From cc7293b66a07d5d75e133017752d82753d8b3f36 Mon Sep 17 00:00:00 2001 From: Sebastian G Date: Mon, 2 Mar 2026 20:09:45 -0500 Subject: [PATCH] feat: Implement advanced diversity selection algorithms, comprehensive quality filtering, and caching for improved image curation. --- .gitignore | 1 + LICENSE | 21 + README.md | 157 ++-- if_curator/__init__.py | 8 + if_curator/cache.py | 87 ++ if_curator/cli.py | 143 +++- if_curator/config.py | 16 +- if_curator/diversity.py | 364 ++++++++- if_curator/embeddings.py | 89 ++- if_curator/image_processing.py | 66 +- if_curator/immich_api.py | 118 ++- if_curator/logging.py | 4 +- if_curator/quality.py | 132 +++ pyproject.toml | 43 +- uv.lock | 1368 +++++++++++++++++--------------- 15 files changed, 1836 insertions(+), 781 deletions(-) create mode 100644 LICENSE create mode 100644 if_curator/cache.py create mode 100644 if_curator/quality.py diff --git a/.gitignore b/.gitignore index d03b50b..b104c37 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ yolov9c.pt .insightface/ .huggingface/ .cache/huggingface +.if_cache/ .immich_config.json # Python-generated files diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..a4bf177 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Sebastian + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index 65ef7e3..0a30db1 100644 --- a/README.md +++ b/README.md @@ -11,31 +11,6 @@ - ---- - -> [!WARNING] -> This is 100% vibe-coded. - - - - - -## โšก Why This Tool? - -> **"Diversity matters far more than volume."** โ€” *Frigate Developer Tips* - -Training AI models on "bulk" data is often harmful. If you feed the model 50 images from the same 10-second video clip, it learns to recognize the *lighting and background*, not the actual *face* or *object*. - -`if-curator` solves this using **AI-powered diversity selection**: - -| Mode | Embedding Model | Algorithm | -| :--- | :--- | :--- | -| **๐Ÿ‘ค Face** | InsightFace (ArcFace) | Farthest Point Sampling | -| **๐Ÿถ Object** | SigLIP (Vision Transformer) | Farthest Point Sampling | - -Both use the same **Farthest Point Sampling (FPS)** algorithm that mathematically selects images until redundancy starts, ensuring optimal diversity whether that's 20 or 150 images. - > [!WARNING] > **Regarding Object Classification** > @@ -44,25 +19,67 @@ Both use the same **Farthest Point Sampling (FPS)** algorithm that mathematicall --- +## โšก Why This Tool? + +> **"Diversity matters far more than volume."** โ€” *Frigate Developer Tips* + +Training AI models on "bulk" data is often harmful. If you feed the model 50 images from the same 10-second video clip, it learns to recognize the *lighting and background*, not the actual *face* or *object*. + +`if-curator` solves this using **AI-powered diversity selection** and **quality filtering**: + +| Mode | Embedding Model | Algorithm | +| :--- | :--- | :--- | +| **๐Ÿ‘ค Face** | InsightFace (ArcFace) | K-Medoids Clustering + FPS + Hard Example Weighting | +| **๐Ÿถ Object** | SigLIP (Vision Transformer) | K-Medoids Clustering + FPS | + +The pipeline uses **K-Medoids clustering** to guarantee coverage of every distinct "look", then fills the remaining budget with **Farthest Point Sampling (FPS)** biased toward **hard examples** (unusual angles, partial occlusions). An **adaptive threshold** stops selection automatically when adding more images becomes redundant. + +--- + ## โœจ Features -### ๐ŸŽฏ Unified Selection Strategies -Both Face and Object modes offer the same powerful options: -- **Auto (Objective Diversity) [Recommended]**: Dynamically selects images until redundancy starts +### ๐ŸŽฏ Smart Selection +- **Auto Diversity [Recommended]**: Clusters images by visual similarity, selects representatives from each cluster, then fills with maximally-diverse picks until redundancy starts (capped at 80) - **Standard (30 images)**: Balanced set using Smart Diversity - **Broad (100 images)**: Extensive set using Smart Diversity - **Custom Count**: You choose the limit +### ๏ฟฝ Quality Filtering +Bad training data hurts ArcFace models. Images are automatically rejected if they are: +- **Blurry** โ€” Laplacian variance below threshold +- **Grayscale / IR** โ€” ArcFace is trained on color images only +- **Over/Underexposed** โ€” Washed-out or too dark to use +- **Low confidence** โ€” Partial or occluded face detections +- **Too small** โ€” Faces under 100px (configurable) lack features + ### ๐Ÿ‘ค Face Recognition Prep -- Uses **InsightFace** (ArcFace/Buffalo_L) embeddings -- Extracts faces using Immich's metadata -- **Auto-Diversity** picks the optimal set size based on visual distinctness +- Uses **InsightFace** (ArcFace/Buffalo_L) embeddings on **face crops** (not full images โ€” avoids wrong-face in group photos) +- **Hard example prioritization** โ€” unusual angles, sunglasses, and low-confidence detections are biased for selection +- **Face alignment** via InsightFace landmarks (standard 112ร—112 ArcFace input) +- Downloads **full-resolution** originals for final crops (falls back to JPEG preview for HEIC/RAW) +- Configurable crop margin (default 15%) ### ๐Ÿ“ฆ Object/State Classification Prep - Uses **SigLIP** (Vision Transformer) embeddings for semantic diversity - **YOLOv9c** to detect and crop specific objects (dogs, cars, etc.) - Captures variation in poses, lighting, and backgrounds -- *Note: As mentioned, Frigate upload is pending support.* + +### โšก Performance +- **Concurrent thumbnail downloads** (8 parallel workers) +- **Batch-capable** SigLIP embeddings for GPU efficiency +- Optional **disk-based embedding cache** for faster re-runs +- **Multi-person batch mode** โ€” process multiple people in one session + +### ๐Ÿ“‹ Preview Before Download +After selection, a summary table shows what will be processed: +``` + ๐Ÿ“‹ Training Job Preview +โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ +โ”ƒ Person โ”ƒ Mode โ”ƒ Images โ”ƒ Date Range โ”ƒ +โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ +โ”‚ Sebastian โ”‚ face โ”‚ 80 โ”‚ 2021-04-03 โ†’ 2026-02-18 โ”‚ +โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ +``` --- @@ -93,22 +110,25 @@ uv sync --extra gpu ## ๐Ÿ’ป Usage -Run the command-line interface: - ```bash -uv run -m if_curator +uv run if-curator ``` ### Interactive Flow The tool will guide you through: -1. **Select Person/Subject**: Choose from your Immich people. -2. **Training Mode**: Face (Recognition) or Object (Classification). -3. **Strategy**: Auto, Standard, Broad, etc. +1. **Select Person** โ€” Choose from your Immich people (supports multi-person batch) +2. **Training Mode** โ€” Face (Recognition) or Object (Classification) +3. **Strategy** โ€” Auto, Standard, Broad, or Custom +4. **Preview** โ€” Review the selection summary before downloading +5. **Execute** โ€” Downloads and processes images with progress tracking ```text -Using SigLIP (visual embeddings) for diversity analysis... -Computing embeddings for 69 images... โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ 100% -Auto-diversity selected 38 optimally diverse images. +Using InsightFace (face embeddings) for diversity analysis... +Quality filtering removed 76 images. +Adaptive threshold: 0.1721 (median_dist=0.8605, fraction=0.2) +Clustering 223 embeddings into 20 groups (K-Medoids)... +Selected 20 cluster medoids as initial picks. +Selection complete: 80 images (0 hard examples with confidence < 0.85). ``` --- @@ -117,18 +137,55 @@ Auto-diversity selected 38 optimally diverse images. The tool prompts for your Immich URL and API Key on the first run and saves them to `.immich_config.json`. -| Variable | Description | -| :--- | :--- | -| `IMMICH_URL` | Full URL to Immich (e.g. `http://192.168.1.10:2283`) | -| `API_KEY` | Your Immich API Key | -| `FORCE_CPU` | Set to `true` to disable GPU acceleration | +### Environment Variables + +| Variable | Default | Description | +| :--- | :--- | :--- | +| `IMMICH_URL` | โ€” | Full URL to Immich (e.g. `http://192.168.1.10:2283`) | +| `API_KEY` | โ€” | Your Immich API Key | +| `FORCE_CPU` | `false` | Disable GPU acceleration | +| `MIN_FACE_WIDTH` | `100` | Minimum face crop size (pixels) | +| `BLUR_THRESHOLD` | `100.0` | Laplacian variance threshold for blur detection | +| `MIN_CONFIDENCE` | `0.7` | Minimum Immich detection confidence | +| `MAX_AUTO_IMAGES` | `80` | Safety cap for auto-diversity mode | +| `FACE_MARGIN` | `0.15` | Crop margin around face (fraction) | +| `USE_FULL_RESOLUTION` | `true` | Download originals for final crops | +| `ENABLE_FACE_ALIGNMENT` | `true` | Align faces to ArcFace 112ร—112 format | +| `ENABLE_CACHE` | `false` | Cache embeddings to disk for faster re-runs | +| `CACHE_DIR` | `.if_cache` | Directory for embedding cache | --- ## ๐Ÿง  Technical Details -- **InsightFace**: Face detection and embedding (ArcFace) -- **SigLIP**: Visual embeddings via `transformers` (OpenAI CLIP alternative) -- **YOLOv9c**: State-of-the-art object detection for cropping -- **Rich**: Beautiful terminal UI +### Models +- **InsightFace (Buffalo_L)** โ€” Face detection and embedding (ArcFace, 512-d) +- **SigLIP** โ€” Visual embeddings via `transformers` (google/siglip-base-patch16-224, 768-d) +- **YOLOv9c** โ€” Object detection for cropping +### Algorithms +- **K-Medoids Clustering** โ€” Groups embeddings into k clusters using cosine distance, selecting actual data points (medoids) as cluster centers. Guarantees one representative from every distinct "look" +- **Farthest Point Sampling** โ€” After medoid selection, fills remaining budget by iteratively selecting the most distant point from the current set +- **Hard Example Weighting** โ€” Candidates with detection confidence < 0.85 get a 1.2โ€“1.5ร— distance boost, biasing selection toward challenging images (unusual angles, occlusions) +- **Adaptive Auto-Threshold** โ€” Computed as 20% of the median pairwise cosine distance; stops when the next-best image is too similar +- **Quality Filtering** โ€” Blur (Laplacian), grayscale/IR (channel comparison), exposure (histogram), confidence (Immich metadata) +- **Face Crop Embedding** โ€” Extracts the target person's face (using Immich bbox) before embedding, preventing wrong-face selection in group photos + +### Architecture +``` +Immich API โ”€โ–บ Fetch Assets by Person โ”€โ–บ Time Filter + โ”‚ + Concurrent Thumbnail Download (8 workers) + โ”‚ + Quality Filtering (blur, IR, exposure...) + โ”‚ + Face Crop Extraction (bbox from Immich metadata) + โ”‚ + Compute Embeddings (InsightFace / SigLIP) + โ”‚ + K-Medoids Clustering โ†’ FPS + Hard Example Weighting + โ”‚ + Preview Summary Table + โ”‚ + Download Full-Res โ”€โ–บ Face Alignment โ”€โ–บ Save +``` diff --git a/if_curator/__init__.py b/if_curator/__init__.py index e69de29..96fcefe 100644 --- a/if_curator/__init__.py +++ b/if_curator/__init__.py @@ -0,0 +1,8 @@ +"""Immich to Frigate training set curator. + +AI-powered tool to extract high-quality, diverse training images from your +Immich library for Frigate's Face Recognition (ArcFace) and Object/State +Classification models. +""" + +__version__ = "0.1.0" diff --git a/if_curator/cache.py b/if_curator/cache.py new file mode 100644 index 0000000..21a36be --- /dev/null +++ b/if_curator/cache.py @@ -0,0 +1,87 @@ +"""Disk-based embedding cache. + +Caches embeddings keyed by (asset_id, model_version) to avoid +recomputing on reruns. Uses numpy binary format for fast I/O. +""" + +import hashlib +import logging +import os + +import numpy as np + +logger = logging.getLogger(__name__) + +# Model versions โ€” bump these when the upstream model changes +MODEL_VERSIONS = { + "insightface": "buffalo_l_v1", + "siglip": "siglip-base-patch16-224_v1", + "immich": "immich_buffalo_l_v1", +} + + +class EmbeddingCache: + """Simple disk-based embedding cache. + + Embeddings are stored as .npy files in a flat directory, + keyed by a hash of (asset_id, model_version). + """ + + def __init__(self, cache_dir: str = ".if_cache") -> None: + self.cache_dir = cache_dir + self._ensured = False + + def _ensure_dir(self) -> None: + if not self._ensured: + os.makedirs(self.cache_dir, exist_ok=True) + self._ensured = True + + @staticmethod + def _key(asset_id: str, model: str) -> str: + version = MODEL_VERSIONS.get(model, model) + raw = f"{asset_id}:{version}" + return hashlib.sha256(raw.encode()).hexdigest()[:16] + + def _path(self, asset_id: str, model: str) -> str: + return os.path.join(self.cache_dir, f"{self._key(asset_id, model)}.npy") + + def get(self, asset_id: str, model: str = "insightface") -> np.ndarray | None: + """Retrieve cached embedding, or None if not cached.""" + path = self._path(asset_id, model) + if os.path.exists(path): + try: + return np.load(path) + except Exception: + return None + return None + + def put(self, asset_id: str, embedding: np.ndarray, model: str = "insightface") -> None: + """Store an embedding in