stable version v0.1

.gitignore

@@ -4,6 +4,7 @@ __pycache__/
 *.py[cod]
 *$py.class
 .idea/
+saves/
 # C extensions
 *.so
 

_dataset/FlowPic/classes_csvs/chat/reg/my_chat_raw.csv

@@ -0,0 +1,22 @@
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+my_whatsapp_chat2,185.60.216.53,443,10.0.0.9,60461,TCP,1503130929.938558,210,0.0,0.064899,0.119151,0.120185,0.120501,0.121558,0.185194,0.185375,0.200732,0.201655,0.202242,0.203047,0.203657,0.203976,0.204572,0.20495,0.248791,0.24902,0.249692,0.249988,0.264997,0.265364,0.266048,0.266392,0.267549,0.267769,0.268561,0.268791,0.269053,0.26958,0.270259,0.270625,0.271246,0.271667,0.27234,0.272725,0.312867,0.313122,0.313829,0.314198,0.314511,0.315157,0.315773,0.31603,0.328799,0.328964,0.329845,0.330079,0.330389,0.330943,0.334562,0.334895,0.335222,0.335778,0.336499,0.336882,0.337521,0.337933,0.338718,0.339105,0.339958,0.340284,0.340875,0.341243,0.341857,0.342624,0.343053,0.343391,0.343958,0.344546,0.344841,0.345398,0.346021,0.346295,0.346935,0.347228,0.376841,0.377083,0.377388,0.378052,0.378264,0.37877,0.379154,0.379727,0.380367,0.380728,0.381015,0.381634,0.382061,0.38269,0.383,0.383542,0.392051,0.392255,0.392629,0.393082,0.393753,0.394092,0.394384,0.394928,0.395191,0.398212,0.398532,0.399107,0.400204,0.400495,0.401129,0.401477,0.402141,0.402973,0.403568,0.403871,0.561086,0.561289,0.561933,0.562176,0.562452,0.563054,0.563346,0.563774,0.564429,0.564725,0.565163,0.565398,0.644158,0.644367,0.644727,0.645318,0.645561,0.646209,0.646888,0.647154,0.64741,0.648003,0.648657,0.648929,0.649671,0.649915,0.650596,0.652103,0.652405,0.652856,0.653814,0.654202,0.655461,0.65577,0.683542,0.683773,0.684143,0.684613,0.726841,0.727145,0.730048,0.730291,0.730926,0.731276,0.731906,0.732106,0.732726,0.733126,0.73375,0.734329,0.735226,0.735497,0.736462,0.736671,0.737335,0.737769,0.738053,0.738636,0.739402,0.739698,0.740246,0.744093,0.7445,0.744789,0.745325,0.746091,0.746408,0.746693,0.747286,0.748048,0.748344,0.749405,0.74961,0.750244,0.750611,0.750873,0.751393,0.751689,0.7522,0.752839,0.75353,0.753814,0.754529,0.754808,0.76631,0.766726,0.76695,0.768162,0.809547,0.809887,0.810239,0.810602,45.955576,65.360512,65.360728,65.361163,65.385839,65.425075,,44,44,40,1320,1320,688,298,121,78,82,82,376,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,290,1320,1320,1320,228,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,1320,1320,1320,1320,1320,1320,899,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,1320,1320,1320,638,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,1320,1320,1320,638,1320,1320,1320,339,1320,1320,1320,339,1320,1320,1320,58,40,86,71,40,117,40
+my_whatsapp_chat3,185.60.216.53,443,10.0.0.9,60489,TCP,1503131767.611236,68,0.0,0.06671,0.118544,0.118918,0.182453,0.184939,0.185988,0.285655,0.644056,0.644278,0.64458,0.645107,0.645425,0.646041,0.646396,0.646894,0.647177,0.745883,0.746122,0.746398,0.747631,0.747959,0.748455,0.749124,0.749405,0.750146,0.750478,0.751129,0.751482,0.75179,0.752452,0.752748,0.75339,0.754014,0.754329,0.80957,0.810355,0.810725,0.81125,0.81156,0.812147,0.81282,0.81319,0.847669,0.847914,0.848203,0.849012,0.849645,0.849929,0.850518,0.851524,0.851825,0.85214,0.852678,0.853316,0.853659,0.854558,0.855164,0.85589,0.85658,0.856859,0.857488,0.857765,46.114359,65.31913,65.319333,65.319757,65.383573,,44,44,40,186,121,78,82,40,1320,358,1320,1320,1320,1320,1320,1320,456,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,1320,424,1320,1320,1320,1320,1320,1320,456,1320,1320,1320,1320,1320,1320,456,1320,1320,1320,1320,1320,905,40,86,71,40,40
+my_whatsapp_chat3,10.0.0.9,60489,185.60.216.53,443,TCP,1503131767.601515,47,0.0,0.009844,0.010076,0.128714,0.128856,0.131369,0.131393,0.1314,0.131626,0.192277,0.192401,0.194693,0.195746,0.654094,0.654882,0.655818,0.65668,0.656945,0.755927,0.75743,0.75825,0.759194,0.760263,0.761263,0.762224,0.763174,0.764108,0.820182,0.821022,0.821931,0.82299,0.857689,0.858805,0.859701,0.8603,0.861598,0.862442,0.86341,0.864311,0.865648,0.866641,0.867537,46.054353,65.328969,65.329115,65.329155,65.329532,,64,40,557,40,91,93,96,82,337,40,78,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40,40

