Important information (05/2020)

05/2020: The label format changed from .yml to .json. This should help to load the dataset significantly faster (especially in Python). Moreover the following changed:

Class Identity is replaced by attributes dictionary. The attributes dictionary contains the keys "direction", "relevance", "occlusion", "orientation", "aspects", "state" and "pictogram". New since v2.0 is the "reflection" attribute.
Some labels may have the items ("aspects", "unknown"), ("state", "unknown") or ("pictogram", "unknown"). This differs to the labels in the yml format, which always had one default value even if the label information changed. Note that due to this your results on json max differ to .yml
MATLAB reader is deprecated. If you want to use .json in MATLAB you have to write your own reader. Do not hesitate to create a pull request.
API in C++ and Python slightly changed. Take a look into the code!

The amount of overall labels/images is NOT changed. The image data itself is also untouched.

For everybody who still wants to work with the old .yml files: You can simply checkout the state tagged as "v1". If you are not able to see the tag do not forget to fetch & pull.

The DriveU Traffic Light Dataset (DTLD): Introduction and Comparison with Existing Datasets

This repository provides code for parsing the DriveU Traffic Light Dataset (DTLD), which is published in the course of our 2018 ICRA publication "The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets".

Paper

Paper see https://ieeexplore.ieee.org/document/8460737.

Download the dataset

INFO (11/27/2018): The Dataset is online now!

The data can be downloaded from http://www.traffic-light-data.com/.

NEW v2 04/2021: json label format

.
├── DTLD                 # DTLD
    ├── Berlin           # Contains all Routes of Berlin
    ├── Bochum           # Contains all routes of Bochum
    ├── Bremen           # Contains all routes of Bremen
    ├── Dortmund         # Contains all routes of Dortmund
    ├── Duesseldorf      # Contains all routes of Duesseldorf
    ├── Essen            # Contains all routes of Essen
    ├── Frankfurt        # Contains all routes of Frankfurt
    ├── Fulda            # Contains all routes of Fulda
    ├── Hannover         # Contains all routes of Hannover
    ├── Kassel           # Contains all routes of Kassel
    ├── Koeln            # Contains all routes of Cologne
    ├── DTLD_labels_v1.0 # Old labels (v1.0) in yml-format
    ├── DTLD_labels_v2.0 # New labels (v2.0) in json-format
    ├── LICENSE          # License
    └── README.md        # Readme

DEPRECATED: DTLD_v1 (yml-Files)

.
├── DTLD                 # DTLD
    ├── Berlin           # Contains all Routes of Berlin
    ├── Bochum           # Contains all routes of Bochum
    ├── Bremen           # Contains all routes of Bremen
    ├── Dortmund         # Contains all routes of Dortmund
    ├── Duesseldorf      # Contains all routes of Duesseldorf
    ├── Essen            # Contains all routes of Essen
    ├── Frankfurt        # Contains all routes of Frankfurt
    ├── Fulda            # Contains all routes of Fulda
    ├── Hannover         # Contains all routes of Hannover
    ├── Kassel           # Contains all routes of Kassel
    ├── Koeln            # Contains all routes of Cologne
    ├── DTLD_labels      # Labels in yml-format
    ├── LICENSE          # License
    └── README.md        # Readme

Route structure

We separated each drive in one city into different routes

.
├── Berlin                # Berlin
    ├── Berlin1           # First route
    ├── Berlin2           # Second route
    ├── Berlin3           # Third route
    ├── ...

Sequence structure

We separated each route into several sequences. One sequence describes one unique intersection up to passing it. The foldername indicates date and time.

.
├── Berlin 1                    # Route Berlin1
    ├── 2015-04-17_10-50-05     # First intersection
    ├── 2015-04-17_10-50-41     # Second intersection
    ├── ...

Image structure

For each sequences, images and disparity images are available. Filename indicates time and date

.
├── 2015-04-17_10-50-05                                      # Route Berlin1
    ├── DE_BBBR667_2015-04-17_10-50-13-633939_k0.tiff        # First left camera image
    ├── DE_BBBR667_2015-04-17_10-50-13-633939_nativeV2.tiff  # First disparity image
    ├── DE_BBBR667_2015-04-17_10-50-14-299876_k0.tiff        # Second left camera image
    ├── DE_BBBR667_2015-04-17_10-50-14-299876_nativeV2       # Second disparity image
    ├── ...

Before starting

1. Check our documentation

Documentation is stored at /dtld_parsing/doc/. We give insights into the data and explain how to interpret it.

2. Change absolute paths

Do not forget to change the absolute paths of the images in all label files (.yml).

Using the dataset

C++

Clone the dtld_parsing respository

git clone https://github.com/julimueller/dtld_parsing

Build everything
cd dtld_parsing/c++/driveu_dataset/
mkdir build && cd build
cmake .. -DCMAKE_INSTALL_PREFIX="YOUR_PATH" && make -j12 install
driveu_test -label_file -calib_dir -data_base_dir

Note: "YOUR_PATH" has to be in LD_LIBRARY_PATH. DTLD_DIR is the directory where all .zips should be unpacked.
The visualization should look like this

![alt text](https://github.com/julimueller/dtld_parsing/blob/master/images/c%2B%2B_demo.png)
### Python
UPDATE 03-26-2020: Python modules were changed to Python 3.
```Shell
git clone https://github.com/julimueller/dtld_parsing
cd dtld_parsing
python3 setup.py install
cd python
python3 load_dtld.py --label_file <label_file_path.yml> --calib_dir <path_to_calib> --data_base_dir <dtld_dir>

Result should look like above

MATLAB

NOTE 05/2020: MATLAB support is deprecated and will likely not be added for the new label format (json).

Run main.m

Results should look like this alt text

Citation

Do not forget to cite our work for the case you used DTLD

Citation:

@INPROCEEDINGS{8460737,
author={A. Fregin and J. Müller and U. Kreβel and K. Dietmayer},
booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
title={The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets},
year={2018},
volume={},
number={},
pages={3376-3383},
keywords={computer vision;image recognition;traffic engineering computing;DriveU traffic light dataset;traffic light recognition;autonomous driving;computer vision;University of Ulm Traffic Light Dataset;Daimler AG;Cameras;Urban areas;Benchmark testing;Lenses;Training;Visualization;Detectors},
doi={10.1109/ICRA.2018.8460737},
ISSN={2577-087X},
month={May},}

julimueller / dtld_parsing

readme