the cache.""" + self._ensure_dir() + try: + np.save(self._path(asset_id, model), embedding) + except Exception as e: + logger.debug(f"Cache write failed for {asset_id}: {e}") + + def clear(self) -> None: + """Delete all cached embeddings.""" + if not os.path.isdir(self.cache_dir): + return + count = 0 + for f in os.listdir(self.cache_dir): + if f.endswith(".npy"): + os.remove(os.path.join(self.cache_dir, f)) + count += 1 + logger.info(f"Cleared {count} cached embeddings.") + + +# Singleton instance +_cache: EmbeddingCache | None = None + + +def get_cache(cache_dir: str = ".if_cache") -> EmbeddingCache: + """Get or create the singleton cache instance.""" + global _cache + if _cache is None: + _cache = EmbeddingCache(cache_dir) + return _cache diff --git a/if_curator/cli.py b/if_curator/cli.py index 4a743a9..5ad9037 100644 --- a/if_curator/cli.py +++ b/if_curator/cli.py @@ -9,13 +9,15 @@ from PIL import Image 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, ConfigManager from .diversity import select_diverse_assets from .embeddings import is_embedding_available from .image_processing import process_face_mode, process_full_mode, process_object_mode -from .immich_api import fetch_all_assets, filter_recent_assets, get_people +from .immich_api import fetch_all_assets, fetch_full_image, filter_recent_assets, get_people from .logging import console, setup_logging + logger = logging.getLogger(__name__) # Strategy presets: (limit, mode_name) @@ -62,23 +64,9 @@ def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | s return limits.get(choice, 0), "time" if choice != "4" else "skip" -def interactive_configure(people: list[dict]) -> list[dict]: - """Interactive phase: select person, mode, and configure training strategy.""" - 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 [] - - # Select person - console.print("\n[bold cyan]Select Person to Train:[/bold cyan]") - for idx, p in enumerate(valid_people, 1): - console.print(f" [bold]{idx}.[/bold] {p['name']}") - - p_choice = IntPrompt.ask("Enter Number", choices=[str(i) for i in range(1, len(valid_people) + 1)]) - person = valid_people[p_choice - 1] +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 @@ -105,7 +93,7 @@ def interactive_configure(people: list[dict]) -> list[dict]: if not recent_assets: rprint(" [dim]Skipping (0 recent images).[/dim]") - return [] + return None # Strategy selection has_embedding = is_embedding_available(entity_type) @@ -113,18 +101,52 @@ def interactive_configure(people: list[dict]) -> list[dict]: limit, selection_mode = _get_strategy_choice(has_embedding, entity_type) if selection_mode == "skip": - return [] + 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}] + return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config} -def _perform_selection( - assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str -) -> list: +def interactive_configure(people: list[dict]) -> list[dict]: + """Interactive phase: select person(s), mode, and configure training strategy. + + Supports multi-person batch mode โ€” 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 _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)" @@ -134,12 +156,17 @@ def _perform_selection( is_embedding_available(entity_type) with Progress( - SpinnerColumn(), TextColumn("[progress.description]{task.description}"), - BarColumn(), TaskProgressColumn(), console=console, + 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, + assets, + limit, + name, selection_mode=selection_mode, entity_type=entity_type, progress_callback=lambda c, t: progress.update(task, completed=c, total=t), @@ -156,6 +183,30 @@ def _perform_selection( return selected +def _show_preview(jobs: list[dict]) -> None: + """Show a summary table of all queued jobs before execution.""" + table = Table(title="๐Ÿ“‹ 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]} โ†’ {dates[-1]}" if len(dates) >= 2 else (dates[0] if dates else "โ€”") + + table.add_row(name, mode, count, date_range) + + console.print() + console.print(table) + console.print() + + def execute_jobs(jobs: list[dict]) -> None: """Download and process images for all jobs.""" if not jobs: @@ -163,9 +214,14 @@ def execute_jobs(jobs: list[dict]) -> None: 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, + 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) @@ -181,17 +237,22 @@ def execute_jobs(jobs: list[dict]) -> None: count = 0 for asset in assets: try: - 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, - ) - if not resp.ok: + # 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: progress.console.print(f"[red]Failed download {asset['id']}[/red]") else: - img = Image.open(BytesIO(resp.content)) saved = ( - process_face_mode(img, asset, person, person_dir, count, min_width=Config.MIN_FACE_WIDTH) + process_face_mode(img, asset, person, person_dir, count) if mode == "face" else process_object_mode(img, config, person_dir, count) if mode == "object" @@ -236,10 +297,12 @@ def main() -> None: jobs = interactive_configure(people) - if jobs and Confirm.ask(f"\nReady to process {sum(j['limit'] for j in jobs)} images?"): - execute_jobs(jobs) - rprint("\n[bold green]Done! Happy Training.[/bold green]") - elif not jobs: + if jobs: + _show_preview(jobs) + if Confirm.ask(f"Ready to process {sum(j['limit'] for j in jobs)} images?"): + execute_jobs(jobs) + rprint("\n[bold green]Done! Happy Training.[/bold green]") + else: rprint("[yellow]No jobs configured.[/yellow]") except KeyboardInterrupt: diff --git a/if_curator/config.py b/if_curator/config.py index 813d2ca..4a87b41 100644 --- a/if_curator/config.py +++ b/if_curator/config.py @@ -24,7 +24,21 @@ class Config: API_KEY: str | None = None OUTPUT_DIR: str = "./frigate_train" YEARS_FILTER: int = 10 - MIN_FACE_WIDTH: int = 50 + + # Quality filtering + MIN_FACE_WIDTH: int = 100 + BLUR_THRESHOLD: float = 100.0 + MIN_CONFIDENCE: float = 0.7 + MAX_AUTO_IMAGES: int = 80 + + # Output quality + FACE_MARGIN: float = 0.15 + USE_FULL_RESOLUTION: bool = True + ENABLE_FACE_ALIGNMENT: bool = True + + # Caching (opt-in to avoid unexpected files) + ENABLE_CACHE: bool = False + CACHE_DIR: str = ".if_cache" def __new__(cls) -> "Config": if cls._instance is None: diff --git a/if_curator/diversity.py b/if_curator/diversity.py index 9fb354b..4e53e5b 100644 --- a/if_curator/diversity.py +++ b/if_curator/diversity.py @@ -1,9 +1,12 @@ """ Diversity selection for training data