nsfwapp/backend/ai_server.py

# backend\ai_server.py

import json
import math
import os
import secrets
import tempfile
from pathlib import Path
from typing import List, Optional

from fastapi import Depends, FastAPI, HTTPException, Request, status
from pydantic import BaseModel
from ultralytics import YOLO


BASE_DIR = Path(__file__).resolve().parent
KEYPOINT_NAMES = [
    "nose",
    "left_eye", "right_eye",
    "left_ear", "right_ear",
    "left_shoulder", "right_shoulder",
    "left_elbow", "right_elbow",
    "left_wrist", "right_wrist",
    "left_hip", "right_hip",
    "left_knee", "right_knee",
    "left_ankle", "right_ankle",
]

NO_SEX_POSITION_LABEL = "keine"
NO_SEX_POSITION_ALIASES = {
    "",
    NO_SEX_POSITION_LABEL,
}


def normalize_sex_position_label(value) -> str:
    clean = str(value or "").strip().lower()
    if clean in NO_SEX_POSITION_ALIASES:
        return NO_SEX_POSITION_LABEL
    return clean


def is_no_sex_position_label(value) -> bool:
    return normalize_sex_position_label(value) == NO_SEX_POSITION_LABEL


def normalize_sex_position_labels(values) -> set[str]:
    labels = set()
    has_no_position = False

    for value in values or []:
        clean = normalize_sex_position_label(value)
        if is_no_sex_position_label(clean):
            has_no_position = True
            continue
        if clean:
            labels.add(clean)

    if has_no_position:
        labels.add(NO_SEX_POSITION_LABEL)

    return labels


def existing_file(path: Path) -> Optional[Path]:
    try:
        if path.exists() and path.is_file() and path.stat().st_size > 0:
            return path
    except OSError:
        pass
    return None


def existing_model_dir(path: Path) -> Optional[Path]:
    try:
        if path.exists() and path.is_dir() and existing_file(path / "config.json"):
            return path
    except OSError:
        pass
    return None


def resolve_training_root() -> Path:
    env_root = os.environ.get("TRAINING_ROOT", "").strip()
    if env_root:
        root = Path(env_root).expanduser().resolve()
        root.mkdir(parents=True, exist_ok=True)
        return root

    candidates = [
        # Wenn ai_server.py aus backend/ läuft:
        BASE_DIR / "generated" / "training",

        # Wenn ai_server.py aus backend/ml/ laufen würde:
        BASE_DIR.parent / "generated" / "training",

        # Wenn ai_server.py embedded aus Temp läuft, aber backendRoot als cwd gesetzt wurde:
        Path.cwd() / "generated" / "training",

        # Wenn Working Directory Projektroot ist:
        Path.cwd() / "backend" / "generated" / "training",
    ]

    for root in candidates:
        if (
            existing_file(root / "detection_labels.json")
            or existing_file(root / "detector" / "model" / "best.pt")
            or existing_model_dir(root / "videomae" / "model")
        ):
            root.mkdir(parents=True, exist_ok=True)
            return root.resolve()

    # Fallback: Server soll trotzdem starten.
    root = (Path.cwd() / "generated" / "training").resolve()
    root.mkdir(parents=True, exist_ok=True)
    return root


TRAINING_ROOT = resolve_training_root()
DEFAULT_MODEL_PATH = TRAINING_ROOT / "detector" / "model" / "best.pt"
DEFAULT_POSE_MODEL_PATH = TRAINING_ROOT / "pose" / "model" / "best.pt"
DEFAULT_VIDEOMAE_MODEL_PATH = TRAINING_ROOT / "videomae" / "model"


def resolve_detection_labels_path() -> Path:
    env_path = os.environ.get("DETECTION_LABELS_PATH", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return p
        raise RuntimeError(f"DETECTION_LABELS_PATH not found: {p}")

    p = TRAINING_ROOT / "detection_labels.json"
    if existing_file(p):
        return p.resolve()

    raise RuntimeError(f"detection_labels.json not found: {p}")


def resolve_model_path() -> str:
    env_path = os.environ.get("YOLO_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return str(p)
        raise RuntimeError(f"YOLO_MODEL not found: {p}")

    if existing_file(DEFAULT_MODEL_PATH):
        return str(DEFAULT_MODEL_PATH)

    raise RuntimeError(f"YOLO model not found: {DEFAULT_MODEL_PATH}")


def resolve_pose_model_path() -> Optional[Path]:
    env_path = os.environ.get("YOLO_POSE_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return p
        raise RuntimeError(f"YOLO_POSE_MODEL not found: {p}")

    if existing_file(DEFAULT_POSE_MODEL_PATH):
        return DEFAULT_POSE_MODEL_PATH.resolve()

    return None


def base_pose_model_candidates() -> list[Path]:
    return [
        BASE_DIR / "yolo26n-pose.pt",
        Path.cwd() / "yolo26n-pose.pt",
        TRAINING_ROOT / "yolo26n-pose.pt",
        TRAINING_ROOT.parent / "yolo26n-pose.pt",
        TRAINING_ROOT.parent.parent / "yolo26n-pose.pt",
        Path(tempfile.gettempdir()) / "nsfwapp-ml" / "yolo26n-pose.pt",
    ]


def resolve_base_pose_model_path() -> Optional[Path]:
    env_path = os.environ.get("YOLO_BASE_POSE_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_file(p):
            return p
        raise RuntimeError(f"YOLO_BASE_POSE_MODEL not found: {p}")

    for candidate in base_pose_model_candidates():
        p = existing_file(candidate)
        if p:
            return p.resolve()

    return None


def resolve_videomae_model_path() -> Optional[Path]:
    env_path = os.environ.get("VIDEOMAE_MODEL", "").strip()
    if env_path:
        p = Path(env_path).expanduser().resolve()
        if existing_model_dir(p):
            return p
        raise RuntimeError(f"VIDEOMAE_MODEL not found: {p}")

    if existing_model_dir(DEFAULT_VIDEOMAE_MODEL_PATH):
        return DEFAULT_VIDEOMAE_MODEL_PATH.resolve()

    return None


# Server darf auch ohne Labels/Model starten.
DETECTION_LABELS_PATH: Optional[Path] = None

LABEL_GROUPS = {
    "people": set(),
    "sexPositions": {NO_SEX_POSITION_LABEL},
    "bodyParts": set(),
    "objects": set(),
    "clothing": set(),
}

BODY_LABELS = LABEL_GROUPS["bodyParts"]
OBJECT_LABELS = LABEL_GROUPS["objects"]
CLOTHING_LABELS = LABEL_GROUPS["clothing"]
POSITION_LABELS = set()

PERSON_LABELS = {
    "person_male",
    "person_female",
}

_MODEL_PATH = ""
_MODEL_ERROR = ""
_POSE_MODEL_PATH = ""
_POSE_MODEL_ERROR = ""
_BASE_POSE_MODEL_PATH = ""
_BASE_POSE_MODEL_ERROR = ""
_VIDEOMAE_MODEL_PATH = ""
_VIDEOMAE_MODEL_ERROR = ""
_VIDEOMAE_DEVICE_ACTIVE = ""
_LABEL_ERROR = ""

