Cross-reference photos against assets DB

- Scanned all 15 photos in uploads/photos/ via vision + Tesseract OCR
- Matched keurig_label.jpg → Asset #5144 (machine_id 636671)
- Updated Asset #5144 photo_path in assets.db
- Created scripts/crossref_photos.py for reusable batch matching
- Reported unmatched IDs for coca_cola_label.jpg and telemetry_device.jpg
- Generated crossref_report.md with full findings

crossref_report.md

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+# Photo Cross-Reference Report
+**Generated:** 2026-05-29
+
+## Database
+- assets.db: 7,488 assets
+
+## Photos Scanned
+15 files in uploads/photos/
+
+## Results
+
+### Matched (1 photo → 1 asset update)
+
+| Photo | Asset ID | Machine ID | Name | Action |
+|-------|----------|------------|------|--------|
+| keurig_label.jpg | 5144 | 636671 | 636671 / 956 Cypress Way | ✅ photo_path set to /uploads/photos/keurig_label.jpg |
+
+### Unmatched — For Investigation (2 photos)
+
+**coca_cola_label.jpg** — Coca-Cola branded label
+- Extracted IDs: RY10006338, RVCC-660-8, 201037BA00039
+- RY10006338 is Royal-pattern serial but not in DB
+- 201037BA00039 is Royal serial format; closest DB match: Asset #6815 (A201037BA00030)
+- Likely a pulled/retired or unregistered machine
+
+**telemetry_device.jpg** — 4G telemetry device
+- Extracted IDs: 4434331624226353 (ICCID), 48602143 (Monyx ID)
+- These are telemetry/sim identifiers, not vending machine assets
+- Consider tracking in a separate telemetry devices table
+
+### Test Data (3 photos)
+- 557ba8d1..., 59dd8174..., a8c7e553...: "Machine 1D 12845-678901" — app test pattern, no DB match
+
+### No Text (9 photos)
+- Icon-sized images (100x100 to 200x100): no readable text
+
+## Files Changed
+- `/home/oplabs/projects/canteen-asset-tracker/assets.db` — Asset #5144 photo_path updated
+- `/home/oplabs/projects/canteen-asset-tracker/scripts/crossref_photos.py` — reusable batch matching script

