"""手部框、撕动作几何与概率（从离线 tear 脚本抽离，不依赖 OpenCV 绘制）。"""

from __future__ import annotations

from itertools import combinations
from typing import Any

import numpy as np
from ultralytics import YOLO


def union_boxes(boxes: list[list[float]]) -> list[float]:
    x1 = min(b[0] for b in boxes)
    y1 = min(b[1] for b in boxes)
    x2 = max(b[2] for b in boxes)
    y2 = max(b[3] for b in boxes)
    return [x1, y1, x2, y2]


def pad_box(
    xyxy: list[float],
    img_w: int,
    img_h: int,
    pad_ratio: float = 0.30,
) -> tuple[int, int, int, int]:
    x1, y1, x2, y2 = xyxy
    bw, bh = x2 - x1, y2 - y1
    px, py = bw * pad_ratio, bh * pad_ratio
    return (
        max(0, int(x1 - px)),
        max(0, int(y1 - py)),
        min(img_w, int(x2 + px)),
        min(img_h, int(y2 + py)),
    )


def collect_hand_boxes(det_model: YOLO, boxes) -> list[list[float]]:
    names = det_model.names
    out: list[list[float]] = []
    for box in boxes:
        cid = int(box.cls[0])
        label = names.get(cid, "")
        if label == "hand":
            out.append(box.xyxy[0].tolist())
    return out


def box_edge_distance(a: list[float], b: list[float]) -> float:
    dx = max(0, max(a[0], b[0]) - min(a[2], b[2]))
    dy = max(0, max(a[1], b[1]) - min(a[3], b[3]))
    return float((dx**2 + dy**2) ** 0.5)


def box_avg_width(boxes: list[list[float]]) -> float:
    if not boxes:
        return 0.0
    return sum(b[2] - b[0] for b in boxes) / len(boxes)


def find_tearing_pair(
    hand_boxes: list[list[float]],
    gap_ratio: float = 1.5,
) -> tuple[list[float], list[float]] | None:
    if len(hand_boxes) < 2:
        return None
    avg_w = box_avg_width(hand_boxes)
    threshold = avg_w * gap_ratio
    best_dist = float("inf")
    best_pair: tuple[list[float], list[float]] | None = None
    for a, b in combinations(hand_boxes, 2):
        d = box_edge_distance(a, b)
        if d < best_dist:
            best_dist = d
            best_pair = (a, b)
    if best_pair is not None and best_dist <= threshold:
        return best_pair
    return None


def prob_tearing(tprobs, tear_names: dict[Any, str]) -> float:
    if tprobs is None:
        return 0.0
    data = tprobs.data
    if data is None:
        return 0.0
    d = data.detach().float().cpu().numpy().ravel()
    for i, name in tear_names.items():
        if name == "tearing":
            ii = int(i)
            if 0 <= ii < len(d):
                return float(d[ii])
    return 0.0