Datasets:
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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
} |