feat: script refactor: csv

.gitattributes

@@ -77,3 +77,4 @@ attacks/6.MOV filter=lfs diff=lfs merge=lfs -text
 attacks/7.MOV filter=lfs diff=lfs merge=lfs -text
 attacks/8.MOV filter=lfs diff=lfs merge=lfs -text
 attacks/9.MOV filter=lfs diff=lfs merge=lfs -text
+data filter=lfs diff=lfs merge=lfs -text

MacBook-Attacks-Dataset.py

@@ -0,0 +1,83 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {MacBook-Attacks-Dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of 40,000 videos and selfies with unique people. 15,000
+attack replays from 4,000 unique devices. 10,000 attacks with A4 printouts and
+10,000 attacks with cut-out printouts.
+"""
+_NAME = 'MacBook-Attacks-Dataset'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = "cc-by-nc-nd-4.0"
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class MacBookAttacksDataset(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(description=_DESCRIPTION,
+                                    features=datasets.Features({
+                                        'file': datasets.Value('string'),
+                                        'phone': datasets.Value('string'),
+                                        'computer': datasets.Value('string'),
+                                        'gender': datasets.Value('string'),
+                                        'age': datasets.Value('int16'),
+                                        'country': datasets.Value('string'),
+                                    }),
+                                    supervised_keys=None,
+                                    homepage=_HOMEPAGE,
+                                    citation=_CITATION,
+                                    license=_LICENSE)
+
+    def _split_generators(self, dl_manager):
+        attacks = dl_manager.download(f"{_DATA}attacks.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        attacks = dl_manager.iter_archive(attacks)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "attacks": attacks,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, attacks, annotations):
+        annotations_df = pd.read_csv(annotations, sep=';')
+        for idx, (video_path, video) in enumerate(attacks):
+            file_name = '/'.join(video_path.split('/')[-2:])
+
+            yield idx, {
+                'file':
+                    file_name,
+                'phone':
+                    annotations_df.loc[
+                        annotations_df['file'].str.lower() == file_name]
+                    ['phone'].values[0],
+                'computer':
+                    annotations_df.loc[
+                        annotations_df['file'].str.lower() == file_name]
+                    ['computer'].values[0],
+                'gender':
+                    annotations_df.loc[
+                        annotations_df['file'].str.lower() == file_name]
+                    ['gender'].values[0],
+                'age':
+                    annotations_df.loc[
+                        annotations_df['file'].str.lower() == file_name]
+                    ['age'].values[0],
+                'country':
+                    annotations_df.loc[
+                        annotations_df['file'].str.lower() == file_name]
+                    ['country'].values[0]
+            }