Land README CI badge on main

- frigate_api._get_faces_data now validates the /api/faces response is a
  dict before returning it, so a malformed body no longer crashes
  data.items() in get_all_frigate_person_files (and the equivalent
  data.get() in get_frigate_person_files).
- upload_tracker now takes an exclusive flock on a DATA_DIR lock file for
  every tracker load-mutate-save cycle, so a scheduled run and a manual
  docker exec against the same DATA_DIR can no longer race a
  read-modify-write and silently drop the loser's marks.
- begin_batch()/flush_batch() now flush to disk every 10 marks instead of
  deferring the whole per-person upload loop, bounding how many uploaded
  marks a crash mid-batch can lose.

.githooks/pre-commit

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+if git diff --cached --name-only | grep -q "^pyproject\.toml$"; then
+    uv lock
+    git add uv.lock
+fi

.github/FUNDING.yml

@@ -0,0 +1,2 @@
+github: sudolulo
+ko_fi: sudolulo

.github/workflows/docker-publish.yml

@@ -54,7 +54,7 @@ jobs:
           df -h
 
       - name: Checkout repository
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
         with:
           ref: ${{ inputs.tag || github.ref }}
 
@@ -178,7 +178,7 @@ jobs:
           df -h
 
       - name: Checkout repository
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
         with:
           ref: ${{ inputs.tag || github.ref }}
 
@@ -264,7 +264,7 @@ jobs:
           df -h
 
       - name: Checkout repository
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
         with:
           ref: ${{ inputs.tag || github.ref }}
 
@@ -347,7 +347,7 @@ jobs:
           df -h
 
       - name: Checkout repository
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
         with:
           ref: ${{ inputs.tag || github.ref }}
 

.github/workflows/lint.yml

@@ -23,7 +23,7 @@ jobs:
           echo "Disk space freed."
 
       - name: Checkout code
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
 
       - name: Run Ruff
         uses: astral-sh/ruff-action@0ce1b0bf8b818ef400413f810f8a11cdbda0034b # v4.0.0

.github/workflows/release.yml

@@ -32,7 +32,7 @@ jobs:
           echo "Disk space freed."
 
       - name: Checkout
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
         with:
           fetch-depth: 0
 

.github/workflows/test.yml

@@ -13,7 +13,7 @@ jobs:
       contents: read
     steps:
       - name: Checkout code
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
+        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
 
       - name: Install uv
         uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
@@ -21,6 +21,9 @@ jobs:
       - name: Set up Python
         run: uv python install 3.13
 
+      - name: Check lockfile is up to date
+        run: uv lock --check
+
       - name: Install dependencies
         run: uv sync --extra cpu
 

.github/workflows/update-lockfile.yml

@@ -1,46 +0,0 @@
-# .github/workflows/update-lockfile.yml
-name: Update lockfile
-
-on:
-  push:
-    branches:
-      - '**'
-    paths:
-      - 'pyproject.toml'
-  workflow_dispatch:
-
-jobs:
-  update-lockfile:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
-
-      - name: Set up Python
-        run: uv python install 3.13
-
-      - name: Regenerate lockfile
-        run: uv lock
-
-      - name: Check for changes
-        id: diff
-        run: |
-          if git diff --quiet uv.lock; then
-            echo "changed=false" >> "$GITHUB_OUTPUT"
-          else
-            echo "changed=true" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Commit and push updated lockfile
-        if: steps.diff.outputs.changed == 'true'
-        run: |
-          git config user.name "github-actions[bot]"
-          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
-          git add uv.lock
-          git commit -m "chore: update lockfile"
-          git push

.gitignore

@@ -34,3 +34,7 @@ wheels/
 .pytest_cache/
 .mypy_cache/
 .python-version
+
+# Runtime data: face embeddings + upload trackers keyed by real person names.
+# Never commit — this is the DATA_DIR the README tells you to mount.
+data/

CHANGELOG.md

@@ -7,6 +7,52 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Fixed
+
+- **`get_all_frigate_person_files` crashed on a non-dict `/api/faces` response**: `data.items()` assumed the JSON body was always an object; a malformed response from Frigate crashed the call instead of degrading gracefully like the rest of the API layer. `_get_faces_data` now validates the response shape and returns `None` on a non-dict body, protecting both `get_all_frigate_person_files` and `get_frigate_person_files`.
+
+- **Tracker JSON files had no cross-process locking**: two winnow invocations against the same `DATA_DIR` (e.g. a scheduled run overlapping a manual `docker exec`, which the docs explicitly instruct) could race a read-modify-write on `frigate_uploaded_ids.json`/`frigate_rejected_ids.json` and silently lose the loser's marks. Tracker load-mutate-save cycles now hold an exclusive `flock` on a `DATA_DIR` lock file for the duration of the operation.
+
+- **`begin_batch`/`flush_batch` could lose an entire person's upload marks on a crash**: tracker writes were deferred for the whole per-person upload loop, so a SIGKILL/OOM/host crash mid-batch lost every successful Frigate upload from the tracker even though the files were already live in Frigate, causing duplicate re-uploads on the next run. Batches now flush to disk every 10 marks, bounding the loss to a small, fixed window.
+
+## [0.6.6] - 2026-06-18
+
+### Changed
+
+- **`MAX_AUTO_IMAGES` default lowered from 20 to 5** — existing users who have not set this variable and already have more than 5 winnow-managed images in Frigate will find themselves at cap on the next run. With `QUALITY_REPLACEMENT=true` (the default), winnow will attempt to swap weaker images rather than uploading new ones. Set `MAX_AUTO_IMAGES=20` to restore the previous behaviour.
+
+## [0.6.5] - 2026-06-17
+
+### Added
+
+- **Version displayed in startup banner**: winnow now prints its installed version at launch.
+
+### Fixed
+
+- **GPU image: `CUDAExecutionProvider` missing due to parallel install race**: `insightface` declares `onnxruntime` (CPU) as a dependency, causing `uv sync` to install both `onnxruntime` and `onnxruntime-gpu` in parallel — both packages claim the same `pybind11_state.so` binary. On GitHub Actions the CPU binary consistently won the race, leaving the GPU build without CUDA support at runtime despite all CUDA libraries being present. Fixed by reinstalling `onnxruntime-gpu` sequentially after `uv sync` to guarantee its GPU binary is on disk.
+
+- **GPU extra was missing three required nvidia pip packages**: `onnxruntime-gpu` 1.26.0 gates CUDA EP loading on the Python-importability of `nvidia-cuda-runtime-cu12`, `nvidia-cufft-cu12`, and `nvidia-curand-cu12`. These packages were not declared in the `gpu` extra and were absent on fresh installs, silently disabling GPU inference.
+
+- **`_handle_duplicate_people` raises `KeyError` on id-less person records**: bare `p["id"]` subscripts in the auto-merge loop and `_smaller_duplicate_ids` raised `KeyError` when Immich returned a person dict without an `id` field (e.g. unconfirmed face clusters). Fixed by using `p.get("id")` and filtering `None` from `skip_ids`.
+
+- **`_smaller_duplicate_ids` could include `None` in the skip set**: `p.get("id")` without a `None` guard populated `skip_ids` with `None`, causing `p.get("id") not in skip_ids` to pass for every id-less person, so unnamed face clusters were silently re-included in all return paths.
+
+- **`_handle_duplicate_people` dead code removed**: guards `if not survivor_id` and `if not merge_ids` became unreachable after the id-gate fix; their presence suggested they still ran.
+
+- **`_valid_people` in `jobs.py` used wrong name filter**: whitespace-only names (e.g. `"  "`) passed the `p.get("name")` truthiness check and were included in the person list. Fixed using `(p.get("name") or "").strip()` consistent with the cli.py gate.
+
+- **`interactive_configure` queued-marker check was O(N²)**: `[j for j in jobs if j["person"]["id"] == p.get("id")]` ran a full scan over jobs for every person in the display loop. Replaced with a `queued_ids` set hoisted before the loop.
+
+- **`executor.py` slot restore did not clear `min_quality_score_for_slot`**: when a replacement upload failed all retries after a deletion, `effective_count` was restored but the stale quality-score floor from the deleted file remained, blocking the next candidate from filling the slot.
+
+- **`get_immich_version` swallowed `KeyError` on unexpected schema**: bare `data["major"]` / `data["minor"]` / `data["patch"]` subscripts were silently caught by the surrounding `except Exception`, returning `None` without logging. Replaced with `.get()` calls that log a debug warning on unexpected schemas.
+
+- **Face embedding selects nearest face to crop centre, not largest by area**: a 25 % margin on the crop window can pull a larger neighbouring face into the bounding box; selecting the biggest face by area then embeds the wrong person. Centre-proximity is now used instead.
+
+- **Zero-norm face embeddings skipped before diversity selection**: InsightFace occasionally returns a zero vector for low-quality detections; zero embeddings pass deduplication with similarity 0 and score distance 1.0, causing them to be selected first as maximally diverse.
+
+- **`executor.py` slot restore did not clear `min_quality_score_for_slot`**: stale quality floor from the deleted file blocked the next candidate from filling the restored slot in quality-replacement mode.
+
 ## [0.6.4] - 2026-06-17
 
