这篇教程CUB200-2011鸟类细粒度数据集训练集和测试集划分python代码写得很实用，希望能帮到您。
下载的数据集中，包含了如下文件：
bounding_boxes.txt；classes.txt；image_class_labels.txt； images.txt； train_test_split.txt.
其中，bounding_boxes.txt为图像中鸟类的边界框信息；classes.txt为鸟类的类别信息，共有200类； image_class_labels.txt为图像标签和所属类别标签信息；images.txt为图像的标签和图像路径信息；train_test_split.txt为训练集和测试集划分。
本博客主要是根据train_test_split.txt文件和images.txt文件将原始下载的CUB200-2011数据集划分为训练集和测试集。在深度学习Pytorch框架下采用ImageFolder和DataLoader读取数据集较为方便。相关的python代码如下：
(1) CUB200-2011训练集和测试集划分代码
# *_*coding: utf-8 *_*
# author --liming--
"""
读取images.txt文件,获得每个图像的标签
读取train_test_split.txt文件,获取每个图像的train, test标签.其中1为训练,0为测试.
"""
import os
import shutil
import numpy as np
import config
import time
time_start = time.time()
# 文件路径
path_images = config.path + 'images.txt'
path_split = config.path + 'train_test_split.txt'
trian_save_path = config.path + 'dataset/train/'
test_save_path = config.path + 'dataset/test/'
# 读取images.txt文件
images = []
with open(path_images,'r') as f:
for line in f:
images.append(list(line.strip('\n').split(',')))
# 读取train_test_split.txt文件
split = []
with open(path_split, 'r') as f_:
for line in f_:
split.append(list(line.strip('\n').split(',')))
# 划分
num = len(images) # 图像的总个数
for k in range(num):
file_name = images[k][0].split(' ')[1].split('/')[0]
aaa = int(split[k][0][-1])
if int(split[k][0][-1]) == 1: # 划分到训练集
#判断文件夹是否存在
if os.path.isdir(trian_save_path + file_name):
shutil.copy(config.path + 'images/' + images[k][0].split(' ')[1], trian_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])
else:
os.makedirs(trian_save_path + file_name)
shutil.copy(config.path + 'images/' + images[k][0].split(' ')[1], trian_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])
print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])
else:
#判断文件夹是否存在
if os.path.isdir(test_save_path + file_name):
aaaa = config.path + 'images/' + images[k][0].split(' ')[1]
bbbb = test_save_path+file_name+'/'+images[k][0].split(' ')[1]
shutil.copy(config.path + 'images/' + images[k][0].split(' ')[1], test_save_path+file_name+'/'+images[k][0].split(' ')[1].split('/')[1])
else:
os.makedirs(test_save_path + file_name)
shutil.copy(config.path + 'images/' + images[k][0].split(' ')[1], test_save_path + file_name + '/' + images[k][0].split(' ')[1].split('/')[1])
print('%s处理完毕!' % images[k][0].split(' ')[1].split('/')[1])
time_end = time.time()
print('CUB200训练集和测试集划分完毕, 耗时%s!!' % (time_end - time_start))
config文件
# *_*coding: utf-8 *_*
# author --liming--
path = '/media/lm/C3F680DFF08EB695/细粒度数据集/birds/CUB200/CUB_200_2011/'
ROOT_TRAIN = path + 'images/train/'
ROOT_TEST = path + 'images/test/'
BATCH_SIZE = 16
(2) 利用Pytorch方式读取数据
# *_*coding: utf-8 *_*
# author --liming--
"""
用于已下载数据集的转换,便于pytorch的读取
"""
import torch
import torchvision
import config
from torchvision import datasets, transforms
data_transform = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def train_data_load():
# 训练集
root_train = config.ROOT_TRAIN
train_dataset = torchvision.datasets.ImageFolder(root_train,
transform=data_transform)
CLASS = train_dataset.class_to_idx
print('训练数据label与文件名的关系:', CLASS)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=config.BATCH_SIZE,
shuffle=True)
return CLASS, train_loader
def test_data_load():
# 测试集
root_test = config.ROOT_TEST
test_dataset = torchvision.datasets.ImageFolder(root_test,
transform=data_transform)
CLASS = test_dataset.class_to_idx
print('测试数据label与文件名的关系：',CLASS)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=config.BATCH_SIZE,
shuffle=True)
return CLASS, test_loader
if __name__ == '__main___':
train_data_load()
test_data_load()
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版权声明：本文为CSDN博主「CV-小生」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/limingkaoyan/article/details/102680185
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