refactor: all data

.gitattributes

@@ -57,3 +57,4 @@ live_videos/0001d815c0--61eeaf9928213259ef95a20a.mp4 filter=lfs diff=lfs merge=l
 live_videos/0001d815c0--61f3c3321e087a11af822fdc.mp4 filter=lfs diff=lfs merge=lfs -text
 live_videos/0001d815c0--6207d93b43afdc2f8d25853a.mp4 filter=lfs diff=lfs merge=lfs -text
 live_videos/0001db3fa7--61e9938abb27377cdd4ea53a.mp4 filter=lfs diff=lfs merge=lfs -text
+data filter=lfs diff=lfs merge=lfs -text

live_videos/0001d815c0--61f3c3321e087a11af822fdc.mp4 → data/videos.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7b7faa0bb5385931698c5a5a680e3edfabe7497340aaf628cabf17a2a3601521
-size 1517577
+oid sha256:c6ef5625cbdb0d58d44fe60ff6eae8bf72a46719f06727783fd6dae05a2b8d9f
+size 8073719

live_videos/0001d815c0--61e561016090114967d86569.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9f4d20cee120aeb2d21529d32fbc30de519ca655216ed980f63f775037bb5c50
-size 1172009

live_videos/0001d815c0--61eeaf9928213259ef95a20a.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b59295e4404d7830b5345504e5a61121c40605412706c6668d8a7893f304fbf0
-size 1059547

live_videos/0001d815c0--6207d93b43afdc2f8d25853a.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4df148f35adf452cf4d6d26158047d9e861e0f834c54ded4b20aac1cdf86233d
-size 1795725

live_videos/0001db3fa7--61e9938abb27377cdd4ea53a.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:012637a839449add13860d2d7faa49af81a0bcd7e68bda9bc189f04495fff312
-size 1265421

low_quality_webcam_video_attacks.py

@@ -0,0 +1,87 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {low_quality_webcam_video_attacks},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset includes live-recorded Anti-Spoofing videos from around the world,
+captured via low-quality webcams with resolutions like QVGA, QQVGA and QCIF.
+"""
+_NAME = 'low_quality_webcam_video_attacks'
+
+_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 LowQualityWebcamVideoAttacks(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'video_file': datasets.Value('string'),
+                'assignment_id': datasets.Value('string'),
+                'worker_id': datasets.Value('string'),
+                'gender': datasets.Value('string'),
+                'age': datasets.Value('uint8'),
+                'country': datasets.Value('string'),
+                'resolution': 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)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "videos": videos,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, videos, annotations):
+        annotations_df = pd.read_csv(annotations, sep=';')
+        for idx, (image_path, video) in enumerate(videos):
+            file_name = image_path.split('/')[-1]
+            assignment_id = file_name.split('.')[0]
+
+            yield idx, {
+                "video_file":
+                    file_name,
+                'assignment_id':
+                    assignment_id,
+                'worker_id':
+                    annotations_df.loc[
+                        annotations_df['assignment_id'] == assignment_id]
+                    ['worker_id'].values[0],
+                'gender':
+                    annotations_df.loc[
+                        annotations_df['assignment_id'] == assignment_id]
+                    ['gender'].values[0],
+                'age':
+                    annotations_df.loc[
+                        annotations_df['assignment_id'] == assignment_id]
+                    ['age'].values[0],
+                'country':
+                    annotations_df.loc[
+                        annotations_df['assignment_id'] == assignment_id]
+                    ['country'].values[0],
+                'resolution':
+                    annotations_df.loc[
+                        annotations_df['assignment_id'] == assignment_id]
+                    ['resolution'].values[0]
+            }