 ### Fixed

CONTRIBUTING.md

@@ -17,8 +17,11 @@ git clone https://github.com/sudolulo/winnow.git
 cd winnow
 git checkout dev
 uv sync
+git config core.hooksPath .githooks
 ```
 
+The last line activates the project's git hooks. The pre-commit hook automatically runs `uv lock` and stages the result whenever `pyproject.toml` is part of a commit, keeping the lockfile in sync without any extra steps.
+
 ## Running Tests and Lint
 
 ```bash

Dockerfile

@@ -8,7 +8,7 @@
 
 ARG VARIANT=gpu
 
-FROM --platform=$BUILDPLATFORM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04 AS base-amd64-gpu
+FROM --platform=$BUILDPLATFORM nvidia/cuda:12.9.2-cudnn-runtime-ubuntu24.04 AS base-amd64-gpu
 FROM ubuntu:26.04 AS base-amd64-rocm
 FROM ubuntu:22.04 AS base-amd64-intel
 FROM ubuntu:26.04 AS base-amd64-cpu
@@ -50,7 +50,9 @@ RUN if [ "$VARIANT" = "cpu" ]; then \
     elif [ "$VARIANT" = "intel" ]; then \
         uv sync --frozen --no-dev --extra intel; \
     elif [ "$VARIANT" = "gpu" ]; then \
-        uv sync --frozen --no-dev --extra gpu; \
+        uv sync --frozen --no-dev --extra gpu && \
+        ORT_GPU_VER=$(.venv/bin/python -c "import importlib.metadata; print(importlib.metadata.version('onnxruntime-gpu'))") && \
+        uv pip install --python .venv/bin/python --no-deps --reinstall "onnxruntime-gpu==$ORT_GPU_VER"; \
     else \
         echo "Unknown VARIANT: '$VARIANT'. Must be one of: cpu, rocm, intel, gpu" >&2; \
         exit 1; \

NOTICE

@@ -0,0 +1,3 @@
+winnow
+
+Portions of this project were written with assistance from Claude (Anthropic).

README.md

@@ -1,6 +1,6 @@
 # winnow
 
-[![Docker](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml) [![Test](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/test.yml) [![GitHub release](https://img.shields.io/github/v/release/sudolulo/winnow)](https://github.com/sudolulo/winnow/releases/latest) [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](LICENSE) [![Immich](https://img.shields.io/badge/Immich-v1.106%2B-blueviolet)](https://immich.app) [![Frigate](https://img.shields.io/badge/Frigate-Ready-brightgreen)](https://frigate.video)
+[![Tests](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions)
 
 > **Note:** winnow's approach to training Frigate face recognition is not an officially documented workflow — results may vary.
 
@@ -187,7 +187,7 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `MAX_AUTO_IMAGES` | `20` | Maximum training images per person in Frigate |
+| `MAX_AUTO_IMAGES` | `5` | Maximum training images per person in Frigate |
 | `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
 
 #### Advanced Tuning *(calibrated — do not adjust)*
@@ -273,3 +273,8 @@ When run with a terminal attached, winnow starts an interactive session: select
 ## Attribution
 
 Based on [if_curator](https://github.com/ds-sebastian/if_curator) by Sebastian, licensed MIT.
+
+## Support
+
+If winnow is useful to you, consider supporting development via
+[GitHub Sponsors](https://github.com/sponsors/sudolulo) or [Ko-fi](https://ko-fi.com/sudolulo).

compose.yml

@@ -31,7 +31,7 @@ services:
       # - USE_FULL_RESOLUTION=true    # Use full-res images vs thumbnails (default: true)
       # - MIN_CONFIDENCE=0.7          # Minimum face detection confidence (default: 0.7)
       # - BLUR_THRESHOLD=100.0        # Laplacian blur threshold; lower = accept more blur (default: 100.0)
-      # - MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 20)
+      # - MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 5)
 
       # ── Caching & Models ──────────────────────────────────────────────────
       # - FORCE_CPU=true              # Disable GPU, fall back to CPU

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.6.4"
+version = "0.6.6"
 description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"
@@ -17,7 +17,7 @@ classifiers = [
 dependencies = [
     "croniter>=5.0.2",
     "insightface>=0.7.3",
-    "numpy>=2.2.6",
+    "numpy>=2.5.0",
     "opencv-python-headless>=4.12.0.88",
     "pillow>=12.1.0",
     "python-dotenv>=1.2.1",
@@ -27,12 +27,15 @@ dependencies = [
 
