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在计算机视觉和深度学习领域，标注文件是模型训练的重要组成部分。无论是图像分类、目标检测还是图像分割，正确的标注能够显著提升模型的性能。在实际应用中，我们需要快速了解每个类别的样本数量，以便进行数据分析、平衡类别分布或优化模型训练。

以下是各个格式的文件代码，输出均按照标签数量从多到少排序，其中txt输入需要修改列表中标签。

统计YOLO格式的TXT文件

import os
from collections import Counterstring_table = ['hat','nohat']  #按顺序修改为类别列表
folder_path = r' '  #修改为txt文件夹
category_counter = Counter()for filename in os.listdir(folder_path):if filename.endswith('.txt'):file_path = os.path.join(folder_path, filename)with open(file_path, 'r') as file:for line in file:category_index = int(line.split()[0])if category_index < len(string_table):category = string_table[category_index]category_counter[category] += 1
print("各类别数量:")
for category in string_table:count = category_counter[category]print(f"{category}: {count}")

统计VOC格式的XML文件

import os
import xml.etree.ElementTree as ETclass_count = {}
folder_path = r' '  # 此处修改为xml文件夹
for filename in os.listdir(folder_path):if filename.endswith('.xml'):tree = ET.parse(os.path.join(folder_path, filename))root = tree.getroot()for obj in root.findall('object'):name = obj.find('name').textif name in class_count:class_count[name] += 1else:class_count[name] = 1
sorted_class_count = sorted(class_count.items(), key=lambda x: x[1], reverse=True)
print("各类别数量:")
for name, count in sorted_class_count:print(f"{name}: {count}")

统计JSON文件

import os
import json
from collections import Counterjson_folder = r' '  # 修改JSON文件夹路径
json_files = [f for f in os.listdir(json_folder) if f.endswith('.json')]
category_counter = Counter()
for json_file in json_files:with open(os.path.join(json_folder, json_file), 'r') as f:data = json.load(f)for shape in data['shapes']:category = shape['label']category_counter[category] += 1
sorted_category_count = sorted(category_counter.items(), key=lambda x: x[1], reverse=True)
print("各类别数量：")
for category, count in sorted_category_count:print(f"{category}: {count}")