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Cityscapes#

Format specification#

Cityscapes format overview is available here.

Cityscapes format specification is available here.

Supported annotation types:

  • Masks

Supported annotation attributes:

  • is_crowd (boolean). Specifies if the annotation label can distinguish between different instances. If False, the annotation id field encodes the instance id.

Import Cityscapes dataset#

The Cityscapes dataset is available for free download.

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

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

Cityscapes dataset directory should have the following structure:

└─ Dataset/
    ├── dataset_meta.json # a list of non-Cityscapes labels (optional)
    ├── label_colors.txt # a list of non-Cityscapes labels in other format (optional)
    ├── leftImg8bit/
    │   ├── <split: train,val, ...>
    │   │   ├── {city1}
    │   │   |   ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_leftImg8bit.png
    │   │   │   └── ...
    │   │   ├── {city2}
    │   │   └── ...
    └── gtFine/
        ├── <split: train,val, ...>
        │   ├── {city1}
        │   |   ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_color.png
        │   |   ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_instanceIds.png
        │   |   ├── {city1}_{seq:[0...6]}_{frame:[0...6]}_gtFine_labelIds.png
        │   │   └── ...
        │   ├── {city2}
        │   └── ...
        └── ...

Annotated files description:

  1. *_leftImg8bit.png - left images in 8-bit LDR format

  2. *_color.png - class labels encoded by its color

  3. *_labelIds.png - class labels are encoded by its index

  4. *_instanceIds.png - class and instance labels encoded by an instance ID. The pixel values encode class and the individual instance: the integer part of a division by 1000 of each ID provides class ID, the remainder is the instance ID. If a certain annotation describes multiple instances, then the pixels have the regular ID of that class

To add custom classes, you can use dataset_meta.json and label_colors.txt. If the dataset_meta.json is not represented in the dataset, then label_colors.txt will be imported if possible.

In label_colors.txt you can define custom color map and non-cityscapes labels, for example:

# label_colors [color_rgb name]
0 124 134 elephant

To make sure that the selected dataset has been added to the project, you can run datum project info, which will display the project information.

Export to other formats#

Datumaro can convert a Cityscapes dataset into any other format Datumaro supports. To get the expected result, convert the dataset to formats that support the segmentation task (e.g. PascalVOC, CamVID, etc.)

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

datum project create
datum project import -f cityscapes <path/to/cityscapes>
datum project export -f voc -o <output/dir>

or

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

Or, using Python API:

import datumaro as dm

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

Export to Cityscapes#

There are several ways to convert a dataset to Cityscapes format:

# export dataset into Cityscapes format from existing project
datum project export -p <path/to/project> -f cityscapes -o <output/dir> \
    -- --save-media

# converting to Cityscapes format from other format
datum convert -if voc -i <path/to/dataset> \
    -f cityscapes -o <output/dir> -- --save-media

Extra options for exporting to Cityscapes format:

  • --save-media allow to export dataset with saving media files (by default False)

  • --image-ext IMAGE_EXT allow to specify image extension for exporting dataset (by default - keep original or use .png, if none)

  • --save-dataset-meta - allow to export dataset with saving dataset meta file (by default False)

  • --label_map allow to define a custom colormap. Example:

# mycolormap.txt :
# 0 0 255 sky
# 255 0 0 person
#...
datum project export -f cityscapes -- --label-map mycolormap.txt

or you can use original cityscapes colomap:

datum project export -f cityscapes -- --label-map cityscapes

Examples#

Datumaro supports filtering, transformation, merging etc. for all formats and for the Cityscapes format in particular. Follow the user manual to get more information about these operations.

There are several examples of using Datumaro operations to solve particular problems with a Cityscapes dataset:

Example 1. Load the original Cityscapes dataset and convert to Pascal VOC#

datum project create -o project
datum project import -p project -f cityscapes ./Cityscapes/
datum stats -p project
datum project export -p project -o dataset/ -f voc -- --save-media

Example 2. Create a custom Cityscapes-like dataset#

from collections import OrderedDict

import numpy as np
import datumaro as dm
import datumaro.plugins.cityscapes_format as Cityscapes

label_map = OrderedDict()
label_map['background'] = (0, 0, 0)
label_map['label_1'] = (1, 2, 3)
label_map['label_2'] = (3, 2, 1)
categories = Cityscapes.make_cityscapes_categories(label_map)

dataset = dm.Dataset.from_iterable([
    dm.DatasetItem(id=1,
        image=np.ones((1, 5, 3)),
        annotations=[
            dm.Mask(image=np.array([[1, 0, 0, 1, 1]]), label=1),
            dm.Mask(image=np.array([[0, 1, 1, 0, 0]]), label=2, id=2,
                attributes={'is_crowd': False}),
        ]
    ),
], categories=categories)

dataset.export('./dataset', format='cityscapes')

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

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