Create waste_dumpsites.py

waste_dumpsites.py

@@ -0,0 +1,109 @@
+import json
+import os
+from pathlib import Path
+import datasets
+
+class WasteDumpsitesConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+
+class WasteDumpsites(datasets.GeneratorBasedBuilder):
+    """Waste Dumpsites Drone Imagery dataset in COCO format."""
+
+    BUILDER_CONFIGS = [
+        WasteDumpsitesConfig(name="default", description="Default config for Waste Dumpsites dataset")
+    ]
+
+    def _info(self):
+        print("datasets.DatasetInfo")
+        return datasets.DatasetInfo(
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "image_id": datasets.Value("int64"),
+                "width": datasets.Value("int64"),
+                "height": datasets.Value("int64"),
+                "annotations": datasets.Sequence({
+                    "id": datasets.Value("int64"),
+                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                    "area": datasets.Value("float32"),
+                    "category_id": datasets.Value("int64"),
+                    "category_name": datasets.Value("string"),
+                    "iscrowd": datasets.Value("int64"),
+                })
+            }),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        print("Returns SplitGenerators.")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"split": "valid"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, split):
+        """Yields examples for COCO format."""
+        print("Yields examples for COCO format.")
+        # Using your exact directory and file structure
+        split_dir = os.path.join(self.config.data_dir, split)
+        annotations_file = os.path.join(split_dir, "annotations_coco.json")
+        
+        # Read COCO format annotations
+        with open(annotations_file, 'r') as f:
+            coco_data = json.load(f)
+            
+        # Create category id to name mapping
+        categories = {cat['id']: cat['name'] for cat in coco_data['categories']}
+        
+        # Group annotations by image
+        image_annotations = {}
+        for ann in coco_data['annotations']:
+            img_id = ann['image_id']
+            if img_id not in image_annotations:
+                image_annotations[img_id] = []
+            image_annotations[img_id].append(ann)
+        
+        # Yield examples
+        for image in coco_data['images']:
+            image_id = image['id']
+            print(image_id)
+            
+            # Get all annotations for this image
+            annotations = image_annotations.get(image_id, [])
+            
+            # Prepare annotations in the required format
+            formatted_annotations = {
+                "id": [],
+                "bbox": [],
+                "area": [],
+                "category_id": [],
+                "category_name": [],
+                "iscrowd": []
+            }
+            
+            for ann in annotations:
+                formatted_annotations["id"].append(ann["id"])
+                formatted_annotations["bbox"].append(ann["bbox"])
+                formatted_annotations["area"].append(ann["area"])
+                formatted_annotations["category_id"].append(ann["category_id"])
+                formatted_annotations["category_name"].append(categories[ann["category_id"]])
+                formatted_annotations["iscrowd"].append(ann.get("iscrowd", 0))
+            
+            yield str(image_id), {
+                "image": os.path.join(split_dir, image["file_name"]),
+                "image_id": image_id,
+                "width": image["width"],
+                "height": image["height"],
+                "annotations": formatted_annotations
+            }