diff --git a/.env.example b/.env.example index 9aa6d33..3daa6d6 100644 --- a/.env.example +++ b/.env.example @@ -8,19 +8,16 @@ FRIGATE_URL=http://192.168.1.10:5000 # Set AUTO_MODE=true to force auto mode even in an interactive terminal. # AUTO_MODE=true # VERBOSE=true # Enable DEBUG-level console output (log file is always DEBUG) -# TRAINING_MODE: face = upload to Frigate face recognition API -# object = save crops to output dir for manual Frigate placement -TRAINING_MODE=face -# STRATEGY: auto = objective diversity (recommended), standard = 30 imgs, broad = 100 imgs -STRATEGY=auto +# STRATEGY: adaptive = embedding diversity (recommended), standard = 30 imgs, broad = 100 imgs +STRATEGY=adaptive # LIMIT=50 # Custom image count; overrides STRATEGY preset -# OBJECT_CLASS=dog # Object label for object mode (e.g. dog, cat, car) # ── People Filtering ────────────────────────────────────────────────────────── # ONLY_PEOPLE=John,Jane # Comma-separated; process only these people # SKIP_PEOPLE=Unknown # Comma-separated; skip these people -# MIN_FACE_COUNT=5 # Skip people with fewer than N assets in Immich +# MIN_FACE_COUNT=3 # Skip people with fewer than N assets in Immich (default: 3) # YEARS_FILTER=10 # Only include images from the last N years (default: 10) +# MERGE_DUPLICATE_PEOPLE=false # Merge duplicate Immich person records permanently (default: false — warn and skip) # ── Image Quality ───────────────────────────────────────────────────────────── # MIN_FACE_WIDTH=90 # Minimum face width in pixels (default: 90, guarantees ≥8,100px crop) @@ -29,22 +26,21 @@ STRATEGY=auto # 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=120.0 # Laplacian blur threshold; lower = accept more blur (default: 120.0) -# MAX_AUTO_IMAGES=80 # Hard cap on auto-diversity selection (default: 80) +# MAX_AUTO_IMAGES=20 # Hard cap on auto-diversity selection (default: 20) # QUALITY_REPLACEMENT=true # At cap, replace a weaker tracked image with a better candidate (default: true) -# FRIGATE_SCORE_CEILING=0.0 # Skip uploads already well-covered (pre-upload score > ceiling = redundant; 0 = disabled; requires at least one prior run) +# FRIGATE_SCORE_CEILING= # Below-cap novelty gate: unset = dynamic (default), 0 = disabled, e.g. 0.85 = fixed ceiling # ENABLE_FRIGATE_SCORES=true # Call Frigate's recognize endpoint pre-upload to store diversity scores (default: true; adds ~200ms per upload) # ── Caching & Models ────────────────────────────────────────────────────────── # FORCE_CPU=true # Disable GPU, fall back to CPU # ENABLE_CACHE=false # Disable embedding cache (default: true) CACHE_DIR=/app/.if_cache -HF_HOME=/models/huggingface INSIGHTFACE_HOME=/models/.insightface # ── Tracker overrides (one-shot — remove after use) ─────────────────────────── # DRY_RUN=true # Preview selection without downloading/uploading # RETRY_REJECTED=true # Re-attempt previously rejected images -# RESET_PERSON=John # Clear uploaded+rejected history for one person +# RESET_PERSON=John # Clear uploaded+rejected history for one person (use * for all) # ── Scheduling ──────────────────────────────────────────────────────────────── # CRON_SCHEDULE controls container lifetime: diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index e910bb9..d86d266 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -22,7 +22,7 @@ jobs: run: uv python install 3.13 - name: Install dependencies - run: uv sync --all-extras + run: uv sync --extra cpu - name: Run tests run: uv run pytest diff --git a/.github/workflows/update-lockfile.yml b/.github/workflows/update-lockfile.yml index b677152..65c7987 100644 --- a/.github/workflows/update-lockfile.yml +++ b/.github/workflows/update-lockfile.yml @@ -1,5 +1,5 @@ # .github/workflows/update-lockfile.yml -name: Update lockfiles +name: Update lockfile on: push: @@ -7,9 +7,6 @@ on: - '**' paths: - 'pyproject.toml' - - 'pyproject-cpu.toml' - - 'pyproject-rocm.toml' - - 'pyproject-intel.toml' workflow_dispatch: jobs: @@ -18,14 +15,6 @@ jobs: permissions: contents: write steps: - - name: Free up disk space - run: | - sudo rm -rf /usr/share/dotnet - sudo rm -rf /opt/ghc - sudo rm -rf "/usr/local/share/boost" - sudo rm -rf "$AGENT_TOOLSDIRECTORY" - echo "Disk space freed." - - name: Checkout repository uses: actions/checkout@v6 @@ -35,32 +24,23 @@ jobs: - name: Set up Python run: uv python install 3.13 - - name: Regenerate all lockfiles - run: | - cp pyproject.toml _pyproject_orig.toml - for variant in cpu rocm intel; do - cp pyproject-${variant}.toml pyproject.toml - uv lock - cp uv.lock uv-${variant}.lock - done - cp _pyproject_orig.toml pyproject.toml - uv lock - rm _pyproject_orig.toml + - name: Regenerate lockfile + run: uv lock - name: Check for changes id: diff run: | - if git diff --quiet uv.lock uv-cpu.lock uv-rocm.lock uv-intel.lock; then + if git diff --quiet uv.lock; then echo "changed=false" >> "$GITHUB_OUTPUT" else echo "changed=true" >> "$GITHUB_OUTPUT" fi - - name: Commit and push updated lockfiles + - 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 uv-cpu.lock uv-rocm.lock uv-intel.lock - git commit -m "chore: update lockfiles" + git add uv.lock + git commit -m "chore: update lockfile" git push diff --git a/CHANGELOG.md b/CHANGELOG.md index f9b8aa5..7f9687c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,44 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.5.0] - 2026-06-14 + +### Changed + +- **SQLite upload tracker**: `upload_tracker.py` is fully rewritten on top of SQLite (stdlib `sqlite3`). The JSON pair (`frigate_uploaded_ids.json` / `frigate_rejected_ids.json`) is replaced by a single `winnow_tracker.db` (WAL journal, `check_same_thread=False`). Existing JSON files are migrated atomically on first run and renamed to `.json.bak`. No user action required; the tracker API (`mark_uploaded`, `mark_rejected`, `filter_already_uploaded`, `get_person_summary`, etc.) is unchanged. + +- **Config lazy singleton**: `_Config` now uses `__getattr__` to defer all I/O until the first attribute access. `load_dotenv()` no longer runs at module import time — it runs on the first access to any `Config` attribute. Empty-string env vars (`IMMICH_URL=`, `OUTPUT_DIR=`) are now correctly distinguished from unset ones so a `.env` file value never silently overrides an explicit `""` set in the environment. `Config.reset()` clears the loaded state for clean test isolation. + +- **Reconcile module extracted**: `reconcile_frigate_mappings` and `enrich_asset_with_face_data` are extracted from `executor.py` into a new `winnow/reconcile.py` module. No behaviour change; reduces `executor.py` length and clarifies responsibility boundaries. + +- **Single lockfile**: `pyproject-gpu.toml`, `pyproject-cpu.toml`, `pyproject-rocm.toml`, `pyproject-intel.toml` and their separate lockfiles are removed. GPU/ROCm/Intel/CPU variant deps are now declared as `[project.optional-dependencies]` extras in `pyproject.toml` with `[tool.uv] conflicts` for mutual exclusion. A single `uv.lock` covers all variants. The Dockerfile selects the correct extra via `uv sync --extra $VARIANT`. + +- **Ubuntu base bumped**: amd64 GPU base updated from `nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04` to `nvidia/cuda:12.8.1-cudnn-runtime-ubuntu24.04`. amd64 ROCm and CPU bases updated from Ubuntu 22.04 to Ubuntu 26.04. arm64 bases remain Ubuntu 24.04. + +### Fixed + +- **Frigate API unreachable at upload start no longer crashes reconciliation**: when the Frigate `GET /api/faces` call fails at upload start, reconciliation is now skipped entirely for that batch (`_skip_reconcile = True`). Previously, falling back to the tracker's known filenames as the pre-upload baseline caused the `> target` guard to fire on unmapped manual files, silently dropping all mappings. + +- **Polling `== target` guards against wrong-file mapping**: the reconcile poll loop now breaks on `len(new_files) == target` and sets an "external upload detected" flag when `> target`. The old `>= target` break would have proceeded with an incorrect file set when a concurrent external upload was present, causing wrong asset-ID mappings. The poll loop now also exits early on `> target` rather than exhausting all four retry intervals (up to 15 s wasted per person with a concurrent external uploader). + +- **`auto_cap` post-selection truncation removed**: the diversity selector now receives the correct upper bound (`capacity` or `min(limit, capacity)`) directly instead of selecting up to `MAX_AUTO_IMAGES` and then silently truncating the result list. The old approach produced a selection biased toward the first `capacity` items in embedding space rather than the globally optimal diverse subset. + +- **Dockerfile unknown VARIANT now fails loudly**: added an explicit `elif [ "$VARIANT" = "gpu" ]` branch and an `else … exit 1` for unrecognised values. Previously, any unknown variant silently fell through to the `cpu` branch. + +- **JSON migration partial-rename data loss**: if the rename of one of the two JSON files failed (e.g. a `PermissionError`), the other file's data was committed to SQLite but the `COUNT(*) > 0` guard on the next run would skip re-migration of the remaining file, permanently losing its data. The guard is removed (idempotent `INSERT OR IGNORE` makes re-running safe). Each rename is now wrapped in its own `try/except OSError` so a failure on one file is logged and does not prevent the other from completing. + +- **SQL column allowlist in `_pick_mapped_file`**: the `score_col` f-string interpolation into SQL is now guarded by a `frozenset` allowlist at the function boundary, raising `ValueError` on any value outside `{"blur_score", "frigate_score"}`. + +- **`load_dotenv` no longer runs at import time**: moving `load_dotenv()` to the first line of `_load()` prevents side-effects during module import (which could interfere with