Yolo-to-COCO-format-converter

When you use Yolo-model, you might create annotation labels with Yolo-mark.
For example,

obj.names - example of list with object names
train.txt - example with list of image filenames for training Yolo model
train/ - example of folder that contain images and labels

*.jpg : example of list of image

*.txt : example of list of label

But, when you want to use another model(ex. efficientdet), you need another annotation format! 😥

This code will help you make a COCO format annotations with Yolo format!

How to use

Requirements

numpy
OpenCV

You can make same environment with anaconda environment.

conda create -n Yolo-to-COCO python=3.8
conda activate Yolo-to-COCO
pip install numpy
pip install opencv

Just clone this repository.

git clone https://github.com/Taeyoung96/Yolo-to-COCO-format-converter.git
cd Yolo-to-COCO-format-converter

It will be easy to understand if you refer to the tutorial folder.

When you have your own Yolo annotation format, just change a little bit!

1. Change `classes` with your own dataset.

In main.py, there is a code that declare the classes. You will change this with your obj.names.

2. Check the absolute path in `train.txt`.

Make sure that it points to the absolute path to the folder where the image and text files are located.
You can easily change the path with Text Editor(Ubuntu 18.04) or NotePad (Window 10).

3. Just run the code.

You need to provide 2 argments(essential) & 1 argments(optional).

path : Absolute path of train.txt
output : Name of the json file
debug : If you want to check the bounding boxes or annotation information.

When you want to make json file,

python main.py --path [Absolute Path of train.txt] --output [Name of the json file]
(For example)python main.py --path /home/taeyoungkim/Desktop/Yolo-to-COCO-format-converter/tutorial/train.txt --output train

Or when you want to check the bounding boxes,

python main.py --path [Absolute Path of train.txt] --output [Name of the json file] --debug
(For example)python main.py --path /home/taeyoungkim/Desktop/Yolo-to-COCO-format-converter/tutorial/train.txt --output train --debug

Tips

If you want to read json files more clearly, you should use JQ!

JQ Manual
(For example) cd output
jq . train.json > train_jq.json

Results

Result of Json file

On debug mode, you can check bounding boxes

On debug mode, you can check annotation information on terminal

Contributors

I created a repository by referring to chrise96/image-to-coco-json-converter.
GeeJae Lee helped to make it.

hxdaze / yolo-to-coco-format-converter Goto Github PK