wangyinuest/VOC_to_COCO

最简单的VOC转COCO，一条指令完整转换

Python

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

请先确认你的VOC如下面的格式

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

克隆本仓到你指定的地址

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

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
执行生成, 确认是python3版本以上解释器，否则报错
```
python3 voc_to_coco.py
```
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 !

First need to comfirm that your VOC path looks like:

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

Clone this repo

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

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
do it ！ only for python3
```
python3 voc_to_coco.py
```
Done ! your COCO dataset and your VOC dataset in the same path. So easy !