620 lines
25 KiB
Python
620 lines
25 KiB
Python
"""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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from urllib.parse import quote
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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 rich.table import Table
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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, load_embedding_model
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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, fetch_face_data, fetch_full_image, 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 _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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# 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 None
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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 None
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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 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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mode = os.environ.get("TRAINING_MODE", "face")
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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(f" Filtered to {len(valid_people)} people with ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})")
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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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entity_type = mode
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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"] = os.environ.get("OBJECT_CLASS", "dog")
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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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if not recent_assets:
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rprint(f" [dim]Skipping {name} (0 recent images).[/dim]")
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continue
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has_embedding = is_embedding_available(entity_type)
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limit, selection_mode = _resolve_strategy(strategy, has_embedding)
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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, entity_type)
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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 _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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if not has_embedding:
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return 30, "time"
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strategy_map = {
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"auto": ("auto", "smart"),
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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 upload_to_frigate(jobs: list[dict]) -> None:
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"""Upload processed face crops to Frigate via API with detailed logging."""
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frigate_url = os.environ.get("FRIGATE_URL", "")
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if not frigate_url:
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rprint("[yellow]⚠️ FRIGATE_URL not set, skipping upload.[/yellow]")
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return
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rprint("\n[bold cyan]📤 Uploading to Frigate[/bold cyan]")
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rprint(f" Target: [dim]{frigate_url}[/dim]")
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# Count total files to upload
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total_files = 0
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for job in jobs:
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name = job["person"]["name"]
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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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if not os.path.isdir(person_dir):
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continue
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total_files += sum(
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1 for f in os.listdir(person_dir)
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if f.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))
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)
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if total_files == 0:
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rprint(" [yellow]No images found to upload.[/yellow]")
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return
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rprint(f" People: [bold]{len(jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
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uploaded, failed = 0, 0
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max_retries = 2
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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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upload_task = progress.add_task("[green]Uploading to Frigate", total=total_files)
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for job in jobs:
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name = job["person"]["name"]
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# URL-encode the name for the API (handles spaces, special chars)
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encoded_name = quote(name, safe="")
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if " " in name:
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progress.console.print(f" ℹ️ URL-encoded name for Frigate API: '{name}' → '{encoded_name}'")
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person_dir = os.path.join(Config.OUTPUT_DIR, name)
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if not os.path.isdir(person_dir):
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progress.console.print(f" [dim]⏭️ {name}: no output directory, skipping[/dim]")
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continue
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person_files = sorted(
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f for f in os.listdir(person_dir)
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if f.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))
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)
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if not person_files:
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progress.console.print(f" [dim]⏭️ {name}: no images found[/dim]")
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continue
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progress.console.print(f" 📁 {name}: uploading {len(person_files)} image(s)...")
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person_uploaded = 0
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person_failed = 0
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for fname in person_files:
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fpath = os.path.join(person_dir, fname)
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success = False
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for attempt in range(1, max_retries + 1):
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try:
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with open(fpath, "rb") as f:
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resp = requests.post(
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f"{frigate_url}/api/faces/train/{encoded_name}/classify",
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files={"file": (fname, f, "image/jpeg")},
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timeout=30,
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)
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if resp.status_code == 200:
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uploaded += 1
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person_uploaded += 1
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success = True
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break
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else:
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if attempt < max_retries:
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logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: HTTP {resp.status_code}, retrying...")
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continue
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failed += 1
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person_failed += 1
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progress.console.print(
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f" [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
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)
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try:
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error_detail = resp.json().get("message", resp.text[:100])
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progress.console.print(f" [dim]{error_detail}[/dim]")
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except Exception:
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progress.console.print(f" [dim]{resp.text[:100]}[/dim]")
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except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
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if attempt < max_retries:
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logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: {type(exc).__name__}, retrying...")
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continue
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failed += 1
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person_failed += 1
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label = "Connection refused" if isinstance(exc, requests.exceptions.ConnectionError) else "Request timed out (30s)"
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progress.console.print(
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f" [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
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)
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except Exception as e:
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if attempt < max_retries:
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logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: {type(e).__name__}, retrying...")
