from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, DownloadManager from typing import Any, Dict, List, Tuple import os import json class ProcessedImageDataset(GeneratorBasedBuilder): VERSION = "1.0.0" def _info(self) -> DatasetInfo: # Specify dataset info here return DatasetInfo( # You can add description, citation, homepage, etc. features=self._features(), supervised_keys=("image", "text"), ) def _features(self): # Define the features of your dataset: image file, text, etc. from datasets import Features, Image, Value return Features({"image_file": Image(), "text": Value("string")}) def _split_generators(self, dl_manager) -> List[SplitGenerator]: # This method is tasked with downloading/extracting the data and defining the splits print(self.config.data_dir) if self.config.data_dir is None: raise ValueError(f'Data directory unspecified. Correct usage is: load_dataset(script_path, data_dir=data_dir_path)') return [SplitGenerator(name="train", gen_kwargs={"data_dir": self.config.data_dir})] def _generate_examples(self, data_dir): # def _generate_examples(self, data_dir: str) -> Tuple[int, Dict[str, Any]]: # This method will read the data and yield examples metadata_file_path = os.path.join(data_dir, "metadata.jsonl") # Read metadata and store it in a dictionary metadata = {} with open(metadata_file_path, "r") as f: for line in f: item = json.loads(line) metadata[item["file_name"]] = item # Iterate through each file in the data directory for filename in os.listdir(data_dir): if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".jpeg"): if filename in metadata: metadata_entry = metadata[filename] yield filename, { "image_file": os.path.join(data_dir, filename), "text": metadata_entry["text"], }