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"""v12 ULTIMATE cross-source manifest.
Integrates EVERY valid source:
1. Hitit original (17k) + aug (5k)
2. Pseudo from unsup cluster (36k)
3. Pseudo iter2 from ensemble (3k)
4. eBL classification overlap (43k, cap 500/cls)
5. OB zip after extraction (55k → capped)
6. DeepScribe classification overlap (3k)
7. eBL LMU 158k metadata (new!) — filter for Hitit-label overlap
8. compvis bbox extracted — mzl→abz mapping where possible
"""
import json, argparse, os, re
from pathlib import Path
from collections import Counter
ROOT = Path("/arf/scratch/stakan/hitit-proje")
def load_mzl_abz_map(path=None):
"""Load MZL→ABZ mapping JSON built from Oracc OSL.
Returns (mzl_to_sign, mzl_to_abz) where:
- mzl_to_sign: "839" → "KUR" (canonical sign name, Hitit-compatible)
- mzl_to_abz: "839" → "470" (ABZ/ABZL number)
"""
if path is None:
path = ROOT / 'hitit_ocr/data/mzl_abz_map.json'
if not Path(path).exists(): return {}, {}
with open(path) as f:
d = json.load(f)
return d.get('mzl_to_sign', {}), d.get('mzl_to_abz', {})
def extract_compvis_bboxes(label_to_idx, cap_per_label=200, mzl_map=None):
"""Extract sign bboxes from compvis manifest to per-bbox classification records.
Uses the MZL→ABZ sign mapping built from Oracc OSL to translate
compvis `mzl_label` numerical codes (e.g. "839") into canonical sign names
(e.g. "KUR"). Only records whose mapped sign name exists in `label_to_idx`
are kept.
"""
mzl_to_sign = mzl_map or {}
src_path = ROOT / 'datasets/sources/compvis/manifest.jsonl'
if not src_path.exists(): return []
out = []
per_label = Counter()
n_mapped = 0; n_unmapped = 0
for line in open(src_path):
r = json.loads(line)
if r.get('task') != 'detection': continue
extra = r.get('extra') or {}
bboxes = extra.get('bboxes', [])
# Find image path. compvis images live in cdli/<tablet_id>.jpg;
# manifest stores image_name like "P334926_Obv" (strip _Obv/_Rev).
img_path = r.get('path')
if img_path is None or not os.path.exists(img_path):
iname = r.get('image_name') or ''
tid = r.get('tablet_id') or ''
base = re.sub(r'_(Obv|Rev|obverse|reverse)$', '', iname)
cand_names = [base, tid, iname]
img_path = None
for name in cand_names:
if not name: continue
for sub in ('cdli', 'saa05', 'test'):
c = ROOT / f'datasets/sources/compvis/cuneiform-sign-detection-dataset/images/{sub}/{name}.jpg'
if c.exists():
img_path = str(c); break
if img_path: break
if not img_path or not os.path.exists(img_path): continue
for b in bboxes:
mzl = str(b.get('mzl_label', '')).strip()
if not mzl: continue
# Try (1) direct match, (2) MZL-map translation to sign name
if mzl in label_to_idx:
lab = mzl
else:
lab = mzl_to_sign.get(mzl) or mzl_to_sign.get(mzl.lstrip('0'))
if not lab or lab not in label_to_idx:
n_unmapped += 1; continue
n_mapped += 1
if per_label[lab] >= cap_per_label: continue
bb = b.get('bbox') or b.get('relative_bbox')
if not bb or len(bb) != 4: continue
out.append({
'task': 'classification',
'storage': 'fs',
'path': img_path,
'bbox': bb,
'unified_label': lab,
'cross_source': True,
'cross_source_origin': 'compvis_bbox',
'tablet_view_fold': 1,
'integrity_ok': True,
})
per_label[lab] += 1
print(f" [compvis] bbox records: {len(out)} (mzl-mapped: {n_mapped}, unmapped: {n_unmapped})")
return out
def extract_yeni_veri_bboxes(label_to_idx, cap_per_label=500):
"""yeni_veri: 38 Hittite tablets with YOLO bboxes + class_name (ABZ-compatible).
