feat: add script

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

@@ -0,0 +1,109 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {selfie_and_video},
+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).
+"""
+_NAME = 'selfie_and_video'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class SelfieAndVideo(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')
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}data.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        images = dl_manager.iter_archive(images)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "images": images,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, images, 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