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/fuse_eval.py

@@ -2,7 +2,7 @@ import sys,os,json,pickle,csv,numpy as np
 S=os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0,S)
 from ocr_fuse import norm,sim  # noqa (module runs OCR of packshots on import; cheap)
 sig=pickle.load(open(f'{S}/sig.pkl','rb')); O=pickle.load(open(f'{S}/ocr.pkl','rb'))
-L={f['index']:f['label'] for f in json.load(open(f'{S}/image6_v2.json'))['facings']}
+L={f['index']:f['label'] for f in json.load(open(f'{S}/../../../../eval/labels/image6.json'))['facings']}
 def conf(ev):
     r=sorted(ev.items(),key=lambda kv:-kv[1]['inliers']*(0.25+kv[1]['color']))
     (k1,e1),(k2,e2)=r[0],r[1]; i1,i2=e1['inliers'],e2['inliers']