release: v0.2.3 — security fixes and CI improvements

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.github/workflows/docker-publish.yml

@@ -15,22 +15,8 @@ env:
   IMAGE_NAME: sudolulo/winnow
 
 jobs:
-  verify-lockfile:
-    name: Verify uv.lock is current
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Check lockfile is up to date
-        run: uv lock --check
-
   build:
     name: Build (${{ matrix.platform }})
-    needs: verify-lockfile
     runs-on: ${{ matrix.runner }}
     strategy:
       matrix:
@@ -140,4 +126,3 @@ jobs:
       - name: Inspect image
         run: |
           docker buildx imagetools inspect ${{ steps.tags.outputs.tags }}
-

.github/workflows/lint.yml

@@ -9,6 +9,8 @@ on:
 jobs:
   lint:
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
     env:
       FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true
     steps:

.github/workflows/release.yml

@@ -14,22 +14,8 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
-  verify-lockfile:
-    name: Verify uv.lock is current
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-
-      - name: Check lockfile is up to date
-        run: uv lock --check
-
   release:
     name: Create GitHub Release & Build Image
-    needs: verify-lockfile
     runs-on: ubuntu-latest
     permissions:
       contents: write
@@ -48,6 +34,12 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v4
+
+      - name: Ensure uv.lock is current
+        run: uv lock
+
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
 
@@ -138,4 +130,3 @@ jobs:
           tags: |
             ghcr.io/sudolulo/winnow:latest
             ghcr.io/sudolulo/winnow:${{ steps.tag.outputs.TAG }}
-

.github/workflows/test.yml

@@ -9,6 +9,8 @@ on:
 jobs:
   test:
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
     steps:
       - name: Checkout code
         uses: actions/checkout@v4

CHANGELOG.md

@@ -7,6 +7,34 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.2.3] - 2026-06-12
+
+### Fixed
+
+- **Clear-text API key storage**: `config.py` no longer writes `API_KEY` to `.immich_config.json`. The key must come from an environment variable or `.env` file. Interactive mode now prints a tip directing users to `.env`. Resolves CodeQL `py/clear-text-storage-sensitive-data`.
+
+### Changed
+
+- **CI workflow permissions**: `test.yml` and `lint.yml` now declare `permissions: contents: read`, following least-privilege principle and resolving `actions/missing-workflow-permissions` scanner alerts.
+- **CI lockfile race condition**: removed the `verify-lockfile` pre-job from `docker-publish.yml` and `release.yml`. The `update-lockfile.yml` bot maintains the lockfile; the verify step raced against it on the same push event and caused false failures. `release.yml` now runs `uv lock` inline so tag-triggered builds are always self-consistent.
+- **Docs moved to wiki**: `docs/` folder removed from the repository. Setup, Troubleshooting, and FAQ pages are now at the [GitHub wiki](https://github.com/sudolulo/winnow/wiki).
+
+## [0.2.2] - 2026-06-12
+
+### Added
+
+- **Confidence scores in upload tracker**: Immich face confidence scores are now stored per asset in `frigate_uploaded_ids.json` under `by_person[name].scores`. Lays the groundwork for future replacement logic (remove low-confidence uploads when better images are found).
+- **Frigate-authoritative capacity tracking**: At startup, `GET /api/faces` is queried on the Frigate host to retrieve the actual number of trained images per person from the `train` directory (pending/unclassified queue is excluded). This count is stored as `frigate_count` in the tracker JSON so it survives Frigate downtime.
+- **Lifetime cap uses Frigate count**: `MAX_AUTO_IMAGES` is now enforced against Frigate's live training image count rather than the local uploaded-asset tally. Fallback priority: live Frigate API → last cached `frigate_count` in JSON → local uploaded count.
+- **Startup summary shows Frigate count**: Tracker summary at startup now includes the last known Frigate training count per person (e.g. `78 uploaded, 2 rejected, 42 in Frigate`).
+- **`winnow/frigate_api.py`**: new module encapsulating Frigate API helpers; currently exposes `get_frigate_face_counts()`.
+
+### Changed
+
+- `upload_tracker.py`: `by_person` entries migrated from flat list to `{asset_ids, scores, frigate_count}` dict. Old list format is read and migrated transparently on first write.
+- `mark_uploaded()` now accepts an optional `score` keyword argument.
+- `get_person_summary()` now returns `frigate_count` and `scores` fields alongside `uploaded` and `rejected`.
+
 ## [0.2.1] - 2026-06-12
 
