refactor: rename project to winnow
- Rename Python package directory if_curator/ → winnow/ - Update all imports, entry points, and CLI references - Update pyproject.toml: name, scripts, package list, repository URL - Update Dockerfile, compose.yml, entrypoint.sh, scheduler.py - Update GitHub Actions workflow image names - Update README Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Claude Sonnet 4.6
+302
@@ -0,0 +1,302 @@
|
||||
"""Configuration phase: strategy selection and job building."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
|
||||
from rich import print as rprint
|
||||
from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
|
||||
from rich.prompt import Confirm, IntPrompt, Prompt
|
||||
from rich.table import Table
|
||||
|
||||
from .config import Config
|
||||
from .diversity import select_diverse_assets
|
||||
from .embeddings import is_embedding_available, load_embedding_model
|
||||
from .immich_api import fetch_all_assets, filter_recent_assets
|
||||
from .logging import console
|
||||
from .upload_tracker import filter_already_uploaded
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Strategy presets: (limit, mode_name)
|
||||
STRATEGY_PRESETS = {
|
||||
"1": ("auto", "Auto Diversity"),
|
||||
"2": (30, "Standard (30)"),
|
||||
"3": (100, "Broad (100)"),
|
||||
}
|
||||
|
||||
|
||||
def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | str, str]:
|
||||
"""Prompt user for training strategy and return (limit, selection_mode)."""
|
||||
model_name = "InsightFace" if entity_type == "face" else "SigLIP"
|
||||
|
||||
if has_embedding:
|
||||
rprint(" [bold]1.[/bold] Auto (Objective Diversity) [green][Recommended][/green]")
|
||||
rprint(" [dim]• Dynamically selects images until redundancy starts[/dim]")
|
||||
rprint(" [bold]2.[/bold] Standard (30 images)")
|
||||
rprint(" [bold]3.[/bold] Broad (100 images)")
|
||||
rprint(" [bold]4.[/bold] Custom Count")
|
||||
rprint(" [bold]5.[/bold] Skip")
|
||||
|
||||
choice = Prompt.ask("Choice", choices=["1", "2", "3", "4", "5"], default="1")
|
||||
|
||||
if choice == "5":
|
||||
return 0, "skip"
|
||||
if choice == "4":
|
||||
limit = IntPrompt.ask("Enter number of images", default=30)
|
||||
mode = "smart" if Confirm.ask("Use Smart Diversity?", default=True) else "time"
|
||||
return limit, mode
|
||||
if choice in STRATEGY_PRESETS:
|
||||
return STRATEGY_PRESETS[choice][0], "smart"
|
||||
return 30, "smart"
|
||||
|
||||
# Fallback when embedding model not available
|
||||
rprint(f" [yellow]Note: {model_name} not available. Using Time Spread.[/yellow]")
|
||||
rprint(" [bold]1.[/bold] Standard (30 images) [green][Recommended][/green]")
|
||||
rprint(" [bold]2.[/bold] Broad (100 images)")
|
||||
rprint(" [bold]3.[/bold] Custom Count")
|
||||
rprint(" [bold]4.[/bold] Skip")
|
||||
|
||||
choice = Prompt.ask("Choice", choices=["1", "2", "3", "4"], default="1")
|
||||
limits = {"1": 30, "2": 100, "3": IntPrompt.ask("Enter number of images", default=30)}
|
||||
return limits.get(choice, 0), "time" if choice != "4" else "skip"
|
||||
|
||||
|
||||
def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, str]:
|
||||
"""Resolve env var strategy to (limit, selection_mode) without prompts."""
|
||||
custom_limit = os.environ.get("LIMIT", "").strip()
|
||||
|
||||
if not has_embedding:
|
||||
limit = int(custom_limit) if custom_limit else 30
|
||||
return limit, "time"
|
||||
|
||||
if custom_limit:
|
||||
return int(custom_limit), "smart"
|
||||
|
||||
strategy_map = {
|
||||
"auto": ("auto", "smart"),
|
||||
"standard": (30, "smart"),
|
||||
"broad": (100, "smart"),
|
||||
}
|
||||
return strategy_map.get(strategy, ("auto", "smart"))
|
||||
|
||||
|
||||
def _perform_selection(assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str) -> list:
|
||||
"""Run diversity selection with progress display."""
