"""Tier 2 缩放不变性 + Tier 3 落点可视化 + 数值回归（均走方案 A，torch 无关、确定性）。"""
import os

import cv2
import numpy as np
from conftest import fixture, OUTPUT

from face_analysis.detector import detector
from face_analysis.measure import measure_face


def _run(img):
    h, w = img.shape[:2]
    lms = detector.detect(img)
    assert lms is not None
    # 固定走方案 A（mask=None），保证确定性与 torch 无关
    return measure_face(lms, None, w, h)


def test_scale_invariance():
    """等比放大 2×：占比几乎不变（±0.5%），cm 近似不变（±2%）。"""
    img = cv2.imread(fixture("frontal.jpg"))
    big = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
    r1, r2 = _run(img), _run(big)
    cm1 = {"top": r1.top_cm, "upper": r1.upper_cm, "middle": r1.middle_cm, "lower": r1.lower_cm}
    cm2 = {"top": r2.top_cm, "upper": r2.upper_cm, "middle": r2.middle_cm, "lower": r2.lower_cm}
    d1, d2 = r1.to_response(), r2.to_response()
    for k in ["top_court", "upper_court", "middle_court", "lower_court"]:
        a = d1["four_courts"]["ratios"][k]
        b = d2["four_courts"]["ratios"][k]
        assert abs(a - b) < 0.005, f"ratio {k} 漂移过大: {a} vs {b}"
    for k in ["top", "upper", "middle", "lower"]:
        assert abs(cm1[k] - cm2[k]) / cm1[k] < 0.02, f"cm {k} 漂移过大: {cm1[k]} vs {cm2[k]}"


def test_landmark_overlay():
    """生成 5 纵向点叠加图供人工核验，并断言坐标在图内且自上而下有序。"""
    img = cv2.imread(fixture("frontal.jpg"))
    h, w = img.shape[:2]
    r = _run(img)
    pts = r.to_response()["landmarks"]
    order = ["hair_top", "hairline", "brow_center", "nose_bottom", "chin_tip"]
    ys = [pts[n]["y"] for n in order]
    # 坐标在图像范围内
    for n in order:
        assert 0 <= pts[n]["x"] <= w
        assert 0 <= pts[n]["y"] <= h
    # 自上而下严格递增
    assert ys == sorted(ys), f"纵向点未自上而下有序: {ys}"
    # dump 叠加图
    canvas = img.copy()
    for n in order:
        cv2.circle(canvas, (pts[n]["x"], pts[n]["y"]), 4, (0, 0, 255), -1)
        cv2.line(canvas, (0, pts[n]["y"]), (w, pts[n]["y"]), (0, 255, 0), 1)
    cv2.imwrite(os.path.join(OUTPUT, "frontal_landmarks.png"), canvas)


# 数值回归基线：frontal.jpg 方案A 首次实测值，防重构回归（容差 1%）。
_BASELINE = {
    "face_total_height_cm": 24.01,
    "top_court_cm": 5.06, "upper_court_cm": 6.07,
    "middle_court_cm": 7.23, "lower_court_cm": 5.64,
    "eye_width_cm": 2.52, "face_width_cm": 12.49, "inter_eye_distance_cm": 3.24,
}


def test_numeric_regression():
    img = cv2.imread(fixture("frontal.jpg"))
    d = _run(img).to_response()
    got = {
        "face_total_height_cm": d["face_total_height_cm"],
        **{k: d["four_courts"][k] for k in
           ["top_court_cm", "upper_court_cm", "middle_court_cm", "lower_court_cm"]},
        **{k: d["seven_eyes"][k] for k in
           ["eye_width_cm", "face_width_cm", "inter_eye_distance_cm"]},
    }
    for k, base in _BASELINE.items():
        assert abs(got[k] - base) / base < 0.01, f"{k} 回归: 基线 {base}, 实测 {got[k]}"