 [project.optional-dependencies]
 gpu   = [
-    "onnxruntime-gpu>=1.23.2; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "onnxruntime-gpu>=1.27.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
-    "nvidia-cudnn-cu12>=9.0.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-cudnn-cu12>=9.23.2.1; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-cuda-runtime-cu12>=12.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-cufft-cu12>=11.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-curand-cu12>=10.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
 ]
 rocm  = ["onnxruntime-rocm>=1.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'"]
 intel = ["onnxruntime-openvino>=1.20.0; sys_platform == 'linux' and platform_machine == 'x86_64'"]
-cpu   = ["onnxruntime>=1.23.2"]
+cpu   = ["onnxruntime>=1.27.0"]
 
 [project.scripts]
 winnow = "winnow.cli:main"
@@ -60,8 +63,8 @@ required-environments = [
 
 [dependency-groups]
 dev = [
-    "pytest>=8.0",
+    "pytest>=9.1.1",
-    "ruff>=0.15.17",
+    "ruff>=0.15.20",
 ]
 
 [tool.hatch.build.targets.wheel]

scheduler.py

@@ -49,11 +49,12 @@ def _run_scheduler() -> None:
             try:
                 main()
                 print("winnow run complete", flush=True)
-            except KeyboardInterrupt:
+            except (KeyboardInterrupt, SystemExit):
                 raise
             except Exception as e:
                 logger.error("winnow run failed: %s", e, exc_info=True)
                 print(f"winnow run failed: {e}", flush=True)
+            cron = croniter(schedule, time.time())
             next_run = cron.get_next(float)
             print(f"Next run: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(next_run))}", flush=True)
         time.sleep(min(60, max(1, next_run - time.time())))

tests/test_config.py

@@ -21,7 +21,7 @@ def test_config_loads_defaults(monkeypatch):
     assert cfg.MIN_FACE_COUNT == 3
     assert cfg.BLUR_THRESHOLD == 120.0
     assert cfg.MIN_CONFIDENCE == 0.7
-    assert cfg.MAX_AUTO_IMAGES == 20
+    assert cfg.MAX_AUTO_IMAGES == 5
     assert cfg.QUALITY_REPLACEMENT is True
     assert cfg.FACE_MARGIN == 0.15
     assert cfg.USE_FULL_RESOLUTION is True

tests/test_frigate_api.py

@@ -0,0 +1,56 @@
+"""Tests for Frigate API helpers (no network required)."""
+
+import pytest
+
+
+class _FakeResponse:
+    def __init__(self, json_body, status_code=200):
+        self._json_body = json_body
+        self.status_code = status_code
+        self.ok = 200 <= status_code < 300
+
+    def raise_for_status(self):
+        if not self.ok:
+            raise Exception(f"HTTP {self.status_code}")
+
+    def json(self):
+        return self._json_body
+
+
+@pytest.fixture(autouse=True)
+def frigate_url(monkeypatch):
+    monkeypatch.setenv("FRIGATE_URL", "http://frigate.test")
+    yield
+
+
+def test_get_all_frigate_person_files_happy_path(monkeypatch):
+    from winnow import frigate_api
+    body = {"Alice": ["a-1.webp", "a-2.webp"], "train": ["pending.webp"]}
+    monkeypatch.setattr(frigate_api.requests, "get", lambda *a, **k: _FakeResponse(body))
+    result = frigate_api.get_all_frigate_person_files()
+    assert result == {"Alice": ["a-1.webp", "a-2.webp"]}
+
+
+def test_get_all_frigate_person_files_rejects_non_dict_body(monkeypatch):
+    """A non-dict /api/faces response must not crash data.items() — it should degrade to None."""
+    from winnow import frigate_api
+    monkeypatch.setattr(frigate_api.requests, "get", lambda *a, **k: _FakeResponse(["not", "a", "dict"]))
+    assert frigate_api.get_all_frigate_person_files() is None
+
+
+def test_get_frigate_person_files_rejects_non_dict_body(monkeypatch):
+    from winnow import frigate_api
+    monkeypatch.setattr(frigate_api.requests, "get", lambda *a, **k: _FakeResponse(["not", "a", "dict"]))
+    assert frigate_api.get_frigate_person_files("Alice") is None
+
+
+def test_get_frigate_face_counts_rejects_non_dict_body(monkeypatch):
+    from winnow import frigate_api
+    monkeypatch.setattr(frigate_api.requests, "get", lambda *a, **k: _FakeResponse("oops"))
+    assert frigate_api.get_frigate_face_counts() is None
+
+
+def test_get_all_frigate_person_files_rejects_scalar_body(monkeypatch):
+    from winnow import frigate_api
+    monkeypatch.setattr(frigate_api.requests, "get", lambda *a, **k: _FakeResponse(42))
+    assert frigate_api.get_all_frigate_person_files() is None

tests/test_upload_tracker.py

@@ -1,5 +1,8 @@
 """Tests for upload tracker — mark, filter, reset, and summary logic."""
 
