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KITTI#

Format specification#

The KITTI dataset has many annotations for different tasks. Datumaro supports only a few of them.

Supported tasks / formats:

  • Object Detection - kitti_detection The format specification is available in README.md here.

  • Segmentation - kitti_segmentation The format specification is available in README.md here.

  • Raw 3D / Velodyne Points - described here

Supported annotation types:

  • Bbox (object detection)

  • Mask (segmentation)

Supported annotation attributes:

  • truncated (boolean) - indicates that the bounding box specified for the object does not correspond to the full extent of the object

  • occluded (boolean) - indicates that a significant portion of the object within the bounding box is occluded by another object

  • score (float) - indicates confidence in detection

Convert KITTI dataset#

The KITTI left color images for object detection are available here. The KITTI object detection labels are available here. The KITTI segmentation dataset is available here.

A KITTI dataset can be converted in the following way:

datum convert --input-format kitti --input-path <path/to/dataset> \
    --output-format <desired_format> --output-dir <output/dir>

KITTI detection dataset directory should have the following structure:

└─ Dataset/
    ├── testing/
    │   └── image_2/
    │       ├── <name_1>.<img_ext>
    │       ├── <name_2>.<img_ext>
    │       └── ...
    └── training/
        ├── image_2/ # left color camera images
        │   ├── <name_1>.<img_ext>
        │   ├── <name_2>.<img_ext>
        │   └── ...
        └─── label_2/ # left color camera label files
            ├── <name_1>.txt
            ├── <name_2>.txt
            └── ...

KITTI segmentation dataset directory should have the following structure:

└─ Dataset/
    ├── dataset_meta.json # a list of non-format labels (optional)
    ├── label_colors.txt # optional, color map for non-original segmentation labels
    ├── testing/
    │   └── image_2/
    │       ├── <name_1>.<img_ext>
    │       ├── <name_2>.<img_ext>
    │       └── ...
    └── training/
        ├── image_2/ # left color camera images
        │   ├── <name_1>.<img_ext>
        │   ├── <name_2>.<img_ext>
        │   └── ...
        ├── label_2/ # left color camera label files
        │   ├── <name_1>.txt
        │   ├── <name_2>.txt
        │   └── ...
        ├── instance/ # instance segmentation masks
        │   ├── <name_1>.png
        │   ├── <name_2>.png
        │   └── ...
        ├── semantic/ # semantic segmentation masks (labels are encoded by its id)
        │   ├── <name_1>.png
        │   ├── <name_2>.png
        │   └── ...
        └── semantic_rgb/ # semantic segmentation masks (labels are encoded by its color)
            ├── <name_1>.png
            ├── <name_2>.png
            └── ...

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.

You can convert a dataset for specific tasks of KITTI dataset instead of the whole dataset, for example:

datum convert --input-format kitti_detection --input-path <path/to/dataset> \
    --output-format <desired_format> --output-dir <output/dir>

Export to other formats#

Datumaro can convert a KITTI dataset into any other format Datumaro supports.

Such conversion will only be successful if the output format can represent the type of dataset you want to convert, e.g. segmentation annotations can be saved in Cityscapes format, but not as COCO keypoints.

There are several ways to convert a KITTI dataset to other dataset formats:

datum convert --input-format kitti --input-path <path/to/kitti> \
    --output-format cityscapes --output-dir <output/dir>

Or, using Python API:

import datumaro as dm

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

Export to KITTI#

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

# converting to KITTI format from other format
datum convert --input-format cityscapes --input-path <path/to/dataset> \
    --output-format kitti --output-dir <output/dir> -- --save-media

Extra options for exporting to KITTI 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)

  • --apply-colormap APPLY_COLORMAP allow to use colormap for class masks (in folder semantic_rgb, by default True)

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

# mycolormap.txt :
# 0 0 255 sky
# 255 0 0 person
#...
datum convert --input-format <source-format> --input-path <path/to/dataset> \
    --output-format kitti --output-dir <output/dir> -- --label-map mycolormap.txt

or you can use original kitti colormap:

datum convert --input-format <source-format> --input-path <path/to/dataset> \
    --output-format kitti --output-dir <output/dir> -- --label-map kitti

  • --tasks TASKS allow to specify tasks for export dataset, by default Datumaro uses all tasks. Example:

datum convert --input-format <source-format> --input-path <path/to/dataset> \
    --output-format kitti --output-dir <output/dir> -- --tasks detection

  • --allow-attributes ALLOW_ATTRIBUTES allow export of attributes (by default True).

Examples#

Datumaro supports filtering, transformation, merging etc. for all formats and for the KITTI 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 KITTI dataset:

Example 1. How to load an original KITTI dataset and convert to Cityscapes#

datum convert --input-format kitti --input-path ./KITTI/ \
    --output-format cityscapes --output-dir ./output/ -- --save-media

Example 2. How to create a custom KITTI-like dataset#

import numpy as np
import datumaro as dm

import datumaro.plugins.kitti_format as KITTI

label_map = {}
label_map['background'] = (0, 0, 0)
label_map['label_1'] = (1, 2, 3)
label_map['label_2'] = (3, 2, 1)
categories = KITTI.make_kitti_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, id=0,
        attributes={'is_crowd': False}),
      dm.Mask(image=np.array([[0, 1, 1, 0, 0]]), label=2, id=0,
        attributes={'is_crowd': False}),
    ]
  ),
], categories=categories)

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

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

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