_DEVICE = os.environ.get("YOLO_DEVICE", "")
_CONF = float(os.environ.get("YOLO_CONF", "0.25"))
_POSE_CONF = float(os.environ.get("YOLO_POSE_CONF", "0.30"))
_BASE_POSE_CONF = float(os.environ.get("YOLO_BASE_POSE_CONF", "0.10"))
_POSE_RELIABLE_MIN_SCORE = float(os.environ.get("YOLO_POSE_RELIABLE_MIN_SCORE", "0.30"))
_POSE_RELIABLE_MIN_KEYPOINTS = int(os.environ.get("YOLO_POSE_RELIABLE_MIN_KEYPOINTS", "6"))
_POSE_RELIABLE_MIN_QUALITY = float(os.environ.get("YOLO_POSE_RELIABLE_MIN_QUALITY", "0.45"))
_POSITION_CONTEXT_MIN_SCORE = float(os.environ.get("YOLO_POSITION_CONTEXT_MIN_SCORE", "0.22"))
_POSITION_CONTEXT_MAX_SCORE = float(os.environ.get("YOLO_POSITION_CONTEXT_MAX_SCORE", "0.44"))
_POSITION_CONTEXT_BOOST_WEIGHT = float(os.environ.get("YOLO_POSITION_CONTEXT_BOOST_WEIGHT", "0.60"))
_POSE_CONFIRMING_CONTEXT_MIN_SCORE = float(os.environ.get("YOLO_POSE_CONFIRMING_CONTEXT_MIN_SCORE", "0.14"))
_POSE_UNCONFIRMED_MAX_SCORE = float(os.environ.get("YOLO_POSE_UNCONFIRMED_MAX_SCORE", "0.38"))
_POSE_STRONG_UNCONFIRMED_MIN_SCORE = float(os.environ.get("YOLO_POSE_STRONG_UNCONFIRMED_MIN_SCORE", "0.70"))
_POSE_STRONG_UNCONFIRMED_MAX_SCORE = float(os.environ.get("YOLO_POSE_STRONG_UNCONFIRMED_MAX_SCORE", "0.46"))
_BATCH = int(os.environ.get("YOLO_BATCH", "16"))
_IMGSZ = int(os.environ.get("YOLO_IMGSZ", "640"))
_HALF = os.environ.get("YOLO_HALF", "0").lower() in {"1", "true", "yes", "on"}
_VIDEOMAE_DEVICE = os.environ.get("VIDEOMAE_DEVICE", "auto")
_VIDEOMAE_NUM_FRAMES = int(os.environ.get("VIDEOMAE_NUM_FRAMES", "16"))

model = None
pose_model = None
base_pose_model = None
videomae_model = None
videomae_processor = None

app = FastAPI()

AI_SERVER_TOKEN = os.environ.get("AI_SERVER_TOKEN", "").strip()
AI_SERVER_AUTH_REQUIRED = os.environ.get("AI_SERVER_AUTH_REQUIRED", "1").strip().lower() not in {
    "0",
    "false",
    "no",
    "off",
}


def require_ai_server_auth(request: Request) -> None:
    if not AI_SERVER_AUTH_REQUIRED:
        return

    if not AI_SERVER_TOKEN:
        raise HTTPException(
            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
            detail="AI server auth token not configured",
        )

    auth = request.headers.get("authorization", "").strip()
    header_token = request.headers.get("x-ai-server-token", "").strip()

    provided = ""

    if auth.lower().startswith("bearer "):
        provided = auth[7:].strip()
    elif header_token:
        provided = header_token

    if not provided or not secrets.compare_digest(provided, AI_SERVER_TOKEN):
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="unauthorized",
            headers={"WWW-Authenticate": "Bearer"},
        )


class PredictBatchRequest(BaseModel):
    paths: List[str]
    detectorOnly: bool = False
    imageSize: int = 640
    model: Optional[str] = None


class PositionClipItem(BaseModel):
    time: float = 0.0
    start: float = 0.0
    end: float = 0.0
    paths: List[str]


class PredictPositionClipsRequest(BaseModel):
    clips: List[PositionClipItem]
    numFrames: int = 16


def empty_prediction(source: str = "model_missing") -> dict:
    return {
        "modelAvailable": False,
        "source": source,
        "sexPosition": NO_SEX_POSITION_LABEL,
        "sexPositionScore": 0.0,
        "peoplePresent": [],
        "bodyPartsPresent": [],
        "objectsPresent": [],
        "clothingPresent": [],
        "boxes": [],
        "persons": [],
    }


def load_label_groups_safe() -> None:
    global DETECTION_LABELS_PATH
    global LABEL_GROUPS
    global BODY_LABELS
    global OBJECT_LABELS
    global CLOTHING_LABELS
    global POSITION_LABELS
    global PERSON_LABELS
    global _LABEL_ERROR

    try:
        path = resolve_detection_labels_path()
        DETECTION_LABELS_PATH = path

        with path.open("r", encoding="utf-8") as f:
            data = json.load(f)

        LABEL_GROUPS = {
            "people": set(
                str(x).strip().lower()
                for x in data.get("people", [])
                if str(x).strip()
            ),
            "sexPositions": normalize_sex_position_labels(data.get("sexPositions", [])),
            "bodyParts": set(
                str(x).strip().lower()
                for x in data.get("bodyParts", [])
                if str(x).strip()
            ),
            "objects": set(
                str(x).strip().lower()
                for x in data.get("objects", [])
                if str(x).strip()
            ),
            "clothing": set(
                str(x).strip().lower()
                for x in data.get("clothing", [])
                if str(x).strip()
            ),
        }

        if not LABEL_GROUPS["sexPositions"]:
            LABEL_GROUPS["sexPositions"] = {NO_SEX_POSITION_LABEL}

        _LABEL_ERROR = ""

    except Exception as exc:
        DETECTION_LABELS_PATH = None
        _LABEL_ERROR = str(exc)

        LABEL_GROUPS = {
            "people": set(),
            "sexPositions": {NO_SEX_POSITION_LABEL},
            "bodyParts": set(),
            "objects": set(),
            "clothing": set(),
        }

    BODY_LABELS = LABEL_GROUPS["bodyParts"]
    OBJECT_LABELS = LABEL_GROUPS["objects"]
    CLOTHING_LABELS = LABEL_GROUPS["clothing"]

    POSITION_LABELS = {
        label for label in LABEL_GROUPS["sexPositions"]
        if label and not is_no_sex_position_label(label)
    }

    PERSON_LABELS = {
        label for label in LABEL_GROUPS["people"]
        if label
    }


def get_model():
    global model
    global _MODEL_PATH
    global _MODEL_ERROR

    if model is not None:
        return model

    try:
        path = resolve_model_path()
        loaded = YOLO(path)

        model = loaded
        _MODEL_PATH = path
        _MODEL_ERROR = ""

        # Labels erst laden, wenn Inference wirklich gebraucht wird.
        load_label_groups_safe()

        return model

    except Exception as exc:
        model = None
        _MODEL_PATH = ""
        _MODEL_ERROR = str(exc)
        return None


def get_pose_model():
    global pose_model
    global _POSE_MODEL_PATH
    global _POSE_MODEL_ERROR

    if pose_model is not None:
        return pose_model

    try:
        path = resolve_pose_model_path()
        if path is None:
            pose_model = None
            _POSE_MODEL_PATH = ""
            _POSE_MODEL_ERROR = ""
            return None

        loaded = YOLO(str(path))

        pose_model = loaded
        _POSE_MODEL_PATH = str(path)
        _POSE_MODEL_ERROR = ""

        return pose_model

    except Exception as exc:
        pose_model = None
        _POSE_MODEL_PATH = ""
        _POSE_MODEL_ERROR = str(exc)
        return None


def get_base_pose_model():
    global base_pose_model
    global _BASE_POSE_MODEL_PATH
    global _BASE_POSE_MODEL_ERROR

    if base_pose_model is not None:
        return base_pose_model

    try:
        path = resolve_base_pose_model_path()
        if path is None:
            base_pose_model = None
            _BASE_POSE_MODEL_PATH = ""
            _BASE_POSE_MODEL_ERROR = ""
            return None

        loaded = YOLO(str(path))

        base_pose_model = loaded
        _BASE_POSE_MODEL_PATH = str(path)
        _BASE_POSE_MODEL_ERROR = ""

        return base_pose_model

    except Exception as exc:
        base_pose_model = None
        _BASE_POSE_MODEL_PATH = ""
        _BASE_POSE_MODEL_ERROR = str(exc)
        return None


def get_videomae_components():
    global videomae_model
    global videomae_processor
    global _VIDEOMAE_MODEL_PATH
    global _VIDEOMAE_MODEL_ERROR
    global _VIDEOMAE_DEVICE_ACTIVE

    if videomae_model is not None and videomae_processor is not None:
        return videomae_model, videomae_processor

    try:
        path = resolve_videomae_model_path()
        if path is None:
            videomae_model = None
            videomae_processor = None
            _VIDEOMAE_MODEL_PATH = ""
            _VIDEOMAE_MODEL_ERROR = ""
            _VIDEOMAE_DEVICE_ACTIVE = ""
            return None, None

        import torch
        from transformers import AutoImageProcessor, VideoMAEForVideoClassification

        if str(_VIDEOMAE_DEVICE).lower() == "auto":
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        else:
            device = torch.device(_VIDEOMAE_DEVICE)

