File size: 2,133 Bytes
d2ce7dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | 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() |