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.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

FE-Wireframe.py

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+# -*- encoding: utf-8 -*-
+
+"""
+@File    :   FE-Wireframe.py
+@Time    :   2025/08/31 23:00:00
+@Author  :   lh9171338
+@Version :   1.0
+@Contact :   [email protected]
+"""
+
+import os
+import numpy as np
+import json
+import datasets
+from datasets import Features, Image, Sequence, Value
+
+
+_CITATION = """\
+@ARTICLE{10323537,
+  author={Yu, Huai and Li, Hao and Yang, Wen and Yu, Lei and Xia, Gui-Song},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title={Detecting Line Segments in Motion-Blurred Images With Events},
+  year={2023},
+  pages={1-16},
+  doi={10.1109/TPAMI.2023.3334877}
+}
+"""
+_DESCRIPTION = """\
+This new dataset is designed for motion-blurred image line segment detection with events.
+"""
+_HOMEPAGE = ""
+_LICENSE = "mit"
+
+
+class FEBlurframe(datasets.GeneratorBasedBuilder):
+    """FE-Blurframe Dataset"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        """infos"""
+        features = Features(
+            {
+                "blur_image": Image(),
+                "start_image": Image(),
+                "end_image": Image(),
+                "events": {
+                    "image_size": Sequence(Value("int32")),
+                    "x": Sequence(Value("int16")),
+                    "y": Sequence(Value("int16")),
+                    "t": Sequence(Value("int32")),
+                    "p": Sequence(Value("bool")),
+                },
+                "H": Sequence(Sequence(Value("float32"))),  # shape [3, 3]
+                "image_size": Sequence(Value("int32")),  # shape [2]
+                "junc": Sequence(Sequence(Value("float32"))),  # shape [M, 2]
+                "flow": Sequence(Sequence(Value("float32"))),  # shape [M, 2]
+                "lines": Sequence(Sequence(Value("float32"))),  # shape [N, 4]
+                "edges_positive": Sequence(Sequence(Value("float32"))),  # shape [Np, 2]
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """split generators"""
+        data_files = {
+            "train": "train.jsonl",
+            "test": "test.jsonl",
+            "events_raw": "events_raw.zip",
+            "images-blur": "images-blur.zip",
+            "images-start": "images-start.zip",
+            "images-end": "images-end.zip",
+        }
+        data_files = dl_manager.download_and_extract(data_files)
+        print(f"data_files: {data_files}")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files["train"],
+                    "data_files": data_files,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_files["test"],
+                    "data_files": data_files,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, data_files):
+        """generate examples"""
+        with open(filepath, encoding="utf-8") as f:
+            lines = f.readlines()[:10]
+        for idx, line in enumerate(lines):
+            info = json.loads(line)
+            new_info = dict()
+            new_info["blur_image"] = os.path.join(data_files["images-blur"], "images-blur", info["filename"])
+            new_info["start_image"] = os.path.join(data_files["images-start"], "images-start", info["filename"])
+            new_info["end_image"] = os.path.join(data_files["images-end"], "images-end", info["filename"])
+            events = np.load(
+                os.path.join(data_files["events_raw"], "events_raw", info["filename"].replace(".png", ".npz"))
+            )
+            new_info["events"] = dict(**events)
+            for key in ["image_size", "H", "junc", "flow", "lines", "edges_positive"]:
+                new_info[key] = info[key]
+            yield idx, new_info

