automated update: 2026-06-10 22:25:47

Dockerfile

@@ -22,8 +22,10 @@ RUN curl -LsSf https://astral.sh/uv/install.sh | sh \
     && cp /root/.local/bin/uv /usr/local/bin/uv
 
 WORKDIR /app
-RUN git clone --depth 1 https://github.com/sudolulo/if_curator_headless.git . \
-    && uv sync --extra gpu && uv add croniter && uv cache clean
+# Copy project files instead of git clone for reproducible builds
+COPY pyproject.toml uv.lock* ./
+COPY if_curator/ if_curator/
+RUN uv sync --extra gpu --extra object && uv add croniter && uv cache clean
 
 COPY entrypoint.sh scheduler.py /app/
 RUN chmod +x /app/entrypoint.sh

compose.yml

@@ -13,7 +13,7 @@ services:
       - ENABLE_CACHE=true
       - CACHE_DIR=/app/.if_cache
       - HF_HOME=/models/huggingface
-      - INSIGHTFACE_HOME=/models/insightface
+      - INSIGHTFACE_HOME=/models
       # - ONLY_PEOPLE=John,Jane
       # - SKIP_PEOPLE=Unknown
       # - MIN_FACE_COUNT=5
@@ -37,3 +37,4 @@ services:
             - driver: nvidia
               count: all
               capabilities: [gpu]
+

if_curator/__main__.py

@@ -1,4 +1,4 @@
-from .cli import main
+from if_curator.cli import main
 
 if __name__ == "__main__":
     main()

if_curator/cache.py

@@ -80,8 +80,15 @@ _cache: EmbeddingCache | None = None
 
 
 def get_cache(cache_dir: str = ".if_cache") -> EmbeddingCache:
-    """Get or create the singleton cache instance."""
+    """Get or create the singleton cache instance.
+
+    Note: The ``cache_dir`` parameter is only used when creating the
+    singleton for the first time. Subsequent calls return the existing
+    instance regardless of ``cache_dir``. If you need a cache with a
+    different directory, instantiate ``EmbeddingCache`` directly.
+    """
     global _cache
     if _cache is None:
         _cache = EmbeddingCache(cache_dir)
     return _cache
+

if_curator/cli.py

@@ -14,7 +14,7 @@ from rich.table import Table
 
 from .config import Config, ConfigManager
 from .diversity import select_diverse_assets
-from .embeddings import is_embedding_available
+from .embeddings import is_embedding_available, load_embedding_model
 from .image_processing import process_face_mode, process_full_mode, process_object_mode
 from .immich_api import fetch_all_assets, fetch_full_image, filter_recent_assets, get_people
 from .logging import console, setup_logging
@@ -146,6 +146,7 @@ def interactive_configure(people: list[dict]) -> list[dict]:
 
     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"])
@@ -164,6 +165,13 @@ def auto_configure(people: list[dict]) -> list[dict]:
     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 ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})")
+
     jobs = []
     for person in valid_people:
         name = person["name"]
@@ -239,6 +247,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
     rprint(f"  People: [bold]{len(jobs)}[/bold], Total images: [bold]{total_files}[/bold]")
 
     uploaded, failed = 0, 0
+    max_retries = 2
 
     with Progress(
         SpinnerColumn(),
@@ -276,6 +285,9 @@ def upload_to_frigate(jobs: list[dict]) -> None:
 