+import fcntl
+import json
+import os
 
 import pytest
 
@@ -260,3 +263,121 @@ def test_get_most_redundant_exclude_all_returns_none():
     mark_uploaded("asset-a", person_name="Alice", score=0.5, frigate_score=0.70)
     record_frigate_file("Alice", "Alice-a.webp", "asset-a")
     assert get_most_redundant_mapped_file("Alice", exclude={"Alice-a.webp"}) is None
+
+
+# ── cross-process locking ──────────────────────────────────────────────────────
+
+def test_tracker_lock_blocks_concurrent_exclusive_access():
+    """While upload_tracker holds the tracker lock, a second (non-blocking) attempt
+    to exclusively lock the same file must fail — proving the lock is real and
+    guards a second winnow process from racing a read-modify-write."""
+    from winnow import upload_tracker as ut
+
+    ut._acquire_lock()
+    try:
+        lock_path = ut._lock_path()
+        assert lock_path.exists()
+        fd = os.open(lock_path, os.O_RDWR)
+        try:
+            with pytest.raises(OSError):
+                fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        finally:
+            os.close(fd)
+    finally:
+        ut._release_lock()
+
+    # Released — a second exclusive, non-blocking lock now succeeds immediately.
+    fd = os.open(ut._lock_path(), os.O_RDWR)
+    try:
+        fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        fcntl.flock(fd, fcntl.LOCK_UN)
+    finally:
+        os.close(fd)
+
+
+def test_tracker_lock_reentrant_within_process():
+    """Nested acquisitions (e.g. begin_batch() for both tracker files, or a tracker
+    call made while a batch is open) must not deadlock the process that holds them."""
+    from winnow import upload_tracker as ut
+
+    assert ut._lock_depth == 0
+    ut._acquire_lock()
+    ut._acquire_lock()
+    assert ut._lock_depth == 2
+    ut._release_lock()
+    assert ut._lock_depth == 1
+    ut._release_lock()
+    assert ut._lock_depth == 0
+
+
+def test_begin_flush_batch_releases_lock_for_next_caller():
+    """begin_batch()/flush_batch() (including a nested mark_uploaded call inside the
+    batch) must fully release the lock so it doesn't stay held for the rest of the run."""
+    from winnow import upload_tracker as ut
+    from winnow.upload_tracker import (
+        REJECT_TRACKER_FILE,
+        UPLOAD_TRACKER_FILE,
+        begin_batch,
+        flush_batch,
+        mark_uploaded,
+    )
+
+    begin_batch(UPLOAD_TRACKER_FILE)
+    begin_batch(REJECT_TRACKER_FILE)
+    mark_uploaded("a1", person_name="Alice")
+    flush_batch(UPLOAD_TRACKER_FILE)
+    flush_batch(REJECT_TRACKER_FILE)
+
+    assert ut._lock_depth == 0
+    fd = os.open(ut._lock_path(), os.O_RDWR)
+    try:
+        fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        fcntl.flock(fd, fcntl.LOCK_UN)
+    finally:
+        os.close(fd)
+
+
+# ── batched writes: incremental flush bounds crash loss ───────────────────────
+
+def test_batch_defers_writes_below_flush_threshold(isolated_cache):
+    """Below the flush threshold, writes stay in memory — batching still avoids
+    a disk write per mark."""
+    from winnow.upload_tracker import UPLOAD_TRACKER_FILE, begin_batch, flush_batch, mark_uploaded
+
+    tracker_path = isolated_cache / UPLOAD_TRACKER_FILE
+    begin_batch(UPLOAD_TRACKER_FILE)
+    try:
+        mark_uploaded("asset-0", person_name="Alice")
+        assert not tracker_path.exists()
+    finally:
+        flush_batch(UPLOAD_TRACKER_FILE)
+    assert tracker_path.exists()
+
+
+def test_batch_flushes_incrementally_bounding_crash_loss(isolated_cache):
+    """A crash mid-batch (SIGKILL/OOM/host crash) must lose at most a bounded number
+    of marks, not the whole per-person batch — verified by reading the file straight
+    off disk before flush_batch() is ever called."""
+    from winnow.upload_tracker import _BATCH_FLUSH_EVERY, UPLOAD_TRACKER_FILE, begin_batch, flush_batch, mark_uploaded
+
+    tracker_path = isolated_cache / UPLOAD_TRACKER_FILE
+    begin_batch(UPLOAD_TRACKER_FILE)
+    try:
+        for i in range(_BATCH_FLUSH_EVERY):
+            mark_uploaded(f"asset-{i}", person_name="Alice")
+        # Threshold reached: disk already reflects all marks so far, even though
+        # flush_batch() has not run yet — simulates surviving a crash here.
+        on_disk = json.loads(tracker_path.read_text())
+        ids = on_disk["by_person"]["Alice"]["asset_ids"]
+        assert len(ids) == _BATCH_FLUSH_EVERY
+
+        # One more mark past the threshold stays deferred again until the next
+        # periodic flush or flush_batch().
+        mark_uploaded("asset-extra", person_name="Alice")
+        on_disk = json.loads(tracker_path.read_text())
+        assert len(on_disk["by_person"]["Alice"]["asset_ids"]) == _BATCH_FLUSH_EVERY
+    finally:
+        flush_batch(UPLOAD_TRACKER_FILE)
+
+    on_disk = json.loads(tracker_path.read_text())
+    assert len(on_disk["by_person"]["Alice"]["asset_ids"]) == _BATCH_FLUSH_EVERY + 1

uv.lock

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+    { url = "https://files.pythonhosted.org/packages/d7/2b/9555445e1201d92b3195f45cdb153a0b68f24e0a4273f6e3d5ab46e212bb/ruff-0.15.20-py3-none-win_arm64.whl", hash = "sha256:2f5b2a6d614e8700388806a14996c40fab2c47b819ef57d790a34878858ed9ca", size = 11343498, upload-time = "2026-06-25T17:20:35.03Z" },
 ]
 
 [[package]]
@@ -862,7 +895,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.6.2"
+version = "0.6.6"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },
@@ -880,7 +913,10 @@ cpu = [
     { name = "onnxruntime" },
 ]
 gpu = [
+    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
 ]
 intel = [
@@ -900,10 +936,13 @@ dev = [
 requires-dist = [
     { name = "croniter", specifier = ">=5.0.2" },
     { name = "insightface", specifier = ">=0.7.3" },
-    { name = "numpy", specifier = ">=2.2.6" },
+    { name = "numpy", specifier = ">=2.5.0" },
-    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=9.0.0" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=12.0" },
-    { name = "onnxruntime", marker = "extra == 'cpu'", specifier = ">=1.23.2" },
+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=9.23.2.1" },
-    { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=1.23.2" },
+    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=11.0" },
+    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=10.0" },
+    { name = "onnxruntime", marker = "extra == 'cpu'", specifier = ">=1.27.0" },
+    { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=1.27.0" },
     { name = "onnxruntime-openvino", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'intel'", specifier = ">=1.20.0" },
     { name = "onnxruntime-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'rocm'", specifier = ">=1.16.0" },
     { name = "opencv-python-headless", specifier = ">=4.12.0.88" },
@@ -916,6 +955,6 @@ provides-extras = ["gpu", "rocm", "intel", "cpu"]
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "pytest", specifier = ">=8.0" },
+    { name = "pytest", specifier = ">=9.1.1" },
-    { name = "ruff", specifier = ">=0.15.17" },
+    { name = "ruff", specifier = ">=0.15.20" },
 ]

winnow/cli.py

@@ -7,6 +7,7 @@ import sys
 from rich import print as rprint
 from rich.prompt import Confirm
 
+from . import __version__
 from .config import Config, _getenv_bool
 from .executor import execute_jobs, upload_to_frigate
 from .immich_api import get_immich_version, get_people, merge_people
@@ -71,7 +72,7 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
     by_name: dict[str, list[dict]] = defaultdict(list)
     for p in people:
         name = (p.get("name") or "").strip()
-        if name:
+        if name and p.get("id"):
             by_name[name].append(p)
 
     duplicates = {name: ps for name, ps in by_name.items() if len(ps) > 1}
@@ -81,19 +82,23 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
     def _smaller_duplicate_ids(groups: dict) -> set[str]:
         """IDs of all but the largest person in each duplicate group."""
         return {
-            p.get("id")
+            pid
             for ps in groups.values()
             for p in sorted(ps, key=lambda x: x.get("assetCount", 0), reverse=True)[1:]
+            if (pid := p.get("id"))
         }
 
     skip_ids = _smaller_duplicate_ids(duplicates)
 
