问题描述
准备使用yolov5训练自己的模型,自己将下载的开源数据集按照自己的要求重新标注了一下,然后现在对其进行划分。
问题分析
划分数据集主要的步骤就是,首先要将数据集打乱顺序,然后按照一定的比例将其分为训练集,验证集和测试集。
这里我定的比例是7:1:2。
步骤流程
1、将数据集打乱顺序
数据集有图片和标注文件,我们需要把两种文件绑定然后将其打乱顺序。
首先读取数据后,将两种文件通过zip函数绑定
each_class_image = []
each_class_label = []
for image in os.listdir(file_path):
each_class_image.append(image)
for label in os.listdir(xml_path):
each_class_label.append(label)
data=list(zip(each_class_image,each_class_label))
random.shuffle(data)
each_class_image,each_class_label=zip(*data)
2、按照确定好的比例将两个列表元素分割
train_images = each_class_image[0:int(train_rate * total)]
val_images = each_class_image[int(train_rate * total):int((train_rate + val_rate) * total)]
test_images = each_class_image[int((train_rate + val_rate) * total):]
train_labels = each_class_label[0:int(train_rate * total)]
val_labels = each_class_label[int(train_rate * total):int((train_rate + val_rate) * total)]
test_labels = each_class_label[int((train_rate + val_rate) * total):]
3、在本地生成文件夹,将划分好的数据集分别保存
这样就保存好了。
for image in train_images:
#print(image)
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in train_labels:
#print(label)
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'train' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in val_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in val_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'val' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in test_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in test_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'test' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
运行结果展示
直接运行单个python文件即可。
运行完毕
去本地查看
图片和标注文件乱序,且一一对应。
完整代码分享
import os
import shutil
import random
random.seed(0)
def split_data(file_path,xml_path, new_file_path, train_rate, val_rate, test_rate):
each_class_image = []
each_class_label = []
for image in os.listdir(file_path):
each_class_image.append(image)
for label in os.listdir(xml_path):
each_class_label.append(label)
data=list(zip(each_class_image,each_class_label))
total = len(each_class_image)
random.shuffle(data)
each_class_image,each_class_label=zip(*data)
train_images = each_class_image[0:int(train_rate * total)]
val_images = each_class_image[int(train_rate * total):int((train_rate + val_rate) * total)]
test_images = each_class_image[int((train_rate + val_rate) * total):]
train_labels = each_class_label[0:int(train_rate * total)]
val_labels = each_class_label[int(train_rate * total):int((train_rate + val_rate) * total)]
test_labels = each_class_label[int((train_rate + val_rate) * total):]
for image in train_images:
print(image)
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in train_labels:
print(label)
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'train' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in val_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in val_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'val' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in test_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in test_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'test' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
if __name__ == '__main__':
file_path = "D:/Files/dataSet/drone_images"
xml_path = 'D:/Files/dataSet/drone_labels'
new_file_path = "D:/Files/dataSet/droneData"
split_data(file_path,xml_path, new_file_path, train_rate=0.7, val_rate=0.1, test_rate=0.2)
原文地址:https://blog.csdn.net/freezing_00/article/details/129097738
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