test environment setup) and makes the load order deterministic relative to `os.environ` overrides. + +- **Empty-string env var priority fix**: `if self.IMMICH_URL or …` treated `IMMICH_URL=""` as falsy and silently fell through to the config file. Changed to `if self.IMMICH_URL is None` so an empty-string explicit env var is respected. + +### Added + +- **Diversity test suite expanded** (PR #11): 33 new tests covering k-medoids clustering, farthest-point sampling, adaptive threshold computation, near-duplicate deduplication, and time-spread selection. Total: 93 tests (was 60). + +- **Known-limitation annotations** (PR #12): `TODO(frigate-api)` comments placed at each FIFO-ordering assumption, manual-file-invisibility note, and async-rebuild limitation in `executor.py` and `reconcile.py`. These mark spots where a richer Frigate API would allow a deeper fix. + ## [0.4.11] - 2026-06-14 ### Removed diff --git a/Dockerfile b/Dockerfile index c890d21..9e8c2e9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,16 +1,17 @@ # ── Base images ─────────────────────────────────────────────────────────────── -# amd64 + gpu: NVIDIA CUDA 12.8 + cuDNN (GPU acceleration via NVIDIA Container Toolkit) -# amd64 + rocm: Ubuntu 22.04 (AMD GPU via ROCm — pass /dev/kfd and /dev/dri) +# amd64 + gpu: NVIDIA CUDA 12.8 + cuDNN on Ubuntu 24.04 (highest Ubuntu NVIDIA publishes) +# amd64 + rocm: Ubuntu 26.04 (AMD GPU via ROCm — pass /dev/kfd and /dev/dri) # amd64 + intel: Ubuntu 22.04 (Intel Arc / iGPU via OpenVINO — pass /dev/dri) -# amd64 + cpu: Ubuntu 22.04 (CPU-only, ~2 GB smaller image) +# Note: intel stays on 22.04 — Intel's GPU repo only publishes for jammy +# amd64 + cpu: Ubuntu 26.04 (CPU-only, ~2 GB smaller image) # arm64: Ubuntu 24.04 (CPU-only; no CUDA/ROCm wheels on ARM) ARG VARIANT=gpu -FROM --platform=$BUILDPLATFORM nvidia/cuda:12.8.1-cudnn-runtime-ubuntu22.04 AS base-amd64-gpu -FROM ubuntu:22.04 AS base-amd64-rocm +FROM --platform=$BUILDPLATFORM nvidia/cuda:12.8.1-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:22.04 AS base-amd64-cpu +FROM ubuntu:26.04 AS base-amd64-cpu FROM ubuntu:24.04 AS base-arm64-gpu FROM ubuntu:24.04 AS base-arm64-rocm FROM ubuntu:24.04 AS base-arm64-intel @@ -24,8 +25,9 @@ FROM base-${TARGETARCH}-${VARIANT} AS build ARG VARIANT=gpu ENV DEBIAN_FRONTEND=noninteractive -# Both Ubuntu 22.04 and 24.04 get Python 3.13 from the deadsnakes PPA. -# GNUPGHOME is isolated so gpg never contacts an agent socket under QEMU. +# All base images get Python 3.13 from the deadsnakes PPA (26.04 ships 3.14 natively; +# 3.13 is used to keep dependencies tested and aligned). GNUPGHOME is isolated +# so gpg never contacts an agent socket under QEMU. RUN apt-get update && apt-get install -y --no-install-recommends \ ca-certificates curl gnupg software-properties-common \ && GNUPGHOME=$(mktemp -d) add-apt-repository ppa:deadsnakes/ppa -y \ @@ -41,18 +43,20 @@ RUN curl -LsSf https://astral.sh/uv/install.sh | sh \ WORKDIR /app -# Swap in the variant-specific pyproject and lockfile before syncing. -COPY pyproject.toml uv.lock pyproject-cpu.toml uv-cpu.lock \ - pyproject-rocm.toml uv-rocm.lock pyproject-intel.toml uv-intel.lock ./ +COPY pyproject.toml uv.lock ./ RUN if [ "$VARIANT" = "cpu" ]; then \ - cp pyproject-cpu.toml pyproject.toml && cp uv-cpu.lock uv.lock; \ + uv sync --frozen --no-dev --extra cpu; \ elif [ "$VARIANT" = "rocm" ]; then \ - cp pyproject-rocm.toml pyproject.toml && cp uv-rocm.lock uv.lock; \ + uv sync --frozen --no-dev --extra rocm; \ elif [ "$VARIANT" = "intel" ]; then \ - cp pyproject-intel.toml pyproject.toml && cp uv-intel.lock uv.lock; \ + uv sync --frozen --no-dev --extra intel; \ + elif [ "$VARIANT" = "gpu" ]; then \ + uv sync --frozen --no-dev --extra gpu; \ + else \ + echo "Unknown VARIANT: '$VARIANT'. Must be one of: cpu, rocm, intel, gpu" >&2; \ + exit 1; \ fi && \ - uv sync --frozen --no-dev \ - && uv cache clean + uv cache clean COPY winnow/ winnow/ COPY entrypoint.sh scheduler.py ./ diff --git a/README.md b/README.md index ab33214..2009d52 100644 --- a/README.md +++ b/README.md @@ -79,8 +79,6 @@ Uploaded and rejected asset IDs are persisted across runs. The same image is nev --- ---- - ## Running in Docker ### Image Tags diff --git a/pyproject-cpu.toml b/pyproject-cpu.toml deleted file mode 100644 index 4b2ff89..0000000 --- a/pyproject-cpu.toml +++ /dev/null @@ -1,74 +0,0 @@ -[project] -name = "winnow" -version = "0.4.11" -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" -authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }] -keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", - "Programming Language :: Python :: 3.13", - "Topic :: Scientific/Engineering :: Image Recognition", -] -dependencies = [ - "croniter>=5.0.2", - "insightface>=0.7.3", - "numpy>=2.2.6", - "onnxruntime>=1.23.2", - "opencv-python-headless>=4.12.0.88", - "pillow>=12.1.0", - "python-dotenv>=1.2.1", - "requests>=2.32.5", - "rich>=14.2.0", -] - -[project.scripts] -winnow = "winnow.cli:main" - -[project.urls] -Repository = "https://github.com/sudolulo/winnow" -Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md" - -[tool.uv] -required-environments = [ - "sys_platform == 'linux' and platform_machine == 'x86_64'", -] - -[dependency-groups] -dev = [ - "pytest>=8.0", - "ruff>=0.15.17", -] - -[tool.hatch.build.targets.wheel] -packages = ["winnow"] - -[tool.ruff] -line-length = 120 -target-version = "py313" - -[tool.ruff.lint] -select = ["E", "F", "I"] - -[tool.deptry] -pep621_dev_dependency_groups = ["dev"] - -[tool.deptry.package_module_name_map] -pillow = "PIL" -opencv-python-headless = "cv2" -python-dotenv = "dotenv" -insightface = "insightface" -numpy = "numpy" -onnxruntime = "onnxruntime" -requests = "requests" -rich = "rich" - -[tool.pytest.ini_options] -testpaths = ["tests"] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/pyproject-intel.toml b/pyproject-intel.toml deleted file mode 100644 index 0b637ba..0000000 --- a/pyproject-intel.toml +++ /dev/null @@ -1,84 +0,0 @@ -[project] -name = "winnow" -version = "0.4.11" -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" -authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }] -keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", - "Programming Language :: Python :: 3.13", - "Topic :: Scientific/Engineering :: Image Recognition", -] -dependencies = [ - "croniter>=5.0.2", - "insightface>=0.7.3", - "numpy>=2.2.6", - "onnxruntime-openvino>=1.20.0", - "opencv-python-headless>=4.12.0.88", - "pillow>=12.1.0", - "python-dotenv>=1.2.1", - "requests>=2.32.5", - "rich>=14.2.0", -] - -[project.scripts] -winnow = "winnow.cli:main" - -[project.urls] -Repository = "https://github.com/sudolulo/winnow" -Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md" - -[tool.uv] -conflicts = [ - [ - { package = "onnxruntime" }, - { package = "onnxruntime-gpu" }, - { package = "onnxruntime-openvino" }, - ], -] -required-environments = [ - "sys_platform == 'linux' and platform_machine == 'x86_64'", -] - -[dependency-groups] -dev = [ - "pytest>=8.0", - "ruff>=0.15.17", -] - -[tool.hatch.build.targets.wheel] -packages = ["winnow"] - -[tool.ruff] -line-length = 120 -target-version = "py313" - -[tool.ruff.lint] -select = ["E", "F", "I"] - -[tool.deptry] -pep621_dev_dependency_groups = ["dev"] - -[tool.deptry.package_module_name_map] -pillow = "PIL" -opencv-python-headless = "cv2" -python-dotenv = "dotenv" -insightface = "insightface" -numpy = "numpy" -onnxruntime-openvino = "onnxruntime" -requests = "requests" -rich = "rich" - -[tool.deptry.per_rule_ignores] -DEP002 = ["onnxruntime-openvino"] - -[tool.pytest.ini_options] -testpaths = ["tests"] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/pyproject-rocm.toml b/pyproject-rocm.toml deleted file mode 100644 index 3053179..0000000 --- a/pyproject-rocm.toml +++ /dev/null @@ -1,85 +0,0 @@ -[project] -name = "winnow" -version = "0.4.11" -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" -authors = [{ name = "Holden Salomon", email = "holden@arch.fyi" }] -keywords = ["immich", "frigate", "face-recognition", "training-data", "arcface", "insightface"] -classifiers = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Developers", - "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", - "Programming Language :: Python :: 3.13", - "Topic :: Scientific/Engineering :: Image Recognition", -] -dependencies = [ - "croniter>=5.0.2", - "insightface>=0.7.3", - "numpy>=2.2.6", - "onnxruntime-rocm>=1.16.0", - "opencv-python-headless>=4.12.0.88", - "pillow>=12.1.0", - "python-dotenv>=1.2.1", - "requests>=2.32.5", - "rich>=14.2.0", -] - -[project.scripts] -winnow = "winnow.cli:main" - -[project.urls] -Repository = "https://github.com/sudolulo/winnow" -Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md" - -[tool.uv] -index-strategy = "unsafe-best-match" -conflicts = [ - [ - { package = "onnxruntime" }, - { package = "onnxruntime-gpu" }, - { package = "onnxruntime-rocm" }, - ], -] -required-environments = [ - "sys_platform == 'linux' and platform_machine == 'x86_64'", -] - -[dependency-groups] -dev = [ - "pytest>=8.0", - "ruff>=0.15.17", -] - -[tool.hatch.build.targets.wheel] -packages = ["winnow"] - -[tool.ruff] -line-length = 120 -target-version = "py313" - -[tool.ruff.lint] -select = ["E", "F", "I"] - -[tool.deptry] -pep621_dev_dependency_groups = ["dev"] - -[tool.deptry.package_module_name_map] -pillow = "PIL" -opencv-python-headless = "cv2" -python-dotenv = "dotenv" -insightface = "insightface" -numpy = "numpy" -onnxruntime-rocm = "onnxruntime" -requests = "requests" -rich = "rich" - -[tool.deptry.per_rule_ignores] -DEP002 = ["onnxruntime-rocm"] - -[tool.pytest.ini_options] -testpaths = ["tests"] - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" diff --git a/pyproject.toml b/pyproject.toml index 