        processor = AutoImageProcessor.from_pretrained(path)
        loaded = VideoMAEForVideoClassification.from_pretrained(path)
        loaded.to(device)
        loaded.eval()

        videomae_model = loaded
        videomae_processor = processor
        _VIDEOMAE_MODEL_PATH = str(path)
        _VIDEOMAE_MODEL_ERROR = ""
        _VIDEOMAE_DEVICE_ACTIVE = str(device)

        return videomae_model, videomae_processor

    except Exception as exc:
        videomae_model = None
        videomae_processor = None
        _VIDEOMAE_MODEL_PATH = ""
        _VIDEOMAE_DEVICE_ACTIVE = ""
        _VIDEOMAE_MODEL_ERROR = str(exc)
        return None, None


def resample_values(values: list, count: int) -> list:
    if not values:
        return []
    if count <= 1:
        return [values[0]]
    if len(values) == 1:
        return [values[0] for _ in range(count)]

    last = len(values) - 1
    return [
        values[int(round((i * last) / max(1, count - 1)))]
        for i in range(count)
    ]


def load_videomae_clip_frames(paths: list[str], num_frames: int):
    from PIL import Image

    clean_paths = [
        str(path).strip()
        for path in paths or []
        if str(path).strip()
    ]
    selected = resample_values(clean_paths, max(2, int(num_frames or _VIDEOMAE_NUM_FRAMES or 16)))
    frames = []

    for path in selected:
        with Image.open(path) as img:
            frames.append(img.convert("RGB").copy())

    return frames


def predict_videomae_clip(clip: PositionClipItem, num_frames: int) -> dict:
    current_model, current_processor = get_videomae_components()
    if current_model is None or current_processor is None:
        return {
            "time": float(clip.time or 0.0),
            "start": float(clip.start or 0.0),
            "end": float(clip.end or 0.0),
            "sexPosition": NO_SEX_POSITION_LABEL,
            "sexPositionScore": 0.0,
            "source": "videomae_missing",
            "scores": [],
        }

    import torch

    frames = load_videomae_clip_frames(clip.paths, num_frames)
    if not frames:
        return {
            "time": float(clip.time or 0.0),
            "start": float(clip.start or 0.0),
            "end": float(clip.end or 0.0),
            "sexPosition": NO_SEX_POSITION_LABEL,
            "sexPositionScore": 0.0,
            "source": "videomae_no_frames",
            "scores": [],
        }

    inputs = current_processor(frames, return_tensors="pt")
    device = next(current_model.parameters()).device
    pixel_values = inputs["pixel_values"].to(device)

    with torch.no_grad():
        logits = current_model(pixel_values=pixel_values).logits
        probs = torch.softmax(logits, dim=-1)[0].detach().cpu().tolist()

    id_to_label = getattr(current_model.config, "id2label", {}) or {}
    scores = []
    best_label = NO_SEX_POSITION_LABEL
    best_score = 0.0

    for idx, score in enumerate(probs):
        raw_label = id_to_label.get(idx, id_to_label.get(str(idx), idx))
        label = normalize_sex_position_label(raw_label)
        score = clamp01(score)
        scores.append({
            "label": label,
            "score": score,
        })

        if score > best_score:
            best_label = label
            best_score = score

    scores.sort(key=lambda item: item["score"], reverse=True)

    return {
        "time": float(clip.time or 0.0),
        "start": float(clip.start or 0.0),
        "end": float(clip.end or 0.0),
        "sexPosition": best_label,
        "sexPositionScore": best_score,
        "source": "videomae",
        "scores": scores[:10],
    }


def scored(label: str, score: float) -> dict:
    return {
        "label": label,
        "score": float(score),
    }


def best_score(items: list[dict], label: str, score: float) -> None:
    for item in items:
        if item["label"] == label:
            if score > item["score"]:
                item["score"] = float(score)
            return

    items.append(scored(label, score))


def prediction_from_result(result) -> dict:
    names = result.names or {}

    boxes_out = []
    people_present = []
    body_parts = []
    objects = []
    clothing = []

    if result.boxes is not None:
        xywhn = result.boxes.xywhn.cpu().tolist()
        cls_values = result.boxes.cls.cpu().tolist()
        conf_values = result.boxes.conf.cpu().tolist()

        for box_xywhn, cls_id, conf in zip(xywhn, cls_values, conf_values):
            label = str(names.get(int(cls_id), int(cls_id))).strip().lower()
            score = float(conf)

            if not label:
                continue

            cx, cy, w, h = [float(v) for v in box_xywhn]

            x = max(0.0, min(1.0, cx - w / 2.0))
            y = max(0.0, min(1.0, cy - h / 2.0))
            w = max(0.0, min(1.0 - x, w))
            h = max(0.0, min(1.0 - y, h))

            is_person = label in PERSON_LABELS
            is_body = label in BODY_LABELS
            is_object = label in OBJECT_LABELS
            is_clothing = label in CLOTHING_LABELS

            if not (is_person or is_body or is_object or is_clothing):
                continue

            boxes_out.append({
                "label": label,
                "score": score,
                "x": x,
                "y": y,
                "w": w,
                "h": h,
            })

            if is_person:
                best_score(people_present, label, score)

            if is_body:
                best_score(body_parts, label, score)

            if is_object:
                best_score(objects, label, score)

            if is_clothing:
                best_score(clothing, label, score)

    return {
        "modelAvailable": True,
        "source": f"yolo-server:{Path(_MODEL_PATH).name}",
        "sexPosition": NO_SEX_POSITION_LABEL,
        "sexPositionScore": 0.0,
        "peoplePresent": people_present,
        "bodyPartsPresent": body_parts,
        "objectsPresent": objects,
        "clothingPresent": clothing,
        "boxes": boxes_out,
        "persons": [],
    }


def pose_persons_from_result(result) -> list[dict]:
    names = result.names or {}
    persons = []

    if result.boxes is None:
        return persons

    kpts_xyn = None
    kpts_conf = None

    if result.keypoints is not None:
        try:
            kpts_xyn = result.keypoints.xyn.cpu().tolist()
        except Exception:
            kpts_xyn = None
        try:
            kpts_conf = result.keypoints.conf.cpu().tolist()
        except Exception:
            kpts_conf = None

    xywhn_values = result.boxes.xywhn.cpu().tolist()
    cls_values = result.boxes.cls.cpu().tolist()
    conf_values = result.boxes.conf.cpu().tolist()

    for i, (box_xywhn, cls_id, conf) in enumerate(
        zip(xywhn_values, cls_values, conf_values)
    ):
        label = str(names.get(int(cls_id), int(cls_id))).strip().lower()
        score = float(conf)

        cx, cy, w, h = [float(v) for v in box_xywhn]
        x = max(0.0, min(1.0, cx - w / 2.0))
        y = max(0.0, min(1.0, cy - h / 2.0))
        w = max(0.0, min(1.0 - x, w))
        h = max(0.0, min(1.0 - y, h))

        if w <= 0 or h <= 0:
            continue

        keypoints = []
        if kpts_xyn is not None and i < len(kpts_xyn):
            for ki, point in enumerate(kpts_xyn[i]):
                if len(point) < 2:
                    continue

                kconf = 0.0
                if (
                    kpts_conf is not None
                    and i < len(kpts_conf)
                    and ki < len(kpts_conf[i])
                ):
                    kconf = float(kpts_conf[i][ki])

                keypoints.append({
                    "name": KEYPOINT_NAMES[ki] if ki < len(KEYPOINT_NAMES) else str(ki),
                    "x": float(point[0]),
                    "y": float(point[1]),
                    "conf": kconf,
                })

        persons.append(annotate_pose_person_quality({
            "label": label,
            "score": score,
            "box": {
                "x": x,
                "y": y,
                "w": w,
                "h": h,
            },
            "keypoints": keypoints,
        }))

    return persons


def clamp01(value) -> float:
    try:
        n = float(value)
    except Exception:
        return 0.0

    if not math.isfinite(n):
        return 0.0

    return max(0.0, min(1.0, n))


def is_finite01(value) -> bool:
    try:
        n = float(value)
    except Exception:
        return False

    return math.isfinite(n) and 0.0 <= n <= 1.0


def normalized_box(box: dict | None) -> dict | None:
    if not isinstance(box, dict):
        return None

