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# Architecture Reference
> **See also:** composition.md · effects.md · scenes.md · shaders.md · inputs.md · optimization.md · troubleshooting.md
## Grid System
### Resolution Presets
```python
RESOLUTION_PRESETS = {
"landscape": (1920, 1080), # 16:9 — YouTube, default
"portrait": (1080, 1920), # 9:16 — TikTok, Reels, Stories
"square": (1080, 1080), # 1:1 — Instagram feed
"ultrawide": (2560, 1080), # 21:9 — cinematic
"landscape4k":(3840, 2160), # 16:9 — 4K
"portrait4k": (2160, 3840), # 9:16 — 4K portrait
}
def get_resolution(preset="landscape", custom=None):
"""Returns (VW, VH) tuple."""
if custom:
return custom
return RESOLUTION_PRESETS.get(preset, RESOLUTION_PRESETS["landscape"])
```
### Multi-Density Grids
Pre-initialize multiple grid sizes. Switch per section for visual variety. Grid dimensions auto-compute from resolution:
**Landscape (1920x1080):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| xs | 8 | 400x108 | Ultra-dense data fields |
| sm | 10 | 320x83 | Dense detail, rain, starfields |
| md | 16 | 192x56 | Default balanced, transitions |
| lg | 20 | 160x45 | Quote/lyric text (readable at 1080p) |
| xl | 24 | 137x37 | Short quotes, large titles |
| xxl | 40 | 80x22 | Giant text, minimal |
**Portrait (1080x1920):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| xs | 8 | 225x192 | Ultra-dense, tall data columns |
| sm | 10 | 180x148 | Dense detail, vertical rain |
| md | 16 | 112x100 | Default balanced |
| lg | 20 | 90x80 | Readable text (~30 chars/line centered) |
| xl | 24 | 75x66 | Short quotes, stacked |
| xxl | 40 | 45x39 | Giant text, minimal |
**Square (1080x1080):**
| Key | Font Size | Grid (cols x rows) | Use |
|-----|-----------|-------------------|-----|
| sm | 10 | 180x83 | Dense detail |
| md | 16 | 112x56 | Default balanced |
| lg | 20 | 90x45 | Readable text |
**Key differences in portrait mode:**
- Fewer columns (90 at `lg` vs 160) — lines must be shorter or wrap
- Many more rows (80 at `lg` vs 45) — vertical stacking is natural
- Aspect ratio correction flips: `asp = cw / ch` still works but the visual emphasis is vertical
- Radial effects appear as tall ellipses unless corrected
- Vertical effects (rain, embers, fire columns) are naturally enhanced
- Horizontal effects (spectrum bars, waveforms) need rotation or compression
**Grid sizing for text in portrait**: Use `lg` (20px) for 2-3 word lines. Max comfortable line length is ~25-30 chars. For longer quotes, break aggressively into many short lines stacked vertically — portrait has vertical space to spare. `xl` (24px) works for single words or very short phrases.
Grid dimensions: `cols = VW // cell_width`, `rows = VH // cell_height`.
### Font Selection
Don't hardcode a single font. Choose fonts to match the project's mood. Monospace fonts are required for grid alignment but vary widely in personality:
| Font | Personality | Platform |
|------|-------------|----------|
| Menlo | Clean, neutral, Apple-native | macOS |
| Monaco | Retro terminal, compact | macOS |
| Courier New | Classic typewriter, wide | Cross-platform |
| SF Mono | Modern, tight spacing | macOS |
| Consolas | Windows native, clean | Windows |
| JetBrains Mono | Developer, ligature-ready | Install |
| Fira Code | Geometric, modern | Install |
| IBM Plex Mono | Corporate, authoritative | Install |
| Source Code Pro | Adobe, balanced | Install |
**Font detection at init**: probe available fonts and fall back gracefully:
```python
import platform
def find_font(preferences):
"""Try fonts in order, return first that exists."""
for name, path in preferences:
if os.path.exists(path):
return path
raise FileNotFoundError(f"No monospace font found. Tried: {[p for _,p in preferences]}")
FONT_PREFS_MACOS = [
("Menlo", "/System/Library/Fonts/Menlo.ttc"),
("Monaco", "/System/Library/Fonts/Monaco.ttf"),
("SF Mono", "/System/Library/Fonts/SFNSMono.ttf"),
("Courier", "/System/Library/Fonts/Courier.ttc"),
]
FONT_PREFS_LINUX = [
("DejaVu Sans Mono", "/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf"),
("Liberation Mono", "/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf"),
("Noto Sans Mono", "/usr/share/fonts/truetype/noto/NotoSansMono-Regular.ttf"),
("Ubuntu Mono", "/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf"),
]
FONT_PREFS_WINDOWS = [
("Consolas", r"C:\Windows\Fonts\consola.ttf"),
("Courier New", r"C:\Windows\Fonts\cour.ttf"),
("Lucida Console", r"C:\Windows\Fonts\lucon.ttf"),
("Cascadia Code", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaCode.ttf")),
("Cascadia Mono", os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\Windows\Fonts\CascadiaMono.ttf")),
]
def _get_font_prefs():
s = platform.system()
if s == "Darwin":
return FONT_PREFS_MACOS
elif s == "Windows":
return FONT_PREFS_WINDOWS
return FONT_PREFS_LINUX
FONT_PREFS = _get_font_prefs()
```
**Multi-font rendering**: use different fonts for different layers (e.g., monospace for background, a bolder variant for overlay text). Each GridLayer owns its own font:
```python
grid_bg = GridLayer(find_font(FONT_PREFS), 16) # background
grid_text = GridLayer(find_font(BOLD_PREFS), 20) # readable text
```
### Collecting All Characters
Before initializing grids, gather all characters that need bitmap pre-rasterization:
```python
all_chars = set()
for pal in [PAL_DEFAULT, PAL_DENSE, PAL_BLOCKS, PAL_RUNE, PAL_KATA,
PAL_GREEK, PAL_MATH, PAL_DOTS, PAL_BRAILLE, PAL_STARS,
PAL_HALFFILL, PAL_HATCH, PAL_BINARY, PAL_MUSIC, PAL_BOX,
PAL_CIRCUIT, PAL_ARROWS, PAL_HERMES]: # ... all palettes used in project
all_chars.update(pal)
# Add any overlay text characters
all_chars.update("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 .,-:;!?/|")
all_chars.discard(" ") # space is never rendered
```
### GridLayer Initialization
Each grid pre-computes coordinate arrays for vectorized effect math. The grid automatically adapts to any resolution (landscape, portrait, square):
```python
class GridLayer:
def __init__(self, font_path, font_size, vw=None, vh=None):
"""Initialize grid for any resolution.
vw, vh: video width/height in pixels. Defaults to global VW, VH."""
vw = vw or VW; vh = vh or VH
self.vw = vw; self.vh = vh
self.font = ImageFont.truetype(font_path, font_size)
asc, desc = self.font.getmetrics()
bbox = self.font.getbbox("M")
self.cw = bbox[2] - bbox[0] # character cell width
self.ch = asc + desc # CRITICAL: not textbbox height
self.cols = vw // self.cw
self.rows = vh // self.ch
self.ox = (vw - self.cols * self.cw) // 2 # centering
self.oy = (vh - self.rows * self.ch) // 2
# Aspect ratio metadata
self.aspect = vw / vh # >1 = landscape, <1 = portrait, 1 = square
self.is_portrait = vw < vh
self.is_landscape = vw > vh
# Index arrays
self.rr = np.arange(self.rows, dtype=np.float32)[:, None]
self.cc = np.arange(self.cols, dtype=np.float32)[None, :]
# Polar coordinates (aspect-corrected)
cx, cy = self.cols / 2.0, self.rows / 2.0
asp = self.cw / self.ch
self.dx = self.cc - cx
self.dy = (self.rr - cy) * asp
self.dist = np.sqrt(self.dx**2 + self.dy**2)
self.angle = np.arctan2(self.dy, self.dx)
# Normalized (0-1 range) -- for distance falloff
self.dx_n = (self.cc - cx) / max(self.cols, 1)
self.dy_n = (self.rr - cy) / max(self.rows, 1) * asp
self.dist_n = np.sqrt(self.dx_n**2 + self.dy_n**2)
# Pre-rasterize all characters to float32 bitmaps
self.bm = {}
for c in all_chars:
img = Image.new("L", (self.cw, self.ch), 0)
ImageDraw.Draw(img).text((0, 0), c, fill=255, font=self.font)
self.bm[c] = np.array(img, dtype=np.float32) / 255.0
```
### Character Render Loop
The bottleneck. Composites pre-rasterized bitmaps onto pixel canvas:
```python
def render(self, chars, colors, canvas=None):
if canvas is None:
canvas = np.zeros((VH, VW, 3), dtype=np.uint8)
for row in range(self.rows):
y = self.oy + row * self.ch
if y + self.ch > VH: break
for col in range(self.cols):
c = chars[row, col]
if c == " ": continue
x = self.ox + col * self.cw
if x + self.cw > VW: break
a = self.bm[c] # float32 bitmap
canvas[y:y+self.ch, x:x+self.cw] = np.maximum(
canvas[y:y+self.ch, x:x+self.cw],
(a[:, :, None] * colors[row, col]).astype(np.uint8))
return canvas
```
Use `np.maximum` for additive blending (brighter chars overwrite dimmer ones, never darken).
### Multi-Layer Rendering
Render multiple grids onto the same canvas for depth:
```python
canvas = np.zeros((VH, VW, 3), dtype=np.uint8)
canvas = grid_lg.render(bg_chars, bg_colors, canvas) # background layer
canvas = grid_md.render(main_chars, main_colors, canvas) # main layer
canvas = grid_sm.render(detail_chars, detail_colors, canvas) # detail overlay
```
---
## Character Palettes
### Design Principles
Character palettes are the primary visual texture of ASCII video. They control not just brightness mapping but the entire visual feel. Design palettes intentionally:
- **Visual weight**: characters sorted by the amount of ink/pixels they fill. Space is always index 0.
- **Coherence**: characters within a palette should belong to the same visual family.
- **Density curve**: the brightness-to-character mapping is nonlinear. Dense palettes (many chars) give smoother gradients; sparse palettes (5-8 chars) give posterized/graphic looks.
- **Rendering compatibility**: every character in the palette must exist in the font. Test at init and remove missing glyphs.
### Palette Library
Organized by visual family. Mix and match per project -- don't default to PAL_DEFAULT for everything.
#### Density / Brightness Palettes
```python
PAL_DEFAULT = " .`'-:;!><=+*^~?/|(){}[]#&$@%" # classic ASCII art
PAL_DENSE = " .:;+=xX$#@\u2588" # simple 11-level ramp
PAL_MINIMAL = " .:-=+#@" # 8-level, graphic
PAL_BINARY = " \u2588" # 2-level, extreme contrast
PAL_GRADIENT = " \u2591\u2592\u2593\u2588" # 4-level block gradient
```
#### Unicode Block Elements
```python
PAL_BLOCKS = " \u2591\u2592\u2593\u2588\u2584\u2580\u2590\u258c" # standard blocks
PAL_BLOCKS_EXT = " \u2596\u2597\u2598\u2599\u259a\u259b\u259c\u259d\u259e\u259f\u2591\u2592\u2593\u2588" # quadrant blocks (more detail)
PAL_SHADE = " \u2591\u2592\u2593\u2588\u2587\u2586\u2585\u2584\u2583\u2582\u2581" # vertical fill progression
```
#### Symbolic / Thematic
```python
PAL_MATH = " \u00b7\u2218\u2219\u2022\u00b0\u00b1\u2213\u00d7\u00f7\u2248\u2260\u2261\u2264\u2265\u221e\u222b\u2211\u220f\u221a\u2207\u2202\u2206\u03a9" # math symbols
PAL_BOX = " \u2500\u2502\u250c\u2510\u2514\u2518\u251c\u2524\u252c\u2534\u253c\u2550\u2551\u2554\u2557\u255a\u255d\u2560\u2563\u2566\u2569\u256c" # box drawing
PAL_CIRCUIT = " .\u00b7\u2500\u2502\u250c\u2510\u2514\u2518\u253c\u25cb\u25cf\u25a1\u25a0\u2206\u2207\u2261" # circuit board
PAL_RUNE = " .\u16a0\u16a2\u16a6\u16b1\u16b7\u16c1\u16c7\u16d2\u16d6\u16da\u16de\u16df" # elder futhark runes
PAL_ALCHEMIC = " \u2609\u263d\u2640\u2642\u2643\u2644\u2645\u2646\u2647\u2648\u2649\u264a\u264b" # planetary/alchemical symbols
PAL_ZODIAC = " \u2648\u2649\u264a\u264b\u264c\u264d\u264e\u264f\u2650\u2651\u2652\u2653" # zodiac
PAL_ARROWS = " \u2190\u2191\u2192\u2193\u2194\u2195\u2196\u2197\u2198\u2199\u21a9\u21aa\u21bb\u27a1" # directional arrows
PAL_MUSIC = " \u266a\u266b\u266c\u2669\u266d\u266e\u266f\u25cb\u25cf" # musical notation
```
#### Script / Writing System
```python
PAL_KATA = " \u00b7\uff66\uff67\uff68\uff69\uff6a\uff6b\uff6c\uff6d\uff6e\uff6f\uff70\uff71\uff72\uff73\uff74\uff75\uff76\uff77" # katakana halfwidth (matrix rain)
PAL_GREEK = " \u03b1\u03b2\u03b3\u03b4\u03b5\u03b6\u03b7\u03b8\u03b9\u03ba\u03bb\u03bc\u03bd\u03be\u03c0\u03c1\u03c3\u03c4\u03c6\u03c8\u03c9" # Greek lowercase
PAL_CYRILLIC = " \u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u043a\u043b\u043c\u043d\u043e\u043f\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448" # Cyrillic lowercase
PAL_ARABIC = " \u0627\u0628\u062a\u062b\u062c\u062d\u062e\u062f\u0630\u0631\u0632\u0633\u0634\u0635\u0636\u0637" # Arabic letters (isolated forms)
```
#### Dot / Point Progressions
```python
PAL_DOTS = " ⋅∘∙●◉◎◆✦★" # dot size progression
PAL_BRAILLE = " ⠁⠂⠃⠄⠅⠆⠇⠈⠉⠊⠋⠌⠍⠎⠏⠐⠑⠒⠓⠔⠕⠖⠗⠘⠙⠚⠛⠜⠝⠞⠟⠿" # braille patterns
PAL_STARS = " ·✧✦✩✨★✶✳✸" # star progression
PAL_HALFFILL = " ◔◑◕◐◒◓◖◗◙" # directional half-fill progression
PAL_HATCH = " ▣▤▥▦▧▨▩" # crosshatch density ramp
```
#### Project-Specific (examples -- invent new ones per project)
```python
PAL_HERMES = " .\u00b7~=\u2248\u221e\u26a1\u263f\u2726\u2605\u2295\u25ca\u25c6\u25b2\u25bc\u25cf\u25a0" # mythology/tech blend
PAL_OCEAN = " ~\u2248\u2248\u2248\u223c\u2307\u2248\u224b\u224c\u2248" # water/wave characters
PAL_ORGANIC = " .\u00b0\u2218\u2022\u25e6\u25c9\u2742\u273f\u2741\u2743" # growing/botanical
PAL_MACHINE = " _\u2500\u2502\u250c\u2510\u253c\u2261\u25a0\u2588\u2593\u2592\u2591" # mechanical/industrial
```
### Creating Custom Palettes
When designing for a project, build palettes from the content's theme:
1. **Choose a visual family** (dots, blocks, symbols, script)
2. **Sort by visual weight** -- render each char at target font size, count lit pixels, sort ascending
3. **Test at target grid size** -- some chars collapse to blobs at small sizes
4. **Validate in font** -- remove chars the font can't render:
```python
def validate_palette(pal, font):
"""Remove characters the font can't render."""
valid = []
for c in pal:
if c == " ":
valid.append(c)
continue
img = Image.new("L", (20, 20), 0)
ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
if np.array(img).max() > 0: # char actually rendered something
valid.append(c)
return "".join(valid)
```
### Mapping Values to Characters
```python
def val2char(v, mask, pal=PAL_DEFAULT):
"""Map float array (0-1) to character array using palette."""
n = len(pal)
idx = np.clip((v * n).astype(int), 0, n - 1)
out = np.full(v.shape, " ", dtype="U1")
for i, ch in enumerate(pal):
out[mask & (idx == i)] = ch
return out
```
**Nonlinear mapping** for different visual curves:
```python
def val2char_gamma(v, mask, pal, gamma=1.0):
"""Gamma-corrected palette mapping. gamma<1 = brighter, gamma>1 = darker."""
v_adj = np.power(np.clip(v, 0, 1), gamma)
return val2char(v_adj, mask, pal)
def val2char_step(v, mask, pal, thresholds):
"""Custom threshold mapping. thresholds = list of float breakpoints."""
