INFO.md

GeDa

GeDa is a Python package that helps you to Get the Data for your project.

Installation

pip install geda

Usage

Using specific data provider class

from geda.data_providers.voc import VOCSemanticSegmentationDataProvider

root = "<directory>/<to>/<store>/<data>" # e.g. "data/VOC"
dataprovider = VOCSemanticSegmentationDataProvider(root)
dataprovider.get_data()

Using get_data shortcut

from geda import get_data

root = "<directory>/<to>/<store>/<data>" # e.g. "data/VOC"
dataprovider = get_data(name="VOC_SemanticSegmentation", root=root)
dataprovider.get_data()

The get_data function currently supported names: DUTS, NYUDv2, VOC_InstanceSegmentation, VOC_SemanticSegmentation, VOC_PersonPartSegmentation, VOC_Main, VOC_Action, VOC_Layout

What it does

By using dataprovider.get_data() functionality, the data is subjected to the following pipeline:

1. Download the data from source (specified by the _URLS variable in each module)
2. Unzip the files if needed (in case of tar, zip or gz files downloaded)
3. Move the files to <root>/raw directory
4. Find the split ids (file basenames or indices - depending on the dataset)
5. Arrange files, i.e. move (or copy) files from <root>/raw directory to task-specific directories
6. [Optional] Create labels in specific format (f.e. YOLO)

Example

Resulting directory structure of the get_data(name="VOC_SemanticSegmentation", root="data/VOC")

.
└── data
    └── VOC
        ├── raw
        │   ├── Annotations
        │   ├── ImageSets
        │   ├── JPEGImages
        │   ├── SegmentationClass
        │   └── SegmentationObject
        ├── SegmentationClass
        │   ├── annots
        │   ├── images
        │   ├── labels
        │   └── masks
        └── trainval_2012.tar

Currently supported datasets

Image Segmentation

• PASCAL VOC 2012
• NYUDv2
• Person-Parts
• DUTS

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT