File size: 8,238 Bytes
ed46c5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | """Procedural synthetic photographs of carved runic inscriptions.
Lexicon: words of the *train* split only (frequency >= 2), sampled with temperature 0.7 into
1-5 word phrases. Each transliteration is mapped to runes with a fixed table (Younger Futhark
long-branch core, dotted medieval runes and Elder Futhark signs for the remaining letters),
rendered with one of three runic fonts, and composited onto a noise-based stone texture as
an incised groove (height map + directional light), optionally painted, laid out straight,
vertical, in a framed band or along a curve, then degraded (lichen, blur, perspective,
exposure, JPEG). The label is the transliteration string itself.
"""
import argparse
import io
import json
import math
import os
import random
from collections import Counter
import numpy as np
from PIL import Image, ImageDraw, ImageFilter, ImageFont
RUNE = {
"f": "ᚠ", "u": "ᚢ", "þ": "ᚦ", "o": "ᚬ", "ą": "ᚭ", "r": "ᚱ", "k": "ᚴ", "h": "ᚼ", "n": "ᚾ",
"i": "ᛁ", "a": "ᛅ", "s": "ᛋ", "t": "ᛏ", "b": "ᛒ", "m": "ᛘ", "l": "ᛚ", "R": "ᛦ",
"e": "ᛂ", "g": "ᚵ", "d": "ᛑ", "p": "ᛔ", "y": "ᚤ", "c": "ᛍ", "v": "ᚡ", "z": "ᛎ",
"x": "ᛪ", "q": "ᛩ", "w": "ᚹ", "j": "ᛃ", "ð": "ᚧ", "ï": "ᛇ", "ŋ": "ᛜ", "æ": "ᛆ",
"ø": "ᚯ", "ö": "ᚯ", "å": "ᚮ", "ä": "ᛅ",
}
SEPARATORS = ["᛫", "᛬", "×", ":", "᛭", " "]
FONT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fonts")
FONTS = ["NotoSansRunic-Regular.ttf", "Junicode-Regular.ttf", "Junicode-Bold.ttf"]
def lexicon(corpus_path):
words = Counter()
for l in open(corpus_path):
r = json.loads(l)
if r["split"] == "train":
words.update(w for w in r["norm"].split() if 2 <= len(w) <= 20)
return {w: c for w, c in words.items() if c >= 2}
def sample_phrase(rng, words, weights):
n = rng.choices([1, 2, 3, 4, 5], weights=[2, 3, 3, 2, 1])[0]
return " ".join(rng.choices(words, weights=weights, k=n))
def smooth_noise(rng, h, w, octaves=5):
nrng = np.random.default_rng(rng.randint(0, 2**31))
out = np.zeros((h, w), np.float32)
amp = 1.0
for o in range(octaves):
sh, sw = max(2, h // (2 ** (octaves - o))), max(2, w // (2 ** (octaves - o)))
base = nrng.standard_normal((sh, sw)).astype(np.float32)
up = np.array(Image.fromarray(base).resize((w, h), Image.BICUBIC))
out += amp * up
amp *= 0.55
out -= out.min()
return out / (out.max() + 1e-6)
def stone(rng, h, w):
base = np.array([rng.uniform(90, 175)] * 3, np.float32)
tint = np.array([rng.uniform(-18, 22), rng.uniform(-12, 10), rng.uniform(-25, 8)], np.float32)
n1 = smooth_noise(rng, h, w)
nrng = np.random.default_rng(rng.randint(0, 2**31))
speck = (nrng.random((h, w)) < rng.uniform(0.01, 0.08)).astype(np.float32)
speck = np.array(Image.fromarray((speck * 255).astype(np.uint8)).filter(ImageFilter.GaussianBlur(0.8)), np.float32) / 255.0
img = base + tint + (n1[..., None] - 0.5) * rng.uniform(40, 110)
img += (speck[..., None] * rng.uniform(-60, 60))
return img
def text_mask(rng, runes, W, H):
font = ImageFont.truetype(os.path.join(FONT_DIR, rng.choice(FONTS)), 10)
size = int(H * rng.uniform(0.12, 0.3))
for _ in range(40):
font = font.font_variant(size=size)
bbox = font.getbbox(runes)
if bbox[2] - bbox[0] <= W * 0.92:
break
size = int(size * 0.9)
tw, th = bbox[2] - bbox[0], bbox[3] - bbox[1]
m = Image.new("L", (W, H), 0)
d = ImageDraw.Draw(m)
x = (W - tw) // 2 - bbox[0] + rng.randint(-W // 20, W // 20)
y = (H - th) // 2 - bbox[1] + rng.randint(-H // 12, H // 12)
stroke = rng.choice([0, 0, 1, 2])
d.text((x, y), runes, fill=255, font=font, stroke_width=stroke, stroke_fill=255)
band = rng.random() < 0.45
if band:
pad = int(th * rng.uniform(0.25, 0.5))
lw = max(2, size // 12)
d.line([(x + bbox[0] - pad, y + bbox[1] - pad), (x + bbox[2] + pad, y + bbox[1] - pad)], fill=255, width=lw)