README.md

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+---
+datasets: lh9171338/FE-Wireframe
+pretty_name: FE-Wireframe Dataset
+license: mit
+tags:
+- computer-vision
+- line-segment-detection
+- wireframe-parsing
+- blurred image
+- event data
+size_categories: n<1K
+---
+
+# FE-Wireframe Dataset
+
+<p align="center">
+    ✏️ <a href="https://github.com/lh9171338/FE-LSD"><b>Github</b></a>&nbsp&nbsp | &nbsp&nbsp📑 <a href="https://arxiv.org/abs/2211.07365">Paper</a> &nbsp&nbsp | &nbsp&nbsp🖼️ <a href="https://huggingface.co/spaces/lh9171338/FE-LineViewer">Viewer</a>
+</p>
+
+
+This is the FE-Wireframe dataset, designed for motion-blurred image line segment detection with events.
+
+## Summary
+
+The FE-Wireframe dataset is constructed to provide synthetic data for motion-blur scenarios without requiring manual line annotations. Starting from the labeled [Wireframe dataset](https://huggingface.co/datasets/lh9171338/Wireframe), we employ the ESIM simulator to generate event-based synthetic sequences. The overall data generation pipeline is shown as follows.
+
+![pipeline](figures/sim_data.png)
+
+- File structure:
+```
+|-- events_raw
+    |-- 0000001.npz
+    |-- 0000002.npz
+    |-- ...
+|-- images-blur
+    |-- 0000001.png
+    |-- 0000002.png
+    |-- ...
+|-- images-start
+    |-- 0000001.png
+    |-- 0000002.png
+    |-- ...
+|-- images-end
+    |-- 0000001.png
+    |-- 0000002.png
+    |-- ...
+|-- train.jsonl
+|-- test.jsonl
+```
+
+- 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/FE-Wireframe --local-dir ./
+```
+
+- Download with Git
+
+```shell
+git lfs install
+git clone https://huggingface.co/datasets/lh9171338/FE-Wireframe
+```
+
+## Usage
+
+- Load the dataset from Hugging Face Hub
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("lh9171338/FE-Wireframe", trust_remote_code=True)
+print(ds)
+# DatasetDict({
+#     train: Dataset({
+#         features: ['blur_image', 'start_image', 'end_image', 'events', 'H', 'image_size', 'junc', 'flow', 'lines', 'edges_positive'],
+#         num_rows: 5000
+#     })
+#     test: Dataset({
+#         features: ['blur_image', 'start_image', 'end_image', 'events', 'H', 'image_size', 'junc', 'flow', 'lines', 'edges_positive'],
+#         num_rows: 462
+#     })
+# })
+```
+
+- Load the dataset from local
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("FE-Wireframe", trust_remote_code=True)
+print(ds)
+# DatasetDict({
+#     train: Dataset({
+#         features: ['blur_image', 'start_image', 'end_image', 'events', 'H', 'image_size', 'junc', 'flow', 'lines', 'edges_positive'],
+#         num_rows: 5000
+#     })
+#     test: Dataset({
+#         features: ['blur_image', 'start_image', 'end_image', 'events', 'H', 'image_size', 'junc', 'flow', 'lines', 'edges_positive'],
+#         num_rows: 462
+#     })
+# })
+```
+
+- Load the dataset with jsonl files
+```python
+import jsonlines
+
+with jsonlines.open("test.jsonl") as reader:
+    infos = list(reader)
+print(infos[0].keys())
+# dict_keys(['filename', 'image_size', 'H', 'junc', 'flow', 'lines', 'edges_positive'])
+```
+
+## Citation
+
+```
+@ARTICLE{10323537,
+  author={Yu, Huai and Li, Hao and Yang, Wen and Yu, Lei and Xia, Gui-Song},
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
+  title={Detecting Line Segments in Motion-Blurred Images With Events}, 
+  year={2023},
+  pages={1-16},
+  doi={10.1109/TPAMI.2023.3334877}
+}
+```

events_raw.zip

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images-blur.zip

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images-end.zip

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images-start.zip

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test.jsonl

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test.py

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+from datasets import load_dataset
+
+ds = load_dataset("lh9171338/FE-Wireframe", trust_remote_code=True)
+# ds = load_dataset("/Users/lihao57/workspace/FE-Blurframe", trust_remote_code=True)
+# ds = load_dataset("FE-Wireframe.py", trust_remote_code=True)
+print(ds)
+print(ds["test"][0].keys())
+print(ds["test"][0]["events"]["image_size"])
+
+# import os
+# import numpy as np
+# import glob
+# from PIL import Image
+# from lh_tool.iterator import AutoParallelProcess
+
+
+# def func(event_file, save_path):
+#     events = np.load(event_file)
+#     x = events["x"].astype("int16")
+#     y = events["y"].astype("int16")
+#     p = events["p"].astype("int32")
+#     t = events["t"].astype("bool")
+#     image_file = event_file.replace("events_raw", "images-blur").replace(".npz", ".png")
+#     image = Image.open(image_file)
+#     image_size = image.size
+#     w, h = image_size
+#     mask = (x >=0 ) & (x < w) & (y >= 0) & (y < h)
+#     x = x[mask]
+#     y = y[mask]
+#     p = p[mask]
+#     t = t[mask]
+#     data = dict(
+#         image_size=image_size,
+#         x=x,
+#         y=y,
+#         p=p,
+#         t=t,
+#     )
+#     np.savez_compressed(os.path.join(save_path, os.path.basename(event_file)), **data)
+
+
+# if __name__ == "__main__":
+#     event_files = glob.glob("events_raw/*.npz")
+#     save_path = "events"
+#     os.makedirs(save_path, exist_ok=True)
+#     AutoParallelProcess(func).run(
+#         event_file=event_files,
+#         save_path=save_path,
+#     )

train.jsonl

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