out = np.full(v.shape, pal[0], dtype="U1")
for i, thr in enumerate(thresholds):
out[mask & (v > thr)] = pal[min(i + 1, len(pal) - 1)]
return out
```
---
## Color System
### HSV->RGB (Vectorized)
All color computation in HSV for intuitive control, converted at render time:
```python
def hsv2rgb(h, s, v):
"""Vectorized HSV->RGB. h,s,v are numpy arrays. Returns (R,G,B) uint8 arrays."""
h = h % 1.0
c = v * s; x = c * (1 - np.abs((h*6) % 2 - 1)); m = v - c
# ... 6 sector assignment ...
return (np.clip((r+m)*255, 0, 255).astype(np.uint8),
np.clip((g+m)*255, 0, 255).astype(np.uint8),
np.clip((b+m)*255, 0, 255).astype(np.uint8))
```
### Color Mapping Strategies
Don't default to a single strategy. Choose based on the visual intent:
| Strategy | Hue source | Effect | Good for |
|----------|------------|--------|----------|
| Angle-mapped | `g.angle / (2*pi)` | Rainbow around center | Radial effects, kaleidoscopes |
| Distance-mapped | `g.dist_n * 0.3` | Gradient from center | Tunnels, depth effects |
| Frequency-mapped | `f["cent"] * 0.2` | Timbral color shifting | Audio-reactive |
| Value-mapped | `val * 0.15` | Brightness-dependent hue | Fire, heat maps |
| Time-cycled | `t * rate` | Slow color rotation | Ambient, chill |
| Source-sampled | Video frame pixel colors | Preserve original color | Video-to-ASCII |
| Palette-indexed | Discrete color lookup | Flat graphic style | Retro, pixel art |
| Temperature | Blend between warm/cool | Emotional tone | Mood-driven scenes |
| Complementary | `hue` and `hue + 0.5` | High contrast | Bold, dramatic |
| Triadic | `hue`, `hue + 0.33`, `hue + 0.66` | Vibrant, balanced | Psychedelic |
| Analogous | `hue +/- 0.08` | Harmonious, subtle | Elegant, cohesive |
| Monochrome | Fixed hue, vary S and V | Restrained, focused | Noir, minimal |
### Color Palettes (Discrete RGB)
For non-HSV workflows -- direct RGB color sets for graphic/retro looks:
```python
# Named color palettes -- use for flat/graphic styles or per-character coloring
COLORS_NEON = [(255,0,102), (0,255,153), (102,0,255), (255,255,0), (0,204,255)]
COLORS_PASTEL = [(255,179,186), (255,223,186), (255,255,186), (186,255,201), (186,225,255)]
COLORS_MONO_GREEN = [(0,40,0), (0,80,0), (0,140,0), (0,200,0), (0,255,0)]
COLORS_MONO_AMBER = [(40,20,0), (80,50,0), (140,90,0), (200,140,0), (255,191,0)]
COLORS_CYBERPUNK = [(255,0,60), (0,255,200), (180,0,255), (255,200,0)]
COLORS_VAPORWAVE = [(255,113,206), (1,205,254), (185,103,255), (5,255,161)]
COLORS_EARTH = [(86,58,26), (139,90,43), (189,154,91), (222,193,136), (245,230,193)]
COLORS_ICE = [(200,230,255), (150,200,240), (100,170,230), (60,130,210), (30,80,180)]
COLORS_BLOOD = [(80,0,0), (140,10,10), (200,20,20), (255,50,30), (255,100,80)]
COLORS_FOREST = [(10,30,10), (20,60,15), (30,100,20), (50,150,30), (80,200,50)]
def rgb_palette_map(val, mask, palette):
"""Map float array (0-1) to RGB colors from a discrete palette."""
n = len(palette)
idx = np.clip((val * n).astype(int), 0, n - 1)
R = np.zeros(val.shape, dtype=np.uint8)
G = np.zeros(val.shape, dtype=np.uint8)
B = np.zeros(val.shape, dtype=np.uint8)
for i, (r, g, b) in enumerate(palette):
m = mask & (idx == i)
R[m] = r; G[m] = g; B[m] = b
return R, G, B
```
### OKLAB Color Space (Perceptually Uniform)
HSV hue is perceptually non-uniform: green occupies far more visual range than blue. OKLAB / OKLCH provide perceptually even color steps — hue increments of 0.1 look equally different regardless of starting hue. Use OKLAB for:
- Gradient interpolation (no unwanted intermediate hues)
- Color harmony generation (perceptually balanced palettes)
- Smooth color transitions over time
```python
# --- sRGB <-> Linear sRGB ---
def srgb_to_linear(c):
"""Convert sRGB [0,1] to linear light. c: float32 array."""
return np.where(c <= 0.04045, c / 12.92, ((c + 0.055) / 1.055) ** 2.4)
def linear_to_srgb(c):
"""Convert linear light to sRGB [0,1]."""
return np.where(c <= 0.0031308, c * 12.92, 1.055 * np.power(np.maximum(c, 0), 1/2.4) - 0.055)
# --- Linear sRGB <-> OKLAB ---
def linear_rgb_to_oklab(r, g, b):
"""Linear sRGB to OKLAB. r,g,b: float32 arrays [0,1].
Returns (L, a, b) where L=[0,1], a,b=[-0.4, 0.4] approx."""
l_ = 0.4122214708 * r + 0.5363325363 * g + 0.0514459929 * b
m_ = 0.2119034982 * r + 0.6806995451 * g + 0.1073969566 * b
s_ = 0.0883024619 * r + 0.2817188376 * g + 0.6299787005 * b
l_c = np.cbrt(l_); m_c = np.cbrt(m_); s_c = np.cbrt(s_)
L = 0.2104542553 * l_c + 0.7936177850 * m_c - 0.0040720468 * s_c
a = 1.9779984951 * l_c - 2.4285922050 * m_c + 0.4505937099 * s_c
b_ = 0.0259040371 * l_c + 0.7827717662 * m_c - 0.8086757660 * s_c
return L, a, b_
def oklab_to_linear_rgb(L, a, b):
"""OKLAB to linear sRGB. Returns (r, g, b) float32 arrays [0,1]."""
l_ = L + 0.3963377774 * a + 0.2158037573 * b
m_ = L - 0.1055613458 * a - 0.0638541728 * b
s_ = L - 0.0894841775 * a - 1.2914855480 * b
l_c = l_ ** 3; m_c = m_ ** 3; s_c = s_ ** 3
r = +4.0767416621 * l_c - 3.3077115913 * m_c + 0.2309699292 * s_c
g = -1.2684380046 * l_c + 2.6097574011 * m_c - 0.3413193965 * s_c
b_ = -0.0041960863 * l_c - 0.7034186147 * m_c + 1.7076147010 * s_c
return np.clip(r, 0, 1), np.clip(g, 0, 1), np.clip(b_, 0, 1)
# --- Convenience: sRGB uint8 <-> OKLAB ---
def rgb_to_oklab(R, G, B):
"""sRGB uint8 arrays to OKLAB."""
r = srgb_to_linear(R.astype(np.float32) / 255.0)
g = srgb_to_linear(G.astype(np.float32) / 255.0)
b = srgb_to_linear(B.astype(np.float32) / 255.0)
return linear_rgb_to_oklab(r, g, b)
def oklab_to_rgb(L, a, b):
"""OKLAB to sRGB uint8 arrays."""
r, g, b_ = oklab_to_linear_rgb(L, a, b)
R = np.clip(linear_to_srgb(r) * 255, 0, 255).astype(np.uint8)
G = np.clip(linear_to_srgb(g) * 255, 0, 255).astype(np.uint8)
B = np.clip(linear_to_srgb(b_) * 255, 0, 255).astype(np.uint8)
return R, G, B
# --- OKLCH (cylindrical form of OKLAB) ---
def oklab_to_oklch(L, a, b):
"""OKLAB to OKLCH. Returns (L, C, H) where H is in [0, 1] (normalized)."""
C = np.sqrt(a**2 + b**2)
H = (np.arctan2(b, a) / (2 * np.pi)) % 1.0
return L, C, H
def oklch_to_oklab(L, C, H):
"""OKLCH to OKLAB. H in [0, 1]."""
angle = H * 2 * np.pi
a = C * np.cos(angle)
b = C * np.sin(angle)
return L, a, b
```
### Gradient Interpolation (OKLAB vs HSV)
Interpolating colors through OKLAB avoids the hue detours that HSV produces:
```python
def lerp_oklab(color_a, color_b, t_array):
"""Interpolate between two sRGB colors through OKLAB.
color_a, color_b: (R, G, B) tuples 0-255
t_array: float32 array [0,1] — interpolation parameter per pixel.
Returns (R, G, B) uint8 arrays."""
La, aa, ba = rgb_to_oklab(
np.full_like(t_array, color_a[0], dtype=np.uint8),
np.full_like(t_array, color_a[1], dtype=np.uint8),
np.full_like(t_array, color_a[2], dtype=np.uint8))
Lb, ab, bb = rgb_to_oklab(
np.full_like(t_array, color_b[0], dtype=np.uint8),
np.full_like(t_array, color_b[1], dtype=np.uint8),
np.full_like(t_array, color_b[2], dtype=np.uint8))
L = La + (Lb - La) * t_array
a = aa + (ab - aa) * t_array
b = ba + (bb - ba) * t_array
return oklab_to_rgb(L, a, b)
def lerp_oklch(color_a, color_b, t_array, short_path=True):
"""Interpolate through OKLCH (preserves chroma, smooth hue path).
short_path: take the shorter arc around the hue wheel."""
La, aa, ba = rgb_to_oklab(
np.full_like(t_array, color_a[0], dtype=np.uint8),
np.full_like(t_array, color_a[1], dtype=np.uint8),
np.full_like(t_array, color_a[2], dtype=np.uint8))
Lb, ab, bb = rgb_to_oklab(
np.full_like(t_array, color_b[0], dtype=np.uint8),
np.full_like(t_array, color_b[1], dtype=np.uint8),
np.full_like(t_array, color_b[2], dtype=np.uint8))
L1, C1, H1 = oklab_to_oklch(La, aa, ba)
L2, C2, H2 = oklab_to_oklch(Lb, ab, bb)
# Shortest hue path
if short_path:
dh = H2 - H1
dh = np.where(dh > 0.5, dh - 1.0, np.where(dh < -0.5, dh + 1.0, dh))
H = (H1 + dh * t_array) % 1.0
else:
H = H1 + (H2 - H1) * t_array
L = L1 + (L2 - L1) * t_array
C = C1 + (C2 - C1) * t_array
Lout, aout, bout = oklch_to_oklab(L, C, H)
return oklab_to_rgb(Lout, aout, bout)
```
### Color Harmony Generation
Auto-generate harmonious palettes from a seed color:
```python
def harmony_complementary(seed_rgb):
"""Two colors: seed + opposite hue."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb, _oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0)]
def harmony_triadic(seed_rgb):
"""Three colors: seed + two at 120-degree offsets."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.333) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.667) % 1.0)]
def harmony_analogous(seed_rgb, spread=0.08, n=5):
"""N colors spread evenly around seed hue."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
offsets = np.linspace(-spread * (n-1)/2, spread * (n-1)/2, n)
return [_oklch_to_srgb_tuple(L[0], C[0], (H[0] + off) % 1.0) for off in offsets]
def harmony_split_complementary(seed_rgb, split=0.08):
"""Three colors: seed + two flanking the complement."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
comp = (H[0] + 0.5) % 1.0
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (comp - split) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (comp + split) % 1.0)]
def harmony_tetradic(seed_rgb):
"""Four colors: two complementary pairs at 90-degree offset."""
L, a, b = rgb_to_oklab(np.array([seed_rgb[0]]), np.array([seed_rgb[1]]), np.array([seed_rgb[2]]))
_, C, H = oklab_to_oklch(L, a, b)
return [seed_rgb,
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.25) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.5) % 1.0),
_oklch_to_srgb_tuple(L[0], C[0], (H[0] + 0.75) % 1.0)]
def _oklch_to_srgb_tuple(L, C, H):
"""Helper: single OKLCH -> sRGB (R,G,B) int tuple."""
La = np.array([L]); Ca = np.array([C]); Ha = np.array([H])
Lo, ao, bo = oklch_to_oklab(La, Ca, Ha)
R, G, B = oklab_to_rgb(Lo, ao, bo)
return (int(R[0]), int(G[0]), int(B[0]))
```
### OKLAB Hue Fields
Drop-in replacements for `hf_*` generators that produce perceptually uniform hue variation:
```python
def hf_oklch_angle(offset=0.0, chroma=0.12, lightness=0.7):
"""OKLCH hue mapped to angle from center. Perceptually uniform rainbow.
Returns (R, G, B) uint8 color array instead of a float hue.
NOTE: Use with _render_vf_rgb() variant, not standard _render_vf()."""
def fn(g, f, t, S):
H = (g.angle / (2 * np.pi) + offset + t * 0.05) % 1.0
L = np.full_like(H, lightness)
C = np.full_like(H, chroma)
Lo, ao, bo = oklch_to_oklab(L, C, H)
R, G, B = oklab_to_rgb(Lo, ao, bo)
return mkc(R, G, B, g.rows, g.cols)
return fn
```
### Compositing Helpers
```python
def mkc(R, G, B, rows, cols):
"""Pack 3 uint8 arrays into (rows, cols, 3) color array."""
o = np.zeros((rows, cols, 3), dtype=np.uint8)
o[:,:,0] = R; o[:,:,1] = G; o[:,:,2] = B
return o
def layer_over(base_ch, base_co, top_ch, top_co):
"""Composite top layer onto base. Non-space chars overwrite."""
m = top_ch != " "
base_ch[m] = top_ch[m]; base_co[m] = top_co[m]
return base_ch, base_co
def layer_blend(base_co, top_co, alpha):
"""Alpha-blend top color layer onto base. alpha is float array (0-1) or scalar."""
if isinstance(alpha, (int, float)):
alpha = np.full(base_co.shape[:2], alpha, dtype=np.float32)
a = alpha[:,:,None]
return np.clip(base_co * (1 - a) + top_co * a, 0, 255).astype(np.uint8)
def stamp(ch, co, text, row, col, color=(255,255,255)):
"""Write text string at position."""
for i, c in enumerate(text):
cc = col + i
if 0 <= row < ch.shape[0] and 0 <= cc < ch.shape[1]:
ch[row, cc] = c; co[row, cc] = color
```
---
## Section System
Map time ranges to effect functions + shader configs + grid sizes:
```python
SECTIONS = [
(0.0, "void"), (3.94, "starfield"), (21.0, "matrix"),
(46.0, "drop"), (130.0, "glitch"), (187.0, "outro"),
]
FX_DISPATCH = {"void": fx_void, "starfield": fx_starfield, ...}
SECTION_FX = {"void": {"vignette": 0.3, "bloom": 170}, ...}
SECTION_GRID = {"void": "md", "starfield": "sm", "drop": "lg", ...}
SECTION_MIRROR = {"drop": "h", "bass_rings": "quad"}
def get_section(t):
sec = SECTIONS[0][1]
for ts, name in SECTIONS:
if t >= ts: sec = name
return sec
```
---
## Parallel Encoding
Split frames across N workers. Each pipes raw RGB to its own ffmpeg subprocess:
```python
def render_batch(batch_id, frame_start, frame_end, features, seg_path):
r = Renderer()
cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{VW}x{VH}", "-r", str(FPS), "-i", "pipe:0",
"-c:v", "libx264", "-preset", "fast", "-crf", "18",
"-pix_fmt", "yuv420p", seg_path]
# CRITICAL: stderr to file, not pipe
stderr_fh = open(os.path.join(workdir, f"err_{batch_id:02d}.log"), "w")
pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE,
stdout=subprocess.DEVNULL, stderr=stderr_fh)
for fi in range(frame_start, frame_end):
t = fi / FPS
sec = get_section(t)
f = {k: float(features[k][fi]) for k in features}
ch, co = FX_DISPATCH[sec](r, f, t)
canvas = r.render(ch, co)
canvas = apply_mirror(canvas, sec, f)
canvas = apply_shaders(canvas, sec, f, t)
pipe.stdin.write(canvas.tobytes())
pipe.stdin.close()
pipe.wait()
stderr_fh.close()
```
Concatenate segments + mux audio:
```python
# Write concat file
with open(concat_path, "w") as cf:
for seg in segments:
cf.write(f"file '{seg}'\n")
subprocess.run(["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_path,
"-i", audio_path, "-c:v", "copy", "-c:a", "aac", "-b:a", "192k",
"-shortest", output_path])
```
## Effect Function Contract
### v2 Protocol (Current)
Every scene function: `(r, f, t, S) -> canvas_uint8` — where `r` = Renderer, `f` = features dict, `t` = time float, `S` = persistent state dict
```python
def fx_example(r, f, t, S):
"""Scene function returns a full pixel canvas (uint8 H,W,3).
Scenes have full control over multi-grid rendering and pixel-level composition.
