2992a0e650
- Add SERVICE_INVENTORY_V1.0.0.md (25 source-verified tools, 3.7GB) - Add ERP_SELECTION_REPORT.md (Odoo CE vs ERPNext comparison) - Add SFTPGO_ODOO_REPLACEMENT.md (SFTPGo migration plan) - Add SERVICE_GO_GITEA_BUILD.md (Go compiler + Gitea build report) - Add release visualize command (face trace heatmap + identity filter) - Add sqlite-vec integration (160MB SQLite with vec0 vector tables) - Add export_identities.py, export_sqlite.py, render_face_heatmap.py - Add Go, Gitea, Rust/Cargo, Swift, yt-dlp, SQLite, sqlite-vec to service CLI - Fix package to include identities and identity_bindings in data.sql - Update release list to show all deployed video stats - Add V1.0.0 YAML frontmatter to all docs (DOCS_STANDARD compliant)
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Export identity data for a video UUID as JSON (for offline analysis).
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Usage: python3 export_identities.py <file_uuid> [output.json]
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"""
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import sys, json, psycopg2
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UUID = sys.argv[1] if len(sys.argv) > 1 else "aeed71342a899fe4b4c57b7d41bcb692"
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OUT = sys.argv[2] if len(sys.argv) > 2 else f"/Users/accusys/momentry/output_dev/{UUID}.identities.json"
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conn = psycopg2.connect("dbname=momentry user=accusys")
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cur = conn.cursor()
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# Get identities referenced by this file's face_detections
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cur.execute("""
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SELECT DISTINCT i.id, i.name, i.uuid, i.identity_type, i.source, i.status,
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i.face_embedding, i.voice_embedding, i.reference_data, i.tmdb_id, i.tmdb_profile
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FROM dev.identities i
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INNER JOIN dev.face_detections fd ON fd.identity_id = i.id
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WHERE fd.file_uuid = %s
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ORDER BY i.id
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""", (UUID,))
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rows = cur.fetchall()
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identities = []
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for r in rows:
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identities.append({
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"id": r[0],
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"name": r[1],
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"uuid": str(r[2]) if r[2] else None,
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"identity_type": r[3],
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"source": r[4],
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"status": r[5],
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"tmdb_id": r[9],
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"tmdb_profile": r[10],
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})
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# Get identity_bindings for these identities' traces
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cur.execute("""
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SELECT DISTINCT ib.identity_id, ib.identity_type, ib.identity_value, ib.confidence
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FROM dev.identity_bindings ib
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WHERE ib.identity_id IN (
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SELECT DISTINCT fd.identity_id FROM dev.face_detections fd WHERE fd.file_uuid = %s
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)
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""", (UUID,))
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bindings = [{"identity_id": r[0], "identity_type": r[1], "identity_value": r[2], "confidence": float(r[3])} for r in cur.fetchall()]
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# Get trace-to-identity mapping from face_detections
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cur.execute("""
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SELECT DISTINCT trace_id, identity_id, COUNT(*) as face_count
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FROM dev.face_detections
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WHERE file_uuid = %s AND identity_id IS NOT NULL AND trace_id IS NOT NULL
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GROUP BY trace_id, identity_id ORDER BY trace_id
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""", (UUID,))
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trace_map = [{"trace_id": r[0], "identity_id": r[1], "face_count": r[2]} for r in cur.fetchall()]
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cur.close(); conn.close()
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output = {
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"file_uuid": UUID,
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"identity_count": len(identities),
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"binding_count": len(bindings),
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"trace_mapping_count": len(trace_map),
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"identities": identities,
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"bindings": bindings,
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"trace_to_identity": trace_map,
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}
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with open(OUT, 'w') as f:
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json.dump(output, f, indent=2, ensure_ascii=False)
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size_kb = len(json.dumps(output)) / 1024
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print(f"Exported {len(identities)} identities, {len(bindings)} bindings, {len(trace_map)} trace mappings")
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print(f" → {OUT} ({size_kb:.0f}KB)")
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