VOC_to_COCO

update 2019.10.5

最简单的VOC数据集转换为COCO数据集方式

修改路径后， 只要一条指令就能转换

任何报错请issue我

1. 请先确认你的VOC如下面的格式

     VOC
       |-- Annotations
       		|-- all xml files
       |-- JPEGImages
       		|-- all your samples
       |-- ImageSets
       		|-- 。。。。
   
   

2. 克隆本仓到你指定的地址

git clone https://github.com/Stephenfang51/VOC_to_COCO

3. cd 到VOC_to_COCO 编辑 voc_to_coco.py修改以下3点

   1. 设定验证集样本数, 如果设定为100， 则将从你的样本随机提取100个作为验证集
   2. 设定测试集样本数
   3. VOC Annotations 资料夹路径（最后面的"/"务必加上， 否则报错）

   例如

   val_files_num = 100
   test_files_num = 100
   voc_annotations = '././VOC/Annotations/'  #remember to modify the path

   修改到这边已经结束

4. 执行生成, 确认是python3版本以上解释器， 否则报错

   python3 voc_to_coco.py
   

5. COCO 数据集已经生成与VOC同一主目录下

---

The simplest way to covert VOC style dataset to COCO style dataset, only for object detection tesk for now.

Any problem feel free to issue me !

1. First need to comfirm that your VOC path looks like:

     VOC
       |-- Annotations
       		|-- all xml files
       |-- JPEGImages
       		|-- all your samples
       |-- ImageSets
       		|-- 。。。。
   

2. Clone this repo

   git clone https://github.com/Stephenfang51/VOC_to_COCO
   

3. command cd to VOC_to_COCO path, and set the following three parts

   val_files_num = 100
   test_files_num = 100
   voc_annotations = '././VOC/Annotations/'  #remember to modify the path

   ex.

   if you set val_files_num to 100, which means it wil randomly choice 100 samples from your dataset

4. do it ！ only for python3

   python3 voc_to_coco.py

5. Done ! your COCO dataset and your VOC dataset in the same path. So easy !