initial commit

.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
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* 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
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+.vscode
+.gradio
+**/__pycache__

.pre-commit-config.yaml

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+repos:
+-   repo: https://github.com/psf/black
+    rev: 22.3.0
+    hooks:
+    - id: black
+      args: [--line-length=120]
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.2.0
+    hooks:
+      - id: check-json
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: requirements-txt-fixer

README.md

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+---
+title: FE-LineViewer
+emoji: 🌖
+colorFrom: pink
+colorTo: indigo
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Frame-Event Line Segment Detection Dataset Viewer Space
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Install
+```shell
+git clone https://huggingface.co/spaces/lh9171338/FE-LineViewer
+cd FE-LineViewer
+python3 -m pip install -r requirements.txt
+```
+
+# Run
+
+```shell
+python3 app.py
+```

app.py

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+# -*- encoding: utf-8 -*-
+
+"""
+@File    :   app.py
+@Time    :   2025/8/29 15:25:00
+@Author  :   lh9171338
+@Version :   1.0
+@Contact :   2909171338@qq.com
+"""
+
+import os
+import gradio as gr
+from PIL import Image
+import io
+import logging
+import matplotlib.pyplot as plt
+import numpy as np
+from datasets import load_dataset, DatasetDict
+from utils.event import Event
+
+
+dataset_dict = dict()
+dataset = None
+default_split_selector_info = dict(
+    choices=["train", "test"],
+    label="Split",
+    value="train",
+    interactive=False,
+)
+default_index_slider_info = dict(
+    minimum=0,
+    maximum=1,
+    step=1,
+    label="Index",
+    value=0,
+    interactive=False,
+)
+sample_info = dict(
+    dataset=dataset,
+    split="train",
+    index=0,
+    blur_image=None,
+    event_image=None,
+    start_image=None,
+    end_image=None,
+)
+
+
+def get_dataset(dataset_name):
+    """
+    Get dataset
+
+    Args:
+        dataset_name (str): dataset name or path
+
+    Returns:
+        dataset (datasets.Dataset): dataset
+    """
+    global dataset_dict
+    if dataset_name in dataset_dict:
+        dataset = dataset_dict[dataset_name]
+    else:
+        if os.path.exists(dataset_name):
+            dataset = load_dataset(dataset_name, data_dir=dataset_name, trust_remote_code=True)
+        else:
+            dataset = load_dataset(dataset_name, trust_remote_code=True)
+        dataset_dict[dataset_name] = dataset
+    return dataset
+
+
+def submit_callback(dataset_name):
+    """
+    Submit callback function
+
+    Args:
+        dataset_name (str): dataset name or path
+
+    Returns:
+        split_selector_info (dict): updated split selector info
+        index_slider_info (dict): updated index slider info
+        blur_image (PIL.Image): updated blur image
+        event_image (PIL.Image): updated event image
+        start_image (PIL.Image): updated start image
+        end_image (PIL.Image): updated end image
+    """
+    global dataset
+    try:
+        dataset = get_dataset(dataset_name)
+    except Exception as e:
+        dataset = None
+        logging.error(f"Load dataset failed: {e}")
+        split_selector_info = gr.update(**default_split_selector_info)
+        index_slider_info = gr.update(**default_index_slider_info)
+        return split_selector_info, index_slider_info, None, None, None, None
+
+    if not isinstance(dataset, DatasetDict):
+        dataset = {str(dataset.split): dataset}
+    splits = list(dataset.keys())
+    split = splits[0]
+    maximum = len(dataset[split]) - 1
+    index = 0
+    split_selector_info = gr.update(choices=splits, value=split, interactive=True)
+    index_slider_info = gr.update(minimum=0, maximum=maximum, value=index, interactive=True)
+    blur_image, event_image, start_image, end_image = show_image(split=split, index=index)
+    return split_selector_info, index_slider_info, blur_image, event_image, start_image, end_image
+
+
+def selector_change_callback(split):
+    """
+    Selector change callback function
+
+    Args:
+        split (str): selected split, value must be one of ["train", "test"]
+
+    Returns:
+        index_slider_info (dict): updated slider info
+        blur_image (PIL.Image): updated blur image
+        event_image (PIL.Image): updated event image
+        start_image (PIL.Image): updated start image
+        end_image (PIL.Image): updated end image
+    """
+    global dataset
+    if dataset is None:
+        index_slider_info = gr.update(**default_index_slider_info)
+        return index_slider_info, None, None, None, None
+
+    maximum = len(dataset[split]) - 1
+    index = 0
+    index_slider_info = gr.update(minimum=0, maximum=maximum, value=index)
+    blur_image, event_image, start_image, end_image = show_image(split=split, index=index)
+    return index_slider_info, blur_image, event_image, start_image, end_image
+
+
+def draw_lines(image, lines):
+    """
+    Draw lines on image
+
+    Args:
+        image (np.ndarray): input image
+        lines (np.ndarray): list of lines, with shape [N, 2, 2]
+
+    Returns:
+        image (PIL.Image): drawn image
+    """
+    height, width = image.shape[:2]
+    fig = plt.figure()
+    fig.set_size_inches(width / height, 1, forward=False)
+    ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
+    ax.set_axis_off()
+    fig.add_axes(ax)
+    plt.xlim([-0.5, width - 0.5])
+    plt.ylim([height - 0.5, -0.5])
+    plt.imshow(image)
+    for pts in lines:
+        pts = pts - 0.5
+        plt.plot(pts[:, 0], pts[:, 1], color="orange", linewidth=0.5)
+        plt.scatter(pts[:, 0], pts[:, 1], color="#33FFFF", s=1.2, edgecolors="none", zorder=5)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=height, bbox_inches=0)
+    buf.seek(0)
+    plt.close(fig)
+    image = Image.open(buf)
+    return image
+
+
+def show_image(split, index):
+    """
+    Show image
+
+    Args:
+        split (str): split name, value must be one of ["train", "test"]
+        index (int): index of the sample
+
+    Returns:
+        blur_image (PIL.Image): drawn blurred image
+        event_image (PIL.Image): drawn event image
+        start_image (PIL.Image): drawn start image
+        end_image (PIL.Image): drawn end image
+    """
+    global dataset
+    if dataset is None:
+        return None, None, None, None
+
+    global sample_info
+    old_sample_info = dict(
+        dataset=sample_info["dataset"],
+        split=sample_info["split"],
+        index=sample_info["index"],
+    )
+    new_sample_info = dict(dataset=dataset, split=split, index=index)
+    if old_sample_info == new_sample_info:  # No need to update
+        logging.info("No need to update")
+        return sample_info["blur_image"], sample_info["event_image"], sample_info["start_image"], sample_info["end_image"]
+
+    sample = dataset[split][index]
+    blur_image = sample["blur_image"]
+    start_image = np.array(sample["start_image"])
+    end_image = np.array(sample["end_image"])
+    lines = np.array(sample["lines"]).reshape(-1, 2, 2)
+    event_image = Image.fromarray(Event(events=sample["events"]).event2image())
+    event_image = event_image.resize(blur_image.size)
+    start_image = draw_lines(start_image, lines)
+    end_image = draw_lines(end_image, lines)
+    sample_info.update(new_sample_info)
+    sample_info["blur_image"] = blur_image
+    sample_info["event_image"] = event_image
+    sample_info["start_image"] = start_image
+    sample_info["end_image"] = end_image
+    logging.info("Update")
+    return blur_image, event_image, start_image, end_image
+
+
+def main():
+    """
+    Main
+
+    Args:
+        None
+
+    Returns:
+        None
+    """
+    with gr.Blocks() as demo:
+        dataset_textbox = gr.Textbox(value="lh9171338/FE-Blurframe", label="Dataset name or path")
+        split_selector = gr.Dropdown(**default_split_selector_info)
+        index_slider = gr.Slider(**default_index_slider_info)
+        with gr.Row():
+            blur_image = gr.Image(label="Blurred Image")
+            event_image = gr.Image(label="Event Image")
+            start_image = gr.Image(label="Start Image")
+            end_image = gr.Image(label="End Image")
+
+        dataset_textbox.submit(
+            submit_callback,
+            dataset_textbox,
+            [split_selector, index_slider, blur_image, event_image, start_image, end_image],
+        )
+        split_selector.change(selector_change_callback, split_selector, [index_slider, blur_image, event_image, start_image, end_image])
+        index_slider.change(show_image, [split_selector, index_slider], [blur_image, event_image, start_image, end_image])
+        demo.load(
+            submit_callback,
+            dataset_textbox,
+            [split_selector, index_slider, blur_image, event_image, start_image, end_image],
+        )
+        demo.launch(share=False)
+
+
+if __name__ == "__main__":
+    # set base logging config
+    fmt = "[%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s] %(message)s"
+    logging.basicConfig(format=fmt, level=logging.INFO)
+
+    main()

requirements.txt

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+datasets
+matplotlib
+numpy<2
+pillow

utils/event.py

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+# -*- encoding: utf-8 -*-
+
+"""
+@File    :   event.py
+@Time    :   2025/9/3 15:25:00
+@Author  :   lh9171338
+@Version :   1.0
+@Contact :   2909171338@qq.com
+"""
+
+import numpy as np
+
+
+class Event:
+    """
+    Event
+
+    Args:
+        filename (str | None): path to the event file
+        events (dict | None): event data
+    """
+    def __init__(self, filename=None, events=None, **kwargs):
+        assert filename is not None or events is not None
+        if filename is not None:
+            events = np.load(filename)
+            events = dict(**events)
+        events["x"] = np.array(events["x"], dtype="int16")
+        events["y"] = np.array(events["y"], dtype="int16")
+        events["p"] = np.array(events["p"], dtype="int16")
+        events["t"] = np.array(events["t"], dtype="int32")
+
+        self.filename = filename
+        self.events = events
+
+    def event2image(self):
+        """
+        Convert event to image
+
+        Args:
+            None
+
+        Returns:
+            image (numpy.ndarray): event image
+        """
+        width, height = self.events["image_size"]
+        xs = self.events["x"]
+        ys = self.events["y"]
+        ps = self.events["p"]
+
+        event = np.zeros((height, width, 2), dtype="float32")
+        np.add.at(event, (ys, xs, ps), 1)
+        np.add.at(event, (ys, xs, ps), 1)
+        neg = event[:, :, 0]
+        pos = event[:, :, 1]
+        image = np.stack((neg, np.zeros_like(neg), pos), axis=-1)
+        image = (((image / image.max()) ** 0.5) * 255).astype(np.uint8)
+        return image