"""
# Render multiple layers at different grid densities
canvas_a = _render_vf(r, "md", vf_plasma, hf_angle(0.0), PAL_DENSE, f, t, S)
canvas_b = _render_vf(r, "sm", vf_vortex, hf_time_cycle(0.1), PAL_RUNE, f, t, S)
# Pixel-level blend
result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
return result
```
See `references/scenes.md` for the full scene protocol, the Renderer class, `_render_vf()` helper, and complete scene examples.
See `references/composition.md` for blend modes, tone mapping, feedback buffers, and multi-grid composition.
### v1 Protocol (Legacy)
Simple scenes that use a single grid can still return `(chars, colors)` and let the caller handle rendering, but the v2 canvas protocol is preferred for all new code.
```python
def fx_simple(r, f, t, S):
g = r.get_grid("md")
val = np.sin(g.dist * 0.1 - t * 3) * f.get("bass", 0.3) * 2
val = np.clip(val, 0, 1); mask = val > 0.03
ch = val2char(val, mask, PAL_DEFAULT)
R, G, B = hsv2rgb(np.full_like(val, 0.6), np.full_like(val, 0.7), val)
co = mkc(R, G, B, g.rows, g.cols)
return g.render(ch, co) # returns canvas directly
```
### Persistent State
Effects that need state across frames (particles, rain columns) use the `S` dict parameter (which is `r.S` — same object, but passed explicitly for clarity):
```python
def fx_with_state(r, f, t, S):
if "particles" not in S:
S["particles"] = initialize_particles()
update_particles(S["particles"])
# ...
```
State persists across frames within a single scene/clip. Each worker process (and each scene) gets its own independent state.
### Helper Functions
```python
def hsv2rgb_scalar(h, s, v):
"""Single-value HSV to RGB. Returns (R, G, B) tuple of ints 0-255."""
h = h % 1.0
c = v * s; x = c * (1 - abs((h * 6) % 2 - 1)); m = v - c
if h * 6 < 1: r, g, b = c, x, 0
elif h * 6 < 2: r, g, b = x, c, 0
elif h * 6 < 3: r, g, b = 0, c, x
elif h * 6 < 4: r, g, b = 0, x, c
elif h * 6 < 5: r, g, b = x, 0, c
else: r, g, b = c, 0, x
return (int((r+m)*255), int((g+m)*255), int((b+m)*255))
def log(msg):
"""Print timestamped log message."""
print(msg, flush=True)
```

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# Composition & Brightness Reference
The composable system is the core of visual complexity. It operates at three levels: pixel-level blend modes, multi-grid composition, and adaptive brightness management. This document covers all three, plus the masking/stencil system for spatial control.
> **See also:** architecture.md · effects.md · scenes.md · shaders.md · troubleshooting.md
## Pixel-Level Blend Modes
### The `blend_canvas()` Function
All blending operates on full pixel canvases (`uint8 H,W,3`). Internally converts to float32 [0,1] for precision, blends, lerps by opacity, converts back.
```python
def blend_canvas(base, top, mode="normal", opacity=1.0):
af = base.astype(np.float32) / 255.0
bf = top.astype(np.float32) / 255.0
fn = BLEND_MODES.get(mode, BLEND_MODES["normal"])
result = fn(af, bf)
if opacity < 1.0:
result = af * (1 - opacity) + result * opacity
return np.clip(result * 255, 0, 255).astype(np.uint8)
```
### 20 Blend Modes
```python
BLEND_MODES = {
# Basic arithmetic
"normal": lambda a, b: b,
"add": lambda a, b: np.clip(a + b, 0, 1),
"subtract": lambda a, b: np.clip(a - b, 0, 1),
"multiply": lambda a, b: a * b,
"screen": lambda a, b: 1 - (1 - a) * (1 - b),
# Contrast
"overlay": lambda a, b: np.where(a < 0.5, 2*a*b, 1 - 2*(1-a)*(1-b)),
"softlight": lambda a, b: (1 - 2*b)*a*a + 2*b*a,
"hardlight": lambda a, b: np.where(b < 0.5, 2*a*b, 1 - 2*(1-a)*(1-b)),
# Difference
"difference": lambda a, b: np.abs(a - b),
"exclusion": lambda a, b: a + b - 2*a*b,
# Dodge / burn
"colordodge": lambda a, b: np.clip(a / (1 - b + 1e-6), 0, 1),
"colorburn": lambda a, b: np.clip(1 - (1 - a) / (b + 1e-6), 0, 1),
# Light
"linearlight": lambda a, b: np.clip(a + 2*b - 1, 0, 1),
"vividlight": lambda a, b: np.where(b < 0.5,
np.clip(1 - (1-a)/(2*b + 1e-6), 0, 1),
np.clip(a / (2*(1-b) + 1e-6), 0, 1)),
"pin_light": lambda a, b: np.where(b < 0.5,
np.minimum(a, 2*b), np.maximum(a, 2*b - 1)),
"hard_mix": lambda a, b: np.where(a + b >= 1.0, 1.0, 0.0),
# Compare
"lighten": lambda a, b: np.maximum(a, b),
"darken": lambda a, b: np.minimum(a, b),
# Grain
"grain_extract": lambda a, b: np.clip(a - b + 0.5, 0, 1),
"grain_merge": lambda a, b: np.clip(a + b - 0.5, 0, 1),
}
```
### Blend Mode Selection Guide
**Modes that brighten** (safe for dark inputs):
- `screen` — always brightens. Two 50% gray layers screen to 75%. The go-to safe blend.
- `add` — simple addition, clips at white. Good for sparkles, glows, particle overlays.
- `colordodge` — extreme brightening at overlap zones. Can blow out. Use low opacity (0.3-0.5).
- `linearlight` — aggressive brightening. Similar to add but with offset.
**Modes that darken** (avoid with dark inputs):
- `multiply` — darkens everything. Only use when both layers are already bright.
- `overlay` — darkens when base < 0.5, brightens when base > 0.5. Crushes dark inputs: `2 * 0.12 * 0.12 = 0.03`. Use `screen` instead for dark material.
- `colorburn` — extreme darkening at overlap zones.
**Modes that create contrast**:
- `softlight` — gentle contrast. Good for subtle texture overlay.
- `hardlight` — strong contrast. Like overlay but keyed on the top layer.
- `vividlight` — very aggressive contrast. Use sparingly.
**Modes that create color effects**:
- `difference` — XOR-like patterns. Two identical layers difference to black; offset layers create wild colors. Great for psychedelic looks.
- `exclusion` — softer version of difference. Creates complementary color patterns.
- `hard_mix` — posterizes to pure black/white/saturated color at intersections.
**Modes for texture blending**:
- `grain_extract` / `grain_merge` — extract a texture from one layer, apply it to another.
### Multi-Layer Chaining
```python
# Pattern: render layers -> blend sequentially
canvas_a = _render_vf(r, "md", vf_plasma, hf_angle(0.0), PAL_DENSE, f, t, S)
canvas_b = _render_vf(r, "sm", vf_vortex, hf_time_cycle(0.1), PAL_RUNE, f, t, S)
canvas_c = _render_vf(r, "lg", vf_rings, hf_distance(), PAL_BLOCKS, f, t, S)
result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
result = blend_canvas(result, canvas_c, "difference", 0.6)
```
Order matters: `screen(A, B)` is commutative, but `difference(screen(A,B), C)` differs from `difference(A, screen(B,C))`.
### Linear-Light Blend Modes
Standard `blend_canvas()` operates in sRGB space — the raw byte values. This is fine for most uses, but sRGB is perceptually non-linear: blending in sRGB darkens midtones and shifts hues slightly. For physically accurate blending (matching how light actually combines), convert to linear light first.
Uses `srgb_to_linear()` / `linear_to_srgb()` from `architecture.md` § OKLAB Color System.
```python
def blend_canvas_linear(base, top, mode="normal", opacity=1.0):
"""Blend in linear light space for physically accurate results.
Identical API to blend_canvas(), but converts sRGB → linear before
blending and linear → sRGB after. More expensive (~2x) due to the
gamma conversions, but produces correct results for additive blending,
screen, and any mode where brightness matters.
"""
af = srgb_to_linear(base.astype(np.float32) / 255.0)
bf = srgb_to_linear(top.astype(np.float32) / 255.0)
fn = BLEND_MODES.get(mode, BLEND_MODES["normal"])
result = fn(af, bf)
if opacity < 1.0:
result = af * (1 - opacity) + result * opacity
result = linear_to_srgb(np.clip(result, 0, 1))
return np.clip(result * 255, 0, 255).astype(np.uint8)
```
**When to use `blend_canvas_linear()` vs `blend_canvas()`:**
| Scenario | Use | Why |
|----------|-----|-----|
| Screen-blending two bright layers | `linear` | sRGB screen over-brightens highlights |
| Add mode for glow/bloom effects | `linear` | Additive light follows linear physics |
| Blending text overlay at low opacity | `srgb` | Perceptual blending looks more natural for text |
| Multiply for shadow/darkening | `srgb` | Differences are minimal for darken ops |
| Color-critical work (matching reference) | `linear` | Avoids sRGB hue shifts in midtones |
| Performance-critical inner loop | `srgb` | ~2x faster, good enough for most ASCII art |
**Batch version** for compositing many layers (converts once, blends multiple, converts back):
```python
def blend_many_linear(layers, modes, opacities):
"""Blend a stack of layers in linear light space.
Args:
layers: list of uint8 (H,W,3) canvases
modes: list of blend mode strings (len = len(layers) - 1)
opacities: list of floats (len = len(layers) - 1)
Returns:
uint8 (H,W,3) canvas
"""
# Convert all to linear at once
linear = [srgb_to_linear(l.astype(np.float32) / 255.0) for l in layers]
result = linear[0]
for i in range(1, len(linear)):
fn = BLEND_MODES.get(modes[i-1], BLEND_MODES["normal"])
blended = fn(result, linear[i])
op = opacities[i-1]
if op < 1.0:
blended = result * (1 - op) + blended * op
result = np.clip(blended, 0, 1)
result = linear_to_srgb(result)
return np.clip(result * 255, 0, 255).astype(np.uint8)
```
---
## Multi-Grid Composition
This is the core visual technique. Rendering the same conceptual scene at different grid densities (character sizes) creates natural texture interference, because characters at different scales overlap at different spatial frequencies.
### Why It Works
- `sm` grid (10pt font): 320x83 characters. Fine detail, dense texture.
- `md` grid (16pt): 192x56 characters. Medium density.
- `lg` grid (20pt): 160x45 characters. Coarse, chunky characters.
When you render a plasma field on `sm` and a vortex on `lg`, then screen-blend them, the fine plasma texture shows through the gaps in the coarse vortex characters. The result has more visual complexity than either layer alone.
### The `_render_vf()` Helper
This is the workhorse function. It takes a value field + hue field + palette + grid, renders to a complete pixel canvas:
```python
def _render_vf(r, grid_key, val_fn, hue_fn, pal, f, t, S, sat=0.8, threshold=0.03):
"""Render a value field + hue field to a pixel canvas via a named grid.
Args:
r: Renderer instance (has .get_grid())
grid_key: "xs", "sm", "md", "lg", "xl", "xxl"
val_fn: (g, f, t, S) -> float32 [0,1] array (rows, cols)
hue_fn: callable (g, f, t, S) -> float32 hue array, OR float scalar
pal: character palette string
f: feature dict
t: time in seconds
S: persistent state dict
sat: HSV saturation (0-1)
threshold: minimum value to render (below = space)
Returns:
uint8 array (VH, VW, 3) — full pixel canvas
"""
g = r.get_grid(grid_key)
val = np.clip(val_fn(g, f, t, S), 0, 1)
mask = val > threshold
ch = val2char(val, mask, pal)
# Hue: either a callable or a fixed float
if callable(hue_fn):
h = hue_fn(g, f, t, S) % 1.0
else:
h = np.full((g.rows, g.cols), float(hue_fn), dtype=np.float32)
# CRITICAL: broadcast to full shape and copy (see Troubleshooting)
h = np.broadcast_to(h, (g.rows, g.cols)).copy()
R, G, B = hsv2rgb(h, np.full_like(val, sat), val)
co = mkc(R, G, B, g.rows, g.cols)
return g.render(ch, co)
```
### Grid Combination Strategies
| Combination | Effect | Good For |
|-------------|--------|----------|
| `sm` + `lg` | Maximum contrast between fine detail and chunky blocks | Bold, graphic looks |
| `sm` + `md` | Subtle texture layering, similar scales | Organic, flowing looks |
| `md` + `lg` + `xs` | Three-scale interference, maximum complexity | Psychedelic, dense |
| `sm` + `sm` (different effects) | Same scale, pattern interference only | Moire, interference |
### Complete Multi-Grid Scene Example
```python
def fx_psychedelic(r, f, t, S):
"""Three-layer multi-grid scene with beat-reactive kaleidoscope."""
# Layer A: plasma on medium grid with rainbow hue
canvas_a = _render_vf(r, "md",
lambda g, f, t, S: vf_plasma(g, f, t, S) * 1.3,
hf_angle(0.0), PAL_DENSE, f, t, S, sat=0.8)
# Layer B: vortex on small grid with cycling hue
canvas_b = _render_vf(r, "sm",
lambda g, f, t, S: vf_vortex(g, f, t, S, twist=5.0) * 1.2,
hf_time_cycle(0.1), PAL_RUNE, f, t, S, sat=0.7)
# Layer C: rings on large grid with distance hue
canvas_c = _render_vf(r, "lg",
lambda g, f, t, S: vf_rings(g, f, t, S, n_base=8, spacing_base=3) * 1.4,
hf_distance(0.3, 0.02), PAL_BLOCKS, f, t, S, sat=0.9)
# Blend: A screened with B, then difference with C
result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
result = blend_canvas(result, canvas_c, "difference", 0.6)
# Beat-triggered kaleidoscope
if f.get("bdecay", 0) > 0.3:
result = sh_kaleidoscope(result.copy(), folds=6)
return result
```
---
## Adaptive Tone Mapping
### The Brightness Problem
ASCII characters are small bright dots on a black background. Most pixels in any frame are background (black). This means:
- Mean frame brightness is inherently low (often 5-30 out of 255)
- Different effect combinations produce wildly different brightness levels
- A spiral scene might be 50 mean, while a fire scene is 9 mean
- Linear multipliers (e.g., `canvas * 2.0`) either leave dark scenes dark or blow out bright scenes
### The `tonemap()` Function
Replaces linear brightness multipliers with adaptive per-frame normalization + gamma correction:
```python
def tonemap(canvas, target_mean=90, gamma=0.75, black_point=2, white_point=253):
"""Adaptive tone-mapping: normalizes + gamma-corrects so no frame is
fully dark or washed out.
1. Compute 1st and 99.5th percentile on 4x subsample (16x fewer values,
negligible accuracy loss, major speedup at 1080p+)
2. Stretch that range to [0, 1]
3. Apply gamma curve (< 1 lifts shadows, > 1 darkens)
4. Rescale to [black_point, white_point]
"""
f = canvas.astype(np.float32)
sub = f[::4, ::4] # 4x subsample: ~390K values vs ~6.2M at 1080p
lo = np.percentile(sub, 1)
hi = np.percentile(sub, 99.5)
if hi - lo < 10:
hi = max(hi, lo + 10) # near-uniform frame fallback
f = np.clip((f - lo) / (hi - lo), 0.0, 1.0)
np.power(f, gamma, out=f) # in-place: avoids allocation
np.multiply(f, (white_point - black_point), out=f)
np.add(f, black_point, out=f)
return np.clip(f, 0, 255).astype(np.uint8)
```
### Why Gamma, Not Linear
Linear multiplier `* 2.0`:
```
input 10 -> output 20 (still dark)
input 100 -> output 200 (ok)
input 200 -> output 255 (clipped, lost detail)
```
Gamma 0.75 after normalization:
```
input 0.04 -> output 0.08 (lifted from invisible to visible)
input 0.39 -> output 0.50 (moderate lift)
input 0.78 -> output 0.84 (gentle lift, no clipping)
```
Gamma < 1 compresses the highlights and expands the shadows. This is exactly what we need: lift dark ASCII content into visibility without blowing out the bright parts.