750e5cd..5ccbf97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "winnow" -version = "0.4.11" +version = "0.5.0" 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" @@ -17,11 +17,7 @@ classifiers = [ dependencies = [ "croniter>=5.0.2", "insightface>=0.7.3", - "nvidia-cudnn-cu12>=9.0.0", "numpy>=2.2.6", - "onnxruntime-gpu>=1.23.2; sys_platform == 'linux' and platform_machine == 'x86_64'", - "onnxruntime>=1.23.2; sys_platform == 'linux' and platform_machine != 'x86_64'", - "onnxruntime>=1.23.2; sys_platform != 'linux'", "opencv-python-headless>=4.12.0.88", "pillow>=12.1.0", "python-dotenv>=1.2.1", @@ -29,6 +25,15 @@ dependencies = [ "rich>=14.2.0", ] +[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'", +] +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"] + [project.scripts] winnow = "winnow.cli:main" @@ -38,10 +43,13 @@ Changelog = "https://github.com/sudolulo/winnow/blob/main/CHANGELOG.md" Documentation = "https://github.com/sudolulo/winnow/wiki" [tool.uv] +index-strategy = "unsafe-best-match" conflicts = [ [ - { package = "onnxruntime" }, - { package = "onnxruntime-gpu" }, + { extra = "gpu" }, + { extra = "rocm" }, + { extra = "intel" }, + { extra = "cpu" }, ], ] required-environments = [ @@ -77,11 +85,14 @@ insightface = "insightface" numpy = "numpy" nvidia-cudnn-cu12 = "nvidia.cudnn" onnxruntime-gpu = "onnxruntime" +onnxruntime-rocm = "onnxruntime" +onnxruntime-openvino = "onnxruntime" +onnxruntime = "onnxruntime" requests = "requests" rich = "rich" [tool.deptry.per_rule_ignores] -DEP002 = ["onnxruntime-gpu", "nvidia-cudnn-cu12"] +DEP002 = ["onnxruntime-gpu", "nvidia-cudnn-cu12", "onnxruntime-rocm", "onnxruntime-openvino", "onnxruntime"] [tool.pytest.ini_options] testpaths = ["tests"] @@ -89,4 +100,3 @@ testpaths = ["tests"] [build-system] requires = ["hatchling"] build-backend = "hatchling.build" - diff --git a/scheduler.py b/scheduler.py index 0f7bb25..931fafb 100644 --- a/scheduler.py +++ b/scheduler.py @@ -30,6 +30,7 @@ def check_models() -> None: NOW = time.time() cron = croniter(SCHEDULE, NOW) next_run = cron.get_next(float) +print(f"Next run: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(next_run))}", flush=True) while True: now = time.time() @@ -42,7 +43,8 @@ while True: except KeyboardInterrupt: raise except Exception as e: - logger.error(f"winnow run failed: {e}", exc_info=True) + logger.error("winnow run failed: %s", e, exc_info=True) print(f"winnow run failed: {e}", flush=True) 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(max(1, next_run - time.time())) diff --git a/tests/test_diversity.py b/tests/test_diversity.py new file mode 100644 index 0000000..6a72824 --- /dev/null +++ b/tests/test_diversity.py @@ -0,0 +1,336 @@ +"""Tests for core diversity selection algorithms (pure functions, no network).""" + +import numpy as np +import pytest +from PIL import Image + + +def _unit_embeddings(n: int, d: int = 512, seed: int = 42) -> list: + """Return n normalised random embeddings — well-separated in high-dim space.""" + rng = np.random.default_rng(seed) + embs = rng.standard_normal((n, d)).astype(np.float32) + embs /= np.linalg.norm(embs, axis=1, keepdims=True) + return list(embs) + + +def _asset_with_face( + asset_id="a1", person_id="p1", + x1=10, y1=10, x2=60, y2=60, + img_w=100, img_h=100, score=0.9, +): + return { + "id": asset_id, + "people": [{ + "id": person_id, + "faces": [{ + "boundingBoxX1": x1, "boundingBoxY1": y1, + "boundingBoxX2": x2, "boundingBoxY2": y2, + "imageWidth": img_w, "imageHeight": img_h, + "score": score, + }], + }], + } + + +# ── _get_face_bbox ───────────────────────────────────────────────────────────── + +def test_get_face_bbox_returns_coords(): + from winnow.diversity import _get_face_bbox + asset = _asset_with_face(x1=5, y1=10, x2=55, y2=70) + assert _get_face_bbox(asset) == (5, 10, 55, 70) + + +def test_get_face_bbox_filters_by_person_id(): + from winnow.diversity import _get_face_bbox + asset = _asset_with_face(person_id="p1") + assert _get_face_bbox(asset, person_id="p999") is None + + +def test_get_face_bbox_returns_none_empty_faces(): + from winnow.diversity import _get_face_bbox + asset = {"id": "a1", "people": [{"id": "p1", "faces": []}]} + assert _get_face_bbox(asset) is None + + +def test_get_face_bbox_returns_none_no_people(): + from winnow.diversity import _get_face_bbox + assert _get_face_bbox({"id": "a1"}) is None + + +# ── _get_face_confidence ─────────────────────────────────────────────────────── + +def test_get_face_confidence_returns_score(): + from winnow.diversity import _get_face_confidence + asset = _asset_with_face(score=0.92) + assert _get_face_confidence(asset) == pytest.approx(0.92) + + +def test_get_face_confidence_filters_by_person_id(): + from winnow.diversity import _get_face_confidence + asset = _asset_with_face(person_id="p1", score=0.9) + assert _get_face_confidence(asset, person_id="p999") is None + + +def test_get_face_confidence_returns_none_empty_faces(): + from winnow.diversity import _get_face_confidence + asset = {"id": "a1", "people": [{"id": "p1", "faces": []}]} + assert _get_face_confidence(asset) is None + + +# ── _crop_face_from_thumbnail ────────────────────────────────────────────────── + +def test_crop_face_returns_image(): + from winnow.diversity import _crop_face_from_thumbnail + img = Image.new("RGB", (200, 200), color=(128, 64, 32)) + asset = _asset_with_face(x1=50, y1=50, x2=150, y2=150, img_w=200, img_h=200) + crop = _crop_face_from_thumbnail(img, asset) + assert crop is not None + assert crop.width > 0 and crop.height > 0 + + +def test_crop_face_scales_bbox_to_thumbnail(): + """When thumbnail is half the metadata dimensions, bbox is scaled accordingly.""" + from winnow.diversity import _crop_face_from_thumbnail + img = Image.new("RGB", (200, 200)) + # Metadata says 400×400; bbox covers the centre quarter + asset = _asset_with_face(x1=100, y1=100, x2=300, y2=300, img_w=400, img_h=400) + crop = _crop_face_from_thumbnail(img, asset) + assert crop is not None + assert crop.width <= 200 and crop.height <= 200 + + +def test_crop_face_returns_none_no_metadata(): + from winnow.diversity import _crop_face_from_thumbnail + img = Image.new("RGB", (100, 100)) + assert _crop_face_from_thumbnail(img, {"id": "a1"}) is None + + +def test_crop_face_returns_none_for_sub_30px_bbox(): + """A 1×1 bbox produces a crop too small to embed — should be rejected.""" + from winnow.diversity import _crop_face_from_thumbnail + img = Image.new("RGB", (100, 100)) + asset = _asset_with_face(x1=50, y1=50, x2=51, y2=51, img_w=100, img_h=100) + assert _crop_face_from_thumbnail(img, asset) is None + + +def test_crop_face_respects_person_id_filter(): + from winnow.diversity import _crop_face_from_thumbnail + img = Image.new("RGB", (200, 200)) + asset = _asset_with_face(person_id="p1", x1=50, y1=50, x2=150, y2=150) + assert _crop_face_from_thumbnail(img, asset, person_id="p999") is None + + +# ── _dedup_embeddings ────────────────────────────────────────────────────────── + +def test_dedup_keeps_all_diverse_embeddings(): + from winnow.diversity import _dedup_embeddings + embs = _unit_embeddings(20) + candidates = [{"id": str(i)} for i in range(20)] + _, out_cands, _ = _dedup_embeddings(embs, candidates, [None] * 20) + # Random 512-dim unit vectors are far apart — all should survive + assert len(out_cands) == 20 + + +def test_dedup_removes_near_duplicate(): + from winnow.diversity import _dedup_embeddings + base = np.zeros(512, dtype=np.float32) + base[0] = 1.0 + # Cosine distance ≈ 0.01 — well within the 0.20 dedup threshold + near_dup = base.copy() + near_dup[1] = 0.014 + near_dup /= np.linalg.norm(near_dup) + + embs = [base, near_dup] + candidates = [{"id": "base", "quality_score": 0.9}, {"id": "dup", "quality_score": 0.5}] + _, out_cands, _ = _dedup_embeddings(embs, candidates, [None, None]) + assert len(out_cands) == 1 + assert out_cands[0]["id"] == "base" + + +def test_dedup_keeps_higher_quality_from_duplicate_pair(): + from winnow.diversity import _dedup_embeddings + base = np.zeros(512, dtype=np.float32) + base[0] = 1.0 + near_dup = base.copy() + near_dup[1] = 0.014 + near_dup /= np.linalg.norm(near_dup) + + # Reversed quality: near_dup is sharper + embs = [base, near_dup] + candidates = [{"id": "base", "quality_score": 0.3}, {"id": "dup", "quality_score": 0.95}] + _, out_cands, _ = _dedup_embeddings(embs, candidates, [None, None]) + assert len(out_cands) == 1 + assert out_cands[0]["id"] == "dup" + + +def test_dedup_single_embedding_passes_through(): + from winnow.diversity import _dedup_embeddings + embs = _unit_embeddings(1) + out_embs, out_cands, _ = _dedup_embeddings(embs, [{"id": "only"}], [None]) + assert len(out_cands) == 1 + + +def test_dedup_treats_zero_quality_score_as_zero_not_missing(): + """quality_score=0.0 is a valid score — should not be treated as absent.""" + from winnow.diversity import _dedup_embeddings + base = np.zeros(512, dtype=np.float32) + base[0] = 1.0 + near_dup = base.copy() + near_dup[1] = 0.014 + near_dup /= np.linalg.norm(near_dup) + + embs = [base, near_dup] + # base has explicit 0.0; near_dup has 0.5 — near_dup should win + candidates = [{"id": "base", "quality_score": 0.0}, {"id": "dup", "quality_score": 0.5}] + _, out_cands, _ = _dedup_embeddings(embs, candidates, [None, None]) + assert out_cands[0]["id"] == "dup" + + +# ── _kmedoids ────────────────────────────────────────────────────────────────── + +def _dist_matrix(embs): + m = np.vstack(embs) + m /= np.linalg.norm(m, axis=1, keepdims=True) + return 1 - m @ m.T + + +def test_kmedoids_returns_k_distinct_medoids(): + from winnow.diversity import _kmedoids + dist = _dist_matrix(_unit_embeddings(30)) + medoids, _ = _kmedoids(dist, k=5) + assert len(medoids) == 5 + assert len(set(medoids)) == 5 + + +def test_kmedoids_labels_cover_all_points(): + from winnow.diversity import _kmedoids + dist = _dist_matrix(_unit_embeddings(20)) + medoids, labels = _kmedoids(dist, k=4) + assert len(labels) == 20 + assert set(labels).issubset(set(range(4))) + + +def test_kmedoids_medoids_are_valid_indices(): + from winnow.diversity import _kmedoids + n = 15 + dist = _dist_matrix(_unit_embeddings(n)) + medoids, _ = _kmedoids(dist, k=3) + assert all(0 <= m < n for m in medoids) + + +def test_kmedoids_k_equals_n_selects_all(): + from winnow.diversity import _kmedoids + n = 5 + dist = _dist_matrix(_unit_embeddings(n)) + medoids, _ = _kmedoids(dist, k=n) + assert len(medoids) == n + + +# ── _compute_adaptive_threshold ──────────────────────────────────────────────── + +def test_adaptive_threshold_positive(): + from winnow.diversity import _compute_adaptive_threshold + embs = np.array(_unit_embeddings(50)) + assert _compute_adaptive_threshold(embs) > 0 + + +def test_adaptive_threshold_floor_for_identical_embeddings(): + """All-identical embeddings → median pairwise distance = 0 → floor at 0.05.""" + from winnow.diversity import _compute_adaptive_threshold + base = np.zeros((10, 512), dtype=np.float32) + base[:, 0] = 1.0 + assert _compute_adaptive_threshold(base) == pytest.approx(0.05) + + +def test_adaptive_threshold_single_point_returns_floor(): + from winnow.diversity import _compute_adaptive_threshold + single = np.ones((1, 512), dtype=np.float32) + single /= np.linalg.norm(single) + assert _compute_adaptive_threshold(single) == pytest.approx(0.05) + + +def test_adaptive_threshold_scales_with_spread(): + """A more spread-out embedding set should produce a higher threshold.""" + from winnow.diversity import _compute_adaptive_threshold + tight = np.array(_unit_embeddings(30, seed=0)) * 0.001 + np.array([1.0] + [0.0] * 511) + tight /= np.linalg.norm(tight, axis=1, keepdims=True) + diverse = np.array(_unit_embeddings(30, seed=1)) + assert _compute_adaptive_threshold(diverse) > _compute_adaptive_threshold(tight) + + +# ── _select_time_spread ──────────────────────────────────────────────────────── + +def test_time_spread_returns_exact_n(): + from winnow.diversity import _select_time_spread + assets = [{"id": str(i)} for i in range(100)] + assert len(_select_time_spread(assets, limit=10)) == 10 + + +def test_time_spread_returns_all_when_under_limit(): + from winnow.diversity import _select_time_spread + assets = [{"id": str(i)} for i in range(5)] + assert len(_select_time_spread(assets, limit=20)) == 5 + + +def test_time_spread_auto_defaults_to_30(): + from winnow.diversity import _select_time_spread + assets = [{"id": str(i)} for i in range(200)] + assert len(_select_time_spread(assets, limit="auto")) == 30 + + +def test_time_spread_includes_first_and_last(): + from winnow.diversity import _select_time_spread + assets = [{"id": str(i)} for i in range(100)] + result = _select_time_spread(assets, limit=5) + ids = [int(a["id"]) for a in result] + assert ids[0] == 0 + assert ids[-1] == 99 + + +# ── _cluster_aware_selection ─────────────────────────────────────────────────── + +def test_cluster_selection_returns_exact_limit(monkeypatch): + from winnow.diversity import _cluster_aware_selection + monkeypatch.setattr("winnow.diversity.Config.MAX_AUTO_IMAGES", 20) + embs = _unit_embeddings(50) + candidates = [{"id": str(i)} for i in range(50)] + result = _cluster_aware_selection(embs, candidates, limit=10) + assert len(result) == 10 + + +def test_cluster_selection_output_is_subset_of_input(monkeypatch): + from winnow.diversity import _cluster_aware_selection + monkeypatch.setattr("winnow.diversity.Config.MAX_AUTO_IMAGES", 20) + embs = _unit_embeddings(30) + candidates = [{"id": str(i)} for i in range(30)] + result = _cluster_aware_selection(embs, candidates, limit=10) + result_ids = {a["id"] for a in result} + assert result_ids.issubset({a["id"] for a in candidates}) + + +def test_cluster_selection_auto_stops_early_on_tight_cluster(monkeypatch): + """When all embeddings are nearly identical auto mode should stop early.""" + from winnow.diversity import _cluster_aware_selection + monkeypatch.setattr("winnow.diversity.Config.MAX_AUTO_IMAGES", 20) + rng = np.random.default_rng(0) + base = np.zeros(512, dtype=np.float32) + base[0] = 1.0 + embs = [] + for _ in range(50): + v = base + rng.standard_normal(512).astype(np.float32) * 0.001 + v /= np.linalg.norm(v) + embs.append(v) + candidates = [{"id": str(i)} for i in range(50)] + result = _cluster_aware_selection(list(embs), candidates, limit="auto") + assert len(result) < 20 + + +def test_cluster_selection_hard_example_weighting_accepted(monkeypatch): + """Confidence scores are accepted without error.""" + from winnow.diversity import _cluster_aware_selection + monkeypatch.setattr("winnow.diversity.Config.MAX_AUTO_IMAGES", 20) + embs = _unit_embeddings(20) + candidates = [{"id": str(i)} for i in range(20)] + conf = [0.7 if i % 2 == 0 else 0.95 for i in range(20)] + result = _cluster_aware_selection(embs, candidates, limit=5, confidence_scores=conf) + assert len(result) == 5 diff --git a/tests/test_upload_tracker.py b/tests/test_upload_tracker.py index f5afb9c..7b57ae4 100644 --- a/tests/test_upload_tracker.py +++ b/tests/test_upload_tracker.py @@ -10,7 +10,19 @@ def isolated_cache(monkeypatch, tmp_path): monkeypatch.setenv("CACHE_DIR", str(tmp_path)) from winnow.config import _Config _Config.reset() + # Also reset the SQLite connection so the next call opens the new path + import winnow.upload_tracker as ut + ut._conn = None + ut._conn_path = None yield tmp_path + # Teardown + if ut._conn is not None: + try: + ut._conn.close() + except Exception: + pass + ut._conn = None + ut._conn_path = None _Config.reset() @@ -73,12 +85,12 @@ def test_duplicate_marks_are_idempotent(): # ── frigate_files mapping ───────────────────────────────────────────────────── -def test_record_and_remove_frigate_file(): - from winnow.upload_tracker import get_person_summary, record_frigate_file, remove_frigate_file - record_frigate_file("Alice", "Alice-1000.webp", "asset-a1") +def test_record_and_remove_frigate_files_batch(): + from winnow.upload_tracker import get_person_summary, record_frigate_files_batch, remove_frigate_file + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a1"}) assert "Alice-1000.webp" in get_person_summary()["Alice"]["frigate_files"] remove_frigate_file("Alice", "Alice-1000.webp") - assert "Alice-1000.webp" not in get_person_summary()["Alice"]["frigate_files"] + assert "Alice-1000.webp" not in get_person_summary().get("Alice", {}).get("frigate_files", {}) def test_remove_nonexistent_frigate_file_is_safe(): @@ -92,11 +104,11 @@ def test_remove_frigate_file_does_not_unmark_asset(): from winnow.upload_tracker import ( filter_already_uploaded, mark_uploaded, - record_frigate_file, + record_frigate_files_batch, remove_frigate_file, ) mark_uploaded("asset-a1", person_name="Alice") - record_frigate_file("Alice", "Alice-1000.webp", "asset-a1") + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a1"}) remove_frigate_file("Alice", "Alice-1000.webp") # Asset must still be excluded — it was deliberately replaced, not lost assert filter_already_uploaded(["asset-a1"]) == [] @@ -108,14 +120,14 @@ def test_get_tracked_frigate_file_count_zero_when_empty(): def test_get_tracked_frigate_file_count_counts_only_mapped(): - """Only files explicitly recorded via record_frigate_file count toward the cap.""" - from winnow.upload_tracker import get_tracked_frigate_file_count, mark_uploaded, record_frigate_file + """Only files explicitly recorded via record_frigate_files_batch count toward the cap.""" + from winnow.upload_tracker import get_tracked_frigate_file_count, mark_uploaded, record_frigate_files_batch mark_uploaded("asset-a", person_name="Alice") mark_uploaded("asset-b", person_name="Alice") - record_frigate_file("Alice", "Alice-1000.webp", "asset-a") + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a"}) # asset-b is uploaded but not yet mapped — does not count assert get_tracked_frigate_file_count("Alice") == 1 - record_frigate_file("Alice", "Alice-1001.webp", "asset-b") + record_frigate_files_batch("Alice", {"Alice-1001.webp": "asset-b"}) assert get_tracked_frigate_file_count("Alice") == 2 @@ -128,12 +140,11 @@ def test_get_lowest_quality_mapped_file_returns_lowest(): from winnow.upload_tracker import ( get_lowest_quality_mapped_file, mark_uploaded, - record_frigate_file, + record_frigate_files_batch, ) mark_uploaded("asset-hi", person_name="Alice", score=0.95) mark_uploaded("asset-lo", person_name="Alice", score=0.71) - record_frigate_file("Alice", "Alice-1000.webp", "asset-hi") - record_frigate_file("Alice", "Alice-1001.webp", "asset-lo") + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-hi", "Alice-1001.webp": "asset-lo"}) result = get_lowest_quality_mapped_file("Alice") assert result is not None frigate_filename, asset_id, score = result @@ -147,12 +158,11 @@ def test_get_lowest_quality_mapped_file_skips_unscored(): from winnow.upload_tracker import ( get_lowest_quality_mapped_file, mark_uploaded, - record_frigate_file, + record_frigate_files_batch, ) mark_uploaded("asset-scored", person_name="Alice", score=0.85) mark_uploaded("asset-noscr", person_name="Alice") - record_frigate_file("Alice", "Alice-1000.webp", "asset-scored") - record_frigate_file("Alice", "Alice-1001.webp", "asset-noscr") + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-scored", "Alice-1001.webp": "asset-noscr"}) result = get_lowest_quality_mapped_file("Alice") assert result is not None assert result[1] == "asset-scored" # only scored file is a candidate @@ -166,28 +176,26 @@ def test_get_tracked_frigate_filenames_empty(): def test_get_tracked_frigate_filenames_returns_mapped(): - from winnow.upload_tracker import get_tracked_frigate_filenames, record_frigate_file - record_frigate_file("Alice", "Alice-1000.webp", "asset-a") - record_frigate_file("Alice", "Alice-1001.webp", "asset-b") + from