import time
from bil2rgb import process_bil_files
from unet_pytorch import predict_rgb
from shape_spectral import process_images
import cv2
from classification_model.Parallel.predict_plastic import predict_and_save
import numpy as np
import os
import matplotlib
from shape_spectral_background import process_images_background
matplotlib.use('TkAgg')

def read_hdr_file(bil_path):
    # 读取 .bil 文件对应的 .hdr 文件
    hdr_path = bil_path.replace('.bil', '.hdr')  # 假设 .hdr 文件与 .bil 文件同名
    with open(hdr_path, 'r') as f:
        header = f.readlines()

    samples, lines = None, None

    # 从 .hdr 文件中提取样本和行数
    for line in header:
        if line.startswith('samples'):
            samples = int(line.split('=')[-1].strip())
        if line.startswith('lines'):
            lines = int(line.split('=')[-1].strip())

    return samples, lines

def save_envi_classification(bil_path, df, savepath):
    if not os.path.exists(savepath):
        os.makedirs(savepath)
        print(f"创建目录: {savepath}")
    # 获取 .bil 文件的图像尺寸
    samples, lines = read_hdr_file(bil_path)

    # 初始化一个大小为 (lines, samples) 的空白图像
    classification_result = np.zeros((lines, samples), dtype=np.uint8)

    # 循环遍历 df 中的每个轮廓和对应的类别
    for _, row in df.iterrows():
        contour = row['contour']  # 轮廓为一个列表

        prediction = int(row['Predictions']) + 1  # 预测类别

        # 将预测类别10和11转换为0
        if prediction == 10 or prediction == 11:
            prediction = 0

        # 将该轮廓的所有点设置为预测类别
        contour = np.array(contour, dtype=np.int32)
        cv2.fillPoly(classification_result, [contour], prediction)

    # 根据原 .bil 文件名生成输出路径
    base_name = os.path.splitext(os.path.basename(bil_path))[0]  # 获取文件名，不带扩展名
    output_path = os.path.join(savepath, f"{base_name}_predict.dat")

    # 保存 ENVI 格式的文件
    with open(output_path, 'wb') as f:
        classification_result.tofile(f)

    # 保存头文件（关键修正点）
    header_content = f"""ENVI
description = {{
Classification Result.}}
samples = {samples}
lines = {lines}
bands = 1
header offset = 0
file type = ENVI Standard
data type = 1  # 修正为8-bit byte类型
interleave = bil
classes = 10
class = {{ back, ABS, HDPE, LDPE, PA6, PET, PP, PS, PTFE, PVC }}
single pixel area = 0.000036
unit = mm2
byte order = 0
wavelength units = nm
"""
    header_filename = os.path.join(savepath, f"{base_name}_predict.hdr")
    with open(header_filename, 'w') as header_file:
        header_file.write(header_content)

def change_hdr_file(bil_path):
    # 定义要追加的波长信息
    wavelength_info = """wavelength = {898.82, 903.64, 908.46, 913.28, 918.1, 922.92, 927.75, 932.57, 937.4, 942.22, 947.05, 951.88, 956.71, 961.54, 966.38, 971.21, 976.05, 980.88, 985.72, 990.56, 995.4, 1000.2, 1005.1, 1009.9, 1014.8, 1019.6, 1024.5, 1029.3, 1034.2, 1039, 1043.9, 1048.7, 1053.6, 1058.4, 1063.3, 1068.2, 1073, 1077.9, 1082.7, 1087.6, 1092.5, 1097.3, 1102.2, 1107.1, 1111.9, 1116.8, 1121.7, 1126.6, 1131.4, 1136.3, 1141.2, 1146.1, 1150.9, 1155.8, 1160.7, 1165.6, 1170.5, 1175.4, 1180.2, 1185.1, 1190, 1194.9, 1199.8, 1204.7, 1209.6, 1214.5, 1219.4, 1224.3, 1229.2, 1234.1, 1239, 1243.9, 1248.8, 1253.7, 1258.6, 1263.5, 1268.4, 1273.3, 1278.2, 1283.1, 1288.1, 1293, 1297.9, 1302.8, 1307.7, 1312.6, 1317.6, 1322.5, 1327.4, 1332.3, 1337.3, 1342.2, 1347.1, 1352, 1357, 1361.9, 1366.8, 1371.8, 1376.7, 1381.6, 1386.6, 1391.5, 1396.5, 1401.4, 1406.3, 1411.3, 1416.2, 1421.2, 1426.1, 1431.1, 1436, 1441, 1445.9, 1450.9, 1455.8, 1460.8, 1465.8, 1470.7, 1475.7, 1480.6, 1485.6, 1490.6, 1495.5, 1500.5, 1505.5, 1510.4, 1515.4, 1520.4, 1525.3, 1530.3, 1535.3, 1540.3, 1545.2, 1550.2, 1555.2, 1560.2, 1565.2, 1570.1, 1575.1, 1580.1, 1585.1, 1590.1, 1595.1, 1600.1, 1605.1, 1610, 1615, 1620, 1625, 1630, 1635, 1640, 1645, 1650, 1655, 1660, 1665, 1670.1, 1675.1, 1680.1, 1685.1, 1690.1, 1695.1, 1700.1, 1705.1, 1710.2, 1715.2, 1720.2}"""