             for fname in person_files:
                 fpath = os.path.join(person_dir, fname)
+                success = False
+
+                for attempt in range(1, max_retries + 1):
                     try:
                         with open(fpath, "rb") as f:
                             resp = requests.post(
@@ -286,34 +298,40 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         if resp.status_code == 200:
                             uploaded += 1
                             person_uploaded += 1
+                            success = True
+                            break
                         else:
+                            if attempt < max_retries:
+                                logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: HTTP {resp.status_code}, retrying...")
+                                continue
                             failed += 1
                             person_failed += 1
                             progress.console.print(
-                            f"    [red]✗ {fname}: HTTP {resp.status_code}[/red]"
+                                f"    [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
                             )
                             try:
                                 error_detail = resp.json().get("message", resp.text[:100])
                                 progress.console.print(f"      [dim]{error_detail}[/dim]")
                             except Exception:
                                 progress.console.print(f"      [dim]{resp.text[:100]}[/dim]")
-                except requests.exceptions.ConnectionError:
+                    except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
+                        if attempt < max_retries:
+                            logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: {type(exc).__name__}, retrying...")
+                            continue
                         failed += 1
                         person_failed += 1
+                        label = "Connection refused" if isinstance(exc, requests.exceptions.ConnectionError) else "Request timed out (30s)"
                         progress.console.print(
-                        f"    [red]✗ {fname}: Connection refused[/red]"
-                    )
-                except requests.exceptions.Timeout:
-                    failed += 1
-                    person_failed += 1
-                    progress.console.print(
-                        f"    [red]✗ {fname}: Request timed out (30s)[/red]"
+                            f"    [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
                         )
                     except Exception as e:
+                        if attempt < max_retries:
+                            logger.warning(f"Upload attempt {attempt}/{max_retries} for {fname}: {type(e).__name__}, retrying...")
+                            continue
                         failed += 1
                         person_failed += 1
                         progress.console.print(
-                        f"    [red]✗ {fname}: {type(e).__name__} - {e}[/red]"
+                            f"    [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
                         )
 
                 progress.advance(upload_task)
@@ -349,8 +367,8 @@ def _perform_selection(assets: list, limit: int | str, name: str, selection_mode
         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 to avoid interference with progress bar
-        is_embedding_available(entity_type)
+        # Pre-load model explicitly (separate from availability check)
+        load_embedding_model(entity_type)
 
         with Progress(
             SpinnerColumn(),

if_curator/config.py

@@ -14,10 +14,10 @@ load_dotenv()
 CONFIG_FILE = Path(".immich_config.json")
 
 
-class Config:
+class _Config:
     """Singleton configuration with uppercase attribute access for backward compatibility."""
 
-    _instance: ClassVar["Config | None"] = None
+    _instance: ClassVar["_Config | None"] = None
 
     # Configuration values
     IMMICH_URL: str | None = None
@@ -26,11 +26,14 @@ class Config:
     YEARS_FILTER: int = 10
 
     # Quality filtering
-    MIN_FACE_WIDTH: int = 100
+    MIN_FACE_WIDTH: int = 50
     BLUR_THRESHOLD: float = 100.0
     MIN_CONFIDENCE: float = 0.7
     MAX_AUTO_IMAGES: int = 80
 
+    # People filtering
+    MIN_FACE_COUNT: int = 0
+
     # Output quality
     FACE_MARGIN: float = 0.15
     USE_FULL_RESOLUTION: bool = True
@@ -40,7 +43,7 @@ class Config:
     ENABLE_CACHE: bool = False
     CACHE_DIR: str = ".if_cache"
 
-    def __new__(cls) -> "Config":
+    def __new__(cls) -> "_Config":
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._load()
@@ -54,6 +57,9 @@ class Config:
         self.OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./frigate_train")
         self.YEARS_FILTER = int(os.getenv("YEARS_FILTER", "10"))
         self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "50"))
+        self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "0"))
+        self.ENABLE_CACHE = os.getenv("ENABLE_CACHE", "false").lower() in ("true", "1", "yes")
+        self.CACHE_DIR = os.getenv("CACHE_DIR", ".if_cache")
 
         # Fall back to config file for missing values
         if CONFIG_FILE.exists():
@@ -66,6 +72,11 @@ class Config:
             except (json.JSONDecodeError, OSError) as e:
                 logging.warning(f"Failed to load config file: {e}")
 
+    @classmethod
+    def reset(cls) -> None:
+        """Reset the singleton — mainly useful for testing or delayed env setup."""
+        cls._instance = None
+
     def save(self) -> None:
         """Persist configuration to file."""
         try:
@@ -105,11 +116,47 @@ class Config:
             raise ValueError("Missing Immich URL or API Key.")
 
