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LLAMAS

Prepare the dataset

  1. The LLAMAS dataset can be downloaded in their official website.

  2. Change the LLAMAS_ROOT in configs/lane_detection/common/datasets/_utils.py to your dataset's location..

  3. Pre-processing:

  python tools/llamas_list_convertor.py

LLAMAS dataset provides both color and gray images. We use color images in our framework.

  1. Prepare official evaluation scripts:
cd tools/llamas_evaluation
mkdir output

Then change data_dir to your LLAMAS base directory in autotest_llamas.sh. Mind that you need extra ../../ if relative path is used.

  1. If you use Bézier curve methods, download Bézier curve GT from here and unzip them in LLAMAS_ROOT/bezier_labels/. More info on curves are in CURVE.md.

Description

Directory Structure

  <LLAMAS.BASE_DIR>
     ├─ color_images    
     │  ├─ test
     │  ├─ train
     │  └─ valid
     ├─ labels    
     │  ├─ train
     │  └─ valid
     ├─ laneseg_labels
     │  ├─ train
     │  └─ valid
     ├─ bezier_labels
     │  ├─ train_3.json
     │  └─ ... 
     └─ lists

The test set' s annotations are not public.

Label Data Format

{
    "image_name": "...",
    "projection_matrix": [[x11, 0, x13], [0, x21, x23], [0, 0, 1]],
    "lanes":
        [
            {
                "lane_id": "...", 
                "markers": 
                 [
                     {
                        "lane_marker_id": "...", 
                        "world_start":{"x":"...", "y": "...", "z": "..."},  "pixel_start": {"x": "...", "y": "..."},
                        "world_end":{"x":"...", "y": "...", "z": "..."}, "pixel_start": {"x": "...", "y": "..."}}
                     },
                     ...                 
                 ]
            },
            ...     
        ]
}

LLAMAS dataset employs json files to save annotations. Each image corresponds to a json file.

We utilize the format of culane to reformat LLAMAS dataset.

We use the script provided by Xingang Pan to generate per-pixel labels.

The generated labels can be downloaded from Google Drive.