curation. -Uses Farthest Point Sampling (FPS) algorithm with embeddings: -- Faces: InsightFace embeddings -- Objects: SigLIP embeddings +Selection pipeline: +1. Concurrent thumbnail download +2. Quality filtering (blur, IR, exposure, confidence, face size) +3. Face crop extraction (embed person's face, not full image) +4. Embedding computation (InsightFace or SigLIP) +5. Cluster-aware selection (K-Medoids + FPS with hard example weighting) """ import logging @@ -15,6 +18,7 @@ from PIL import Image from .config import Config, get_headers from .embeddings import get_embedding, is_embedding_available +from .quality import assess_quality logger = logging.getLogger(__name__) @@ -28,7 +32,7 @@ def select_diverse_assets( progress_callback=None, ) -> list: """ - Select diverse assets using Farthest Point Sampling or time spread. + Select diverse assets using cluster-aware FPS or time spread. Args: assets: List of asset dicts from Immich API @@ -61,6 +65,11 @@ def select_diverse_assets( return _select_time_spread(assets, limit) +# ============================================================================= +# Thumbnail & Metadata Helpers +# ============================================================================= + + def _fetch_thumbnail(asset_id: str, timeout: int = 10) -> Image.Image | None: """Fetch thumbnail from Immich API.""" try: @@ -71,13 +80,106 @@ def _fetch_thumbnail(asset_id: str, timeout: int = 10) -> Image.Image | None: return None +def _get_face_bbox(asset: dict) -> tuple[float, float, float, float] | None: + """Extract face bounding box from asset metadata if available.""" + for person in asset.get("people", []): + faces = person.get("faces", []) + if faces: + f = faces[0] + return ( + f.get("boundingBoxX1", 0), + f.get("boundingBoxY1", 0), + f.get("boundingBoxX2", 0), + f.get("boundingBoxY2", 0), + ) + return None + + +def _get_face_confidence(asset: dict) -> float | None: + """Extract face detection confidence from asset metadata if available.""" + for person in asset.get("people", []): + faces = person.get("faces", []) + if faces: + return faces[0].get("score") or faces[0].get("confidence") + return None + + +def _crop_face_from_thumbnail( + img: Image.Image, + asset: dict, + margin: float = 0.25, +) -> Image.Image | None: + """Crop the face region from a thumbnail using Immich bbox metadata. + + By cropping before embedding, we guarantee InsightFace embeds the + correct person's face (not the largest face in a group photo). + + Args: + img: Full preview thumbnail + asset: Asset dict with people/faces metadata + margin: Extra margin around the bbox (fraction, default 25%) + + Returns: + Cropped face PIL image, or None if no face metadata available + """ + bbox = _get_face_bbox(asset) + if bbox is None: + return None + + x1, y1, x2, y2 = bbox + img_w, img_h = img.size + + # Get metadata dimensions to scale bbox + for person in asset.get("people", []): + faces = person.get("faces", []) + if faces: + meta_w = faces[0].get("imageWidth") or img_w + meta_h = faces[0].get("imageHeight") or img_h + scale_x, scale_y = img_w / meta_w, img_h / meta_h + x1, y1 = x1 * scale_x, y1 * scale_y + x2, y2 = x2 * scale_x, y2 * scale_y + break + + face_w, face_h = x2 - x1, y2 - y1 + + # Add margin so InsightFace's internal alignment has context + mx, my = face_w * margin, face_h * margin + crop = img.crop( + ( + max(0, x1 - mx), + max(0, y1 - my), + min(img_w, x2 + mx), + min(img_h, y2 + my), + ) + ) + + # Skip if too small for meaningful embedding + if crop.width < 30 or crop.height < 30: + return None + + return crop + + +# ============================================================================= +# Embedding Collection +# ============================================================================= + + def _select_by_embedding( assets: list, limit: int | str, entity_type: str, progress_callback=None, ) -> list: - """Select assets using embedding-based Farthest Point Sampling.""" + """Select assets using embedding-based cluster-aware FPS. + + Pipeline: + 1. Concurrent thumbnail download + 2. Quality filtering + 3. Face crop extraction (face mode only) + 4. Embedding computation + 5. Cluster-aware selection with hard example weighting + """ # Determine candidate pool (cap at 3000 for performance) effective_limit = 30 if limit == "auto" else limit pool_size = min(3000, max(effective_limit * 20, len(assets))) @@ -89,24 +191,68 @@ def _select_by_embedding( else: candidates = assets - # Compute embeddings - embeddings, valid_candidates = [], [] - for i, asset in enumerate(candidates): - if progress_callback: - progress_callback(i, len(candidates)) + # --- Phase 1: Concurrent thumbnail download --- + from concurrent.futures import ThreadPoolExecutor, as_completed - img = _fetch_thumbnail(asset["id"]) + thumbnail_map: dict[str, Image.Image] = {} + with ThreadPoolExecutor(max_workers=8) as pool: + futures = {pool.submit(_fetch_thumbnail, a["id"]): a for a in candidates} + for i, future in enumerate(as_completed(futures)): + if progress_callback: + progress_callback(i, len(candidates)) + asset = futures[future] + try: + img = future.result() + if img is not None: + thumbnail_map[asset["id"]] = img + except Exception: + continue + + # --- Phase 2-4: Quality filter โ†’ Crop โ†’ Embed --- + embeddings, valid_candidates, confidence_scores = [], [], [] + quality_filtered = 0 + + for asset in candidates: + img = thumbnail_map.get(asset["id"]) if img is None: continue - emb = get_embedding(img, entity_type) + confidence = _get_face_confidence(asset) + + # Quality gate: filter before expensive embedding computation + if entity_type == "face": + face_bbox = _get_face_bbox(asset) + quality = assess_quality( + img, + face_bbox=face_bbox, + confidence=confidence, + blur_threshold=Config.BLUR_THRESHOLD, + min_face_px=Config.MIN_FACE_WIDTH, + min_confidence=Config.MIN_CONFIDENCE, + ) + if not quality.passed: + quality_filtered += 1 + logger.debug(f"Quality filtered {asset['id']}: {quality.reason}") + continue + + # Crop the target person's face before embedding + face_crop = _crop_face_from_thumbnail(img, asset) + embed_img = face_crop if face_crop is not None else img + else: + embed_img = img + + emb = get_embedding(embed_img, entity_type, asset_id=asset["id"]) if emb is not None: embeddings.append(emb) valid_candidates.append(asset) + confidence_scores.append(confidence) if progress_callback: progress_callback(len(candidates), len(candidates)) + if quality_filtered > 0: + logger.info(f"Quality filtering removed {quality_filtered} images.") + if not embeddings: logger.warning("No valid embeddings found. Falling back to time spread.") return _select_time_spread(assets, limit) @@ -115,58 +261,212 @@ def _select_by_embedding( logger.warning(f"Only {len(valid_candidates)} valid embeddings. Returning all.") return valid_candidates - # Farthest Point Sampling with vectorized distance computation - return _farthest_point_sampling( - embeddings, valid_candidates, limit, auto_threshold=0.15 + # --- Phase 5: Cluster-aware selection --- + return _cluster_aware_selection( + embeddings, + valid_candidates, + limit, + entity_type=entity_type, + confidence_scores=confidence_scores, ) -def _farthest_point_sampling( +# ============================================================================= +# K-Medoids (Lightweight Implementation) +# ============================================================================= + + +def _kmedoids(dist_matrix: np.ndarray, k: int, max_iter: int = 50) -> tuple[list[int], np.ndarray]: + """Lightweight K-Medoids clustering using cosine distance matrix. + + Args: + dist_matrix: (N, N) pairwise distance matrix + k: Number of clusters + max_iter: Maximum iterations for swap step + + Returns: + (medoid_indices, cluster_labels) tuple + """ + n = dist_matrix.shape[0] + rng = np.random.default_rng(42) + + # Initialize medoids: first = most central point, rest = farthest from chosen + total_dist = dist_matrix.sum(axis=1) + medoids = [int(np.argmin(total_dist))] + + for _ in range(k - 1): + dists_to_chosen = dist_matrix[:, medoids].min(axis=1) + dists_to_chosen[medoids] = -np.inf + medoids.append(int(np.argmax(dists_to_chosen))) + + # Iterative swap step + medoids = list(medoids) + labels = np.argmin(dist_matrix[:, medoids], axis=1) + cost = sum(dist_matrix[i, medoids[labels[i]]] for i in range(n)) + + for _ in range(max_iter): + improved = False + # Try swapping each medoid with a random non-medoid + non_medoids = [i for i in range(n) if i not in medoids] + if not non_medoids: + break + + for m_idx in range(k): + candidates = rng.choice(non_medoids, size=min(10, len(non_medoids)), replace=False) + for cand in candidates: + new_medoids = medoids.copy() + new_medoids[m_idx] = cand + new_labels = np.argmin(dist_matrix[:, new_medoids], axis=1) + new_cost = sum(dist_matrix[i, new_medoids[new_labels[i]]] for i in range(n)) + if new_cost < cost: + medoids = new_medoids + labels = new_labels + cost = new_cost + improved = True + break + if improved: + break + + if not improved: + break + + return medoids, labels + + +# ============================================================================= +# Cluster-Aware Selection (K-Medoids + FPS Hybrid) +# ============================================================================= + + +def _compute_adaptive_threshold(emb_normed: np.ndarray, entity_type: str) -> float: + """Compute adaptive FPS stop threshold based on actual embedding distribution. + + Instead of a hardcoded threshold, samples pairwise distances and sets + the threshold as a fraction of the median pairwise distance. + """ + n = len(emb_normed) + sample_size = min(200, n) + rng = np.random.default_rng(42) + indices = rng.choice(n, sample_size, replace=False) if n > sample_size else np.arange(n) + sample = emb_normed[indices] + + # Compute pairwise cosine distances for the sample + pairwise = 1 - sample @ sample.T + upper_tri = pairwise[np.triu_indices(len(sample), k=1)] + median_dist = float(np.median(upper_tri)) + + # Faces: 20% of median (tighter โ€” want fewer, more distinct images) + # Objects: 10% of median (wider โ€” want more diversity) + fraction = 0.20 if entity_type == "face" else 0.10 + threshold = max(0.05, median_dist * fraction) + + logger.info( + f"Adaptive threshold: {threshold:.4f} " + f"(median_dist={median_dist:.4f}, fraction={fraction}, type={entity_type})" + ) + return threshold + + +def _cluster_aware_selection( embeddings: list, candidates: list, limit: int | str, - auto_threshold: float = 0.15, + entity_type: str = "face", + confidence_scores: list | None = None, ) -> list: - """Vectorized Farthest Point Sampling.""" + """Two-stage selection: K-Medoids clustering โ†’ FPS with hard example weighting. + + Stage 1: Cluster embeddings into k groups, select medoids as initial picks. + This guarantees at least one representative from every distinct "look". + + Stage 2: Fill remaining budget with FPS across cluster boundaries, + biasing toward hard examples (low-confidence candidates). + """ emb_matrix = np.vstack(embeddings) # (N, D) n = len(emb_matrix) - # Normalize for cosine distance (cosine_dist = 1 - cosine_sim) + # Normalize for cosine distance norms = np.linalg.norm(emb_matrix, axis=1, keepdims=True) emb_normed = emb_matrix / np.maximum(norms, 1e-8) - # Start from median-time sample - selected = [n // 2] + # Build confidence weight array for hard example boosting + conf_array = np.ones(n) + if confidence_scores and entity_type == "face": + for i, c in enumerate(confidence_scores): + if c is not None: + conf_array[i] = c + + # Compute adaptive threshold for auto mode + auto_threshold = _compute_adaptive_threshold(emb_normed, entity_type) if limit == "auto" else 0.0 + target = Config.MAX_AUTO_IMAGES if limit == "auto" else limit + + # --- Stage 1: K-Medoids clustering --- + k = min(max(5, target // 4), n // 3, n) # e.g., 5-20 clusters + logger.info(f"Clustering {n} embeddings into {k} groups (K-Medoids)...") + + # Compute full cosine distance matrix + dist_matrix = 1 - emb_normed @ emb_normed.T + + medoid_indices, cluster_labels = _kmedoids(dist_matrix, k) + selected = list(medoid_indices) + selected_set = set(selected) + + logger.info(f"Selected {len(selected)} cluster medoids as initial picks.") + + # --- Stage 2: FPS with hard example weighting --- min_dists = np.full(n, np.inf) - target = 500 if limit == "auto" else limit + # Initialize min distances from all medoids + for idx in selected: + dists = dist_matrix[idx] + min_dists = np.minimum(min_dists, dists) + for idx in selected: + min_dists[idx] = -np.inf while len(selected) < target: - # Update min distances with last selected point - last_emb = emb_normed[selected[-1]] - dists_to_last = 1 - emb_normed @ last_emb # Cosine distance - min_dists = np.minimum(min_dists, dists_to_last) - min_dists[selected[-1]] = -np.inf # Exclude already selected + # Hard example weighting: boost distance for low-confidence candidates + # Confidence < 0.85 gets up to 1.5ร— distance boost + hard_weight = np.where(conf_array < 0.85, 1.0 + (0.85 - conf_array) * 2.0, 1.0) + weighted_dists = min_dists * hard_weight - # Find farthest point - best_idx = np.argmax(min_dists) - best_dist = min_dists[best_idx] + best_idx = int(np.argmax(weighted_dists)) + best_dist = min_dists[best_idx] # Use unweighted for threshold comparison if best_dist == -np.inf: break # All points selected if limit == "auto" and best_dist < auto_threshold: logger.info( - f"Auto-stop: Next best image {best_dist:.3f} away (threshold {auto_threshold})." + f"Auto-stop: Next best image {best_dist:.3f} away " f"(adaptive threshold {auto_threshold:.4f})." ) break selected.append(best_idx) + selected_set.add(best_idx) + + # Update min distances + dists_to_new = dist_matrix[best_idx] + min_dists = np.minimum(min_dists, dists_to_new) + min_dists[best_idx] = -np.inf + + # Log hard example stats + if entity_type == "face": + selected_conf = [conf_array[i] for i in selected if conf_array[i] < 1.0] + hard_count = sum(1 for c in selected_conf if c < 0.85) + logger.info( + f"Selection complete: {len(selected)} images " f"({hard_count} hard examples with confidence < 0.85)." + ) + else: + logger.info(f"Selection complete: {len(selected)} diverse images.") - logger.info(f"Smart selection complete. Picked {len(selected)} diverse images.") return [candidates[i] for i in selected] +# ============================================================================= +# Time Spread Fallback +# ============================================================================= + + def _select_time_spread(assets: list, limit: int | str) -> list: """Select N assets evenly distributed in time.""" if limit == "auto": diff --git a/if_curator/embeddings.py b/if_curator/embeddings.py index 1f9c1f5..c21b660 100644 --- a/if_curator/embeddings.py +++ b/if_curator/embeddings.py @@ -1,8 +1,9 @@ """ Unified embedding interface for faces and objects. -- Faces: InsightFace (ArcFace/Buffalo_L) +- Faces: InsightFace (ArcFace/Buffalo_L) โ€” or reuse from Immich - Objects: SigLIP (Vision Transformer via transformers) +- Caching: Disk-based cache avoids recomputation on reruns """ import contextlib @@ -14,6 +15,8 @@ import cv2 import numpy as np from PIL import Image +from .cache import get_cache + logger = logging.getLogger(__name__) # Lazy-loaded singletons @@ -48,8 +51,10 @@ def get_insightface_app(): # Determine device: 0 for GPU, -1 for CPU gpu_providers = { - "CUDAExecutionProvider", "ROCmExecutionProvider", - "MPSExecutionProvider", "CoreMLExecutionProvider", + "CUDAExecutionProvider", + "ROCmExecutionProvider", + "MPSExecutionProvider", + "CoreMLExecutionProvider", } ctx_id = -1 if _is_force_cpu() else (0 if gpu_providers & set(providers) else -1) @@ -73,6 +78,7 @@ def get_insightface_app(): logger.warning("Retrying InsightFace on CPU...") try: from insightface.app import FaceAnalysis + _insightface_app = FaceAnalysis(name="buffalo_l", root="~/.insightface") _insightface_app.prepare(ctx_id=-1, det_size=(640, 640)) return _insightface_app @@ -179,14 +185,83 @@ def get_object_embedding(img_pil: Image.Image) -> np.ndarray | None: return None +def get_object_embeddings_batch(images: list[Image.Image]) -> list[np.ndarray | None]: + """Get SigLIP embeddings for a batch of images (GPU-efficient).""" + model, processor = get_siglip_model() + if model is None: + return [None] * len(images) + + try: + import torch + + inputs = processor(images=images, return_tensors="pt", padding=True) + device = next(model.parameters()).device + inputs = {k: v.to(device) for k, v in inputs.items()} + + with torch.no_grad(): + outputs = model(**inputs) + embeddings = outputs.pooler_output.cpu().numpy() + return [embeddings[i] for i in range(len(embeddings))] + except Exception as e: + logger.error(f"Error in batch embedding: {e}") + # Fall back to individual computation + return [get_object_embedding(img) for img in images] + + # ============================================================================= -# Unified Interface +# Unified Interface with Caching # ============================================================================= -def get_embedding(img_pil: Image.Image, entity_type: str = "face") -> np.ndarray | None: - """Get embedding for an image based on entity type ('face' or 'object').""" - return get_face_embedding(img_pil) if entity_type == "face" else get_object_embedding(img_pil) +def get_embedding( + img_pil: Image.Image, + entity_type: str = "face", + asset_id: str | None = None, + immich_embedding: np.ndarray | None = None, +) -> np.ndarray | None: + """Get embedding for an image based on entity type. + + Priority: + 1. Pre-fetched Immich embedding (if provided) + 2. Disk cache (if enabled and asset_id provided) + 3. Local model computation (InsightFace or SigLIP) + + Args: + img_pil: The image to embed + entity_type: 'face' or 'object' + asset_id: Optional asset ID for cache lookup + immich_embedding: Optional pre-fetched embedding from Immich API + """ + from .config import Config + + use_cache = Config.ENABLE_CACHE and asset_id is not None + cache = get_cache(Config.CACHE_DIR) if use_cache else None + model_key = "immich" if entity_type == "face" else "siglip" + + # 1. Use Immich embedding if provided + if immich_embedding is not None: + if cache: + cache.put(asset_id, immich_embedding, model_key) + return immich_embedding + + # 2. Check disk cache + if cache: + cached = cache.get(asset_id, model_key) + if cached is not None: + return cached + + # 3. Compute locally + if entity_type == "face": + emb = get_face_embedding(img_pil) + model_key = "insightface" + else: + emb = get_object_embedding(img_pil) + + # Cache the result + if emb is not None and cache: + cache.put(asset_id, emb, model_key) + + return emb def is_embedding_available(entity_type: str = "face") -> bool: diff --git a/if_curator/image_processing.py b/if_curator/image_processing.py index f65dc3e..66a926f 100644 --- a/if_curator/image_processing.py +++ b/if_curator/image_processing.py @@ -3,8 +3,11 @@ import logging import os +import numpy as np from PIL import Image +from .config import Config + logger = logging.getLogger(__name__) # Lazy singleton @@ -16,20 +19,59 @@ def get_yolo_model(): global _yolo_model if _yolo_model is None: from