 ### Fixed

docs/faq.md

@@ -1,63 +0,0 @@
-# FAQ
-
-## Does winnow modify my Immich library?
-
-No. winnow only reads from Immich (assets, people, face bounding boxes). It never writes back to Immich or deletes anything.
-
----
-
-## How many images should I upload to Frigate?
-
-The `auto` strategy decides this for you — it keeps selecting until adding more images would be redundant. In practice this is usually 20–60 per person. You can cap it with `MAX_AUTO_IMAGES` (default 80).
-
-Quality and diversity matter far more than volume. 30 well-spread images outperform 200 from the same week.
-
----
-
-## What's the difference between face mode and object mode?
-
-- **Face mode**: Extracts and aligns face crops, uploads them directly to Frigate's face training API. This is for teaching Frigate to recognize specific people.
-- **Object mode**: Runs YOLO detection on full images and saves crops of a target class (dog, cat, car, etc.) to disk. Frigate has no API for object training data, so you place them manually.
-
----
-
-## Can I run it without Frigate?
-
-Yes — in object mode, `FRIGATE_URL` is not used and crops are saved to the output volume. In face mode you need Frigate to receive the uploads, but you can use `DRY_RUN=true` to preview selection without uploading.
-
----
-
-## How does auto-diversity mode work?
-
-winnow computes a vector embedding for each candidate image (what the face/object actually looks like — angle, lighting, expression). It then clusters those embeddings and picks representatives that are maximally spread across the embedding space. It stops when the next-most-different image is already close to something already selected. See the README for the full pipeline.
-
----
-
-## Does it support multiple people in one run?
-
-Yes. By default it processes every named person in your Immich library. Use `ONLY_PEOPLE` to whitelist specific names or `SKIP_PEOPLE` to exclude them.
-
----
-
-## What GPU is needed?
-
-Any NVIDIA GPU with CUDA 12.x support. The models (InsightFace Buffalo_L + SigLIP) fit comfortably in 4 GB VRAM. CPU mode works but is significantly slower.
-
-ARM builds (linux/arm64) use CPU-only — CUDA is not available on ARM.
-
----
-
-## Does it work on Unraid / Proxmox / bare Docker?
-
-Yes — the `compose.yml` uses standard Docker volume mounts. Replace the example paths with whatever absolute paths suit your setup.
-
----
-
-## How do I update winnow?
-
-```bash
-docker compose pull
-docker compose up -d
-```
-
-The `latest` tag on GHCR tracks the `main` branch. Pinning to a version tag (e.g. `ghcr.io/sudolulo/winnow:v0.2.0`) is recommended for stability.

docs/setup.md

@@ -1,95 +0,0 @@
-# Setup Guide
-
-## Prerequisites
-
-- [Immich](https://immich.app) v1.106+ with face recognition enabled and people tagged
-- [Frigate](https://frigate.video) v0.16+ (face mode only)
-- Docker with the NVIDIA container toolkit (optional but strongly recommended)
-
----
-
-## 1. Get your Immich API key
-
-1. Open Immich → **Account Settings** → **API Keys**
-2. Click **New API Key**, give it a name (e.g. `winnow`), copy the key
-
----
-
-## 2. Get your Frigate URL
-
-This is the base URL of your Frigate instance, e.g. `http://192.168.1.10:5000`. Only needed for face mode — omit it entirely if you're using object mode.
-
----
-
-## 3. Deploy with Docker Compose
-
-Copy [`compose.yml`](../compose.yml) and [`.env.example`](../.env.example) to a directory on your host:
-
-```bash
-mkdir winnow && cd winnow
-curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/compose.yml
-curl -O https://raw.githubusercontent.com/sudolulo/winnow/main/.env.example
-cp .env.example .env
-```
-
-Edit `.env` with your values:
-
-```bash
-IMMICH_URL=http://192.168.1.10:2283
-API_KEY=your-immich-api-key
-FRIGATE_URL=http://192.168.1.10:5000
-```
-
-Edit the volume paths in `compose.yml` to point to directories on your host where models, cache, and output crops should be stored:
-
-```yaml
-volumes:
-  - /your/path/to/models:/models
-  - /your/path/to/cache:/app/.if_cache
-  - /your/path/to/output:/app/frigate_train
-```
-
-These directories will be created automatically by Docker if they don't exist.
-
-Start it:
-
-```bash
-docker compose up -d
-```
-
-Logs:
-
-```bash
-docker compose logs -f winnow
-```
-
----
-
-## 4. First run
-
-On the first run, winnow downloads the embedding models (~1–2 GB) from HuggingFace and InsightFace. This happens once — subsequent runs use the cached models from your mounted volume and start immediately.
-
----
-
-## 5. Scheduling
-
-Set `CRON_SCHEDULE` in your `.env` to keep winnow running on a schedule:
-
-```
-CRON_SCHEDULE=0 3 * * 0   # Every Sunday at 3 AM
-```
-
-Without `CRON_SCHEDULE`, the container runs once and exits.
-
----
-
-## GPU passthrough
-
-To enable GPU acceleration, include the `deploy` block in `compose.yml` (already present in the example) and ensure the NVIDIA container toolkit is installed on your host:
-
-```bash
-# Verify GPU is accessible to Docker
-docker run --rm --gpus all nvidia/cuda:12.9.2-base-ubuntu22.04 nvidia-smi
-```
-
-CPU mode works without any GPU setup — set `FORCE_CPU=true` to disable GPU explicitly.

