| import argparse,csv,json |
| from collections import Counter |
| from pathlib import Path |
| TASKS={1:'Image Description',2:'Amodal Counting',3:'Pattern Recognition',4:'Occluded Count Reasoning'};META=('difficulty_score','pattern_type','shape_type','occluder_style','total_object_count','visible_object_count','occluded_object_count','canvas_size','seed') |
| def flatten(root): |
| root=Path(root);rows=[];images=0;bad=[] |
| for line in (root/'annotations.jsonl').open(encoding='utf8'): |
| item=json.loads(line);images+=1;qs=item.get('questions',[]) |
| if len(qs)!=4:bad.append(item['id']) |
| metadata=json.dumps({k:item[k] for k in META},separators=(',',':')) |
| for q in qs:rows.append({'task':TASKS[q['difficulty_level']],'image':Path(item['image_path']).name,'prompt':q['question_text'],'groundtruth':str(q['ground_truth']),'metadata':metadata}) |
| fields=('task','image','prompt','groundtruth','metadata') |
| with (root/'dataset_final.csv').open('w',encoding='utf-8-sig',newline='') as f:w=csv.DictWriter(f,fieldnames=fields);w.writeheader();w.writerows(rows) |
| with (root/'dataset_final.jsonl').open('w',encoding='utf8',newline='\n') as f: |
| for row in rows:f.write(json.dumps(row,separators=(',',':'))+'\n') |
| with (root/'dataset_final.csv').open(encoding='utf-8-sig',newline='') as f:readback=sum(1 for _ in csv.DictReader(f)) |
| counts=Counter(r['task'] for r in rows);print(f'Images read: {images}\nTotal rows written: {len(rows)}\nCSV rows re-read: {readback}') |
| for task,count in sorted(counts.items()):print(f'{task}: {count}') |
| print('Images without exactly 4 questions: '+(', '.join(bad) if bad else 'none')) |
| if readback!=images*4 or bad:raise SystemExit(1) |
| def main():p=argparse.ArgumentParser();p.add_argument('--dataset-dir',default='.');flatten(p.parse_args().dataset_dir) |
| if __name__=='__main__':main() |
|
|