+    def _excl(lst: list[dict]) -> list[dict]:
+        return [p for p in lst if p.get("id") not in skip_ids]
+
     if not Config.MERGE_DUPLICATE_PEOPLE:
         rprint("\n[bold yellow]⚠  Duplicate person names detected in Immich:[/bold yellow]")
         for name, ps in sorted(duplicates.items()):
             ordered = sorted(ps, key=lambda x: x.get("assetCount", 0), reverse=True)
             entries = ", ".join(
-                f"[dim]{p['id'][:8]}…[/dim] ({p.get('assetCount', 0)} assets)"
+                f"[dim]{(p.get('id') or '?')[:8]}…[/dim] ({p.get('assetCount', 0)} assets)"
                 for p in ordered
             )
             rprint(f"  [yellow]{name}[/yellow] → {len(ps)} people: {entries}")
@@ -109,20 +114,21 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
         )
         # Return deduplicated list — keep only the largest per name so that
         # downstream job creation never runs two jobs for the same Frigate folder.
-        return [p for p in people if p.get("id") not in skip_ids]
+        return _excl(people)
 
     # Auto-merge: survivor = largest asset count, rest merge into it inside Immich
     merged_any = False
     for name, ps in sorted(duplicates.items()):
         ordered = sorted(ps, key=lambda x: x.get("assetCount", 0), reverse=True)
         survivor = ordered[0]
-        merge_ids = [p["id"] for p in ordered[1:]]
+        survivor_id = survivor.get("id")
+        merge_ids = [pid for p in ordered[1:] if (pid := p.get("id")) is not None]
         rprint(
             f"  [cyan]Merging {name!r} inside Immich:[/cyan] keeping "
-            f"[dim]{survivor['id'][:8]}…[/dim] ({survivor.get('assetCount', 0)} assets), "
+            f"[dim]{survivor_id[:8]}…[/dim] ({survivor.get('assetCount', 0)} assets), "
             f"absorbing {len(merge_ids)} smaller duplicate(s)..."
         )
-        if merge_people(survivor["id"], merge_ids):
+        if merge_people(survivor_id, merge_ids):
             rprint(f"  [green]✓ Merged {name!r}[/green]")
             merged_any = True
         else:
@@ -141,12 +147,12 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
                 " — possible transient error or expired API key;"
                 " proceeding with pre-merge list. Check IMMICH_API_KEY if this recurs."
             )
-            return [p for p in people if p.get("id") not in skip_ids]
+            return _excl(people)
         # Filter out the smaller duplicate from any group whose merge failed — those
         # IDs still exist in Immich and would produce two jobs for the same folder.
         # IDs from groups that merged successfully are already gone from Immich, so
         # this filter is a no-op for them.
-        return [p for p in fresh if p.get("id") not in skip_ids]
+        return _excl(fresh)
 
     # All merges failed — fall back to local deduplication (keep largest per name) so
     # downstream job creation never runs two jobs for the same Frigate folder.
@@ -154,7 +160,7 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
         "  [yellow]All merges failed — applying local deduplication"
         " to avoid overwriting output.[/yellow]"
     )
-    return [p for p in people if p.get("id") not in skip_ids]
+    return _excl(people)
 
 
 _UNSUPPORTED_VARS = [
@@ -179,8 +185,8 @@ def main() -> None:
         if trace_size:
             _handle_trace_crop(trace_size)
 
-        console.print(r"""
+        console.print(f"""
-    [bold blue]winnow[/bold blue]
+    [bold blue]winnow[/bold blue] [dim]v{__version__}[/dim]
     [dim]Immich -> Frigate Training Data Curator[/dim]
         """)
 

winnow/config.py

@@ -127,12 +127,15 @@ class _Config:
         self.API_KEY = os.getenv("API_KEY")
         self.OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./frigate_train")
         self.YEARS_FILTER = _getenv_int("YEARS_FILTER", 10)
+        if self.YEARS_FILTER < 0:
+            logging.warning("YEARS_FILTER=%s is negative — using default 10", self.YEARS_FILTER)
+            self.YEARS_FILTER = 10
         self.MIN_FACE_WIDTH = _getenv_int("MIN_FACE_WIDTH", 90)
         self.MIN_FACE_COUNT = _getenv_int("MIN_FACE_COUNT", 3)
         self.MERGE_DUPLICATE_PEOPLE = _getenv_bool("MERGE_DUPLICATE_PEOPLE", False)
         self.BLUR_THRESHOLD = _getenv_float("BLUR_THRESHOLD", 120.0)
         self.MIN_CONFIDENCE = _getenv_float("MIN_CONFIDENCE", 0.7)
-        self.MAX_AUTO_IMAGES = _getenv_int("MAX_AUTO_IMAGES", 20)
+        self.MAX_AUTO_IMAGES = _getenv_int("MAX_AUTO_IMAGES", 5)
         self.QUALITY_REPLACEMENT = _getenv_bool("QUALITY_REPLACEMENT", True)
         self.FRIGATE_SCORE_CEILING = _getenv_optional_float("FRIGATE_SCORE_CEILING")
         self.ENABLE_FRIGATE_SCORES = _getenv_bool("ENABLE_FRIGATE_SCORES", True)

winnow/diversity.py

@@ -303,6 +303,9 @@ def _select_by_embedding(
 
             emb = get_embedding(embed_img, asset_id=asset["id"])
             if emb is not None:
+                if np.linalg.norm(emb) < 1e-6:
+                    logger.debug("Zero-norm embedding for asset %s, skipping", asset["id"])
+                    continue
                 embeddings.append(emb)
                 valid_candidates.append(asset)
                 confidence_scores.append(confidence)

winnow/embeddings.py

@@ -107,7 +107,6 @@ def get_insightface_app():
     global _insightface_app, _insightface_loaded
     if _insightface_loaded:
         return _insightface_app
-    _insightface_loaded = True
 
     ctx_id = -1
     insightface_home = os.environ.get("INSIGHTFACE_HOME", os.path.expanduser("~/.insightface"))
@@ -172,10 +171,12 @@ def get_insightface_app():
             _insightface_app.prepare(ctx_id=ctx_id, det_size=(640, 640))
 
         logger.info("InsightFace Buffalo_L: ready on %s (%.1fs)", device_str, time.time() - t0)
+        _insightface_loaded = True
         return _insightface_app
 
     except ImportError:
         logger.error("InsightFace not installed!")
+        _insightface_loaded = True
         return None
     except Exception as e:
         logger.error("Failed to load InsightFace: %s", e)
@@ -193,9 +194,11 @@ def get_insightface_app():
                     )
                     _insightface_app.prepare(ctx_id=-1, det_size=(640, 640))
                 logger.info("InsightFace Buffalo_L: ready on CPU (fallback, %.1fs)", time.time() - t0)
+                _insightface_loaded = True
                 return _insightface_app
             except Exception as ex:
                 logger.error("InsightFace CPU fallback failed: %s", ex)
+        _insightface_loaded = True
         return None
 
 
@@ -218,9 +221,14 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None:
         if not faces:
             return None
 
