https://github.com/tillbeemelmanns/cityscapes-to-coco-conversion

Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron
https://github.com/tillbeemelmanns/cityscapes-to-coco-conversion

cityscapes cityscapes-dataset coco-conversion coco-format cocodataset mask-rcnn

Last synced: 7 months ago
JSON representation

Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron

• Host: GitHub
• URL: https://github.com/tillbeemelmanns/cityscapes-to-coco-conversion
• Owner: TillBeemelmanns
• Created: 2020-01-10T15:07:31.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-21T16:45:00.000Z (over 2 years ago)
• Last Synced: 2025-03-25T18:40:07.265Z (7 months ago)
• Topics: cityscapes, cityscapes-dataset, coco-conversion, coco-format, cocodataset, mask-rcnn
• Language: Python
• Homepage:
• Size: 5.44 MB
• Stars: 87
• Watchers: 1
• Forks: 15
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
## Cityscapes to CoCo Conversion Tool

![](assets/preview.png)

This script allows to convert the [Cityscapes Dataset](https://www.cityscapes-dataset.com/) to Mircosoft's [CoCo Format](http://cocodataset.org/). The code heavily relies on Facebook's [Detection Repo](https://github.com/facebookresearch/Detectron/blob/master/tools/convert_cityscapes_to_coco.py) and [Cityscapes Scripts](https://github.com/mcordts/cityscapesScripts). 

The converted annotations can be easily used for [Mask-RCNN](https://github.com/matterport/Mask_RCNN) or other deep learning projects.

## Folder Structure

Download the Cityscapes Dataset and organize the files in the following structure. Create an empty `annotations` directory.

```

data/

└── cityscapes

    ├── annotations

    ├── gtFine

    │   ├── test

    │   ├── train

    │   └── val

    └── leftImg8bit

        ├── test

        ├── train

        └── val

main.py

inspect_coco.py

README.md

requirements.txt

```

## Installation

```

pip install -r requirements.txt 

```

## Run

To run the conversion execute the following

```

python main.py --dataset cityscapes --datadir data/cityscapes --outdir data/cityscapes/annotations

```

In order to run the visualization of the CoCo dataset you may run

```

python inspect_coco.py --coco_dir data/cityscapes

```

## Output

![vis1](assets/plot1.png "Cityscapes in CoCo format") ![vis2](assets/plot2.png "Cityscapes in CoCo format")