File size: 3,270 Bytes
0dc87cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | """Match detected crops to catalogue products using CLIP + Qwen2.5-VL fallback."""
import numpy as np
from embedder import embed_catalogue, embed_crops
from vlm_fallback import identify as vlm_identify
CLIP_THRESHOLD = 0.65
def match_detections(
detections: list[dict],
catalogue_path: str = "catalogue/catalogue.json",
clip_threshold: float = CLIP_THRESHOLD,
use_vlm: bool = True,
device: str = None,
) -> list[dict]:
"""
Match each detection to a catalogue product.
Augments each detection dict with:
- matched: True/False
- product_name: matched product name (or 'unknown')
- sku_id: matched SKU (or None)
- similarity: cosine similarity score
- match_method: 'clip' or 'vlm'
"""
import torch
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
cat = embed_catalogue(catalogue_path)
cat_embs = cat["embeddings"]
products = cat["products"]
product_names = [p["product_name"] for p in products]
crop_embs = embed_crops(detections, device=device)
sims = crop_embs @ cat_embs.T
vlm_queue = []
for i, det in enumerate(detections):
max_idx = int(np.argmax(sims[i]))
max_sim = float(sims[i][max_idx])
det["similarity"] = round(max_sim, 4)
if max_sim >= clip_threshold:
det["matched"] = True
det["product_name"] = products[max_idx]["product_name"]
det["sku_id"] = products[max_idx]["sku_id"]
det["match_method"] = "clip"
else:
vlm_queue.append(i)
if vlm_queue and use_vlm:
print(f"[matcher] Sending {len(vlm_queue)} low-confidence crops to Qwen2.5-VL...")
for i in vlm_queue:
det = detections[i]
result = vlm_identify(det["crop"], product_names, device=device)
if result in product_names:
idx = product_names.index(result)
det["matched"] = True
det["product_name"] = result
det["sku_id"] = products[idx]["sku_id"]
det["match_method"] = "vlm"
else:
det["matched"] = False
det["product_name"] = "unknown"
det["sku_id"] = None
det["match_method"] = "vlm"
elif vlm_queue:
for i in vlm_queue:
det = detections[i]
det["matched"] = False
det["product_name"] = "unknown"
det["sku_id"] = None
det["match_method"] = "clip"
matched = sum(1 for d in detections if d.get("matched"))
print(f"[matcher] {matched}/{len(detections)} products matched")
return detections
if __name__ == "__main__":
import sys
from detector import detect_products
from cropper import crop_detections
path = sys.argv[1] if len(sys.argv) > 1 else "shelf_images/shelf_01.jpg"
dets = detect_products(path)
dets = crop_detections(path, dets)
dets = match_detections(dets)
print(f"\n{'='*60}")
print(f"Results for {path}")
print(f"{'='*60}")
for d in dets:
status = "MATCH" if d["matched"] else "UNKNOWN"
print(f" [{status}] {d['product_name']:40s} sim={d['similarity']:.4f} via={d['match_method']}")
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