Delete cleanup_dataset.py with huggingface_hub

cleanup_dataset.py

@@ -1,180 +0,0 @@
-#!/usr/bin/env python3
-"""
-Clean dataset: remove black JPGs (brightness < 30), update metadata.
-No PCD issues found — all PCD sizes are 2-6 MB (healthy).
-"""
-import pandas as pd
-import zipfile
-import io
-import shutil
-import concurrent.futures
-from pathlib import Path
-from collections import defaultdict
-from PIL import Image
-
-ZIP_DIR = Path('/workspace/3D_o')
-MERGED_FILE = '/workspace/merged_result.parquet'
-INDEX_FILE = '/workspace/archive_index.parquet'
-CATALOG_FILE = '/workspace/file_catalog.parquet'
-BAD_FILE = '/workspace/bad_files.parquet'
-
-BRIGHTNESS_THRESHOLD = 30  # below this = black frame
-
-print("=" * 60)
-print("Finding black JPGs (brightness < 30)...")
-print("=" * 60)
-
-catalog = pd.read_parquet(CATALOG_FILE)
-jpg_catalog = catalog[catalog['path'].str.startswith('jpg/')]
-suspicious = jpg_catalog[jpg_catalog['size'] < 500_000]
-print(f"  Suspicious JPGs (< 500 KB): {len(suspicious)}")
-
-def check_archive(archive):
-    bad = []
-    rows = suspicious[suspicious['archive'] == archive]
-    if len(rows) == 0:
-        return bad
-    try:
-        with zipfile.ZipFile(ZIP_DIR / archive, 'r') as zf:
-            for _, row in rows.iterrows():
-                data = zf.read(row['path'])
-                img = Image.open(io.BytesIO(data))
-                gray = img.convert('L')
-                pixels = gray.getdata()
-                n = gray.width * gray.height
-                mean = sum(pixels) / n
-                if mean < BRIGHTNESS_THRESHOLD:
-                    bad.append((archive, row['path'], row['size'], mean))
-    except Exception as e:
-        print(f"  ERROR {archive}: {e}")
-    return bad
-
-archives = suspicious['archive'].unique()
-bad_jpgs = []
-with concurrent.futures.ThreadPoolExecutor(max_workers=8) as ex:
-    results = ex.map(check_archive, archives)
-    for r in results:
-        bad_jpgs.extend(r)
-
-print(f"  Black JPGs found: {len(bad_jpgs)}")
-
-bad_records = []
-for a, p, sz, b in bad_jpgs:
-    bad_records.append({'archive': a, 'path': p, 'type': 'jpg', 'reason': f'black_frame:brightness={b:.1f}'})
-
-bad_df = pd.DataFrame(bad_records)
-bad_df.to_parquet(BAD_FILE)
-print(f"  Saved to {BAD_FILE}")
-
-if len(bad_df) == 0:
-    print("  No bad files found. Dataset is clean!")
-    exit(0)
-
-# Show affected archives
-arc_counts = bad_df.groupby('archive').size().sort_index()
-print(f"\n  Affected archives:")
-for arc, cnt in arc_counts.items():
-    print(f"    {arc}: {cnt} black JPGs")
-
-# ─── Phase 2: Update merged_result.parquet ────────────────────────────────
-print("\n" + "=" * 60)
-print("Updating merged_result.parquet...")
-print("=" * 60)
-
-bad_set = set(zip(bad_df['archive'], bad_df['path']))
-
-merged = pd.read_parquet(MERGED_FILE)
-print(f"  Original rows: {len(merged)}")
-
-# Find rows with bad JPGs
-merged['_bad_jpg'] = merged.apply(
-    lambda r: pd.notna(r['jpg_path']) and (r['archive'], r['jpg_path']) in bad_set, axis=1
-)
-n_bad = merged['_bad_jpg'].sum()
-print(f"  Rows with black JPG: {n_bad}")
-
-# Clear jpg_path for rows with bad JPGs (keep the row, PCD is fine)
-merged.loc[merged['_bad_jpg'], 'jpg_path'] = None
-merged = merged.drop(columns=['_bad_jpg'])
-
-merged.to_parquet(MERGED_FILE)
-print(f"  Saved {len(merged)} rows")
-
-# ─── Phase 3: Repack ZIPs ────────────────────────────────────────────────
-print("\n" + "=" * 60)
-print("Repacking affected ZIP archives...")
