labelme2yolo 0.4.0

This script converts the JSON format output by LabelMe to the text format required by YOLO serirs.

Labelme2YOLO

Labelme2YOLO efficiently converts LabelMe's JSON format to the YOLOv5 dataset format. It also supports YOLOv5/YOLOv8 segmentation datasets, making it simple to convert existing LabelMe segmentation datasets to YOLO format.

New Features

• export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
• Now you can choose the output format of the label text. The two available alternatives are polygon and bounding box(bbox).

Performance

Labelme2YOLO is implemented in Rust, which makes it significantly faster than equivalent Python implementations. In fact, it can be up to 100 times faster, allowing you to process large datasets more efficiently.

Installation

pip install labelme2yolo

Arguments

[LABEL_LIST]... Comma-separated list of labels in the dataset.

Options

-d, --json_dir <JSON_DIR> Directory containing LabelMe JSON files. Repeat the flag to convert multiple directories in one run (requires --output_dir).

-o, --output_dir <OUTPUT_DIR> Output directory for the converted dataset [default: <json_dir>/YOLODataset]. Required when multiple --json_dir values are given.

--val_size <VAL_SIZE> Proportion of the dataset to use for validation (between 0.0 and 1.0) [default: 0.2].

--test_size <TEST_SIZE> Proportion of the dataset to use for testing (between 0.0 and 1.0) [default: 0].

--output_format <OUTPUT_FORMAT> Output format for YOLO annotations: 'bbox' or 'polygon' [default: bbox] [aliases: format] [possible values: polygon, bbox].

--seed Seed for random shuffling [default: 42].

-h, --help Print help.

-V, --version Print version.

How to Use

1. Converting JSON files and splitting training, validation datasets

You may need to place all LabelMe JSON files under labelme_json_dir and then run the following command:

labelme2yolo --json_dir /path/to/labelme_json_dir/

This tool will generate dataset labels and images with YOLO format in different folders, such as

/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml

2. Converting JSON files and splitting training, validation, and test datasets with --val_size and --test_size

You may need to place all LabelMe JSON files under labelme_json_dir and then run the following command:

labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15

This tool will generate dataset labels and images with YOLO format in different folders, such as

/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/test/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/test/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml

3. Converting multiple directories into one dataset

Pass --json_dir multiple times to combine several source directories. An explicit --output_dir is required in this case:

labelme2yolo -d /path/to/src-a/ -d /path/to/src-b/ -o /path/to/output/

Files with the same name in different source directories are kept apart automatically.

How to build package/wheel

pip install maturin
maturin develop