chore(deps): bump the python-deps group across 1 directory with 5 updates

Bumps the python-deps group with 5 updates in the / directory:

| Package | From | To |
| --- | --- | --- |
| [croniter](https://github.com/pallets-eco/croniter) | `6.2.2` | `6.2.3` |
| [numpy](https://github.com/numpy/numpy) | `2.5.0` | `2.5.1` |
| [opencv-python-headless](https://github.com/opencv/opencv-python) | `4.13.0.92` | `5.0.0.93` |
| [pillow](https://github.com/python-pillow/Pillow) | `12.2.0` | `12.3.0` |
| [nvidia-cudnn-cu12](https://developer.nvidia.com/cuda-zone) | `9.23.2.1` | `9.24.0.43` |



Updates `croniter` from 6.2.2 to 6.2.3
- [Release notes](https://github.com/pallets-eco/croniter/releases)
- [Changelog](https://github.com/pallets-eco/croniter/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pallets-eco/croniter/compare/6.2.2...6.2.3)

Updates `numpy` from 2.5.0 to 2.5.1
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.5.0...v2.5.1)

Updates `opencv-python-headless` from 4.13.0.92 to 5.0.0.93
- [Release notes](https://github.com/opencv/opencv-python/releases)
- [Commits](https://github.com/opencv/opencv-python/commits)

Updates `pillow` from 12.2.0 to 12.3.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](https://github.com/python-pillow/Pillow/compare/12.2.0...12.3.0)

Updates `nvidia-cudnn-cu12` from 9.23.2.1 to 9.24.0.43

---
updated-dependencies:
- dependency-name: croniter
  dependency-version: 6.2.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: python-deps
- dependency-name: numpy
  dependency-version: 2.5.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
  dependency-group: python-deps
- dependency-name: opencv-python-headless
  dependency-version: 5.0.0.93
  dependency-type: direct:production
  update-type: version-update:semver-major
  dependency-group: python-deps
- dependency-name: pillow
  dependency-version: 12.3.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: python-deps
- dependency-name: nvidia-cudnn-cu12
  dependency-version: 9.24.0.43
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: python-deps
...

Signed-off-by: dependabot[bot] <support@github.com>

.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/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

@@ -1,47 +0,0 @@
-# .github/workflows/update-lockfile.yml
-name: Update lockfile
-
-on:
-  push:
-    branches:
-      - '**'
-      - '!main'
-    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

CHANGELOG.md

@@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [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

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

README.md

@@ -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)*

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 = "0.6.5"
+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"
@@ -15,11 +15,11 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Image Recognition",
 ]
 dependencies = [
-    "croniter>=5.0.2",
+    "croniter>=6.2.3",
     "insightface>=0.7.3",
-    "numpy>=2.2.6",
-    "opencv-python-headless>=4.12.0.88",
-    "pillow>=12.1.0",
+    "numpy>=2.5.1",
+    "opencv-python-headless>=5.0.0.93",
+    "pillow>=12.3.0",
     "python-dotenv>=1.2.1",
     "requests>=2.32.5",
     "rich>=14.2.0",
@@ -27,15 +27,15 @@ dependencies = [
 
 [project.optional-dependencies]
 gpu   = [
-    "onnxruntime-gpu>=1.23.2; sys_platform == 'linux' and platform_machine == 'x86_64'",
-    "nvidia-cudnn-cu12>=9.0.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "onnxruntime-gpu>=1.27.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "nvidia-cudnn-cu12>=9.24.0.43; 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"
@@ -63,8 +63,8 @@ required-environments = [
 
