feat: script

selfie-and-video-on-back-camera.py

@@ -0,0 +1,84 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {selfie-and-video-on-back-camera},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of selfies and video of real people made on a back camera
+of the smartphone. The dataset solves tasks in the field of anti-spoofing and
+it is useful for buisness and safety systems.
+"""
+_NAME = 'selfie-and-video-on-back-camera'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class SelfieAndVideoOnBackCamera(datasets.GeneratorBasedBuilder):
+    """Small sample of image-text pairs"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                'photo': datasets.Image(),
+                'video': datasets.Value('string'),
+                'phone': datasets.Value('string'),
+                'gender': datasets.Value('string'),
+                'age': datasets.Value('int8'),
+                'country': datasets.Value('string'),
+            }),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}photo.tar.gz")
+        videos = dl_manager.download(f"{_DATA}video.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        images = dl_manager.iter_archive(images)
+        videos = dl_manager.iter_archive(videos)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "images": images,
+                                        'videos': videos,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, images, videos, annotations):
+        annotations_df = pd.read_csv(annotations, sep=';')
+
+        for idx, ((image_path, image),
+                  (video_path, video)) in enumerate(zip(images, videos)):
+            yield idx, {
+                "photo": {
+                    "path": image_path,
+                    "bytes": image.read()
+                },
+                "video":
+                    video_path,
+                'phone':
+                    annotations_df.loc[annotations_df['photo'].str.startswith(
+                        str(idx))]['phone'].values[0],
+                'gender':
+                    annotations_df.loc[annotations_df['photo'].str.startswith(
+                        str(idx))]['gender'].values[0],
+                'age':
+                    annotations_df.loc[annotations_df['photo'].str.startswith(
+                        str(idx))]['age'].values[0],
+                'country':
+                    annotations_df.loc[annotations_df['photo'].str.startswith(
+                        str(idx))]['country'].values[0],
+            }