# -*- coding: utf-8 -*-
"""
依赖注入模块
提供 FastAPI 依赖项
"""

import base64
from typing import Optional

import cv2
import numpy as np
from fastapi import Depends, File, Form, Request, UploadFile

from api.exceptions import InvalidImageError, ModelNotLoadedError
from api.schemas.request import OCRRequestBase64, OCRRequestParams, ROIParams
from api.security import decode_base64_image, validate_uploaded_file
from ocr.pipeline import OCRPipeline, OCRResult
from utils.config import OCRConfig, PipelineConfig, ROIConfig


def get_ocr_pipeline(request: Request) -> OCRPipeline:
    """
    获取 OCR Pipeline 实例

    Args:
        request: FastAPI 请求对象

    Returns:
        OCRPipeline 实例

    Raises:
        ModelNotLoadedError: 模型未加载
    """
    pipeline = getattr(request.app.state, "ocr_pipeline", None)
    if pipeline is None:
        raise ModelNotLoadedError()
    return pipeline


async def parse_multipart_image(
    file: UploadFile = File(..., description="图片文件"),
) -> bytes:
    """
    解析 multipart 上传的图片文件

    Args:
        file: 上传的文件

    Returns:
        验证后的图片字节数据
    """
    content = await file.read()
    return validate_uploaded_file(
        content=content,
        filename=file.filename,
        content_type=file.content_type,
    )


async def parse_multipart_params(
    lang: str = Form(default="ch", description="识别语言"),
    use_gpu: bool = Form(default=False, description="是否使用 GPU"),
    drop_score: float = Form(default=0.5, ge=0.0, le=1.0, description="置信度阈值"),
    roi_x: Optional[float] = Form(default=None, ge=0.0, le=1.0, description="ROI X"),
    roi_y: Optional[float] = Form(default=None, ge=0.0, le=1.0, description="ROI Y"),
    roi_width: Optional[float] = Form(
        default=None, gt=0.0, le=1.0, description="ROI 宽度"
    ),
    roi_height: Optional[float] = Form(
        default=None, gt=0.0, le=1.0, description="ROI 高度"
    ),
    return_annotated_image: bool = Form(
        default=False, description="是否返回标注图片"
    ),
) -> OCRRequestParams:
    """
    解析 multipart 表单参数

    Returns:
        OCR 请求参数对象
    """
    return OCRRequestParams(
        lang=lang,
        use_gpu=use_gpu,
        drop_score=drop_score,
        roi_x=roi_x,
        roi_y=roi_y,
        roi_width=roi_width,
        roi_height=roi_height,
        return_annotated_image=return_annotated_image,
    )


def decode_image_bytes(content: bytes) -> np.ndarray:
    """
    将图片字节解码为 numpy 数组

    Args:
        content: 图片字节数据

    Returns:
        OpenCV 图像 (BGR 格式)

    Raises:
        InvalidImageError: 图片解码失败或尺寸不符合要求
    """
    # 图片尺寸限制
    MIN_IMAGE_SIZE = 10  # 最小 10x10 像素
    MAX_IMAGE_SIZE = 10000  # 最大 10000x10000 像素

    try:
        nparr = np.frombuffer(content, np.uint8)
        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
        if image is None:
            raise InvalidImageError("图片解码失败")

        # 验证图片尺寸
        height, width = image.shape[:2]
        if width < MIN_IMAGE_SIZE or height < MIN_IMAGE_SIZE:
            raise InvalidImageError(
                f"图片尺寸过小，最小要求 {MIN_IMAGE_SIZE}x{MIN_IMAGE_SIZE} 像素，"
                f"当前尺寸 {width}x{height}"
            )
        if width > MAX_IMAGE_SIZE or height > MAX_IMAGE_SIZE:
            raise InvalidImageError(
                f"图片尺寸过大，最大允许 {MAX_IMAGE_SIZE}x{MAX_IMAGE_SIZE} 像素，"
                f"当前尺寸 {width}x{height}"
            )

        return image
    except Exception as e:
        if isinstance(e, InvalidImageError):
            raise
        raise InvalidImageError(f"图片解码失败: {str(e)}")


def encode_image_base64(image: np.ndarray, format: str = ".jpg") -> str:
    """
    将图像编码为 Base64 字符串

    Args:
        image: OpenCV 图像
        format: 图片格式 (如 '.jpg', '.png')

    Returns:
        Base64 编码的图片字符串
    """
    success, encoded = cv2.imencode(format, image)
    if not success:
        return ""
    return base64.b64encode(encoded.tobytes()).decode("utf-8")


def build_pipeline_config(roi: Optional[ROIParams]) -> PipelineConfig:
    """
    根据 ROI 参数构建管道配置

    Args:
        roi: ROI 参数

    Returns:
        管道配置对象
    """
    if roi is None:
        return PipelineConfig(roi=ROIConfig(enabled=False))

    return PipelineConfig(
        roi=ROIConfig(
            enabled=True,
            x_ratio=roi.x,
            y_ratio=roi.y,
            width_ratio=roi.width,
            height_ratio=roi.height,
        )
    )