tngo
aaed5d388f
Replaces the DINO gallery as the recognition path with the measured design from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue check, fused with an OCR text lane that vetoes a match whose package text names a different catalog row and answers on its own when geometry finds nothing. - identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane builds each row's signature from the catalog name plus its packshot OCR, so a row with no enrolled image is still answerable. - engine.py: every backend groups scores by canonical SKU, so no view-suffixed id reaches the wire and a name and price always resolve. decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both lanes are built in warm_up (non-fatally) so the first frame is not the slow one, and the text lane shares the engine's OCR lock. - live.py: decides per crop on the lane's own rank order. The lane's order comes from its rank key and its value is only the gate, so re-sorting by value named a different SKU than the lane chose; averaging in an empty observation also diluted a good confidence below the accept line. - identity.py: a `local` score scale whose gate is one absolute line, because the value is already a margin over the runner-up. - app.py, run-demo-server.sh: `--identity local` is the default; the demo server no longer starts on the leaked `stock-lopia` catalog. - eval_live.py: the replay vocabulary accepts `local`, so a recording from the shipped server can be scored. - README.md: documents the three backends and what each is for. Measured on the corrected image6 labels at live resolution: 36/61 in-catalog facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced from the text lane alone. Through the real server: /v1/recognize 90% precision, /v1/stream 100% precision, no view-suffixed ids on either.
30 lines
1.7 KiB
Python
30 lines
1.7 KiB
Python
import sys,os,glob,json,time,numpy as np,cv2,random
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sys.path.insert(0,'code'); sys.path.insert(0,os.path.dirname(__file__)); os.environ['YOLO_OFFLINE']='1'
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from gallery import ProductGallery
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from detector import ShelfProductDetector
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from diag import crops, sku_of
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from PIL import Image
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S=os.path.dirname(__file__); random.seed(0)
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g=ProductGallery('stock-lopia-clean',['sku_l01','sku_l02','sku_l03','sku_l05','sku_l06','sku_l09','sku_l11']); assert g.load()
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refs=[(sku_of(os.path.basename(p)[:-4]),g.embed(cv2.imread(p))) for p in sorted(glob.glob('stock-lopia-clean/*.jpg'))]
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det=ShelfProductDetector(); dis=[]
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for p in sorted(glob.glob('add/*')):
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if 'image6' in p: continue
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im=cv2.cvtColor(np.array(Image.open(p).convert('RGB')),cv2.COLOR_RGB2BGR)
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for j,b in enumerate(det.detect(im).get('boxes',[])):
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x1,y1,x2,y2=[int(v) for v in b['box']]; c=im[max(0,y1):y2,max(0,x1):x2]
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if min(c.shape[:2])>=40: dis.append((f'd{j}_{os.path.basename(p)}',c))
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random.shuffle(dis)
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D=[(k,g.embed(c)) for k,c in dis]
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L=json.load(open(f'{S}/image6_v2.json')); E={k for k,_ in refs}
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Q=[(fc['label'],g.embed(c)) for fc,c in crops('add/image6.jpg',L,True) if fc['label'] in E]
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t=time.perf_counter(); _=g.embed(Q and dis[0][1]); print('embed ms',round((time.perf_counter()-t)*1000,1))
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for N in [0,50,200,len(D)]:
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R=refs+D[:N]; M=np.stack([e for _,e in R]); names=[k for k,_ in R]
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rec={1:0,5:0,10:0,20:0}
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for truth,q in Q:
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s=M@q; order=[]
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for i in np.argsort(-s):
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if names[i] not in order: order.append(names[i])
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for K in rec: rec[K]+= truth in order[:K]
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print(f'skus={len(set(names)):4d} DINO recall@1/5/10/20 '+'/'.join(str(rec[K]) for K in rec)+f' of {len(Q)}')
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