    x = clamp01(box.get("x", 0.0))
    y = clamp01(box.get("y", 0.0))
    w = clamp01(box.get("w", 0.0))
    h = clamp01(box.get("h", 0.0))

    if w <= 0 or h <= 0:
        return None

    if x + w > 1:
        w = 1 - x
    if y + h > 1:
        h = 1 - y

    if w <= 0 or h <= 0:
        return None

    out = dict(box)
    out.update({"x": x, "y": y, "w": w, "h": h})
    return out


def box_area(box: dict) -> float:
    clean = normalized_box(box)
    if not clean:
        return 0.0
    return float(clean["w"]) * float(clean["h"])


def box_center(box: dict) -> tuple[float, float] | None:
    clean = normalized_box(box)
    if not clean:
        return None
    return float(clean["x"]) + float(clean["w"]) / 2.0, float(clean["y"]) + float(clean["h"]) / 2.0


def box_gap(a: dict, b: dict) -> float:
    a = normalized_box(a)
    b = normalized_box(b)
    if not a or not b:
        return 1.0

    dx = max(0.0, max(float(a["x"]) - (float(b["x"]) + float(b["w"])), float(b["x"]) - (float(a["x"]) + float(a["w"]))))
    dy = max(0.0, max(float(a["y"]) - (float(b["y"]) + float(b["h"])), float(b["y"]) - (float(a["y"]) + float(a["h"]))))
    return math.sqrt(dx * dx + dy * dy)


def box_overlap_ratio(a: dict, b: dict) -> float:
    a = normalized_box(a)
    b = normalized_box(b)
    if not a or not b:
        return 0.0

    left = max(float(a["x"]), float(b["x"]))
    top = max(float(a["y"]), float(b["y"]))
    right = min(float(a["x"]) + float(a["w"]), float(b["x"]) + float(b["w"]))
    bottom = min(float(a["y"]) + float(a["h"]), float(b["y"]) + float(b["h"]))

    if right <= left or bottom <= top:
        return 0.0

    min_area = min(box_area(a), box_area(b))
    if min_area <= 0:
        return 0.0

    return clamp01(((right - left) * (bottom - top)) / min_area)


def box_horizontal_overlap_ratio(a: dict, b: dict) -> float:
    a = normalized_box(a)
    b = normalized_box(b)
    if not a or not b:
        return 0.0

    left = max(float(a["x"]), float(b["x"]))
    right = min(float(a["x"]) + float(a["w"]), float(b["x"]) + float(b["w"]))
    if right <= left:
        return 0.0

    min_width = min(float(a["w"]), float(b["w"]))
    if min_width <= 0:
        return 0.0

    return clamp01((right - left) / min_width)


def box_vertical_overlap_ratio(a: dict, b: dict) -> float:
    a = normalized_box(a)
    b = normalized_box(b)
    if not a or not b:
        return 0.0

    top = max(float(a["y"]), float(b["y"]))
    bottom = min(float(a["y"]) + float(a["h"]), float(b["y"]) + float(b["h"]))
    if bottom <= top:
        return 0.0

    min_height = min(float(a["h"]), float(b["h"]))
    if min_height <= 0:
        return 0.0

    return clamp01((bottom - top) / min_height)


def is_person_like_label(label: str) -> bool:
    clean = str(label or "").strip().lower()
    return clean == "person" or clean.startswith("person_")


def boxes_by_label(prediction: dict, *labels: str) -> list[dict]:
    wanted = {str(label).strip().lower() for label in labels if str(label).strip()}
    out = []

    for box in prediction.get("boxes", []) or []:
        label = str(box.get("label", "")).strip().lower() if isinstance(box, dict) else ""
        if label not in wanted:
            continue

        clean = normalized_box(box)
        if clean:
            out.append(clean)

    return out


def any_boxes_near(left_boxes: list[dict], right_boxes: list[dict], margin: float) -> bool:
    for left in left_boxes:
        for right in right_boxes:
            if box_gap(left, right) <= margin or box_overlap_ratio(left, right) > 0:
                return True
    return False


def point_near_box(x: float, y: float, box: dict, margin: float) -> bool:
    if not is_finite01(x) or not is_finite01(y):
        return False

    clean = normalized_box(box)
    if not clean:
        return False

    return (
        float(clean["x"]) - margin <= x <= float(clean["x"]) + float(clean["w"]) + margin
        and float(clean["y"]) - margin <= y <= float(clean["y"]) + float(clean["h"]) + margin
    )


def any_pose_keypoint_near_boxes(
    persons: list[dict],
    keypoint_names: list[str],
    boxes: list[dict],
    margin: float,
) -> bool:
    if not boxes:
        return False

    wanted = {str(name).strip().lower() for name in keypoint_names}

    for person in reliable_pose_persons(persons):
        for point in person.get("keypoints", []) or []:
            name = str(point.get("name", "")).strip().lower()
            if name not in wanted:
                continue
            if float(point.get("conf") or 0.0) < 0.20:
                continue

            x = float(point.get("x") or 0.0)
            y = float(point.get("y") or 0.0)

            for box in boxes:
                if point_near_box(x, y, box, margin):
                    return True

    return False


def pose_keypoint_stats(person: dict) -> tuple[int, float]:
    keypoints = person.get("keypoints", []) or []
    if not keypoints:
        return 0, 0.0

    visible = 0
    total_conf = 0.0

    for point in keypoints:
        x = float(point.get("x") or 0.0)
        y = float(point.get("y") or 0.0)
        conf = float(point.get("conf") or 0.0)

        if conf < 0.20 or not is_finite01(x) or not is_finite01(y):
            continue

        visible += 1
        total_conf += clamp01(conf)

    if visible == 0:
        return 0, 0.0

    coverage = clamp01(visible / max(1, len(KEYPOINT_NAMES)))
    avg_conf = clamp01(total_conf / visible)
    return visible, clamp01(coverage * 0.45 + avg_conf * 0.55)


def pose_keypoint_quality(person: dict) -> float:
    _, quality = pose_keypoint_stats(person)
    return quality


def annotate_pose_person_quality(person: dict) -> dict:
    visible, quality = pose_keypoint_stats(person)
    score = clamp01(float(person.get("score") or 0.0))

    person["visibleKeypoints"] = visible
    person["quality"] = quality
    person["reliable"] = (
        score >= _POSE_RELIABLE_MIN_SCORE
        and visible >= _POSE_RELIABLE_MIN_KEYPOINTS
        and quality >= _POSE_RELIABLE_MIN_QUALITY
    )

    return person


def reliable_pose_persons(persons: list[dict]) -> list[dict]:
    out = []

    for person in persons:
        if not isinstance(person, dict):
            continue

        if "reliable" not in person:
            annotate_pose_person_quality(person)

        if bool(person.get("reliable")):
            out.append(person)

    return out


def scene_person_boxes(prediction: dict, persons: list[dict]) -> list[dict]:
    detector_boxes = []
    pose_boxes = []

    for box in prediction.get("boxes", []) or []:
        if not isinstance(box, dict) or not is_person_like_label(box.get("label", "")):
            continue

        clean = normalized_box(box)
        if clean:
            detector_boxes.append(clean)

    for person in reliable_pose_persons(persons):
        clean = normalized_box(person.get("box"))
        if clean:
            pose_boxes.append(clean)

    if detector_boxes and len(detector_boxes) >= len(pose_boxes):
        return detector_boxes

    return pose_boxes


def scene_person_pair_signals(boxes: list[dict]) -> dict:
    signals = {
        "close": False,
        "overlap": False,
        "horizontal": False,
        "vertical": False,
        "stacked": False,
    }

    for i, left in enumerate(boxes):
        left = normalized_box(left)
        if not left:
            continue

        for raw_right in boxes[i + 1:]:
            right = normalized_box(raw_right)
            if not right:
                continue

            gap = box_gap(left, right)
            overlap = box_overlap_ratio(left, right)
            close = gap <= 0.08 or overlap >= 0.08

            if close:
                signals["close"] = True
            if overlap >= 0.18:
                signals["overlap"] = True
            if close and float(left["w"]) > float(left["h"]) * 1.15 and float(right["w"]) > float(right["h"]) * 1.15:
                signals["horizontal"] = True
            if close and float(left["h"]) > float(left["w"]) * 1.25 and float(right["h"]) > float(right["w"]) * 1.25:
                signals["vertical"] = True