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continue
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failed += 1
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person_failed += 1
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progress.console.print(
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f" [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
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)
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progress.advance(upload_task)
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# Per-person summary
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if person_failed == 0:
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progress.console.print(
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f" ✅ {name}: {person_uploaded}/{person_uploaded} uploaded"
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)
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else:
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progress.console.print(
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f" ⚠️ {name}: {person_uploaded} succeeded, {person_failed} failed"
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)
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# Grand summary
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rprint("\n [bold]Frigate Upload Summary:[/bold]")
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rprint(f" ✅ Succeeded: [green]{uploaded}[/green]")
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if failed:
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rprint(f" ❌ Failed: [red]{failed}[/red]")
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else:
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rprint(" ❌ Failed: 0")
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if failed > 0:
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rprint(" [yellow]Check logs above for per-file error details.[/yellow]")
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if failed == total_files and total_files > 0:
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rprint(" [bold red]All uploads failed. Verify FRIGATE_URL is reachable and API is enabled.[/bold red]")
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def _perform_selection(assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str) -> 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 explicitly (separate from availability check)
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load_embedding_model(entity_type)
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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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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 _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("Mode", style="dim")
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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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mode = job["config"].get("mode", "face")
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count = str(job["limit"])
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# Date range
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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, mode, 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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def _enrich_asset_with_face_data(asset: dict, person: dict) -> dict:
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"""Enrich an asset dict with face data from the Immich faces API.
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The search/metadata endpoint does not include face bounding box data,
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so we fetch it from GET /api/faces?id={asset_id} and inject it into
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the asset's "people" field so process_face_mode can find it.
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Returns the enriched asset dict (modifies in place and returns it).
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"""
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person_id = person["id"]
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face_data = fetch_face_data(asset["id"], person_id=person_id)
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if face_data is None:
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logger.debug(
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f"Face data API returned nothing for {person.get('name')} "
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f"in asset {asset.get('id')} — Immich may not have detected a face"
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)
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return asset
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# Skip zero-area bounding boxes (face detection failed or no face found)
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if face_data.bbox == (0, 0, 0, 0):
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logger.debug(
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f"Zero-area bounding box for {person.get('name')} in asset {asset.get('id')}"
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)
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return asset
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logger.debug(
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f"Got face data for {person.get('name')} in asset {asset.get('id')}: "