 
-# Singleton instance and backward-compatible aliases
-Config = Config()  # type: ignore[misc]
-ConfigManager = type("ConfigManager", (), {"get": staticmethod(lambda: Config)})
+# Singleton instance — use a lazy property pattern to avoid import-time side effects
+# when env vars aren't yet set. Call Config.instance() or just access attributes on
+# the module-level `Config` (which delegates to the singleton).
+class _ConfigAccessor:
+    """Lazy accessor that defers singleton creation until first attribute access.
+
+    This avoids reading .env and config files at import time, so environment
+    variables set after importing the module are properly picked up.
+    """
+
+    def __getattr__(self, name: str):
+        return getattr(_Config(), name)
+
+    def __setattr__(self, name: str, value):
+        if name.startswith("_"):
+            super().__setattr__(name, value)
+        else:
+            setattr(_Config(), name, value)
+
+    def reset(self) -> None:
+        """Reset the underlying singleton."""
+        _Config.reset()
+
+    def interactive_setup(self) -> None:
+        """Delegate to the singleton."""
+        _Config().interactive_setup()
+
+    def validate(self) -> None:
+        """Delegate to the singleton."""
+        _Config().validate()
+
+    def save(self) -> None:
+        """Delegate to the singleton."""
+        _Config().save()
+
+
+Config = _ConfigAccessor()
+ConfigManager = type("ConfigManager", (), {"get": staticmethod(lambda: _Config())})
 
 
 def get_headers() -> dict[str, str]:
     """Return HTTP headers for Immich API requests."""
     return {"x-api-key": Config.API_KEY or "", "Accept": "application/json"}
+

if_curator/embeddings.py

@@ -7,6 +7,7 @@ Unified embedding interface for faces and objects.
 """
 
 import contextlib
+import importlib
 import logging
 import os
 import warnings
@@ -21,8 +22,10 @@ logger = logging.getLogger(__name__)
 
 # Lazy-loaded singletons
 _insightface_app = None
+_insightface_loaded = False
 _siglip_model = None
 _siglip_processor = None
+_siglip_loaded = False
 
 
 def _is_force_cpu() -> bool:
@@ -37,9 +40,10 @@ def _is_force_cpu() -> bool:
 
 def get_insightface_app():
     """Singleton for InsightFace app with automatic GPU/CPU fallback."""
-    global _insightface_app
-    if _insightface_app is not None:
+    global _insightface_app, _insightface_loaded
+    if _insightface_loaded:
         return _insightface_app
+    _insightface_loaded = True
 
     try:
         import onnxruntime as ort
@@ -120,9 +124,10 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None:
 
 def get_siglip_model():
     """Singleton for SigLIP model and processor with GPU auto-detection."""
-    global _siglip_model, _siglip_processor
-    if _siglip_model is not None:
+    global _siglip_model, _siglip_processor, _siglip_loaded
+    if _siglip_loaded:
         return _siglip_model, _siglip_processor
+    _siglip_loaded = True
 
     try:
         import warnings
@@ -264,9 +269,45 @@ def get_embedding(
     return emb
 
 
-def is_embedding_available(entity_type: str = "face") -> bool:
-    """Check if embedding model is available for the given entity type."""
+def _is_module_available(module_name: str) -> bool:
+    """Check if a Python module is importable without importing it fully."""
+    try:
+        importlib.util.find_spec(module_name)
+        return True
+    except (ModuleNotFoundError, ValueError):
+        return False
+
+
+def is_embedding_available(entity_type: str = "face", *, load: bool = False) -> bool:
+    """Check if embedding model is available for the given entity type.
+
+    By default this performs a lightweight import-check only (no model loading).
+    Pass ``load=True`` to actually load the model (expensive, hundreds of MB).
+
+    Args:
+        entity_type: 'face' or 'object'
+        load: If True, fully load the model to verify. If False (default),
+              only check that the required packages are importable.
+    """
+    if load:
         if entity_type == "face":
             return get_insightface_app() is not None
         model, _ = get_siglip_model()
         return model is not None
+
+    # Lightweight check: just verify the packages are importable
+    if entity_type == "face":
+        return _is_module_available("insightface") and _is_module_available("onnxruntime")
+    return _is_module_available("transformers") and _is_module_available("torch")
+
+
+def load_embedding_model(entity_type: str = "face") -> bool:
+    """Explicitly load the embedding model for the given entity type.
+
+    Returns True if the model loaded successfully.
+    """
+    if entity_type == "face":
+        return get_insightface_app() is not None
+    model, _ = get_siglip_model()
+    return model is not None
+

