fix

.hand-gesture-recognition-dataset

@@ -1,83 +0,0 @@
-# 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, files_set in enumerate(files):
-#             set_id = int(files_set[0].split('/')[2])
-#             data = {'set_id': set_id}
-
-#             for file in files_set:
-#                 file_name = file.split('/')[3]
-#                 if 'fist' in file_name.lower():
-#                     data['fist'] = file
-#                 elif 'four' in file_name.lower():
-#                     data['four'] = file
-#                 elif 'me' in file_name.lower():
-#                     data['me'] = file
-#                 elif 'one' in file_name.lower():
-#                     data['one'] = file
-#                 elif 'small' in file_name.lower():
-#                     data['small'] = file
-#             yield idx, data

hand-gesture-recognition-dataset.py

@@ -0,0 +1,93 @@
+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, file_path in enumerate(files):
+            set_id = int(file_path.split('/')[-2])
+            file_name = file_path.split('/')[-1]
+
+            print(set_id, file_name)
+            if 'fist' in file_name:
+                data = {
+                    'set_id':
+                        set_id,
+                    'fist':
+                        annotations_df.loc[annotations_df['set_id'] == set_id]
+                        ['fist'].values[0],
+                    'four':
+                        annotations_df.loc[annotations_df['set_id'] == set_id]
+                        ['four'].values[0],
+                    'me':
+                        annotations_df.loc[annotations_df['set_id'] == set_id]
+                        ['me'].values[0],
+                    'one':
+                        annotations_df.loc[annotations_df['set_id'] == set_id]
+                        ['one'].values[0],
+                    'small':
+                        annotations_df.loc[annotations_df['set_id'] == set_id]
+                        ['small'].values[0]
+                }
+
+                yield idx, data