-        # Return embedding of largest face
+        # Return embedding of the face nearest the crop centre; a large margin can pull
-        largest = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
+        # a bigger neighbouring face into frame, and max-by-area would pick the wrong person.
-        return largest.embedding
+        cx, cy = img_pil.width / 2, img_pil.height / 2
+        nearest = min(
+            faces,
+            key=lambda f: ((f.bbox[0] + f.bbox[2]) / 2 - cx) ** 2 + ((f.bbox[1] + f.bbox[3]) / 2 - cy) ** 2,
+        )
+        return nearest.embedding
     except Exception as e:
         logger.error("Error getting face embedding: %s", e)
         return None

winnow/executor.py

@@ -186,11 +186,10 @@ def execute_jobs(jobs: list[dict]) -> None:
                             saved = process_face_mode(
                                 img, asset, person, person_dir, count, insightface_app=insightface_app
                             )
-                            if saved:
+                            if isinstance(saved, tuple):
                                 filename = f"{count}.jpg"
                                 asset_map[filename] = asset["id"]
                                 score_map[filename] = asset.get("quality_score")
-                                if isinstance(saved, tuple):
                                 dims_map[filename] = saved
                                 # Time-spread path: compute blur score from the downloaded
                                 # image. Capped at 1440px via blur_score_from_image() so the
@@ -202,8 +201,9 @@ def execute_jobs(jobs: list[dict]) -> None:
 
                                 count += 1
                             else:
+                                reason = saved if isinstance(saved, str) else "no usable face data"
                                 progress.console.print(
-                                    f"[yellow]Skipped {asset['id']} (no usable face data)[/yellow]"
+                                    f"[yellow]Skipped {asset['id']} ({reason})[/yellow]"
                                 )
                     except Exception as e:
                         logger.error("Failed to process asset %s: %s", asset.get("id", "<unknown>"), e)
@@ -384,9 +384,9 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                     # freed slot isn't filled with something worse than what we removed.
                     if min_quality_score_for_slot is not None:
                         file_score = score_map.get(fname)
-                        if file_score is not None and file_score <= min_quality_score_for_slot:
+                        if file_score is not None and file_score < min_quality_score_for_slot:
                             progress.console.print(
-                                f"    [dim]⏭  {fname}: score {file_score:.3f} ≤ freed slot floor"
+                                f"    [dim]⏭  {fname}: score {file_score:.3f} < freed slot floor"
                                 f" {min_quality_score_for_slot:.3f}, skipping[/dim]"
                             )
                             progress.advance(upload_task)
@@ -514,8 +514,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                 uploaded += 1
                                 person_uploaded += 1
                                 effective_count += 1
-                                min_quality_score_for_slot = None
+                                min_quality_score_for_slot = None  # for/else rollback mirrors this pair
-
                                 asset_id = asset_map.get(fname)
                                 if asset_id:
                                     try:
@@ -567,13 +566,14 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                     error_detail = resp.json().get("message", full_body[:100])
                                 except Exception:
                                     error_detail = full_body[:100]
-                                if resp.status_code == 400:
+                                if resp.status_code in (400, 500):
                                     progress.console.print(f"      [dim]{error_detail}[/dim]")
                                 else:
                                     logger.debug("%s HTTP %s: %s", fname, resp.status_code, error_detail)
                                 _is_permanent = (
                                     (resp.status_code == 400 and "face" in full_body.lower())
                                     or resp.status_code == 422
+                                    or (resp.status_code == 500 and "could not process" in full_body.lower())
                                 )
                                 if _is_permanent:
                                     asset_id = asset_map.get(fname)
@@ -608,6 +608,16 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                             progress.console.print(
                                 f"    [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
                             )
+                    else:
+                        # All retries exhausted without a successful upload.
+                        # Restore the slot freed by the preceding delete so the next
+                        # candidate still sees at_cap=True and must beat the replacement gate.
+                        # Also clear the quality floor — the deleted file's score no longer
+                        # represents any live Frigate file, and leaving it blocks the next
+                        # candidate from filling the restored slot.
+                        if at_cap:
+                            effective_count += 1
+                            min_quality_score_for_slot = None
 
                     progress.advance(upload_task)
 

winnow/frigate_api.py

@@ -32,14 +32,18 @@ def get_frigate_version() -> str | None:
 
 
 def _get_faces_data() -> dict | None:
-    """Fetch raw GET /api/faces response. Returns None if unavailable."""
+    """Fetch raw GET /api/faces response. Returns None if unavailable or malformed."""
     frigate_url = _get_frigate_url()
     if not frigate_url:
         return None
     try:
         resp = requests.get(f"{frigate_url}/api/faces", timeout=10)
         resp.raise_for_status()
-        return resp.json()
+        data = resp.json()
+        if not isinstance(data, dict):
+            logger.warning("Unexpected response shape from Frigate /api/faces: %s", type(data).__name__)
+            return None
+        return data
     except Exception as e:
         logger.warning("Could not query Frigate faces API: %s", e)
         return None
@@ -146,8 +150,10 @@ def delete_frigate_person_files(person_name: str, filenames: list[str]) -> bool:
     Returns True on success, False if unreachable or the request fails.
     """
     frigate_url = _get_frigate_url()
-    if not frigate_url or not filenames:
+    if not frigate_url:
         return False
+    if not filenames:
+        return True
     from urllib.parse import quote
     encoded_name = quote(person_name, safe="")
     try:

winnow/image_processing.py

@@ -48,6 +48,8 @@ def align_face(img: Image.Image, landmarks: list[list[float]] | np.ndarray) -> I
         if lm.shape != (5, 2):
             logger.debug("Invalid landmark shape: %s, expected (5, 2)", lm.shape)
             return None
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message=".*estimate.*is deprecated", category=FutureWarning)
             aligned = norm_crop(img_np, lm)
         return Image.fromarray(aligned)
     except ImportError:
@@ -66,10 +68,11 @@ def process_face_mode(
     count: int,
     min_width: int | None = None,
     insightface_app=None,
-) -> tuple[int, int] | None:
+) -> tuple[int, int] | str:
     """Crop face based on Immich metadata and save to output directory.
 