            left_center = box_center(left)
            right_center = box_center(right)
            if (
                left_center
                and right_center
                and gap <= 0.12
                and box_horizontal_overlap_ratio(left, right) >= 0.35
                and abs(left_center[1] - right_center[1]) >= 0.12
            ):
                signals["stacked"] = True

    return signals


def pose_point(person: dict, name: str, min_conf: float = 0.20) -> tuple[float, float] | None:
    wanted = str(name or "").strip().lower()
    if not wanted:
        return None

    for point in person.get("keypoints", []) or []:
        point_name = str(point.get("name", "")).strip().lower()
        if point_name != wanted:
            continue

        conf = float(point.get("conf") or 0.0)
        x = float(point.get("x") or 0.0)
        y = float(point.get("y") or 0.0)

        if conf >= min_conf and is_finite01(x) and is_finite01(y):
            return x, y

    return None


def pose_midpoint(
    person: dict,
    names: list[str],
    min_conf: float = 0.20,
    min_points: int = 1,
) -> tuple[float, float] | None:
    points = [
        point
        for name in names
        if (point := pose_point(person, name, min_conf)) is not None
    ]

    if len(points) < min_points:
        return None

    x = sum(point[0] for point in points) / len(points)
    y = sum(point[1] for point in points) / len(points)
    return x, y


def point_distance(a: tuple[float, float] | None, b: tuple[float, float] | None) -> float:
    if not a or not b:
        return 0.0

    return math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2)


def projected_point_distance(
    a: tuple[float, float] | None,
    b: tuple[float, float] | None,
    axis_x: float,
    axis_y: float,
) -> float:
    if not a or not b:
        return 0.0

    return abs((a[0] - b[0]) * axis_x + (a[1] - b[1]) * axis_y)


def pose_extents_along_axis(
    person: dict,
    origin: tuple[float, float],
    axis_x: float,
    axis_y: float,
    perp_x: float,
    perp_y: float,
    min_conf: float = 0.20,
) -> tuple[float, float] | None:
    long_values: list[float] = []
    cross_values: list[float] = []

    for point in person.get("keypoints", []) or []:
        conf = float(point.get("conf") or 0.0)
        x = float(point.get("x") or 0.0)
        y = float(point.get("y") or 0.0)

        if conf < min_conf or not is_finite01(x) or not is_finite01(y):
            continue

        dx = x - origin[0]
        dy = y - origin[1]
        long_values.append(dx * axis_x + dy * axis_y)
        cross_values.append(dx * perp_x + dy * perp_y)

    if len(long_values) < 3:
        return None

    return abs(max(long_values) - min(long_values)), abs(max(cross_values) - min(cross_values))


def pose_person_geometry(person: dict) -> dict:
    box = normalized_box(person.get("box"))
    box_center_point = box_center(box) if box else None

    left_hip = pose_point(person, "left_hip")
    right_hip = pose_point(person, "right_hip")
    left_knee = pose_point(person, "left_knee")
    right_knee = pose_point(person, "right_knee")

    hip = pose_midpoint(person, ["left_hip", "right_hip"])
    shoulder = pose_midpoint(person, ["left_shoulder", "right_shoulder"])
    knee = pose_midpoint(person, ["left_knee", "right_knee"])
    head = pose_midpoint(person, ["nose", "left_eye", "right_eye", "left_ear", "right_ear"])

    center = hip or box_center_point or (0.5, 0.5)

    torso_dx = 0.0
    torso_dy = 0.0
    torso_len = 0.0
    torso_angle = 0.0
    has_torso_axis = False
    axis_x = 0.0
    axis_y = 1.0
    perp_x = -1.0
    perp_y = 0.0
    if hip and shoulder:
        raw_dx = hip[0] - shoulder[0]
        raw_dy = hip[1] - shoulder[1]
        torso_dx = abs(raw_dx)
        torso_dy = abs(raw_dy)
        torso_len = math.sqrt(raw_dx * raw_dx + raw_dy * raw_dy)
        if torso_len >= 0.07:
            has_torso_axis = True
            axis_x = raw_dx / torso_len
            axis_y = raw_dy / torso_len
            perp_x = -axis_y
            perp_y = axis_x
            torso_angle = math.atan2(axis_y, axis_x)

    hip_width = point_distance(left_hip, right_hip)
    knee_width = point_distance(left_knee, right_knee)

    knees_below_hips = bool(knee and hip and knee[1] > hip[1] + 0.045)
    if has_torso_axis and knee and hip:
        knee_projection = (knee[0] - hip[0]) * axis_x + (knee[1] - hip[1]) * axis_y
        knees_below_hips = knee_projection > 0.045

    if has_torso_axis and left_knee and right_knee:
        knee_width = projected_point_distance(left_knee, right_knee, perp_x, perp_y)
        if left_hip and right_hip:
            hip_width = projected_point_distance(left_hip, right_hip, perp_x, perp_y)

    knees_wide = bool(
        knee_width > 0
        and knee_width >= max(0.11, hip_width * 1.12)
    )
    straddling = knees_below_hips and knees_wide

    body_long = torso_len
    body_cross = max(hip_width, knee_width)

    if has_torso_axis:
        pose_extents = pose_extents_along_axis(person, center, axis_x, axis_y, perp_x, perp_y)
        if pose_extents:
            body_long = max(body_long, pose_extents[0])
            body_cross = max(body_cross, pose_extents[1])

        if box:
            box_long = abs(axis_x) * float(box["w"]) + abs(axis_y) * float(box["h"])
            box_cross = abs(perp_x) * float(box["w"]) + abs(perp_y) * float(box["h"])
            body_long = max(body_long, box_long)
            body_cross = max(body_cross, box_cross)
    elif box:
        body_long = max(float(box["w"]), float(box["h"]))
        body_cross = min(float(box["w"]), float(box["h"]))

    elongated = body_long >= max(0.18, body_cross * 1.18)

    torso_horizontal = torso_len >= 0.07 and torso_dx >= torso_dy * 1.15
    torso_vertical = torso_len >= 0.07 and torso_dy >= torso_dx * 1.15

    box_horizontal = bool(box and float(box["w"]) >= float(box["h"]) * 1.05)
    box_vertical = bool(box and float(box["h"]) >= float(box["w"]) * 1.25)

    lying = torso_horizontal or box_horizontal or (has_torso_axis and elongated and not box_vertical)
    upright = torso_vertical or box_vertical
    all_fours = knees_below_hips and not straddling and (torso_horizontal or (has_torso_axis and elongated))
    bent_or_kneeling = all_fours or (knees_below_hips and not straddling)

    return {
        "box": box,
        "center": center,
        "head": head,
        "hip": hip,
        "shoulder": shoulder,
        "knee": knee,
        "torso_angle": torso_angle,
        "has_torso_axis": has_torso_axis,
        "axis_x": axis_x,
        "axis_y": axis_y,
        "perp_x": perp_x,
        "perp_y": perp_y,
        "body_long": body_long,
        "body_cross": body_cross,
        "elongated": elongated,
        "lying": lying,
        "upright": upright,
        "straddling": straddling,
        "knees_below_hips": knees_below_hips,
        "knees_wide": knees_wide,
        "all_fours": all_fours,
        "bent_or_kneeling": bent_or_kneeling,
    }


def combine_position_score(scores: dict[str, float], label: str, score: float) -> None:
    label = normalize_sex_position_label(label)
    if is_no_sex_position_label(label) or label not in POSITION_LABELS or score <= 0:
        return

    score = clamp01(score)
    current = clamp01(scores.get(label, 0.0))
    scores[label] = clamp01(1 - (1 - current) * (1 - score))


def pose_axis_alignment(a: dict, b: dict) -> tuple[float, bool]:
    if not a.get("has_torso_axis") or not b.get("has_torso_axis"):
        return 0.0, False