_reference/TrafficParser/datasets_generator.py

@@ -10,12 +10,12 @@ from sklearn.model_selection import train_test_split
 
 CLASS = "browsing"
 TEST_SIZE = 0.1
-DATASET_DIR = "../datasets/"
+DATASET_DIR = "../dataset"
-
+BASE_PATH = '../../_dataset/FlowPic/classes_csvs/'
 VPN_TYPES = {
-    "reg": glob.glob("../raw_csvs/classes/**/reg/*.npy"),
+    "reg": glob.glob(f"{BASE_PATH}**/reg/*.npy"),
-    "vpn": glob.glob("../raw_csvs/classes/**/vpn/*.npy"),
+    "vpn": glob.glob(f"{BASE_PATH}**/vpn/*.npy"),
-    "tor": glob.glob("../raw_csvs/classes/**/tor/*.npy")
+    "tor": glob.glob(f"{BASE_PATH}**/tor/*.npy")
 }
 
 

_reference/TrafficParser/traffic_csv_converter.py

@@ -9,7 +9,7 @@ import csv
 from sessions_plotter import *
 import glob
 import re
-
+from loguru import logger
 FLAGS = None
 INPUT = "../raw_csvs/classes/browsing/reg/CICNTTor_browsing.raw.csv"#"../dataset/iscxNTVPN2016/CompletePCAPs" # ""
 INPUT_DIR = "../raw_csvs/classes/chat/vpn/"
@@ -37,26 +37,26 @@ MIN_TPS = 50
 
 
 def export_dataset(dataset):
-    print("Start export dataset")
+    logger.info("Start export dataset")
     np.save(os.path.splitext(INPUT)[0], dataset)
-    print(dataset.shape)
+    logger.info(dataset.shape)
 
 
 def export_class_dataset(dataset, class_dir):
-    print("Start export dataset")
+    logger.info("Start export dataset")
     np.save(class_dir + "/" + "_".join(re.findall(r"[\w']+", class_dir)[-2:]), dataset)
-    print(dataset.shape)
+    logger.info(dataset.shape)
 
 
 def import_dataset():
-    print("Import dataset")
+    logger.info("Import dataset")
     dataset = np.load(os.path.splitext(INPUT)[0] + ".npy")
-    print(dataset.shape)
+    logger.info(dataset.shape)
     return dataset
 
 
 def traffic_csv_converter(file_path):
-    print("Running on " + file_path)
+    logger.info("Running on " + file_path)
     dataset = []
     # labels = []
     counter = 0
@@ -101,7 +101,7 @@ def traffic_csv_converter(file_path):
                         dataset.append([h])
                         counter += 1
                         if counter % 100 == 0:
-                            print(counter)
+                            logger.info(counter)
 
     return np.asarray(dataset) #, np.asarray(labels)
 
