import os
import pandas as pd
from pathlib import Path
import numpy as np
def process_single_bil_background(full_bil_path):
    """
    处理单个BIL文件，提取滤纸背景的平均光谱（不需要mask）

    参数:
        full_bil_path: BIL文件路径

    返回:
        mean_spectrum: 归一化的平均光谱数组
    """
    try:
        from plantcv import plantcv as pcv
        import cv2
    except ImportError:
        raise ImportError("需要导入plantcv和cv2库：from plantcv import plantcv as pcv; import cv2")

    # 设置 PlantCV 的全局参数
    pcv.params.debug = "None"
    pcv.params.dpi = 100
    pcv.params.text_size = 1
    pcv.params.text_thickness = 1

    # 读取光谱图像（ENVI 格式）
    spectral_array = pcv.readimage(filename=full_bil_path, mode='envi')
    bath_100 = spectral_array.array_data[:, :, 100]

    # 使用阈值提取滤纸区域（背景区域，阈值>5500）
    bath_over = pcv.threshold.binary(gray_img=bath_100, threshold=5500, object_type='light')

    # 对滤纸掩膜进行形态学操作
    bath_erode = pcv.erode(gray_img=bath_over, ksize=3, i=4)
    dilate_img = pcv.dilate(gray_img=bath_erode, ksize=3, i=4)

    # 填充大的孔洞
    dilate_img = pcv.fill(bin_img=dilate_img, size=10000)

    # 使用cv2.connectedComponents找到连通区域
    num1, labeled_spectral_mask = cv2.connectedComponents(dilate_img.astype(np.uint8))

    print(f"  检测到 {num1 - 1} 个滤纸区域")

    # 找到最大面积的对象
    max_area = 0
    max_label = 0

    for label in range(1, num1):
        obj_mask = labeled_spectral_mask == label
        area = np.sum(obj_mask)
        if area > max_area:
            max_area = area
            max_label = label

    # 提取最大面积对象的光谱
    if max_label > 0:
        max_obj_mask = labeled_spectral_mask == max_label
        spectral_values = spectral_array.array_data[max_obj_mask, :]
        mean_spectrum = np.mean(spectral_values, axis=0) / 10000  # 归一化
        return mean_spectrum
    else:
        print(f"  警告: 未找到有效的滤纸区域")
        n_bands = spectral_array.array_data.shape[-1]
        return np.zeros(n_bands)


def batch_process_bil_background(bil_folder, output_csv_path):
    """
    批量处理文件夹中的BIL文件，提取滤纸背景光谱并保存到CSV

    参数:
        bil_folder: 包含.bil文件的文件夹路径
        output_csv_path: 输出CSV文件路径（宽格式：每行一个文件，每列一个波段）

    返回:
        df: 包含所有文件光谱的DataFrame
    """
    bil_folder = Path(bil_folder)
    if not bil_folder.exists():
        raise ValueError(f"文件夹不存在: {bil_folder}")

    # 查找所有.bil文件
    bil_files = []
    for ext in ['.bil', '.BIL']:
        bil_files.extend(list(bil_folder.glob(f'*{ext}')))

    if len(bil_files) == 0:
        print(f"警告: 在 {bil_folder} 中未找到任何.bil文件")
        return None

    print(f"找到 {len(bil_files)} 个BIL文件")

    # 确保输出目录存在
    output_path = Path(output_csv_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    # 存储所有结果
    all_spectra = []
    filenames = []
    failed_files = []

    # 处理每个文件
    for i, bil_path in enumerate(bil_files, 1):
        print(f"\n[{i}/{len(bil_files)}] 处理文件: {bil_path.name}")
        print(f"{'=' * 60}")

        try:
            spectrum = process_single_bil_background(str(bil_path))

            if spectrum is not None and len(spectrum) > 0 and np.sum(spectrum) > 0:
                all_spectra.append(spectrum)
                filenames.append(bil_path.stem)
                print(f"  ✓ 成功提取光谱 ({len(spectrum)} 个波段)")
            else:
                failed_files.append(bil_path.name)
                print(f"  ✗ 未能提取有效光谱")

        except Exception as e:
            failed_files.append(bil_path.name)
            print(f"  ✗ 处理失败: {str(e)}")
            import traceback
            traceback.print_exc()
            continue

    # 合并结果
    if len(all_spectra) == 0:
        print("\n没有有效的结果可以保存")
        return None

    # 创建宽格式DataFrame
    n_bands = len(all_spectra[0])
    column_names = [f'band_{i}' for i in range(n_bands)]

    df = pd.DataFrame(all_spectra, columns=column_names, index=filenames)
    df.index.name = 'filename'
    df = df.reset_index()

    # 保存到CSV
    df.to_csv(output_csv_path, index=False)

    # 打印统计信息
    print(f"\n{'=' * 60}")
    print(f"批量处理完成！")
    print(f"  总共文件数: {len(bil_files)}")
    print(f"  成功处理: {len(all_spectra)}")
    print(f"  失败文件: {len(failed_files)}")
    if failed_files:
        print(f"  失败列表: {', '.join(failed_files)}")
    print(f"  光谱波段数: {n_bands}")
    print(f"  输出文件: {output_csv_path}")
    print(f"{'=' * 60}")

    return df



bil_folder = r"D:\Data\Traindata-09\whitebord"
output_csv = r"D:\Data\Traindata-09\whitebord\filter_paper_spectra.csv"

batch_process_bil_background(bil_folder, output_csv)