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| """VGGFace2-HQ audio-visual human speech dataset.""" |
|
|
| import json |
| import os |
| import re |
| from urllib.parse import urlparse, parse_qs |
| from getpass import getpass |
| from hashlib import sha256 |
| from itertools import repeat |
| from multiprocessing import Manager, Pool, Process |
| from pathlib import Path |
| from shutil import copyfileobj |
| from warnings import catch_warnings, filterwarnings |
| from urllib3.exceptions import InsecureRequestWarning |
|
|
| import pandas as pd |
| import requests |
|
|
| import datasets |
|
|
| _DESCRIPTION = "VGGFace2-HQ is a large-scale face recognition dataset. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession." |
| _CITATION = """\ |
| @article{DBLP:journals/corr/abs-1710-08092, |
| author = {Qiong Cao and |
| Li Shen and |
| Weidi Xie and |
| Omkar M. Parkhi and |
| Andrew Zisserman}, |
| title = {VGGFace2-HQ: {A} dataset for recognising faces across pose and age}, |
| journal = {CoRR}, |
| volume = {abs/1710.08092}, |
| year = {2017}, |
| url = {http://arxiv.org/abs/1710.08092}, |
| eprinttype = {arXiv}, |
| eprint = {1710.08092}, |
| timestamp = {Wed, 04 Aug 2021 07:50:14 +0200}, |
| biburl = {https://dblp.org/rec/journals/corr/abs-1710-08092.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| """ |
|
|
|
|
|
|
| _URLS = { |
| "default": { |
| "train": ("https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac01.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac02.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac03.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac04.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac05.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac06.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac07.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac08.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac09.zip", |
| "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/train/VGGFac10.zip" |
|
|
| ), |
| "test": "https://huggingface.co/datasets/ProgramComputer/VGGFace2-HQ/resolve/main/test/test.zip", |
| } |
| } |
|
|
|
|
|
|
| class VGGFace2-HQ(datasets.GeneratorBasedBuilder): |
| """VGGFace2-HQ is dataset contains faces from Google Search""" |
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig( version=VERSION |
| ) |
| ] |
|
|
| def _info(self): |
| features = { |
| "image": datasets.Image(), |
| "image_id": datasets.Value("string"), |
| "class_id": datasets.Value("string"), |
| "identity": datasets.Value("string"), |
| 'gender': datasets.Value("string"), |
| 'sample_num':datasets.Value("uint64"), |
| 'flag':datasets.Value("bool"), |
| "male": datasets.Value("bool"), |
| "black_hair": datasets.Value("bool"), |
| "gray_hair": datasets.Value("bool"), |
| "blond_hair": datasets.Value("bool"), |
| "long_hair": datasets.Value("bool"), |
| "mustache_or_beard": datasets.Value("bool"), |
| "wearing_hat": datasets.Value("bool"), |
| "eyeglasses": datasets.Value("bool"), |
| "sunglasses": datasets.Value("bool"), |
| "mouth_open": datasets.Value("bool"), |
| } |
|
|
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| supervised_keys=datasets.info.SupervisedKeysData("file", "class_id"), |
| features=datasets.Features(features), |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| targets = ( |
| ["01-Male.txt", "02-Black_Hair.txt","03-Brown_Hair.txt","04-Gray_Hair.txt","05-Blond_Hair.txt","06-Long_Hair.txt","07-Mustache_or_Beard.txt","08-Wearing_Hat.txt","09-Eyeglasses.txt","10-Sunglasses.txt","11-Mouth_Open.txt"] |
| ) |
| target_dict = dict( |
| ( |
| re.sub(r"^\d+-|\.txt$","",target), |
| f"https://raw.githubusercontent.com/ox-vgg/vgg_face2/master/attributes/{target}", |
| ) |
| for target in targets |
| ) |
| target_dict['identity'] = "https://huggingface.co/datasets/ProgramComputer/VGGFace2/raw/main/meta/identity_meta.csv" |
| metadata = dl_manager.download( |
| target_dict |
| ) |
|
|
| mapped_paths_train = dl_manager.download_and_extract( |
| _URLS["default"]["train"] |
| ) |
| mapped_paths_test = dl_manager.download_and_extract( |
| _URLS["default"]["test"] |
| ) |
| return [ |
| datasets.SplitGenerator( |
| name="train", |
| gen_kwargs={ |
| "paths": mapped_paths_train, |
| "meta_paths": metadata, |
| }, |
| ), |
| datasets.SplitGenerator( |
| name="test", |
| gen_kwargs={ |
| "paths": mapped_paths_test, |
| "meta_paths": metadata, |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, paths, meta_paths): |
| key = 0 |
| meta = pd.read_csv( |
| meta_paths["identity"], |
| sep=", " |
| ) |
| for key,conf in [(k,v) for (k,v) in meta_paths.items() if k != "identity"]: |
| |
| temp = pd.read_csv(conf,sep='\t', header=None) |
| temp.columns = ['Image_Path', key] |
| |
| temp['Class_ID'] = temp['Image_Path'].str.split('/').str[0] |
| |
| |
| temp.drop(columns=['Image_Path'], inplace=True) |
| |
| meta = meta.merge(temp, on='Class_ID', how='left') |
| raise Exception(meta) |
| for file_path, file_obj in paths: |
|
|
| label = file_path.split("/")[2] |
| yield file_path, { |
| "image": {"path": file_path, "bytes": file_obj.read()}, |
| |
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| } |
| key+= 1 |
| |