ultralytics import YOLO + logger.info("Loading YOLOv9c model...") _yolo_model = YOLO("yolov9c.pt") return _yolo_model +def align_face(img: Image.Image, landmarks: list[list[float]] | np.ndarray) -> Image.Image | None: + """Align face using 5-point landmarks to standard ArcFace input format (112x112). + + This produces a normalized face crop that matches exactly what ArcFace + was trained on, improving recognition accuracy. + + Args: + img: Full PIL image containing the face + landmarks: 5-point facial landmarks [[x,y], ...] (eyes, nose, mouth corners) + + Returns: + Aligned 112x112 face image, or None if alignment fails + """ + try: + from insightface.utils.face_align import norm_crop + + img_np = np.asarray(img) + lm = np.array(landmarks, dtype=np.float32) + if lm.shape != (5, 2): + logger.debug(f"Invalid landmark shape: {lm.shape}, expected (5, 2)") + return None + aligned = norm_crop(img_np, lm) + return Image.fromarray(aligned) + except ImportError: + logger.debug("InsightFace not available for face alignment") + return None + except Exception as e: + logger.debug(f"Face alignment failed: {e}") + return None + + def process_face_mode( img: Image.Image, asset: dict, person: dict, output_dir: str, count: int, - min_width: int = 50, + min_width: int | None = None, ) -> bool: - """Crop face based on Immich metadata and save to output directory.""" + """Crop face based on Immich metadata and save to output directory. + + If face alignment is enabled and landmarks are available, produces + an aligned 112x112 crop. Otherwise falls back to bounding box crop + with configurable margin. + """ + min_width = min_width or Config.MIN_FACE_WIDTH + # Find face metadata for this person face_info = None for p in asset.get("people", []): @@ -57,8 +99,20 @@ def process_face_mode( logger.debug(f"Face too small ({face_w:.1f}x{face_h:.1f})") return False - # Add 10% margin - margin_x, margin_y = face_w * 0.10, face_h * 0.10 + # Try face alignment if enabled and landmarks available + if Config.ENABLE_FACE_ALIGNMENT: + landmarks = face_info.get("landmarks") or face_info.get("landmark") + if landmarks: + # Scale landmarks + scaled_landmarks = [[lm[0] * scale_x, lm[1] * scale_y] for lm in landmarks] + aligned = align_face(img, scaled_landmarks) + if aligned is not None: + aligned.save(os.path.join(output_dir, f"{count}.jpg"), format="JPEG") + return True + + # Fall back to bounding box crop with configurable margin + margin = Config.FACE_MARGIN + margin_x, margin_y = face_w * margin, face_h * margin crop_box = ( max(0, x1 - margin_x), max(0, y1 - margin_y), @@ -87,9 +141,7 @@ def process_object_mode( 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 + (box, int(box.cls[0]), float(box.conf[0])) for r in results for box in r.boxes ): 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() diff --git a/if_curator/immich_api.py b/if_curator/immich_api.py index dd05967..b81a563 100644 --- a/if_curator/immich_api.py +++ b/if_curator/immich_api.py @@ -1,15 +1,30 @@ -"""Immich API client for fetching people and assets.""" +"""Immich API client for fetching people, assets, and face data.""" import logging +from dataclasses import dataclass from datetime import datetime, timedelta, timezone +from io import BytesIO +import numpy as np import requests +from PIL import Image from .config import Config, get_headers logger = logging.getLogger(__name__) +@dataclass +class FaceData: + """Pre-computed face data from Immich.""" + + embedding: np.ndarray | None + bbox: tuple[float, float, float, float] # (x1, y1, x2, y2) + confidence: float | None + image_width: int + image_height: int + + def get_people() -> list[dict]: """Fetch all people from Immich.""" try: @@ -68,6 +83,107 @@ def fetch_all_assets(person: dict) -> list[dict]: return assets +def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | None: + """Fetch pre-computed face data (embedding, bbox, confidence) from Immich. + + Queries GET /api/faces?id={asset_id} to retrieve face detection results + that Immich already computed using InsightFace Buffalo_L. + + Args: + asset_id: The asset to get face data for + person_id: Optional person ID to match the specific face + + Returns: + FaceData with embedding, bbox, and confidence, or None if unavailable + """ + try: + resp = requests.get( + f"{Config.IMMICH_URL}/api/faces", + params={"id": asset_id}, + headers=get_headers(), + timeout=10, + ) + + if not resp.ok: + logger.debug(f"Face data endpoint returned {resp.status_code} for {asset_id}") + return None + + faces = resp.json() + if not faces: + return None + + # Match the target person if specified + face = None + if person_id: + face = next((f for f in faces if f.get("person", {}).get("id") == person_id), None) + if face is None: + face = faces[0] # Fall back to first/largest face + + # Extract embedding if available + embedding = None + if "embedding" in face: + embedding = np.array(face["embedding"], dtype=np.float32) + + # Extract bounding box + bbox = ( + face.get("boundingBoxX1", 0), + face.get("boundingBoxY1", 0), + face.get("boundingBoxX2", 0), + face.get("boundingBoxY2", 0), + ) + + return FaceData( + embedding=embedding, + bbox=bbox, + confidence=face.get("score") or face.get("confidence"), + image_width=face.get("imageWidth", 0), + image_height=face.get("imageHeight", 0), + ) + + except requests.RequestException as e: + logger.debug(f"Failed to fetch face data for {asset_id}: {e}") + return None + except (KeyError, TypeError, ValueError) as e: + logger.debug(f"Failed to parse face data for {asset_id}: {e}") + return None + + +def fetch_full_image(asset_id: str, timeout: int = 60) -> Image.Image | None: + """Fetch full-resolution image from Immich, falling back to preview thumbnail. + + The /original endpoint may return HEIC, RAW, or video files that PIL + cannot open directly. In that case, we fall back to the JPEG thumbnail. + """ + # Try original first + try: + resp = requests.get( + f"{Config.IMMICH_URL}/api/assets/{asset_id}/original", + headers=get_headers(), + timeout=timeout, + ) + if resp.ok: + try: + return Image.open(BytesIO(resp.content)) + except Exception: + logger.debug(f"PIL can't open original for {asset_id}, falling back to preview") + except requests.RequestException: + logger.debug(f"Original request failed for {asset_id}, falling back to preview") + + # Fall back to preview thumbnail (always JPEG) + try: + resp = requests.get( + f"{Config.IMMICH_URL}/api/assets/{asset_id}/thumbnail?size=preview&format=JPEG", + headers=get_headers(), + timeout=30, + ) + if resp.ok: + return Image.open(BytesIO(resp.content)) + except Exception as e: + logger.error(f"Failed to fetch image {asset_id}: {e}") + + return None + + def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[dict]: """Filter assets to keep only those from the last N years.""" years = years or Config.YEARS_FILTER diff --git a/if_curator/logging.py b/if_curator/logging.py index 9340760..870cce5 100644 --- a/if_curator/logging.py +++ b/if_curator/logging.py @@ -36,9 +36,7 @@ def setup_logging(verbose: bool = False) -> logging.Logger: # File handler (always debug level) file_handler = logging.FileHandler("immich_export.log") file_handler.setLevel(logging.DEBUG) - file_handler.setFormatter( - logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") - ) + file_handler.setFormatter(logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")) root.addHandler(file_handler) # Silence noisy libraries diff --git a/if_curator/quality.py b/if_curator/quality.py new file mode 100644 index 0000000..477391a --- /dev/null +++ b/if_curator/quality.py @@ -0,0 +1,132 @@ +"""Image quality assessment for training data curation. + +Filters out images that would hurt Frigate's ArcFace model training: +blur, grayscale/IR, over/underexposure, tiny faces, low-confidence detections. +""" + +import logging +from dataclasses import dataclass, field + +import cv2 +import numpy as np +from PIL import Image + +logger = logging.getLogger(__name__) + + +@dataclass +class QualityResult: + """Result of quality assessment on a face/image crop.""" + + passed: bool + reasons: list[str] = field(default_factory=list) + + @property + def reason(self) -> str: + return "; ".join(self.reasons) if self.reasons else "OK" + + +def check_blur(img_np: np.ndarray, threshold: float = 100.0) -> tuple[bool, str]: + """Detect blur using Laplacian variance. + + Lower variance = blurrier image. ArcFace needs clear facial features. + """ + gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np + variance = cv2.Laplacian(gray, cv2.CV_64F).var() + if variance < threshold: + return False, f"Blurry (laplacian={variance:.1f}, threshold={threshold})" + return True, "" + + +def check_grayscale(img_np: np.ndarray) -> tuple[bool, str]: + """Detect grayscale/IR images by checking channel similarity. + + ArcFace is trained on color images; IR/grayscale degrades recognition. + """ + if img_np.ndim != 3 or img_np.shape[2] < 3: + return False, "Grayscale (single channel)" + + # Compare channel means โ€” IR/grayscale has nearly identical R, G, B + means = img_np[:, :, :3].mean(axis=(0, 1)) + max_diff = max(abs(means[0] - means[1]), abs(means[1] - means[2]), abs(means[0] - means[2])) + + if max_diff < 5.0: + return False, f"Grayscale/IR (channel diff={max_diff:.1f})" + return True, "" + + +def check_exposure(img_np: np.ndarray, lo: float = 30.0, hi: float = 225.0) -> tuple[bool, str]: + """Check for severe under/overexposure using mean brightness. + + Extremely dark or blown-out faces lack usable features. + """ + gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np + mean_brightness = gray.mean() + + if mean_brightness < lo: + return False, f"Underexposed (brightness={mean_brightness:.1f}, min={lo})" + if mean_brightness > hi: + return False, f"Overexposed (brightness={mean_brightness:.1f}, max={hi})" + return True, "" + + +def check_face_size(face_width: float, face_height: float, min_px: int = 100) -> 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)" + return True, "" + + +def check_confidence(score: float | None, min_conf: float = 0.7) -> tuple[bool, str]: + """Check detection confidence from Immich. + + Low confidence often means partial, occluded, or false-positive faces. + """ + if score is None: + return True, "" # No score available, pass by default + if score < min_conf: + return False, f"Low confidence ({score:.2f}, min={min_conf})" + return True, "" + + +def assess_quality( + img: Image.Image, + face_bbox: tuple[float, float, float, float] | None = None, + confidence: float | None = None, + blur_threshold: float = 100.0, + min_face_px: int = 100, + min_confidence: float = 0.7, +) -> QualityResult: + """Run all quality checks on an image. + + Args: + img: PIL Image (RGB) + face_bbox: (x1, y1, x2, y2) bounding box, or None to skip face-size check + confidence: Detection confidence score from Immich, or None + blur_threshold: Laplacian variance threshold for blur detection + min_face_px: Minimum face dimension in pixels + min_confidence: Minimum acceptable detection confidence + + Returns: + QualityResult with passed=True if all checks pass + """ + img_np = np.asarray(img) + reasons = [] + + # Run all checks, collect failures + checks = [ + check_blur(img_np, blur_threshold), + check_grayscale(img_np), + check_exposure(img_np), + check_confidence(confidence, min_confidence), + ] + + if face_bbox is not None: + x1, y1, x2, y2 = face_bbox + checks.append(check_face_size(x2 - x1, y2 - y1, min_face_px)) + + for passed, reason in checks: + if not passed: + reasons.append(reason) + + return QualityResult(passed=len(reasons) == 0, reasons=reasons) diff --git a/pyproject.toml b/pyproject.toml index 1ef6b57..d770981 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,24 +3,37 @@ name = "if-curator" version = "0.1.0" description = "Immich to Frigate training sets" readme = "README.md" +license = "MIT" requires-python = ">=3.12" +authors = [{ name = "ds-sebastian" }] +keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Image Recognition", +] dependencies = [ "insightface>=0.7.3", "numpy>=2.2.6", "onnxruntime>=1.23.2", - "opencv-python>=4.12.0.88", "opencv-python-headless>=4.12.0.88", "pillow>=12.1.0", "python-dotenv>=1.2.1", "requests>=2.32.5", "rich>=14.2.0", - "scipy>=1.17.0", - "sentencepiece>=0.2.1", "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" + [project.optional-dependencies] gpu = [ "onnxruntime-gpu>=1.23.2", @@ -28,6 +41,7 @@ gpu = [ [dependency-groups] dev = [ + "pytest>=8.0", "ruff>=0.9.2", ] @@ -38,3 +52,26 @@ target-version = "py312" [tool.ruff.lint] select = ["E", "F", "I"] +[tool.deptry] +pep621_dev_dependency_groups = ["dev"] + +[tool.deptry.package_module_name_map] +pillow = "PIL" +opencv-python-headless = "cv2" +python-dotenv = "dotenv" +insightface = 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