SYNTHIA

Format specification

The original SYNTHIA dataset is available here.

Datumaro supports all SYNTHIA formats except SYNTHIA-AL.

Supported annotation types:

• Mask

Supported annotation attributes:

• dynamic_object (boolean): whether the object moving

Import SYNTHIA dataset

A Datumaro project with a SYNTHIA source can be created in the following way:

datum project create
datum project import --format synthia <path/to/dataset>

It is also possible to import the dataset using Python API:

import datumaro as dm

synthia_dataset = dm.Dataset.import_from('<path/to/dataset>', 'synthia')

SYNTHIA dataset directory should have the following structure:

dataset/
├── dataset_meta.json # a list of non-format labels (optional)
├── GT/
│   ├── COLOR/
│   │   ├── Stereo_Left/
│   │   │   ├── Omni_B
│   │   │   │   ├── 000000.png
│   │   │   │   ├── 000001.png
│   │   │   │   └── ...
│   │   │   └── ...
│   │   └── Stereo_Right
│   │       ├── Omni_B
│   │       │   ├── 000000.png
│   │       │   ├── 000001.png
│   │       │   └── ...
│   │       └── ...
│   └── LABELS
│       ├── Stereo_Left
│       │   ├── Omni_B
│       │   │   ├── 000000.png
│       │   │   ├── 000001.png
│       │   │   └── ...
│       │   └── ...
│       └── Stereo_Right
│           ├── Omni_B
│           │   ├── 000000.png
│           │   ├── 000001.png
│           │   └── ...
│           └── ...
└── RGB
    ├── Stereo_Left
    │   ├── Omni_B
    │   │   ├── 000000.png
    │   │   ├── 000001.png
    │   │   └── ...
    │   └── ...
    └── Stereo_Right
        ├── Omni_B
        │   ├── 000000.png
        │   ├── 000001.png
        │   └── ...
        └── ...

• RGB folder containing standard RGB images used for training.

• GT/LABELS folder containing containing PNG files (one per image). Annotations are given in three channels. The red channel contains the class of that pixel. The green channel contains the class only for those objects that are dynamic (cars, pedestrians, etc.), otherwise it contains 0.

• GT/COLOR folder containing png files (one per image). Annotations are given using a color representation.

When importing a dataset, only GT/LABELS folder will be used. If it is missing, GT/COLOR folder will be used.

The original dataset also contains depth information, but Datumaro does not currently support it.

To add custom classes, you can use dataset_meta.json.

Export to other formats

Datumaro can convert a SYNTHIA dataset into any other format Datumaro supports. To get the expected result, convert the dataset to a format that supports segmentation masks.

There are several ways to convert a SYNTHIA dataset to other dataset formats using CLI:

datum project create
datum project import -f synthia <path/to/dataset>
datum project export -f voc -o <output/dir> -- --save-media

or

datum convert -if synthia -i <path/to/dataset> \
    -f voc -o <output/dir> -- --save-media

Or, using Python API:

import datumaro as dm

dataset = dm.Dataset.import_from('<path/to/dataset>', 'synthia')
dataset.export('save_dir', 'voc')

Examples

Examples of using this format from the code can be found in the format tests