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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Datasets Authors and ProgramComputer. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # Lint as: python3 | |
| """VGGFace2 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 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: {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/resolve/main/data/vggface2_train.tar.gz", | |
| "test": "https://huggingface.co/datasets/ProgramComputer/VGGFace2/resolve/main/data/vggface2_test.tar.gz", | |
| } | |
| } | |
| class VGGFace2(datasets.GeneratorBasedBuilder): | |
| """VGGFace2 is dataset contains faces from Google Search""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( version=VERSION | |
| ) | |
| ] | |
| def _info(self): | |
| features = { | |
| "file": datasets.Image(), | |
| "image_id": datasets.Value("string"), | |
| "class_id": datasets.Value("string"), | |
| "identity": datasets.Value("string"), | |
| "male": datasets.Value("binary"), | |
| "black_hair": datasets.Value("string"), | |
| "gray_hair": datasets.Value("string"), | |
| "blond_hair": datasets.Value("string"), | |
| "long_hair": datasets.Value("string"), | |
| "mustache_or_beard": datasets.Value("string"), | |
| "wearing_hat": datasets.Value("string"), | |
| "eyeglasses": datasets.Value("string"), | |
| "sunglasses": datasets.Value("string"), | |
| "mouth_open": datasets.Value("string"), | |
| } | |
| 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.iter_archive( | |
| _URLS["default"]["train"] | |
| ) | |
| mapped_paths_test = dl_manager.iter_archive( | |
| _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=",", | |
| index_col=0, | |
| engine="python", | |
| ) | |
| for conf in [x for x in meta_paths.values() if x != meta_paths["identity"]]: | |
| temp = pd.read_csv(conf,sep='\t', header=None) | |
| temp.columns = ['Image_Path', conf] | |
| temp['Class_ID'] = temp['Image_Path'].str.split('/').str[0] | |
| temp['Image_Name'] = temp['Image_Path'].str.split('/').str[1] | |
| # Drop the 'Image_Path' column from df2 | |
| temp.drop(columns=['Image_Path'], inplace=True) | |
| # Merge the two DataFrames on 'Class_ID' | |
| result_df = meta.merge(df2, on='Class_ID', how='outer') | |
| print(meta) | |
| for file_path, file_obj in images: | |
| if file_path.startswith(_IMAGES_DIR): | |
| if file_path[len(_IMAGES_DIR) : -len(".jpg")] in files_to_keep: | |
| clip_index = int(clip.stem) | |
| label = file_path.split("/")[2] | |
| yield file_path, { | |
| "image": {"path": file_path, "bytes": file_obj.read()}, | |
| "label": label, | |
| } | |
| key+= 1 | |