refactor: script and readme

README.md

@@ -4,6 +4,58 @@ task_categories:
 - image-to-image
 tags:
 - code
+dataset_info:
+  features:
+  - name: id_1
+    dtype: image
+  - name: id_2
+    dtype: image
+  - name: selfie_1
+    dtype: image
+  - name: selfie_2
+    dtype: image
+  - name: selfie_3
+    dtype: image
+  - name: selfie_4
+    dtype: image
+  - name: selfie_5
+    dtype: image
+  - name: selfie_6
+    dtype: image
+  - name: selfie_7
+    dtype: image
+  - name: selfie_8
+    dtype: image
+  - name: selfie_9
+    dtype: image
+  - name: selfie_10
+    dtype: image
+  - name: selfie_11
+    dtype: image
+  - name: selfie_12
+    dtype: image
+  - name: selfie_13
+    dtype: image
+  - name: user_id
+    dtype: string
+  - name: set_id
+    dtype: string
+  - name: user_race
+    dtype: string
+  - name: name
+    dtype: string
+  - name: age
+    dtype: int8
+  - name: country
+    dtype: string
+  - name: gender
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 376371811
+    num_examples: 10
+  download_size: 374658409
+  dataset_size: 376371811
 ---
 # Selfies, ID Images dataset
 **4083** sets, which includes *2 photos of a person from his documents and 13 selfies*. **571** sets of Hispanics and **3512** sets of Caucasians.

selfies_and_id.py

@@ -0,0 +1,118 @@
+import io
+
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {selfies_and_id},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+4083 sets, which includes 2 photos of a person from his documents and
+13 selfies. 571 sets of Hispanics and 3512 sets of Caucasians.
+Photo documents contains only a photo of a person.
+All personal information from the document is hidden.
+"""
+_NAME = 'selfies_and_id'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class SelfiesAndId(datasets.GeneratorBasedBuilder):
+    """Small sample of image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'id_1': datasets.Image(),
+                'id_2': datasets.Image(),
+                'selfie_1': datasets.Image(),
+                'selfie_2': datasets.Image(),
+                'selfie_3': datasets.Image(),
+                'selfie_4': datasets.Image(),
+                'selfie_5': datasets.Image(),
+                'selfie_6': datasets.Image(),
+                'selfie_7': datasets.Image(),
+                'selfie_8': datasets.Image(),
+                'selfie_9': datasets.Image(),
+                'selfie_10': datasets.Image(),
+                'selfie_11': datasets.Image(),
+                'selfie_12': datasets.Image(),
+                'selfie_13': datasets.Image(),
+                'user_id': datasets.Value('string'),
+                'set_id': datasets.Value('string'),
+                'user_race': datasets.Value('string'),
+                'name': 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}images.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=['URL', 'Bytes'])
+        for idx, (image_path, image) in enumerate(images):
+            images_data.loc[idx] = {'URL': image_path, 'Bytes': image.read()}
+
+        annotations_df = pd.merge(annotations_df,
+                                  images_data,
+                                  how='left',
+                                  on=['URL'])
+        for idx, worker_id in enumerate(pd.unique(annotations_df['UserId'])):
+            annotation = annotations_df.loc[annotations_df['UserId'] ==
+                                            worker_id]
+            annotation = annotation.sort_values(['FName'])
+            data = {
+                row[5].lower(): {
+                    'path': row[6],
+                    'bytes': row[10]
+                } for row in annotation.itertuples()
+            }
+
+            age = annotation.loc[annotation['FName'] ==
+                                 'ID_1']['Age'].values[0]
+            country = annotation.loc[annotation['FName'] ==
+                                     'ID_1']['Country'].values[0]
+            gender = annotation.loc[annotation['FName'] ==
+                                    'ID_1']['Gender'].values[0]
+            set_id = annotation.loc[annotation['FName'] ==
+                                    'ID_1']['SetId'].values[0]
+            user_race = annotation.loc[annotation['FName'] ==
+                                       'ID_1']['UserRace'].values[0]
+            name = annotation.loc[annotation['FName'] ==
+                                  'ID_1']['Name'].values[0]
+
+            data['user_id'] = worker_id
+            data['age'] = age
+            data['country'] = country
+            data['gender'] = gender
+            data['set_id'] = set_id
+            data['user_race'] = user_race
+            data['name'] = name
+
+            yield idx, data