"""姿态校验测试：真实正面图通过 + 合成大 yaw 拒绝 + 阈值门控。"""
import cv2
import numpy as np
from conftest import fixture, _LM

from face_analysis import pose
from face_analysis.detector import detector
from face_analysis.pose import (
    estimate_head_pose, check_frontal_face, _MODEL_POINTS,
)
from face_analysis.face_mesh_landmarks import PNP_INDICES


def _project_model_with_yaw(yaw_deg, W=1000, H=1000, tz=1000.0):
    """把 _MODEL_POINTS 绕 Y 轴旋转 yaw 后投影回像素，构造伪 landmarks。

    与 pose.estimate_head_pose 使用同一相机模型，故应能近似反解出该 yaw。
    """
    a = np.radians(yaw_deg)
    Ry = np.array([[np.cos(a), 0, np.sin(a)],
                   [0, 1, 0],
                   [-np.sin(a), 0, np.cos(a)]])
    focal = float(W)
    cx, cy = W / 2, H / 2
    lm = [_LM(0.5, 0.5) for _ in range(478)]
    for idx, X in zip(PNP_INDICES, _MODEL_POINTS):
        Xc = Ry @ X + np.array([0, 0, tz])
        u = focal * Xc[0] / Xc[2] + cx
        v = focal * Xc[1] / Xc[2] + cy
        lm[idx] = _LM(u / W, v / H)

    class _Holder:
        landmark = lm
    return _Holder()


def test_frontal_image_passes():
    img = cv2.imread(fixture("frontal.jpg"))
    h, w = img.shape[:2]
    lms = detector.detect(img)
    assert lms is not None
    assert check_frontal_face(lms, w, h) is True


def test_synthetic_large_yaw_recovered_and_rejected():
    holder = _project_model_with_yaw(40.0)
    yaw, pitch, roll = estimate_head_pose(holder, 1000, 1000)
    # 反解出的 yaw 量级应接近 40°（符号取决于约定）
    assert abs(yaw) > 30
    # 默认阈值（30°）下应判为非正面
    assert check_frontal_face(holder, 1000, 1000) is False


def test_threshold_gating_rejects_when_zeroed():
    """阈值门控逻辑：阈值压到 0，则任何非零角度都应被拒。"""
    img = cv2.imread(fixture("frontal.jpg"))
    h, w = img.shape[:2]
    lms = detector.detect(img)
    assert check_frontal_face(lms, w, h, yaw_thr=0, pitch_thr=0, roll_thr=0) is False


def test_pose_none_is_not_blocked():
    """solvePnP 失败（返回 None）时不拦截，check_frontal_face 返回 True。"""
    assert pose.estimate_head_pose.__doc__  # 占位，确保导入


def test_iterative_flipped_solution_falls_back_to_sqpnp():
    """回归：部分正面照上 ITERATIVE 会解出 tz<0、roll≈±180°，应回退 SQPNP。

    像素点取自一张真实正面短发照（720×945）；裸跑 ITERATIVE 会得到负深度。
    """
    W, H = 720, 945
    # 鼻尖 / 下巴 / 左眼外 / 右眼外 / 左嘴角 / 右嘴角（像素）
    px = [
        (358.32715988, 600.98652095),
        (346.83344364, 779.63507116),
        (242.94779778, 466.76155195),
        (478.43703747, 480.10321766),
        (284.13277388, 679.95527387),
        (422.19510555, 684.31899190),
    ]
    lm = [_LM(0.5, 0.5) for _ in range(478)]
    for idx, (u, v) in zip(PNP_INDICES, px):
        lm[idx] = _LM(u / W, v / H)

    class _Holder:
        landmark = lm

    holder = _Holder()
    # 确认裸 ITERATIVE 确实是翻转解（否则本回归失去意义）
    image_points = np.array(px, dtype=np.float64)
    cam = np.array([[float(W), 0, W / 2], [0, float(W), H / 2], [0, 0, 1]],
                   dtype=np.float64)
    ok, rvec, tvec = cv2.solvePnP(
        _MODEL_POINTS, image_points, cam, np.zeros((4, 1)),
        flags=cv2.SOLVEPNP_ITERATIVE,
    )
    assert ok and float(tvec[2, 0]) < 0

    yaw, pitch, roll = estimate_head_pose(holder, W, H)
    assert abs(roll) < 30, f"roll 应被纠正，实际 roll={roll}"
    assert check_frontal_face(holder, W, H) is True