https://github.com/inf800/labelme_to_detectron2

https://github.com/inf800/labelme_to_detectron2

Last synced: 24 days ago
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• Host: GitHub
• URL: https://github.com/inf800/labelme_to_detectron2
• Owner: INF800
• License: apache-2.0
• Created: 2021-11-03T04:23:11.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-03T08:50:32.000Z (about 3 years ago)
• Last Synced: 2024-08-10T10:57:13.503Z (4 months ago)
• Language: Python
• Size: 7.81 KB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# labeleme_to_detectron2

Convert labelme annotations to detectron2 annotation format.

**USAGE:**

```

python -m labelme_to_detectron2.convert_annotations {labelme_annotations_dir}

```

Copy the unique labels that will be displayed.

Files will be created in `*.d2json` format (same as json)

Divide into train and test datasets and then register dataset using the code below:

```python

from pathlib import Path

unique_labels=['person', 'vehicle']

def get_dataset_dicts(img_dir):

    dataset_dicts = []

    img_dir = Path(img_dir)

    for i, jsonpath in enumerate([*img_dir.glob("*.d2json")]):

      with open(str(jsonpath), 'r') as fp:

        annot = json.load(fp)

        annot['image_id'] = i

        annot['file_name'] = str(img_dir/Path(annot['file_name']).name) 

        

        dataset_dicts.append(annot)

    

    return dataset_dicts

for d in ["train", "test"]:

    DatasetCatalog.register("dataset_" + d, lambda d=d: get_dataset_dicts(f"{base}/{d}"))

    MetadataCatalog.get("dataset_" + d).set(thing_classes=unique_labels)

```

### Insatallation

Install locally with sym-link for fast code updates

```

pip install -e .

```

Generate distributable file

```

python3 setup.py sdist

```

Can install using: `pip install https://github.com/INF800/labelme_to_detectron2/raw/main/dist/labelme_to_detectron2-x.x.x.tar.gz`

Share distributable using pypi

```

twine upload dist/* 

```