"""场景分类：规则驱动，无需模型"""
import math
from enum import Enum
from dataclasses import dataclass
import numpy as np
from PIL import Image


class Scene(str, Enum):
    SCREENSHOT = "screenshot"   # PNG + 宽屏 w/h > 1.4
    DOCUMENT = "document"       # 长宽比接近 A4, h/w > 1.3
    POSTER = "poster"           # 低熵值 < 5 + JPEG
    UNKNOWN = "unknown"         # 最严处理


# OCR 置信度阈值
OCR_THRESHOLD = {
    Scene.SCREENSHOT: 0.85,
    Scene.DOCUMENT: 0.75,
    Scene.POSTER: 0.65,
    Scene.UNKNOWN: 0.85,
}


@dataclass
class SceneResult:
    scene: Scene
    ocr_threshold: float


def _image_entropy(img: Image.Image) -> float:
    gray = img.convert("L")
    hist = gray.histogram()
    total = sum(hist)
    entropy = 0.0
    for count in hist:
        if count > 0:
            p = count / total
            entropy -= p * math.log2(p)
    return entropy


def classify_scene(img: Image.Image, fmt: str) -> SceneResult:
    """
    img: PIL Image
    fmt: 文件格式，如 'PNG', 'JPEG'
    """
    w, h = img.size
    fmt = fmt.upper()

    if fmt == "PNG" and w / h > 1.4:
        scene = Scene.SCREENSHOT
    elif h / w > 1.3:
        scene = Scene.DOCUMENT
    elif fmt in ("JPEG", "JPG") and _image_entropy(img) < 5:
        scene = Scene.POSTER
    else:
        scene = Scene.UNKNOWN

    return SceneResult(scene=scene, ocr_threshold=OCR_THRESHOLD[scene])