Welcome to the exciting world of image annotations! If you’ve been using the LabelMe annotation tool and need to convert your annotations into the COCO (Common Objects in Context) format for your deep learning models, you’re in the right place. This blog will guide you through the installation and usage of the labelme2coco package, simplifying the conversion process for you!

What is LabelMe and COCO?

LabelMe is a graphical image annotation tool that allows users to do a multitude of tasks such as classification, segmentation, and object detection. However, many popular frameworks like Yolact, Detectron, and MMDetection require annotations in the COCO format to function effectively. The labelme2coco package bridges this gap by converting LabelMe annotations into a format that these models can leverage.

Getting Started

To kickstart your conversion journey, follow these simple steps:

Step 1: Installation

Begin by installing the package via pip. Open your command-line interface and run the following command:

pip install -U labelme2coco

Step 2: Basic Usage

Once installed, you can start using it right away. Here’s how:

• For a straightforward conversion:

python labelme2coco path/to/labelme_dir

• To split your dataset into training and validation sets:

python labelme2coco path/to/labelme_dir --train_split_rate 0.85

• To specify a starting category ID:

python labelme2coco path/to/labelme_dir --category_id_start 1

Advanced Usage

If you want to dive deeper into customization, here is an advanced approach:

python
# import package
import labelme2coco

# set directory that contains LabelMe annotations and image files
labelme_folder = 'testdata/labelme_annot'

# set export directory
export_dir = 'testdata'

# set train split rate
train_split_rate = 0.85

# set category ID start value
category_id_start = 1

# convert LabelMe annotations to COCO
labelme2coco.convert(labelme_folder, export_dir, train_split_rate, category_id_start=category_id_start)

# import functions for advanced manipulation
from labelme2coco import get_coco_from_labelme_folder, save_json

# set directories for training and validation data
labelme_train_folder = 'testdata/labelme_annot'
labelme_val_folder = 'testdata/labelme_annot'

# path for COCO JSON export
export_dir = 'testdata'
category_id_start = 1

# create train COCO object
train_coco = get_coco_from_labelme_folder(labelme_train_folder, category_id_start=category_id_start)

# export train COCO JSON
save_json(train_coco.json, export_dir + 'train.json')

# create validation COCO object
val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories, category_id_start=category_id_start)

# export validation COCO JSON
save_json(val_coco.json, export_dir + 'val.json')

Understanding the Code with an Analogy

Think of the labelme2coco package as an efficient translator. Just like a translator converts one language into another while preserving the meaning, this package takes in the annotations (written in the LabelMe ‘language’) and translates them into COCO format(another structured language for object detection) that many algorithms can understand.

In the advanced usage section, each piece of the code represents a step in this translation process. First, you identify the documents (the folders containing your annotations) you want to convert. Then, you set the parameters for how you would like to split or identify categories, ensuring that this process fits your needs, much like guiding a translator to maintain the original context while adjusting the output language style and format accordingly.

Troubleshooting

If you encounter any issues during installation or conversion, here are some troubleshooting ideas:

• Make sure you have the latest version of pip installed. Use pip install --upgrade pip.
• If you face file path errors, double-check that the paths to your directories are correct.
• Ensure that your LabelMe annotations are not corrupted and are in the expected format; otherwise, the conversion may fail.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

With labelme2coco, the process of converting your LabelMe annotations into COCO format is as easy as pie! By following the steps provided, you’re now all set to prepare your dataset for powerful object detection models. Happy coding!