VGGFace2-HQ.py

# 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-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['Image_Name'] = temp['Image_Path'].str.split('/').str[1]
            
            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()},
            # "image_id": datasets.Value("string"),
            # "class_id": datasets.Value("string"),
            # "identity": datasets.Value("string"),
            # 'gender': dataset.Value("string"),
            # 'sample_num':dataset.Value("uint64"), 
            # 'flag':dataset.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")
                    }
                key+= 1