Create VGGFace2.py

VGGFace2.py

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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
+        )
+    ]
+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"
+    def _info(self):
+        features = {
+            "file": datasets.Image(),
+            "path": 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,
+            homepage=_URL,
+            supervised_keys=datasets.info.SupervisedKeysData("file", "speaker_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 = dl_manager.iter_archive(
+                dict(
+                    (
+                        placeholder_key,
+                        dict(
+                            (target, _URLS[target][placeholder_key])
+                            for target in targets
+                        ),
+                    )
+                    for placeholder_key in ("train", "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
+