-print("=" * 60)
-
-bad_by_archive = defaultdict(list)
-for _, row in bad_df.iterrows():
-    bad_by_archive[row['archive']].append(row['path'])
-
-def repack(archive):
-    zip_path = ZIP_DIR / archive
-    tmp_path = zip_path.with_suffix('.tmp.zip')
-    bad_files = set(bad_by_archive[archive])
-    try:
-        with zipfile.ZipFile(zip_path, 'r') as zin:
-            with zipfile.ZipFile(tmp_path, 'w', zipfile.ZIP_DEFLATED, compresslevel=1) as zout:
-                for info in zin.infolist():
-                    if info.filename not in bad_files:
-                        zout.writestr(info, zin.read(info.filename))
-        orig_size = zip_path.stat().st_size
-        shutil.move(tmp_path, zip_path)
-        new_size = zip_path.stat().st_size
-        removed = sum(1 for f in bad_files)
-        return (archive, 'ok', orig_size, new_size, removed)
-    except Exception as e:
-        if tmp_path.exists():
-            tmp_path.unlink()
-        return (archive, 'error', str(e)[:100])
-
-archives_to_fix = sorted(bad_by_archive.keys())
-print(f"  Archives to repack: {len(archives_to_fix)}")
-
-with concurrent.futures.ThreadPoolExecutor(max_workers=4) as ex:
-    results = list(ex.map(repack, archives_to_fix))
-
-for r in results:
-    if r[1] == 'ok':
-        saving = (r[2] - r[3]) / 1e6
-        print(f"    {r[0]}: {r[2]/1e6:.0f} MB -> {r[3]/1e6:.0f} MB ({r[4]} files removed, -{saving:.0f} MB)")
-    else:
-        print(f"    {r[0]}: ERROR - {r[2]}")
-
-# ─── Phase 4: Update archive_index.parquet ────────────────────────────────
-print("\n" + "=" * 60)
-print("Updating archive_index.parquet...")
-print("=" * 60)
-
-records = []
-for zp in sorted(ZIP_DIR.glob('*.zip')):
-    try:
-        with zipfile.ZipFile(zp, 'r') as zf:
-            info_list = zf.infolist()
-        records.append({
-            'archive': zp.name,
-            'size_mb': round(zp.stat().st_size / 1e6, 1),
-            'file_count': len(info_list),
-            'pcd_count': sum(1 for i in info_list if i.filename.startswith('pcd/')),
-            'jpg_count': sum(1 for i in info_list if i.filename.startswith('jpg/')),
-        })
-    except Exception as e:
-        print(f"  ERROR {zp.name}: {e}")
-
-new_index = pd.DataFrame(records)
-new_index.to_parquet(INDEX_FILE)
-print(f"  Saved {len(new_index)} entries")
-
-# ─── Summary ──────────────────────────────────────────────────────────────
-print("\n" + "=" * 60)
-print("CLEANUP SUMMARY")
-print("=" * 60)
-print(f"  Black JPGs removed: {len(bad_jpgs)}")
-print(f"  Archives repacked: {len(archives_to_fix)}")
-print(f"  Rows with cleared jpg_path: {n_bad}")
-print(f"  Total dataset size: {new_index['size_mb'].sum():.0f} MB")
-print(f"  Total valid files: {new_index['file_count'].sum()}")
-print(f"  Total valid PCDs: {new_index['pcd_count'].sum()}")
-print(f"  Total valid JPGs: {new_index['jpg_count'].sum()}")
-print("\nDone! Dataset is clean.")