import numpy as np
from spectral.io import envi
from PIL import Image
import matplotlib.pyplot as plt
import os
def linear_stretch_2_percent(data):
    """
    应用2%线性拉伸到数据

    参数:
    data: 输入的单波段数据

    返回:
    拉伸后的数据 (0-255)
    """
    # 计算2%和98%的分位数
    low = np.percentile(data, 2)
    high = np.percentile(data, 98)

    # 应用线性拉伸
    stretched = np.clip((data - low) / (high - low), 0, 1) * 255
    return stretched.astype(np.uint8)


def process_bil_files(input_folder):
    """
    处理BIL文件：读取10、60、160波段并导出为PNG

    参数:
    input_folder: 包含BIL文件的输入文件夹
    """
    # 读取BIL文件
    img = envi.open(input_folder.replace('.bil', '.hdr'), input_folder)

    # 读取指定波段（10, 60, 160）
    # 注意：波段索引从0开始，所以10波段是索引9，以此类推
    band_10 = img.read_band(9)
    band_60 = img.read_band(59)
    band_160 = img.read_band(159)

    # 应用2%线性拉伸到每个波段
    band_10_stretched = linear_stretch_2_percent(band_10)
    band_60_stretched = linear_stretch_2_percent(band_60)
    band_160_stretched = linear_stretch_2_percent(band_160)

    # 创建RGB图像（分别对应10,60,160波段）
    rgb_img = np.stack([band_10_stretched, band_60_stretched, band_160_stretched], axis=-1)

    # 将NumPy数组转换为PIL图像
    # 确保值在0-255范围内并转换为uint8类型
    rgb_img_pil = Image.fromarray((rgb_img).astype(np.uint8))

    return rgb_img_pil