|
||||
if selection_mode == "smart":
|
||||
model_display = "InsightFace (face embeddings)" if entity_type == "face" else "SigLIP (visual embeddings)"
|
||||
rprint(f"\n[cyan]Using {model_display} for diversity analysis...[/cyan]")
|
||||
|
||||
# Pre-load model explicitly (separate from availability check)
|
||||
load_embedding_model(entity_type)
|
||||
|
||||
with Progress(
|
||||
SpinnerColumn(),
|
||||
TextColumn("[progress.description]{task.description}"),
|
||||
BarColumn(),
|
||||
TaskProgressColumn(),
|
||||
console=console,
|
||||
) as progress:
|
||||
task = progress.add_task(f"[cyan]Computing embeddings for {len(assets)} images...", total=None)
|
||||
selected = select_diverse_assets(
|
||||
assets,
|
||||
limit,
|
||||
name,
|
||||
selection_mode=selection_mode,
|
||||
entity_type=entity_type,
|
||||
progress_callback=lambda c, t: progress.update(task, completed=c, total=t),
|
||||
)
|
||||
|
||||
label = f"Auto-diversity selected {len(selected)}" if limit == "auto" else f"Selected {len(selected)}"
|
||||
rprint(f" [green]{label} diverse images.[/green]")
|
||||
return selected
|
||||
|
||||
rprint(f"\n[cyan]Using time-spread selection for {limit} images...[/cyan]")
|
||||
with console.status(f"[bold]Selecting {limit} images evenly distributed over time...[/bold]"):
|
||||
selected = select_diverse_assets(assets, limit, name, selection_mode="time", entity_type=entity_type)
|
||||
rprint(f" [green]Selected {len(selected)} images using time spread.[/green]")
|
||||
return selected
|
||||
|
||||
|
||||
def _configure_person(person: dict, people: list[dict]) -> dict | None:
|
||||
"""Configure training for a single person. Returns job dict or None."""
|
||||
name = person["name"]
|
||||
console.print(f"\nSelected: [bold green]{name}[/bold green]")
|
||||
|
||||
# Select training mode
|
||||
rprint("\n[bold cyan]Training Mode:[/bold cyan]")
|
||||
rprint(" [bold]1.[/bold] Face (Frigate Face Recognition)")
|
||||
rprint(" [bold]2.[/bold] Object (Frigate Object Classification)")
|
||||
|
||||
mode_choice = Prompt.ask("Choice", choices=["1", "2"], default="1")
|
||||
entity_type = "face" if mode_choice == "1" else "object"
|
||||
|
||||
config = {"name": name, "mode": entity_type}
|
||||
if entity_type == "object":
|
||||
config["object_class"] = Prompt.ask("Enter Object Class (e.g. dog, cat, car)", default="dog")
|
||||
|
||||
# Fetch and filter assets
|
||||
years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER)
|
||||
|
||||
console.print(f"Scanning for {name} ({entity_type})...")
|
||||
with console.status("[bold green]Fetching assets...[/bold green]"):
|
||||
all_assets = fetch_all_assets(person)
|
||||
recent_assets = filter_recent_assets(all_assets, years=years)
|
||||
|
||||
rprint(f" Found [bold]{len(all_assets)}[/bold] total, [bold]{len(recent_assets)}[/bold] in range ({years} years).")
|
||||
|
||||
# Filter out assets already uploaded to Frigate
|
||||
retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
|
||||
before_dedup = len(recent_assets)
|
||||
new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected))
|
||||
recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids]
|
||||
skipped = before_dedup - len(recent_assets)
|
||||
if skipped:
|
||||
rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]")
|
||||
|
||||
if not recent_assets:
|
||||
rprint(" [dim]Skipping (0 new images after dedup).[/dim]")
|
||||
return None
|
||||
|
||||
# Strategy selection
|
||||
has_embedding = is_embedding_available(entity_type)
|
||||
rprint(f"\n[bold cyan]Select Training Strategy for {name}:[/bold cyan]")
|
||||
|
||||
limit, selection_mode = _get_strategy_choice(has_embedding, entity_type)
|
||||
if selection_mode == "skip":
|
||||
return None
|
||||
|
||||
# Perform selection
|
||||
selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, entity_type)
|
||||
|
||||
rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]")
|
||||
return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config}
|
||||
|
||||
|
||||
def interactive_configure(people: list[dict]) -> list[dict]:
|
||||
"""Interactive phase: select person(s), mode, and configure training strategy.
|
||||
|
||||
Supports multi-person batch mode — after configuring one person,
|
||||
prompts to add another.
