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

本文链接：https://blog.youkuaiyun.com/com_fang_bean/article/details/108686173

该脚本用于生成FastRCNN训练所需的train.txt和val.txt文件。首先，它检查VOC2012的Annotations文件夹是否存在，然后设置验证集比例为50%，对文件名进行排序并随机选取一部分作为验证集，剩余作为训练集。最后，将训练集和验证集写入对应的txt文件中。

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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