import os import json from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split, Features, Value, Sequence, Image as HFImage class LineGraphicsDataset(GeneratorBasedBuilder): def _info(self): return DatasetInfo( features=Features({ "image_name": Value("string"), "image": HFImage(), "width": Value("int32"), "height": Value("int32"), "instances": Sequence({ "category_id": Value("int32"), "mask": Sequence(Sequence(Value("float32"))) # List of polygons (each polygon = list of floats) }) }), description="Line Graphics Dataset with polygon instance masks in COCO format.", ) def _split_generators(self, dl_manager): data_dir = dl_manager.download_and_extract(self.config.data_dir or ".") return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={"split_dir": os.path.join(data_dir, "train")}, ), SplitGenerator( name=Split.VALIDATION, gen_kwargs={"split_dir": os.path.join(data_dir, "val")}, ), SplitGenerator( name=Split.TEST, gen_kwargs={"split_dir": os.path.join(data_dir, "test")}, ), ] def _generate_examples(self, split_dir): # Load annotations annotation_file = os.path.join(split_dir, "annotations.json") with open(annotation_file, "r") as f: coco = json.load(f) # Map image_id to metadata image_id_to_info = {img["id"]: img for img in coco["images"]} # Group annotations by image from collections import defaultdict anns_by_image = defaultdict(list) for ann in coco["annotations"]: anns_by_image[ann["image_id"]].append(ann) # Generate examples for image_id, image_info in image_id_to_info.items(): file_name = image_info["file_name"] image_path = os.path.join(split_dir, "images", file_name) instances = [] for ann in anns_by_image[image_id]: # Each mask is a list of polygons segmentation = ann["segmentation"] if not isinstance(segmentation, list): continue # skip if malformed instances.append({ "category_id": ann["category_id"], "mask": segmentation }) yield file_name, { "image_name": file_name, "image": image_path, "width": image_info["width"], "height": image_info["height"], "instances": instances }