### Pipeline Ordering
The pipeline in `render_clip()` is:
```
scene_fn(r, f, t, S) -> canvas
|
tonemap(canvas, gamma=scene_gamma)
|
FeedbackBuffer.apply(canvas, ...)
|
ShaderChain.apply(canvas, f=f, t=t)
|
ffmpeg pipe
```
Tonemap runs BEFORE feedback and shaders. This means:
- Feedback operates on normalized data (consistent behavior regardless of scene brightness)
- Shaders like solarize, posterize, contrast operate on properly-ranged data
- The brightness shader in the chain is no longer needed (tonemap handles it)
### Per-Scene Gamma Tuning
Default gamma is 0.75. Scenes that apply destructive post-processing need more aggressive lift because the destruction happens after tonemap:
| Scene Type | Recommended Gamma | Why |
|------------|-------------------|-----|
| Standard effects | 0.75 | Default, works for most scenes |
| Solarize post-process | 0.50-0.60 | Solarize inverts bright pixels, reducing overall brightness |
| Posterize post-process | 0.50-0.55 | Posterize quantizes, often crushing mid-values to black |
| Heavy difference blending | 0.60-0.70 | Difference mode creates many near-zero pixels |
| Already bright scenes | 0.85-1.0 | Don't over-boost scenes that are naturally bright |
Configure via the scene table:
```python
SCENES = [
{"start": 9.17, "end": 11.25, "name": "fire", "gamma": 0.55,
"fx": fx_fire, "shaders": [("solarize", {"threshold": 200}), ...]},
{"start": 25.96, "end": 27.29, "name": "diamond", "gamma": 0.5,
"fx": fx_diamond, "shaders": [("bloom", {"thr": 90}), ...]},
]
```
### Brightness Verification
After rendering, spot-check frame brightness:
```python
# In test-frame mode
canvas = scene["fx"](r, feat, t, r.S)
canvas = tonemap(canvas, gamma=scene.get("gamma", 0.75))
chain = ShaderChain()
for sn, kw in scene.get("shaders", []):
chain.add(sn, **kw)
canvas = chain.apply(canvas, f=feat, t=t)
print(f"Mean brightness: {canvas.astype(float).mean():.1f}, max: {canvas.max()}")
```
Target ranges after tonemap + shaders:
- Quiet/ambient scenes: mean 30-60
- Active scenes: mean 40-100
- Climax/peak scenes: mean 60-150
- If mean < 20: gamma is too high or a shader is destroying brightness
- If mean > 180: gamma is too low or add is stacking too much
---
## FeedbackBuffer Spatial Transforms
The feedback buffer stores the previous frame and blends it into the current frame with decay. Spatial transforms applied to the buffer before blending create the illusion of motion in the feedback trail.
### Implementation
```python
class FeedbackBuffer:
def __init__(self):
self.buf = None
def apply(self, canvas, decay=0.85, blend="screen", opacity=0.5,
transform=None, transform_amt=0.02, hue_shift=0.0):
if self.buf is None:
self.buf = canvas.astype(np.float32) / 255.0
return canvas
# Decay old buffer
self.buf *= decay
# Spatial transform
if transform:
self.buf = self._transform(self.buf, transform, transform_amt)
# Hue shift the feedback for rainbow trails
if hue_shift > 0:
self.buf = self._hue_shift(self.buf, hue_shift)
# Blend feedback into current frame
result = blend_canvas(canvas,
np.clip(self.buf * 255, 0, 255).astype(np.uint8),
blend, opacity)
# Update buffer with current frame
self.buf = result.astype(np.float32) / 255.0
return result
def _transform(self, buf, transform, amt):
h, w = buf.shape[:2]
if transform == "zoom":
# Zoom in: sample from slightly inside (creates expanding tunnel)
m = int(h * amt); n = int(w * amt)
if m > 0 and n > 0:
cropped = buf[m:-m or None, n:-n or None]
# Resize back to full (nearest-neighbor for speed)
buf = np.array(Image.fromarray(
np.clip(cropped * 255, 0, 255).astype(np.uint8)
).resize((w, h), Image.NEAREST)).astype(np.float32) / 255.0
elif transform == "shrink":
# Zoom out: pad edges, shrink center
m = int(h * amt); n = int(w * amt)
small = np.array(Image.fromarray(
np.clip(buf * 255, 0, 255).astype(np.uint8)
).resize((w - 2*n, h - 2*m), Image.NEAREST))
new = np.zeros((h, w, 3), dtype=np.uint8)
new[m:m+small.shape[0], n:n+small.shape[1]] = small
buf = new.astype(np.float32) / 255.0
elif transform == "rotate_cw":
# Small clockwise rotation via affine
angle = amt * 10 # amt=0.005 -> 0.05 degrees per frame
cy, cx = h / 2, w / 2
Y = np.arange(h, dtype=np.float32)[:, None]
X = np.arange(w, dtype=np.float32)[None, :]
cos_a, sin_a = np.cos(angle), np.sin(angle)
sx = (X - cx) * cos_a + (Y - cy) * sin_a + cx
sy = -(X - cx) * sin_a + (Y - cy) * cos_a + cy
sx = np.clip(sx.astype(int), 0, w - 1)
sy = np.clip(sy.astype(int), 0, h - 1)
buf = buf[sy, sx]
elif transform == "rotate_ccw":
angle = -amt * 10
cy, cx = h / 2, w / 2
Y = np.arange(h, dtype=np.float32)[:, None]
X = np.arange(w, dtype=np.float32)[None, :]
cos_a, sin_a = np.cos(angle), np.sin(angle)
sx = (X - cx) * cos_a + (Y - cy) * sin_a + cx
sy = -(X - cx) * sin_a + (Y - cy) * cos_a + cy
sx = np.clip(sx.astype(int), 0, w - 1)
sy = np.clip(sy.astype(int), 0, h - 1)
buf = buf[sy, sx]
elif transform == "shift_up":
pixels = max(1, int(h * amt))
buf = np.roll(buf, -pixels, axis=0)
buf[-pixels:] = 0 # black fill at bottom
elif transform == "shift_down":
pixels = max(1, int(h * amt))
buf = np.roll(buf, pixels, axis=0)
buf[:pixels] = 0
elif transform == "mirror_h":
buf = buf[:, ::-1]
return buf
def _hue_shift(self, buf, amount):
"""Rotate hues of the feedback buffer. Operates on float32 [0,1]."""
rgb = np.clip(buf * 255, 0, 255).astype(np.uint8)
hsv = np.zeros_like(buf)
# Simple approximate RGB->HSV->shift->RGB
r, g, b = buf[:,:,0], buf[:,:,1], buf[:,:,2]
mx = np.maximum(np.maximum(r, g), b)
mn = np.minimum(np.minimum(r, g), b)
delta = mx - mn + 1e-10
# Hue
h = np.where(mx == r, ((g - b) / delta) % 6,
np.where(mx == g, (b - r) / delta + 2, (r - g) / delta + 4))
h = (h / 6 + amount) % 1.0
# Reconstruct with shifted hue (simplified)
s = delta / (mx + 1e-10)
v = mx
c = v * s; x = c * (1 - np.abs((h * 6) % 2 - 1)); m = v - c
ro = np.zeros_like(h); go = np.zeros_like(h); bo = np.zeros_like(h)
for lo, hi, rv, gv, bv in [(0,1,c,x,0),(1,2,x,c,0),(2,3,0,c,x),
(3,4,0,x,c),(4,5,x,0,c),(5,6,c,0,x)]:
mask = ((h*6) >= lo) & ((h*6) < hi)
ro[mask] = rv[mask] if not isinstance(rv, (int,float)) else rv
go[mask] = gv[mask] if not isinstance(gv, (int,float)) else gv
bo[mask] = bv[mask] if not isinstance(bv, (int,float)) else bv
return np.stack([ro+m, go+m, bo+m], axis=2)
```
### Feedback Presets
| Preset | Config | Visual Effect |
|--------|--------|---------------|
| Infinite zoom tunnel | `decay=0.8, blend="screen", transform="zoom", transform_amt=0.015` | Expanding ring patterns |
| Rainbow trails | `decay=0.7, blend="screen", transform="zoom", transform_amt=0.01, hue_shift=0.02` | Psychedelic color trails |
| Ghostly echo | `decay=0.9, blend="add", opacity=0.15, transform="shift_up", transform_amt=0.01` | Faint upward smearing |
| Kaleidoscopic recursion | `decay=0.75, blend="screen", transform="rotate_cw", transform_amt=0.005, hue_shift=0.01` | Rotating mandala feedback |
| Color evolution | `decay=0.8, blend="difference", opacity=0.4, hue_shift=0.03` | Frame-to-frame color XOR |
| Rising heat haze | `decay=0.5, blend="add", opacity=0.2, transform="shift_up", transform_amt=0.02` | Hot air shimmer |
---
## Masking / Stencil System
Masks are float32 arrays `(rows, cols)` or `(VH, VW)` in range [0, 1]. They control where effects are visible: 1.0 = fully visible, 0.0 = fully hidden. Use masks to create figure/ground relationships, focal points, and shaped reveals.
### Shape Masks
```python
def mask_circle(g, cx_frac=0.5, cy_frac=0.5, radius=0.3, feather=0.05):
"""Circular mask centered at (cx_frac, cy_frac) in normalized coords.
feather: width of soft edge (0 = hard cutoff)."""
asp = g.cw / g.ch if hasattr(g, 'cw') else 1.0
dx = (g.cc / g.cols - cx_frac)
dy = (g.rr / g.rows - cy_frac) * asp
d = np.sqrt(dx**2 + dy**2)
if feather > 0:
return np.clip(1.0 - (d - radius) / feather, 0, 1)
return (d <= radius).astype(np.float32)
def mask_rect(g, x0=0.2, y0=0.2, x1=0.8, y1=0.8, feather=0.03):
"""Rectangular mask. Coordinates in [0,1] normalized."""
dx = np.maximum(x0 - g.cc / g.cols, g.cc / g.cols - x1)
dy = np.maximum(y0 - g.rr / g.rows, g.rr / g.rows - y1)
d = np.maximum(dx, dy)
if feather > 0:
return np.clip(1.0 - d / feather, 0, 1)
return (d <= 0).astype(np.float32)
def mask_ring(g, cx_frac=0.5, cy_frac=0.5, inner_r=0.15, outer_r=0.35,
feather=0.03):
"""Ring / annulus mask."""
inner = mask_circle(g, cx_frac, cy_frac, inner_r, feather)
outer = mask_circle(g, cx_frac, cy_frac, outer_r, feather)
return outer - inner
def mask_gradient_h(g, start=0.0, end=1.0):
"""Left-to-right gradient mask."""
return np.clip((g.cc / g.cols - start) / (end - start + 1e-10), 0, 1).astype(np.float32)
def mask_gradient_v(g, start=0.0, end=1.0):
"""Top-to-bottom gradient mask."""
return np.clip((g.rr / g.rows - start) / (end - start + 1e-10), 0, 1).astype(np.float32)
def mask_gradient_radial(g, cx_frac=0.5, cy_frac=0.5, inner=0.0, outer=0.5):
"""Radial gradient mask — bright at center, dark at edges."""
d = np.sqrt((g.cc / g.cols - cx_frac)**2 + (g.rr / g.rows - cy_frac)**2)
return np.clip(1.0 - (d - inner) / (outer - inner + 1e-10), 0, 1)
```
### Value Field as Mask
Use any `vf_*` function's output as a spatial mask:
```python
def mask_from_vf(vf_result, threshold=0.5, feather=0.1):
"""Convert a value field to a mask by thresholding.
feather: smooth edge width around threshold."""
if feather > 0:
return np.clip((vf_result - threshold + feather) / (2 * feather), 0, 1)
return (vf_result > threshold).astype(np.float32)
def mask_select(mask, vf_a, vf_b):
"""Spatial conditional: show vf_a where mask is 1, vf_b where mask is 0.
mask: float32 [0,1] array. Intermediate values blend."""
return vf_a * mask + vf_b * (1 - mask)
```
### Text Stencil
Render text to a mask. Effects are visible only through the letterforms:
```python
def mask_text(grid, text, row_frac=0.5, font=None, font_size=None):
"""Render text string as a float32 mask [0,1] at grid resolution.
Characters = 1.0, background = 0.0.
row_frac: vertical position as fraction of grid height.
font: PIL ImageFont (defaults to grid's font if None).
font_size: override font size for the mask text (for larger stencil text).
"""
from PIL import Image, ImageDraw, ImageFont
f = font or grid.font
if font_size and font != grid.font:
f = ImageFont.truetype(font.path, font_size)
# Render text to image at pixel resolution, then downsample to grid
img = Image.new("L", (grid.cols * grid.cw, grid.ch), 0)
draw = ImageDraw.Draw(img)
bbox = draw.textbbox((0, 0), text, font=f)
tw = bbox[2] - bbox[0]
x = (grid.cols * grid.cw - tw) // 2
draw.text((x, 0), text, fill=255, font=f)
row_mask = np.array(img, dtype=np.float32) / 255.0
# Place in full grid mask
mask = np.zeros((grid.rows, grid.cols), dtype=np.float32)
target_row = int(grid.rows * row_frac)
# Downsample rendered text to grid cells
for c in range(grid.cols):
px = c * grid.cw
if px + grid.cw <= row_mask.shape[1]:
cell = row_mask[:, px:px + grid.cw]
if cell.mean() > 0.1:
mask[target_row, c] = cell.mean()
return mask
def mask_text_block(grid, lines, start_row_frac=0.3, font=None):
"""Multi-line text stencil. Returns full grid mask."""
mask = np.zeros((grid.rows, grid.cols), dtype=np.float32)
for i, line in enumerate(lines):
row_frac = start_row_frac + i / grid.rows
line_mask = mask_text(grid, line, row_frac, font)
mask = np.maximum(mask, line_mask)
return mask
```
### Animated Masks
Masks that change over time for reveals, wipes, and morphing:
```python
def mask_iris(g, t, t_start, t_end, cx_frac=0.5, cy_frac=0.5,
max_radius=0.7, ease_fn=None):
"""Iris open/close: circle that grows from 0 to max_radius.
ease_fn: easing function (default: ease_in_out_cubic from effects.md)."""
if ease_fn is None:
ease_fn = lambda x: x * x * (3 - 2 * x) # smoothstep fallback
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
radius = ease_fn(progress) * max_radius
return mask_circle(g, cx_frac, cy_frac, radius, feather=0.03)
def mask_wipe_h(g, t, t_start, t_end, direction="right"):
"""Horizontal wipe reveal."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
if direction == "left":
progress = 1 - progress
return mask_gradient_h(g, start=progress - 0.05, end=progress + 0.05)
def mask_wipe_v(g, t, t_start, t_end, direction="down"):
"""Vertical wipe reveal."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
if direction == "up":
progress = 1 - progress
return mask_gradient_v(g, start=progress - 0.05, end=progress + 0.05)
def mask_dissolve(g, t, t_start, t_end, seed=42):
"""Random pixel dissolve — noise threshold sweeps from 0 to 1."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
rng = np.random.RandomState(seed)
noise = rng.random((g.rows, g.cols)).astype(np.float32)
return (noise < progress).astype(np.float32)
```
### Mask Boolean Operations
```python
def mask_union(a, b):
"""OR — visible where either mask is active."""
return np.maximum(a, b)
def mask_intersect(a, b):
"""AND — visible only where both masks are active."""
return np.minimum(a, b)
def mask_subtract(a, b):
"""A minus B — visible where A is active but B is not."""
return np.clip(a - b, 0, 1)
def mask_invert(m):
"""NOT — flip mask."""
return 1.0 - m
```
### Applying Masks to Canvases
```python
def apply_mask_canvas(canvas, mask, bg_canvas=None):
"""Apply a grid-resolution mask to a pixel canvas.
Expands mask from (rows, cols) to (VH, VW) via nearest-neighbor.
canvas: uint8 (VH, VW, 3)
mask: float32 (rows, cols) [0,1]
bg_canvas: what shows through where mask=0. None = black.
"""
# Expand mask to pixel resolution
mask_px = np.repeat(np.repeat(mask, canvas.shape[0] // mask.shape[0] + 1, axis=0),
canvas.shape[1] // mask.shape[1] + 1, axis=1)
mask_px = mask_px[:canvas.shape[0], :canvas.shape[1]]
if bg_canvas is not None:
return np.clip(canvas * mask_px[:, :, None] +
bg_canvas * (1 - mask_px[:, :, None]), 0, 255).astype(np.uint8)
return np.clip(canvas * mask_px[:, :, None], 0, 255).astype(np.uint8)
def apply_mask_vf(vf_a, vf_b, mask):
"""Apply mask at value-field level — blend two value fields spatially.
All arrays are (rows, cols) float32."""
return vf_a * mask + vf_b * (1 - mask)
```
---
## PixelBlendStack
Higher-level wrapper for multi-layer compositing:
```python
class PixelBlendStack:
def __init__(self):
self.layers = []
def add(self, canvas, mode="normal", opacity=1.0):
self.layers.append((canvas, mode, opacity))
return self
def composite(self):
if not self.layers:
return np.zeros((VH, VW, 3), dtype=np.uint8)
result = self.layers[0][0]
for canvas, mode, opacity in self.layers[1:]:
result = blend_canvas(result, canvas, mode, opacity)
return result
```
## Text Backdrop (Readability Mask)
When placing readable text over busy multi-grid ASCII backgrounds, the text will blend into the background and become illegible. **Always apply a dark backdrop behind text regions.**
The technique: compute the bounding box of all text glyphs, create a gaussian-blurred dark mask covering that area with padding, and multiply the background by `(1 - mask * darkness)` before rendering text on top.