Each bbox becomes a classification record (native Hittite)."""
src_path = ROOT / 'datasets/sources/yeni_veri/manifest.jsonl'
if not src_path.exists(): return []
out = []; per_label = Counter()
for line in open(src_path):
try: r = json.loads(line)
except: continue
if r.get('task') != 'detection': continue
img_path = r.get('path') or ''
if img_path and not os.path.isabs(img_path):
img_path = str(ROOT / img_path)
if not os.path.exists(img_path): continue
extra = r.get('extra') or {}
for b in extra.get('bboxes', []):
lab = b.get('class_name')
if not lab or lab not in label_to_idx: continue
if per_label[lab] >= cap_per_label: continue
yb = b.get('yolo_bbox')
if not yb or len(yb) != 4: continue
out.append({
'task': 'classification',
'storage': 'fs',
'path': img_path,
'bbox': yb,
'bbox_format': 'yolo',
'unified_label': lab,
'cross_source': True,
'cross_source_origin': 'yeni_veri',
'tablet_view_fold': 1,
'integrity_ok': True,
'period': 'Hittite',
})
per_label[lab] += 1
print(f" [yeni_veri] bbox records: {len(out)}")
return out
def extract_maicubeda(label_to_idx, cap_per_label=300):
"""maicubeda: 27.5k classification crops (char-level).
Filter by label overlap, keep fs storage only."""
src_path = ROOT / 'datasets/sources/maicubeda/manifest_classification.jsonl'
if not src_path.exists():
src_path = ROOT / 'datasets/sources/maicubeda/manifest.jsonl'
if not src_path.exists(): return []
out = []; per_label = Counter()
for line in open(src_path):
try: r = json.loads(line)
except: continue
if r.get('task') != 'classification': continue
if r.get('storage') != 'fs': continue
if r.get('integrity_ok') is False: continue
lab = r.get('unified_label')
if not lab or lab not in label_to_idx: continue
if per_label[lab] >= cap_per_label: continue
p = r.get('path')
if not p or not os.path.exists(p): continue
out.append({
'task': 'classification',
'storage': 'fs',
'path': p,
'unified_label': lab,
'cross_source': True,
'cross_source_origin': 'maicubeda',
'tablet_view_fold': 1,
'integrity_ok': True,
})
per_label[lab] += 1
print(f" [maicubeda] records: {len(out)}")
return out
def extract_ebl_lmu(label_to_idx, cap_per_label=200, max_take=80000):
"""Extract Hitit-overlapping records from eBL LMU 158k dataset.
After tar extraction the jpeg files sit directly in
datasets/sources/ebl_lmu/<_id>.jpeg (no snippets/ subdir). We also
normalize signName: eBL uses 'ŠU₂' with Unicode subscripts while Hittite
labels are plain ASCII ('ŠU2'), so we try both forms.
"""
meta_path = ROOT / 'datasets/sources/ebl_lmu/metadata.json'
if not meta_path.exists(): return []
archive_dirs = [
ROOT / 'datasets/sources/ebl_lmu',
ROOT / 'datasets/sources/ebl_lmu/snippets',
ROOT / 'datasets/sources/ebl_ocr/v2_20251219/snippets',
]
archive_dir = next((d for d in archive_dirs if d.exists()), None)
if archive_dir is None: return []
# Unicode subscript → ASCII digit map for signName normalization
sub_map = str.maketrans('₀₁₂₃₄₅₆₇₈₉', '0123456789')
with open(meta_path) as f:
meta = json.load(f)
if not isinstance(meta, list): return []
out = []; per_label = Counter()
n_label_miss = 0; n_file_miss = 0
for r in meta:
raw_sn = r.get('signName') or ''
# Try (1) raw, (2) ASCII-subscript form, (3) uppercase
for cand in (raw_sn, raw_sn.translate(sub_map), raw_sn.upper().translate(sub_map)):
if cand in label_to_idx:
sn = cand; break
else:
n_label_miss += 1; continue
if per_label[sn] >= cap_per_label: continue
if len(out) >= max_take: break
sid = r.get('_id', '')
# Expected file: {archive_dir}/{_id}.jpeg (tar stored them flat)
p_candidates = [archive_dir / f'{sid}.jpeg', archive_dir / f'{sid}.jpg']
p = next((x for x in p_candidates if x.exists()), None)
if p is None:
n_file_miss += 1; continue
out.append({
'task': 'classification',
'storage': 'fs',
