# -*- coding: utf-8 -*-
"""
可视化模块
在图像上绘制 OCR 识别结果
"""

import os
import sys
import cv2
import numpy as np
from typing import List, Optional, Tuple

from ocr.engine import TextBlock
from ocr.pipeline import OCRResult
from utils.config import VisualizeConfig


# Windows 系统常用中文字体列表（按优先级排序）
_WINDOWS_CHINESE_FONTS = [
    "msyh.ttc",      # 微软雅黑
    "msyhbd.ttc",    # 微软雅黑粗体
    "simhei.ttf",    # 黑体
    "simsun.ttc",    # 宋体
    "simkai.ttf",    # 楷体
]

# Linux 系统常用中文字体路径
_LINUX_CHINESE_FONTS = [
    "/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
    "/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc",
    "/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc",
    "/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf",
]


def _find_system_chinese_font() -> Optional[str]:
    """
    自动查找系统中文字体

    Returns:
        字体文件路径，未找到返回 None
    """
    if sys.platform == "win32":
        # Windows 字体目录
        fonts_dir = os.path.join(os.environ.get("WINDIR", "C:\\Windows"), "Fonts")
        for font_name in _WINDOWS_CHINESE_FONTS:
            font_path = os.path.join(fonts_dir, font_name)
            if os.path.exists(font_path):
                return font_path
    else:
        # Linux/macOS
        for font_path in _LINUX_CHINESE_FONTS:
            if os.path.exists(font_path):
                return font_path

    return None


class OCRVisualizer:
    """
    OCR 结果可视化器
    在图像上绘制文本框和识别结果
    """

    def __init__(self, config: VisualizeConfig):
        """
        初始化可视化器

        Args:
            config: 可视化配置
        """
        self._config = config
        self._font = cv2.FONT_HERSHEY_SIMPLEX

        # 尝试加载中文字体
        self._pil_font = None
        self._use_pil = False

        # 确定字体路径：优先使用配置的路径，否则自动检测系统字体
        font_path = config.font_path or _find_system_chinese_font()

        if font_path:
            try:
                from PIL import ImageFont
                self._pil_font = ImageFont.truetype(font_path, 20)
                self._use_pil = True
            except Exception:
                # 字体加载失败，使用 OpenCV 默认字体
                pass

    def draw_text_blocks(
        self,
        frame: np.ndarray,
        text_blocks: List[TextBlock],
        copy: bool = True
    ) -> np.ndarray:
        """
        在帧上绘制文本块

        Args:
            frame: 输入帧
            text_blocks: 文本块列表
            copy: 是否复制帧（避免修改原帧）

        Returns:
            绘制后的帧
        """
        if copy:
            frame = frame.copy()

        for block in text_blocks:
            self._draw_single_block(frame, block)

        return frame

    def draw_result(
        self,
        frame: np.ndarray,
        result: Optional[OCRResult],
        copy: bool = True
    ) -> np.ndarray:
        """
        在帧上绘制 OCR 结果

        Args:
            frame: 输入帧
            result: OCR 结果
            copy: 是否复制帧

        Returns:
            绘制后的帧
        """
        if copy:
            frame = frame.copy()

        if result is None:
            return frame

        # 绘制 ROI 区域（如果启用）
        if result.roi_applied and result.roi_rect:
            self._draw_roi(frame, result.roi_rect)

        # 绘制所有文本块
        for block in result.text_blocks:
            self._draw_single_block(frame, block)

        # 绘制状态信息
        self._draw_status(frame, result)

        return frame

    def _draw_single_block(
        self,
        frame: np.ndarray,
        block: TextBlock
    ) -> None:
        """
        绘制单个文本块

        Args:
            frame: 帧
            block: 文本块
        """
        # 获取带偏移的边界框坐标
        bbox = block.bbox_with_offset
        points = np.array(bbox, dtype=np.int32)

        # 绘制多边形边框
        cv2.polylines(
            frame,
            [points],
            isClosed=True,
            color=self._config.box_color,
            thickness=self._config.box_thickness
        )

        # 准备显示文本
        display_text = block.text
        if self._config.show_confidence:
            display_text = f"{block.text} ({block.confidence:.2f})"