 [dependency-groups]
 dev = [
-    "pytest>=8.0",
-    "ruff>=0.15.17",
+    "pytest>=9.1.1",
+    "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

uv.lock

@@ -105,14 +105,14 @@ wheels = [
 
 [[package]]
 name = "croniter"
-version = "6.2.2"
+version = "6.2.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "python-dateutil" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/df/de/5832661ed55107b8a09af3f0a2e71e0957226a59eb1dcf0a445cce6daf20/croniter-6.2.2.tar.gz", hash = "sha256:ba60832a5ec8e12e51b8691c3309a113d1cf6526bdf1a48150ce8ec7a532d0ab", size = 113762, upload-time = "2026-03-15T08:43:48.112Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/03/35/96ad0a71eb0b27ab4476a7ed23facd0713d82da9c911edc8af7f34a62d6a/croniter-6.2.3.tar.gz", hash = "sha256:fb129986ef7e2c44e3f4c9f503da83ad914d2afa48f40a43ee3dca4b5c41d476", size = 166174, upload-time = "2026-07-02T14:34:22.166Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/d0/39/783980e78cb92c2d7bdb1fc7dbc86e94ccc6d58224d76a7f1f51b6c51e30/croniter-6.2.2-py3-none-any.whl", hash = "sha256:a5d17b1060974d36251ea4faf388233eca8acf0d09cbd92d35f4c4ac8f279960", size = 45422, upload-time = "2026-03-15T08:43:46.626Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/dd/6466498a8b69754cffbd7237ce4c66446ca5ffcf53fb397d437666f056d2/croniter-6.2.3-py3-none-any.whl", hash = "sha256:137a97001b4d52fb71c10b750e303db79e6e42d40fff8ff77126102176c9f786", size = 46446, upload-time = "2026-07-02T14:34:20.889Z" },
 ]
 
 [[package]]
@@ -269,52 +269,42 @@ wheels = [
 
 [[package]]
 name = "numpy"
-version = "2.4.6"
+version = "2.5.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/22/fd/89965aa4ac08c74998539fcbf24fa3540f3e15237fbeb6bcf9c908f4aade/numpy-2.5.1.tar.gz", hash = "sha256:a48a113e6afea91f5608793bafa7ef2ad481fefbda87ec5069f483de61cb9fa3", size = 20755553, upload-time = "2026-07-04T17:08:00.933Z" }
 wheels = [
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 source = { editable = "." }
 dependencies = [
     { name = "croniter" },
@@ -944,19 +939,19 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "croniter", specifier = ">=5.0.2" },
+    { name = "croniter", specifier = ">=6.2.3" },
     { name = "insightface", specifier = ">=0.7.3" },
-    { name = "numpy", specifier = ">=2.2.6" },
+    { name = "numpy", specifier = ">=2.5.1" },
     { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=12.0" },
-    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=9.0.0" },
+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=9.24.0.43" },
     { 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 = ">=1.23.2" },
-    { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=1.23.2" },
+    { 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" },
-    { name = "pillow", specifier = ">=12.1.0" },
+    { name = "opencv-python-headless", specifier = ">=5.0.0.93" },
+    { name = "pillow", specifier = ">=12.3.0" },
     { name = "python-dotenv", specifier = ">=1.2.1" },
     { name = "requests", specifier = ">=2.32.5" },
     { name = "rich", specifier = ">=14.2.0" },
@@ -965,6 +960,6 @@ provides-extras = ["gpu", "rocm", "intel", "cpu"]
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "pytest", specifier = ">=8.0" },
-    { name = "ruff", specifier = ">=0.15.17" },
+    { name = "pytest", specifier = ">=9.1.1" },
+    { name = "ruff", specifier = ">=0.15.20" },
 ]

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/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
 
 

winnow/executor.py

@@ -186,12 +186,11 @@ 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
+                                dims_map[filename] = saved
                                 # Time-spread path: compute blur score from the downloaded
                                 # image. Capped at 1440px via blur_score_from_image() so the
                                 # scale matches the preview thumbnails the embedding path uses
@@ -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)
@@ -566,13 +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)

winnow/frigate_api.py

@@ -146,8 +146,10 @@ 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

@@ -48,7 +48,9 @@ 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
-        aligned = norm_crop(img_np, lm)
+        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:
         logger.debug("InsightFace not available for face alignment")
@@ -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

@@ -134,7 +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
 
@@ -303,7 +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(
@@ -244,13 +248,13 @@ def auto_configure(people: list[dict]) -> list[dict]:
         return []
 
     strategy = os.environ.get("STRATEGY", "auto")
-    skip = [s.strip() 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()]
+    skip = {s.strip().casefold() for s in os.environ.get("SKIP_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