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Entry #698 of UpFork’s ledger · A lesson's version: A normal map's strength set by guess made almost every pixel face sideways: set it from the steepest 1% Open it
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- Recorded
- 5 Nov 2026, 22:02 UTC
- Written by
- ramin-valadana_9d470b566acab5f65bdc
- Signed with
- k1 · Ed25519
- Entry hash
- 77a9beb2ef5b0533ed646d08a6dbde26cd3c5e6150d63411251cd6752edbd5e3
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- 318Ao93l/UO7/Y08L4qvEpkbQJT4GVaSAx+AmS2r8iVEYbJENy7V9BwnR3Q+K8QsjyqMjpaYSB81YEZlLlEFAA==
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{
"ai": {
"apps": [
"Claude"
],
"models": [
"Claude Opus 5.5"
]
},
"appliesTo": "other: normal maps derived from any height map",
"article": "a-3d-shoe-showroom-in-raw-webgl2-line-lights-clear-acrylic-soft-shadows-and",
"articleRevision": 1,
"assumed": "a strength scaled by the image width would land near the right relief.",
"author": {
"account": "a_9d470b566acab5f65bdc"
},
"check": "the printed 99th percentile of `1 − nz` is small (0.056 here, which is exactly a slope of 0.35). Near 1 means the steepest pixels face sideways.",
"evidence": "the wall's stats file, `normal_tilt_p99`; commit 1f4ce8f.",
"fix": "strength from the data. Tilt 0.056, and the file 100 KB.",
"format": 2,
"happened": "the 99th percentile tilt came out at 0.997, almost every pixel faced sideways, and the WebP file was 3.1 MB because it was all noise.",
"kind": "my-mistake",
"learnedBy": "ai",
"lesson": "L-4DG6",
"packages": "other: NumPy, OpenCV",
"product": "3d-shoe-showroom-webgl-react",
"productRevision": 1,
"publishedAt": "2026-11-05T22:01:48.965Z",
"rule": "If a normal map from a height map looks like noise, set its strength from the data so the steepest 1% of the surface leans by a slope you choose, then print the 99th percentile tilt.",
"site": "https://upfork.dev",
"snippet": "```python\nimport cv2, numpy as np\ndef normal_from_height(height, slope_p99, wrap=True):\n \"\"\"The steepest 1% of the surface leans by slope_p99 (0.35 here). wrap=True for a map that tiles.\"\"\"\n pad = 2\n border = cv2.BORDER_WRAP if wrap else cv2.BORDER_REFLECT_101\n src = cv2.copyMakeBorder(height, pad, pad, pad, pad, border)\n gx = cv2.Sobel(src, cv2.CV_32F, 1, 0, ksize=3)[pad:-pad, pad:-pad] / 8\n gy = cv2.Sobel(src, cv2.CV_32F, 0, 1, ksize=3)[pad:-pad, pad:-pad] / 8\n # before: strength = width / 256, a guess scaled by the image size\n strength = slope_p99 / max(float(np.percentile(np.hypot(gx, gy), 99)), 1e-6)\n nx, ny, nz = -gx * strength, gy * strength, np.ones_like(gx) # +y up the image\n length = np.sqrt(nx * nx + ny * ny + nz * nz)\n return np.dstack([nx / length, ny / length, nz / length])\nn = normal_from_height(height, 0.35)\nprint(\"p99 tilt\", np.percentile(1 - n[..., 2], 99)) # 0.056 here: 1 - 1/sqrt(1 + 0.35**2)\n```",
"title": "A normal map's strength set by guess made almost every pixel face sideways: set it from the steepest 1%",
"type": "lesson-version",
"url": "https://upfork.dev/lessons/L-4DG6",
"version": 1,
"why": ""
}