cvat/utils/coco/converter.md

Utility for converting CVAT XML annotation file to MS COCO json format

Description

This utility gets annotation obtained from CVAT and converts to annotation in COCO representation. Input annotation must contain segmentation because bounding boxes of objects are calculated from its segments.

Installation

Install necessary packages and create a virtual environment.

$ sudo apt-get update
$ sudo apt-get install -y --no-install-recommends python3-pip python3-venv python3-dev python3-tk libgtk-3-dev gcc

$ python3 -m venv .env
$ . .env/bin/activate
$ cat ../requirements.txt requirements.txt | xargs -n 1 -L 1 pip install

Usage

Run the script inside the virtual environment.

python converter.py --cvat-xml </path/to/cvat/annotation.xml> --output </path/to/output/coco/annotation.json> --image-dir </path/to/directory/with/images> --labels </path/to/file/with/labels.txt> --draw </path/to/save/directory> --draw_labels --use_background_label

Please run python converter.py --help for more details.

Labels

If '--labels' argument is used, the script gets names of labels from a file. If file with labels is not defined, the script parses input annotation and find field labels to find which labels are presented. File with labels should include labels in one string separated by spaces or one label per string and also their combinations. For example:

label1 label2
label3

Merge several annotations in COCO representation into one

Run the script merge_annotations.py

python merge_annotations.py --input-dir /path/to/directory/with/datasets --output /path/to/result/annotation.json --images-map /path/to/file/with/matched/datasets/and/images.txt --draw /path/to/directory/where/save/images

Please run python merge_annotations.py --help for more details.

Example of a file for --images-map:

{
    "dataset1_part1.json": "images/dataset1/part1",
    "dataset1_part2.json": "images/dataset1/part2",
    "dataset2_part1.json": "images/dataset2/part1",
    "dataset2_part2.json": "images/dataset2/part2"
}