When you use Yolo-model, you might create annotation labels with Yolo-mark.
For example,
obj.names- example of list with object namestrain.txt- example with list of image filenames for training Yolo modeltrain/- 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! 😥
- Oct 13th, 2021 - We could support not only
Yolo-markoutputs, but alsoOpenLabelingoutputs!
Also, We could make segmentation mask polygons information in json file.
Thanks to @NauchtanRobotics!
- numpy
- OpenCV
You can make same environment with anaconda environment.
conda create -n Yolo-to-COCO python=3.8conda activate Yolo-to-COCOpip install numpypip install opencvpip install imagesize
Just clone this repository.
git clone https://github.com/Taeyoung96/Yolo-to-COCO-format-converter.gitcd Yolo-to-COCO-format-converter
When you have your own Yolo annotation format, just change a little bit!
In main.py, there is a code that declare the classes. You will change this with your obj.names.
Next, follow step 2 if you have your annotations in separate text files, one for each image. Alternatively, follow step 3 if you wish to work from YOLO annotations which are concatenated into a single file.
Use this approach if your training data file structure looks like this:
dataset_root_dir/
Photo_00001.jpg
Photo_00001.txt
Photo_00002.jpg
Photo_00003.txt
You don't need to specify yolo-subdir argument.
python main.py --path <Absolute path to dataset_root_dir> --output <Name of the json file>- (For example)
python main.py --path /home/taeyoungkim/Desktop/Yolo-to-COCO-format-converter/tutorial/ --output train
Use this approach if your annotations are in nested a level below the image files like this:
dataset_root_dir/
YOLO_darknet/
Photo_00001.txt
Photo_00002.txt
Photo_00001.jpg
Photo_00002.jpg
Command to use:
python main.py --yolo-subdir --path <Absolute path to dataset_root_dir> --output <Name of the json file>python main.py --yolo-subdir --box2seg --path <Absolute path to dataset_root_dir> --output <Name of the json file>
The arg --box2seg initializes segmentation mask polygons that have box shapes.
This is useful for when changing your modeling from object detection to image segmentation.
These masks can then be reshaped using software such as the interface provided by makesense.ai
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).
If you want to quickly create a train.txt file in Ubuntu, you can use path_replacer.py.
Works with 2 simple arguments.
- path_image_folder: File path where the images are located.
- path_txt: File path of the 'txt' file you want to create.
When you want to use
python path_replacer.py --path_image_folder <File path where the images are located> --path_txt <File path of the 'txt' file you want to create>- (For example)
python path_replacer.py --path_image_folder /home/taeyoungkim/Desktop/Yolo-to-COCO-format-converter/tutorial/train --path_txt /home/taeyoungkim/Desktop/Yolo-to-COCO-format-converter/tutorial/train.txt
You need to provide 2 argments(essential) & 3 argments(optional).
essential
- path : Absolute path of train.txt
- output : Name of the json file
optional
- yolo-subdir : If your annotation label have OpenLabeling output.
- box2seg : If you want to make segmentation mask polygons that have box shapes.
- 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
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
Result of Json file
On debug mode, you can check bounding boxes
On debug mode, you can check annotation information on terminal
- I created a repository by referring to chrise96/image-to-coco-json-converter.
- GeeJae Lee helped to make it.
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