"""Which facings are falsely accepted, and as what."""
import sys, cv2
sys.path.insert(0, "code")
from eval_identity import LocalLaneAdapter, load_labels, at_live_resolution, leaked, UNKNOWN, crop_of
from identity_lanes import DecisionPolicy

payload = load_labels("eval/labels/image6.json")
image = cv2.imread("add/image6.jpg")
facings = [f for f in payload["facings"] if f["label"] != "exclude"]
facings = [f for f in facings if leaked(image, f["box"], "stock-lopia-clean") is None]
enrolled = {"sku_l01","sku_l02","sku_l03","sku_l05","sku_l06","sku_l09","sku_l10","sku_l11"}

for live in (False, True):
    picture = at_live_resolution(image) if live else image
    scaled = facings if not live else [
        {**f, "box": [int(round(v * picture.shape[1] / image.shape[1])) for v in f["box"]]}
        for f in facings]
    lane = LocalLaneAdapter("stock-lopia-clean")
    print(f"=== {'live' if live else 'full'} ===")
    for f in scaled:
        crop = crop_of(picture, f["box"])
        if crop.size == 0: continue
        ranked = lane.rank(crop)
        d = lane.decide(crop, ranked, accept=2.0, margin=0.0, scale="local")
        ans = d.get("best_sku") if d.get("identity_level") == "accepted" else None
        if ans and ans != f["label"]:
            print(f"  #{f['index']:>3} truth={f['label']:<9} answered={ans:<9} enrolled={f['label'] in enrolled}")