'path': str(p),
'unified_label': sn,
'cross_source': True,
'cross_source_origin': 'ebl_lmu',
'tablet_view_fold': 1,
'integrity_ok': True,
'period': r.get('script') or 'Mesopotamian',
})
per_label[sn] += 1
print(f" [ebl_lmu] records: {len(out)} (label_miss={n_label_miss}, file_miss={n_file_miss})")
return out
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--base-manifest', required=True,
help='Base Hitit manifest to extend')
ap.add_argument('--label-to-idx', required=True, help='v4 ckpt for label set')
ap.add_argument('--output', required=True)
ap.add_argument('--include-pseudo', action='store_true', default=True)
ap.add_argument('--cap-per-label', type=int, default=500)
ap.add_argument('--mzl-map', default=str(ROOT / 'hitit_ocr/data/mzl_abz_map.json'))
args = ap.parse_args()
import torch
ck = torch.load(args.label_to_idx, map_location='cpu', weights_only=False)
l2i = ck['label_to_idx']
print(f"Hitit classes: {len(l2i)}")
mzl_to_sign, _ = load_mzl_abz_map(args.mzl_map)
print(f"MZL→sign mappings: {len(mzl_to_sign)}")
added = Counter()
with open(args.output, 'w') as out:
# Base
with open(args.base_manifest) as f:
for line in f:
out.write(line); added['base'] += 1
# Already integrated pseudo manifests (check they exist and not already in base)
for src_name, src_path in [
('unsup_cluster', ROOT / 'hitit_ocr/runs/h100/unsup_cluster_v4/pseudo_labels.jsonl'),
('pseudo_iter2_ebl', ROOT / 'datasets/sources/hitit_local/manifest_pseudo_iter2_ebl.jsonl'),
]:
if not src_path.exists(): continue
with open(src_path) as f:
for line in f:
try: r = json.loads(line)
except: continue
if r.get('unified_label') not in l2i: continue
if r.get('tablet_view_fold', 0) == 0: r['tablet_view_fold'] = 1
out.write(json.dumps(r) + '\n'); added[src_name] += 1
# Cross-source from previously-built v11 (Hitit + eBL + OB + DS)
# Reuse build logic: read each source, filter by overlap
for src in ['ebl_ocr', 'old_babylonian_signs', 'deepscribe']:
# Check both regular and fs-rewritten manifest
src_mf = ROOT / f'datasets/sources/{src}/manifest_fs.jsonl'
if not src_mf.exists():
src_mf = ROOT / f'datasets/sources/{src}/manifest.jsonl'
if not src_mf.exists(): continue
per_label = Counter()
with open(src_mf) as f:
for line in f:
try: r = json.loads(line)
except: continue
if r.get('task') != 'classification': continue
lab = r.get('unified_label')
if not lab or lab not in l2i: continue
if r.get('storage') != 'fs' or not r.get('path'): continue
if r.get('integrity_ok') is False: continue
if per_label[lab] >= args.cap_per_label: continue
r['cross_source'] = True; r['cross_source_origin'] = src
r['tablet_view_fold'] = 1
out.write(json.dumps(r) + '\n'); added[src] += 1
per_label[lab] += 1
# eBL LMU 158k
ebl_rows = extract_ebl_lmu(l2i, cap_per_label=args.cap_per_label, max_take=30000)
for r in ebl_rows:
out.write(json.dumps(r) + '\n')
added['ebl_lmu'] = len(ebl_rows)
# compvis bbox with MZL→ABZ map
cv_rows = extract_compvis_bboxes(l2i, cap_per_label=args.cap_per_label,
mzl_map=mzl_to_sign)
for r in cv_rows:
out.write(json.dumps(r) + '\n')
added['compvis_bbox'] = len(cv_rows)
# yeni_veri: Hitit tablet bboxes with native ABZ class_name
yv_rows = extract_yeni_veri_bboxes(l2i, cap_per_label=args.cap_per_label)
for r in yv_rows:
out.write(json.dumps(r) + '\n')
added['yeni_veri'] = len(yv_rows)
# maicubeda: char-level classification crops
mai_rows = extract_maicubeda(l2i, cap_per_label=args.cap_per_label)
for r in mai_rows:
out.write(json.dumps(r) + '\n')
added['maicubeda'] = len(mai_rows)
print(f"\nManifest v12 breakdown:")
total = 0
for k, v in added.items():
print(f" {k}: {v}")
total += v
print(f" TOTAL: {total}")
print(f"Output: {args.output}")
if __name__ == '__main__':
main()
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