```python
from scipy.ndimage import gaussian_filter
def apply_text_backdrop(canvas, glyphs, padding=80, darkness=0.75):
"""Darken the background behind text for readability.
Call AFTER rendering background, BEFORE rendering text.
Args:
canvas: (VH, VW, 3) uint8 background
glyphs: list of {"x": float, "y": float, ...} glyph positions
padding: pixel padding around text bounding box
darkness: 0.0 = no darkening, 1.0 = fully black
Returns:
darkened canvas (uint8)
"""
if not glyphs:
return canvas
xs = [g['x'] for g in glyphs]
ys = [g['y'] for g in glyphs]
x0 = max(0, int(min(xs)) - padding)
y0 = max(0, int(min(ys)) - padding)
x1 = min(VW, int(max(xs)) + padding + 50) # extra for char width
y1 = min(VH, int(max(ys)) + padding + 60) # extra for char height
# Soft dark mask with gaussian blur for feathered edges
mask = np.zeros((VH, VW), dtype=np.float32)
mask[y0:y1, x0:x1] = 1.0
mask = gaussian_filter(mask, sigma=padding * 0.6)
factor = 1.0 - mask * darkness
return (canvas.astype(np.float32) * factor[:, :, np.newaxis]).astype(np.uint8)
```
### Usage in render pipeline
Insert between background rendering and text rendering:
```python
# 1. Render background (multi-grid ASCII effects)
bg = render_background(cfg, t)
# 2. Darken behind text region
bg = apply_text_backdrop(bg, frame_glyphs, padding=80, darkness=0.75)
# 3. Render text on top (now readable against dark backdrop)
bg = text_renderer.render(bg, frame_glyphs, color=(255, 255, 255))
```
Combine with **reverse vignette** (see shaders.md) for scenes where text is always centered — the reverse vignette provides a persistent center-dark zone, while the backdrop handles per-frame glyph positions.
## External Layout Oracle Pattern
For text-heavy videos where text needs to dynamically reflow around obstacles (shapes, icons, other text), use an external layout engine to pre-compute glyph positions and feed them into the Python renderer via JSON.
### Architecture
```
Layout Engine (browser/Node.js) → layouts.json → Python ASCII Renderer
↑ ↑
Computes per-frame Reads glyph positions,
glyph (x,y) positions renders as ASCII chars
with obstacle-aware reflow with full effect pipeline
```
### JSON interchange format
```json
{
"meta": {
"canvas_width": 1080, "canvas_height": 1080,
"fps": 24, "total_frames": 1248,
"fonts": {
"body": {"charW": 12.04, "charH": 24, "fontSize": 20},
"hero": {"charW": 24.08, "charH": 48, "fontSize": 40}
}
},
"scenes": [
{
"id": "scene_name",
"start_frame": 0, "end_frame": 96,
"frames": {
"0": {
"glyphs": [
{"char": "H", "x": 287.1, "y": 400.0, "alpha": 1.0},
{"char": "e", "x": 311.2, "y": 400.0, "alpha": 1.0}
],
"obstacles": [
{"type": "circle", "cx": 540, "cy": 540, "r": 80},
{"type": "rect", "x": 300, "y": 500, "w": 120, "h": 80}
]
}
}
}
]
}
```
### When to use
- Text that dynamically reflows around moving objects
- Per-glyph animation (reveal, scatter, physics)
- Variable typography that needs precise measurement
- Any case where Python's Pillow text layout is insufficient
### When NOT to use
- Static centered text (just use PIL `draw.text()` directly)
- Text that only fades in/out without spatial animation
- Simple typewriter effects (handle in Python with a character counter)
### Running the oracle
Use Playwright to run the layout engine in a headless browser:
```javascript
// extract.mjs
import { chromium } from 'playwright';
const browser = await chromium.launch({ headless: true });
const page = await browser.newPage();
await page.goto(`file://${oraclePath}`);
await page.waitForFunction(() => window.__ORACLE_DONE__ === true, null, { timeout: 60000 });
const result = await page.evaluate(() => window.__ORACLE_RESULT__);
writeFileSync('layouts.json', JSON.stringify(result));
await browser.close();
```
### Consuming in Python
```python
# In the renderer, map pixel positions to the canvas:
for glyph in frame_data['glyphs']:
char, px, py = glyph['char'], glyph['x'], glyph['y']
alpha = glyph.get('alpha', 1.0)
# Render using PIL draw.text() at exact pixel position
draw.text((px, py), char, fill=(int(255*alpha),)*3, font=font)
```
Obstacles from the JSON can also be rendered as glowing ASCII shapes (circles, rectangles) to visualize the reflow zones.

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# Input Sources
> **See also:** architecture.md · effects.md · scenes.md · shaders.md · optimization.md · troubleshooting.md
## Audio Analysis
### Loading
```python
tmp = tempfile.mktemp(suffix=".wav")
subprocess.run(["ffmpeg", "-y", "-i", input_path, "-ac", "1", "-ar", "22050",
"-sample_fmt", "s16", tmp], capture_output=True, check=True)
with wave.open(tmp) as wf:
sr = wf.getframerate()
raw = wf.readframes(wf.getnframes())
samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
```
### Per-Frame FFT
```python
hop = sr // fps # samples per frame
win = hop * 2 # analysis window (2x hop for overlap)
window = np.hanning(win)
freqs = rfftfreq(win, 1.0 / sr)
bands = {
"sub": (freqs >= 20) & (freqs < 80),
"bass": (freqs >= 80) & (freqs < 250),
"lomid": (freqs >= 250) & (freqs < 500),
"mid": (freqs >= 500) & (freqs < 2000),
"himid": (freqs >= 2000)& (freqs < 6000),
"hi": (freqs >= 6000),
}
```
For each frame: extract chunk, apply window, FFT, compute band energies.
### Feature Set
| Feature | Formula | Controls |
|---------|---------|----------|
| `rms` | `sqrt(mean(chunk²))` | Overall loudness/energy |
| `sub`..`hi` | `sqrt(mean(band_magnitudes²))` | Per-band energy |
| `centroid` | `sum(freq*mag) / sum(mag)` | Brightness/timbre |
| `flatness` | `geomean(mag) / mean(mag)` | Noise vs tone |
| `flux` | `sum(max(0, mag - prev_mag))` | Transient strength |
| `sub_r`..`hi_r` | `band / sum(all_bands)` | Spectral shape (volume-independent) |
| `cent_d` | `abs(gradient(centroid))` | Timbral change rate |
| `beat` | Flux peak detection | Binary beat onset |
| `bdecay` | Exponential decay from beats | Smooth beat pulse (0→1→0) |
**Band ratios are critical** — they decouple spectral shape from volume, so a quiet bass section and a loud bass section both read as "bassy" rather than just "loud" vs "quiet".
### Smoothing
EMA prevents visual jitter:
```python
def ema(arr, alpha):
out = np.empty_like(arr); out[0] = arr[0]
for i in range(1, len(arr)):
out[i] = alpha * arr[i] + (1 - alpha) * out[i-1]
return out
# Slow-moving features (alpha=0.12): centroid, flatness, band ratios, cent_d
# Fast-moving features (alpha=0.3): rms, flux, raw bands
```
### Beat Detection
```python
flux_smooth = np.convolve(flux, np.ones(5)/5, mode="same")
peaks, _ = signal.find_peaks(flux_smooth, height=0.15, distance=fps//5, prominence=0.05)
beat = np.zeros(n_frames)
bdecay = np.zeros(n_frames, dtype=np.float32)
for p in peaks:
beat[p] = 1.0
for d in range(fps // 2):
if p + d < n_frames:
bdecay[p + d] = max(bdecay[p + d], math.exp(-d * 2.5 / (fps // 2)))
```
`bdecay` gives smooth 0→1→0 pulse per beat, decaying over ~0.5s. Use for flash/glitch/mirror triggers.
### Normalization
After computing all frames, normalize each feature to 0-1:
```python
for k in features:
a = features[k]
lo, hi = a.min(), a.max()
features[k] = (a - lo) / (hi - lo + 1e-10)
```
## Video Sampling
### Frame Extraction
```python
# Method 1: ffmpeg pipe (memory efficient)
cmd = ["ffmpeg", "-i", input_video, "-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{target_w}x{target_h}", "-r", str(fps), "-"]
pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
frame_size = target_w * target_h * 3
for fi in range(n_frames):
raw = pipe.stdout.read(frame_size)
if len(raw) < frame_size: break
frame = np.frombuffer(raw, dtype=np.uint8).reshape(target_h, target_w, 3)
# process frame...
# Method 2: OpenCV (if available)
cap = cv2.VideoCapture(input_video)
```
### Luminance-to-Character Mapping
Convert video pixels to ASCII characters based on brightness:
```python
def frame_to_ascii(frame_rgb, grid, pal=PAL_DEFAULT):
"""Convert video frame to character + color arrays."""
rows, cols = grid.rows, grid.cols
# Resize frame to grid dimensions
small = np.array(Image.fromarray(frame_rgb).resize((cols, rows), Image.LANCZOS))
# Luminance
lum = (0.299 * small[:,:,0] + 0.587 * small[:,:,1] + 0.114 * small[:,:,2]) / 255.0
# Map to chars
chars = val2char(lum, lum > 0.02, pal)
# Colors: use source pixel colors, scaled by luminance for visibility
colors = np.clip(small * np.clip(lum[:,:,None] * 1.5 + 0.3, 0.3, 1), 0, 255).astype(np.uint8)
return chars, colors
```
### Edge-Weighted Character Mapping
Use edge detection for more detail in contour regions:
```python
def frame_to_ascii_edges(frame_rgb, grid, pal=PAL_DEFAULT, edge_pal=PAL_BOX):
gray = np.mean(frame_rgb, axis=2)
small_gray = resize(gray, (grid.rows, grid.cols))
lum = small_gray / 255.0
# Sobel edge detection
gx = np.abs(small_gray[:, 2:] - small_gray[:, :-2])
gy = np.abs(small_gray[2:, :] - small_gray[:-2, :])
edge = np.zeros_like(small_gray)
edge[:, 1:-1] += gx; edge[1:-1, :] += gy
edge = np.clip(edge / edge.max(), 0, 1)
# Edge regions get box drawing chars, flat regions get brightness chars
is_edge = edge > 0.15
chars = val2char(lum, lum > 0.02, pal)
edge_chars = val2char(edge, is_edge, edge_pal)
chars[is_edge] = edge_chars[is_edge]
return chars, colors
```
### Motion Detection
Detect pixel changes between frames for motion-reactive effects:
```python
prev_frame = None
def compute_motion(frame):
global prev_frame
if prev_frame is None:
prev_frame = frame.astype(np.float32)
return np.zeros(frame.shape[:2])
diff = np.abs(frame.astype(np.float32) - prev_frame).mean(axis=2)
prev_frame = frame.astype(np.float32) * 0.7 + prev_frame * 0.3 # smoothed
return np.clip(diff / 30.0, 0, 1) # normalized motion map
```
Use motion map to drive particle emission, glitch intensity, or character density.
### Video Feature Extraction
Per-frame features analogous to audio features, for driving effects:
```python
def analyze_video_frame(frame_rgb):
gray = np.mean(frame_rgb, axis=2)
return {
"brightness": gray.mean() / 255.0,
"contrast": gray.std() / 128.0,
"edge_density": compute_edge_density(gray),
"motion": compute_motion(frame_rgb).mean(),
"dominant_hue": compute_dominant_hue(frame_rgb),
"color_variance": compute_color_variance(frame_rgb),
}
```
## Image Sequence
### Static Image to ASCII
Same as single video frame conversion. For animated sequences:
```python
import glob
frames = sorted(glob.glob("frames/*.png"))
for fi, path in enumerate(frames):
img = np.array(Image.open(path).resize((VW, VH)))
chars, colors = frame_to_ascii(img, grid, pal)
```
### Image as Texture Source
Use an image as a background texture that effects modulate:
```python
def load_texture(path, grid):
img = np.array(Image.open(path).resize((grid.cols, grid.rows)))
lum = np.mean(img, axis=2) / 255.0
return lum, img # luminance for char mapping, RGB for colors
```
## Text / Lyrics
### SRT Parsing
```python
import re
def parse_srt(path):
"""Returns [(start_sec, end_sec, text), ...]"""
entries = []
with open(path) as f:
content = f.read()
blocks = content.strip().split("\n\n")
for block in blocks:
lines = block.strip().split("\n")
if len(lines) >= 3:
times = lines[1]
m = re.match(r"(\d+):(\d+):(\d+),(\d+) --> (\d+):(\d+):(\d+),(\d+)", times)
if m:
g = [int(x) for x in m.groups()]
start = g[0]*3600 + g[1]*60 + g[2] + g[3]/1000
end = g[4]*3600 + g[5]*60 + g[6] + g[7]/1000
text = " ".join(lines[2:])
entries.append((start, end, text))
return entries
```
### Lyrics Display Modes
- **Typewriter**: characters appear left-to-right over the time window
- **Fade-in**: whole line fades from dark to bright
- **Flash**: appear instantly on beat, fade out
- **Scatter**: characters start at random positions, converge to final position
- **Wave**: text follows a sine wave path
```python
def lyrics_typewriter(ch, co, text, row, col, t, t_start, t_end, color):
"""Reveal characters progressively over time window."""
progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
n_visible = int(len(text) * progress)
stamp(ch, co, text[:n_visible], row, col, color)
```
## Generative (No Input)
For pure generative ASCII art, the "features" dict is synthesized from time:
```python
def synthetic_features(t, bpm=120):
"""Generate audio-like features from time alone."""
beat_period = 60.0 / bpm
beat_phase = (t % beat_period) / beat_period
return {
"rms": 0.5 + 0.3 * math.sin(t * 0.5),
"bass": 0.5 + 0.4 * math.sin(t * 2 * math.pi / beat_period),
"sub": 0.3 + 0.3 * math.sin(t * 0.8),
"mid": 0.4 + 0.3 * math.sin(t * 1.3),
"hi": 0.3 + 0.2 * math.sin(t * 2.1),
"cent": 0.5 + 0.2 * math.sin(t * 0.3),
"flat": 0.4,
"flux": 0.3 + 0.2 * math.sin(t * 3),
"beat": 1.0 if beat_phase < 0.05 else 0.0,
"bdecay": max(0, 1.0 - beat_phase * 4),
# ratios
"sub_r": 0.2, "bass_r": 0.25, "lomid_r": 0.15,
"mid_r": 0.2, "himid_r": 0.12, "hi_r": 0.08,
"cent_d": 0.1,
}
```
## TTS Integration
For narrated videos (testimonials, quotes, storytelling), generate speech audio per segment and mix with background music.
### ElevenLabs Voice Generation
```python
import requests, time, os
def generate_tts(text, voice_id, api_key, output_path, model="eleven_multilingual_v2"):
"""Generate TTS audio via ElevenLabs API. Streams response to disk."""
# Skip if already generated (idempotent re-runs)
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
return
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
data = {
"text": text,
"model_id": model,
"voice_settings": {
"stability": 0.65,
"similarity_boost": 0.80,
"style": 0.15,
"use_speaker_boost": True,
},
}
resp = requests.post(url, json=data, headers=headers, stream=True)
resp.raise_for_status()
with open(output_path, "wb") as f:
for chunk in resp.iter_content(chunk_size=4096):
f.write(chunk)
time.sleep(0.3) # rate limit: avoid 429s on batch generation
```
Voice settings notes:
- `stability` 0.65 gives natural variation without drift. Lower (0.3-0.5) for more expressive reads, higher (0.7-0.9) for monotone/narration.
- `similarity_boost` 0.80 keeps it close to the voice profile. Lower for more generic sound.
- `style` 0.15 adds slight stylistic variation. Keep low (0-0.2) for straightforward reads.
- `use_speaker_boost` True improves clarity at the cost of slightly more processing time.
### Voice Pool
ElevenLabs has ~20 built-in voices. Use multiple voices for variety across quotes. Reference pool:
```python
VOICE_POOL = [
("JBFqnCBsd6RMkjVDRZzb", "George"),
("nPczCjzI2devNBz1zQrb", "Brian"),
("pqHfZKP75CvOlQylNhV4", "Bill"),
("CwhRBWXzGAHq8TQ4Fs17", "Roger"),
("cjVigY5qzO86Huf0OWal", "Eric"),
("onwK4e9ZLuTAKqWW03F9", "Daniel"),
("IKne3meq5aSn9XLyUdCD", "Charlie"),
("iP95p4xoKVk53GoZ742B", "Chris"),
("bIHbv24MWmeRgasZH58o", "Will"),
("TX3LPaxmHKxFdv7VOQHJ", "Liam"),
("SAz9YHcvj6GT2YYXdXww", "River"),
("EXAVITQu4vr4xnSDxMaL", "Sarah"),
("Xb7hH8MSUJpSbSDYk0k2", "Alice"),
("pFZP5JQG7iQjIQuC4Bku", "Lily"),
("XrExE9yKIg1WjnnlVkGX", "Matilda"),
("FGY2WhTYpPnrIDTdsKH5", "Laura"),
("SOYHLrjzK2X1ezoPC6cr", "Harry"),
("hpp4J3VqNfWAUOO0d1Us", "Bella"),
("N2lVS1w4EtoT3dr4eOWO", "Callum"),
("cgSgspJ2msm6clMCkdW9", "Jessica"),
("pNInz6obpgDQGcFmaJgB", "Adam"),
]
```
### Voice Assignment
Shuffle deterministically so re-runs produce the same voice mapping:
```python
import random as _rng
def assign_voices(n_quotes, voice_pool, seed=42):
"""Assign a different voice to each quote, cycling if needed."""
r = _rng.Random(seed)
ids = [v[0] for v in voice_pool]
r.shuffle(ids)
return [ids[i % len(ids)] for i in range(n_quotes)]
```
### Pronunciation Control
TTS text must be separate from display text. The display text has line breaks for visual layout; the TTS text is a flat sentence with phonetic fixes.
