Update script to hub

Boat_dataset.py

@@ -38,67 +38,101 @@ _URLS = {
 class BoatDataset(datasets.GeneratorBasedBuilder):
 
     VERSION = datasets.Version("1.1.0")
+    
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Dataset for detecting boats in aerial images."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "Boat_dataset"  # Provide a default configuration
 
     def _info(self):
-        features=datasets.Features({
-            'image_id': datasets.Value('int32'),
-            'image_path': datasets.Value('string'),
-            'width': datasets.Value('int32'),
-            'height': datasets.Value('int32'),
-            'objects': datasets.Features({
-                'id': datasets.Sequence(datasets.Value('int32')),
-                'area': datasets.Sequence(datasets.Value('float32')),
-                'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
-                'category': datasets.Sequence(datasets.Value('int32'))
-            }),
-        })
-        
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=features,
+            features=datasets.Features({
+                'image_id': datasets.Value('int32'),
+                'image_path': datasets.Value('string'),
+                'width': datasets.Value('int32'),
+                'height': datasets.Value('int32'),
+                'objects': datasets.Features({
+                    'id': datasets.Sequence(datasets.Value('int32')),
+                    'area': datasets.Sequence(datasets.Value('float32')),
+                    'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
+                    'category': datasets.Sequence(datasets.Value('int32'))
+                }),
+            }),
             homepage=_HOMEPAGE,
             license=_LICENSE,
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
+        # Download all files and extract them
         downloaded_files = dl_manager.download_and_extract(_URLS)
 
+        # Paths to files and archives
+        image_archive_path = downloaded_files["images"]
+        classes_file_path = downloaded_files["classes"]
+        annotations_paths = downloaded_files["anno"]
+
+        # Load class labels from the classes file
+        with open(classes_file_path, 'r') as file:
+            classes = [line.strip() for line in file.readlines()]
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    'annotations_file': downloaded_files['anno']['train'],
-                    'split': 'train'
+                    "image_archive_path": image_archive_path,
+                    "annotations_file": annotations_paths["train"],
+                    "classes": classes,
+                    "split": "train",
                 }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    'annotations_file': downloaded_files['anno']['val'],
-                    'split': 'val'
+                    "image_archive_path": image_archive_path,
+                    "annotations_file": annotations_paths["val"],
+                    "classes": classes,
+                    "split": "val",
                 }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    'annotations_file': downloaded_files['anno']['test'],
-                    'split': 'val_real'
+                    "image_archive_path": image_archive_path,
+                    "annotations_file": annotations_paths["test"],
+                    "classes": classes,
+                    "split": "val_real",
                 }
-            )
+            ),
         ]
 
-    def _generate_examples(self, annotations_file, split):
+    def _generate_examples(self, dl_manager, image_archive_path, annotations_file, classes, split):
+        # Open the archive using iter_archive to access files without extracting them
+        with dl_manager.iter_archive(image_archive_path) as archive:
+            # Convert path: file-like object pairs to a dict for quick access
+            images_dict = {os.path.basename(path): file for path, file in archive}
+
+        # Process annotations
         with open(annotations_file, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 try:
                     data = json.loads(row.strip())
+                    file = images_dict.get(data["file_name"])
+                    if not file:
+                        continue  # Skip if file is not found in the archive
                     yield key, {
                         "image_id": data["image_id"],
-                        "image_path": data["image_path"],
+                        "image_path": file,  # You can save the path or file object depending on your processing needs
                         "width": data["width"],
                         "height": data["height"],
-                        "objects": data["objects"],
+                        "objects": {
+                            "id": data["objects"]["id"],
+                            "area": data["objects"]["area"],
+                            "bbox": data["objects"]["bbox"],
+                            "category": [classes[idx] for idx in data["objects"]["category"]]
+                        },
                     }
                 except json.JSONDecodeError:
                     print(f"Skipping invalid JSON at line {key + 1}: {row}")