scripts/crossref_photos.py

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+#!/usr/bin/env python3
+"""
+Cross-reference extracted photo IDs against assets DB.
+
+Processes all photos in uploads/photos/, runs vision/OCR text extraction,
+matches identifiers against assets.machine_id / serial_number / connect_id,
+updates matched assets with photo_path, and reports unmatched IDs.
+
+Usage:
+    python3 scripts/crossref_photos.py              # process all photos
+    python3 scripts/crossref_photos.py --photo <name>  # single photo
+    python3 scripts/crossref_photos.py --text "S/N: 1234"  # from text only
+"""
+
+import argparse
+import json
+import os
+import re
+import sqlite3
+import sys
+from datetime import datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from classify_makes import normalize_identifier, find_asset_by_normalized_id
+
+DB_PATH = str(Path(__file__).resolve().parent.parent / "assets.db")
+PHOTOS_DIR = str(Path(__file__).resolve().parent.parent / "uploads" / "photos")
+
+
+def process_photo(photo_path: str, db_path: str = DB_PATH) -> dict:
+    """Process a single photo: extract text, match DB, update photo_path."""
+    fname = os.path.basename(photo_path)
+    result = {
+        "photo": fname,
+        "photo_path": f"/uploads/photos/{fname}",
+        "size_bytes": os.path.getsize(photo_path),
+        "vision_text": "",
+        "extracted_ids": [],
+        "matches": [],
+        "updated": False,
+    }
+
+    # Try Tesseract OCR
+    try:
+        import pytesseract
+        from PIL import Image as PILImage
+
+        img = PILImage.open(photo_path)
+        img_gray = img.convert("L")
+        text = pytesseract.image_to_string(img_gray, config="--psm 6")
+        text = text.strip()
+        clean_chars = sum(1 for c in text if c.isalnum() or c in " \n/-.")
+        if text and clean_chars >= 4:
+            result["vision_text"] = text
+    except ImportError:
+        pass
+
+    if not result["vision_text"]:
+        result["vision_source"] = "none"
+        return result
+
+    result["vision_source"] = "tesseract"
+
+    # Extract identifiers from text
+    identifiers = []
+    lines = result["vision_text"].split("\n")
+    for line in lines:
+        line = line.strip()
+        if not line or len(line) < 4:
+            continue
+        norm = normalize_identifier(line)
+        if norm and len(norm) >= 4:
+            identifiers.append(norm)
+        tokens = re.findall(r"[A-Za-z0-9]{4,}", line)
+        for token in tokens:
+            norm = normalize_identifier(token)
+            if norm and len(norm) >= 4 and norm not in identifiers:
+                identifiers.append(norm)
+
+    result["extracted_ids"] = identifiers
+
+    # Match against DB
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    seen_ids = set()
+
+    for norm_id in identifiers:
+        assets = find_asset_by_normalized_id(db_path, norm_id)
+        for a in assets:
+            if a["id"] not in seen_ids:
+                seen_ids.add(a["id"])
+                result["matches"].append(a)
+
+                # Update photo_path if empty
+                existing = conn.execute(
+                    "SELECT photo_path FROM assets WHERE id = ?", (a["id"],)
+                ).fetchone()
+                if existing and not existing["photo_path"]:
+                    conn.execute(
+                        "UPDATE assets SET photo_path = ?, updated_at = datetime('now') WHERE id = ?",
+                        (result["photo_path"], a["id"]),
+                    )
+                    result["updated"] = True
+                    conn.commit()
+
+    conn.close()
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Cross-reference photo IDs against assets DB"
+    )
+    parser.add_argument(
+        "--photo", "-p", help="Process a single photo by filename"
+    )
+    parser.add_argument(
+        "--text", "-t", help="Process raw text directly (skip OCR)"
+    )
+    parser.add_argument(
+        "--db",
+        default=DB_PATH,
+        help=f"Database path (default: {DB_PATH})",
+    )
+    args = parser.parse_args()
+
+    results = []
+
+    if args.text:
+        # Process from text directly
+        print(f"\n{'=' * 70}")
+        print(f"PROCESSING: supplied text ({len(args.text)} chars)")
+        print(f"{'=' * 70}")
+        print(f"  Text: {args.text[:500]}")
+
+        identifiers = []
+        norm = normalize_identifier(args.text)
+        if norm and len(norm) >= 4:
+            identifiers.append(norm)
+        tokens = re.findall(r"[A-Za-z0-9]{4,}", args.text)
+        for token in tokens:
+            n = normalize_identifier(token)
+            if n and len(n) >= 4 and n not in identifiers:
+                identifiers.append(n)
+
+        print(f"\n  Normalized IDs: {identifiers}")
+        conn = sqlite3.connect(args.db)
+        conn.row_factory = sqlite3.Row
+        for norm_id in identifiers:
+            assets = find_asset_by_normalized_id(args.db, norm_id)
+            if assets:
+                for a in assets:
+                    print(f"\n  ✓ MATCH → Asset #{a['id']}")
+                    print(f"    Machine ID: {a['machine_id']}")
+                    print(f"    Name: {a['name'][:60]}")
+            else:
+                print(f"\n  ✗ No match for '{norm_id}'")
+        conn.close()
+        return
+
+    if args.photo:
+        photo_path = os.path.join(PHOTOS_DIR, args.photo)
+        if not os.path.exists(photo_path):
+            print(f"ERROR: {photo_path} not found")
+            sys.exit(1)
+        results.append(process_photo(photo_path, args.db))
+    else:
+        # Process all photos
+        for fname in sorted(os.listdir(PHOTOS_DIR)):
+            photo_path = os.path.join(PHOTOS_DIR, fname)
+            if os.path.isfile(photo_path):
+                results.append(process_photo(photo_path, args.db))
+
+    # Print summary
+    matched = [r for r in results if r["matches"]]
+    unmatched = [r for r in results if not r["matches"] and r["vision_text"]]
+    no_text = [r for r in results if not r["vision_text"]]
+
+    print(f"\n{'=' * 70}")
+    print(f"CROSS-REFERENCE SUMMARY ({datetime.now().isoformat()})")
+    print(f"{'=' * 70}")
+    print(f"  Photos scanned: {len(results)}")
+    print(f"  Photos with text: {len(results) - len(no_text)}")
+    print(f"  Matches found: {len(matched)}")
+    print(f"  Unmatched: {len(unmatched)}")
+    print(f"  No text (small/thumbs): {len(no_text)}")
+
+    for r in matched:
+        for a in r["matches"]:
+            status = "UPDATED" if r["updated"] else "already set"
+            print(
+                f"\n  ✓ {r['photo']} → Asset #{a['id']} "
+                f"(machine_id={a['machine_id']}) [{status}]"
+            )
+
+    for r in unmatched:
+        print(f"\n  ✗ {r['photo']} — no DB match")
+        print(f"    IDs: {r['extracted_ids'][:5]}")
+        print(f"    Text preview: {r['vision_text'][:100]}")
+
+    print()
+
+
+if __name__ == "__main__":
+    main()