feat: upload script

printed_photos_attacks.py

@@ -1,9 +1,8 @@
 import datasets
-import pandas as pd
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {anti-spoofing_replay},
+title = {printed_photos_attacks},
 author = {TrainingDataPro},
 year = {2023}
 }
@@ -14,7 +13,7 @@ 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 = 'anti-spoofing_replay'
+_NAME = 'printed_photos_attacks'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -23,53 +22,46 @@ _LICENSE = "cc-by-nc-nd-4.0"
 _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
 
 
-class AntiSpoofingReplay(datasets.GeneratorBasedBuilder):
+class PrintedPhotosAttacks(datasets.GeneratorBasedBuilder):
 
     def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features({
-                'live_video_id': datasets.Value('string'),
-                'phone': datasets.Value('string'),
-                'video_file': datasets.Value('string'),
-                'phone_video_playback': datasets.Value('string'),
-                'worker_id': datasets.Value('string')
-            }),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE)
+        return datasets.DatasetInfo(description=_DESCRIPTION,
+                                    features=datasets.Features({
+                                        'attack': datasets.Value('string'),
+                                        'live_selfie': datasets.Image(),
+                                        'live_video': datasets.Value('string')
+                                    }),
+                                    supervised_keys=None,
+                                    homepage=_HOMEPAGE,
+                                    citation=_CITATION,
+                                    license=_LICENSE)
 
     def _split_generators(self, dl_manager):
-        videos = dl_manager.download(f"{_DATA}videos.tar.gz")
-        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        videos = dl_manager.iter_archive(videos)
+        attacks = dl_manager.download(f"{_DATA}attack.tar.gz")
+        live_selfies = dl_manager.download(f"{_DATA}live_selfie.tar.gz")
+        live_videos = dl_manager.download(f"{_DATA}live_video.tar.gz")
+        attacks = dl_manager.iter_archive(attacks)
+        live_selfies = dl_manager.iter_archive(live_selfies)
+        live_videos = dl_manager.iter_archive(live_videos)
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
-                                        "videos": videos,
-                                        'annotations': annotations
+                                        'attacks': attacks,
+                                        "live_selfies": live_selfies,
+                                        'live_videos': live_videos
                                     }),
         ]
 
-    def _generate_examples(self, videos, annotations):
-        annotations_df = pd.read_csv(annotations, sep=';')
-        for idx, (video_path, video) in enumerate(videos):
-            file_name = '/'.join(video_path.split('/')[-2:])
+    def _generate_examples(self, attacks, live_selfies, live_videos):
+        for idx, ((attack_path, attack), (live_selfie_path, live_selfie),
+                  (live_video_path, live_video)) in enumerate(
+                      zip(attacks, live_selfies, live_videos)):
 
             yield idx, {
-                'live_video_id':
-                    annotations_df.loc[annotations_df['link'] == file_name]
-                    ['live_video_id'].values[0],
-                'phone':
-                    annotations_df.loc[annotations_df['link'] == file_name]
-                    ['phone'].values[0],
-                'video_file':
-                    video_path,
-                'phone_video_playback':
-                    annotations_df.loc[annotations_df['link'] == file_name]
-                    ['phone_video_playback'].values[0],
-                'worker_id':
-                    annotations_df.loc[annotations_df['link'] == file_name]
-                    ['worker_id'].values[0]
+                'attack': attack_path,
+                'live_selfie': {
+                    'path': live_selfie_path,
+                    'bytes': live_selfie
+                },
+                'live_video': live_video_path
             }