42dot
Open Dataset

자율주행 연구 개발 생태계 조성을 위한
42dot의 데이터셋을 소개합니다.

SDLane

The SDLane dataset is a novel lane marking dataset for autonomous driving. We provide high resolution images of 1920 X 1208 pixels which capture challenging scenarios in highways and urban areas. The dataset consists of 39K training and 4K test images with accurate ground truth labels. For each scene, we manually annotated the 2D lane geometry of all visible lane markings on the road. In addition, to better infer the position of the ego vehicle, we annotated the index of each lane relative to the leftmost lane marking.

Download the SDLane Dataset

Label Format

Annotations in JSON format are as follows:

{
"geometry": [[(x1,y1),(x2,y2)],[(x3,y3),(x4,y4)]]     # array of x,y coordinates   
"idx": [0, 1]                                                               # index relative to the leftmost lane   
}

Folder Structure

For both the training and test sets, we provide a TXT file containing a list of image paths, JPEG images, and their
corresponding annotation JSON files.

SDLane/    
  └── train/   
              ├── train_list.txt   
              ├── images/   
                        ├── 5bbb9c8402a48a0f7eab3af7692743953136faf4/   
                                  ├── 0001.jpg/   
                                  ├── 0002.jpg/   
              ├── labels/   
                        ├── 5bbb9c8402a48a0f7eab3af7692743953136faf4/   
                                  ├── 0001.json/   
                                  ├── 0002.json/   
  └── test/ㅍ
            ├── test_list.txt   
            ├── images/   
                      ├── 5bbb9c8402a48a0f7eab3af7692743953136faf4/   
                                ├── 1313.jpg/   
                                ├── 1315.jpg/   
            ├── labels/   
                      ├── 5bbb9c8402a48a0f7eab3af7692743953136faf4/   
                                ├── 1313.json/   
                                ├── 1315.json/

Citation

If you use the SDLane dataset, please use the following citation:

@inproceedings{SDLane,   
        author = {Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, Chang-Su Kim},   
        title = {Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes},   
        booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},   
        year = {2022}

Tutorial

SDLane Dataset Tutorial

In this tutorial, you will learn how to generate a segmentation label for each corresponding image. We also provide a code block to overlay lane segmentation labels on the image.

To run this tutorial, please download the dataset from https://42dot.ai/akit/dataset/ and install the following packages, os, json,matplotlib, numpy,PIL and opencv_python.

In [1]:

import os   
import json   
import matplotlib.pyplot as plt   
import numpy as np   
from PIL import Image   
import cv2

In [2]:

# Dataset directory   
ROOT = # please update   
DATALIST_PATH = os.path.join(ROOT, 'test_list.txt')

Load Image and Annotation

In [3]:

# Load datalist   
with open(DATALIST_PATH) as f:   
      datalist = [line.rstrip('
') for line in f]

In [4]:

def get_label(datalist, idx):   
    """   
    returns the corresponding label path for each image path   
    """   
    image_path = datalist[idx]   
    label_path = image_path.replace('images', 'labels').replace('.jpg', '.json')   
    return image_path, label_path   


def load_json(label_path):   
    with open(label_path, "r") as f:   
            annotation = json.load(f)   
    return annotation   

      
image_path, label_path = get_label(datalist, idx=0)   


# absolute path   
image_path = os.path.join(ROOT, image_path)   
label_path = os.path.join(ROOT, label_path)

In [5]:

# visualize image   
img = plt.imread(image_path)   
plt.imshow(img)

<matplotlib.image.AxesImage at 0x7fd86951f050>

In [6]:

# load raw annotation data   
annotation = load_json(label_path) # key = ['geometry', 'idx']

Visualize Segmentation Labels

In [7]:

def generate_seg_label(annotation, vis=None, height=1208, width=1920):   
    """   
    generates segmentation Labels   
    """   
    if vis is None:   
          vis = np.zeros((height, width, 3), dtype=np.uint8)   
          vis = np.ascontiguousarray(vis)   


    lane_geometry = annotation['geometry']   
    pts = [np.int32(lane) for lane in lane_geometry if not len(lane)==0]   
    vis = cv2.polylines(vis, pts, False, (0, 255, 0), 10)   
    return vis

In [8]:

# visualize segmentation label   
vis = generate_seg_label(annotation)   
plt.imshow(vis)

<matplotlib.image.AxesImage at 0x7fd8691fac10>

In [9]:

# overlay segmentation label on the original image   
vis = generate_seg_label(annotation, img)   
plt.imshow(vis)

<matplotlib.image.AxesImage at 0x7fd8691f29d0>

Download the SDLane Dataset