#!/usr/bin/env python3
"""Agent Calibration Layer (ACL): per-camera score statistics from normal data.

Scores a subset of TRAIN (all-normal) samples with the saved memory bank and
records each camera agent's median / IQR of its anomaly-score reports. Fusion
then operates on per-agent standardized scores, removing systematic camera
bias before pooling. Label-free (uses normal training data only).
"""
from __future__ import annotations

import json
import os
import sys

import numpy as np

HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(HERE)
sys.path.insert(0, ROOT)

from src.data import load_samples, subsample_samples  # noqa: E402
from src.scorer import PatchScorer  # noqa: E402

DATA = "/home/z/my-project/data"
IMG_ROOT = os.path.join(DATA, "realiad_256")
JSON_ROOT = os.path.join(DATA, "mirror/realiad_jsons")
RES = os.path.join(ROOT, "results")

cats = ["usb", "pcb", "mint", "eraser", "bottle_cap", "zipper"]
out = {}
for cat in cats:
    bank_path = os.path.join(RES, "scores", f"{cat}_bank.npy")
    scorer = PatchScorer(seed=0)
    scorer.load(bank_path)
    train = load_samples(cat, IMG_ROOT, JSON_ROOT, "train")
    sub = subsample_samples(train, n_ok=150, n_ng=0, seed=1)[:150]
    flat = [p for s in sub for p in s.image_paths]
    sc, _ = scorer.score_images(flat)
    sc = sc.reshape(len(sub), 5)
    per_cam = {}
    for v in range(5):
        col = sc[:, v]
        per_cam[f"C{v+1}"] = {
            "median": float(np.median(col)),
            "iqr": float(np.subtract(*np.percentile(col, [75, 25]))) + 1e-9,
            "mean": float(np.mean(col)),
        }
    out[cat] = per_cam
    print(cat, {k: round(v["median"], 4) for k, v in per_cam.items()})

with open(os.path.join(RES, "agent_calibration.json"), "w") as f:
    json.dump(out, f, indent=1)
print("saved", os.path.join(RES, "agent_calibration.json"))