering
TTSElevenLabs API (optional)Generate narration clips
OptionalOpenCVVideo frame sampling, edge detection

Pipeline Architecture

Every mode follows the same 6-stage pipeline:

INPUT → ANALYZE → SCENE_FN → TONEMAP → SHADE → ENCODE
  1. INPUT — Load/decode source material (video frames, audio samples, images, or nothing)
  2. ANALYZE — Extract per-frame features (audio bands, video luminance/edges, motion vectors)
  3. SCENE_FN — Scene function renders to pixel canvas (uint8 H,W,3). Composes multiple character grids via _render_vf() + pixel blend modes. See references/composition.md
  4. TONEMAP — Percentile-based adaptive brightness normalization. See references/composition.md § Adaptive Tonemap
  5. SHADE — Post-processing via ShaderChain + FeedbackBuffer. See references/shaders.md
  6. ENCODE — Pipe raw RGB frames to ffmpeg for H.264/GIF encoding

Creative Direction

Aesthetic Dimensions

DimensionOptionsReference
Character paletteDensity ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), project-specificarchitecture.md § Palettes
Color strategyHSV, OKLAB/OKLCH, discrete RGB palettes, auto-generated harmony, monochrome, temperaturearchitecture.md § Color System
Background textureSine fields, fBM noise, domain warp, voronoi, reaction-diffusion, cellular automata, videoeffects.md
Primary effectsRings, spirals, tunnel, vortex, waves, interference, aurora, fire, SDFs, strange attractorseffects.md
ParticlesSparks, snow, rain, bubbles, runes, orbits, flocking boids, flow-field followers, trailseffects.md § Particles
Shader moodRetro CRT, clean modern, glitch art, cinematic, dreamy, industrial, psychedelicshaders.md
Grid densityxs(8px) through xxl(40px), mixed per layerarchitecture.md § Grid System
Coordinate spaceCartesian, polar, tiled, rotated, fisheye, Möbius, domain-warpedeffects.md § Transforms
FeedbackZoom tunnel, rainbow trails, ghostly echo, rotating mandala, color evolutioncomposition.md § Feedback
MaskingCircle, ring, gradient, text stencil, animated iris/wipe/dissolvecomposition.md § Masking
TransitionsCrossfade, wipe, dissolve, glitch cut, iris, mask-based revealshaders.md § Transitions

Per-Section Variation

Never use the same config for the entire video. For each section/scene:

  • Different background effect (or compose 2-3)
  • Different character palette (match the mood)
  • Different color strategy (or at minimum a different hue)
  • Vary shader intensity (more bloom during peaks, more grain during quiet)
  • Different particle types if particles are active

Project-Specific Invention

For every project, invent at least one of:

  • A custom character palette matching the theme
  • A custom background effect (combine/modify existing building blocks)
  • A custom color palette (discrete RGB set matching the brand/mood)
  • A custom particle character set
  • A novel scene transition or visual moment

Don't just pick from the catalog. The catalog is vocabulary — you write the poem.

Workflow

Step 1: Creative Vision

Before any code, articulate the creative concept:

  • Mood/atmosphere: What should the viewer feel? Energetic, meditative, chaotic, elegant, ominous?
  • Visual story: What happens over the duration? Build tension? Transform? Dissolve?
  • Color world: Warm/cool? Monochrome? Neon? Earth tones? What's the dominant hue?
  • Character texture: Dense data? Sparse stars? Organic dots? Geometric blocks?
  • What makes THIS different: What's the one thing that makes this project unique?
  • Emotional arc: How do scenes progress? Open with energy, build to climax, resolve?

Map the user's prompt to aesthetic choices. A "chill lo-fi visualizer" demands different everything from a "glitch cyberpunk data stream."

Step 2: Technical Design

  • Mode — which of the 6 modes above
  • Resolution — landscape 1920x1080 (default), portrait 1080x1920, square 1080x1080 @ 24fps
  • Hardware detection — auto-detect cores/RAM, set quality profile. See references/optimization.md
  • Sections — map timestamps to scene functions, each with its own effect/palette/color/shader config
  • Output format — MP4 (default), GIF (640x360 @ 15fps), PNG sequence

Step 3: Build the Script

Single Python file. Components (with references):

  1. Hardware detection + quality profile — references/optimization.md
  2. Input loader — mode-dependent; references/inputs.md
  3. Feature analyzer — audio FFT, video luminance, or synthetic
  4. Grid + renderer — multi-density grids with bitmap cache; references/architecture.md
  5. Character palettes — multiple per project; references/architecture.md § Palettes
  6. Color system — HSV + discrete RGB + harmony generation; references/architecture.md § Color
  7. Scene functions — each returns canvas (uint8 H,W,3); references/scenes.md
  8. Tonemap — adaptive brightness normalization; references/composition.md
  9. Shader pipeline — ShaderChain + FeedbackBuffer; references/shaders.md
  10. Scene table + dispatcher — time → scene function + config; references/scenes.md
  11. Parallel encoder — N-worker clip rendering with ffmpeg pipes
  12. Main — orchestrate full pipeline

