tools/data_processing/quality_control/clean_excel.py

import os
import pandas as pd


def main():
    # --- 1. 路径配置 ---
    original_excel_path = "/data/team/huchengwei/fundus/fundus_csv/paired_fundus_image_dramdrvo.xlsx"
    missing_log_path = "/data/team/huchengwei/fundus/fundus_csv/missing_images_dramdrvo.txt"  # 上一步脚本自动生成的缺失名单
    # 新的干净表格保存路径
    new_excel_path = "/data/team/huchengwei/fundus/fundus_csv/dramdrvo_cleaned.xlsx"

    # --- 2. 加载缺失黑名单 ---
    if not os.path.exists(missing_log_path):
        raise FileNotFoundError(f"找不到 {missing_log_path}，请确认上一步脚本是否正确生成了该文件。")

    with open(missing_log_path, "r", encoding="utf-8") as f:
        # 去除换行符并存入哈希集合 (Set) 以获得 O(1) 的查询速度
        missing_basenames = set(line.strip() for line in f if line.strip())

    print(f"[*] 成功加载缺失名单，共计 {len(missing_basenames)} 个目标。")

    # --- 3. 读取原始 DataFrame ---
    print(f"[*] 正在读取原始 Excel 表格...")
    df = pd.read_excel(original_excel_path)
    original_len = len(df)

    # --- 4. 核心清洗逻辑 ---
    # 定义过滤条件：提取 image_name 的无后缀基础名，判断其是否在黑名单中
    def is_valid_row(image_name):
        basename = os.path.splitext(str(image_name))[0]
        return basename not in missing_basenames

    # 应用掩码 (Boolean Mask) 过滤
    mask = df['image_name'].apply(is_valid_row)
    df_cleaned = df[mask]

    cleaned_len = len(df_cleaned)
    removed_count = original_len - cleaned_len

    # --- 5. 校验与保存 ---
    print(f"[*] 清洗完成！")
    print(f"    -> 原始数据行数: {original_len}")
    print(f"    -> 清洗后数据行数: {cleaned_len} (正好对应你成功复制的 5505 张图)")
    print(f"    -> 实际排除行数: {removed_count}")

    # 保存新的 Excel
    df_cleaned.to_excel(new_excel_path, index=False)
    print(f"[*] 新的干净表格已生成并保存至:\n    {new_excel_path}")


if __name__ == "__main__":
    main()