# -*- coding: utf-8 -*-
"""
GDAL 底层 IO 工具模块

提供遥感影像读写、格式转换等底层 GDAL 操作功能。
这些函数不依赖任何业务逻辑，可在其他项目中独立复用。
"""
import os
from pathlib import Path
from typing import Tuple, Optional

import numpy as np

# GDAL 导入（可选）
try:
    from osgeo import gdal, ogr, gdal_array
    GDAL_AVAILABLE = True
except ImportError:
    GDAL_AVAILABLE = False

# hdr 文件工具
try:
    from src.utils.util import write_fields_to_hdrfile, get_hdr_file_path
    UTIL_AVAILABLE = True
except ImportError:
    UTIL_AVAILABLE = False
    write_fields_to_hdrfile = None
    get_hdr_file_path = None


# ============================================================
# 影像信息读取
# ============================================================

def get_image_geo_info(img_path: str) -> Tuple[tuple, str, int, int, int]:
    """
    获取影像的地理信息（不加载图像数据，节省内存）

    Args:
        img_path: 影像文件路径

    Returns:
        tuple: (geotransform, projection, width, height, n_bands)
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法读取影像文件")

    dataset = gdal.Open(img_path, gdal.GA_ReadOnly)
    if dataset is None:
        raise ValueError(f"无法打开影像文件: {img_path}")

    try:
        width = dataset.RasterXSize
        height = dataset.RasterYSize
        n_bands = dataset.RasterCount
        geotransform = dataset.GetGeoTransform()
        projection = dataset.GetProjection()
        return geotransform, projection, width, height, n_bands
    finally:
        dataset = None


def load_image_as_array(img_path: str) -> Tuple[np.ndarray, tuple, str]:
    """
    加载影像文件为numpy数组

    注意：此方法会将所有波段加载到内存，对于大图像会消耗大量内存。
    建议直接传递文件路径给算法类，让算法类使用GDAL逐波段处理。

    Args:
        img_path: 影像文件路径

    Returns:
        tuple: (image_array, geotransform, projection)
            image_array: numpy数组，形状为(height, width, bands)
            geotransform: 地理变换参数
            projection: 投影信息
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法读取影像文件")

    dataset = gdal.Open(img_path, gdal.GA_ReadOnly)
    if dataset is None:
        raise ValueError(f"无法打开影像文件: {img_path}")

    try:
        width = dataset.RasterXSize
        height = dataset.RasterYSize
        n_bands = dataset.RasterCount
        geotransform = dataset.GetGeoTransform()
        projection = dataset.GetProjection()

        image_bands = []
        for i in range(1, n_bands + 1):
            band = dataset.GetRasterBand(i)
            band_data = band.ReadAsArray()
            image_bands.append(band_data)

        image_array = np.dstack(image_bands)
        return image_array, geotransform, projection
    finally:
        dataset = None


def read_band_as_array(img_path: str, band_index: int) -> np.ndarray:
    """
    读取单个波段为 numpy 数组

    Args:
        img_path: 影像文件路径
        band_index: 波段索引（从 0 开始）

    Returns:
        numpy 数组，形状为 (height, width)
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法读取影像文件")

    dataset = gdal.Open(img_path, gdal.GA_ReadOnly)
    if dataset is None:
        raise ValueError(f"无法打开影像文件: {img_path}")

    try:
        band = dataset.GetRasterBand(band_index + 1)
        return band.ReadAsArray()
    finally:
        dataset = None


def read_multiple_bands(img_path: str, band_indices: list) -> Tuple[list, tuple, str]:
    """
    读取多个指定波段为列表

    Args:
        img_path: 影像文件路径
        band_indices: 波段索引列表

    Returns:
        tuple: (band_list, geotransform, projection)
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法读取影像文件")

    dataset = gdal.Open(img_path, gdal.GA_ReadOnly)
    if dataset is None:
        raise ValueError(f"无法打开影像文件: {img_path}")

    try:
        geotransform = dataset.GetGeoTransform()
        projection = dataset.GetProjection()
        bands = []
        for idx in band_indices:
            band = dataset.GetRasterBand(idx + 1)
            bands.append(band.ReadAsArray())
        return bands, geotransform, projection
    finally:
        dataset = None