    # Körperachsen sind richtungslos: 0° und 180° gelten beide als parallel.
    return abs(math.cos(float(a.get("torso_angle") or 0.0) - float(b.get("torso_angle") or 0.0))), True


def append_prediction_source(prediction: dict, source: str) -> None:
    source = str(source or "").strip()
    if not source:
        return

    current = str(prediction.get("source") or "").strip()
    if not current:
        prediction["source"] = source
        return

    if source in {part.strip() for part in current.split("+")}:
        return

    prediction["source"] = f"{current}+{source}"


def fuse_hybrid_position_scores(
    pose_scores: dict[str, float],
    context_scores: dict[str, float],
) -> tuple[str, float, bool, bool]:
    labels = {
        label
        for label in set(pose_scores.keys()) | set(context_scores.keys())
        if not is_no_sex_position_label(label)
    }

    best_position = ""
    best_score = 0.0
    best_has_pose = False
    best_has_context = False

    for label in labels:
        pose_score = clamp01(pose_scores.get(label, 0.0))
        context_score = clamp01(context_scores.get(label, 0.0))
        has_pose = pose_score > 0
        has_context = context_score > 0

        score = 0.0
        if has_pose:
            if has_context and context_score >= _POSE_CONFIRMING_CONTEXT_MIN_SCORE:
                score = pose_score
                boost = clamp01(context_score * _POSITION_CONTEXT_BOOST_WEIGHT)
                score = clamp01(1 - (1 - score) * (1 - boost))
            else:
                max_unconfirmed_score = _POSE_UNCONFIRMED_MAX_SCORE
                if pose_score >= _POSE_STRONG_UNCONFIRMED_MIN_SCORE:
                    max_unconfirmed_score = _POSE_STRONG_UNCONFIRMED_MAX_SCORE
                score = min(max_unconfirmed_score, pose_score)
        elif context_score >= _POSITION_CONTEXT_MIN_SCORE:
            score = min(_POSITION_CONTEXT_MAX_SCORE, context_score)

        if score > best_score:
            best_position = label
            best_score = score
            best_has_pose = has_pose
            best_has_context = has_context

    return best_position, clamp01(best_score), best_has_pose, best_has_context


def add_pose_pair_geometry_scores(scores: dict[str, float], persons: list[dict]) -> None:
    def add(label: str, score: float) -> None:
        combine_position_score(scores, label, score)

    reliable_persons = reliable_pose_persons(persons)
    if len(reliable_persons) < 2:
        return

    geometries = [pose_person_geometry(person) for person in reliable_persons]

    for i, left in enumerate(geometries):
        left_box = left["box"]
        if not left_box:
            continue

        for right in geometries[i + 1:]:
            right_box = right["box"]
            if not right_box:
                continue

            gap = box_gap(left_box, right_box)
            overlap = box_overlap_ratio(left_box, right_box)
            horizontal_overlap = box_horizontal_overlap_ratio(left_box, right_box)
            vertical_overlap = box_vertical_overlap_ratio(left_box, right_box)
            close = gap <= 0.12 or overlap >= 0.08

            if not close:
                continue

            left_center = left["center"]
            right_center = right["center"]
            top = left if left_center[1] <= right_center[1] else right
            bottom = right if top is left else left

            top_above = top["center"][1] <= bottom["center"][1] - 0.055
            horizontal_stack = horizontal_overlap >= 0.35 and (top_above or overlap >= 0.22)
            lateral_stack = vertical_overlap >= 0.35 and overlap >= 0.12
            strong_stack = horizontal_stack or lateral_stack

            axis_alignment, has_axis_alignment = pose_axis_alignment(left, right)
            axes_parallel = has_axis_alignment and axis_alignment >= 0.74
            axes_crossed = has_axis_alignment and axis_alignment <= 0.56

            def has_strong_rider_shape(geometry: dict) -> bool:
                return bool(
                    geometry["straddling"]
                    or (
                        geometry["knees_wide"]
                        and geometry["knees_below_hips"]
                        and (geometry["upright"] or axes_crossed)
                    )
                )

            def has_weak_rider_shape(geometry: dict) -> bool:
                return bool(geometry["knees_wide"] and geometry["knees_below_hips"])

            left_has_strong_rider_shape = has_strong_rider_shape(left)
            right_has_strong_rider_shape = has_strong_rider_shape(right)
            top_has_strong_rider_shape = has_strong_rider_shape(top)
            top_has_weak_rider_shape = has_weak_rider_shape(top)

            rider = top
            base = bottom
            rider_has_strong_shape = top_has_strong_rider_shape
            rider_has_weak_shape = top_has_weak_rider_shape
            if left_has_strong_rider_shape != right_has_strong_rider_shape:
                if left_has_strong_rider_shape:
                    rider = left
                    base = right
                    rider_has_strong_shape = True
                    rider_has_weak_shape = has_weak_rider_shape(left)
                else:
                    rider = right
                    base = left
                    rider_has_strong_shape = True
                    rider_has_weak_shape = has_weak_rider_shape(right)

            if strong_stack and rider_has_strong_shape and not axes_parallel:
                add("cowgirl", 0.20)
                add("reverse_cowgirl", 0.17)

                if base["lying"]:
                    add("cowgirl", 0.12)
                    add("reverse_cowgirl", 0.10)
                if rider["straddling"]:
                    add("cowgirl", 0.08)
                    add("reverse_cowgirl", 0.06)
            elif strong_stack and rider_has_weak_shape and not has_axis_alignment:
                # Ohne verwertbare Achsen bleibt Cowgirl nur ein schwaches Signal.
                # Sobald die Achsen parallel sind, sieht die Szene eher nach
                # Missionary/Überlagerung aus und soll nicht in Cowgirl kippen.
                add("cowgirl", 0.08)
                add("reverse_cowgirl", 0.06)

            if strong_stack and (bottom["lying"] or (axes_parallel and top_above)):
                if not top_has_strong_rider_shape or axes_parallel:
                    add("missionary", 0.14)
                if axes_parallel:
                    add("missionary", 0.08)
                if not top["straddling"] and not top_has_strong_rider_shape:
                    add("missionary", 0.08)

            both_lying = left["lying"] and right["lying"]
            same_level = abs(left_center[1] - right_center[1]) <= 0.15
            side_by_side = abs(left_center[0] - right_center[0]) >= 0.10

            parallel_side_by_side = axes_parallel and left["elongated"] and right["elongated"] and close and not strong_stack
            if (both_lying and (same_level or side_by_side)) or parallel_side_by_side:
                add("spooning", 0.18)
                if overlap >= 0.10:
                    add("prone_bone", 0.07)

            left_bent = left["all_fours"] or left["bent_or_kneeling"]
            right_bent = right["all_fours"] or right["bent_or_kneeling"]

            if left_bent != right_bent:
                other = right if left_bent else left
                add("doggy", 0.16)
                if other["upright"]:
                    add("doggy", 0.06)
                    add("standing_doggy", 0.07)
                if left["lying"] or right["lying"]:
                    add("prone_bone", 0.06)
            elif left["all_fours"] or right["all_fours"]:
                add("doggy", 0.13)
                if left["lying"] or right["lying"]:
                    add("prone_bone", 0.07)


def add_pose_scene_context_scores(scores: dict[str, float], prediction: dict, persons: list[dict]) -> None:
    def add(label: str, score: float) -> None:
        combine_position_score(scores, label, score)

    reliable_persons = reliable_pose_persons(persons)
    person_boxes = scene_person_boxes(prediction, reliable_persons)
    person_count = max(len(person_boxes), len(reliable_persons))
    pair = scene_person_pair_signals(person_boxes)
    add_pose_pair_geometry_scores(scores, reliable_persons)

    penis_boxes = boxes_by_label(prediction, "penis")
    pussy_boxes = boxes_by_label(prediction, "pussy", "vagina", "vulva", "labia")
    ass_boxes = boxes_by_label(prediction, "ass", "anus")
    breast_boxes = boxes_by_label(prediction, "breasts")
    tongue_boxes = boxes_by_label(prediction, "tongue")
    toy_boxes = boxes_by_label(prediction, "dildo", "vibrator", "strapon", "buttplug")

    has_penis = bool(penis_boxes)
    has_pussy = bool(pussy_boxes)
    has_ass = bool(ass_boxes)
    has_breasts = bool(breast_boxes)
    has_tongue = bool(tongue_boxes)
    has_toy = bool(toy_boxes)

    head_names = ["nose", "left_eye", "right_eye", "left_ear", "right_ear"]
    hand_names = ["left_wrist", "right_wrist"]
    hip_names = ["left_hip", "right_hip"]

