import math

from edge.models import (
    Keypoint, BBox, NOSE, L_SHOULDER, R_SHOULDER, L_HIP, R_HIP, CORE_JOINTS,
)


def _mean(points: list[tuple[float, float]]) -> tuple[float, float]:
    n = len(points)
    return (sum(p[0] for p in points) / n, sum(p[1] for p in points) / n)


def com(keypoints: list[Keypoint]) -> tuple[float, float]:
    pts = [(keypoints[i].x, keypoints[i].y) for i in CORE_JOINTS]
    return _mean(pts)


def _shoulder_center(kps: list[Keypoint]) -> tuple[float, float]:
    return _mean([(kps[L_SHOULDER].x, kps[L_SHOULDER].y),
                  (kps[R_SHOULDER].x, kps[R_SHOULDER].y)])


def _hip_center(kps: list[Keypoint]) -> tuple[float, float]:
    return _mean([(kps[L_HIP].x, kps[L_HIP].y),
                  (kps[R_HIP].x, kps[R_HIP].y)])


def torso_angle_deg(keypoints: list[Keypoint]) -> float:
    """몸통(어깨중심→엉덩이중심) 벡터가 수직축과 이루는 각. 0=수직, 90=수평."""
    sx, sy = _shoulder_center(keypoints)
    hx, hy = _hip_center(keypoints)
    vx, vy = hx - sx, hy - sy
    # 수직축(0,1) 대비 편차. 위/아래 무관하게 |성분| 사용.
    return math.degrees(math.atan2(abs(vx), abs(vy))) if (vx or vy) else 0.0


def head_below_hips(keypoints: list[Keypoint]) -> bool:
    _, hy = _hip_center(keypoints)
    return keypoints[NOSE].y > hy


def visible_count(keypoints: list[Keypoint], thr: float = 0.2) -> int:
    return sum(1 for k in keypoints if k.conf >= thr)


def core_visible(keypoints: list[Keypoint], thr: float = 0.5) -> bool:
    return all(keypoints[i].conf >= thr for i in CORE_JOINTS)


def height_of(bbox: BBox) -> float:
    return bbox.height


def normalize(value: float, bbox_height: float) -> float:
    return value / bbox_height if bbox_height else 0.0


def track_displacement(track) -> float:
    """Mean per-keypoint displacement between the last two frames, normalized by bbox height. 1.0 if <2 frames."""
    if len(track.frames) < 2:
        return 1.0
    a, b = track.frames[-2], track.frames[-1]
    h = b.bbox.height or 1.0
    d = sum(((p.x - q.x) ** 2 + (p.y - q.y) ** 2) ** 0.5
            for p, q in zip(a.keypoints, b.keypoints)) / len(b.keypoints)
    return normalize(d, h)