Ethanfel
6ddfcde8ee
- Scan regions can be disabled (Del/Backspace) instead of deleted, shown greyed out - Resize scan regions by dragging timeline edges or editing table cells - Grey ghost overlay shows trimmed portions of resized regions - Ctrl+Z undo for disable, resize, drag, and negative toggle actions - Fix training stats including scan-exported clips when checkbox unchecked - Switch classifier to HistGradientBoostingClassifier (multi-threaded) - Timestamped model saves with latest copy at base path - Fix next-folder counter not detecting scan export folders - Each scan area exports to its own numbered clip folder - Platform-aware HW encoder detection (Linux/Windows/macOS) - Auto-detect VAAPI render device instead of hardcoding - Use shutil.move for cross-drive safety on Windows - Comprehensive README rewrite with scan workflow documentation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
625 lines
25 KiB
Python
625 lines
25 KiB
Python
import os
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import sqlite3
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import threading
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from datetime import datetime, timezone
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from pathlib import Path
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from .paths import _log
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class ProcessedDB:
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_SCHEMA_VERSION = 4 # bump when schema changes
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def __init__(self, db_path: str | None = None):
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if db_path is None:
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db_path = str(Path.home() / ".8cut.db")
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self._path = db_path
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self._lock = threading.Lock()
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try:
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self._con = sqlite3.connect(db_path, check_same_thread=False)
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self._migrate()
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self._enabled = True
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_log(f"DB opened: {db_path}")
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except Exception as e:
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_log(f"DB unavailable: {e}")
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self._con = None
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self._enabled = False
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def _migrate(self) -> None:
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"""Create table if missing, then add any new columns for old DBs."""
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cols = {
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row[1]
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for row in self._con.execute("PRAGMA table_info(processed)").fetchall()
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}
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if not cols:
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# Fresh DB — create from scratch
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self._con.execute(
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"CREATE TABLE IF NOT EXISTS processed ("
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" id INTEGER PRIMARY KEY AUTOINCREMENT,"
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" filename TEXT NOT NULL,"
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" start_time REAL NOT NULL,"
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" output_path TEXT NOT NULL,"
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" label TEXT NOT NULL DEFAULT '',"
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" category TEXT NOT NULL DEFAULT '',"
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" short_side INTEGER DEFAULT 512,"
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" portrait_ratio TEXT NOT NULL DEFAULT '',"
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" crop_center REAL NOT NULL DEFAULT 0.5,"
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" format TEXT NOT NULL DEFAULT 'MP4',"
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" clip_count INTEGER NOT NULL DEFAULT 3,"
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" spread REAL NOT NULL DEFAULT 3.0,"
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" profile TEXT NOT NULL DEFAULT 'default',"
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" source_path TEXT NOT NULL DEFAULT '',"
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" processed_at TEXT NOT NULL"
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")"
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)
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else:
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# Add missing columns to legacy tables
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new_cols = {
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"label": "TEXT NOT NULL DEFAULT ''",
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"category": "TEXT NOT NULL DEFAULT ''",
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"short_side": "INTEGER DEFAULT 512",
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"portrait_ratio": "TEXT NOT NULL DEFAULT ''",
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"crop_center": "REAL NOT NULL DEFAULT 0.5",
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"format": "TEXT NOT NULL DEFAULT 'MP4'",
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"clip_count": "INTEGER NOT NULL DEFAULT 3",
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"spread": "REAL NOT NULL DEFAULT 3.0",
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"profile": "TEXT NOT NULL DEFAULT 'default'",
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"source_path": "TEXT NOT NULL DEFAULT ''",
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"scan_export": "INTEGER NOT NULL DEFAULT 0",
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}
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for col, typedef in new_cols.items():
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if col not in cols:
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self._con.execute(
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f"ALTER TABLE processed ADD COLUMN {col} {typedef}"
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)
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self._con.execute(
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"CREATE INDEX IF NOT EXISTS idx_filename ON processed(filename)"
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)
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self._con.execute(
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"CREATE TABLE IF NOT EXISTS hidden_files ("
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" filename TEXT NOT NULL,"
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" profile TEXT NOT NULL DEFAULT 'default',"
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" PRIMARY KEY (filename, profile)"
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")"
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)
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self._con.execute(
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"CREATE TABLE IF NOT EXISTS scan_results ("
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" id INTEGER PRIMARY KEY AUTOINCREMENT,"
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" filename TEXT NOT NULL,"
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" profile TEXT NOT NULL DEFAULT 'default',"
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" model TEXT NOT NULL,"
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" start_time REAL NOT NULL,"
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" end_time REAL NOT NULL,"
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" score REAL NOT NULL,"
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" disabled INTEGER NOT NULL DEFAULT 0,"