@@ -122,7 +122,7 @@ def traffic_csv_converter_splitted(file_path):
                 for ts_split, sizes_split in zip(np.split(ts, [len(ts)/2]), np.split(sizes, [len(sizes)/2])):
                     split_converter(ts_split, sizes_split, dataset, counter)
 
-    print("Running on " + file_path)
+    logger.info("Running on " + file_path)
     dataset = []
     # labels = []
     counter = 0
@@ -156,24 +156,24 @@ def traffic_class_converter(dir_path):
 def iterate_all_classes():
     for class_dir in glob.glob(CLASSES_DIR):
         if "other" not in class_dir: #"browsing" not in class_dir and
-            print("working on " + class_dir)
+            logger.info("working on " + class_dir)
             dataset = traffic_class_converter(class_dir)
-            print(dataset.shape)
+            logger.info(dataset.shape)
             export_class_dataset(dataset, class_dir)
 
 
 def random_sampling_dataset(input_array, size=2000):
-    print("Import dataset " + input_array)
+    logger.info("Import dataset " + input_array)
     dataset = np.load(input_array)
-    print(dataset.shape)
+    logger.info(dataset.shape)
     p = size*1.0/len(dataset)
-    print(p)
+    logger.info(p)
     if p >= 1:
         raise Exception
 
     mask = np.random.choice([True, False], len(dataset), p=[p, 1-p])
     dataset = dataset[mask]
-    print("Start export dataset")
+    logger.info("Start export dataset")
 
     np.save(os.path.splitext(input_array)[0] + "_samp", dataset)
 
@@ -181,13 +181,12 @@ def random_sampling_dataset(input_array, size=2000):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--input', type=str, default=INPUT, help='Path to csv file')
-
     FLAGS = parser.parse_args()
     ##
-    # iterate_all_classes()
+    iterate_all_classes()
-
+    INPUT = '../../_dataset/FlowPic/classes_csvs/browsing/reg/CICNTTor_browsing_raw.csv'
     # dataset = traffic_class_converter(INPUT_DIR)
-    # dataset = traffic_csv_converter(INPUT)
+    dataset = traffic_csv_converter(INPUT)
 
     input_array = "../raw_csvs/classes/browsing/reg/browsing_reg.npy"
     random_sampling_dataset(input_array)