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f"bbox={face_data.bbox}, img_size={face_data.image_width}x{face_data.image_height}"
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)
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face_info = {
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"boundingBoxX1": face_data.bbox[0],
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"boundingBoxY1": face_data.bbox[1],
|
||
"boundingBoxX2": face_data.bbox[2],
|
||
"boundingBoxY2": face_data.bbox[3],
|
||
"imageWidth": face_data.image_width,
|
||
"imageHeight": face_data.image_height,
|
||
}
|
||
|
||
# Inject into asset so process_face_mode can find it via asset["people"]
|
||
asset["people"] = [{"id": person_id, "faces": [face_info]}]
|
||
return asset
|
||
|
||
|
||
def execute_jobs(jobs: list[dict]) -> None:
|
||
"""Download and process images for all jobs."""
|
||
if not jobs:
|
||
return
|
||
|
||
console.rule("[bold blue]Execution Phase")
|
||
|
||
use_full_res = Config.USE_FULL_RESOLUTION
|
||
|
||
with Progress(
|
||
SpinnerColumn(),
|
||
TextColumn("[progress.description]{task.description}"),
|
||
BarColumn(),
|
||
TaskProgressColumn(),
|
||
console=console,
|
||
) as progress:
|
||
grand_total = sum(j["limit"] for j in jobs)
|
||
overall_task = progress.add_task("[green]Overall Progress", total=grand_total)
|
||
|
||
for job in jobs:
|
||
person, assets, config = job["person"], job["assets"], job["config"]
|
||
name, mode = person["name"], config.get("mode", "face")
|
||
|
||
job_task = progress.add_task(f"Processing {name}...", total=len(assets))
|
||
person_dir = os.path.join(Config.OUTPUT_DIR, name)
|
||
os.makedirs(person_dir, exist_ok=True)
|
||
|
||
count = 0
|
||
skipped_download = 0
|
||
skipped_no_face = 0
|
||
skipped_other = 0
|
||
|
||
for asset in assets:
|
||
try:
|
||
# For face mode, enrich the asset with face bounding box data
|
||
# from the Immich faces API (not included in search/metadata results)
|
||
if mode == "face":
|
||
asset = _enrich_asset_with_face_data(asset, person)
|
||
|
||
# Use full-resolution for final output when configured
|
||
if use_full_res:
|
||
img = fetch_full_image(asset["id"])
|
||
else:
|
||
resp = requests.get(
|
||
f"{Config.IMMICH_URL}/api/assets/{asset['id']}/thumbnail?size=preview&format=JPEG",
|
||
headers={"x-api-key": Config.API_KEY, "Accept": "application/json"},
|
||
timeout=30,
|
||
)
|
||
img = Image.open(BytesIO(resp.content)) if resp.ok else None
|
||
|
||
if img is None:
|
||
skipped_download += 1
|
||
progress.console.print(f"[red]✗ Failed download {asset['id']}[/red]")
|
||
logger.debug(f"Image download failed for asset {asset['id']}")
|
||
else:
|
||
saved = (
|
||
process_face_mode(img, asset, person, person_dir, count)
|
||
if mode == "face"
|
||
else process_object_mode(img, config, person_dir, count)
|
||
if mode == "object"
|
||
else process_full_mode(img, person_dir, count)
|
||
)
|
||
if saved:
|
||
count += 1
|
||
logger.debug(f"Saved image #{count} from asset {asset['id']}")
|
||
else:
|
||
if mode == "face":
|
||
skipped_no_face += 1
|
||
progress.console.print(
|
||
f"[yellow]⏭ No usable face data for {asset['id']}[/yellow]"
|
||
)
|
||
logger.debug(
|
||
f"process_face_mode returned False for asset {asset['id']} — "
|
||
f"people={asset.get('people', 'MISSING')}"
|
||
)
|
||
else:
|
||
skipped_other += 1
|
||
progress.console.print(
|
||
f"[yellow]⏭ Skipped {asset['id']}[/yellow]"
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Failed to process asset {asset['id']}: {e}")
|
||
|
||
progress.advance(job_task)
|
||
progress.advance(overall_task)
|
||
|
||
progress.remove_task(job_task)
|
||
|
||
# Per-person execution summary
|
||
rprint(
|
||
f"\n [bold]{name}:[/bold] saved {count}/{len(assets)} images "
|
||
f"(download_failed={skipped_download}, no_face_data={skipped_no_face}, other={skipped_other})"
|
||
)
|
||
logger.info(
|
||
f"{name}: saved {count}/{len(assets)} "
|
||
f"(download_failed={skipped_download}, no_face_data={skipped_no_face}, other={skipped_other})"
|
||
)
|
||
|
||
|
||
def main() -> None:
|
||
"""Entry point for if-curator CLI."""
|
||
try:
|
||
setup_logging(verbose=False)
|
||
|
||
console.print(r"""
|
||
[bold blue]if-curator[/bold blue]
|
||
[dim]Immich -> Frigate Training Data Curator[/dim]
|
||
""")
|
||
|
||
ConfigManager.get().interactive_setup()
|
||
|
||
try:
|
||
Config.validate()
|
||
except ValueError as e:
|
||
rprint(f"[bold red]Configuration Error:[/bold red] {e}")
|
||
return
|
||
|
||
rprint(f"Server: [dim]{Config.IMMICH_URL}[/dim]")
|
||
rprint(f"Output: [dim]{Config.OUTPUT_DIR}[/dim]")
|
||
|
||
people = get_people()
|
||
if not people:
|
||
rprint("[bold red]Could not fetch people from Immich. Check URL/Key.[/bold red]")
|
||
return
|
||
|
||
# Check for non-interactive mode
|
||
auto_mode = os.environ.get("AUTO_MODE", "false").lower() == "true"
|
||
|
||
if auto_mode:
|
||
rprint("[bold cyan]Running in AUTO mode (non-interactive)[/bold cyan]")
|
||
jobs = auto_configure(people)
|
||
else:
|
||
jobs = interactive_configure(people)
|
||
|
||
if jobs:
|
||
_show_preview(jobs)
|
||
if auto_mode or Confirm.ask(f"Ready to process {sum(j['limit'] for j in jobs)} images?"):
|
||
execute_jobs(jobs)
|
||
upload_to_frigate(jobs)
|
||
rprint("\n[bold green]Done! Happy Training.[/bold green]")
|
||
else:
|
||
rprint("[yellow]No jobs configured.[/yellow]")
|
||
|
||
except KeyboardInterrupt:
|
||
rprint("\n[bold red]Aborted by user.[/bold red]")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
|