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" orig_start_time REAL,"
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" orig_end_time REAL"
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")"
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)
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# Migrate: add new columns to existing scan_results tables
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sr_cols = {
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row[1]
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for row in self._con.execute("PRAGMA table_info(scan_results)").fetchall()
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}
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for col, typedef in [
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("disabled", "INTEGER NOT NULL DEFAULT 0"),
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("orig_start_time", "REAL"),
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("orig_end_time", "REAL"),
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]:
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if col not in sr_cols:
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self._con.execute(
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f"ALTER TABLE scan_results ADD COLUMN {col} {typedef}"
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)
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self._con.execute(
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"CREATE INDEX IF NOT EXISTS idx_scan_file_profile_model"
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" ON scan_results(filename, profile, model)"
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)
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self._con.execute(
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"CREATE TABLE IF NOT EXISTS hard_negatives ("
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" id INTEGER PRIMARY KEY AUTOINCREMENT,"
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" filename TEXT NOT NULL,"
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" profile TEXT NOT NULL DEFAULT 'default',"
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" start_time REAL NOT NULL,"
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" source_path TEXT NOT NULL DEFAULT ''"
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")"
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)
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self._con.execute(
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"CREATE INDEX IF NOT EXISTS idx_hardneg_file_profile"
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" ON hard_negatives(filename, profile)"
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)
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self._con.commit()
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def add(self, filename: str, start_time: float, output_path: str,
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label: str = "", category: str = "",
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short_side: int | None = None, portrait_ratio: str = "",
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crop_center: float = 0.5, fmt: str = "MP4",
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clip_count: int = 3, spread: float = 3.0,
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profile: str = "default", source_path: str = "",
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scan_export: bool = False) -> None:
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if not self._enabled:
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return
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with self._lock:
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self._con.execute(
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"INSERT INTO processed"
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" (filename, start_time, output_path, label, category,"
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" short_side, portrait_ratio, crop_center, format,"
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" clip_count, spread, profile, source_path, scan_export, processed_at)"
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" VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
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(filename, start_time, output_path, label, category,
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short_side, portrait_ratio, crop_center, fmt,
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clip_count, spread, profile, source_path,
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1 if scan_export else 0,
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datetime.now(timezone.utc).isoformat()),
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)
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self._con.commit()
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def get_labels(self) -> list[str]:
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"""Return distinct non-empty labels ordered by most recently used."""
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if not self._enabled:
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return []
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rows = self._con.execute(
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"SELECT DISTINCT label FROM processed"
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" WHERE label != '' ORDER BY processed_at DESC"
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).fetchall()
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# Deduplicate while preserving order (DISTINCT on processed_at DESC
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# may return duplicates if the same label was used multiple times).
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seen: set[str] = set()
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result = []
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for (lbl,) in rows:
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if lbl not in seen:
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seen.add(lbl)
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result.append(lbl)
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return result
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def get_by_output_path(self, output_path: str) -> dict | None:
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"""Return config dict for an output_path, or None."""
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if not self._enabled:
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return None
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cur = self._con.cursor()
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cur.row_factory = sqlite3.Row
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row = cur.execute(
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"SELECT label, category, short_side, portrait_ratio, crop_center, format,"
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" clip_count, spread"
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" FROM processed WHERE output_path = ?",
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(output_path,),
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).fetchone()
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return dict(row) if row else None
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def delete_by_output_path(self, output_path: str) -> None:
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if not self._enabled:
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return
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with self._lock:
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self._con.execute("DELETE FROM processed WHERE output_path = ?", (output_path,))
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self._con.commit()
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def get_group(self, output_path: str, profile: str = "") -> list[str]:
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"""Return all output_paths sharing the same (filename, start_time, profile) as *output_path*."""
