| """Flatten v7 nested-polygon annotations without exposing raw generation metadata.""" |
| import csv,json |
| from pathlib import Path |
|
|
| TASKS={1:"Image Description",2:"Basic Relational Reasoning",3:"Comparative Reasoning",4:"Compound Reasoning",5:"Extrapolative/Counterfactual Reasoning"} |
| META_KEYS=("difficulty_score","rotation_mode","reduction_mode","step_reduction_factors","reduction_factor_span","factor_progression_direction","extrapolation_reduction_factor","line_width_px","seed","parameter_seed","symmetry_modulus_degrees","cumulative_rotation_degrees","cumulative_rotation_fraction","target_cumulative_rotation_degrees","generation_attempt","generation_rejections") |
|
|
| def flatten(annotations): |
| annotations=Path(annotations);root=annotations.parent |
| records=[json.loads(line) for line in annotations.read_text(encoding="utf-8").splitlines() if line] |
| final=[];public=[];private=[] |
| for record in records: |
| metadata=json.dumps({key:record.get(key) for key in META_KEYS},ensure_ascii=False,separators=(",",":"));image=Path(record["image_path"]).name |
| for question in record["questions"]: |
| base={"dataset_version":record["dataset_version"],"question_id":question["question_id"],"task":TASKS[question["difficulty_level"]],"image":image,"prompt":question["question_text"]};groundtruth=str(question["ground_truth"]) |
| public.append(base);private.append({**base,"groundtruth":groundtruth,"answer_format":question["answer_format"]});final.append({"dataset_version":record["dataset_version"],"task":base["task"],"image":image,"prompt":base["prompt"],"groundtruth":groundtruth,"metadata":metadata}) |
| def write(name,fields,rows): |
| with (root/name).open("w",encoding="utf-8-sig",newline="") as handle: |
| writer=csv.DictWriter(handle,fieldnames=fields,lineterminator="\n");writer.writeheader();writer.writerows(rows) |
| write("question_set.csv",["dataset_version","question_id","task","image","prompt"],public) |
| write("answer_key.csv",["dataset_version","question_id","task","image","prompt","groundtruth","answer_format"],private) |
| write("dataset_final.csv",["dataset_version","task","image","prompt","groundtruth","metadata"],final) |
| with (root/"dataset_final.jsonl").open("w",encoding="utf-8",newline="\n") as handle: |
| for row in final:handle.write(json.dumps(row,ensure_ascii=False,separators=(",",":"))+"\n") |
| if len(final)!=5*len(records):raise ValueError("v7 flattened row count mismatch") |
| for path in (root/"question_set.csv",root/"answer_key.csv",root/"dataset_final.csv",root/"dataset_final.jsonl"): |
| text=path.read_text(encoding="utf-8-sig") |
| if "offset_intended" in text or "offset_requested_pre_containment" in text:raise ValueError(f"private offset field leaked into {path.name}") |
| print(f"Images: {len(records)}\nRows written: {len(final)}\nSanity check: PASS") |
|
|
