| """Offline inventory of candidate training supervision; makes no API calls.""" |
| import argparse, collections, hashlib, json |
| from pathlib import Path |
| from PIL import Image |
| from run_contextual_lite import validate_annotation |
|
|
|
|
| def main(): |
| p=argparse.ArgumentParser(description=__doc__) |
| p.add_argument('--run',type=Path,required=True) |
| p.add_argument('--dataset',type=Path,default=Path(__file__).resolve().parents[1]/'captures_1000') |
| p.add_argument('--output',type=Path,required=True) |
| args=p.parse_args();args.output.mkdir(parents=True,exist_ok=True) |
| meta={r['id']:r for line in (args.dataset/'manifest.jsonl').read_text().splitlines() if line.strip() for r in [json.loads(line)]} |
| counts=collections.Counter();apps=collections.Counter();hashes=collections.defaultdict(list);rows=[];errors=[] |
| masks=[] |
| for f in sorted(args.run.glob('screen_*/annotation.json')): |
| a=json.loads(f.read_text());sid=a['screen_id'];src=f.parent/'source.png' |
| try: |
| original=args.dataset/'screens'/f'{sid}.png' |
| actual=hashlib.sha256(original.read_bytes()).hexdigest() |
| if actual!=a['image']['sha256']:raise ValueError('Image hash mismatch') |
| with Image.open(src) as im, Image.open(original) as original_im: |
| if list(im.size)!=[a['image']['width'],a['image']['height']]:raise ValueError('Image dimensions mismatch') |
| pixels=im.convert('RGB').tobytes() |
| if im.size!=original_im.size or pixels!=original_im.convert('RGB').tobytes():raise ValueError('Report image pixels differ from source') |
| pixel_hash=hashlib.sha256(str(im.size).encode()+pixels).hexdigest() |
| props=json.loads((args.dataset/'proposals'/f'{sid}.json').read_text())['proposals'] |
| validate_annotation(a,props) |
| except Exception as e: |
| errors.append({'screen':sid,'error':str(e)[:300]});continue |
| hashes[pixel_hash].append(sid);apps[meta.get(sid,{}).get('app_name','unknown')]+=1 |
| disps=a['coverage']['proposals_disposition'];accepted={pid for pid,d in disps.items() if d['disposition']=='retained'} |
| counts['screens']+=1;counts['nodes']+=len(a['nodes']);counts['proposals']+=len(disps) |
| counts['human_reviewed_screens']+=bool(a['annotation_provenance']['human_reviewed']) |
| usable=0 |
| for n in a['nodes']: |
| counts['level_'+n['level']]+=1 |
| linked=n['proposal_ids'] |
| geometry_ok=not linked or all(pid in accepted for pid in linked) |
| if n['level']!='screen' and geometry_ok:usable+=1 |
| masks.append({'screen':sid,'node':n['id'],'candidate_for_teacher_supervision':n['level']!='screen' and geometry_ok, |
| 'parent_supervision_candidate':n['level']!='screen' and geometry_ok, |
| 'exact_text_supervision':bool(n['visible_text']), |
| 'state_supervision_requires_review':True,'semantic_correctness_verified':False}) |
| counts['nodes_with_visible_text']+=bool(n['visible_text']) |
| counts['candidate_nonroot_nodes']+=usable |
| for d in disps.values():counts['disposition_'+d['disposition']]+=1 |
| rows.append({'screen':sid,'annotation':str(f.resolve()),'image':str(src.resolve()),'sha256':actual, |
| 'app':meta.get(sid,{}).get('app_name','unknown'),'candidate_nodes':usable, |
| 'capture_timestamp':meta.get(sid,{}).get('timestamp'), |
| 'session_group_id':meta.get(sid,{}).get('session_group_id'), |
| 'split':'unassigned_pending_duplicate_and_capture_group_review'}) |
| summary={'counts':dict(counts),'applications':dict(apps),'unique_pixel_images':len(hashes), |
| 'exact_duplicate_groups':[v for v in hashes.values() if len(v)>1], |
| 'errors':errors,'status':'Candidate teacher data; semantic quality unverified; no train/test split created.', |
| 'notes':['Unresolved/rejected proposal-linked nodes are excluded from candidate supervision.', |
| 'Exact decoded-pixel grouping is not near-duplicate detection. Group related captures before splitting.', |
| 'Field masks are eligibility hints, not assertions of ground truth.', |
| 'Current role aliases such as slider->toggle and radio->checkbox require correction or masking before training.']} |
| (args.output/'inventory.json').write_text(json.dumps(summary,indent=2)) |
| for name,records in [('screens.jsonl',rows),('candidate_masks.jsonl',masks)]: |
| (args.output/name).write_text(''.join(json.dumps(r)+'\n' for r in records)) |
| print(json.dumps({'counts':dict(counts),'unique_pixel_images':len(hashes),'duplicate_groups':len(summary['exact_duplicate_groups']),'errors':errors[:3],'total_errors':len(errors)},indent=2)) |
|
|
| if __name__=='__main__':main() |
|
|
