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README.md

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+---
+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},
+}
+```