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if not self._enabled:
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return []
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row = self._con.execute(
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"SELECT filename, start_time, profile FROM processed WHERE output_path = ?",
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(output_path,),
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).fetchone()
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if not row:
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return []
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filename, start_time, row_profile = row
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p = profile or row_profile
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rows = self._con.execute(
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"SELECT output_path FROM processed"
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" WHERE filename = ? AND start_time = ? AND profile = ? ORDER BY output_path",
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(filename, start_time, p),
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).fetchall()
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return [r[0] for r in rows]
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def delete_group(self, output_path: str, profile: str = "") -> list[str]:
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"""Delete all rows sharing the same (filename, start_time, profile) as *output_path*.
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Returns list of deleted output_paths."""
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if not self._enabled:
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return []
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with self._lock:
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row = self._con.execute(
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"SELECT filename, start_time, profile FROM processed WHERE output_path = ?",
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(output_path,),
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).fetchone()
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if not row:
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return []
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filename, start_time, row_profile = row
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p = profile or row_profile
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paths = [r[0] for r in self._con.execute(
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"SELECT output_path FROM processed"
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" WHERE filename = ? AND start_time = ? AND profile = ?",
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(filename, start_time, p),
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).fetchall()]
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self._con.execute(
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"DELETE FROM processed WHERE filename = ? AND start_time = ? AND profile = ?",
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(filename, start_time, p),
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)
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self._con.commit()
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return paths
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def _get_markers_for(self, match: str, profile: str = "default") -> list[tuple[float, int, str]]:
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rows = self._con.execute(
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"SELECT start_time, output_path FROM processed"
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" WHERE filename = ? AND profile = ? AND scan_export = 0"
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" ORDER BY start_time",
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(match, profile),
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).fetchall()
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# Deduplicate by start_time — batch exports share the same cursor.
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seen_times: dict[float, tuple[float, int, str]] = {}
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n = 0
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for t, p in rows:
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if t not in seen_times:
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n += 1
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seen_times[t] = (t, n, p)
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return list(seen_times.values())
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def get_markers(self, filename: str, profile: str = "default") -> list[tuple[float, int, str]]:
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"""Return [(start_time, marker_number, output_path), ...] for exact
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filename match, sorted by start_time. Empty list if no match.
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Excludes scan exports (shown via scan panel instead)."""
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if not self._enabled:
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return []
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return self._get_markers_for(filename, profile)
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def get_clip_count(self, filename: str, profile: str = "default") -> int:
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"""Return total number of exported clips (including scan exports)."""
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if not self._enabled:
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return 0
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row = self._con.execute(
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"SELECT COUNT(*) FROM processed WHERE filename = ? AND profile = ?",
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(filename, profile),
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).fetchone()
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return row[0] if row else 0
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def get_profiles(self) -> list[str]:
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"""Return distinct profile names, ordered alphabetically."""
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if not self._enabled:
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return []
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rows = self._con.execute(
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"SELECT DISTINCT profile FROM processed ORDER BY profile"
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).fetchall()
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return [r[0] for r in rows]
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def get_all_export_paths(self, profile: str = "default") -> list[str]:
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"""Return all unique output_path values for a given profile."""
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if not self._enabled:
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return []
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rows = self._con.execute(
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"SELECT DISTINCT output_path FROM processed WHERE profile = ?",
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(profile,),
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).fetchall()
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return [r[0] for r in rows]
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def get_export_folders(self, profile: str = "default") -> list[str]:
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"""Return distinct export folder names found in output_paths for a profile.
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Export paths follow the structure:
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.../export_folder/group_dir/clip.mp4
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The export folder is 2 levels up from the clip file.
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Returns folder names sorted alphabetically (e.g. ["mp4_Intense", "mp4_Soft"]).