docs/troubleshooting.md

@@ -1,77 +0,0 @@
-# Troubleshooting
-
-## Container exits immediately
-
-Check logs:
-```bash
-docker compose logs winnow
-```
-
-Common causes:
-- **Missing required env var** — `IMMICH_URL` or `API_KEY` not set
-- **Cannot reach Immich** — check the URL and that Immich is running; use `http://` not `https://` unless you have TLS set up
-
----
-
-## "No people found" / nothing processed
-
-- Make sure Immich has completed face recognition and you have named people in your library
-- `YEARS_FILTER` defaults to 10 years — increase it if your tagged photos are older
-- `MIN_FACE_COUNT` skips people with few photos — lower or remove it
-
----
-
-## Frigate upload fails
-
-- Confirm `FRIGATE_URL` is reachable from inside the container: `docker exec winnow curl $FRIGATE_URL/api/stats`
-- Check Frigate v0.16+ — older versions don't have the face training API
-- Set `DRY_RUN=true` to verify selection without uploading
-
----
-
-## Models fail to download
-
-winnow downloads InsightFace and HuggingFace (SigLIP) models on first run.
-
-- Ensure the container has internet access
-- Confirm the model volume is mounted and writable
-- If behind a proxy, set `HTTP_PROXY` / `HTTPS_PROXY` env vars
-
----
-
-## Running on CPU (no GPU)
-
-Set `FORCE_CPU=true`. Everything works but embedding computation is slower — expect several minutes per person instead of seconds.
-
-If you have a GPU but it's not being used:
-- Confirm the NVIDIA container toolkit is installed: `docker run --rm --gpus all nvidia/cuda:12.9.2-base-ubuntu22.04 nvidia-smi`
-- Confirm the `deploy.resources.reservations.devices` block is present in `compose.yml`
-
----
-
-## Same images uploaded every run
-
-The upload tracker is stored in `CACHE_DIR` (`/app/.if_cache` by default). If this volume isn't persisted between runs, the tracker resets and images are re-uploaded.
-
-Make sure `/app/.if_cache` is mounted to a persistent host path.
-
----
-
-## Re-uploading a specific person
-
-To clear the upload history for one person and start fresh:
-
-```env
-RESET_PERSON=John
-```
-
-Remove this after one run — it clears the history and then processes normally.
-
----
-
-## Image quality issues
-
-- **Too blurry**: Lower `BLUR_THRESHOLD` (default 100) — e.g. `50` accepts more blur
-- **Face too small**: Lower `MIN_FACE_WIDTH` (default 50px)
-- **Low confidence detections included**: Raise `MIN_CONFIDENCE` (default 0.7)
-- **Rejected images being re-tried**: Set `RETRY_REJECTED=true` for one run

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.2.1"
+version = "0.2.3"
 description = "Immich to Frigate training sets"
 license = "MIT"
 requires-python = ">=3.12"

uv.lock

@@ -2289,7 +2289,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.2.1"
+version = "0.2.3"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },

winnow/cli.py

@@ -48,7 +48,15 @@ def main() -> None:
         if summary:
             rprint("\n[dim]Tracker summary:[/dim]")
             for person_name, counts in summary.items():
-                rprint(f"  [dim]{person_name}: {counts['uploaded']} uploaded, {counts['rejected']} rejected[/dim]")
+                frigate_part = (
+                    f", {counts['frigate_count']} in Frigate"
+                    if counts.get("frigate_count") is not None
+                    else ""
+                )
+                rprint(
+                    f"  [dim]{person_name}: {counts['uploaded']} uploaded,"
+                    f" {counts['rejected']} rejected{frigate_part}[/dim]"
+                )
 