    # 将.bil路径转换为.hdr路径
    hdr_path = os.path.splitext(bil_path)[0] + '.hdr'

    # 检查.hdr文件是否存在
    if not os.path.exists(hdr_path):
        print(f"错误: 找不到对应的HDR文件: {hdr_path}")
        return

    # 读取文件内容
    with open(hdr_path, 'r') as file:
        content = file.read()

    # 检查是否已包含波长信息
    if 'wavelength' in content:
        print(f"文件 {os.path.basename(hdr_path)} 已包含波长信息，无需修改。")
        return

    # 检查文件是否以换行符结尾
    needs_newline = not content.endswith('\n')

    # 追加波长信息
    with open(hdr_path, 'a') as file:
        if needs_newline:
            file.write('\n')  # 确保新内容从新行开始
        file.write(wavelength_info + '\n')

    print(f"已成功添加波长信息到文件: {os.path.basename(hdr_path)}")
# 记录总开始时间
total_start_time = time.time()

#--------------------------------------------输入参数------------------------------------
# 读取bil文件生成rgb,假彩色显示，拉伸
bil_path = r"E:\Data\Traindata-06\MPData4.bil"
output_path = r'C:\Users\HyperSpec\test\1+2'
unet_path = r"./unet_pytorch/logs/best_epoch_weights.pth"
model_path = r"D:\plastic\plastic\classification_model\modelsave\svm.m"

# 步骤1: 读取BIL文件并生成RGB图像
print("步骤1: 读取BIL文件并生成RGB图像...")
start_time = time.time()
rgb_img = process_bil_files(bil_path)
print(f"步骤1完成，耗时: {time.time() - start_time:.2f}秒")

#修改hdr文件
change_hdr_file(bil_path)

# 步骤2: UNet生成掩膜
print("步骤2: UNet生成掩膜...")
start_time = time.time()
mask = predict_rgb.rgb2mask(rgb_img, unet_path)
mask = np.array(mask)
mask = mask[:, :, 0]
print(f"步骤2完成，耗时: {time.time() - start_time:.2f}秒")

# 步骤3: 处理BIL文件提取特征
print("步骤3: 处理BIL文件提取特征...")
start_time = time.time()
df = process_images(bil_path, mask)
print(f"步骤3完成，耗时: {time.time() - start_time:.2f}秒")

# 步骤4: 获取背景滤纸的平均反射率
print("步骤4: 获取背景滤纸的平均反射率...")
start_time = time.time()
df_correct = process_images_background(bil_path, mask)
print(f"步骤4完成，耗时: {time.time() - start_time:.2f}秒")

# 步骤5: 数据预处理
print("步骤5: 数据预处理...")
start_time = time.time()
# 对 df 的第 2-169 列（索引 1:169）进行除法
df.iloc[:, 1:169] = df.iloc[:, 1:169].div(df_correct, axis=1)
# 去除nan行
df = df.dropna()
# 去除轮廓只有一个点的数据行
df = df[df['contour'].apply(lambda x: len(x) > 1 if isinstance(x, list) else True)]
# 去除面积太小的数据行
df = df[df['area'] >= 400]
print(f"步骤5完成，耗时: {time.time() - start_time:.2f}秒")

# 步骤6: 预测分类结果
print("步骤6: 预测分类结果...")
start_time = time.time()
df_pre = predict_and_save(df, model_path)
print(f"步骤6完成，耗时: {time.time() - start_time:.2f}秒")

# 步骤7: 保存输出结果
print("步骤7: 保存输出结果...")
start_time = time.time()
save_envi_classification(bil_path, df, output_path)
print(f"步骤7完成，耗时: {time.time() - start_time:.2f}秒")

# 输出总耗时
total_time = time.time() - total_start_time
print(f"所有步骤完成，总耗时: {total_time:.2f}秒")
print(f"详细时间报告:")
print(f"- 读取BIL并生成RGB: {time.time() - total_start_time:.2f}秒")
# 这里可以添加更多详细时间报告...