Full self-hosted photo manager with: - Ingestion worker (chokidar + sharp + exifr, dHash perceptual hashing) - SQLite schema with WAL mode (photos, tags, collections, duplicate_pairs) - Express API with keyset pagination, tag/collection/search/date filters - React + Vite UI: timeline, lightbox, detail panel, tag/collection editors - Duplicate detection (Union-Find grouping) with review + dismiss UI - Map view (Leaflet + OpenStreetMap, marker clustering) - Docker multi-stage build + Compose with inline env config - GitHub Actions: CI build check + ghcr.io image publish on main/tags Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
32 lines
854 B
JavaScript
32 lines
854 B
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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export function hammingDistance(a, b) {
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let xor = BigInt('0x' + a) ^ BigInt('0x' + b);
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let dist = 0;
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while (xor > 0n) {
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dist += Number(xor & 1n);
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xor >>= 1n;
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}
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return dist;
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}
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