|
||||
"""
|
||||
valid_people = sorted([p for p in people if p.get("name")], key=lambda x: x["name"])
|
||||
|
||||
if not valid_people:
|
||||
rprint("[red]No people found with names in Immich.[/red]")
|
||||
return []
|
||||
|
||||
jobs = []
|
||||
|
||||
while True:
|
||||
# Select person
|
||||
console.print("\n[bold cyan]Select Person to Train:[/bold cyan]")
|
||||
for idx, p in enumerate(valid_people, 1):
|
||||
# Mark already-queued people
|
||||
marker = " [dim](queued)[/dim]" if any(j["person"]["id"] == p["id"] for j in jobs) else ""
|
||||
console.print(f" [bold]{idx}.[/bold] {p['name']}{marker}")
|
||||
|
||||
p_choice = IntPrompt.ask("Enter Number", choices=[str(i) for i in range(1, len(valid_people) + 1)])
|
||||
person = valid_people[p_choice - 1]
|
||||
|
||||
job = _configure_person(person, valid_people)
|
||||
if job:
|
||||
jobs.append(job)
|
||||
|
||||
# Multi-person: ask to add another
|
||||
if not Confirm.ask("\nAdd another person?", default=False):
|
||||
break
|
||||
|
||||
return jobs
|
||||
|
||||
|
||||
def auto_configure(people: list[dict]) -> list[dict]:
|
||||
"""Non-interactive: configure jobs for all named people automatically."""
|
||||
valid_people = sorted([p for p in people if p.get("name")], key=lambda x: x["name"])
|
||||
|
||||
if not valid_people:
|
||||
rprint("[red]No people found with names in Immich.[/red]")
|
||||
return []
|
||||
|
||||
mode = os.environ.get("TRAINING_MODE", "face")
|
||||
strategy = os.environ.get("STRATEGY", "auto")
|
||||
skip = os.environ.get("SKIP_PEOPLE", "").split(",") if os.environ.get("SKIP_PEOPLE") else []
|
||||
only = os.environ.get("ONLY_PEOPLE", "").split(",") if os.environ.get("ONLY_PEOPLE") else []
|
||||
|
||||
if only:
|
||||
valid_people = [p for p in valid_people if p["name"] in only]
|
||||
if skip:
|
||||
valid_people = [p for p in valid_people if p["name"] not in skip]
|
||||
|
||||
# Filter by minimum face count (Issue #6: previously unimplemented)
|
||||
min_face_count = Config.MIN_FACE_COUNT
|
||||
if min_face_count > 0:
|
||||
valid_people = [p for p in valid_people if p.get("assetCount", 0) >= min_face_count]
|
||||
if valid_people:
|
||||
rprint(
|
||||
f" Filtered to {len(valid_people)} people with"
|
||||
f" ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})"
|
||||
)
|
||||
|
||||
jobs = []
|
||||
for person in valid_people:
|
||||
name = person["name"]
|
||||
entity_type = mode
|
||||
|
||||
config = {"name": name, "mode": entity_type}
|
||||
if entity_type == "object":
|
||||
config["object_class"] = os.environ.get("OBJECT_CLASS", "dog")
|
||||
|
||||
all_assets = fetch_all_assets(person)
|
||||
recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER)
|
||||
|
||||
rprint(f" {name}: {len(all_assets)} total, {len(recent_assets)} recent")
|
||||
|
||||
# Filter out assets already uploaded to Frigate
|
||||
retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes")
|
||||
before_dedup = len(recent_assets)
|
||||
new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected))
|
||||
recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids]
|
||||
skipped = before_dedup - len(recent_assets)
|
||||
if skipped:
|
||||
rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]")
|
||||
|
||||
if not recent_assets:
|
||||
rprint(f" [dim]Skipping {name} (0 new images after dedup).[/dim]")
|
||||
continue
|
||||
|
||||
has_embedding = is_embedding_available(entity_type)
|
||||
limit, selection_mode = _resolve_strategy(strategy, has_embedding)
|
||||
|
||||
if selection_mode == "skip":
|
||||
continue
|
||||
|
||||
selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, entity_type)
|
||||
|
||||
if selected_assets:
|
||||
rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]")
|
||||
jobs.append({"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config})
|
||||
|
||||
return jobs
|
||||
|
||||
|
||||
def _show_preview(jobs: list[dict]) -> None:
|
||||
"""Show a summary table of all queued jobs before execution."""
|
||||
table = Table(title="📋 Training Job Preview", show_header=True, header_style="bold cyan")
|
||||
table.add_column("Person", style="bold")
|
||||
table.add_column("Mode", style="dim")
|
||||
table.add_column("Images", justify="right")
|
||||
table.add_column("Date Range", style="dim")
|
||||
|
||||
for job in jobs:
|
||||
name = job["person"]["name"]
|
||||
mode = job["config"].get("mode", "face")
|
||||
count = str(job["limit"])
|
||||
|
||||
# Date range
|
||||
dates = sorted(a.get("fileCreatedAt", "")[:10] for a in job["assets"] if a.get("fileCreatedAt"))
|
||||
date_range = f"{dates[0]} → {dates[-1]}" if len(dates) >= 2 else (dates[0] if dates else "—")
|
||||
|
||||
table.add_row(name, mode, count, date_range)
|
||||
|
||||
console.print()
|
||||
console.print(table)
|
||||
console.print()
|
||||
Reference in New Issue
Block a user