Datasets:

lh9171338
/

SS360

datasets: lh9171338/SS360
pretty_name: SS360 Dataset
license: mit
tags:
  - computer-vision
  - line-segment-detection
  - wireframe-parsing
  - spherical image
size_categories: 1K<n<10

SS360 Dataset

✏️ Github | 📑 Paper | 🖼️ Viewer

This is the SS360 dataset, designed for spherical image line segment detection.

Summary

The SS360 dataset is constructed by manually annotating images sourced from the SUN360 dataset and the Stanford 2D-3D-S dataset. It is organized into JSONL files (train/metadata.jsonl, test/metadata.jsonl) along with the corresponding images.

Number of samples:

Train: 950
Test: 118

Download

Download with huggingface-hub

python3 -m pip install huggingface-hub
huggingface-cli download --repo-type dataset lh9171338/SS360 --local-dir ./

Download with Git

git lfs install
git clone https://huggingface.co/datasets/lh9171338/SS360

Usage

Load the dataset from Hugging Face Hub

from datasets import load_dataset

ds = load_dataset("lh9171338/SS360")
# or load from `refs/convert/parquet` for acceleration
# from datasets import load_dataset, Features, Image, Sequence, Value
# features = Features({
#     "image": Image(),
#     "image_file": Value("string"),
#     "image_size": Sequence(Value("int32")),
#     "camera_type": Value("string"),
#     "lines": Sequence(Sequence(Sequence(Value("float32")))),
# })
# ds = load_dataset("lh9171338/SS360", features=features, revision="refs/convert/parquet")
print(ds)
# DatasetDict({
#     train: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'lines'],
#         num_rows: 950
#     })
#     test: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'lines'],
#         num_rows: 118
#     })
# })
print(ds["test"][0].keys())
# dict_keys(['image', 'image_file', 'image_size', 'camera_type', 'lines'])

Load the dataset from local

from datasets import load_dataset

ds = load_dataset("imagefolder", data_dir=".")
print(ds)
# DatasetDict({
#     train: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'lines'],
#         num_rows: 950
#     })
#     test: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'lines'],
#         num_rows: 118
#     })
# })
print(ds["test"][0].keys())
# dict_keys(['image', 'image_file', 'image_size', 'camera_type', 'lines'])

Load the dataset with jsonl files

import jsonlines

with jsonlines.open("test/metadata.jsonl") as reader:
    infos = list(reader)
print(infos[0].keys())
# dict_keys(['image', 'image_file', 'image_size', 'camera_type', 'lines'])

Citation

@article{LI2021187,
    title = {ULSD: Unified line segment detection across pinhole, fisheye, and spherical cameras},
    author = {Hao Li and Huai Yu and Jinwang Wang and Wen Yang and Lei Yu and Sebastian Scherer},
    journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {178},
    pages = {187-202},
    year = {2021},
}