Common fixes:
- Brand names: spell phonetically ("Nous" -> "Noose", "nginx" -> "engine-x")
- Abbreviations: expand ("API" -> "A P I", "CLI" -> "C L I")
- Technical terms: add phonetic hints
- Punctuation for pacing: periods create pauses, commas create slight pauses
```python
# Display text: line breaks control visual layout
QUOTES = [
("It can do far more than the Claws,\nand you don't need to buy a Mac Mini.\nNous Research has a winner here.", "Brian Roemmele"),
]
# TTS text: flat, phonetically corrected for speech
QUOTES_TTS = [
"It can do far more than the Claws, and you don't need to buy a Mac Mini. Noose Research has a winner here.",
]
# Keep both arrays in sync -- same indices
```
### Audio Pipeline
1. Generate individual TTS clips (MP3 per quote, skipping existing)
2. Convert each to WAV (mono, 22050 Hz) for duration measurement and concatenation
3. Calculate timing: intro pad + speech + gaps + outro pad = target duration
4. Concatenate into single TTS track with silence padding
5. Mix with background music
```python
def build_tts_track(tts_clips, target_duration, intro_pad=5.0, outro_pad=4.0):
"""Concatenate TTS clips with calculated gaps, pad to target duration.
Returns:
timing: list of (start_time, end_time, quote_index) tuples
"""
sr = 22050
# Convert MP3s to WAV for duration and sample-level concatenation
durations = []
for clip in tts_clips:
wav = clip.replace(".mp3", ".wav")
subprocess.run(
["ffmpeg", "-y", "-i", clip, "-ac", "1", "-ar", str(sr),
"-sample_fmt", "s16", wav],
capture_output=True, check=True)
result = subprocess.run(
["ffprobe", "-v", "error", "-show_entries", "format=duration",
"-of", "csv=p=0", wav],
capture_output=True, text=True)
durations.append(float(result.stdout.strip()))
# Calculate gap to fill target duration
total_speech = sum(durations)
n_gaps = len(tts_clips) - 1
remaining = target_duration - total_speech - intro_pad - outro_pad
gap = max(1.0, remaining / max(1, n_gaps))
# Build timing and concatenate samples
timing = []
t = intro_pad
all_audio = [np.zeros(int(sr * intro_pad), dtype=np.int16)]
for i, dur in enumerate(durations):
wav = tts_clips[i].replace(".mp3", ".wav")
with wave.open(wav) as wf:
samples = np.frombuffer(wf.readframes(wf.getnframes()), dtype=np.int16)
timing.append((t, t + dur, i))
all_audio.append(samples)
t += dur
if i < len(tts_clips) - 1:
all_audio.append(np.zeros(int(sr * gap), dtype=np.int16))
t += gap
all_audio.append(np.zeros(int(sr * outro_pad), dtype=np.int16))
# Pad or trim to exactly target_duration
full = np.concatenate(all_audio)
target_samples = int(sr * target_duration)
if len(full) < target_samples:
full = np.pad(full, (0, target_samples - len(full)))
else:
full = full[:target_samples]
# Write concatenated TTS track
with wave.open("tts_full.wav", "w") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sr)
wf.writeframes(full.tobytes())
return timing
```
### Audio Mixing
Mix TTS (center) with background music (wide stereo, low volume). The filter chain:
1. TTS mono duplicated to both channels (centered)
2. BGM loudness-normalized, volume reduced to 15%, stereo widened with `extrastereo`
3. Mixed together with dropout transition for smooth endings
```python
def mix_audio(tts_path, bgm_path, output_path, bgm_volume=0.15):
"""Mix TTS centered with BGM panned wide stereo."""
filter_complex = (
# TTS: mono -> stereo center
"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=mono,"
"pan=stereo|c0=c0|c1=c0[tts];"
# BGM: normalize loudness, reduce volume, widen stereo
f"[1:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=stereo,"
f"loudnorm=I=-16:TP=-1.5:LRA=11,"
f"volume={bgm_volume},"
f"extrastereo=m=2.5[bgm];"
# Mix with smooth dropout at end
"[tts][bgm]amix=inputs=2:duration=longest:dropout_transition=3,"
"aformat=sample_fmts=s16:sample_rates=44100:channel_layouts=stereo[out]"
)
cmd = [
"ffmpeg", "-y",
"-i", tts_path,
"-i", bgm_path,
"-filter_complex", filter_complex,
"-map", "[out]", output_path,
]
subprocess.run(cmd, capture_output=True, check=True)
```
### Per-Quote Visual Style
Cycle through visual presets per quote for variety. Each preset defines a background effect, color scheme, and text color:
```python
QUOTE_STYLES = [
{"hue": 0.08, "accent": 0.7, "bg": "spiral", "text_rgb": (255, 220, 140)}, # warm gold
{"hue": 0.55, "accent": 0.6, "bg": "rings", "text_rgb": (180, 220, 255)}, # cool blue
{"hue": 0.75, "accent": 0.7, "bg": "wave", "text_rgb": (220, 180, 255)}, # purple
{"hue": 0.35, "accent": 0.6, "bg": "matrix", "text_rgb": (140, 255, 180)}, # green
{"hue": 0.95, "accent": 0.8, "bg": "fire", "text_rgb": (255, 180, 160)}, # red/coral
{"hue": 0.12, "accent": 0.5, "bg": "interference", "text_rgb": (255, 240, 200)}, # amber
{"hue": 0.60, "accent": 0.7, "bg": "tunnel", "text_rgb": (160, 210, 255)}, # cyan
{"hue": 0.45, "accent": 0.6, "bg": "aurora", "text_rgb": (180, 255, 220)}, # teal
]
style = QUOTE_STYLES[quote_index % len(QUOTE_STYLES)]
```
This guarantees no two adjacent quotes share the same look, even without randomness.
### Typewriter Text Rendering
Display quote text character-by-character synced to speech progress. Recently revealed characters are brighter, creating a "just typed" glow:
```python
def render_typewriter(ch, co, lines, block_start, cols, progress, total_chars, text_rgb, t):
"""Overlay typewriter text onto character/color grids.
progress: 0.0 (nothing visible) to 1.0 (all text visible)."""
chars_visible = int(total_chars * min(1.0, progress * 1.2)) # slight overshoot for snappy feel
tr, tg, tb = text_rgb
char_count = 0
for li, line in enumerate(lines):
row = block_start + li
col = (cols - len(line)) // 2
for ci, c in enumerate(line):
if char_count < chars_visible:
age = chars_visible - char_count
bri_factor = min(1.0, 0.5 + 0.5 / (1 + age * 0.015)) # newer = brighter
hue_shift = math.sin(char_count * 0.3 + t * 2) * 0.05
stamp(ch, co, c, row, col + ci,
(int(min(255, tr * bri_factor * (1.0 + hue_shift))),
int(min(255, tg * bri_factor)),
int(min(255, tb * bri_factor * (1.0 - hue_shift)))))
char_count += 1
# Blinking cursor at insertion point
if progress < 1.0 and int(t * 3) % 2 == 0:
# Find cursor position (char_count == chars_visible)
cc = 0
for li, line in enumerate(lines):
for ci, c in enumerate(line):
if cc == chars_visible:
stamp(ch, co, "\u258c", block_start + li,
(cols - len(line)) // 2 + ci, (255, 220, 100))
return
cc += 1
```
### Feature Analysis on Mixed Audio
Run the standard audio analysis (FFT, beat detection) on the final mixed track so visual effects react to both TTS and music:
```python
# Analyze mixed_final.wav (not individual tracks)
features = analyze_audio("mixed_final.wav", fps=24)
```
Visuals pulse with both the music beats and the speech energy.
---
## Audio-Video Sync Verification
After rendering, verify that visual beat markers align with actual audio beats. Drift accumulates from frame timing errors, ffmpeg concat boundaries, and rounding in `fi / fps`.
### Beat Timestamp Extraction
```python
def extract_beat_timestamps(features, fps, threshold=0.5):
"""Extract timestamps where beat feature exceeds threshold."""
beat = features["beat"]
timestamps = []
for fi in range(len(beat)):
if beat[fi] > threshold:
timestamps.append(fi / fps)
return timestamps
def extract_visual_beat_timestamps(video_path, fps, brightness_jump=30):
"""Detect visual beats by brightness jumps between consecutive frames.
Returns timestamps where mean brightness increases by more than threshold."""
import subprocess
cmd = ["ffmpeg", "-i", video_path, "-f", "rawvideo", "-pix_fmt", "gray", "-"]
proc = subprocess.run(cmd, capture_output=True)
frames = np.frombuffer(proc.stdout, dtype=np.uint8)
# Infer frame dimensions from total byte count
n_pixels = len(frames)
# For 1080p: 1920*1080 pixels per frame
# Auto-detect from video metadata is more robust:
probe = subprocess.run(
["ffprobe", "-v", "error", "-select_streams", "v:0",
"-show_entries", "stream=width,height",
"-of", "csv=p=0", video_path],
capture_output=True, text=True)
w, h = map(int, probe.stdout.strip().split(","))
ppf = w * h # pixels per frame
n_frames = n_pixels // ppf
frames = frames[:n_frames * ppf].reshape(n_frames, ppf)
means = frames.mean(axis=1)
timestamps = []
for i in range(1, len(means)):
if means[i] - means[i-1] > brightness_jump:
timestamps.append(i / fps)
return timestamps
```
### Sync Report
```python
def sync_report(audio_beats, visual_beats, tolerance_ms=50):
"""Compare audio beat timestamps to visual beat timestamps.
Args:
audio_beats: list of timestamps (seconds) from audio analysis
visual_beats: list of timestamps (seconds) from video brightness analysis
tolerance_ms: max acceptable drift in milliseconds
Returns:
dict with matched/unmatched/drift statistics
"""
tolerance = tolerance_ms / 1000.0
matched = []
unmatched_audio = []
unmatched_visual = list(visual_beats)
for at in audio_beats:
best_match = None
best_delta = float("inf")
for vt in unmatched_visual:
delta = abs(at - vt)
if delta < best_delta:
best_delta = delta
best_match = vt
if best_match is not None and best_delta < tolerance:
matched.append({"audio": at, "visual": best_match, "drift_ms": best_delta * 1000})
unmatched_visual.remove(best_match)
else:
unmatched_audio.append(at)
drifts = [m["drift_ms"] for m in matched]
return {
"matched": len(matched),
"unmatched_audio": len(unmatched_audio),
"unmatched_visual": len(unmatched_visual),
"total_audio_beats": len(audio_beats),
"total_visual_beats": len(visual_beats),
"mean_drift_ms": np.mean(drifts) if drifts else 0,
"max_drift_ms": np.max(drifts) if drifts else 0,
"p95_drift_ms": np.percentile(drifts, 95) if len(drifts) > 1 else 0,
}
# Usage:
audio_beats = extract_beat_timestamps(features, fps=24)
visual_beats = extract_visual_beat_timestamps("output.mp4", fps=24)
report = sync_report(audio_beats, visual_beats)
print(f"Matched: {report['matched']}/{report['total_audio_beats']} beats")
print(f"Mean drift: {report['mean_drift_ms']:.1f}ms, Max: {report['max_drift_ms']:.1f}ms")
# Target: mean drift < 20ms, max drift < 42ms (1 frame at 24fps)
```
### Common Sync Issues
| Symptom | Cause | Fix |
|---------|-------|-----|
| Consistent late visual beats | ffmpeg concat adds frames at boundaries | Use `-vsync cfr` flag; pad segments to exact frame count |
| Drift increases over time | Floating-point accumulation in `t = fi / fps` | Use integer frame counter, compute `t` fresh each frame |
| Random missed beats | Beat threshold too high / feature smoothing too aggressive | Lower threshold; reduce EMA alpha for beat feature |
| Beats land on wrong frame | Off-by-one in frame indexing | Verify: frame 0 = t=0, frame 1 = t=1/fps (not t=0) |

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# Optimization Reference
> **See also:** architecture.md · composition.md · scenes.md · shaders.md · inputs.md · troubleshooting.md
## Hardware Detection
Detect the user's hardware at script startup and adapt rendering parameters automatically. Never hardcode worker counts or resolution.
### CPU and Memory Detection
```python
import multiprocessing
import platform
import shutil
import os
def detect_hardware():
"""Detect hardware capabilities and return render config."""
cpu_count = multiprocessing.cpu_count()
# Leave 1-2 cores free for OS + ffmpeg encoding
if cpu_count >= 16:
workers = cpu_count - 2
elif cpu_count >= 8:
workers = cpu_count - 1
elif cpu_count >= 4:
workers = cpu_count - 1
else:
workers = max(1, cpu_count)
# Memory detection (platform-specific)
try:
if platform.system() == "Darwin":
import subprocess
mem_bytes = int(subprocess.check_output(["sysctl", "-n", "hw.memsize"]).strip())
elif platform.system() == "Linux":
with open("/proc/meminfo") as f:
for line in f:
if line.startswith("MemTotal"):
mem_bytes = int(line.split()[1]) * 1024
break
else:
mem_bytes = 8 * 1024**3 # assume 8GB on unknown
except Exception:
mem_bytes = 8 * 1024**3
mem_gb = mem_bytes / (1024**3)
# Each worker uses ~50-150MB depending on grid sizes
# Cap workers if memory is tight
mem_per_worker_mb = 150
max_workers_by_mem = int(mem_gb * 1024 * 0.6 / mem_per_worker_mb) # use 60% of RAM
workers = min(workers, max_workers_by_mem)
# ffmpeg availability and codec support
has_ffmpeg = shutil.which("ffmpeg") is not None
return {
"cpu_count": cpu_count,
"workers": workers,
"mem_gb": mem_gb,
"platform": platform.system(),
"arch": platform.machine(),
"has_ffmpeg": has_ffmpeg,
}
```
### Adaptive Quality Profiles
Scale resolution, FPS, CRF, and grid density based on hardware:
```python
def quality_profile(hw, target_duration_s, user_preference="auto"):
"""
Returns render settings adapted to hardware.
user_preference: "auto", "draft", "preview", "production", "max"
"""
if user_preference == "draft":
return {"vw": 960, "vh": 540, "fps": 12, "crf": 28, "workers": min(4, hw["workers"]),
"grid_scale": 0.5, "shaders": "minimal", "particles_max": 200}
if user_preference == "preview":
return {"vw": 1280, "vh": 720, "fps": 15, "crf": 25, "workers": hw["workers"],
"grid_scale": 0.75, "shaders": "standard", "particles_max": 500}
if user_preference == "max":
return {"vw": 3840, "vh": 2160, "fps": 30, "crf": 15, "workers": hw["workers"],
"grid_scale": 2.0, "shaders": "full", "particles_max": 3000}
# "production" or "auto"
# Auto-detect: estimate render time, downgrade if it would take too long
n_frames = int(target_duration_s * 24)
est_seconds_per_frame = 0.18 # ~180ms at 1080p
est_total_s = n_frames * est_seconds_per_frame / max(1, hw["workers"])
if hw["mem_gb"] < 4 or hw["cpu_count"] <= 2:
# Low-end: 720p, 15fps
return {"vw": 1280, "vh": 720, "fps": 15, "crf": 23, "workers": hw["workers"],
"grid_scale": 0.75, "shaders": "standard", "particles_max": 500}
if est_total_s > 3600: # would take over an hour
# Downgrade to 720p to speed up
return {"vw": 1280, "vh": 720, "fps": 24, "crf": 20, "workers": hw["workers"],
"grid_scale": 0.75, "shaders": "standard", "particles_max": 800}
# Standard production: 1080p 24fps
return {"vw": 1920, "vh": 1080, "fps": 24, "crf": 20, "workers": hw["workers"],
"grid_scale": 1.0, "shaders": "full", "particles_max": 1200}
def apply_quality_profile(profile):
"""Set globals from quality profile."""