         people = get_people()
         if not people:

winnow/config.py

@@ -67,12 +67,11 @@ class _Config:
         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
+        # Fall back to config file for non-sensitive values (API_KEY not stored here)
         if CONFIG_FILE.exists():
             try:
                 data = json.loads(CONFIG_FILE.read_text())
                 self.IMMICH_URL = self.IMMICH_URL or data.get("IMMICH_URL")
-                self.API_KEY = self.API_KEY or data.get("API_KEY")
                 if not os.getenv("OUTPUT_DIR"):
                     self.OUTPUT_DIR = data.get("OUTPUT_DIR", self.OUTPUT_DIR)
             except (json.JSONDecodeError, OSError) as e:
@@ -84,13 +83,16 @@ class _Config:
         cls._instance = None
 
     def save(self) -> None:
-        """Persist configuration to file."""
+        """Persist non-sensitive configuration to file.
+
+        API_KEY is intentionally excluded — store it in .env or as an
+        environment variable instead of a plain-text config file.
+        """
         try:
             CONFIG_FILE.write_text(
                 json.dumps(
                     {
                         "IMMICH_URL": self.IMMICH_URL,
-                        "API_KEY": self.API_KEY,
                         "OUTPUT_DIR": self.OUTPUT_DIR,
                     },
                     indent=2,
@@ -113,8 +115,8 @@ class _Config:
 
         if not self.API_KEY:
             console.print("[yellow]Immich API Key not found.[/yellow]")
+            console.print("[dim]Tip: set API_KEY in your .env file to avoid re-entering it.[/dim]")
             self.API_KEY = Prompt.ask("Enter Immich API Key", password=True)
-            self.save()
 
     def validate(self) -> None:
         """Raise ValueError if required config is missing."""

winnow/executor.py

@@ -58,6 +58,7 @@ def _enrich_asset_with_face_data(asset: dict, person: dict) -> dict:
 
     # Inject into asset so process_face_mode can find it via asset["people"]
     asset["people"] = [{"id": person_id, "faces": [face_info]}]
+    asset["face_confidence"] = face_data.confidence
     return asset
 
 
@@ -96,8 +97,9 @@ def execute_jobs(jobs: list[dict]) -> None:
                 shutil.rmtree(person_dir)
             os.makedirs(person_dir, exist_ok=True)
 
-            # Track filename → asset_id mapping for upload dedup
+            # Track filename → asset_id and filename → confidence score
             asset_map: dict[str, str] = {}
+            score_map: dict[str, float | None] = {}
 
             count = 0
             for asset in assets:
@@ -132,11 +134,13 @@ def execute_jobs(jobs: list[dict]) -> None:
                             # Record which asset produced which output file
                             filename = f"{count}.jpg"
                             asset_map[filename] = asset["id"]
+                            score_map[filename] = asset.get("face_confidence")
                             # Also record object-mode variant filenames
                             if mode == "object":
                                 for f in sorted(os.listdir(person_dir)):
                                     if f.startswith(f"{count}_") and f not in asset_map:
                                         asset_map[f] = asset["id"]
+                                        score_map[f] = asset.get("face_confidence")
 
                             count += 1
                         else:
@@ -149,8 +153,9 @@ def execute_jobs(jobs: list[dict]) -> None:
                 progress.advance(job_task)
                 progress.advance(overall_task)
 
-            # Store asset_map on the job so upload_to_frigate can use it
+            # Store maps on the job so upload_to_frigate can use them
             job["asset_map"] = asset_map
+            job["score_map"] = score_map
 
             progress.remove_task(job_task)
 
@@ -230,6 +235,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                 continue
 
             asset_map = filename_to_asset_id.get(name, {})
+            score_map = job.get("score_map", {})
             person_files = sorted(asset_map.keys())
 
             if not person_files:
@@ -257,7 +263,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                             # Mark this asset as uploaded so it's skipped on future runs
                             asset_id = asset_map.get(fname)
                             if asset_id:
-                                mark_uploaded(asset_id, person_name=name)
+                                mark_uploaded(asset_id, person_name=name, score=score_map.get(fname))
 
