---
datasets: lh9171338/F-Wireframe
pretty_name: F-Wireframe Dataset
license: mit
tags:
- computer-vision
- line-segment-detection
- wireframe-parsing
- fisheye image
size_categories: n<1K

---

# F-Wireframe Dataset

<p align="center">
    ✏️ <a href="https://github.com/lh9171338/ULSD-ISPRS"><b>Github</b></a>&nbsp&nbsp | &nbsp&nbsp📑 <a href="https://www.sciencedirect.com/science/article/abs/pii/S0924271621001623">Paper</a> &nbsp&nbsp | &nbsp&nbsp🖼️ <a href="https://huggingface.co/spaces/lh9171338/LineViewer">Viewer</a>
</p>

This is the F-Wireframe dataset, designed for fisheye image line segment detection.

## Summary

The F-Wireframe dataset is derived from the [Wireframe](https://huggingface.co/datasets/lh9171338/Wireframe) dataset by distorting images with the fisheye distortion model, where the fisheye distortion coefficient of each image is randomly generated.

Number of samples:
- Train: 5,000
- Test: 462

## Download

- Download with huggingface-hub

```shell
python3 -m pip install huggingface-hub
huggingface-cli download --repo-type dataset lh9171338/F-Wireframe --local-dir ./
```

- Download with Git

```shell
git lfs install
git clone https://huggingface.co/datasets/lh9171338/F-Wireframe
```

## Usage

- Load the dataset from Hugging Face Hub

```python
from datasets import load_dataset

ds = load_dataset("lh9171338/F-Wireframe")
# 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"),
#     "camera_coeff": {
#         "K": Sequence(Sequence(Value("float32"))),
#         "D": Sequence(Value("float32")),
#     },
#     "lines": Sequence(Sequence(Sequence(Value("float32")))),
# })
# ds = load_dataset("lh9171338/F-Wireframe", features=features, revision="refs/convert/parquet")
print(ds)
# DatasetDict({
#     train: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'],
#         num_rows: 5000
#     })
#     test: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'],
#         num_rows: 462
#     })
# })
print(ds["test"][0].keys())
# dict_keys(['file_name', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'])
```

- Load the dataset from local

```python
from datasets import load_dataset

ds = load_dataset("imagefolder", data_dir=".")
print(ds)
# DatasetDict({
#     train: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'],
#         num_rows: 5000
#     })
#     test: Dataset({
#         features: ['image', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'],
#         num_rows: 462
#     })
# })
print(ds["test"][0].keys())
# dict_keys(['file_name', 'image_file', 'image_size', 'camera_type', 'camera_coeff', 'lines'])
```

- Load the dataset with jsonl files
```python
import jsonlines

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