flan
274d50ee99
After removing the object pipeline, several dead 'mode' artifacts remained: - executor.py: unpack `config` from job even though it was no longer read - jobs.py: set `"mode": "face"` in both configure paths (key never consumed) - compose.yml: TRAINING_MODE=face env, OBJECT_CLASS comment, HF_HOME, stale MAX_AUTO_IMAGES default note - Dockerfile: "and object classification" label, /models/huggingface mkdir, HF_HOME ENV
332 lines
13 KiB
Python
332 lines
13 KiB
Python
"""Configuration phase: strategy selection and job building."""
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import logging
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import os
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from rich import print as rprint
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from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
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from rich.prompt import Confirm, IntPrompt, Prompt
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from rich.table import Table
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from .config import Config
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from .diversity import select_diverse_assets
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from .embeddings import is_embedding_available, load_embedding_model
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from .frigate_api import get_frigate_face_counts
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from .immich_api import fetch_all_assets, filter_recent_assets
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from .log_config import console
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from .upload_tracker import filter_already_uploaded, get_person_summary, update_frigate_count
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logger = logging.getLogger(__name__)
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# Strategy presets: (limit, mode_name)
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STRATEGY_PRESETS = {
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"1": ("auto", "Adaptive Diversity"),
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"2": (30, "Standard (30)"),
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"3": (100, "Broad (100)"),
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}
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def _get_strategy_choice(has_embedding: bool) -> tuple[int | str, str]:
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"""Prompt user for training strategy and return (limit, selection_mode)."""
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if has_embedding:
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rprint(" [bold]1.[/bold] Adaptive Diversity [green][Recommended][/green]")
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rprint(" [dim]• Dynamically selects images until redundancy starts[/dim]")
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rprint(" [bold]2.[/bold] Standard (30 images)")
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rprint(" [bold]3.[/bold] Broad (100 images)")
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rprint(" [bold]4.[/bold] Custom Count")
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rprint(" [bold]5.[/bold] Skip")
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choice = Prompt.ask("Choice", choices=["1", "2", "3", "4", "5"], default="1")
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if choice == "5":
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return 0, "skip"
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if choice == "4":
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limit = IntPrompt.ask("Enter number of images", default=30)
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mode = "smart" if Confirm.ask("Use Smart Diversity?", default=True) else "time"
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return limit, mode
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if choice in STRATEGY_PRESETS:
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return STRATEGY_PRESETS[choice][0], "smart"
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return 30, "smart"
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# Fallback when embedding model not available
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rprint(" [yellow]Note: InsightFace not available. Using Time Spread.[/yellow]")
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rprint(" [bold]1.[/bold] Standard (30 images) [green][Recommended][/green]")
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rprint(" [bold]2.[/bold] Broad (100 images)")
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rprint(" [bold]3.[/bold] Custom Count")
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rprint(" [bold]4.[/bold] Skip")
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choice = Prompt.ask("Choice", choices=["1", "2", "3", "4"], default="1")
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if choice == "4":
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return 0, "skip"
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if choice == "3":
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return IntPrompt.ask("Enter number of images", default=30), "time"
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return {"1": 30, "2": 100}.get(choice, 30), "time"
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def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, str]:
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"""Resolve env var strategy to (limit, selection_mode) without prompts."""
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custom_limit = os.environ.get("LIMIT", "").strip()
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if not has_embedding:
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limit = int(custom_limit) if custom_limit else 30
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return limit, "time"
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if custom_limit:
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return int(custom_limit), "smart"
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strategy_map = {
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"adaptive": ("auto", "smart"),
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"auto": ("auto", "smart"), # legacy alias for adaptive
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"standard": (30, "smart"),
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"broad": (100, "smart"),
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}
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return strategy_map.get(strategy, ("auto", "smart"))
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def _perform_selection(
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assets: list, limit: int | str, name: str, selection_mode: str, person_id: str | None = None
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) -> list:
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"""Run diversity selection with progress display."""