_reference/dataset/sessions_plotter.py

@@ -0,0 +1,163 @@
+#!/usr/bin/env python
+from datetime import datetime
+
+from traffic_csv_converter import *
+import cv2
+"""
+sessions_plotter.py has 3 functions to create spectogram, histogram, 2d_histogram from [(ts, size),..] session.
+sessions_plotter.py 具有 3 个功能，可从 [（ts， size）,..] 会话创建频谱图、直方图2d_histogram。
+"""
+import os
+import matplotlib
+
+import cv2 as cv
+matplotlib.use('agg')
+import matplotlib.pyplot as plt
+import numpy as np
+
+import datetime,time
+
+MTU = 1500
+
+
+def session_spectogram(ts, sizes, name=None):
+    plt.scatter(ts, sizes, marker='.')
+    plt.ylim(0, MTU)#设置y轴坐标区间
+    #plt.ylim(MTU, MTU2)  # 设置y轴坐标区间
+    plt.xlim(ts[0], ts[-1])#设置x轴坐标区间
+    # plt.yticks(np.arange(0, MTU, 10))
+    # plt.xticks(np.arange(int(ts[0]), int(ts[-1]), 10))
+    plt.title(name + " Session Spectogram")#输出表头
+    plt.ylabel('Size [B]')
+    plt.xlabel('Time [sec]')
+
+    plt.grid(True)#显示网格线 1=True=默认显示；0=False=不显示
+    plt.show()
+
+
+
+
+def session_atricle_spectogram(ts, sizes, fpath=None, show=True, tps=None):
+    if tps is None:
+        max_delta_time = ts[-1] - ts[0]
+    else:
+        max_delta_time = tps
+
+    ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
+    plt.figure()
+    plt.scatter(ts_norm, sizes, marker=',', c='k', s=5)
+    plt.ylim(0, MTU)
+    plt.xlim(0, MTU)
+    plt.ylabel('Packet Size [B]')
+    plt.xlabel('Normalized Arrival Time')
+    plt.set_cmap('binary')#如果将当前图窗比作一幅简笔画，则cmap就代表颜料盘的配色，用所提供的颜料盘自动给当前简笔画上色，就是cmap所做的事。
+    #plt.set_cmap('cool')
+    plt.axes().set_aspect('equal')#设置轴缩放的方面，即y-unit与x-unit的比率
+    plt.grid(False)#显示网格线 1=True=默认显示；0=False=不显示
+    if fpath is not None:
+        # plt.savefig(OUTPUT_DIR + fname, bbox_inches='tight', pad_inches=1)
+        plt.savefig(fpath, bbox_inches='tight')
+        #第一个实参指定要以什么样的文件名保存图表。
+        #第二个实参指定将图表多余的空白区域裁减掉。如果要保留图表周围多余的空白区域，可省略这个实参。
+    if show:
+        plt.show()
+    plt.close()
+
+
+def session_histogram(sizes, plot=False):
+    hist, bin_edges = np.histogram(sizes, bins=range(0, MTU + 1, 1))
+    #sizes数组，bins表示的是范围，即直方图纵坐标最大值，(0,MTU + 1)表示只统计[0，MTU]不包括0
+    if plot:
+        plt.bar(bin_edges[:-1], hist, width=1)#hist直方图，width单个直方图的宽度
+        plt.xlim(min(bin_edges), max(bin_edges)+100)
+        plt.show()
+    return hist.astype(np.uint16)#转换数据类型为np.uint16
+
+
+def session_2d_histogram(ts, sizes, plot=False, tps=None, qwe=0):#plot=False不画出图例
+    if tps is None:
+        max_delta_time = ts[-1] - ts[0]
+    else:
+        max_delta_time = tps
+
+    # ts_norm = map(int, ((np.array(ts) - ts[0]) / max_delta_time) * MTU)
+    ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
+    H, xedges, yedges = np.histogram2d(sizes, ts_norm, bins=(range(0, MTU + 1, 1), range(0, MTU + 1, 1)))
+    if plot:
+        plt.pcolormesh(xedges, yedges, H)#pcolormeshp()函数用于创建具有非规则矩形网格的伪彩色图
+        #plt.colorbar()#colorbar 即主图旁一个长条状的小图，能够辅助表示主图中 colormap 的颜色组成和颜色与数值的对应关系
+        plt.scatter(ts_norm, sizes, marker=',', s=1)
+        plt.xlim(0, MTU)
+        plt.ylim(0, MTU)
+
+        # 326
+        plt.ylabel('Packet Size [B]')
+        plt.xlabel('Normalized Arrival Time')
+
+
+        # plt.set_cmap('gnuplot')
+        plt.set_cmap('BuPu')
+        # plt.set_cmap('Greys')
+        plt.axis('off')
+        qwe=str(qwe)
+
+        figure_save_path = "appp111"  # 这里创建了一个文件夹，如果依次创建不同文件夹，可以用name_list[i]
+        # figure_save_path2 = "appp08"  # 这里创建了一个文件夹，如果依次创建不同文件夹，可以用name_list[i]
+        if not os.path.exists(figure_save_path):
+            os.makedirs(figure_save_path)  # 如果不存在目录figure_save_path，则创建
+        # if not os.path.exists(figure_save_path2):
+        #     os.makedirs(figure_save_path2)  # 如果不存在目录figure_save_path，则创建
+        plt.savefig(os.path.join(figure_save_path, qwe+ ".png"),bbox_inches='tight',pad_inches = 0)  # 分别命名图片
+
+
+        # # cv2.imwrite(filename, img)
+        # img = cv2.imread(s)
+        # cv2.imwrite(img, figure_save_path2, qwe+ ".png")
+        #
+        #
+        # #plt.savefig("filename" +qwe+ ".png")
+
+        plt.close()
+        plt.show()
+        # plt.axis('off')
+        #
+        # plt.show()
+        # plt.savefig(os.path.join(figure_save_path2, qwe + ".png"))  # 分别命名图片
+
+
+    return H.astype(np.uint16)#转换数据类型为np.uint16
+
+# def session_2d_histogram1(ts, sizes, plot=False, tps=None, qwe=0):#plot=False不画出图例
+#     if tps is None:
+#         max_delta_time = ts[-1] - ts[0]
+#     else:
+#         max_delta_time = tps
+#
+#     # ts_norm = map(int, ((np.array(ts) - ts[0]) / max_delta_time) * MTU)
+#     ts_norm = ((np.array(ts) - ts[0]) / max_delta_time) * MTU
+#     H, xedges, yedges = np.histogram2d(sizes, ts_norm, bins=(range(0, MTU + 1, 1), range(0, MTU + 1, 1)))
+#     if plot:
+#         plt.pcolormesh(xedges, yedges, H)#pcolormeshp()函数用于创建具有非规则矩形网格的伪彩色图
+#         #plt.colorbar()#colorbar 即主图旁一个长条状的小图，能够辅助表示主图中 colormap 的颜色组成和颜色与数值的对应关系
+#         plt.scatter(ts_norm, sizes, marker=',', s=0.5)
+#         plt.xlim(0, MTU)
+#         plt.ylim(0, MTU)
+#         plt.ylabel('Packet Size [B]')
+#         plt.xlabel('Normalized Arrival Time')
+#         #plt.set_cmap('gnuplot')
+#         plt.set_cmap('BuPu')
+#         qwe=str(qwe)
+#
+#         figure_save_path = "fig_voip_vpn1"  # 这里创建了一个文件夹，如果依次创建不同文件夹，可以用name_list[i]
+#         if not os.path.exists(figure_save_path):
+#             os.makedirs(figure_save_path)  # 如果不存在目录figure_save_path，则创建
+#         plt.savefig(os.path.join(figure_save_path, qwe+ ".png"))  # 分别命名图片
+#         plt.close()
+#         #plt.savefig("filename" +qwe+ ".png")
+#         plt.show()
+
+
+    # return H.astype(np.uint16)#转换数据类型为np.uint16
+
+
+

