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"""PP4AV dataset.""" |
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import os |
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from glob import glob |
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from tqdm import tqdm |
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from pathlib import Path |
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from typing import List |
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import re |
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from collections import defaultdict |
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import datasets |
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datasets.logging.set_verbosity_info() |
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_HOMEPAGE = "http://shuoyang1213.me/WIDERFACE/" |
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_LICENSE = "Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)" |
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_CITATION = """\ |
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@inproceedings{yang2016wider, |
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Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, |
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Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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Title = {WIDER FACE: A Face Detection Benchmark}, |
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Year = {2016}} |
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""" |
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_DESCRIPTION = """\ |
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WIDER FACE dataset is a face detection benchmark dataset, of which images are |
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selected from the publicly available WIDER dataset. We choose 32,203 images and |
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label 393,703 faces with a high degree of variability in scale, pose and |
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occlusion as depicted in the sample images. WIDER FACE dataset is organized |
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based on 61 event classes. For each event class, we randomly select 40%/10%/50% |
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data as training, validation and testing sets. We adopt the same evaluation |
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metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, |
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we do not release bounding box ground truth for the test images. Users are |
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required to submit final prediction files, which we shall proceed to evaluate. |
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""" |
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_REPO = "https://huggingface.co/datasets/khaclinh/testdata/resolve/main/data" |
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_URLS = { |
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"test": f"{_REPO}/fisheye.zip", |
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"annot": f"{_REPO}/annotations.zip", |
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} |
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IMG_EXT = ['png', 'jpeg', 'jpg'] |
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class TestData(datasets.GeneratorBasedBuilder): |
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"""WIDER FACE dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"faces": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=4)), |
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"plates": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=4)), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"split": "test", |
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"data_dir": data_dir["test"], |
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"annot_dir": data_dir["annot"], |
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}, |
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), |
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] |
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def _generate_examples(self, split, data_dir, annot_dir): |
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image_dir = os.path.join(data_dir, "fisheye") |
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annotation_dir = os.path.join(annot_dir, "annotations", "fisheye") |
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files = [] |
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idx = 0 |
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for img_file in glob(os.path.join(image_dir, "*.png")): |
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d = { image_dir: annot_dir, "png": "txt"} |
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gt_path = img_file |
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for i, j in d.items(): |
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gt_path = gt_path.replace(i, j) |
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faces = [] |
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plates = [] |
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faces.append([1,2,3,4]) |
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plates.append([1,2,3,4]) |
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yield idx, {"image": str(img_file), "faces": faces, "plates": plates} |
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idx += 1 |
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