"""Diagnostic identity harness. Logs full per-facing score vectors so every
ceiling (catalog, identity rank, decision) is separable. Read-only on product code."""
import sys, os, json, time, argparse, pickle
sys.path.insert(0, 'code')
import cv2, numpy as np
from feature_matcher import ProductFeatureMatcher

def sku_of(name):
    p = name.split('_'); return '_'.join(p[:2])

def crops(image_path, labels, live, bug=False, long_side=1280):
    im = cv2.imread(image_path); f = 1.0
    if live:
        h, w = im.shape[:2]; f = long_side / max(h, w)
        im = cv2.resize(im, (int(w*f), int(h*f)), interpolation=cv2.INTER_AREA)
    out = []
    for fc in labels['facings']:
        b = fc['box'] if bug else [int(round(v*f)) for v in fc['box']]
        x1, y1, x2, y2 = b
        out.append((fc, im[max(0,y1):y2, max(0,x1):x2]))
    return out

def run(stock, image, labels, live, bug, method):
    m = ProductFeatureMatcher(stock_dir=stock)
    rows = []
    for fc, c in crops(image, labels, live, bug):
        if c.size == 0 or min(c.shape[:2]) < 8: rows.append(dict(index=fc["index"], truth=fc["label"], shape=c.shape[:2], scores={}, templ={}, sub={}, ms=0.0)); continue
        t = time.perf_counter(); s, det = m.score_crop(c, method=method); dt = time.perf_counter()-t
        g = {}
        for k, v in s.items(): g[sku_of(k)] = max(g.get(sku_of(k), 0.0), v)
        sub = {k: det[k].get('sub_details') for k in det}
        rows.append(dict(index=fc['index'], truth=fc['label'], shape=c.shape[:2], scores=g, templ=s, sub=sub, ms=dt*1000))
    return dict(stock=stock, live=live, bug=bug, method=method, enrolled=sorted({sku_of(k) for k in m.db.templates}), rows=rows)

if __name__ == '__main__':
    ap = argparse.ArgumentParser(); ap.add_argument('--stock'); ap.add_argument('--labels'); ap.add_argument('--live', action='store_true')
    ap.add_argument('--bug', action='store_true'); ap.add_argument('--method', default='hybrid'); ap.add_argument('--out')
    a = ap.parse_args(); L = json.load(open(a.labels))
    r = run(a.stock, 'add/image6.jpg', L, a.live, a.bug, a.method)
    pickle.dump(r, open(a.out, 'wb'))
    print(a.out, len(r['rows']), 'rows', 'median ms', round(float(np.median([x['ms'] for x in r['rows']])),1))