if_curator/image_processing.py

@@ -140,15 +140,17 @@ def process_object_mode(
         results = model(img, verbose=False, device=device)
 
         found = False
-        for idx, (box, cls_id, conf) in enumerate(
-            (box, int(box.cls[0]), float(box.conf[0])) for r in results for box in r.boxes
-        ):
+        class_idx = 0  # Sequential counter per target class (Issue #10)
+        for box in (box for r in results for box in r.boxes):
+            cls_id = int(box.cls[0])
+            conf = float(box.conf[0])
             if 0 <= cls_id < len(model.names) and model.names[cls_id] == target_class and conf > 0.5:
                 x1, y1, x2, y2 = box.xyxy[0].tolist()
                 img.crop((x1, y1, x2, y2)).save(
-                    os.path.join(output_dir, f"{count}_{idx}.jpg"),
+                    os.path.join(output_dir, f"{count}_{class_idx}.jpg"),
                     format="JPEG",
                 )
+                class_idx += 1
                 found = True
 
         return found
@@ -161,3 +163,4 @@ def process_full_mode(img: Image.Image, output_dir: str, count: int) -> bool:
     """Save full image."""
     img.save(os.path.join(output_dir, f"{count}.jpg"), format="JPEG")
     return True
+

if_curator/immich_api.py

@@ -13,6 +13,8 @@ from .config import Config, get_headers
 
 logger = logging.getLogger(__name__)
 
+MAX_PAGES = 1000  # Safety limit for pagination
+
 
 @dataclass
 class FaceData:
@@ -50,7 +52,7 @@ def fetch_all_assets(person: dict) -> list[dict]:
     logger.info(f"Fetching assets for {name}...")
 
     assets = []
-    for page in range(1, 1000):  # Safety limit
+    for page in range(1, MAX_PAGES + 1):
         try:
             resp = requests.post(
                 url,
@@ -209,3 +211,4 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d
 
     logger.info(f"Retained {len(recent)} assets (filtered {skipped} old assets).")
     return recent
+

if_curator/logging.py

@@ -1,6 +1,7 @@
 """Logging configuration for if-curator."""
 
 import logging
+import os
 import warnings
 
 from rich.console import Console
@@ -33,8 +34,11 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
     # Rich console handler - uses shared console to avoid breaking progress bars
     root.addHandler(RichHandler(rich_tracebacks=True, markup=True, console=console))
 
-    # File handler (always debug level)
-    file_handler = logging.FileHandler("immich_export.log")
+    # File handler (always debug level) — log file respects OUTPUT_DIR if set
+    log_dir = os.environ.get("OUTPUT_DIR", ".")
+    os.makedirs(log_dir, exist_ok=True)
+    log_path = os.path.join(log_dir, "immich_export.log")
+    file_handler = logging.FileHandler(log_path)
     file_handler.setLevel(logging.DEBUG)
     file_handler.setFormatter(logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s"))
     root.addHandler(file_handler)
@@ -48,3 +52,4 @@ def setup_logging(verbose: bool = False) -> logging.Logger:
     warnings.filterwarnings("ignore", category=FutureWarning, module="transformers")
 
     return root
+

if_curator/quality.py

@@ -70,7 +70,7 @@ def check_exposure(img_np: np.ndarray, lo: float = 30.0, hi: float = 225.0) -> t
     return True, ""
 
 
-def check_face_size(face_width: float, face_height: float, min_px: int = 100) -> tuple[bool, str]:
+def check_face_size(face_width: float, face_height: float, min_px: int = 50) -> tuple[bool, str]:
     """Validate face crop is large enough for meaningful features."""
     if face_width < min_px or face_height < min_px:
         return False, f"Face too small ({face_width:.0f}x{face_height:.0f}, min={min_px}px)"
@@ -94,7 +94,7 @@ def assess_quality(
     face_bbox: tuple[float, float, float, float] | None = None,
     confidence: float | None = None,
     blur_threshold: float = 100.0,
-    min_face_px: int = 100,
+    min_face_px: int = 50,
     min_confidence: float = 0.7,
 ) -> QualityResult:
     """Run all quality checks on an image.
@@ -130,3 +130,4 @@ def assess_quality(
             reasons.append(reason)
 
