data

Data

Download

The files mentioned below can also be downloaded via OpenDataLab. It is recommended to use provided command line interface for acceleration.

Subset	Split	Google Drive	Baidu Yun	md5	Size
subset_A	sample	sample	sample	21c607fa5a1930275b7f1409b25042a0	~300M
subset_A	all	info	info	95bf28ccf22583d20434d75800be065d	~8.8G
	train	image_0	image_0	8ade7daeec1b64f8ab91a50c81d812f6	~14.0G
		image_1	image_1	c78e776f79e2394d2d5d95b7b5985e0f	~14.3G
		image_2	image_2	4bf09079144aa54cb4dcd5ff6e00cf79	~14.2G
		image_3	image_3	fd9e64345445975f462213b209632aee	~14.4G
		image_4	image_4	ae07e48c88ea2c3f6afbdf5ff71e9821	~14.5G
		image_5	image_5	df62c1f6e6b3fb2a2a0868c78ab19c92	~14.2G
		image_6	image_6	7bff1ce30329235f8e0f25f6f6653b8f	~14.4G
	val	image_7	image_7	c73af4a7aef2692b96e4e00795120504	~21.0G
	test	image_8	image_8	fb2f61e7309e0b48e2697e085a66a259	~21.2G
subset_B	coming soon	-	-	-	-

For files in Google Drive, you can use the following command by replacing [FILE_ID] and [FILE_NAME] accordingly:

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=[FILE_ID]' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=[FILE_ID]" -O [FILE_NAME]

Preprocess

The dataset is preprocessed into pickle files representing different collections, which then be used for training models or evaluation:

cd data
python OpenLane-V2/preprocess.py 

Hierarchy

The hierarchy of folder OpenLane-V2/ is described below:

└── OpenLane-V2
    ├── train
    |   ├── [segment_id]
    |   |   ├── image
    |   |   |   ├── [camera]
    |   |   |   |   ├── [timestamp].jpg
    |   |   |   |   └── ...
    |   |   |   └── ...
    |   |   └── info
    |   |       ├── [timestamp].json
    |   |       └── ...
    |   └── ...
    ├── val
    |   └── ...
    ├── test
    |   └── ...
    ├── data_dict_example.json
    ├── data_dict_subset_A.json
    ├── data_dict_subset_B.json
    ├── openlanev2.md5
    └── preprocess.py

• [segment_id] specifies a sequence of frames, and [timestamp] specifies a single frame in a sequence.
• image/ contains images captured by various cameras, and info/ contains meta data and annotations of a single frame.
• data_dict_[xxx].json notes the split of train / val / test under the subset of data.

Meta Data

The json files under the info/ folder contain meta data and annotations for each frame. Each file is formatted as follows:

{
    'version':                              <str> -- version
    'segment_id':                           <str> -- segment_id
    'meta_data': {
        'source':                           <str> -- name of the original dataset
        'source_id':                        <str> -- original identifier of the segment
    }
    'timestamp':                            <int> -- timestamp of the frame
    'sensor': {
        [camera]: {                         <str> -- name of the camera
            'image_path':                   <str> -- image path
            'extrinsic':                    <dict> -- extrinsic parameters of the camera
            'intrinsic':                    <dict> -- intrinsic parameters of the camera
        },
        ...
    }                              
    'pose':                                 <dict> -- ego pose
    'annotation':                           <dict> -- anntations for the current frame
}

Annotations

For a single frame, annotations are formatted as follow:

{
    'lane_centerline': [                    (n lane centerlines in the current frame)
        {   
            'id':                           <int> -- unique ID in the current frame
            'points':                       <float> [n, 3] -- 3D coordiate
            'confidence':                   <float> -- confidence, only for prediction
        },
        ...
    ],
    'traffic_element': [                    (k traffic elements in the current frame)
        {   
            'id':                           <int> -- unique ID in the current frame
            'category':                     <int> -- traffic element category
                                                1: 'traffic_light',
                                                2: 'road_sign',
            'attribute':                    <int> -- attribute of traffic element
                                                0:  'unknown',
                                                1:  'red',
                                                2:  'green',
                                                3:  'yellow',
                                                4:  'go_straight',
                                                5:  'turn_left',
                                                6:  'turn_right',
                                                7:  'no_left_turn',
                                                8:  'no_right_turn',
                                                9:  'u_turn',
                                                10: 'no_u_turn',
                                                11: 'slight_left',
                                                12: 'slight_right',
            'points':                       <float> [2, 2] -- top-left and bottom-right corners of the 2D bounding box
            'confidence':                   <float> -- confidence, only for prediction
        },
        ...
    ],
    'topology_lclc':                        <float> [n, n] -- adjacent matrix among lane centerlines
    'topology_lcte':                        <float> [n, k] -- adjacent matrix between lane centerlines and traffic elements
}

• id is the identifier of a lane centerline or traffic element and is consistent in a sequence. For predictions, it can be randomly assigned but unique in a single frame.
• topology_lclc and topology_lcte are adjacent matrices, where row and column are sorted according to the order of the lists lane_centerline and traffic_element. It is a MUST to keep the ordering the same for correct evaluation. For ground truth, only 0 or 1 is a valid boolean value for an element in the matrix. For predictions, the value varies from 0 to 1, representing the confidence of the predicted relationship.
• #lane_centerline and #traffic_element are not required to be equal between ground truth and predictions. In the process of evaluation, a matching of ground truth and predictions is determined.