Create utils/bg_generator.py

utils/bg_generator.py

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+from __future__ import annotations
+from pathlib import Path
+from typing import List, Tuple
+import time, random
+import numpy as np
+from PIL import Image, ImageFilter, ImageOps
+
+TMP_DIR = Path("/tmp/bgfx"); TMP_DIR.mkdir(parents=True, exist_ok=True)
+
+_PALETTES = {
+    "office": [(240,245,250),(210,220,230),(180,190,200)],
+    "studio": [(18,18,20),(32,32,36),(58,60,64)],
+    "sunset": [(255,183,77),(255,138,101),(244,143,177)],
+    "forest": [(46,125,50),(102,187,106),(165,214,167)],
+    "ocean": [(33,150,243),(3,169,244),(0,188,212)],
+    "minimal": [(245,246,248),(230,232,236),(214,218,224)],
+    "warm": [(255,224,178),(255,204,128),(255,171,145)],
+    "cool": [(197,202,233),(179,229,252),(178,235,242)],
+    "royal": [(63,81,181),(121,134,203),(159,168,218)],
+}
+
+def _save_pil(img: Image.Image, stem: str = "ai_bg", ext: str = "png") -> str:
+    ts = int(time.time() * 1000)
+    p = TMP_DIR / f"{stem}_{ts}.{ext}"
+    img.save(p)
+    return str(p)
+
+def _palette_from_prompt(prompt: str) -> List[tuple]:
+    p = (prompt or "").lower()
+    for key, pal in _PALETTES.items():
+        if key in p:
+            return pal
+    random.seed(hash(p) % (2**32 - 1))
+    return [tuple(random.randint(90, 200) for _ in range(3)) for _ in range(3)]
+
+def _perlin_like_noise(h: int, w: int, octaves: int = 4) -> np.ndarray:
+    acc = np.zeros((h, w), dtype=np.float32)
+    for o in range(octaves):
+        scale = 2 ** o
+        small = np.random.rand(h // scale + 1, w // scale + 1).astype(np.float32)
+        small = Image.fromarray((small * 255).astype(np.uint8)).resize((w, h), Image.BILINEAR)
+        acc += np.array(small, dtype=np.float32) / 255.0 / (o + 1)
+    acc /= max(1e-6, acc.max())
+    return acc
+
+def _blend_palette(noise: np.ndarray, palette: List[tuple]) -> Image.Image:
+    h, w = noise.shape
+    img = np.zeros((h, w, 3), dtype=np.float32)
+    t1, t2 = 0.33, 0.66
+    c0, c1, c2 = [np.array(c, dtype=np.float32) for c in palette]
+    m0, m1, m2 = noise < t1, (noise >= t1) & (noise < t2), noise >= t2
+    img[m0], img[m1], img[m2] = c0, c1, c2
+    return Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
+
+def generate_ai_background(
+    prompt: str, width: int = 1280, height: int = 720,
+    bokeh: float = 0.0, vignette: float = 0.15, contrast: float = 1.05
+) -> Tuple[Image.Image, str]:
+    palette = _palette_from_prompt(prompt)
+    noise = _perlin_like_noise(height, width, octaves=4)
+    img = _blend_palette(noise, palette)
+
+    if bokeh > 0:
+        img = img.filter(ImageFilter.GaussianBlur(radius=max(0, min(50, bokeh))))
+
+    if vignette > 0:
+        import numpy as np
+        base = np.array(img).astype(np.float32) / 255.0
+        y, x = np.ogrid[:height, :width]
+        cx, cy = width / 2, height / 2
+        r = np.sqrt((x - cx) ** 2 + (y - cy) ** 2)
+        mask = 1 - np.clip(r / (max(width, height) / 1.2), 0, 1)
+        mask = (mask ** 2) * (1 - vignette) + vignette
+        out = base * mask[..., None]
+        img = Image.fromarray(np.clip(out * 255, 0, 255).astype(np.uint8))
+
+    if contrast != 1.0:
+        img = ImageOps.autocontrast(img, cutoff=1)
+        arr = np.array(img).astype(np.float32)
+        mean = arr.mean(axis=(0, 1), keepdims=True)
+        arr = (arr - mean) * float(contrast) + mean
+        img = Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8))
+
+    path = _save_pil(img)
+    return img, path