        # 计算文本位置（在边界框左上角上方）
        text_x = int(min(p[0] for p in bbox))
        text_y = int(min(p[1] for p in bbox)) - 5

        # 确保文本不超出画面
        text_y = max(text_y, 20)

        # 绘制文本
        if self._use_pil and self._pil_font:
            self._draw_text_pil(frame, display_text, (text_x, text_y))
        else:
            self._draw_text_cv2(frame, display_text, (text_x, text_y))

    def _draw_text_cv2(
        self,
        frame: np.ndarray,
        text: str,
        position: Tuple[int, int]
    ) -> None:
        """
        使用 OpenCV 绘制文本（不支持中文，会显示方块）

        Args:
            frame: 帧
            text: 文本
            position: 位置 (x, y)
        """
        # 绘制文本背景（提高可读性）
        (text_width, text_height), baseline = cv2.getTextSize(
            text,
            self._font,
            self._config.text_scale,
            self._config.text_thickness
        )

        x, y = position
        cv2.rectangle(
            frame,
            (x, y - text_height - 5),
            (x + text_width + 5, y + 5),
            (255, 255, 255),
            -1
        )

        # 绘制文本
        cv2.putText(
            frame,
            text,
            position,
            self._font,
            self._config.text_scale,
            self._config.text_color,
            self._config.text_thickness,
            cv2.LINE_AA
        )

    def _draw_text_pil(
        self,
        frame: np.ndarray,
        text: str,
        position: Tuple[int, int]
    ) -> None:
        """
        使用 PIL 绘制文本（支持中文）

        Args:
            frame: 帧
            text: 文本
            position: 位置 (x, y)
        """
        from PIL import Image, ImageDraw

        # OpenCV 图像转 PIL
        pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
        draw = ImageDraw.Draw(pil_image)

        # 获取文本尺寸
        bbox = draw.textbbox(position, text, font=self._pil_font)
        text_width = bbox[2] - bbox[0]
        text_height = bbox[3] - bbox[1]

        x, y = position

        # 绘制背景
        draw.rectangle(
            [x - 2, y - text_height - 2, x + text_width + 2, y + 2],
            fill=(255, 255, 255)
        )

        # 绘制文本
        text_color_rgb = (
            self._config.text_color[2],
            self._config.text_color[1],
            self._config.text_color[0]
        )
        draw.text(position, text, font=self._pil_font, fill=text_color_rgb)

        # PIL 图像转回 OpenCV
        result = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
        np.copyto(frame, result)

    def _draw_roi(
        self,
        frame: np.ndarray,
        roi_rect: Tuple[int, int, int, int]
    ) -> None:
        """
        绘制 ROI 区域

        Args:
            frame: 帧
            roi_rect: ROI 矩形 (x, y, width, height)
        """
        x, y, w, h = roi_rect
        cv2.rectangle(
            frame,
            (x, y),
            (x + w, y + h),
            (255, 255, 0),  # 青色
            2,
            cv2.LINE_AA
        )

    def _draw_status(
        self,
        frame: np.ndarray,
        result: OCRResult
    ) -> None:
        """
        绘制状态信息

        Args:
            frame: 帧
            result: OCR 结果
        """
        h, w = frame.shape[:2]

        # 状态文本
        status_lines = [
            f"Image: {result.image_index}",
            f"Texts: {result.text_count}",
            f"Time: {result.processing_time_ms:.1f}ms"
        ]

        y_offset = 25
        for line in status_lines:
            cv2.putText(
                frame,
                line,
                (10, y_offset),
                self._font,
                0.5,
                (0, 255, 0),
                1,
                cv2.LINE_AA
            )
            y_offset += 20

    def show(
        self,
        frame: np.ndarray,
        wait_key: int = 1
    ) -> int:
        """
        显示帧并等待按键

        Args:
            frame: 帧
            wait_key: 等待时间（毫秒）

        Returns:
            按下的键码
        """
        if not self._config.show_window:
            return -1

        cv2.imshow(self._config.window_name, frame)
        return cv2.waitKey(wait_key)

    def close(self) -> None:
        """关闭所有窗口"""
        cv2.destroyAllWindows()

    @property
    def config(self) -> VisualizeConfig:
        """获取配置"""
        return self._config