global VW, VH, FPS, N_WORKERS
VW = profile["vw"]
VH = profile["vh"]
FPS = profile["fps"]
N_WORKERS = profile["workers"]
# Grid sizes scale with resolution
# CRF passed to ffmpeg encoder
# Shader set determines which post-processing is active
```
### CLI Integration
```python
parser = argparse.ArgumentParser()
parser.add_argument("--quality", choices=["draft", "preview", "production", "max", "auto"],
default="auto", help="Render quality preset")
parser.add_argument("--aspect", choices=["landscape", "portrait", "square"],
default="landscape", help="Aspect ratio preset")
parser.add_argument("--workers", type=int, default=0, help="Override worker count (0=auto)")
parser.add_argument("--resolution", type=str, default="", help="Override resolution e.g. 1280x720")
args = parser.parse_args()
hw = detect_hardware()
if args.workers > 0:
hw["workers"] = args.workers
profile = quality_profile(hw, target_duration, args.quality)
# Apply aspect ratio preset (before manual resolution override)
ASPECT_PRESETS = {
"landscape": (1920, 1080),
"portrait": (1080, 1920),
"square": (1080, 1080),
}
if args.aspect != "landscape" and not args.resolution:
profile["vw"], profile["vh"] = ASPECT_PRESETS[args.aspect]
if args.resolution:
w, h = args.resolution.split("x")
profile["vw"], profile["vh"] = int(w), int(h)
apply_quality_profile(profile)
log(f"Hardware: {hw['cpu_count']} cores, {hw['mem_gb']:.1f}GB RAM, {hw['platform']}")
log(f"Render: {profile['vw']}x{profile['vh']} @{profile['fps']}fps, "
f"CRF {profile['crf']}, {profile['workers']} workers")
```
### Portrait Mode Considerations
Portrait (1080x1920) has the same pixel count as landscape 1080p, so performance is equivalent. But composition patterns differ:
| Concern | Landscape | Portrait |
|---------|-----------|----------|
| Grid cols at `lg` | 160 | 90 |
| Grid rows at `lg` | 45 | 80 |
| Max text line chars | ~50 centered | ~25-30 centered |
| Vertical rain | Short travel | Long, dramatic travel |
| Horizontal spectrum | Full width | Needs rotation or compression |
| Radial effects | Natural circles | Tall ellipses (aspect correction handles this) |
| Particle explosions | Wide spread | Tall spread |
| Text stacking | 3-4 lines comfortable | 8-10 lines comfortable |
| Quote layout | 2-3 wide lines | 5-6 short lines |
**Portrait-optimized patterns:**
- Vertical rain/matrix effects are naturally enhanced — longer column travel
- Fire columns rise through more screen space
- Rising embers/particles have more vertical runway
- Text can be stacked more aggressively with more lines
- Radial effects work if aspect correction is applied (GridLayer handles this automatically)
- Spectrum bars can be rotated 90 degrees (vertical bars from bottom)
**Portrait text layout:**
```python
def layout_text_portrait(text, max_chars_per_line=25, grid=None):
"""Break text into short lines for portrait display."""
words = text.split()
lines = []; current = ""
for w in words:
if len(current) + len(w) + 1 > max_chars_per_line:
lines.append(current.strip())
current = w + " "
else:
current += w + " "
if current.strip():
lines.append(current.strip())
return lines
```
## Performance Budget
Target: 100-200ms per frame (5-10 fps single-threaded, 40-80 fps across 8 workers).
| Component | Time | Notes |
|-----------|------|-------|
| Feature extraction | 1-5ms | Pre-computed for all frames before render |
| Effect function | 2-15ms | Vectorized numpy, avoid Python loops |
| Character render | 80-150ms | **Bottleneck** -- per-cell Python loop |
| Shader pipeline | 5-25ms | Depends on active shaders |
| ffmpeg encode | ~5ms | Amortized by pipe buffering |
## Bitmap Pre-Rasterization
Rasterize every character at init, not per-frame:
```python
# At init time -- done once
for c in all_characters:
img = Image.new("L", (cell_w, cell_h), 0)
ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
bitmaps[c] = np.array(img, dtype=np.float32) / 255.0 # float32 for fast multiply
# At render time -- fast lookup
bitmap = bitmaps[char]
canvas[y:y+ch, x:x+cw] = np.maximum(canvas[y:y+ch, x:x+cw],
(bitmap[:,:,None] * color).astype(np.uint8))
```
Collect all characters from all palettes + overlay text into the init set. Lazy-init for any missed characters.
## Pre-Rendered Background Textures
Alternative to `_render_vf()` for backgrounds where characters don't need to change every frame. Pre-bake a static ASCII texture once at init, then multiply by a per-cell color field each frame. One matrix multiply vs thousands of bitmap blits.
Use when: background layer uses a fixed character palette and only color/brightness varies per frame. NOT suitable for layers where character selection depends on a changing value field.
### Init: Bake the Texture
```python
# In GridLayer.__init__:
self._bg_row_idx = np.clip(
(np.arange(VH) - self.oy) // self.ch, 0, self.rows - 1
)
self._bg_col_idx = np.clip(
(np.arange(VW) - self.ox) // self.cw, 0, self.cols - 1
)
self._bg_textures = {}
def make_bg_texture(self, palette):
"""Pre-render a static ASCII texture (grayscale float32) once."""
if palette not in self._bg_textures:
texture = np.zeros((VH, VW), dtype=np.float32)
rng = random.Random(12345)
ch_list = [c for c in palette if c != " " and c in self.bm]
if not ch_list:
ch_list = list(self.bm.keys())[:5]
for row in range(self.rows):
y = self.oy + row * self.ch
if y + self.ch > VH:
break
for col in range(self.cols):
x = self.ox + col * self.cw
if x + self.cw > VW:
break
bm = self.bm[rng.choice(ch_list)]
texture[y:y+self.ch, x:x+self.cw] = bm
self._bg_textures[palette] = texture
return self._bg_textures[palette]
```
### Render: Color Field x Cached Texture
```python
def render_bg(self, color_field, palette=PAL_CIRCUIT):
"""Fast background: pre-rendered ASCII texture * per-cell color field.
color_field: (rows, cols, 3) uint8. Returns (VH, VW, 3) uint8."""
texture = self.make_bg_texture(palette)
# Expand cell colors to pixel coords via pre-computed index maps
color_px = color_field[
self._bg_row_idx[:, None], self._bg_col_idx[None, :]
].astype(np.float32)
return (texture[:, :, None] * color_px).astype(np.uint8)
```
### Usage in a Scene
```python
# Build per-cell color from effect fields (cheap — rows*cols, not VH*VW)
hue = ((t * 0.05 + val * 0.2) % 1.0).astype(np.float32)
R, G, B = hsv2rgb(hue, np.full_like(val, 0.5), val)
color_field = mkc(R, G, B, g.rows, g.cols) # (rows, cols, 3) uint8
# Render background — single matrix multiply, no per-cell loop
canvas_bg = g.render_bg(color_field, PAL_DENSE)
```
The texture init loop runs once and is cached per palette. Per-frame cost is one fancy-index lookup + one broadcast multiply — orders of magnitude faster than the per-cell bitmap blit loop in `render()` for dense backgrounds.
## Coordinate Array Caching
Pre-compute all grid-relative coordinate arrays at init, not per-frame:
```python
# These are O(rows*cols) and used in every effect
self.rr = np.arange(rows)[:, None] # row indices
self.cc = np.arange(cols)[None, :] # col indices
self.dist = np.sqrt(dx**2 + dy**2) # distance from center
self.angle = np.arctan2(dy, dx) # angle from center
self.dist_n = ... # normalized distance
```
## Vectorized Effect Patterns
### Avoid Per-Cell Python Loops in Effects
The render loop (compositing bitmaps) is unavoidably per-cell. But effect functions must be fully vectorized numpy -- never iterate over rows/cols in Python.
Bad (O(rows*cols) Python loop):
```python
for r in range(rows):
for c in range(cols):
val[r, c] = math.sin(c * 0.1 + t) * math.cos(r * 0.1 - t)
```
Good (vectorized):
```python
val = np.sin(g.cc * 0.1 + t) * np.cos(g.rr * 0.1 - t)
```
### Vectorized Matrix Rain
The naive per-column per-trail-pixel loop is the second biggest bottleneck after the render loop. Use numpy fancy indexing:
```python
# Instead of nested Python loops over columns and trail pixels:
# Build row index arrays for all active trail pixels at once
all_rows = []
all_cols = []
all_fades = []
for c in range(cols):
head = int(S["ry"][c])
trail_len = S["rln"][c]
for i in range(trail_len):
row = head - i
if 0 <= row < rows:
all_rows.append(row)
all_cols.append(c)
all_fades.append(1.0 - i / trail_len)
# Vectorized assignment
ar = np.array(all_rows)
ac = np.array(all_cols)
af = np.array(all_fades, dtype=np.float32)
# Assign chars and colors in bulk using fancy indexing
ch[ar, ac] = ... # vectorized char assignment
co[ar, ac, 1] = (af * bri * 255).astype(np.uint8) # green channel
```
### Vectorized Fire Columns
Same pattern -- accumulate index arrays, assign in bulk:
```python
fire_val = np.zeros((rows, cols), dtype=np.float32)
for fi in range(n_cols):
fx_c = int((fi * cols / n_cols + np.sin(t * 2 + fi * 0.7) * 3) % cols)
height = int(energy * rows * 0.7)
dy = np.arange(min(height, rows))
fr = rows - 1 - dy
frac = dy / max(height, 1)
# Width spread: base columns wider at bottom
for dx in range(-1, 2): # 3-wide columns
c = fx_c + dx
if 0 <= c < cols:
fire_val[fr, c] = np.maximum(fire_val[fr, c],
(1 - frac * 0.6) * (0.5 + rms * 0.5))
# Now map fire_val to chars and colors in one vectorized pass
```
## PIL String Rendering for Text-Heavy Scenes
Alternative to per-cell bitmap blitting when rendering many long text strings (scrolling tickers, typewriter sequences, idea floods). Uses PIL's native `ImageDraw.text()` which renders an entire string in one C call, vs one Python-loop bitmap blit per character.
Typical win: a scene with 56 ticker rows renders 56 PIL `text()` calls instead of ~10K individual bitmap blits.
Use when: scene renders many rows of readable text strings. NOT suitable for sparse or spatially-scattered single characters (use normal `render()` for those).
```python
from PIL import Image, ImageDraw
def render_text_layer(grid, rows_data, font):
"""Render dense text rows via PIL instead of per-cell bitmap blitting.
Args:
grid: GridLayer instance (for oy, ch, ox, font metrics)
rows_data: list of (row_index, text_string, rgb_tuple) — one per row
font: PIL ImageFont instance (grid.font)
Returns:
uint8 array (VH, VW, 3) — canvas with rendered text
"""
img = Image.new("RGB", (VW, VH), (0, 0, 0))
draw = ImageDraw.Draw(img)
for row_idx, text, color in rows_data:
y = grid.oy + row_idx * grid.ch
if y + grid.ch > VH:
break
draw.text((grid.ox, y), text, fill=color, font=font)
return np.array(img)
```
### Usage in a Ticker Scene
```python
# Build ticker data (text + color per row)
rows_data = []
for row in range(n_tickers):
text = build_ticker_text(row, t) # scrolling substring
color = hsv2rgb_scalar(hue, 0.85, bri) # (R, G, B) tuple
rows_data.append((row, text, color))
# One PIL pass instead of thousands of bitmap blits
canvas_tickers = render_text_layer(g_md, rows_data, g_md.font)
# Blend with other layers normally
result = blend_canvas(canvas_bg, canvas_tickers, "screen", 0.9)
```
This is purely a rendering optimization — same visual output, fewer draw calls. The grid's `render()` method is still needed for sparse character fields where characters are placed individually based on value fields.
## Bloom Optimization
**Do NOT use `scipy.ndimage.uniform_filter`** -- measured at 424ms/frame.
Use 4x downsample + manual box blur instead -- 84ms/frame (5x faster):
```python
sm = canvas[::4, ::4].astype(np.float32) # 4x downsample
br = np.where(sm > threshold, sm, 0)
for _ in range(3): # 3-pass manual box blur
p = np.pad(br, ((1,1),(1,1),(0,0)), mode='edge')
br = (p[:-2,:-2] + p[:-2,1:-1] + p[:-2,2:] +
p[1:-1,:-2] + p[1:-1,1:-1] + p[1:-1,2:] +
p[2:,:-2] + p[2:,1:-1] + p[2:,2:]) / 9.0
bl = np.repeat(np.repeat(br, 4, axis=0), 4, axis=1)[:H, :W]
```
## Vignette Caching
Distance field is resolution- and strength-dependent, never changes per frame:
```python
_vig_cache = {}
def sh_vignette(canvas, strength):
key = (canvas.shape[0], canvas.shape[1], round(strength, 2))
if key not in _vig_cache:
Y = np.linspace(-1, 1, H)[:, None]
X = np.linspace(-1, 1, W)[None, :]
_vig_cache[key] = np.clip(1.0 - np.sqrt(X**2+Y**2) * strength, 0.15, 1).astype(np.float32)
return np.clip(canvas * _vig_cache[key][:,:,None], 0, 255).astype(np.uint8)
```
Same pattern for CRT barrel distortion (cache remap coordinates).
## Film Grain Optimization
Generate noise at half resolution, tile up:
```python
noise = np.random.randint(-amt, amt+1, (H//2, W//2, 1), dtype=np.int16)
noise = np.repeat(np.repeat(noise, 2, axis=0), 2, axis=1)[:H, :W]
```
2x blocky grain looks like film grain and costs 1/4 the random generation.
## Parallel Rendering
### Worker Architecture
```python
hw = detect_hardware()
N_WORKERS = hw["workers"]
# Batch splitting (for non-clip architectures)
batch_size = (n_frames + N_WORKERS - 1) // N_WORKERS
batches = [(i, i*batch_size, min((i+1)*batch_size, n_frames), features, seg_path) ...]
with multiprocessing.Pool(N_WORKERS) as pool:
segments = pool.starmap(render_batch, batches)
```
### Per-Clip Parallelism (Preferred for Segmented Videos)
```python
from concurrent.futures import ProcessPoolExecutor, as_completed
with ProcessPoolExecutor(max_workers=N_WORKERS) as pool:
futures = {pool.submit(render_clip, seg, features, path): seg["id"]
for seg, path in clip_args}
for fut in as_completed(futures):
clip_id = futures[fut]
try:
fut.result()
log(f" {clip_id} done")
except Exception as e:
log(f" {clip_id} FAILED: {e}")
```
### Worker Isolation
Each worker:
- Creates its own `Renderer` instance (with full grid + bitmap init)
- Opens its own ffmpeg subprocess
- Has independent random seed (`random.seed(batch_id * 10000)`)
- Writes to its own segment file and stderr log
### ffmpeg Pipe Safety
**CRITICAL**: Never `stderr=subprocess.PIPE` with long-running ffmpeg. The stderr buffer fills at ~64KB and deadlocks:
```python
# WRONG -- will deadlock
pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
# RIGHT -- stderr to file
stderr_fh = open(err_path, "w")
pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.DEVNULL, stderr=stderr_fh)
# ... write all frames ...
pipe.stdin.close()
pipe.wait()
stderr_fh.close()
```
### Concatenation
```python
with open(concat_file, "w") as cf:
for seg in segments:
cf.write(f"file '{seg}'\n")
cmd = ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_file]
if audio_path:
cmd += ["-i", audio_path, "-c:v", "copy", "-c:a", "aac", "-b:a", "192k", "-shortest"]
else:
cmd += ["-c:v", "copy"]
cmd.append(output_path)
subprocess.run(cmd, capture_output=True, check=True)
```
## Particle System Performance
Cap particle counts based on quality profile:
| System | Low | Standard | High |
|--------|-----|----------|------|
| Explosion | 300 | 1000 | 2500 |
| Embers | 500 | 1500 | 3000 |
| Starfield | 300 | 800 | 1500 |
| Dissolve | 200 | 600 | 1200 |
Cull by truncating lists:
```python
MAX_PARTICLES = profile.get("particles_max", 1200)
if len(S["px"]) > MAX_PARTICLES:
for k in ("px", "py", "vx", "vy", "life", "char"):
S[k] = S[k][-MAX_PARTICLES:] # keep newest
```
## Memory Management
- Feature arrays: pre-computed for all frames, shared across workers via fork semantics (COW)
- Canvas: allocated once per worker, reused (`np.zeros(...)`)
- Character arrays: allocated per frame (cheap -- rows*cols U1 strings)
- Bitmap cache: ~500KB per grid size, initialized once per worker
Total memory per worker: ~50-150MB. Total: ~400-800MB for 8 workers.
For low-memory systems (< 4GB), reduce worker count and use smaller grids.
