Upload set-classification.py

set-classification.py

@@ -0,0 +1,64 @@
+import datasets
+import pandas as pd
+from datasets import DownloadManager
+
+class SetClassification(datasets.GeneratorBasedBuilder):
+    """Set-Classification Images dataset"""
+
+    def __init__(self, data_path, *args, **kwargs):
+        super(SetClassification, self).__init__(*args, **kwargs)
+        self.data_path = data_path
+        self.labels = pd.read_csv(f'{self.data_path}/labels.csv')
+        self.train = self.labels[self.labels['split'] == 'train']
+        self.test = self.labels[self.labels['split'] == 'test']
+        self.dl_manager = DownloadManager()
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description='Set Classification Images dataset',
+        )
+    
+
+    def _split_generators(self, dl_manager):
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    'images': [f"{self.data_path}/images/{image.filename}" for image in self.train.itertuples()],
+                    'labels': {
+                        'no': [image.no for image in self.train.itertuples()],
+                        'shape': [image.shape for image in self.train.itertuples()],
+                        'color': [image.color for image in self.train.itertuples()],
+                        'shading': [image.shading for image in self.train.itertuples()]
+                    }
+                }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    'images': [f"{self.data_path}/images/{image.filename}" for image in self.test.itertuples()],
+                    'labels': {
+                        'no': [image.no for image in self.test.itertuples()],
+                        'shape': [image.shape for image in self.test.itertuples()],
+                        'color': [image.color for image in self.test.itertuples()],
+                        'shading': [image.shading for image in self.test.itertuples()]
+                    }
+                }
+            )
+        ]
+
+    def _generate_examples(self, images, labels):
+        for img, label in zip(images, zip(*labels.values())):
+            try:
+                with open(img, 'rb') as img_obj:
+                    no, shape, color, shading = label
+                    yield img, {
+                        'image': {"path": img, "bytes": img_obj.read()},
+                        'no': no,
+                        'shape': shape,
+                        'color': color,
+                        'shading': shading
+                    }
+            except Exception as e:
+                print(f"Error processing image {img}: {e}")