nmemmertandClaude Sonnet 4.6
4f7dd3b49b
- Replace BigInt hammingDistance with 32-bit popcount — safer (no throw on malformed hashes) and ~10× faster - Make scan() async with setImmediate chunking so the event loop stays responsive on large libraries - Batch getGroups() photo lookup in slices of 900 to stay under SQLite's 999-variable limit - Add POST /api/duplicates/keep-best endpoint: scores by resolution then filesize, soft-deletes the losers, dismisses the pair - DuplicateGroup: green "Keep" badge on the best photo, green "Keep best" button in footer Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
37 lines
1.2 KiB
JavaScript
37 lines
1.2 KiB
JavaScript
import sharp from 'sharp';
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// dHash (difference hash): 9×8 → 64-bit comparison → 16-char hex string.
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// Two images with Hamming distance ≤ 10 are near-duplicates.
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export async function computeDHash(filepath) {
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const { data } = await sharp(filepath)
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.resize(9, 8, { fit: 'fill' })
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.greyscale()
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.raw()
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.toBuffer({ resolveWithObject: true });
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let hash = 0n;
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for (let row = 0; row < 8; row++) {
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for (let col = 0; col < 8; col++) {
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const left = data[row * 9 + col];
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const right = data[row * 9 + col + 1];
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hash = (hash << 1n) | (left < right ? 1n : 0n);
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}
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}
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return hash.toString(16).padStart(16, '0');
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}
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function popcount32(n) {
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n = n >>> 0;
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n -= (n >> 1) & 0x55555555;
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n = (n & 0x33333333) + ((n >> 2) & 0x33333333);
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n = (n + (n >> 4)) & 0x0f0f0f0f;
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return Math.imul(n, 0x01010101) >>> 24;
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}
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export function hammingDistance(a, b) {
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if (!a || !b || a.length !== 16 || b.length !== 16) return 64;
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const xorHi = (parseInt(a.slice(0, 8), 16) ^ parseInt(b.slice(0, 8), 16)) >>> 0;
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const xorLo = (parseInt(a.slice(8), 16) ^ parseInt(b.slice(8), 16)) >>> 0;
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return popcount32(xorHi) + popcount32(xorLo);
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}
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