feat: add load script

silicone-masks-biometric-attacks.py

@@ -0,0 +1,74 @@
+from xml.etree import ElementTree as ET
+
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {silicone-masks-biometric-attacks},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of videos of individuals and attacks with printed 2D masks and
+silicone masks . Videos are filmed in different lightning conditions (*in a dark room,
+daylight, light room and nightlight*). Dataset includes videos of people with different
+attributes (*glasses, mask, hat, hood, wigs and mustaches for men*).
+"""
+
+_NAME = "silicone-masks-biometric-attacks"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+_LABELS = ["real", "silicone", "mask"]
+
+
+class SiliconeMasksBiometricAttacks(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "name": datasets.Value("string"),
+                    "video": datasets.Value("string"),
+                    "label": datasets.ClassLabel(
+                        num_classes=len(_LABELS),
+                        names=_LABELS,
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        videos = dl_manager.download(f"{_DATA}videos.tar.gz")
+        videos = dl_manager.iter_archive(videos)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "videos": videos,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, videos):
+        for idx, ((video_path, video)) in enumerate(videos):
+            for lbl in _LABELS:
+                if lbl in video_path:
+                    label = lbl
+
+            yield idx, {
+                "id": idx,
+                "name": video_path.split("/")[-1],
+                "video": video_path,
+                "label": label,
+            }