d.line([(x + bbox[0] - pad, y + bbox[3] + pad), (x + bbox[2] + pad, y + bbox[3] + pad)], fill=255, width=lw)
return np.array(m, np.float32) / 255.0
def bend(mask, rng):
h, w = mask.shape
amp = rng.uniform(0.05, 0.18) * h
freq = rng.uniform(0.5, 1.5)
ys, xs = np.mgrid[0:h, 0:w]
shift = amp * np.sin(2 * math.pi * freq * xs / w + rng.uniform(0, 6.28))
src_y = np.clip((ys - shift).astype(np.int32), 0, h - 1)
return mask[src_y, xs]
def render(rng, translit, out_long=768):
runes = "".join(RUNE.get(ch, "") if ch != " " else "\x00" for ch in translit)
sep = rng.choice(SEPARATORS)
runes = runes.replace("\x00", f" {sep} " if sep != " " else " ").replace(" ", " ")
layout = rng.choices(["h", "v", "curve"], weights=[5, 3, 2])[0]
W, H = 1100, rng.choice([320, 420, 520])
mask = text_mask(rng, runes, W, H)
if layout == "curve":
mask = bend(mask, rng)
depth = np.array(Image.fromarray((mask * 255).astype(np.uint8)).filter(
ImageFilter.GaussianBlur(rng.uniform(1.0, 3.0))), np.float32) / 255.0
gy, gx = np.gradient(-depth)
ang = rng.uniform(0, 2 * math.pi)
light = (gx * math.cos(ang) + gy * math.sin(ang)) * rng.uniform(250, 600)
tex = stone(rng, H, W)
groove = depth[..., None] * rng.uniform(20, 70)
img = tex - groove + light[..., None]
if rng.random() < 0.35: # painted runes, as on many restored runestones
paint = np.array(rng.choice([(150, 30, 30), (170, 40, 35), (30, 30, 30), (200, 200, 190)]), np.float32)
a = np.clip(mask * rng.uniform(0.6, 0.95), 0, 1)[..., None]
img = img * (1 - a) + paint * a
if rng.random() < 0.5: # lichen / moss blotches
blot = smooth_noise(rng, H, W, 4) > rng.uniform(0.72, 0.85)
col = np.array(rng.choice([(170, 170, 120), (90, 110, 60), (200, 190, 150)]), np.float32)
img = np.where(blot[..., None], img * 0.4 + col * 0.6, img)
img = np.clip(img, 0, 255).astype(np.uint8)
im = Image.fromarray(img)
if layout == "v":
im = im.rotate(rng.choice([90, -90]), expand=True)
fill = tuple(int(v) for v in img.mean((0, 1)))
im = im.rotate(rng.uniform(-6, 6), expand=True, fillcolor=fill)
w, h = im.size
dx, dy = rng.uniform(-0.06, 0.06) * w, rng.uniform(-0.06, 0.06) * h
coeffs = (1 + rng.uniform(-0.05, 0.05), rng.uniform(-0.05, 0.05), dx, rng.uniform(-0.05, 0.05),
1 + rng.uniform(-0.05, 0.05), dy, rng.uniform(-1e-4, 1e-4), rng.uniform(-1e-4, 1e-4))
im = im.transform(im.size, Image.PERSPECTIVE, coeffs, Image.BICUBIC, fillcolor=fill)
if rng.random() < 0.6:
im = im.filter(ImageFilter.GaussianBlur(rng.uniform(0.3, 1.6)))
arr = np.array(im, np.float32)
arr = arr * rng.uniform(0.6, 1.3) + rng.uniform(-30, 30)
arr += np.random.default_rng(rng.randint(0, 2**31)).normal(0, rng.uniform(2, 10), arr.shape)
im = Image.fromarray(np.clip(arr, 0, 255).astype(np.uint8))
im.thumbnail((out_long, out_long))
buf = io.BytesIO()
im.save(buf, "JPEG", quality=rng.randint(55, 92))
return Image.open(buf).convert("RGB")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--corpus", required=True)
ap.add_argument("--out_dir", required=True)
ap.add_argument("--n", type=int, default=6000)
ap.add_argument("--seed", type=int, default=13)
ap.add_argument("--start", type=int, default=0)
args = ap.parse_args()
rng = random.Random(args.seed + args.start)
lex = lexicon(args.corpus)
words = sorted(lex)
weights = [lex[w] ** 0.7 for w in words]
os.makedirs(os.path.join(args.out_dir, "img"), exist_ok=True)
with open(os.path.join(args.out_dir, f"labels_{args.start}.jsonl"), "w") as f:
for i in range(args.start, args.start + args.n):
t = sample_phrase(rng, words, weights)
name = f"img/syn{i:06d}.jpg"
render(rng, t).save(os.path.join(args.out_dir, name), quality=90)
f.write(json.dumps({"id": f"syn{i:06d}", "image": name, "target": t}, ensure_ascii=False) + "\n")
print("lexicon", len(words), "wrote", args.n)
if __name__ == "__main__":
main()
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