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if selection_mode == "smart":
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rprint("\n[cyan]Using InsightFace (face embeddings) for diversity analysis...[/cyan]")
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load_embedding_model()
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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console=console,
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) as progress:
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task = progress.add_task(f"[cyan]Computing embeddings for {len(assets)} images...", total=None)
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selected = select_diverse_assets(
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assets,
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limit,
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name,
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selection_mode=selection_mode,
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person_id=person_id,
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progress_callback=lambda c, t: progress.update(task, completed=c, total=t),
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)
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label = f"Auto-diversity selected {len(selected)}" if limit == "auto" else f"Selected {len(selected)}"
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rprint(f" [green]{label} diverse images.[/green]")
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return selected
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rprint(f"\n[cyan]Using time-spread selection for {limit} images...[/cyan]")
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with console.status(f"[bold]Selecting {limit} images evenly distributed over time...[/bold]"):
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selected = select_diverse_assets(assets, limit, name, selection_mode="time", person_id=person_id)
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rprint(f" [green]Selected {len(selected)} images using time spread.[/green]")
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return selected
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def _configure_person(person: dict, people: list[dict]) -> dict | None:
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"""Configure training for a single person. Returns job dict or None."""
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name = person["name"]
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console.print(f"\nSelected: [bold green]{name}[/bold green]")
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config = {"name": name, "quality_replacement": Config.QUALITY_REPLACEMENT}
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# Fetch and filter assets
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years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER)
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console.print(f"Scanning for {name}...")
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with console.status("[bold green]Fetching assets...[/bold green]"):
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all_assets = fetch_all_assets(person)
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recent_assets = filter_recent_assets(all_assets, years=years)
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rprint(f" Found [bold]{len(all_assets)}[/bold] total, [bold]{len(recent_assets)}[/bold] in range ({years} years).")
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# Filter out assets already uploaded to Frigate.
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# In interactive mode, ask — use the env var only as the default so it can
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# still be pre-set (e.g. RETRY_REJECTED=true) without forcing the answer.
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retry_env = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
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retry_rejected = Confirm.ask("Include previously rejected images?", default=retry_env)
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before_dedup = len(recent_assets)
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new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected))
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recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids]
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skipped = before_dedup - len(recent_assets)
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if skipped:
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rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]")
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if not recent_assets:
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rprint(" [dim]Skipping (0 new images after dedup).[/dim]")
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return None
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# Strategy selection
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has_embedding = is_embedding_available()
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rprint(f"\n[bold cyan]Select Training Strategy for {name}:[/bold cyan]")
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limit, selection_mode = _get_strategy_choice(has_embedding)
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if selection_mode == "skip":
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return None
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# Perform selection
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selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
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rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]")
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return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config}
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def interactive_configure(people: list[dict]) -> list[dict]:
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"""Interactive phase: select person(s), mode, and configure training strategy.
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Supports multi-person batch mode — after configuring one person,
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prompts to add another.
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"""
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valid_people = sorted([p for p in people if p.get("name")], key=lambda x: x["name"])
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if not valid_people:
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rprint("[red]No people found with names in Immich.[/red]")
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return []
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jobs = []
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while True:
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# Select person
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console.print("\n[bold cyan]Select Person to Train:[/bold cyan]")
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for idx, p in enumerate(valid_people, 1):
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# Mark already-queued people
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marker = " [dim](queued)[/dim]" if any(j["person"]["id"] == p["id"] for j in jobs) else ""
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console.print(f" [bold]{idx}.[/bold] {p['name']}{marker}")
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p_choice = IntPrompt.ask("Enter Number", choices=[str(i) for i in range(1, len(valid_people) + 1)])
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person = valid_people[p_choice - 1]
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job = _configure_person(person, valid_people)
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if job:
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jobs.append(job)
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# Multi-person: ask to add another
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if not Confirm.ask("\nAdd another person?", default=False):
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break
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return jobs
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def auto_configure(people: list[dict]) -> list[dict]:
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"""Non-interactive: configure jobs for all named people automatically."""