    head_near_penis = any_pose_keypoint_near_boxes(reliable_persons, head_names, penis_boxes, 0.09)
    head_near_pussy = any_pose_keypoint_near_boxes(reliable_persons, head_names, pussy_boxes, 0.09)
    head_near_ass = any_pose_keypoint_near_boxes(reliable_persons, head_names, ass_boxes, 0.09)
    hand_near_penis = any_pose_keypoint_near_boxes(reliable_persons, hand_names, penis_boxes, 0.08)
    hand_near_pussy = any_pose_keypoint_near_boxes(reliable_persons, hand_names, pussy_boxes, 0.08)
    hand_near_toy = any_pose_keypoint_near_boxes(reliable_persons, hand_names, toy_boxes, 0.08)
    hips_near_genitals = any_pose_keypoint_near_boxes(reliable_persons, hip_names, penis_boxes + pussy_boxes, 0.08)

    if person_count >= 2:
        for label, score in [
            ("missionary", 0.04),
            ("doggy", 0.04),
            ("cowgirl", 0.04),
            ("reverse_cowgirl", 0.04),
            ("standing_doggy", 0.04),
            ("spooning", 0.04),
            ("69", 0.03),
        ]:
            add(label, score)

    if pair["close"]:
        for label in ["missionary", "doggy", "cowgirl", "spooning"]:
            add(label, 0.04)
    if pair["overlap"]:
        add("missionary", 0.05)
        add("cowgirl", 0.05)
        add("prone_bone", 0.04)
    if pair["horizontal"]:
        add("spooning", 0.12)
        add("prone_bone", 0.07)
    if pair["vertical"]:
        add("standing", 0.08)
        add("standing_doggy", 0.10)
    if pair["stacked"]:
        add("missionary", 0.07)
        add("cowgirl", 0.07)
        add("reverse_cowgirl", 0.06)
        add("facesitting", 0.05)

    if has_penis and has_pussy:
        for label, score in [
            ("missionary", 0.08),
            ("doggy", 0.08),
            ("cowgirl", 0.08),
            ("reverse_cowgirl", 0.07),
            ("prone_bone", 0.06),
            ("standing_doggy", 0.06),
            ("spooning", 0.05),
        ]:
            add(label, score)

        if any_boxes_near(penis_boxes, pussy_boxes, 0.09) or hips_near_genitals:
            add("missionary", 0.08)
            add("doggy", 0.08)
            add("cowgirl", 0.08)
            add("reverse_cowgirl", 0.07)

    if has_penis and (head_near_penis or has_tongue):
        add("blowjob", 0.16)
        if head_near_penis:
            add("blowjob", 0.10)
    if has_pussy and (head_near_pussy or has_tongue or any_boxes_near(tongue_boxes, pussy_boxes, 0.08)):
        add("cunnilingus", 0.16)
        if head_near_pussy or any_boxes_near(tongue_boxes, pussy_boxes, 0.08):
            add("cunnilingus", 0.10)
    if has_penis and hand_near_penis:
        add("handjob", 0.18)
    if has_pussy and hand_near_pussy:
        add("fingering", 0.18)
    if has_penis and has_breasts and any_boxes_near(penis_boxes, breast_boxes, 0.10):
        add("boobjob", 0.20)
    if has_toy:
        add("toy_play", 0.12)
        if (
            hand_near_toy
            or any_boxes_near(toy_boxes, pussy_boxes, 0.10)
            or any_boxes_near(toy_boxes, penis_boxes, 0.10)
            or any_boxes_near(toy_boxes, ass_boxes, 0.10)
        ):
            add("toy_play", 0.12)
    if person_count >= 2 and (head_near_ass or head_near_pussy):
        if has_ass:
            add("facesitting", 0.14)
        if has_pussy and has_penis:
            add("69", 0.10)
    if has_ass and has_pussy and pair["horizontal"]:
        add("doggy", 0.07)
        add("prone_bone", 0.07)


def best_hybrid_pose_scene_position(
    prediction: dict,
    position_persons: list[dict],
    context_persons: list[dict],
) -> tuple[str, float, bool, bool]:
    pose_scores: dict[str, float] = {}
    context_scores: dict[str, float] = {}
    reliable_position_persons = reliable_pose_persons(position_persons)
    reliable_context_persons = reliable_pose_persons(context_persons)

    if not reliable_context_persons:
        reliable_context_persons = reliable_position_persons

    for person in reliable_position_persons:
        label = normalize_sex_position_label(person.get("label"))
        score = float(person.get("score") or 0.0)

        if is_no_sex_position_label(label) or label not in POSITION_LABELS:
            continue

        combine_position_score(pose_scores, label, score)

        quality = pose_keypoint_quality(person)
        if quality > 0:
            combine_position_score(pose_scores, label, 0.04 * quality)

    add_pose_scene_context_scores(context_scores, prediction, reliable_context_persons)

    best_position, best_score, has_pose_signal, has_context_signal = fuse_hybrid_position_scores(
        pose_scores,
        context_scores,
    )

    if best_position and (has_pose_signal or best_score >= _POSITION_CONTEXT_MIN_SCORE):
        return best_position, clamp01(best_score), has_pose_signal, has_context_signal

    return "", 0.0, False, has_context_signal


def best_pose_scene_position(prediction: dict, persons: list[dict]) -> tuple[str, float, bool, bool]:
    return best_hybrid_pose_scene_position(prediction, persons, persons)


def apply_pose_result_to_prediction(prediction: dict, result) -> dict:
    persons = pose_persons_from_result(result)
    if persons:
        prediction["persons"] = persons

    best_position, best_score_value, _has_pose_signal, has_context_signal = best_pose_scene_position(
        prediction,
        persons,
    )

    if best_position:
        prediction["sexPosition"] = best_position
        prediction["sexPositionScore"] = best_score_value

    append_prediction_source(prediction, "yolo_pose")
    if has_context_signal:
        append_prediction_source(prediction, "box_context")

    return prediction


def predict_pose_results(model, paths: list[str], imgsz: int, conf: float):
    return model.predict(
        source=paths,
        imgsz=imgsz,
        conf=conf,
        batch=_BATCH,
        device=_DEVICE or None,
        half=_HALF,
        verbose=False,
    )


def has_reliable_pose_persons(persons: list[dict]) -> bool:
    return bool(reliable_pose_persons(persons))


def apply_hybrid_pose_persons_to_prediction(
    prediction: dict,
    position_persons: list[dict],
    context_persons: list[dict],
) -> None:
    display_persons = context_persons or position_persons
    if display_persons:
        prediction["persons"] = display_persons

    best_position, best_score_value, has_pose_signal, has_context_signal = best_hybrid_pose_scene_position(
        prediction,
        position_persons,
        context_persons,
    )

    if best_position:
        prediction["sexPosition"] = best_position
        prediction["sexPositionScore"] = best_score_value

    if position_persons or context_persons:
        append_prediction_source(prediction, "yolo_pose")
    if context_persons:
        append_prediction_source(prediction, "base_pose")
    if has_pose_signal:
        append_prediction_source(prediction, "pose_position")
    if has_context_signal:
        append_prediction_source(prediction, "box_context")


def apply_pose_batch_to_predictions(paths: list[str], predictions: list[dict], imgsz: int) -> None:
    global _POSE_MODEL_ERROR
    global _BASE_POSE_MODEL_ERROR

    position_persons_by_index: dict[int, list[dict]] = {}
    context_persons_by_index: dict[int, list[dict]] = {}

    current_pose_model = get_pose_model()
    if current_pose_model is not None:
        try:
            pose_results = predict_pose_results(current_pose_model, paths, imgsz, _POSE_CONF)
            for index, pose_result in enumerate(pose_results):
                position_persons_by_index[index] = pose_persons_from_result(pose_result)
        except Exception as exc:
            _POSE_MODEL_ERROR = str(exc)

    needs_base_pose = [
        index
        for index in range(len(paths))
        if not has_reliable_pose_persons(position_persons_by_index.get(index, []))
    ]