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"""
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if not self._enabled:
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return []
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rows = self._con.execute(
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"SELECT DISTINCT output_path FROM processed WHERE profile = ?",
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(profile,),
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).fetchall()
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folder_names: set[str] = set()
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for (op,) in rows:
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grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
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if grandparent:
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folder_names.add(grandparent)
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return sorted(folder_names)
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def get_training_data(self, profile: str, positive_folder: str,
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negative_folder: str = "",
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fallback_video_dir: str = "",
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include_scan_exports: bool = False,
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) -> list[tuple[str, list[float], list[float], list[float]]]:
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"""Build training video_infos from DB data.
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Args:
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profile: profile name
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positive_folder: export folder name for positive class (e.g. "mp4_Intense")
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negative_folder: export folder name for explicit negatives (optional)
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fallback_video_dir: if source_path is empty, try filename in this dir
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include_scan_exports: if True, include auto-exported scan clips
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Returns:
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list of (source_video_path, positive_times, soft_times, negative_times)
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per video. Soft times = clips from any other non-negative folder.
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"""
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if not self._enabled:
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return []
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if include_scan_exports:
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rows = self._con.execute(
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"SELECT filename, start_time, output_path, source_path"
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" FROM processed WHERE profile = ?",
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(profile,),
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).fetchall()
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else:
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rows = self._con.execute(
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"SELECT filename, start_time, output_path, source_path"
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" FROM processed WHERE profile = ? AND scan_export = 0",
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(profile,),
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).fetchall()
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# Collect times by video, split by folder role
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pos_by_video: dict[str, set[float]] = {}
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neg_by_video: dict[str, set[float]] = {}
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soft_by_video: dict[str, set[float]] = {}
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source_by_filename: dict[str, str] = {}
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for fn, st, op, sp in rows:
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if sp:
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source_by_filename[fn] = sp
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grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
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if grandparent == positive_folder:
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pos_by_video.setdefault(fn, set()).add(st)
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elif negative_folder and grandparent == negative_folder:
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neg_by_video.setdefault(fn, set()).add(st)
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else:
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soft_by_video.setdefault(fn, set()).add(st)
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# Include hard negatives from scan feedback
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hard_rows = self._con.execute(
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"SELECT filename, start_time, source_path FROM hard_negatives"
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" WHERE profile = ?",
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(profile,),
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).fetchall()
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for fn, st, sp in hard_rows:
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neg_by_video.setdefault(fn, set()).add(st)
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if sp:
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source_by_filename.setdefault(fn, sp)
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# Remove positive times from soft/neg to avoid conflicting labels
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for fn in pos_by_video:
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if fn in soft_by_video:
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soft_by_video[fn] -= pos_by_video[fn]
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if fn in neg_by_video:
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neg_by_video[fn] -= pos_by_video[fn]
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# Deduplicate nearby markers (spread clips from same position)
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def _dedup_times(times: set[float], min_gap: float = 8.0) -> list[float]:
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if not times:
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return []
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ordered = sorted(times)
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result = [ordered[0]]
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for t in ordered[1:]:
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if t - result[-1] >= min_gap:
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result.append(t)
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return result
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# Include videos that have positives OR explicit negatives
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all_videos = set(pos_by_video) | set(neg_by_video)
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result = []
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for fn in all_videos:
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sp = source_by_filename.get(fn, "")
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if not sp or not os.path.exists(sp):
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if fallback_video_dir:
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sp = os.path.join(fallback_video_dir, fn)
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if not sp or not os.path.exists(sp):
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continue
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gt_pos = _dedup_times(pos_by_video.get(fn, set()))
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gt_soft = _dedup_times(soft_by_video.get(fn, set()))
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gt_neg = _dedup_times(neg_by_video.get(fn, set()))
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result.append((sp, gt_pos, gt_soft, gt_neg))
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return result
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def get_training_stats(self, profile: str,
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include_scan_exports: bool = False) -> dict[str, dict]:
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"""Return per-subprofile stats for training readiness display.