                             break
                         else:

winnow/frigate_api.py

@@ -0,0 +1,28 @@
+"""Frigate API helpers for querying face training state."""
+
+import logging
+import os
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+
+def get_frigate_face_counts() -> dict[str, int] | None:
+    """Return {person_name: training_image_count} from Frigate's train directory.
+
+    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
+    can distinguish "API unavailable" from "person has 0 images."
+    """
+    frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/")
+    if not frigate_url:
+        return None
+    try:
+        resp = requests.get(f"{frigate_url}/api/faces", timeout=10)
+        resp.raise_for_status()
+        data = resp.json()
+        train = data.get("train", {})
+        return {name: len(files) for name, files in train.items() if isinstance(files, list)}
+    except Exception as e:
+        logger.warning(f"Could not query Frigate face counts: {e}")
+        return None

winnow/jobs.py

@@ -11,9 +11,10 @@ from rich.table import Table
 from .config import Config
 from .diversity import select_diverse_assets
 from .embeddings import is_embedding_available, load_embedding_model
+from .frigate_api import get_frigate_face_counts
 from .immich_api import fetch_all_assets, filter_recent_assets
 from .logging import console
-from .upload_tracker import filter_already_uploaded
+from .upload_tracker import filter_already_uploaded, get_person_summary, update_frigate_count
 
 logger = logging.getLogger(__name__)
 
@@ -236,6 +237,12 @@ def auto_configure(people: list[dict]) -> list[dict]:
                 f" ≥{min_face_count} assets (MIN_FACE_COUNT={min_face_count})"
             )
 
+    frigate_counts = get_frigate_face_counts()
+    # Persist each count to tracker so the last known value survives Frigate downtime
+    if frigate_counts is not None:
+        for pname, count in frigate_counts.items():
+            update_frigate_count(pname, count)
+    upload_summary = get_person_summary()
     jobs = []
     for person in valid_people:
         name = person["name"]
@@ -263,9 +270,34 @@ def auto_configure(people: list[dict]) -> list[dict]:
             rprint(f"  [dim]Skipping {name} (0 new images after dedup).[/dim]")
             continue
 
+        # Enforce MAX_AUTO_IMAGES as a lifetime cap per person.
+        # Priority: live Frigate count → last cached Frigate count → local uploaded count.
+        person_summary = upload_summary.get(name, {})
+        if frigate_counts is not None:
+            already_uploaded = frigate_counts.get(name, 0)
+        else:
+            already_uploaded = (
+                person_summary.get("frigate_count")
+                or person_summary.get("uploaded", 0)
+            )
+        capacity = Config.MAX_AUTO_IMAGES - already_uploaded
+        if capacity <= 0:
+            rprint(
+                f"  [dim]Skipping {name} (at lifetime cap:"
+                f" {already_uploaded}/{Config.MAX_AUTO_IMAGES} trained).[/dim]"
+            )
+            continue
+
         has_embedding = is_embedding_available(entity_type)
         limit, selection_mode = _resolve_strategy(strategy, has_embedding)
 
+        # Cap selection to remaining capacity
+        if limit == "auto":
+            if already_uploaded > 0:
+                limit = capacity  # partially filled — select exactly what remains
+        else:
+            limit = min(limit, capacity)
+
         if selection_mode == "skip":
             continue
 

winnow/upload_tracker.py

@@ -8,6 +8,13 @@ Both are excluded from future candidate pools. To reset:
   - All:           delete both files
   - One person:    call reset_person("Name") or set RESET_PERSON=Name
   - Rejects only:  delete frigate_rejected_ids.json, or set RETRY_REJECTED=true
+
+by_person schema (frigate_uploaded_ids.json):
+  {
+    "asset_ids":    ["immich-id-1", ...],   # all assets we attempted to upload
+    "scores":       {"immich-id-1": 0.953}, # Immich face confidence at upload time
+    "frigate_count": 42                     # last known Frigate training image count
+  }
 """
 
 import json
@@ -55,7 +62,23 @@ def _load_flat(filename: str) -> set[str]:
     return set(_load(filename).get(_flat_key(filename), []))
 