-    Returns (width, height) of the saved crop, or None if no crop was saved.
+    Returns (width, height) of the saved crop, or a skip-reason string if the
+    face was filtered out.
     When insightface_app is provided and ENABLE_FACE_ALIGNMENT is True,
     re-detects the face in the Immich bbox region using InsightFace to get
     precise landmarks for a proper 112x112 aligned crop. Falls back to
@@ -89,7 +92,7 @@ def process_face_mode(
 
     if not face_info:
         logger.debug("No face info for %s in asset %s", person.get("name"), asset.get("id"))
-        return None
+        return "no face metadata"
 
     img_w, img_h = img.size
     meta_w = face_info.get("imageWidth") or 0
@@ -108,7 +111,7 @@ def process_face_mode(
     face_w, face_h = x2 - x1, y2 - y1
     if face_w < min_width or face_h < min_width:
         logger.debug("Face too small (%.1fx%.1f)", face_w, face_h)
-        return None
+        return f"face too small ({face_w:.0f}x{face_h:.0f}px, min {min_width}px)"
 
     # Re-detect face with InsightFace for landmark-based alignment.
     # Immich's /api/faces endpoint does not include landmarks, so the

winnow/immich_api.py

@@ -39,7 +39,11 @@ def get_immich_version() -> tuple[int, int, int] | None:
         )
         if resp.ok:
             data = resp.json()
-            return (int(data["major"]), int(data["minor"]), int(data["patch"]))
+            major, minor, patch = data.get("major"), data.get("minor"), data.get("patch")
+            if major is None or minor is None or patch is None:
+                logger.debug("Unexpected Immich version schema: %s", data)
+                return None
+            return (int(major), int(minor), int(patch))
         return None
     except Exception:
         return None
@@ -130,7 +134,7 @@ def fetch_all_assets(person: dict) -> tuple[list[dict], int]:
             # Immich ≥2.x returns {"assets": {"items": [...]}};
             # earlier versions returned {"assets": [...]} directly.
             if isinstance(page_assets, dict):
-                page_assets = page_assets.get("items", [])
+                page_assets = page_assets.get("items") or []
 
             page_count = len(page_assets)  # raw count for termination check before filtering
 
@@ -299,7 +303,7 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d
                 recent.append(asset)
             else:
                 skipped += 1
-        except ValueError:
+        except (ValueError, TypeError):
             bad_timestamp += 1
             continue
 

winnow/jobs.py

@@ -86,7 +86,11 @@ def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, st
         "standard": (30, "smart"),
         "broad": (100, "smart"),
     }
-    return strategy_map.get(strategy, ("auto", "smart"))
+    result = strategy_map.get(strategy)
+    if result is None:
+        logger.warning("Unrecognised STRATEGY=%r — falling back to auto", strategy)
+        return ("auto", "smart")
+    return result
 
 
 def _perform_selection(
@@ -192,13 +196,20 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
     return job
 
 
+def _valid_people(people: list[dict]) -> list[dict]:
+    return sorted(
+        [p for p in people if (p.get("name") or "").strip() and p.get("id")],
+        key=lambda x: x["name"],
+    )
+
+
 def interactive_configure(people: list[dict]) -> list[dict]:
     """Interactive phase: select person(s), mode, and configure training strategy.
 
     Supports multi-person batch mode — 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"])
+    valid_people = _valid_people(people)
 
     if not valid_people:
         rprint("[red]No people found with names in Immich.[/red]")
@@ -209,9 +220,9 @@ def interactive_configure(people: list[dict]) -> list[dict]:
     while True:
         # Select person
         console.print("\n[bold cyan]Select Person to Train:[/bold cyan]")
+        queued_ids = {j["person"]["id"] for j in jobs}
         for idx, p in enumerate(valid_people, 1):
-            # Mark already-queued people
+            marker = " [dim](queued)[/dim]" if p.get("id") in queued_ids else ""
-            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)])
@@ -230,20 +241,20 @@ def interactive_configure(people: list[dict]) -> list[dict]:
 
 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"])
+    valid_people = _valid_people(people)
 
     if not valid_people:
         rprint("[red]No people found with names in Immich.[/red]")
         return []
 
     strategy = os.environ.get("STRATEGY", "auto")
-    skip = [s.strip() for s in os.environ.get("SKIP_PEOPLE", "").split(",") if s.strip()]
+    skip = {s.strip().casefold() for s in os.environ.get("SKIP_PEOPLE", "").split(",") if s.strip()}
-    only = [s.strip() for s in os.environ.get("ONLY_PEOPLE", "").split(",") if s.strip()]
+    only = {s.strip().casefold() for s in os.environ.get("ONLY_PEOPLE", "").split(",") if s.strip()}
 
     if only:
-        valid_people = [p for p in valid_people if p["name"] in only]
+        valid_people = [p for p in valid_people if p["name"].casefold() in only]
     if skip:
-        valid_people = [p for p in valid_people if p["name"] not in skip]
+        valid_people = [p for p in valid_people if p["name"].casefold() not in skip]
 
     min_face_count = Config.MIN_FACE_COUNT
 

winnow/upload_tracker.py

@@ -27,9 +27,11 @@ frigate_files only contains files winnow uploaded — files added manually throu
 Frigate's UI are never mapped here and are never touched by quality replacement.
 """
 
+import fcntl
 import json
 import logging
 import os
+from contextlib import contextmanager
 from pathlib import Path
 
 from .frigate_api import _get_frigate_url, delete_frigate_person_files
@@ -38,6 +40,14 @@ logger = logging.getLogger(__name__)
 
 UPLOAD_TRACKER_FILE = "frigate_uploaded_ids.json"
 REJECT_TRACKER_FILE = "frigate_rejected_ids.json"
+LOCK_FILE = ".tracker.lock"
+
+# Number of deferred _save calls a batch accumulates before it is flushed to disk
+# early. Bounds how many marks a crash mid-batch (SIGKILL/OOM/host crash) can lose —
+# without this, begin_batch()/flush_batch() defer every write for an entire
+# per-person upload loop, so a crash could lose every mark from that person's batch
+# even though the files are already live in Frigate.
+_BATCH_FLUSH_EVERY = 10
 
 # Write-through in-memory cache keyed by the resolved file path.
 # Reduces per-call JSON reads from O(calls) to O(1) after the first load.
@@ -45,6 +55,16 @@ REJECT_TRACKER_FILE = "frigate_rejected_ids.json"
 _cache: dict[str, dict] = {}
 _deferred: set[str] = set()  # paths whose disk writes are batched until flush_batch()
 _dirty: set[str] = set()    # deferred paths that received at least one _save during the batch
+_batch_writes: dict[str, int] = {}  # deferred _save calls since the last disk write, per path
+
+# Cross-process lock guarding the load-mutate-save cycle below. Two winnow
+# invocations against the same DATA_DIR (e.g. a scheduled run overlapping a
+# manual `docker exec`, which the docs explicitly instruct) can otherwise race
+# a read-modify-write and silently lose whichever one saves first. Reentrant
+# within a single process (depth-counted) so begin_batch()/flush_batch() pairs
+# and nested tracker calls made while a batch is open don't self-deadlock.
+_lock_fd: int | None = None
+_lock_depth = 0
 
 
 def _tracker_path(filename: str) -> Path:
@@ -55,6 +75,49 @@ def _tracker_path(filename: str) -> Path:
         return Path(filename)
 
