feat: script

hand-gesture-recognition-dataset.py

@@ -0,0 +1,84 @@
+import datasets
+import pandas as pd
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {hand-gesture-recognition-dataset},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of videos showcasing individuals demonstrating 5 different
+hand gestures (*"one", "four", "small", "fist", and "me"*). Each video captures
+a person prominently displaying a single hand gesture, allowing for accurate
+identification and differentiation of the gestures.
+The dataset offers a diverse range of individuals performing the gestures,
+enabling the exploration of variations in hand shapes, sizes, and movements
+across different individuals. 
+The videos in the dataset are recorded in reasonable lighting conditions and
+with adequate resolution, to ensure that the hand gestures can be easily
+observed and studied.
+"""
+_NAME = 'hand-gesture-recognition-dataset'
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = "cc-by-nc-nd-4.0"
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+
+class HandGestureRecognitionDataset(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(description=_DESCRIPTION,
+                                    features=datasets.Features({
+                                        'set_id': datasets.Value('int32'),
+                                        'fist': datasets.Value('string'),
+                                        'four': datasets.Value('string'),
+                                        'me': datasets.Value('string'),
+                                        'one': datasets.Value('string'),
+                                        "small": datasets.Value('string')
+                                    }),
+                                    supervised_keys=None,
+                                    homepage=_HOMEPAGE,
+                                    citation=_CITATION,
+                                    license=_LICENSE)
+
+    def _split_generators(self, dl_manager):
+        files = dl_manager.download_and_extract(f"{_DATA}files.zip")
+        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
+        files = dl_manager.iter_files(files)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={
+                                        "files": files,
+                                        'annotations': annotations
+                                    }),
+        ]
+
+    def _generate_examples(self, files, annotations):
+        annotations_df = pd.read_csv(annotations, sep=';')
+
+        files = sorted(files)
+        files = [files[i:i + 5] for i in range(0, len(files), 5)]
+
+        for idx, images_set in enumerate(files):
+            set_id = int(images_set[0].split('/')[2])
+            data = {'set_id': set_id}
+
+            for file in images_set:
+                if 'fist' in file.lower():
+                    data['fist'] = file
+                elif 'four' in file.lower():
+                    data['four'] = file
+                elif 'me' in file.lower():
+                    data['me'] = file
+                elif 'one' in file.lower():
+                    data['one'] = file
+                elif 'small' in file.lower():
+                    data['small'] = file
+
+            yield idx, data