fix: update script

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

@@ -3,20 +3,20 @@ import pandas as pd
 
 _CITATION = """\
 @InProceedings{huggingface:dataset,
-title = {selfie_and_video},
+title = {2d-masks-presentation-attack-detection},
 author = {TrainingDataPro},
 year = {2023}
 }
 """
 
 _DESCRIPTION = """\
-4000 people in this dataset. Each person took a selfie on a webcam,
-took a selfie on a mobile phone. In addition, people recorded video from
-the phone and from the webcam, on which they pronounced a given set of numbers.
-Includes folders corresponding to people in the dataset. Each folder includes
-8 files (4 images and 4 videos).
+The dataset consists of videos of individuals wearing printed 2D masks or
+printed 2D masks with cut-out eyes and directly looking at the camera.
+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 = 'selfie_and_video'
+_NAME = '2d-masks-presentation-attack-detection'
 
 _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
 
@@ -25,26 +25,26 @@ _LICENSE = ""
 _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
 
 
-class SelfieAndVideo(datasets.GeneratorBasedBuilder):
+class MasksPresentationAttackDetection(datasets.GeneratorBasedBuilder):
     """Small sample of image-text pairs"""
 
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features({
-                'photo_1': datasets.Image(),
-                'photo_2': datasets.Image(),
-                'video_3': datasets.Value('string'),
-                'video_4': datasets.Value('string'),
-                'photo_5': datasets.Image(),
-                'photo_6': datasets.Image(),
-                'video_7': datasets.Value('string'),
-                'video_8': datasets.Value('string'),
-                'set_id': datasets.Value('string'),
-                'worker_id': datasets.Value('string'),
-                'age': datasets.Value('int8'),
-                'country': datasets.Value('string'),
-                'gender': datasets.Value('string')
+                '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')
             }),
             supervised_keys=None,
             homepage=_HOMEPAGE,
@@ -52,58 +52,60 @@ class SelfieAndVideo(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        images = dl_manager.download(f"{_DATA}data.tar.gz")
+        files = dl_manager.download(f"{_DATA}files.tar.gz")
         annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
-        images = dl_manager.iter_archive(images)
+        files = dl_manager.iter_archive(files)
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN,
                                     gen_kwargs={
-                                        "images": images,
+                                        "files": files,
                                         'annotations': annotations
                                     }),
         ]
 
-    def _generate_examples(self, images, annotations):
+    def _generate_examples(self, files, annotations):
         annotations_df = pd.read_csv(annotations, sep=';')
-        images_data = pd.DataFrame(columns=['Link', 'Bytes'])
-        for idx, (image_path, image) in enumerate(images):
-            if image_path.lower().endswith('.jpg'):
-                images_data.loc[idx] = {
-                    'Link': image_path,
-                    'Bytes': image.read()
-                }
-
-        annotations_df = pd.merge(annotations_df,
-                                  images_data,
-                                  on=['Link'],
-                                  how='left')
-        
-        for idx, worker_id in enumerate(pd.unique(annotations_df['WorkerId'])):
-            annotation = annotations_df.loc[annotations_df['WorkerId'] ==
-                                            worker_id]
-            annotation = annotation.sort_values(['Link'])
-            data = {
-                (f'photo_{row[7][37]}' if row[7].lower().endswith('.jpg') else f'video_{row[7][37]}'):
-                    ({
-                        'path': row[7],
-                        'bytes': row[8]
-                    } if row[7].lower().endswith('.jpg') else row[7])
-                for row in annotation.itertuples()
-            }
-
-            age = annotation.loc[annotation['Link'].str.lower().str.endswith(
-                '1.jpg')]['Age'].values[0]
-            country = annotation.loc[annotation['Link'].str.lower().str.
-                                     endswith('1.jpg')]['Country'].values[0]
-            gender = annotation.loc[annotation['Link'].str.lower().str.
-                                    endswith('1.jpg')]['Gender'].values[0]
-            set_id = annotation.loc[annotation['Link'].str.lower().str.
-                                    endswith('1.jpg')]['SetId'].values[0]
 
-            data['worker_id'] = worker_id
-            data['age'] = age
-            data['country'] = country
-            data['gender'] = gender
-            data['set_id'] = set_id
-
-            yield idx, data
+        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],
+                }