config.py

@@ -0,0 +1,2 @@
+CSV_PATH = './_dataset/DDos3.csv'
+BYPASS_COLUMNS= ('Destination Port', 'Label')

main.py

@@ -0,0 +1,114 @@
+import datetime
+from utils.files import create_dir
+import pandas as pd
+import numpy as np
+from config import *
+import matplotlib.pyplot as plt
+
+
+def is_in_bypass_list(column_name: str, bypass_list: tuple) -> bool:
+    for bypass in bypass_list:
+        if bypass in column_name:
+            return True
+    return False
+
+
+def input_csv_to_df(file_path: str) -> pd.DataFrame:
+    # 使用pandas的read_csv函数读取CSV文件
+    df = pd.read_csv(file_path)
+    return df
+
+
+def averaging_df(df: pd.DataFrame, column_num: int = None):
+    numeric_columns = df.select_dtypes(include=[np.number]).columns
+    max_values = df.max()
+    if column_num is None:
+        column_num = 0
+        for numeric_column in numeric_columns:
+            if is_in_bypass_list(numeric_column, BYPASS_COLUMNS):
+                continue
+            column_num = column_num + 1
+    for numeric_column in numeric_columns:
+        if is_in_bypass_list(numeric_column, BYPASS_COLUMNS):
+            continue
+        df[numeric_column] = df[numeric_column] / max_values[numeric_column] * column_num
+        # fix nan
+        df[numeric_column] = df[numeric_column].fillna(0)
+    return df, column_num
+
+
+def iter_df_to_point(df: pd.DataFrame, column_num: int = None):
+    size = 0
+    points = []
+    for index, row in df.iterrows():
+        x_values = row.values[2:]
+        y_values = np.linspace(0, len(x_values) - 1, len(x_values))
+        size = size + 1
+        points.append({index:(x_values, y_values)})
+    return points
+
+
+def generate_one_plot(x_values, y_values, x_y_size: int) -> plt:
+    yedges = xedges = np.linspace(0, x_y_size, x_y_size)
+    H = np.zeros((x_y_size, x_y_size))
+    plt.pcolormesh(xedges, yedges, H)  # pcolormeshp()函数用于创建具有非规则矩形网格的伪彩色图
+    plt.scatter(x_values, y_values, marker=',', s=1)
+    plt.xlim(0, x_y_size)
+    plt.ylim(0, x_y_size)
+    # 326
+    plt.ylabel('Packet Size [B]')
+    plt.xlabel('Normalized Arrival Time')
+    # plt.set_cmap('gnuplot')
+    plt.set_cmap('BuPu')
+    # plt.set_cmap('Greys')
+    plt.axis('off')
+    return plt
+    # plt.savefig(os.path.join(figure_save_path, qwe + ".png"), bbox_inches='tight', pad_inches=0)  # 分别命名图片
+
+
+def save_plt(plt: plt, base_path: str, num: int):
+    plt.savefig(f"{base_path}/{num}.png", bbox_inches='tight', pad_inches=0)
+
+
+from multiprocessing import Pool, cpu_count
+
+
+def process(df: pd.DataFrame):
+    df, size = averaging_df(df)
+    points = iter_df_to_point(df, size)
+    base_path = f'./saves/{datetime.datetime.now().strftime("%Y%m%d%H%M%S")}'
+    create_dir(base_path)
+
+    pool = Pool(cpu_count())
+    results = []
+
+    for point_dict in points:
+        num = list(point_dict.keys())[0]
+        point = point_dict[num]
+        result = pool.apply_async(generate_and_save, args=(base_path, point, size, num))
+        results.append(result)
+
+    pool.close()
+    pool.join()
+
+def generate_and_save(base_path: str, point: tuple, size: int, calculate):
+    plt = generate_one_plot(point[0], point[1], size)
+    save_plt(plt, base_path, calculate)
+
+def process_single_threaded(df: pd.DataFrame):
+    df, size = averaging_df(df)
+    points = iter_df_to_point(df, size)
+    base_path = f'./saves/{datetime.datetime.now().strftime("%Y%m%d%H%M%S")}'
+    create_dir(base_path)
+    for point_dict in points:
+        num = list(point_dict.keys())[0]
+        point = point_dict[num]
+        size = len(point[0])
+        generate_and_save(base_path, point, size, num)
+        # plt.show()
+    # return df
+
+
+if __name__ == '__main__':
+    df = input_csv_to_df(CSV_PATH)
+    process(df)

