initialize if_curator project with core modules for image embedding, processing, diversity analysis, and Immich API integration.
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Sebastian G
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"""Interactive CLI for if-curator."""
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import logging
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import os
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from io import BytesIO
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import requests
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from PIL import Image
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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 .config import Config, ConfigManager
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from .diversity import select_diverse_assets
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from .embeddings import is_embedding_available
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from .image_processing import process_face_mode, process_full_mode, process_object_mode
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from .immich_api import fetch_all_assets, filter_recent_assets, get_people
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from .logging import console, setup_logging
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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", "Auto 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, entity_type: str) -> tuple[int | str, str]:
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"""Prompt user for training strategy and return (limit, selection_mode)."""
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model_name = "InsightFace" if entity_type == "face" else "SigLIP"
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if has_embedding:
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rprint(" [bold]1.[/bold] Auto (Objective 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(f" [yellow]Note: {model_name} 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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limits = {"1": 30, "2": 100, "3": IntPrompt.ask("Enter number of images", default=30)}
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return limits.get(choice, 0), "time" if choice != "4" else "skip"
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def interactive_configure(people: list[dict]) -> list[dict]:
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"""Interactive phase: select person, mode, and configure training strategy."""
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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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# 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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console.print(f" [bold]{idx}.[/bold] {p['name']}")
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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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name = person["name"]
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console.print(f"\nSelected: [bold green]{name}[/bold green]")
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# Select training mode
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rprint("\n[bold cyan]Training Mode:[/bold cyan]")
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rprint(" [bold]1.[/bold] Face (Frigate Face Recognition)")
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rprint(" [bold]2.[/bold] Object (Frigate Object Classification)")
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mode_choice = Prompt.ask("Choice", choices=["1", "2"], default="1")
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entity_type = "face" if mode_choice == "1" else "object"
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config = {"name": name, "mode": entity_type}
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if entity_type == "object":
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config["object_class"] = Prompt.ask("Enter Object Class (e.g. dog, cat, car)", default="dog")
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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} ({entity_type})...")
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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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if not recent_assets:
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rprint(" [dim]Skipping (0 recent images).[/dim]")
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return []
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# Strategy selection
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has_embedding = is_embedding_available(entity_type)
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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, entity_type)
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if selection_mode == "skip":
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return []
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# Perform selection
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selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, entity_type)
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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 _perform_selection(
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assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str
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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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model_display = "InsightFace (face embeddings)" if entity_type == "face" else "SigLIP (visual embeddings)"
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rprint(f"\n[cyan]Using {model_display} for diversity analysis...[/cyan]")
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# Pre-load model to avoid interference with progress bar
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is_embedding_available(entity_type)
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with Progress(
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SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
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BarColumn(), TaskProgressColumn(), 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, limit, name,
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selection_mode=selection_mode,
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entity_type=entity_type,
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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", entity_type=entity_type)
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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 execute_jobs(jobs: list[dict]) -> None:
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"""Download and process images for all jobs."""
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if not jobs:
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return
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console.rule("[bold blue]Execution Phase")
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with Progress(
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SpinnerColumn(), TextColumn("[progress.description]{task.description}"),
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BarColumn(), TaskProgressColumn(), console=console,
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) as progress:
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grand_total = sum(j["limit"] for j in jobs)
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overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
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for job in jobs:
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person, assets, config = job["person"], job["assets"], job["config"]
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name, mode = person["name"], config.get("mode", "face")
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job_task = progress.add_task(f"Processing {name}...", total=len(assets))
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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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os.makedirs(person_dir, exist_ok=True)
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count = 0
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for asset in assets:
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try:
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resp = requests.get(
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f"{Config.IMMICH_URL}/api/assets/{asset['id']}/thumbnail?size=preview&format=JPEG",
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headers={"x-api-key": Config.API_KEY, "Accept": "application/json"},
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timeout=30,
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)
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if not resp.ok:
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progress.console.print(f"[red]Failed download {asset['id']}[/red]")
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else:
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img = Image.open(BytesIO(resp.content))
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saved = (
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process_face_mode(img, asset, person, person_dir, count, min_width=Config.MIN_FACE_WIDTH)
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if mode == "face"
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else process_object_mode(img, config, person_dir, count)
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if mode == "object"
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else process_full_mode(img, person_dir, count)
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)
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if saved:
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count += 1
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except Exception as e:
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logger.error(f"Failed to process asset {asset['id']}: {e}")
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progress.advance(job_task)
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progress.advance(overall_task)
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progress.remove_task(job_task)
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def main() -> None:
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"""Entry point for if-curator CLI."""
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try:
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setup_logging(verbose=False)
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console.print(r"""
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[bold blue]if-curator[/bold blue]
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[dim]Immich -> Frigate Training Data Curator[/dim]
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""")
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ConfigManager.get().interactive_setup()
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try:
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Config.validate()
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except ValueError as e:
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rprint(f"[bold red]Configuration Error:[/bold red] {e}")
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return
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rprint(f"Server: [dim]{Config.IMMICH_URL}[/dim]")
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rprint(f"Output: [dim]{Config.OUTPUT_DIR}[/dim]")
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people = get_people()
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if not people:
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rprint("[bold red]Could not fetch people from Immich. Check URL/Key.[/bold red]")
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return
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jobs = interactive_configure(people)
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if jobs and Confirm.ask(f"\nReady to process {sum(j['limit'] for j in jobs)} images?"):
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execute_jobs(jobs)
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rprint("\n[bold green]Done! Happy Training.[/bold green]")
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elif not jobs:
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rprint("[yellow]No jobs configured.[/yellow]")
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except KeyboardInterrupt:
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rprint("\n[bold red]Aborted by user.[/bold red]")
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if __name__ == "__main__":
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main()
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