智能数字图像处理之FastRCNN（pytorch）代码解读之spilt_data.py

这是一个脚本生成train.txt等txt文件

1.files_path = "./VOCdevkit/VOC2012/Annotations"-》记住文件路径

2.if not os.path.exists(files_path):

    print("文件夹不存在")

    exit(1)-》检测路径存不存在

3.val_rate = 0.5-》验证集比例

4.files_name = sorted([file.split(".")[0] for file in os.listdir(files_path)])-》拼写并排序并遍历文件路径

5.files_num = len(files_name)-》获取文件长度

6.val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))-》随机采样一部分样本，range(0, files_num)为采样范围，k=int(files_num*val_rate)为采样个数

7.for index, file_name in enumerate(files_name):

    if index in val_index:

        val_files.append(file_name)

    else:

        train_files.append(file_name)-》分割验证集和训练集

8.try:

    train_f = open("train.txt", "x")

    eval_f = open("val.txt", "x")

    train_f.write("\n".join(train_files))

    eval_f.write("\n".join(val_files))-》创建txt文件并放入训练集和验证集

except FileExistsError as e:

    print(e)

    exit(1)

import os
import random


files_path = "./VOCdevkit/VOC2012/Annotations"
if not os.path.exists(files_path):
    print("文件夹不存在")
    exit(1)
val_rate = 0.5

files_name = sorted([file.split(".")[0] for file in os.listdir(files_path)])
files_num = len(files_name)
val_index = random.sample(range(0, files_num), k=int(files_num*val_rate))
train_files = []
val_files = []
for index, file_name in enumerate(files_name):
    if index in val_index:
        val_files.append(file_name)
    else:
        train_files.append(file_name)

try:
    train_f = open("train.txt", "x")
    eval_f = open("val.txt", "x")
    train_f.write("\n".join(train_files))
    eval_f.write("\n".join(val_files))
except FileExistsError as e:
    print(e)
    exit(1)



           

https://github.com/WZMIAOMIAO/deep-learning-for-image-processing