## Brightness Verification
After render, spot-check brightness at sample timestamps:
```python
for t in [2, 30, 60, 120, 180]:
cmd = ["ffmpeg", "-ss", str(t), "-i", output_path,
"-frames:v", "1", "-f", "rawvideo", "-pix_fmt", "rgb24", "-"]
r = subprocess.run(cmd, capture_output=True)
arr = np.frombuffer(r.stdout, dtype=np.uint8)
print(f"t={t}s mean={arr.mean():.1f} max={arr.max()}")
```
Target: mean > 5 for quiet sections, mean > 15 for active sections. If consistently below, increase brightness floor in effects and/or global boost multiplier.
## Render Time Estimates
Scale with hardware. Baseline: 1080p, 24fps, ~180ms/frame/worker.
| Duration | Frames | 4 workers | 8 workers | 16 workers |
|----------|--------|-----------|-----------|------------|
| 30s | 720 | ~3 min | ~2 min | ~1 min |
| 2 min | 2,880 | ~13 min | ~7 min | ~4 min |
| 3.5 min | 5,040 | ~23 min | ~12 min | ~6 min |
| 5 min | 7,200 | ~33 min | ~17 min | ~9 min |
| 10 min | 14,400 | ~65 min | ~33 min | ~17 min |
At 720p: multiply times by ~0.5. At 4K: multiply by ~4.
Heavier effects (many particles, dense grids, extra shader passes) add ~20-50%.
---
## Temp File Cleanup
Rendering generates intermediate files that accumulate across runs. Clean up after the final concat/mux step.
### Files to Clean
| File type | Source | Location |
|-----------|--------|----------|
| WAV extracts | `ffmpeg -i input.mp3 ... tmp.wav` | `tempfile.mktemp()` or project dir |
| Segment clips | `render_clip()` output | `segments/seg_00.mp4` etc. |
| Concat list | ffmpeg concat demuxer input | `segments/concat.txt` |
| ffmpeg stderr logs | piped to file for debugging | `*.log` in project dir |
| Feature cache | pickled numpy arrays | `*.pkl` or `*.npz` |
### Cleanup Function
```python
import glob
import tempfile
import shutil
def cleanup_render_artifacts(segments_dir="segments", keep_final=True):
"""Remove intermediate files after successful render.
Call this AFTER verifying the final output exists and plays correctly.
Args:
segments_dir: directory containing segment clips and concat list
keep_final: if True, only delete intermediates (not the final output)
"""
removed = []
# 1. Segment clips
if os.path.isdir(segments_dir):
shutil.rmtree(segments_dir)
removed.append(f"directory: {segments_dir}")
# 2. Temporary WAV files
for wav in glob.glob("*.wav"):
if wav.startswith("tmp") or wav.startswith("extracted_"):
os.remove(wav)
removed.append(wav)
# 3. ffmpeg stderr logs
for log in glob.glob("ffmpeg_*.log"):
os.remove(log)
removed.append(log)
# 4. Feature cache (optional — useful to keep for re-renders)
# for cache in glob.glob("features_*.npz"):
# os.remove(cache)
# removed.append(cache)
print(f"Cleaned {len(removed)} artifacts: {removed}")
return removed
```
### Integration with Render Pipeline
Call cleanup at the end of the main render script, after the final output is verified:
```python
# At end of main()
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
cleanup_render_artifacts(segments_dir="segments")
print(f"Done. Output: {output_path}")
else:
print("WARNING: final output missing or empty — skipping cleanup")
```
### Temp File Best Practices
- Use `tempfile.mkdtemp()` for segment directories — avoids polluting the project dir
- Name WAV extracts with `tempfile.mktemp(suffix=".wav")` so they're in the OS temp dir
- For debugging, set `KEEP_INTERMEDIATES=1` env var to skip cleanup
- Feature caches (`.npz`) are cheap to store and expensive to recompute — default to keeping them

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# Troubleshooting Reference
> **See also:** composition.md · architecture.md · shaders.md · scenes.md · optimization.md
## Quick Diagnostic
| Symptom | Likely Cause | Fix |
|---------|-------------|-----|
| All black output | tonemap gamma too high or no effects rendering | Lower gamma to 0.5, check scene_fn returns non-zero canvas |
| Washed out / too bright | Linear brightness multiplier instead of tonemap | Replace `canvas * N` with `tonemap(canvas, gamma=0.75)` |
| ffmpeg hangs mid-render | stderr=subprocess.PIPE deadlock | Redirect stderr to file |
| "read-only" array error | broadcast_to view without .copy() | Add `.copy()` after broadcast_to |
| PicklingError | Lambda or closure in SCENES table | Define all fx_* at module level |
| Random dark holes in output | Font missing Unicode glyphs | Validate palettes at init |
| Audio-visual desync | Frame timing accumulation | Use integer frame counter, compute t fresh each frame |
| Single-color flat output | Hue field shape mismatch | Ensure h,s,v arrays all (rows,cols) before hsv2rgb |
| Text unreadable over busy bg | No contrast between text and background | Use `apply_text_backdrop()` (composition.md) + `reverse_vignette` shader (shaders.md) |
| Text garbled/mirrored | Kaleidoscope or mirror shader applied to text scene | **Never apply kaleidoscope, mirror_h/v/quad/diag to scenes with readable text** — radial folding destroys legibility. Apply these only to background layers or text-free scenes |
Common bugs, gotchas, and platform-specific issues encountered during ASCII video development.
## NumPy Broadcasting
### The `broadcast_to().copy()` Trap
Hue field generators often return arrays that are broadcast views — they have shape `(1, cols)` or `(rows, 1)` that numpy broadcasts to `(rows, cols)`. These views are **read-only**. If any downstream code tries to modify them in-place (e.g., `h %= 1.0`), numpy raises:
```
ValueError: output array is read-only
```
**Fix**: Always `.copy()` after `broadcast_to()`:
```python
h = np.broadcast_to(h, (g.rows, g.cols)).copy()
```
This is especially important in `_render_vf()` where hue arrays flow through `hsv2rgb()`.
### The `+=` vs `+` Trap
Broadcasting also fails with in-place operators when operand shapes don't match exactly:
```python
# FAILS if result is (rows,1) and operand is (rows, cols)
val += np.sin(g.cc * 0.02 + t * 0.3) * 0.5
# WORKS — creates a new array
val = val + np.sin(g.cc * 0.02 + t * 0.3) * 0.5
```
The `vf_plasma()` function had this bug. Use `+` instead of `+=` when mixing different-shaped arrays.
### Shape Mismatch in `hsv2rgb()`
`hsv2rgb(h, s, v)` requires all three arrays to have identical shapes. If `h` is `(1, cols)` and `s` is `(rows, cols)`, the function crashes or produces wrong output.
**Fix**: Ensure all inputs are broadcast and copied to `(rows, cols)` before calling.
---
## Blend Mode Pitfalls
### Overlay Crushes Dark Inputs
`overlay(a, b) = 2*a*b` when `a < 0.5`. Two values of 0.12 produce `2 * 0.12 * 0.12 = 0.03`. The result is darker than either input.
**Impact**: If both layers are dark (which ASCII art usually is), overlay produces near-black output.
**Fix**: Use `screen` for dark source material. Screen always brightens: `1 - (1-a)*(1-b)`.
### Colordodge Division by Zero
`colordodge(a, b) = a / (1 - b)`. When `b = 1.0` (pure white pixels), this divides by zero.
**Fix**: Add epsilon: `a / (1 - b + 1e-6)`. The implementation in `BLEND_MODES` should include this.
### Colorburn Division by Zero
`colorburn(a, b) = 1 - (1-a) / b`. When `b = 0` (pure black pixels), this divides by zero.
**Fix**: Add epsilon: `1 - (1-a) / (b + 1e-6)`.
### Multiply Always Darkens
`multiply(a, b) = a * b`. Since both operands are [0,1], the result is always <= min(a,b). Never use multiply as a feedback blend mode — the frame goes black within a few frames.
**Fix**: Use `screen` for feedback, or `add` with low opacity.
---
## Multiprocessing
### Pickling Constraints
`ProcessPoolExecutor` serializes function arguments via pickle. This constrains what you can pass to workers:
| Can Pickle | Cannot Pickle |
|-----------|---------------|
| Module-level functions (`def fx_foo():`) | Lambdas (`lambda x: x + 1`) |
| Dicts, lists, numpy arrays | Closures (functions defined inside functions) |
| Class instances (with `__reduce__`) | Instance methods |
| Strings, numbers | File handles, sockets |
**Impact**: All scene functions referenced in the SCENES table must be defined at module level with `def`. If you use a lambda or closure, you get:
```
_pickle.PicklingError: Can't pickle <function <lambda> at 0x...>
```
**Fix**: Define all scene functions at module top level. Lambdas used inside `_render_vf()` as val_fn/hue_fn are fine because they execute within the worker process — they're not pickled across process boundaries.
### macOS spawn vs Linux fork
On macOS, `multiprocessing` defaults to `spawn` (full serialization). On Linux, it defaults to `fork` (copy-on-write). This means:
- **macOS**: Feature arrays are serialized per worker (~57KB for 30s video, but scales with duration). Each worker re-imports the entire module.
- **Linux**: Feature arrays are shared via COW. Workers inherit the parent's memory.
**Impact**: On macOS, module-level code (like `detect_hardware()`) runs in every worker process. If it has side effects (e.g., subprocess calls), those happen N+1 times.
### Per-Worker State Isolation
Each worker creates its own:
- `Renderer` instance (with fresh grid cache)
- `FeedbackBuffer` (feedback doesn't cross scene boundaries)
- Random seed (`random.seed(hash(seg_id) + 42)`)
This means:
- Particle state doesn't carry between scenes (expected)
- Feedback trails reset at scene cuts (expected)
- `np.random` state is NOT seeded by `random.seed()` — they use separate RNGs
**Fix for deterministic noise**: Use `np.random.RandomState(seed)` explicitly:
```python
rng = np.random.RandomState(hash(seg_id) + 42)
noise = rng.random((rows, cols))
```
---
## Brightness Issues
### Dark Scenes After Tonemap
If a scene is still dark after tonemap, check:
1. **Gamma too high**: Lower gamma (0.5-0.6) for scenes with destructive post-processing
2. **Shader destroying brightness**: Solarize, posterize, or contrast adjustments in the shader chain can undo tonemap's work. Move destructive shaders earlier in the chain, or increase gamma to compensate.
3. **Feedback with multiply**: Multiply feedback darkens every frame. Switch to screen or add.
4. **Overlay blend in scene**: If the scene function uses `blend_canvas(..., "overlay", ...)` with dark layers, switch to screen.
### Diagnostic: Test-Frame Brightness
```bash
python reel.py --test-frame 10.0
# Output: Mean brightness: 44.3, max: 255
```
If mean < 20, the scene needs attention. Common fixes:
- Lower gamma in the SCENES entry
- Change internal blend modes from overlay/multiply to screen/add
- Increase value field multipliers (e.g., `vf_plasma(...) * 1.5`)
- Check that the shader chain doesn't have an aggressive solarize or threshold
### v1 Brightness Pattern (Deprecated)
The old pattern used a linear multiplier:
```python
# OLD — don't use
canvas = np.clip(canvas.astype(np.float32) * 2.0, 0, 255).astype(np.uint8)
```
This fails because:
- Dark scenes (mean 8): `8 * 2.0 = 16` — still dark
- Bright scenes (mean 130): `130 * 2.0 = 255` — clipped, lost detail
Use `tonemap()` instead. See `composition.md` § Adaptive Tone Mapping.
---
## ffmpeg Issues
### Pipe Deadlock
The #1 production bug. If you use `stderr=subprocess.PIPE`:
```python
# DEADLOCK — stderr buffer fills at 64KB, blocks ffmpeg, blocks your writes
pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
```
**Fix**: Always redirect stderr to a file:
```python
stderr_fh = open(err_path, "w")
pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE,
stdout=subprocess.DEVNULL, stderr=stderr_fh)
```
### Frame Count Mismatch
If the number of frames written to the pipe doesn't match what ffmpeg expects (based on `-r` and duration), the output may have:
- Missing frames at the end
- Incorrect duration
- Audio-video desync
**Fix**: Calculate frame count explicitly: `n_frames = int(duration * FPS)`. Don't use `range(int(start*FPS), int(end*FPS))` without verifying the total matches.
### Concat Fails with "unsafe file name"
```
[concat @ ...] Unsafe file name
```
**Fix**: Always use `-safe 0`:
```python
["ffmpeg", "-f", "concat", "-safe", "0", "-i", concat_path, ...]
```
---
## Font Issues
### Cell Height (macOS Pillow)
`textbbox()` and `getbbox()` return incorrect heights on some macOS Pillow versions. Use `getmetrics()`:
```python
ascent, descent = font.getmetrics()
cell_height = ascent + descent # correct
# NOT: font.getbbox("M")[3] # wrong on some versions
```
### Missing Unicode Glyphs
Not all fonts render all Unicode characters. If a palette character isn't in the font, the glyph renders as a blank or tofu box, appearing as a dark hole in the output.
**Fix**: Validate at init:
```python
all_chars = set()
for pal in [PAL_DEFAULT, PAL_DENSE, PAL_RUNE, ...]:
all_chars.update(pal)
valid_chars = set()
for c in all_chars:
if c == " ":
valid_chars.add(c)
continue
img = Image.new("L", (20, 20), 0)
ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
if np.array(img).max() > 0:
valid_chars.add(c)
else:
log(f"WARNING: '{c}' (U+{ord(c):04X}) missing from font")
```
### Platform Font Paths
| Platform | Common Paths |
|----------|-------------|
| macOS | `/System/Library/Fonts/Menlo.ttc`, `/System/Library/Fonts/Monaco.ttf` |
| Linux | `/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf` |
| Windows | `C:\Windows\Fonts\consola.ttf` (Consolas) |
Always probe multiple paths and fall back gracefully. See `architecture.md` § Font Selection.
---
## Performance
### Slow Shaders
Some shaders use Python loops and are very slow at 1080p:
| Shader | Issue | Fix |
|--------|-------|-----|
| `wave_distort` | Per-row Python loop | Use vectorized fancy indexing |
| `halftone` | Triple-nested loop | Vectorize with block reduction |
| `matrix rain` | Per-column per-trail loop | Accumulate index arrays, bulk assign |
### Render Time Scaling
If render is taking much longer than expected:
1. Check grid count — each extra grid adds ~100-150ms/frame for init
2. Check particle count — cap at quality-appropriate limits
3. Check shader count — each shader adds 2-25ms
4. Check for accidental Python loops in effects (should be numpy only)
---
## Common Mistakes
### Using `r.S` vs the `S` Parameter
The v2 scene protocol passes `S` (the state dict) as an explicit parameter. But `S` IS `r.S` — they're the same object. Both work:
```python
def fx_scene(r, f, t, S):
S["counter"] = S.get("counter", 0) + 1 # via parameter (preferred)
r.S["counter"] = r.S.get("counter", 0) + 1 # via renderer (also works)
```
Use the `S` parameter for clarity. The explicit parameter makes it obvious that the function has persistent state.
### Forgetting to Handle Empty Feature Values
Audio features default to 0.0 if the audio is silent. Use `.get()` with sensible defaults:
```python
energy = f.get("bass", 0.3) # default to 0.3, not 0
```
If you default to 0, effects go blank during silence.
### Writing New Files Instead of Editing Existing State
A common bug in particle systems: creating new arrays every frame instead of updating persistent state.
```python
# WRONG — particles reset every frame
S["px"] = []
for _ in range(100):
S["px"].append(random.random())
# RIGHT — only initialize once, update each frame
if "px" not in S:
S["px"] = []
# ... emit new particles based on beats
# ... update existing particles
```
### Not Clipping Value Fields
Value fields should be [0, 1]. If they exceed this range, `val2char()` produces index errors:
```python
# WRONG — vf_plasma() * 1.5 can exceed 1.0
val = vf_plasma(g, f, t, S) * 1.5
# RIGHT — clip after scaling
val = np.clip(vf_plasma(g, f, t, S) * 1.5, 0, 1)
```
The `_render_vf()` helper clips automatically, but if you're building custom scenes, clip explicitly.
## Brightness Best Practices
- Dense animated backgrounds — never flat black, always fill the grid
- Vignette minimum clamped to 0.15 (not 0.12)
- Bloom threshold 130 (not 170) so more pixels contribute to glow
- Use `screen` blend mode (not `overlay`) for dark ASCII layers — overlay squares dark values: `2 * 0.12 * 0.12 = 0.03`
- FeedbackBuffer decay minimum 0.5 — below that, feedback disappears too fast to see
- Value field floor: `vf * 0.8 + 0.05` ensures no cell is truly zero
- Per-scene gamma overrides: default 0.75, solarize 0.55, posterize 0.50, bright scenes 0.85
- Test frames early: render single frames at key timestamps before committing to full render
**Quick checklist before full render:**
1. Render 3 test frames (start, middle, end)
2. Check `canvas.mean() > 8` after tonemap
3. Check no scene is visually flat black
4. Verify per-section variation (different bg/palette/color per scene)
5. Confirm shader chain includes bloom (threshold 130)
6. Confirm vignette strength ≤ 0.25