traffic_csv_converter.py

@@ -259,7 +259,7 @@ if __name__ == '__main__':
     # dataset = traffic_class_converter(INPUT_DIR)
     # dataset = traffic_csv_converter(INPUT)
 
-    input_array = "../raw_csvs/classes/browsing/reg/browsing_reg.npy"
+    input_array = "./_dataset/FlowPic/browsing_reg.npy"
     random_sampling_dataset(input_array)
 
 

utils/files.py

@@ -0,0 +1,64 @@
+import json
+import os
+import shutil
+from loguru import logger
+
+
+# 将字典数据保存为 JSON 文件
+def save_json(data: list, file_path: str):
+    with open(file_path, "w") as f:
+        json.dump(data, f)
+
+
+def load_json(file_path: str):
+    with open(file_path, "r") as f:
+        return json.load(f)
+
+
+def is_dir_empty(path: str) -> bool:
+    return len(os.listdir(path)) == 0
+
+
+def remove_files_in_dir(folder_path: str):
+    for filename in os.listdir(folder_path):
+        file_path = os.path.join(folder_path, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)  # 删除文件
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)  # 递归删除子文件夹及其中文件
+        except Exception as e:
+            print(f"Failed to delete {file_path}. Reason: {e}")
+
+
+def get_files_in_dir(dir):
+    file_list = []
+    for root, dirs, files in os.walk(dir):
+        for file in files:
+            file_list.append(os.path.join(root, file))
+    return file_list
+
+
+def create_dir(dir):
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+        logger.debug(f'文件夹创建成功：{dir}')
+    else:
+        logger.debug(f'文件夹已经存在：{dir}')
+
+
+def get_filename(path):
+    filename = os.path.basename(path)
+    return filename
+
+
+def move_files(src_dir, dst_dir):
+    for file_name in os.listdir(src_dir):
+        src_file = os.path.join(src_dir, file_name)
+        dst_file = os.path.join(dst_dir, file_name)
+        shutil.move(src_file, dst_file)
+
+
+def remove_ext(file_name):
+    name, ext = os.path.splitext(file_name)
+    return name