refactor: all data

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presentation-attack-detection-2d-dataset.py

@@ -0,0 +1,83 @@
+from itertools import pairwise
+
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {presentation-attack-detection-2d-dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of photos of individuals and videos of him/her wearing printed 2D
+mask with cut-out holes for eyes. Videos are filmed in different lightning conditions
+and in different places (*indoors, outdoors*), a person moves his/her head left, right,
+up and down. Each video in the dataset has an approximate duration of 15-17 seconds. 
+"""
+_NAME = "presentation-attack-detection-2d-dataset"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class PresentationAttackDetection2dDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "photo": datasets.Image(),
+                    "video": datasets.Value("string"),
+                    "worker_id": datasets.Value("string"),
+                    "set_id": datasets.Value("string"),
+                    "age": datasets.Value("int8"),
+                    "country": datasets.Value("string"),
+                    "gender": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    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, ((image_path, image), (video_path, video)) in enumerate(
+            pairwise(attacks)
+        ):
+            yield idx, {
+                "photo": {"path": image_path, "bytes": image.read()},
+                "video": video_path,
+                "worker_id": annotations_df.loc[
+                    annotations_df["worker_id"] == image_path.split("/")[-2]
+                ]["worker_id"].values[0],
+                "set_id": annotations_df.loc[
+                    annotations_df["worker_id"] == image_path.split("/")[-2]
+                ]["set_id"].values[0],
+                "age": annotations_df.loc[
+                    annotations_df["worker_id"] == image_path.split("/")[-2]
+                ]["age"].values[0],
+                "country": annotations_df.loc[
+                    annotations_df["worker_id"] == image_path.split("/")[-2]
+                ]["country"].values[0],
+                "gender": annotations_df.loc[
+                    annotations_df["worker_id"] == image_path.split("/")[-2]
+                ]["gender"].values[0],
+            }