"""Cluster PPE detections into per-person candidates."""
from __future__ import annotations

from dataclasses import dataclass, field
from typing import Dict, List, Tuple

from core.detection import PPEItem
from core.geometry import box_distance, expand_bbox, merge_boxes


@dataclass
class PersonCandidate:
    bbox:       Tuple[int, int, int, int]
    items:      Dict[str, float]
    detections: List[PPEItem] = field(default_factory=list)


def should_merge(candidate: PersonCandidate, item: PPEItem) -> bool:
    cx1, cy1, cx2, cy2 = candidate.bbox
    ix1, iy1, ix2, iy2 = item.bbox
    cw, ch = cx2 - cx1, cy2 - cy1
    iw, ih = ix2 - ix1, iy2 - iy1

    cxc, cyc = (cx1 + cx2) / 2, (cy1 + cy2) / 2
    ixc, iyc = (ix1 + ix2) / 2, (iy1 + iy2) / 2

    max_dx = max(cw, iw) * 1.2 + 40
    max_dy = max(ch, ih) * 1.8 + 50

    return abs(ixc - cxc) <= max_dx and abs(iyc - cyc) <= max_dy


def group_detections_to_people(detections: List[PPEItem], w: int, h: int) -> List[PersonCandidate]:
    if not detections:
        return []

    candidates: List[PersonCandidate] = []
    for item in detections:
        merged = False
        for cand in candidates:
            if should_merge(cand, item):
                cand.bbox = merge_boxes(cand.bbox, item.bbox)
                cand.items[item.label] = max(cand.items.get(item.label, 0.0), item.conf)
                cand.detections.append(item)
                merged = True
                break
        if not merged:
            candidates.append(PersonCandidate(
                bbox=item.bbox,
                items={item.label: item.conf},
                detections=[item],
            ))

    again = True
    while again:
        again = False
        merged_list: List[PersonCandidate] = []
        for person in candidates:
            matched = False
            for prev in merged_list:
                pw = prev.bbox[2] - prev.bbox[0]
                ph = prev.bbox[3] - prev.bbox[1]
                dist = box_distance(prev.bbox, person.bbox)
                th = max(pw, ph) * 0.55
                if dist <= th:
                    prev.bbox = merge_boxes(prev.bbox, person.bbox)
                    for label, conf in person.items.items():
                        prev.items[label] = max(prev.items.get(label, 0.0), conf)
                    prev.detections.extend(person.detections)
                    again = True
                    matched = True
                    break
            if not matched:
                merged_list.append(person)
        candidates = merged_list

    for cand in candidates:
        cand.bbox = expand_bbox(cand.bbox, w, h)

    return candidates