Step 4: Quality Verification

  • Test frames first: render single frames at key timestamps before full render
  • Brightness check: canvas.mean() > 8 for all ASCII content. If dark, lower gamma
  • Visual coherence: do all scenes feel like they belong to the same video?
  • Creative vision check: does the output match the concept from Step 1? If it looks generic, go back

Critical Implementation Notes

Brightness — Use tonemap(), Not Linear Multipliers

This is the #1 visual issue. ASCII on black is inherently dark. Never use canvas * N multipliers — they clip highlights. Use adaptive tonemap:

def tonemap(canvas, gamma=0.75):
    f = canvas.astype(np.float32)
    lo, hi = np.percentile(f[::4, ::4], [1, 99.5])
    if hi - lo < 10: hi = lo + 10
    f = np.clip((f - lo) / (hi - lo), 0, 1) ** gamma
    return (f * 255).astype(np.uint8)

Pipeline: scene_fn() → tonemap() → FeedbackBuffer → ShaderChain → ffmpeg

Per-scene gamma: default 0.75, solarize 0.55, posterize 0.50, bright scenes 0.85. Use screen blend (not overlay) for dark layers.

Font Cell Height

macOS Pillow: textbbox() returns wrong height. Use font.getmetrics(): cell_height = ascent + descent. See references/troubleshooting.md.

ffmpeg Pipe Deadlock

Never stderr=subprocess.PIPE with long-running ffmpeg — buffer fills at 64KB and deadlocks. Redirect to file. See references/troubleshooting.md.

Font Compatibility

Not all Unicode chars render in all fonts. Validate palettes at init — render each char, check for blank output. See references/troubleshooting.md.

Per-Clip Architecture

For segmented videos (quotes, scenes, chapters), render each as a separate clip file for parallel rendering and selective re-rendering. See references/scenes.md.

Performance Targets

ComponentBudget
Feature extraction1-5ms
Effect function2-15ms
Character render80-150ms (bottleneck)
Shader pipeline5-25ms
Total~100-200ms/frame

References

FileContents
references/architecture.mdGrid system, resolution presets, font selection, character palettes (20+), color system (HSV + OKLAB + discrete RGB + harmony generation), _render_vf() helper, GridLayer class
references/composition.mdPixel blend modes (20 modes), blend_canvas(), multi-grid composition, adaptive tonemap(), FeedbackBuffer, PixelBlendStack, masking/stencil system
references/effects.mdEffect building blocks: value field generators, hue fields, noise/fBM/domain warp, voronoi, reaction-diffusion, cellular automata, SDFs, strange attractors, particle systems, coordinate transforms, temporal coherence
references/shaders.mdShaderChain, _apply_shader_step() dispatch, 38 shader catalog, audio-reactive scaling, transitions, tint presets, output format encoding, terminal rendering
references/scenes.mdScene protocol, Renderer class, SCENES table, render_clip(), beat-synced cutting, parallel rendering, design patterns (layer hierarchy, directional arcs, visual metaphors, compositional techniques), complete scene examples at every complexity level, scene design checklist
references/inputs.mdAudio analysis (FFT, bands, beats), video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing)
references/optimization.mdHardware detection, quality profiles, vectorized patterns, parallel rendering, memory management, performance budgets
references/troubleshooting.mdNumPy broadcasting traps, blend mode pitfalls, multiprocessing/pickling, brightness diagnostics, ffmpeg issues, font problems, common mistakes

Creative Divergence (use only when user requests experimental/creative/unique output)

If the user asks for creative, experimental, surprising, or unconventional output, select the strategy that best fits and reason through its steps BEFORE generating code.

  • Forced Connections — when the user wants cross-domain inspiration ("make it look organic," "industrial aesthetic")
  • Conceptual Blending — when the user names two things to combine ("ocean meets music," "space + calligraphy")
  • Oblique Strategies — when the user is maximally open ("surprise me," "something I've never seen")

Forced Connections

  1. Pick a domain unrelated to the visual goal (weather systems, microbiology, architecture, fluid dynamics, textile weaving)
  2. List its core visual/structural elements (erosion → gradual reveal; mitosis → splitting duplication; weaving → interlocking patterns)
  3. Map those elements onto ASCII characters and animation patterns
  4. Synthesize — what does "erosion" or "crystallization" look like in a character grid?

Conceptual Blending

  1. Name two distinct visual/conceptual spaces (e.g., ocean waves + sheet music)
  2. Map correspondences (crests = high notes, troughs = rests, foam = staccato)
  3. Blend selectively — keep the most interesting mappings, discard forced ones
  4. Develop emergent properties that exist only in the blend

Oblique Strategies

  1. Draw one: "Honor thy error as a hidden intention" / "Use an old idea" / "What would your closest friend do?" / "Emphasize the flaws" / "Turn it upside down" / "Only a part, not the whole" / "Reverse"
  2. Interpret the directive against the current ASCII animation challenge
  3. Apply the lateral insight to the visual design before writing code

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