# ============================================================
# 影像写入
# ============================================================

def save_array_as_image(image_array: np.ndarray, output_path: str,
                         geotransform: tuple, projection: str,
                         dtype=None) -> str:
    """
    将numpy数组保存为影像文件

    Args:
        image_array: numpy数组，形状为(height, width, bands) 或 (height, width)
        output_path: 输出文件路径
        geotransform: 地理变换参数
        projection: 投影信息
        dtype: GDAL数据类型（默认 gdal.GDT_Float32）

    Returns:
        输出文件路径
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法保存影像文件")

    if dtype is None:
        dtype = gdal.GDT_Float32

    if image_array.ndim == 2:
        height, width = image_array.shape
        n_bands = 1
    else:
        height, width, n_bands = image_array.shape

    driver = gdal.GetDriverByName('ENVI')
    if driver is None:
        driver = gdal.GetDriverByName('GTiff')

    if driver is None:
        raise ValueError("无法创建影像文件，没有可用的驱动")

    dataset = driver.Create(output_path, width, height, n_bands, dtype)
    if dataset is None:
        raise ValueError(f"无法创建输出文件: {output_path}")

    try:
        dataset.SetGeoTransform(geotransform)
        dataset.SetProjection(projection)

        if n_bands == 1:
            band = dataset.GetRasterBand(1)
            band.WriteArray(image_array)
            band.FlushCache()
        else:
            for i in range(n_bands):
                band = dataset.GetRasterBand(i + 1)
                band.WriteArray(image_array[:, :, i])
                band.FlushCache()
    finally:
        dataset = None

    return output_path


def save_bands_as_image(corrected_bands: list, output_path: str,
                        geotransform: tuple, projection: str,
                        dtype=None) -> str:
    """
    直接从波段列表保存影像文件（避免堆叠，节省内存）

    Args:
        corrected_bands: 校正后的波段列表，每个元素是一个(height, width)的numpy数组
        output_path: 输出文件路径
        geotransform: 地理变换参数
        projection: 投影信息
        dtype: GDAL数据类型

    Returns:
        输出文件路径
    """
    if not GDAL_AVAILABLE:
        raise ImportError("GDAL未安装，无法保存影像文件")

    if not corrected_bands:
        raise ValueError("波段列表为空")

    if dtype is None:
        dtype = gdal.GDT_Float32

    n_bands = len(corrected_bands)
    height, width = corrected_bands[0].shape

    driver = gdal.GetDriverByName('ENVI')
    if driver is None:
        driver = gdal.GetDriverByName('GTiff')

    if driver is None:
        raise ValueError("无法创建影像文件，没有可用的驱动")

    dataset = driver.Create(output_path, width, height, n_bands, dtype)
    if dataset is None:
        raise ValueError(f"无法创建输出文件: {output_path}")

    try:
        dataset.SetGeoTransform(geotransform)
        dataset.SetProjection(projection)

        for i, band_array in enumerate(corrected_bands):
            if band_array.shape != (height, width):
                raise ValueError(f"波段 {i} 的尺寸 {band_array.shape} 与预期 {(height, width)} 不匹配")
            band = dataset.GetRasterBand(i + 1)
            band.WriteArray(band_array)
            band.FlushCache()
    finally:
        dataset = None

    return output_path


def copy_hdr_info(source_img_path: str, dest_img_path: str) -> bool:
    """
    复制原始影像的hdr文件信息（如波长等）到目标影像的hdr文件

    Args:
        source_img_path: 源影像文件路径（原始bsq文件）
        dest_img_path: 目标影像文件路径（去耀斑后的bsq文件）

    Returns:
        bool: 是否成功
    """
    if not UTIL_AVAILABLE:
        print("警告: util模块未导入，无法复制hdr文件信息")
        return False

    try:
        source_hdr_path = get_hdr_file_path(source_img_path)
        dest_hdr_path = get_hdr_file_path(dest_img_path)

        if not Path(source_hdr_path).exists():
            print(f"警告: 源hdr文件不存在: {source_hdr_path}")
            return False

        if not Path(dest_hdr_path).exists():
            print(f"警告: 目标hdr文件不存在: {dest_hdr_path}")
            return False

        write_fields_to_hdrfile(source_hdr_path, dest_hdr_path)
        print(f"已复制原始hdr文件信息到: {dest_hdr_path}")
        return True
    except Exception as e:
        print(f"警告: 复制hdr文件信息时出错: {e}")
        return False