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Returns dict mapping subprofile_name → {
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'videos': number of distinct source videos,
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'clips': total clip count,
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}
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"""
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if not self._enabled:
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return {}
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if include_scan_exports:
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rows = self._con.execute(
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"SELECT filename, output_path FROM processed WHERE profile = ?",
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(profile,),
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).fetchall()
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else:
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rows = self._con.execute(
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"SELECT filename, output_path FROM processed"
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" WHERE profile = ? AND scan_export = 0",
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(profile,),
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).fetchall()
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folders = self.get_export_folders(profile)
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stats: dict[str, dict] = {}
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for folder_name in folders:
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videos: set[str] = set()
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clips = 0
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for fn, op in rows:
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grandparent = os.path.basename(os.path.dirname(os.path.dirname(op)))
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if grandparent == folder_name:
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videos.add(fn)
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clips += 1
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stats[folder_name] = {"videos": len(videos), "clips": clips}
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return stats
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# ── Scan results ─────────────────────────────────────────────
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def save_scan_results(self, filename: str, profile: str, model: str,
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regions: list[tuple[float, float, float]]) -> None:
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"""Replace scan results for (filename, profile, model) with new regions.
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regions: list of (start_time, end_time, score).
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"""
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if not self._enabled:
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return
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with self._lock:
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self._con.execute(
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"DELETE FROM scan_results"
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" WHERE filename = ? AND profile = ? AND model = ?",
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(filename, profile, model),
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)
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self._con.executemany(
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"INSERT INTO scan_results"
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" (filename, profile, model, start_time, end_time, score,"
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" orig_start_time, orig_end_time)"
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" VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
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[(filename, profile, model, s, e, sc, s, e) for s, e, sc in regions],
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)
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self._con.commit()
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def get_scan_results(self, filename: str, profile: str
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) -> dict[str, list[tuple[int, float, float, float, bool, float, float]]]:
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"""Return scan results grouped by model.
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|
|
Returns {model: [(row_id, start, end, score, disabled, orig_start, orig_end), ...]}
|
|
sorted by start_time.
|
|
"""
|
|
if not self._enabled:
|
|
return {}
|
|
rows = self._con.execute(
|
|
"SELECT id, model, start_time, end_time, score, disabled,"
|
|
" orig_start_time, orig_end_time"
|
|
" FROM scan_results"
|
|
" WHERE filename = ? AND profile = ?"
|
|
" ORDER BY model, start_time",
|
|
(filename, profile),
|
|
).fetchall()
|
|
result: dict[str, list[tuple[int, float, float, float, bool, float, float]]] = {}
|
|
for row_id, model, s, e, sc, dis, os_, oe in rows:
|
|
# Fall back to current bounds for legacy rows without orig
|
|
result.setdefault(model, []).append(
|
|
(row_id, s, e, sc, bool(dis), os_ if os_ is not None else s,
|
|
oe if oe is not None else e))
|
|
return result
|
|
|
|
def delete_scan_result(self, row_id: int) -> None:
|
|
"""Delete a single scan result row."""
|
|
if not self._enabled:
|
|
return
|
|
with self._lock:
|
|
self._con.execute("DELETE FROM scan_results WHERE id = ?", (row_id,))
|
|
self._con.commit()
|
|
|
|
def toggle_scan_result_disabled(self, row_id: int, disabled: bool) -> None:
|
|
"""Set disabled flag on a scan result row."""
|
|
if not self._enabled:
|
|
return
|
|
with self._lock:
|
|
self._con.execute(
|
|
"UPDATE scan_results SET disabled = ? WHERE id = ?",
|
|
(1 if disabled else 0, row_id),
|
|
)
|
|
self._con.commit()
|
|
|
|
def update_scan_result_times(self, row_id: int,
|
|
start: float, end: float) -> None:
|
|
"""Update start/end times of a scan result row (resize)."""
|
|
if not self._enabled:
|
|
return
|
|
with self._lock:
|
|
self._con.execute(
|
|
"UPDATE scan_results SET start_time = ?, end_time = ? WHERE id = ?",
|
|
(start, end, row_id),
|
|
)
|
|
self._con.commit()
|
|
|
|
def get_scan_models(self, filename: str, profile: str) -> list[str]:
|
|
"""Return model names that have scan results for this file."""
