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