Make the catalog hash honest, share the text helpers, drop the duplicate labels

`catalog_hash` hashed the CSV and the view *names*, so replacing
`sku_l09.jpg` with a different product's packshot left the hash unchanged and
a report could claim the same catalog while every l09 decision in it had
moved. It hashes each view's bytes now.

`identity_lanes` carried its own `normalize_text` and `bigrams`, near-copies
of the ones in `rebuild_catalog` that had already drifted (the lane's table
folded ユ to itself, the catalog's did not). The lane imports the catalog's
now, so one read scores the same either way.

`evidence/image6_v2.json` was byte-identical to `eval/labels/image6.json`; the
evidence scripts and the decision document point at the canonical file and the
copy is gone. `score_crop`'s docstring now states the contract its callers
already depend on: the dict's insertion order is the lane's ranking, and
re-sorting by value names a different SKU than the lane chose.

Tests: the lane is shared by the classify pool, so a concurrent-ranking test
pins that one BFMatcher does not cross crops under eight threads.

recognization/docs/research/2026-09-28-recognition-architecture/evidence/dino_scale.py

@@ -16,7 +16,7 @@ for p in sorted(glob.glob('add/*')):
         if min(c.shape[:2])>=40: dis.append((f'd{j}_{os.path.basename(p)}',c))
 random.shuffle(dis)
 D=[(k,g.embed(c)) for k,c in dis]
-L=json.load(open(f'{S}/image6_v2.json')); E={k for k,_ in refs}
+L=json.load(open(f'{S}/../../../../eval/labels/image6.json')); E={k for k,_ in refs}
 Q=[(fc['label'],g.embed(c)) for fc,c in crops('add/image6.jpg',L,True) if fc['label'] in E]
 t=time.perf_counter(); _=g.embed(Q and dis[0][1]); print('embed ms',round((time.perf_counter()-t)*1000,1))
 for N in [0,50,200,len(D)]: