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>
Merge pull request #48 from sudolulo/dependabot/docker/nvidia/cuda-12.9.2-cudnn-runtime-ubuntu24.04
2026-07-09 18:55:44 +00:00 · 2026-06-26 18:14:53 -04:00 · 2026-06-26 18:14:22 -04:00 · 2026-06-25 18:55:02 +00:00 · 2026-06-25 18:53:34 +00:00 · 2026-06-20 15:06:54 -04:00
24 changed files with 384 additions and 279 deletions
@@ -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
@@ -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 }}

@@ -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
@@ -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

@@ -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

@@ -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
@@ -7,6 +7,44 @@ 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
+
+- **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
@@ -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
@@ -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; \
@@ -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)*
@@ -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
@@ -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"
@@ -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",
@@ -27,12 +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"
@@ -60,8 +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]
@@ -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())))
@@ -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
@@ -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 = [
-    { url = "https://files.pythonhosted.org/packages/fb/82/bdab26d7438c6791ca31b7c024ca37c1eab8b726ba236129005cd4a06e45/numpy-2.4.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0", size = 16684648, upload-time = "2026-05-18T23:34:29.41Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/30/a80189bcc7f5e4258b3fbc3968d909d1756f54d023299ecc39ad6fdb9ef8/numpy-2.4.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb", size = 14693902, upload-time = "2026-05-18T23:34:33.013Z" },
-    { url = "https://files.pythonhosted.org/packages/97/12/70b5d0d7c15e1ebb8a6a84a8caa1d19e181d84fb58bb6d70aca29099dec1/numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f", size = 5198992, upload-time = "2026-05-18T23:34:36.132Z" },
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 ]

 [[package]]
@@ -862,7 +900,7 @@ wheels = [

 [[package]]
 name = "winnow"
-version = "0.6.2"
+version = "0.6.6"
 source = { editable = "." }
 dependencies = [
    { name = "croniter" },
@@ -880,7 +918,10 @@ 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 = [
@@ -898,16 +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 = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'gpu'", specifier = ">=9.0.0" },
-    { 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 = "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 = ">=9.24.0.43" },
+    { 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 = ">=1.27.0" },
+    { 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" },
@@ -916,6 +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" },
 ]
@@ -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"""
-    [bold blue]winnow[/bold blue]
+        console.print(f"""
+    [bold blue]winnow[/bold blue] [dim]v{__version__}[/dim]
    [dim]Immich -> Frigate Training Data Curator[/dim]
        """)

@@ -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)
@@ -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)
@@ -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
-        largest = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
-        return largest.embedding
+        # Return embedding of the face nearest the crop centre; a large margin can pull
+        # a bigger neighbouring face into frame, and max-by-area would pick the wrong person.
+        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
@@ -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)

@@ -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:
@@ -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
@@ -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

@@ -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 any(j["person"]["id"] == p["id"] for j in jobs) else ""
+            marker = " [dim](queued)[/dim]" if p.get("id") in queued_ids 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()]
-    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