File size: 4,057 Bytes
b32aa52 bdc1b09 b32aa52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # -*- encoding: utf-8 -*-
"""
@File : FE-Wireframe.py
@Time : 2025/08/31 23:00:00
@Author : lh9171338
@Version : 1.0
@Contact : 2909171338@qq.com
"""
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()
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
|