|
|
if not self._enabled:
|
|
return []
|
|
rows = self._con.execute(
|
|
"SELECT DISTINCT model FROM scan_results"
|
|
" WHERE filename = ? AND profile = ? ORDER BY model",
|
|
(filename, profile),
|
|
).fetchall()
|
|
return [r[0] for r in rows]
|
|
|
|
def get_scanned_filenames(self, profile: str, model: str) -> set[str]:
|
|
"""Return filenames that already have scan results for this model."""
|
|
if not self._enabled:
|
|
return set()
|
|
rows = self._con.execute(
|
|
"SELECT DISTINCT filename FROM scan_results"
|
|
" WHERE profile = ? AND model = ?",
|
|
(profile, model),
|
|
).fetchall()
|
|
return {r[0] for r in rows}
|
|
|
|
def add_hard_negatives(self, filename: str, profile: str,
|
|
times: list[float], source_path: str = "") -> None:
|
|
"""Save timestamps as hard-negative training examples."""
|
|
if not self._enabled or not times:
|
|
return
|
|
with self._lock:
|
|
for t in times:
|
|
self._con.execute(
|
|
"INSERT INTO hard_negatives (filename, profile, start_time, source_path)"
|
|
" VALUES (?, ?, ?, ?)",
|
|
(filename, profile, t, source_path),
|
|
)
|
|
self._con.commit()
|
|
|
|
def get_hard_negative_times(self, filename: str, profile: str) -> set[float]:
|
|
"""Return start_times marked as hard negatives for this file."""
|
|
if not self._enabled:
|
|
return set()
|
|
rows = self._con.execute(
|
|
"SELECT start_time FROM hard_negatives"
|
|
" WHERE filename = ? AND profile = ?",
|
|
(filename, profile),
|
|
).fetchall()
|
|
return {r[0] for r in rows}
|
|
|
|
def remove_hard_negatives(self, filename: str, profile: str,
|
|
times: list[float]) -> None:
|
|
"""Remove specific hard-negative timestamps."""
|
|
if not self._enabled or not times:
|
|
return
|
|
with self._lock:
|
|
for t in times:
|
|
self._con.execute(
|
|
"DELETE FROM hard_negatives"
|
|
" WHERE filename = ? AND profile = ? AND start_time = ?",
|
|
(filename, profile, t),
|
|
)
|
|
self._con.commit()
|
|
|
|
def get_training_filenames(self, profile: str) -> set[str]:
|
|
"""Return filenames used in training (have exported clips)."""
|
|
if not self._enabled:
|
|
return set()
|
|
rows = self._con.execute(
|
|
"SELECT DISTINCT filename FROM processed WHERE profile = ?",
|
|
(profile,),
|
|
).fetchall()
|
|
return {r[0] for r in rows}
|
|
|
|
# ── Hidden files ───────────────────────────────────────────
|
|
|
|
def hide_file(self, filename: str, profile: str = "default") -> None:
|
|
if not self._enabled:
|
|
return
|
|
with self._lock:
|
|
self._con.execute(
|
|
"INSERT OR IGNORE INTO hidden_files (filename, profile) VALUES (?, ?)",
|
|
(filename, profile),
|
|
)
|
|
self._con.commit()
|
|
|
|
def unhide_file(self, filename: str, profile: str = "default") -> None:
|
|
if not self._enabled:
|
|
return
|
|
with self._lock:
|
|
self._con.execute(
|
|
"DELETE FROM hidden_files WHERE filename = ? AND profile = ?",
|
|
(filename, profile),
|
|
)
|
|
self._con.commit()
|
|
|
|
def get_hidden_files(self, profile: str = "default") -> set[str]:
|
|
if not self._enabled:
|
|
return set()
|
|
rows = self._con.execute(
|
|
"SELECT filename FROM hidden_files WHERE profile = ?", (profile,)
|
|
).fetchall()
|
|
return {r[0] for r in rows}
|