feat: script

cut-2d-masks-presentation-attack-detection.py

@@ -3,7 +3,7 @@ import pandas as pd
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {2d-masks-presentation-attack-detection},
+title = {cut-2d-masks-presentation-attack-detection},
 author = {TrainingDataPro},
 year = {2023}
 }
@@ -16,7 +16,7 @@ Videos are filmed in different lightning conditions and in different places
 (indoors, outdoors). Each video in the dataset has an approximate duration of 2
 seconds.
 """
-_NAME = '2d-masks-presentation-attack-detection'
+_NAME = 'cut-2d-masks-presentation-attack-detection'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -25,26 +25,15 @@ _LICENSE = ""
 _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
 
 
-class MasksPresentationAttackDetection(datasets.GeneratorBasedBuilder):
+class Cut2dMasksPresentationAttackDetection(datasets.GeneratorBasedBuilder):
     """Small sample of image-text pairs"""
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
-                'user': datasets.Value('string'),
-                'real_1': datasets.Value('string'),
-                'real_2': datasets.Value('string'),
-                'real_3': datasets.Value('string'),
-                'real_4': datasets.Value('string'),
-                'mask_1': datasets.Value('string'),
-                'mask_2': datasets.Value('string'),
-                'mask_3': datasets.Value('string'),
-                'mask_4': datasets.Value('string'),
-                'cut_1': datasets.Value('string'),
-                'cut_2': datasets.Value('string'),
-                'cut_3': datasets.Value('string'),
-                'cut_4': datasets.Value('string')
+                'link': datasets.Value('string'),
+                'type': datasets.Value('string')
             }),
             supervised_keys=None,
             homepage=_HOMEPAGE,
@@ -52,60 +41,25 @@ class MasksPresentationAttackDetection(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        files = dl_manager.download(f"{_DATA}files.tar.gz")
+        masks = dl_manager.download(f"{_DATA}masks.tar.gz")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        files = dl_manager.iter_archive(files)
+        masks = dl_manager.iter_archive(masks)
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
-                                        "files": files,
+                                        "masks": masks,
                                         'annotations': annotations
                                     }),
         ]
 
-    def _generate_examples(self, files, annotations):
+    def _generate_examples(self, masks, annotations):
         annotations_df = pd.read_csv(annotations, sep=';')
 
-        for idx, (file_path, file) in enumerate(files):
-            if 'real_1' in file_path.lower():
-                user = file_path.split('/')[-2]
-                yield idx, {
-                    'user':
-                        user,
-                    'real_1':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['real_1'].values[0],
-                    'real_2':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['real_2'].values[0],
-                    'real_3':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['real_3'].values[0],
-                    'real_4':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['real_4'].values[0],
-                    'mask_1':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['mask_1'].values[0],
-                    'mask_2':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['mask_2'].values[0],
-                    'mask_3':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['mask_3'].values[0],
-                    'mask_4':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['mask_4'].values[0],
-                    'cut_1':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['cut_1'].values[0],
-                    'cut_2':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['cut_2'].values[0],
-                    'cut_3':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['cut_3'].values[0],
-                    'cut_4':
-                        annotations_df.loc[annotations_df['user'] == user]
-                        ['cut_4'].values[0],
-                }
+        for idx, (mask_path, mask) in enumerate(masks):
+            yield idx, {
+                'link':
+                    mask_path,
+                'type':
+                    annotations_df.loc[annotations_df['link'] == mask_path]
+                    ['type'].values[0]
+            }