 
-def _mark(filename: str, asset_id: str, person_name: str | None) -> None:
+def _get_ids(entry: list | dict) -> list[str]:
+    """Extract asset_ids from either the old list format or the new dict format."""
+    if isinstance(entry, list):
+        return entry
+    return entry.get("asset_ids", [])
+
+
+def _migrate_entry(entry: list | dict) -> dict:
+    """Ensure by_person entry is in the current dict format."""
+    if isinstance(entry, list):
+        return {"asset_ids": sorted(entry), "scores": {}}
+    entry.setdefault("asset_ids", [])
+    entry.setdefault("scores", {})
+    return entry
+
+
+def _mark(filename: str, asset_id: str, person_name: str | None, score: float | None = None) -> None:
     data = _load(filename)
     flat_key = _flat_key(filename)
     flat = set(data.get(flat_key, []))
@@ -63,9 +86,13 @@ def _mark(filename: str, asset_id: str, person_name: str | None) -> None:
     data[flat_key] = sorted(flat)
     if person_name:
         by_person = data.setdefault("by_person", {})
-        person_ids = set(by_person.get(person_name, []))
-        person_ids.add(asset_id)
-        by_person[person_name] = sorted(person_ids)
+        entry = _migrate_entry(by_person.get(person_name, {}))
+        ids = set(entry["asset_ids"])
+        ids.add(asset_id)
+        entry["asset_ids"] = sorted(ids)
+        if score is not None:
+            entry["scores"][asset_id] = round(score, 4)
+        by_person[person_name] = entry
     _save(filename, data)
 
 
@@ -79,8 +106,8 @@ def load_rejected_ids() -> set[str]:
     return _load_flat(REJECT_TRACKER_FILE)
 
 
-def mark_uploaded(asset_id: str, person_name: str | None = None) -> None:
-    _mark(UPLOAD_TRACKER_FILE, asset_id, person_name)
+def mark_uploaded(asset_id: str, person_name: str | None = None, score: float | None = None) -> None:
+    _mark(UPLOAD_TRACKER_FILE, asset_id, person_name, score=score)
     logger.debug(f"Marked {asset_id} as uploaded ({person_name})")
 
 
@@ -89,14 +116,25 @@ def mark_rejected(asset_id: str, person_name: str | None = None) -> None:
     logger.debug(f"Marked {asset_id} as rejected ({person_name})")
 
 
+def update_frigate_count(person_name: str, count: int) -> None:
+    """Record Frigate's authoritative training image count for a person."""
+    data = _load(UPLOAD_TRACKER_FILE)
+    by_person = data.setdefault("by_person", {})
+    entry = _migrate_entry(by_person.get(person_name, {}))
+    entry["frigate_count"] = count
+    by_person[person_name] = entry
+    _save(UPLOAD_TRACKER_FILE, data)
+
+
 def reset_person(person_name: str) -> None:
     """Remove all uploaded and rejected records for a given person."""
     for filename in (UPLOAD_TRACKER_FILE, REJECT_TRACKER_FILE):
         data = _load(filename)
         flat_key = _flat_key(filename)
         by_person = data.get("by_person", {})
-        person_ids = set(by_person.pop(person_name, []))
-        if person_ids:
+        entry = by_person.pop(person_name, None)
+        if entry is not None:
+            person_ids = set(_get_ids(entry))
             flat = set(data.get(flat_key, [])) - person_ids
             data[flat_key] = sorted(flat)
             data["by_person"] = by_person
@@ -104,18 +142,22 @@ def reset_person(person_name: str) -> None:
     logger.info(f"Reset tracking data for {person_name}")
 
 
-def get_person_summary() -> dict[str, dict[str, int]]:
-    """Return {person_name: {uploaded: N, rejected: N}} for display."""
-    uploaded_by = _load(UPLOAD_TRACKER_FILE).get("by_person", {})
-    rejected_by = _load(REJECT_TRACKER_FILE).get("by_person", {})
-    names = set(uploaded_by) | set(rejected_by)
-    return {
-        name: {
-            "uploaded": len(uploaded_by.get(name, [])),
-            "rejected": len(rejected_by.get(name, [])),
-        }
-        for name in sorted(names)
+def get_person_summary() -> dict[str, dict]:
+    """Return {person_name: {uploaded, rejected, frigate_count, scores}} for display/capacity."""
+    uploaded_data = _load(UPLOAD_TRACKER_FILE).get("by_person", {})
+    rejected_data = _load(REJECT_TRACKER_FILE).get("by_person", {})
+    names = set(uploaded_data) | set(rejected_data)
+    result = {}
+    for name in sorted(names):
+        u_entry = uploaded_data.get(name, {})
+        r_entry = rejected_data.get(name, {})
+        result[name] = {
+            "uploaded": len(_get_ids(u_entry)),
+            "rejected": len(_get_ids(r_entry)),
+            "frigate_count": u_entry.get("frigate_count") if isinstance(u_entry, dict) else None,
+            "scores": u_entry.get("scores", {}) if isinstance(u_entry, dict) else {},
         }
+    return result
 
 
 def filter_already_uploaded(