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valid_people = sorted([p for p in people if p.get("name")], key=lambda x: x["name"])
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if not valid_people:
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rprint("[red]No people found with names in Immich.[/red]")
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return []
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strategy = os.environ.get("STRATEGY", "auto")
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skip = os.environ.get("SKIP_PEOPLE", "").split(",") if os.environ.get("SKIP_PEOPLE") else []
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only = os.environ.get("ONLY_PEOPLE", "").split(",") if os.environ.get("ONLY_PEOPLE") else []
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if only:
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valid_people = [p for p in valid_people if p["name"] in only]
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if skip:
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valid_people = [p for p in valid_people if p["name"] not in skip]
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# Filter by minimum face count (Issue #6: previously unimplemented)
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min_face_count = Config.MIN_FACE_COUNT
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if min_face_count > 0:
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valid_people = [p for p in valid_people if p.get("assetCount", 0) >= min_face_count]
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if valid_people:
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rprint(
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f" Filtered to {len(valid_people)} people with"
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f" ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})"
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)
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frigate_counts = get_frigate_face_counts()
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# Persist each count to tracker so the last known value survives Frigate downtime
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if frigate_counts is not None:
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for pname, count in frigate_counts.items():
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update_frigate_count(pname, count)
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upload_summary = get_person_summary()
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jobs = []
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for person in valid_people:
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name = person["name"]
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config = {"name": name}
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all_assets = fetch_all_assets(person)
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recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER)
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rprint(f" {name}: {len(all_assets)} total, {len(recent_assets)} recent")
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# Filter out assets already uploaded to Frigate
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retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
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before_dedup = len(recent_assets)
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new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected))
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recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids]
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skipped = before_dedup - len(recent_assets)
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if skipped:
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rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]")
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if not recent_assets:
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rprint(f" [dim]Skipping {name} (0 new images after dedup).[/dim]")
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continue
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# Enforce MAX_AUTO_IMAGES against the tracked file count only.
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# Manually-added Frigate files are invisible to this cap so users can
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# curate their own files without shrinking winnow's managed quota.
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person_summary = upload_summary.get(name, {})
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already_uploaded = len(person_summary.get("frigate_files", {}))
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capacity = Config.MAX_AUTO_IMAGES - already_uploaded
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if capacity <= 0:
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if not Config.QUALITY_REPLACEMENT:
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rprint(
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f" [dim]Skipping {name} (at cap:"
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f" {already_uploaded}/{Config.MAX_AUTO_IMAGES}, quality replacement disabled).[/dim]"
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)
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continue
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rprint(
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f" [cyan]{name}: at cap ({already_uploaded}/{Config.MAX_AUTO_IMAGES}),"
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f" checking for quality improvements...[/cyan]"
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)
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quality_replacement_only = True
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else:
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quality_replacement_only = False
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config["quality_replacement"] = quality_replacement_only or Config.QUALITY_REPLACEMENT
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has_embedding = is_embedding_available()
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limit, selection_mode = _resolve_strategy(strategy, has_embedding)
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# Cap selection to remaining capacity (no cap when replacement-only — executor
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# decides per-image whether to swap; any candidate could be an improvement).
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auto_cap = None
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if not quality_replacement_only:
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if limit == "auto":
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if already_uploaded > 0:
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auto_cap = capacity
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else:
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limit = min(limit, capacity)
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if selection_mode == "skip":
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continue
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selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
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if auto_cap is not None:
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selected_assets = selected_assets[:auto_cap]
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if selected_assets:
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rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]")
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jobs.append({"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config})
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return jobs
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def _show_preview(jobs: list[dict]) -> None:
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"""Show a summary table of all queued jobs before execution."""
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table = Table(title="📋 Training Job Preview", show_header=True, header_style="bold cyan")
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table.add_column("Person", style="bold")
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table.add_column("Images", justify="right")
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table.add_column("Date Range", style="dim")
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for job in jobs:
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name = job["person"]["name"]
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count = str(job["limit"])
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dates = sorted(a.get("fileCreatedAt", "")[:10] for a in job["assets"] if a.get("fileCreatedAt"))
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date_range = f"{dates[0]} → {dates[-1]}" if len(dates) >= 2 else (dates[0] if dates else "—")
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table.add_row(name, count, date_range)
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console.print()
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console.print(table)
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console.print()
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