 
+def _lock_path() -> Path:
+    return _tracker_path(LOCK_FILE)
+
+
+def _acquire_lock() -> None:
+    """Acquire the cross-process tracker lock. Reentrant within this process."""
+    global _lock_fd, _lock_depth
+    if _lock_depth == 0:
+        path = _lock_path()
+        path.parent.mkdir(parents=True, exist_ok=True)
+        fd = os.open(path, os.O_CREAT | os.O_RDWR)
+        fcntl.flock(fd, fcntl.LOCK_EX)  # blocks until any other process's lock is released
+        _lock_fd = fd
+        # Cache entries not part of an in-progress deferred batch may be stale —
+        # another process could have written to disk since we last read them.
+        # Drop them so the critical section that follows re-reads from disk.
+        for key in list(_cache):
+            if key not in _deferred:
+                del _cache[key]
+    _lock_depth += 1
+
+
+def _release_lock() -> None:
+    global _lock_fd, _lock_depth
+    if _lock_depth <= 0:
+        return
+    _lock_depth -= 1
+    if _lock_depth == 0 and _lock_fd is not None:
+        fcntl.flock(_lock_fd, fcntl.LOCK_UN)
+        os.close(_lock_fd)
+        _lock_fd = None
+
+
+@contextmanager
+def _locked():
+    """Context manager wrapping a single load-mutate-save cycle in the tracker lock."""
+    _acquire_lock()
+    try:
+        yield
+    finally:
+        _release_lock()
+
+
 def _load(filename: str) -> dict:
     path = _tracker_path(filename)
     key = str(path)
@@ -89,6 +152,15 @@ def _save(filename: str, data: dict) -> None:
     if key in _deferred:
         _cache[key] = data  # accumulate in cache; disk write deferred until flush_batch()
         _dirty.add(key)
+        # Flush early every _BATCH_FLUSH_EVERY marks so a crash mid-batch (SIGKILL/OOM/
+        # host crash) loses at most a bounded number of marks instead of the whole batch.
+        # Safe without re-acquiring the lock: begin_batch() already holds it for the
+        # duration of the batch.
+        _batch_writes[key] = _batch_writes.get(key, 0) + 1
+        if _batch_writes[key] >= _BATCH_FLUSH_EVERY:
+            _write_to_disk(path, data)
+            _dirty.discard(key)
+            _batch_writes[key] = 0
         return
     _write_to_disk(path, data)
     _cache[key] = data  # update cache only after successful write
@@ -96,13 +168,19 @@ def _save(filename: str, data: dict) -> None:
 
 def begin_batch(filename: str) -> None:
     """Defer tracker disk writes for filename. All _save calls accumulate in the
-    in-memory cache until flush_batch() is called. Use around per-person upload loops
+    in-memory cache until flush_batch() is called (with periodic early flushes —
-    to reduce N writes to 1.
+    see _BATCH_FLUSH_EVERY). Use around per-person upload loops to reduce N writes
+    towards 1.
+
+    Acquires the cross-process tracker lock, held until the matching flush_batch()
+    releases it, so a concurrent winnow invocation against the same DATA_DIR can't
+    interleave a read-modify-write with this batch.
 
     If a previous batch for this file was interrupted before flush_batch() was called
     (e.g. an exception escaped the upload loop), the leftover cache state is flushed
     to disk here before starting fresh so that partial progress is not silently lost.
     """
+    _acquire_lock()
     path = _tracker_path(filename)
     key = str(path)
     if key in _deferred and key in _dirty:
@@ -117,16 +195,26 @@ def begin_batch(filename: str) -> None:
         _deferred.discard(key)
         _dirty.discard(key)
     _deferred.add(key)
+    _batch_writes[key] = 0
 
 
 def flush_batch(filename: str) -> None:
-    """Write the accumulated cache state for filename to disk."""
+    """Write the accumulated cache state for filename to disk and release the tracker lock
+    acquired by the matching begin_batch().
+
+    The lock release always runs, even if the disk write raises, so a write failure
+    (e.g. a full disk) can't leave the tracker lock held for the rest of the process.
+    """
     path = _tracker_path(filename)
     key = str(path)
+    try:
         if key in _dirty and key in _cache:
             _write_to_disk(path, _cache[key])
+    finally:
         _deferred.discard(key)
         _dirty.discard(key)
+        _batch_writes.pop(key, None)
+        _release_lock()
 
 
 def _flat_key(filename: str) -> str:
@@ -165,6 +253,7 @@ def _mark(
     if not person_name:
         logger.warning("_mark called with empty person_name for asset %s — asset not recorded", asset_id)
         return
+    with _locked():
         data = _load(filename)
         by_person = dict(data.get("by_person", {}))
         entry = _migrate_entry(by_person.get(person_name, {}))
@@ -229,6 +318,7 @@ def record_frigate_files_batch(person_name: str, mappings: dict[str, str]) -> No
     """Record multiple Frigate filename → asset_id mappings in a single load/save."""
     if not mappings:
         return
+    with _locked():
         src = _load(UPLOAD_TRACKER_FILE)
         by_person = dict(src.get("by_person", {}))
         entry = _migrate_entry(by_person.get(person_name, {}))
@@ -251,6 +341,7 @@ def remove_frigate_file(person_name: str, frigate_filename: str) -> None:
 
 def remove_frigate_files_batch(person_name: str, frigate_filenames: list[str]) -> None:
     """Remove multiple Frigate filenames in a single load/save."""
+    with _locked():
         src = _load(UPLOAD_TRACKER_FILE)
         raw = src.get("by_person", {}).get(person_name)
         if raw is None:
@@ -378,6 +469,7 @@ def find_by_crop_dimension(size: int) -> list[dict]:
 
 def update_frigate_count(person_name: str, count: int) -> None:
     """Record Frigate's authoritative training image count for a person."""
+    with _locked():
         data = _load(UPLOAD_TRACKER_FILE)
         by_person = dict(data.get("by_person", {}))
         entry = _migrate_entry(by_person.get(person_name, {}))
@@ -394,6 +486,7 @@ def reset_all_people() -> None:
     Preferred over calling reset_person() in a loop when RESET_PERSON=* — that
     approach is O(P²) because each call rebuilds the flat list from all remaining entries.
     """
+    with _locked():
         upload_data = _load(UPLOAD_TRACKER_FILE)
         frigate_url = _get_frigate_url()
         if not frigate_url:
@@ -407,7 +500,9 @@ def reset_all_people() -> None:
                 if delete_frigate_person_files(person_name, frigate_filenames):
                     logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}")
                 else:
-                logger.warning(f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway")
+                    logger.warning(
+                        f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway"
+                    )
         _save(UPLOAD_TRACKER_FILE, {})
         _save(REJECT_TRACKER_FILE, {})
     logger.info("Reset all tracking data")
@@ -421,6 +516,7 @@ def reset_person(person_name: str) -> None:
     files (not in frigate_files) are never touched. Proceeds with tracker reset
     even if Frigate is unreachable.
     """
+    with _locked():
         upload_data = _load(UPLOAD_TRACKER_FILE)
         entry = _migrate_entry(upload_data.get("by_person", {}).get(person_name, {}))
         frigate_filenames = list(entry.get("frigate_files", {}).keys())