    current_base_pose_model = get_base_pose_model()
    if current_base_pose_model is not None and needs_base_pose:
        try:
            base_paths = [paths[index] for index in needs_base_pose]
            base_results = predict_pose_results(current_base_pose_model, base_paths, imgsz, _BASE_POSE_CONF)
            for original_index, base_result in zip(needs_base_pose, base_results):
                context_persons_by_index[original_index] = pose_persons_from_result(base_result)
        except Exception as exc:
            _BASE_POSE_MODEL_ERROR = str(exc)

    for index, prediction in enumerate(predictions):
        position_persons = position_persons_by_index.get(index, [])
        context_persons = context_persons_by_index.get(index, [])

        apply_hybrid_pose_persons_to_prediction(
            prediction,
            position_persons,
            context_persons,
        )


def pose_model_status() -> dict:
    try:
        expected = resolve_pose_model_path()
        expected_text = str(expected) if expected else str(DEFAULT_POSE_MODEL_PATH)
        exists = expected is not None
        error = _POSE_MODEL_ERROR
    except Exception as exc:
        expected_text = str(DEFAULT_POSE_MODEL_PATH)
        exists = False
        error = str(exc)

    return {
        "poseModelAvailable": exists,
        "poseModelLoaded": pose_model is not None,
        "poseModel": _POSE_MODEL_PATH or (expected_text if exists else ""),
        "poseModelError": error,
        "expectedPoseModel": expected_text,
    }


def base_pose_model_status() -> dict:
    try:
        expected = resolve_base_pose_model_path()
        expected_text = str(expected) if expected else "yolo26n-pose.pt"
        exists = expected is not None
        error = _BASE_POSE_MODEL_ERROR
    except Exception as exc:
        expected_text = "yolo26n-pose.pt"
        exists = False
        error = str(exc)

    return {
        "basePoseModelAvailable": exists,
        "basePoseModelLoaded": base_pose_model is not None,
        "basePoseModel": _BASE_POSE_MODEL_PATH or (expected_text if exists else ""),
        "basePoseModelError": error,
        "expectedBasePoseModel": expected_text,
    }


def videomae_model_status() -> dict:
    try:
        expected = resolve_videomae_model_path()
        expected_text = str(expected) if expected else str(DEFAULT_VIDEOMAE_MODEL_PATH)
        exists = expected is not None
        error = _VIDEOMAE_MODEL_ERROR
    except Exception as exc:
        expected_text = str(DEFAULT_VIDEOMAE_MODEL_PATH)
        exists = False
        error = str(exc)

    return {
        "videoMAEModelAvailable": exists,
        "videoMAEModelLoaded": videomae_model is not None,
        "videoMAEModel": _VIDEOMAE_MODEL_PATH or (expected_text if exists else ""),
        "videoMAEModelError": error,
        "expectedVideoMAEModel": expected_text,
        "videoMAEDevice": _VIDEOMAE_DEVICE_ACTIVE,
    }


def detector_model_status(load: bool = False) -> dict:
    current_model = get_model() if load else model
    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}

    expected_text = str(DEFAULT_MODEL_PATH)
    exists = False
    error = _MODEL_ERROR

    try:
        expected = resolve_model_path()
        expected_text = str(expected)
        exists = True
    except Exception as exc:
        if not error:
            error = str(exc)

    return {
        "modelAvailable": exists,
        "modelLoaded": current_model is not None,
        "model": _MODEL_PATH or (expected_text if exists else ""),
        "modelError": error,
        "expectedModel": expected_text,
        "classCount": len(names),
        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
    }


@app.post("/predict-batch", dependencies=[Depends(require_ai_server_auth)])
def predict_batch(req: PredictBatchRequest):
    paths = [str(path).strip() for path in req.paths if str(path).strip()]
    if not paths:
        return {
            "ok": False,
            "predictions": [],
            "error": "no paths supplied",
        }

    imgsz = int(req.imageSize or _IMGSZ or 640)

    current_model = get_model()
    if current_model is None:
        predictions = [empty_prediction("detector_model_missing") for _ in paths]
        if not req.detectorOnly:
            apply_pose_batch_to_predictions(paths, predictions, imgsz)

        return {
            "ok": True,
            "available": False,
            "modelAvailable": False,
            "predictions": predictions,
            "modelError": _MODEL_ERROR,
            "expectedModel": str(DEFAULT_MODEL_PATH),
        }

    if DETECTION_LABELS_PATH is None or _LABEL_ERROR:
        return {
            "ok": False,
            "predictions": [],
            "error": f"detection labels missing: {_LABEL_ERROR}",
        }

    try:
        results = current_model.predict(
            source=paths,
            imgsz=imgsz,
            conf=_CONF,
            batch=_BATCH,
            device=_DEVICE or None,
            half=_HALF,
            verbose=False,
        )

        predictions = [prediction_from_result(result) for result in results]

        if not req.detectorOnly:
            apply_pose_batch_to_predictions(paths, predictions, imgsz)

        return {
            "ok": True,
            "available": True,
            "modelAvailable": True,
            "predictions": predictions,
        }

    except Exception as exc:
        return {
            "ok": False,
            "predictions": [],
            "error": str(exc),
        }


@app.post("/predict-position-clips", dependencies=[Depends(require_ai_server_auth)])
def predict_position_clips(req: PredictPositionClipsRequest):
    clips = [clip for clip in req.clips if clip.paths]
    if not clips:
        return {
            "ok": True,
            "available": False,
            "predictions": [],
            "error": "no clips supplied",
        }

    current_model, current_processor = get_videomae_components()
    if current_model is None or current_processor is None:
        return {
            "ok": True,
            "available": False,
            "predictions": [],
            "error": _VIDEOMAE_MODEL_ERROR or f"VideoMAE model not found: {DEFAULT_VIDEOMAE_MODEL_PATH}",
        }

    predictions = []
    for clip in clips:
        try:
            predictions.append(predict_videomae_clip(clip, int(req.numFrames or _VIDEOMAE_NUM_FRAMES or 16)))
        except Exception as exc:
            predictions.append({
                "time": float(clip.time or 0.0),
                "start": float(clip.start or 0.0),
                "end": float(clip.end or 0.0),
                "sexPosition": NO_SEX_POSITION_LABEL,
                "sexPositionScore": 0.0,
                "source": "videomae_predict_failed",
                "error": repr(exc),
                "scores": [],
            })

    return {
        "ok": True,
        "available": True,
        "predictions": predictions,
    }


@app.get("/health", dependencies=[Depends(require_ai_server_auth)])
def health():
    status_payload = {
        "ok": True,
        "ready": True,
        "trainingRoot": str(TRAINING_ROOT),
        "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
        "labelError": _LABEL_ERROR,
    }

    status_payload.update(detector_model_status(load=False))
    status_payload.update(pose_model_status())
    status_payload.update(base_pose_model_status())
    status_payload.update(videomae_model_status())
    return status_payload

@app.post("/reload", dependencies=[Depends(require_ai_server_auth)])
def reload_model():
    global model
    global pose_model
    global base_pose_model
    global videomae_model
    global videomae_processor
    global _MODEL_PATH
    global _MODEL_ERROR
    global _POSE_MODEL_PATH
    global _POSE_MODEL_ERROR
    global _BASE_POSE_MODEL_PATH
    global _BASE_POSE_MODEL_ERROR
    global _VIDEOMAE_MODEL_PATH
    global _VIDEOMAE_MODEL_ERROR
    global _VIDEOMAE_DEVICE_ACTIVE
    global DETECTION_LABELS_PATH

    model = None
    pose_model = None
    base_pose_model = None
    videomae_model = None
    videomae_processor = None
    _MODEL_PATH = ""
    _MODEL_ERROR = ""
    _POSE_MODEL_PATH = ""
    _POSE_MODEL_ERROR = ""
    _BASE_POSE_MODEL_PATH = ""
    _BASE_POSE_MODEL_ERROR = ""
    _VIDEOMAE_MODEL_PATH = ""
    _VIDEOMAE_MODEL_ERROR = ""
    _VIDEOMAE_DEVICE_ACTIVE = ""
    DETECTION_LABELS_PATH = None

    status_payload = {
        "ok": True,
        "ready": True,
        "trainingRoot": str(TRAINING_ROOT),
        "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
        "labelError": _LABEL_ERROR,
    }

    status_payload.update(detector_model_status(load=True))
    status_payload.update(pose_model_status())
    status_payload.update(base_pose_model_status())
    status_payload.update(videomae_model_status())
    return status_payload