     return QualityResult(passed=len(reasons) == 0, reasons=reasons)
+

pyproject.toml

@@ -5,7 +5,7 @@ description = "Immich to Frigate training sets"
 readme = "README.md"
 license = "MIT"
 requires-python = ">=3.12"
-authors = [{ name = "ds-sebastian" }]
+authors = [{ name = "sudolulo" }]
 keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"]
 classifiers = [
     "Development Status :: 3 - Alpha",
@@ -23,21 +23,23 @@ dependencies = [
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
-    "torch>=2.9.1",
-    "transformers>=4.57.6",
-    "ultralytics>=8.3.252",
 ]
 
 [project.scripts]
 if-curator = "if_curator.cli:main"
 
 [project.urls]
-Repository = "https://github.com/ds-sebastian/if_curator"
+Repository = "https://github.com/sudolulo/if_curator_headless"
 
 [project.optional-dependencies]
 gpu = [
     "onnxruntime-gpu>=1.23.2",
 ]
+object = [
+    "torch>=2.9.1",
+    "transformers>=4.57.6",
+    "ultralytics>=8.3.252",
+]
 
 [dependency-groups]
 dev = [
@@ -75,3 +77,4 @@ DEP002 = ["onnxruntime-gpu"]
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
+

scheduler.py

@@ -3,23 +3,56 @@ import os
 import sys
 import subprocess
 import time
+import logging
+from pathlib import Path
+
+try:
     from croniter import croniter
+except ImportError:
+    print("❌ croniter not installed. Run: uv add croniter")
+    sys.exit(1)
+
+SCHEDULE = os.environ["CRON_SCHEDULE"]
+MODELS_DIR = os.environ.get("HF_HOME", "/models/huggingface")
+INSIGHTFACE_BASE = os.environ.get("INSIGHTFACE_HOME", "/models")
 
-SCHEDULE = os.environ.get("CRON_SCHEDULE", "0 3 * * SUN")
 RUN_ENV = {**os.environ, "PYTHONUNBUFFERED": "1"}
 
-def run_curator():
-    print(f"\n▶ [{time.strftime('%Y-%m-%d %H:%M:%S')}] Starting if-curator...", flush=True)
-    subprocess.run(["uv", "run", "python", "-m", "if_curator.cli"], env=RUN_ENV)
+logger = logging.getLogger(__name__)
 
-now = time.time()
-cron = croniter(SCHEDULE, now)
+
+def check_models():
+    """Log model status before each run."""
+    print("📦 Checking models...", flush=True)
+    buffalo = Path(INSIGHTFACE_BASE) / ".insightface" / "models" / "buffalo_l"
+    if buffalo.exists():
+        print("  ✅ InsightFace Buffalo_L: present", flush=True)
+    else:
+        print("  ⬇️  InsightFace Buffalo_L: not found — will download", flush=True)
+
+    hf_hub = Path(MODELS_DIR) / "hub"
+    if hf_hub.exists() and any(hf_hub.iterdir()):
+        print("  ✅ HuggingFace models: present", flush=True)
+    else:
+        print("  ⬇️  HuggingFace models: not found — will download", flush=True)
+    print("🚀 Starting if-curator...", flush=True)
+
+
+NOW = time.time()
+cron = croniter(SCHEDULE, NOW)
 next_run = cron.get_next(float)
 
-print(f"▶ Scheduler active. Next run: {time.ctime(next_run)}")
 while True:
-    if time.time() >= next_run:
-        run_curator()
+    now = time.time()
+    if now >= next_run:
+        print(f"\n▶ [{time.strftime('%Y-%m-%d %H:%M:%S')}] Starting if-curator...", flush=True)
+        check_models()
+        result = subprocess.run(["uv", "run", "if-curator"], env=RUN_ENV)
+        if result.returncode != 0:
+            logger.error(f"if-curator exited with code {result.returncode}")
+            print(f"❌ if-curator failed with exit code {result.returncode}", flush=True)
+        else:
+            print("✅ if-curator completed successfully", flush=True)
         next_run = cron.get_next(float)
     time.sleep(60)