winnow.upload_tracker import get_tracked_frigate_filenames, record_frigate_files_batch + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a", "Alice-1001.webp": "asset-b"}) assert get_tracked_frigate_filenames("Alice") == {"Alice-1000.webp", "Alice-1001.webp"} def test_get_tracked_frigate_filenames_excludes_removed(): from winnow.upload_tracker import ( get_tracked_frigate_filenames, - record_frigate_file, + record_frigate_files_batch, remove_frigate_file, ) - record_frigate_file("Alice", "Alice-1000.webp", "asset-a") - record_frigate_file("Alice", "Alice-1001.webp", "asset-b") + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a", "Alice-1001.webp": "asset-b"}) remove_frigate_file("Alice", "Alice-1000.webp") assert get_tracked_frigate_filenames("Alice") == {"Alice-1001.webp"} def test_get_tracked_frigate_filenames_isolated_by_person(): - from winnow.upload_tracker import get_tracked_frigate_filenames, record_frigate_file - record_frigate_file("Alice", "Alice-1000.webp", "asset-a") - record_frigate_file("Bob", "Bob-2000.webp", "asset-b") + from winnow.upload_tracker import get_tracked_frigate_filenames, record_frigate_files_batch + record_frigate_files_batch("Alice", {"Alice-1000.webp": "asset-a"}) + record_frigate_files_batch("Bob", {"Bob-2000.webp": "asset-b"}) assert get_tracked_frigate_filenames("Alice") == {"Alice-1000.webp"} assert get_tracked_frigate_filenames("Bob") == {"Bob-2000.webp"} @@ -198,12 +206,11 @@ def test_get_lowest_quality_exclude_skips_specified_file(): from winnow.upload_tracker import ( get_lowest_quality_mapped_file, mark_uploaded, - record_frigate_file, + record_frigate_files_batch, ) mark_uploaded("asset-lo", person_name="Alice", score=0.10) mark_uploaded("asset-hi", person_name="Alice", score=0.90) - record_frigate_file("Alice", "Alice-lo.webp", "asset-lo") - record_frigate_file("Alice", "Alice-hi.webp", "asset-hi") + record_frigate_files_batch("Alice", {"Alice-lo.webp": "asset-lo", "Alice-hi.webp": "asset-hi"}) result = get_lowest_quality_mapped_file("Alice", exclude={"Alice-lo.webp"}) assert result is not None assert result[1] == "asset-hi" # lo was excluded; hi is returned @@ -213,29 +220,28 @@ def test_get_lowest_quality_exclude_all_returns_none(): from winnow.upload_tracker import ( get_lowest_quality_mapped_file, mark_uploaded, - record_frigate_file, + record_frigate_files_batch, ) mark_uploaded("asset-a", person_name="Alice", score=0.50) - record_frigate_file("Alice", "Alice-a.webp", "asset-a") + record_frigate_files_batch("Alice", {"Alice-a.webp": "asset-a"}) assert get_lowest_quality_mapped_file("Alice", exclude={"Alice-a.webp"}) is None # ── get_most_redundant_mapped_file ──────────────────────────────────────────── def test_get_most_redundant_none_when_no_frigate_scores(): - from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file + from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_files_batch mark_uploaded("asset-a", person_name="Alice", score=0.80) - record_frigate_file("Alice", "Alice-a.webp", "asset-a") + record_frigate_files_batch("Alice", {"Alice-a.webp": "asset-a"}) # blur score only, no frigate_score → no candidates assert get_most_redundant_mapped_file("Alice") is None def test_get_most_redundant_returns_highest_frigate_score(): - from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file + from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_files_batch mark_uploaded("asset-novel", person_name="Alice", score=0.50, frigate_score=0.31) mark_uploaded("asset-redundant", person_name="Alice", score=0.90, frigate_score=0.88) - record_frigate_file("Alice", "Alice-novel.webp", "asset-novel") - record_frigate_file("Alice", "Alice-redundant.webp", "asset-redundant") + record_frigate_files_batch("Alice", {"Alice-novel.webp": "asset-novel", "Alice-redundant.webp": "asset-redundant"}) result = get_most_redundant_mapped_file("Alice") assert result is not None frigate_filename, asset_id, score = result @@ -245,18 +251,17 @@ def test_get_most_redundant_returns_highest_frigate_score(): def test_get_most_redundant_exclude_skips_file(): - from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file + from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_files_batch mark_uploaded("asset-hi", person_name="Alice", score=0.9, frigate_score=0.85) mark_uploaded("asset-lo", person_name="Alice", score=0.5, frigate_score=0.40) - record_frigate_file("Alice", "Alice-hi.webp", "asset-hi") - record_frigate_file("Alice", "Alice-lo.webp", "asset-lo") + record_frigate_files_batch("Alice", 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"sys_platform == 'win32' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-gpu') or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-intel') or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-rocm') or (extra == 'extra-6-winnow-gpu' and extra == 'extra-6-winnow-intel') or (extra == 'extra-6-winnow-gpu' and extra == 'extra-6-winnow-rocm') or (extra == 'extra-6-winnow-intel' and extra == 'extra-6-winnow-rocm')" }, { name = "iniconfig" }, { name = "packaging" }, { name = "pluggy" }, @@ -733,6 +806,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, ] +[[package]] 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(extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-gpu') or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-intel') or (extra == 'extra-6-winnow-cpu' and extra == 'extra-6-winnow-rocm') or (extra == 'extra-6-winnow-gpu' and extra == 'extra-6-winnow-intel') or (extra == 'extra-6-winnow-gpu' and extra == 'extra-6-winnow-rocm') or (extra == 'extra-6-winnow-intel' and extra == 'extra-6-winnow-rocm')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/85/05/0d5260f1f1ca784f4a4a0def9cbe6affe587f5b4025328d446c3d67765f4/tqdm-4.68.2.tar.gz", hash = "sha256:89c230e8dbc67c7615c142487111222f878c77427ea09549960f62389e258add", size = 171923, upload-time = "2026-06-09T13:26:42.539Z" } wheels = [ @@ -777,15 +862,12 @@ wheels = [ [[package]] name = "winnow" -version = "0.4.11" +version = "0.5.0" source = { editable = "." } dependencies = [ { name = "croniter" }, { name = "insightface" }, { name = "numpy" }, - { name = "nvidia-cudnn-cu12" }, - { name = "onnxruntime", marker = "platform_machine != 'x86_64' or sys_platform != 'linux' or (extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" }, - { name = "onnxruntime-gpu", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu') or (sys_platform != 'linux' and extra == 'project-11-onnxruntime' and extra == 'project-15-onnxruntime-gpu')" }, { name = "opencv-python-headless" }, { name = "pillow" }, { name = "python-dotenv" }, @@ -793,6 +875,21 @@ dependencies = [ { name = "rich" }, ] +[package.optional-dependencies] +cpu = [ + { name = "onnxruntime" }, +] +gpu = [ + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +intel = [ + { name = "onnxruntime-openvino", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +rocm = [ + { name = "onnxruntime-rocm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] + [package.dev-dependencies] dev = [ { name = "pytest" }, @@ -804,16 +901,18 @@ requires-dist = [ { name = "croniter", specifier = ">=5.0.2" }, { name = "insightface", specifier = ">=0.7.3" }, { name = "numpy", specifier = ">=2.2.6" }, - { name = "nvidia-cudnn-cu12", specifier = ">=9.0.0" }, - { name = "onnxruntime", marker = "sys_platform != 'linux'", specifier = ">=1.23.2" }, - { name = "onnxruntime", marker = "platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = ">=1.23.2" }, - { name = "onnxruntime-gpu", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = ">=1.23.2" }, + { 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 = "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 = "python-dotenv", specifier = ">=1.2.1" }, { name = "requests", specifier = ">=2.32.5" }, { name = "rich", specifier = ">=14.2.0" }, ] +provides-extras = ["gpu", "rocm", "intel", "cpu"] [package.metadata.requires-dev] dev = [ diff --git a/winnow/cache.py b/winnow/cache.py index 828139f..53724ac 100644 --- a/winnow/cache.py +++ b/winnow/cache.py @@ -7,37 +7,62 @@ recomputing on reruns. Uses numpy binary format for fast I/O. import hashlib import logging import os +from pathlib import Path import numpy as np logger = logging.getLogger(__name__) -# Model versions — bump these when the upstream model changes MODEL_VERSIONS = { - "insightface": "buffalo_l_v1", "immich": "immich_buffalo_l_v1", } +def _insightface_model_fingerprint() -> str: + """Derive a version string from buffalo_l .onnx file sizes and mtimes. + + Changes automatically when model files are replaced or updated, preventing + stale embeddings from a previous model being served from cache. + Falls back to a static string before the model is downloaded (first run). + """ + insightface_home = os.environ.get("INSIGHTFACE_HOME", os.path.expanduser("~/.insightface")) + model_dir = Path(insightface_home) / "models" / "buffalo_l" + if not model_dir.exists(): + return "buffalo_l_v1" + onnx_files = sorted(model_dir.glob("*.onnx")) + if not onnx_files: + return "buffalo_l_v1" + fingerprint = "|".join( + f"{f.name}:{f.stat().st_size}:{int(f.stat().st_mtime)}" + for f in onnx_files + ) + return hashlib.sha256(fingerprint.encode()).hexdigest()[:12] + + class EmbeddingCache: """Simple disk-based embedding cache. Embeddings are stored as .npy files in a flat directory, - keyed by a hash of (asset_id, model_version). + keyed by a hash of (asset_id, model_version). The InsightFace version + is derived from buffalo_l model file metadata so the cache auto-invalidates + when model files are replaced or updated. """ def __init__(self, cache_dir: str = ".if_cache") -> None: self.cache_dir = cache_dir self._ensured = False + self._model_versions = { + **MODEL_VERSIONS, + "insightface": _insightface_model_fingerprint(), + } def _ensure_dir(self) -> None: if not self._ensured: os.makedirs(self.cache_dir, exist_ok=True) self._ensured = True - @staticmethod - def _key(asset_id: str, model: str) -> str: - version = MODEL_VERSIONS.get(model, model) + def _key(self, asset_id: str, model: str) -> str: + version = self._model_versions.get(model, model) raw = f"{asset_id}:{version}" return hashlib.sha256(raw.encode()).hexdigest()[:16] @@ -60,7 +85,7 @@ class EmbeddingCache: try: np.save(self._path(asset_id, model), embedding) except Exception as e: - logger.debug(f"Cache write failed for {asset_id}: {e}") + logger.debug("Cache write failed for %s: %s", asset_id, e) def clear(self) -> None: """Delete all cached embeddings.""" @@ -71,7 +96,7 @@ class EmbeddingCache: if f.endswith(".npy"): os.remove(os.path.join(self.cache_dir, f)) count += 1 - logger.info(f"Cleared {count} cached embeddings.") + logger.info("Cleared %s cached embeddings.", count) # Singleton instance @@ -90,4 +115,3 @@ def get_cache(cache_dir: str = ".if_cache") -> EmbeddingCache: if _cache is None: _cache = EmbeddingCache(cache_dir) return _cache - diff --git a/winnow/cli.py b/winnow/cli.py index a88ee4e..0b89d77 100644 --- a/winnow/cli.py +++ b/winnow/cli.py @@ -7,9 +7,9 @@ import sys from rich import print as rprint from rich.prompt import Confirm -from .config import Config, ConfigManager +from .config import Config from .executor import execute_jobs, upload_to_frigate -from .immich_api import get_people, merge_people +from .immich_api import get_immich_version, get_people, merge_people from .jobs import _show_preview, auto_configure, interactive_configure from .log_config import console, setup_logging from .upload_tracker import find_by_crop_dimension, get_person_summary, reset_person @@ -168,7 +168,7 @@ def main() -> None: "Image quality issues caused by non-default values will not be investigated.[/dim]\n" ) - ConfigManager.get().interactive_setup() + Config.interactive_setup() try: Config.validate() @@ -216,6 +216,13 @@ def main() -> None: f" {counts['rejected']} rejected{frigate_part}[/dim]" ) + _immich_version = get_immich_version() + if _immich_version is not None and _immich_version < (1, 106, 0): + rprint( + f" [yellow]⚠ Immich {'.'.join(str(x) for x in _immich_version)} detected — " + "winnow requires v1.106+. Some features may not work.[/yellow]" + ) + people = get_people() if not people: rprint("[bold red]Could not fetch people from Immich. Check URL/Key.[/bold red]") diff --git a/winnow/config.py b/winnow/config.py index 419727d..85c8549 100644 --- a/winnow/config.py +++ b/winnow/config.py @@ -9,51 +9,77 @@ from typing import ClassVar from dotenv import load_dotenv from rich.prompt import Prompt -load_dotenv() - CONFIG_FILE = Path(".immich_config.json") class _Config: - """Singleton configuration with uppercase attribute access for backward compatibility.""" + """Singleton configuration with lazy loading via __getattr__. + + Class-level attributes are annotations only (no defaults), so attribute + access on an un-loaded instance falls through to __getattr__, which + triggers _load() exactly once. + """ _instance: ClassVar["_Config | None"] = None - # Configuration values - IMMICH_URL: str | None = None - API_KEY: str | None = None - OUTPUT_DIR: str = "./frigate_train" - YEARS_FILTER: int = 10 + # Annotations only — no class-level defaults so __getattr__ fires on first access + IMMICH_URL: str | None + API_KEY: str | None + OUTPUT_DIR: str + YEARS_FILTER: int # Quality filtering - MIN_FACE_WIDTH: int = 90 - BLUR_THRESHOLD: float = 120.0 - MIN_CONFIDENCE: float = 0.7 - MAX_AUTO_IMAGES: int = 20 - QUALITY_REPLACEMENT: bool = True - FRIGATE_SCORE_CEILING: float | None = None - ENABLE_FRIGATE_SCORES: bool = True + MIN_FACE_WIDTH: int + BLUR_THRESHOLD: float + MIN_CONFIDENCE: float + MAX_AUTO_IMAGES: int + QUALITY_REPLACEMENT: bool + FRIGATE_SCORE_CEILING: float | None + ENABLE_FRIGATE_SCORES: bool # People filtering - MIN_FACE_COUNT: int = 3 - MERGE_DUPLICATE_PEOPLE: bool = False + MIN_FACE_COUNT: int + MERGE_DUPLICATE_PEOPLE: bool # Output quality - FACE_MARGIN: float = 0.15 - USE_FULL_RESOLUTION: bool = True - ENABLE_FACE_ALIGNMENT: bool = True + FACE_MARGIN: float + USE_FULL_RESOLUTION: bool + ENABLE_FACE_ALIGNMENT: bool - ENABLE_CACHE: bool = True - CACHE_DIR: str = ".if_cache" + ENABLE_CACHE: bool + CACHE_DIR: str def __new__(cls) -> "_Config": if cls._instance is None: cls._instance = super().__new__(cls) - cls._instance._load() + # Do NOT call _load() here — keep __new__ I/O-free so that import + # time does not trigger env/file reads. return cls._instance + def __getattr__(self, name: str): + """Called only when the attribute is not found on the instance. + + On first access to any config attribute, load all values from env/file + and return the requested one. Re-registers self as _instance so that + a subsequent reset() correctly finds and clears this object's attrs. + """ + if name.startswith("_"): + raise AttributeError(name) + self._load() + # Re-register self as the singleton so reset() can clear our __dict__. + # This handles the case where __getattr__ is called on the module-level + # Config object after a reset() set _instance to None. + _Config._instance = self + # _load() sets the attribute as an instance attr; retrieve it directly + # to avoid infinite recursion through __getattr__. + try: + return self.__dict__[name] + except KeyError: + raise AttributeError(f"_Config has no attribute {name!r}") + def _load(self) -> None: """Load configuration from environment and config file.""" + load_dotenv() # Load from environment (highest priority) self.IMMICH_URL = os.getenv("IMMICH_URL") self.API_KEY = os.getenv("API_KEY") @@ -75,19 +101,23 @@ class _Config: self.ENABLE_CACHE = os.getenv("ENABLE_CACHE", "true").lower() in ("true", "1", "yes") self.CACHE_DIR = os.getenv("CACHE_DIR", ".if_cache") - # Fall back to config file for non-sensitive values (API_KEY not stored here) + # Fall back to config file only when the env var is genuinely absent (None). + # An explicitly empty env var (IMMICH_URL="") takes priority over the file. if CONFIG_FILE.exists(): try: data = json.loads(CONFIG_FILE.read_text()) - self.IMMICH_URL = self.IMMICH_URL or data.get("IMMICH_URL") - if not os.getenv("OUTPUT_DIR"): + if self.IMMICH_URL is None: + self.IMMICH_URL = data.get("IMMICH_URL") + if os.getenv("OUTPUT_DIR") is None: self.OUTPUT_DIR = data.get("OUTPUT_DIR", self.OUTPUT_DIR) except (json.JSONDecodeError, OSError) as e: - logging.warning(f"Failed to load config file: {e}") + logging.warning("Failed to load config file: %s", e) @classmethod def reset(cls) -> None: """Reset the singleton — mainly useful for testing or delayed env setup.""" + if cls._instance is not None: + cls._instance.__dict__.clear() cls._instance = None def save(self) -> None: @@ -106,9 +136,9 @@ class _Config: indent=2, ) ) - logging.info(f"Configuration saved to {CONFIG_FILE}") + logging.info("Configuration saved to %s", CONFIG_FILE) except OSError as e: - logging.error(f"Failed to save config: {e}") + logging.error("Failed to save config: %s", e) def interactive_setup(self) -> None: """Prompt user for missing configuration.""" @@ -132,52 +162,10 @@ class _Config: raise ValueError("Missing Immich URL or API Key.") -# Singleton instance — use a lazy property pattern to avoid import-time side effects -# when env vars aren't yet set. Call Config.instance() or just access attributes on -# the module-level `Config` (which delegates to the singleton). -class _ConfigAccessor: - """Lazy accessor that defers singleton creation until first attribute access. - - This avoids reading .env and config files at import time, so environment - variables set after importing the module are properly picked up. - """ - - def __getattr__(self, name: str): - return getattr(_Config(), name) - - def __setattr__(self, name: str, value): - if name.startswith("_"): - super().__setattr__(name, value) - else: - setattr(_Config(), name, value) - - def reset(self) -> None: - """Reset the underlying singleton.""" - _Config.reset() - - def interactive_setup(self) -> None: - """Delegate to the singleton.""" - _Config().interactive_setup() - - def validate(self) -> None: - """Delegate to the singleton.""" - _Config().validate() - - def save(self) -> None: - """Delegate to the singleton.""" - _Config().save() - - -Config = _ConfigAccessor() - - -class ConfigManager: - @staticmethod - def get() -> _Config: - return _Config() +# Module-level singleton — lazy: no I/O until first attribute access. +Config = _Config() def get_headers() -> dict[str, str]: """Return HTTP headers for Immich API requests.""" return {"x-api-key": Config.API_KEY or "", "Accept": "application/json"} - diff --git a/winnow/diversity.py b/winnow/diversity.py index dfe6971..8a6ee0e 100644 --- a/winnow/diversity.py +++ b/winnow/diversity.py @@ -10,6 +10,7 @@ Selection pipeline: """ import logging +from concurrent.futures import ThreadPoolExecutor, as_completed from io import BytesIO import numpy as np @@ -30,6 +31,7 @@ def select_diverse_assets( selection_mode: str = "smart", person_id: str | None = None, progress_callback=None, + fetch_fn=None, ) -> list: """ Select diverse assets using cluster-aware FPS or time spread. @@ -40,6 +42,8 @@ def select_diverse_assets( entity_name: Name of the person for logging selection_mode: 'smart' (embedding-based) or 'time' (time spread) progress_callback: Optional callback(current, total) for progress + fetch_fn: Optional callable(asset_id) -> Image | None; defaults to + _fetch_thumbnail. Injected for testability. Returns: List of selected assets @@ -57,9 +61,9 @@ def select_diverse_assets( return _select_time_spread(assets, limit) try: - return _select_by_embedding(assets, limit, person_id, progress_callback) + return _select_by_embedding(assets, limit, person_id, progress_callback, fetch_fn=fetch_fn) except Exception as e: - logger.error(f"Smart Diversity failed: {e}. Falling back to time spread.") + logger.error("Smart Diversity failed: %s. Falling back to time spread.", e) return _select_time_spread(assets, limit) @@ -178,6 +182,7 @@ def _select_by_embedding( limit: int | str, person_id: str | None = None, progress_callback=None, + fetch_fn=None, ) -> list: """Select assets using embedding-based cluster-aware FPS. @@ -203,8 +208,14 @@ def _select_by_embedding( # Process in bounded batches so at most _BATCH decoded images live in RAM # at once. With 472 candidates each thumbnail is ~3-8 MB decoded; loading # all at once easily exhausts a 4 GB container limit on CPU. - from concurrent.futures import ThreadPoolExecutor, as_completed - + # LIMITATION — thumbnail-resolution embeddings drive full-res crop selection: + # diversity selection runs InsightFace on Immich preview thumbnails (~720p) + # to avoid downloading full-res for every candidate, but the training crop + # comes from the full-resolution original. Embeddings from thumbnails are + # representative in practice, but heavy JPEG compression on a preview could + # produce a subtly different embedding than the full-res version. For most + # libraries this is negligible; it matters if Immich preview quality is low. + _fetch = fetch_fn or _fetch_thumbnail _BATCH = 32 embeddings, valid_candidates, confidence_scores = [], [], [] quality_filtered = 0 @@ -216,7 +227,7 @@ def _select_by_embedding( # Download this batch concurrently batch_images: dict[str, Image.Image] = {} with ThreadPoolExecutor(max_workers=min(8, len(batch))) as pool: - futures = {pool.submit(_fetch_thumbnail, a["id"]): a for a in batch} + futures = {pool.submit(_fetch, a["id"]): a for a in batch} for future in as_completed(futures): asset = futures[future] try: @@ -224,7 +235,7 @@ def _select_by_embedding( if img is not None: batch_images[asset["id"]] = img except Exception as e: - logger.debug(f"Failed to fetch thumbnail for {asset['id']}: {e}") + logger.debug("Failed to fetch thumbnail for %s: %s", asset["id"], e) continue # Process each image; batch_images goes out of scope after this loop, @@ -250,7 +261,7 @@ def _select_by_embedding( ) if not quality.passed: quality_filtered += 1 - logger.debug(f"Quality filtered {asset['id']}: {quality.reason}") + logger.debug("Quality filtered %s: %s", asset["id"], quality.reason) continue asset["quality_score"] = quality.blur_score @@ -264,14 +275,14 @@ def _select_by_embedding( confidence_scores.append(confidence) if quality_filtered > 0: - logger.info(f"Quality filtering removed {quality_filtered} images.") + logger.info("Quality filtering removed %s images.", quality_filtered) if not embeddings: logger.warning("No valid embeddings found. Falling back to time spread.") return _select_time_spread(assets, limit) if limit != "auto" and len(valid_candidates) < limit: - logger.warning(f"Only {len(valid_candidates)} valid embeddings. Returning all.") + logger.warning("Only %s valid embeddings. Returning all.", len(valid_candidates)) return valid_candidates # --- Phase 5: Near-duplicate removal --- @@ -285,7 +296,7 @@ def _select_by_embedding( # Re-check after dedup: pool may have shrunk below limit if limit != "auto" and len(valid_candidates) < limit: - logger.warning(f"Only {len(valid_candidates)} embeddings after near-duplicate removal. Returning all.") + logger.warning("Only %s embeddings after near-duplicate removal. Returning all.", len(valid_candidates)) return valid_candidates # --- Phase 6: Cluster-aware selection --- @@ -345,7 +356,7 @@ def _dedup_embeddings( dropped = len(embeddings) - len(kept_indices) if dropped: - logger.info(f"Near-duplicate removal dropped {dropped} images (threshold {_DEDUP_THRESHOLD}).") + logger.info("Near-duplicate removal dropped %s images (threshold %s).", dropped, _DEDUP_THRESHOLD) return ( [embeddings[i] for i in kept_indices], @@ -440,7 +451,7 @@ def _compute_adaptive_threshold(emb_normed: np.ndarray) -> float: median_dist = float(np.median(upper_tri)) threshold = max(0.05, median_dist * 0.20) - logger.debug(f"Adaptive threshold: {threshold:.4f} (median_dist={median_dist:.4f})") + logger.debug("Adaptive threshold: %.4f (median_dist=%.4f)", threshold, median_dist) return threshold @@ -478,7 +489,7 @@ def _cluster_aware_selection( # --- Stage 1: K-Medoids clustering --- k = min(max(5, target // 4), max(1, n // 3), n) # e.g., 1-20 clusters - logger.debug(f"Clustering {n} embeddings into {k} groups (K-Medoids)...") + logger.debug("Clustering %s embeddings into %s groups (K-Medoids)...", n, k) # Compute full cosine distance matrix dist_matrix = 1 - emb_normed @ emb_normed.T @@ -487,7 +498,7 @@ def _cluster_aware_selection( selected = list(medoid_indices) selected_set = set(selected) - logger.debug(f"Selected {len(selected)} cluster medoids as initial picks.") + logger.debug("Selected %s cluster medoids as initial picks.", len(selected)) # --- Stage 2: FPS with hard example weighting --- min_dists = np.full(n, np.inf) @@ -527,7 +538,7 @@ def _cluster_aware_selection( selected_conf = [conf_array[i] for i in selected if conf_array[i] < 1.0] hard_count = sum(1 for c in selected_conf if c < 0.85) - logger.info(f"Selection complete: {len(selected)} images ({hard_count} hard examples with confidence < 0.85).") + logger.info("Selection complete: %s images (%s hard examples with confidence < 0.85).", len(selected), hard_count) return [candidates[i] for i in selected] @@ -542,7 +553,7 @@ def _select_time_spread(assets: list, limit: int | str) -> list: if limit == "auto": limit = 30 - logger.info(f"Selecting {limit} images using time spread.") + logger.info("Selecting %s images using time spread.", limit) if len(assets) <= limit: return assets diff --git a/winnow/embeddings.py b/winnow/embeddings.py index 1f6ed46..a1f470a 100644 --- a/winnow/embeddings.py +++ b/winnow/embeddings.py @@ -66,7 +66,7 @@ def _preload_cuda_libs() -> None: else: logger.debug("onnxruntime.preload_dlls() not available (ORT < 1.21)") except Exception as e: - logger.warning(f"Failed to preload CUDA/cuDNN DLLs: {e}") + logger.warning("Failed to preload CUDA/cuDNN DLLs: %s", e) # ============================================================================= @@ -100,7 +100,7 @@ def get_insightface_app(): # Get providers, excluding TensorRT to avoid noisy errors providers = [p for p in ort.get_available_providers() if p != "TensorrtExecutionProvider"] - logger.debug(f"ONNX providers available: {providers}") + logger.debug("ONNX providers available: %s", providers) gpu_providers = { "CUDAExecutionProvider", @@ -121,7 +121,7 @@ def get_insightface_app(): if p == "OpenVINOExecutionProvider" else p for p in providers ] - logger.debug(f"OpenVINO EP: device_type={openvino_device}") + logger.debug("OpenVINO EP: device_type=%s", openvino_device) if not has_gpu_provider and not _is_force_cpu(): logger.warning( @@ -136,21 +136,21 @@ def get_insightface_app(): device_str = f"OpenVINO ({os.getenv('OPENVINO_DEVICE', 'CPU')})" else: device_str = "GPU" - logger.info(f"InsightFace Buffalo_L: loading into memory on {device_str}...") + logger.info("InsightFace Buffalo_L: loading into memory on %s...", device_str) t0 = time.time() with _suppress_output(): _insightface_app = FaceAnalysis(name="buffalo_l", root=insightface_home, providers=providers) _insightface_app.prepare(ctx_id=ctx_id, det_size=(640, 640)) - logger.info(f"InsightFace Buffalo_L: ready on {device_str} ({time.time() - t0:.1f}s)") + logger.info("InsightFace Buffalo_L: ready on %s (%.1fs)", device_str, time.time() - t0) return _insightface_app except ImportError: logger.error("InsightFace not installed!") return None except Exception as e: - logger.error(f"Failed to load InsightFace: {e}") + logger.error("Failed to load InsightFace: %s", e) if ctx_id == 0: logger.warning("InsightFace GPU load failed — retrying on CPU...") try: @@ -164,10 +164,10 @@ def get_insightface_app(): providers=["CPUExecutionProvider"], ) _insightface_app.prepare(ctx_id=-1, det_size=(640, 640)) - logger.info(f"InsightFace Buffalo_L: ready on CPU (fallback, {time.time() - t0:.1f}s)") + logger.info("InsightFace Buffalo_L: ready on CPU (fallback, %.1fs)", time.time() - t0) return _insightface_app except Exception as ex: - logger.error(f"InsightFace CPU fallback failed: {ex}") + logger.error("InsightFace CPU fallback failed: %s", ex) return None @@ -193,7 +193,7 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None: largest = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1])) return largest.embedding except Exception as e: - logger.error(f"Error getting face embedding: {e}") + logger.error("Error getting face embedding: %s", e) return None diff --git a/winnow/executor.py b/winnow/executor.py index fbf3b51..55eefb1 100644 --- a/winnow/executor.py +++ b/winnow/executor.py @@ -3,7 +3,6 @@ import logging import os import shutil -import time from io import BytesIO from urllib.parse import quote @@ -17,12 +16,14 @@ from .frigate_api import ( delete_frigate_person_files, get_all_frigate_person_files, get_frigate_person_files, + get_frigate_version, recognize_face, ) from .image_processing import process_face_mode -from .immich_api import fetch_face_data, fetch_full_image +from .immich_api import fetch_full_image from .log_config import console from .quality import assess_quality +from .reconcile import enrich_asset_with_face_data, reconcile_frigate_mappings from .upload_tracker import ( get_lowest_quality_mapped_file, get_most_redundant_mapped_file, @@ -31,7 +32,6 @@ from .upload_tracker import ( has_frigate_scores, mark_rejected, mark_uploaded, - record_frigate_files_batch, remove_frigate_file, ) @@ -54,112 +54,6 @@ def _safe_person_dir(output_dir: str, person_name: str) -> str: return candidate -def _reconcile_frigate_mappings( - person_name: str, - known_files_before: set[str], - uploaded: list[tuple[str, str | None]], -) -> None: - """Map Frigate filenames to asset IDs after a batch of uploads. - - Polls until all expected new files appear in the Frigate API, then maps - them to asset IDs by filename timestamp order (Frigate processes the - upload queue in FIFO order, so earlier uploads get earlier timestamps). - - KNOWN LIMITATION — race condition with external uploads: - If another client uploads a face file for this person concurrently, the - count of new files will exceed `len(uploaded)` and we bail out entirely - (the "> target" branch). That's safe — we never record a wrong mapping — - but those uploads become permanently unmapped (they won't be eligible for - quality replacement). The right fix is a Frigate API that returns the - filename in the upload response, removing the need for any post-upload - diffing. Until then, the external-upload guard keeps mappings correct at - the cost of occasionally missing them when another client is active. - """ - target = len(uploaded) - current_files: set[str] = set() - - for delay in (1, 2, 4, 8): - time.sleep(delay) - fresh = get_frigate_person_files(person_name) - if fresh is None: - logger.warning( - f"{person_name}: Frigate API unreachable during mapping reconciliation" - " — quality replacement won't target these files" - ) - return - current_files = set(fresh) - if len(current_files - known_files_before) >= target: - break - - new_files = current_files - known_files_before - - if len(new_files) == target: - def _ts(fname: str) -> float: - try: - return float(fname.rsplit("_", 1)[-1].replace(".webp", "")) - except (ValueError, IndexError): - return 0.0 - - mappings = { - frigate_file: asset_id - for (_, asset_id), frigate_file in zip(uploaded, sorted(new_files, key=_ts)) - if asset_id - } - record_frigate_files_batch(person_name, mappings) - elif len(new_files) > target: - logger.info( - f"{person_name}: {len(new_files)} new Frigate files for {target} uploads" - " (external upload detected) — skipping file mapping" - ) - else: - logger.warning( - f"{person_name}: only {len(new_files)} of {target} expected Frigate files" - " appeared after reconciliation — mapping skipped" - ) - - -def _enrich_asset_with_face_data(asset: dict, person: dict) -> dict: - """Enrich an asset dict with face bounding box data from the Immich faces API. - - The search/metadata endpoint does not include face bounding box data, - so we fetch it from GET /api/faces?id={asset_id} and inject it into - the asset's "people" field so process_face_mode can find it. - - Returns the enriched asset dict (modifies in place and returns it). - """ - person_id = person["id"] - face_data = fetch_face_data(asset["id"], person_id=person_id) - - if face_data is None: - logger.debug(f"No face data returned for {person.get('name')} in asset {asset.get('id')}") - # Clean any None entries from the people list (can come from Immich API) - if "people" in asset: - asset["people"] = [p for p in asset["people"] if p is not None] - return asset - - # Skip zero-area bounding boxes (face detection failed or no face found) - if face_data.bbox == (0, 0, 0, 0): - logger.debug(f"Zero-area bounding box for {person.get('name')} in asset {asset.get('id')}") - # Clean any None entries from the people list (can come from Immich API) - if "people" in asset: - asset["people"] = [p for p in asset["people"] if p is not None] - return asset - - face_info = { - "boundingBoxX1": face_data.bbox[0], - "boundingBoxY1": face_data.bbox[1], - "boundingBoxX2": face_data.bbox[2], - "boundingBoxY2": face_data.bbox[3], - "imageWidth": face_data.image_width, - "imageHeight": face_data.image_height, - } - - # Inject into asset so process_face_mode can find it via asset["people"] - asset["people"] = [{"id": person_id, "faces": [face_info]}] - asset["face_confidence"] = face_data.confidence - return asset - - def execute_jobs(jobs: list[dict]) -> None: """Download and process images for all jobs. @@ -183,7 +77,7 @@ def execute_jobs(jobs: list[dict]) -> None: insightface_app = get_insightface_app() except Exception as e: - logger.debug(f"InsightFace unavailable for crop alignment: {e}") + logger.debug("InsightFace unavailable for crop alignment: %s", e) with Progress( SpinnerColumn(), @@ -220,7 +114,7 @@ def execute_jobs(jobs: list[dict]) -> None: try: # Enrich the asset with face bounding box data from the Immich # faces API (not included in search/metadata results). - asset = _enrich_asset_with_face_data(asset, person) + asset = enrich_asset_with_face_data(asset, person) # Skip download if detection confidence already disqualifies # the asset — avoids fetching a large image we'll discard. conf = asset.get("face_confidence") @@ -270,7 +164,7 @@ def execute_jobs(jobs: list[dict]) -> None: score_img.thumbnail((1440, 1440), Image.LANCZOS) score_map[filename] = assess_quality(score_img).blur_score except Exception as exc: - logger.debug(f"Quality score fallback for {asset['id']}: {exc}") + logger.debug("Quality score fallback for %s: %s", asset["id"], exc) score_map[filename] = 0.0 # unknown quality — treat as lowest count += 1 @@ -279,7 +173,7 @@ def execute_jobs(jobs: list[dict]) -> None: f"[yellow]Skipped {asset['id']} (no usable face data)[/yellow]" ) except Exception as e: - logger.error(f"Failed to process asset {asset['id']}: {e}") + logger.error("Failed to process asset %s: %s", asset["id"], e) progress.advance(job_task) progress.advance(overall_task) @@ -293,7 +187,7 @@ def execute_jobs(jobs: list[dict]) -> None: # Log how many images were actually saved vs selected if count < len(assets): - logger.info(f"{name}: saved {count}/{len(assets)} selected images") + logger.info("%s: saved %s/%s selected images", name, count, len(assets)) def upload_to_frigate(jobs: list[dict]) -> None: @@ -311,6 +205,18 @@ def upload_to_frigate(jobs: list[dict]) -> None: rprint("[yellow]⚠️ FRIGATE_URL not set, skipping upload.[/yellow]") return + _frigate_version = get_frigate_version() + if _frigate_version is not None: + try: + parts = [int(x) for x in _frigate_version.split("-")[0].split(".") if x.isdigit()] + if len(parts) >= 2 and (parts[0], parts[1]) < (0, 16): + rprint( + f" [yellow]⚠ Frigate {_frigate_version} detected — " + "face training API requires v0.16+. Uploads may fail.[/yellow]" + ) + except Exception: + pass + rprint("\n[bold cyan]📤 Uploading to Frigate[/bold cyan]") rprint(f" Target: [dim]{frigate_url}[/dim]") @@ -380,21 +286,34 @@ def upload_to_frigate(jobs: list[dict]) -> None: # manually-added Frigate files don't consume winnow's managed quota. # Replacement targets also come exclusively from the tracker, so manually # added files are never selected for deletion — only winnow-uploaded ones. + # LIMITATION — manual files are invisible to diversity decisions: winnow + # can observe their effect on the Frigate score (indirectly, via recognize) + # but cannot measure their embedding distribution directly. If a user has + # 20 manually-added frontals and winnow has room for 20 more, winnow may + # add more frontals because it can't see that frontals are already covered. + # TODO(frigate-api): if Frigate exposes per-file embeddings, compute + # diversity against the full training set (tracked + manual) rather than + # relying solely on the Frigate score as a proxy signal. _snapshot = ( all_frigate_files.get(name, []) if all_frigate_files is not None else get_frigate_person_files(name) ) if _snapshot is None: - # Frigate GET is down; fall back to the tracker's mapped filenames - # as the pre-upload baseline. reconciliation will still work unless - # there are concurrent manual uploads (handled by >target guard). + # Frigate GET is down. The tracker only knows files winnow mapped + # previously — it is blind to manually-added Frigate files. Using + # the tracker as the baseline would make those unmapped files look + # like new uploads in reconcile, triggering the >target guard and + # silently dropping all mappings. Skip reconciliation entirely when + # we can't get a reliable live snapshot. logger.warning( - f"{name}: Frigate API unreachable at upload start" - " — using tracker baseline for post-upload reconciliation" + "%s: Frigate API unreachable at upload start" + " — file mapping will be skipped for this batch", name ) - known_frigate_files_at_start: set[str] = get_tracked_frigate_filenames(name) + known_frigate_files_at_start: set[str] = set() + _skip_reconcile = True else: known_frigate_files_at_start: set[str] = set(_snapshot) + _skip_reconcile = False # Remove tracker mappings for files that no longer exist in Frigate # (manually deleted, or cleaned up outside winnow). This corrects the # effective_count so those slots are available for new uploads. @@ -447,6 +366,14 @@ def upload_to_frigate(jobs: list[dict]) -> None: # Frigate rebuilds its model asynchronously after any delete (clear + background # thread), so the first recognize call after a deletion returns None — our code # handles this conservatively by skipping that candidate until the next run. + # LIMITATION — async rebuild during multi-replacement runs: each deletion in a + # single run triggers a background model rebuild in Frigate. Subsequent recognize + # calls in the same run may get None (rebuild in progress), causing later + # candidates to fall back to blur-score replacement or be skipped entirely. + # The more replacements that happen in one run, the worse the scoring gets. + # TODO(frigate-api): if Frigate exposes a model generation counter or a + # rebuild-complete signal, poll it between recognize calls during replacement + # sequences rather than accepting stale/None scores. pre_fscore: float | None = None if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0: if not at_cap or person_has_fscores: @@ -529,7 +456,7 @@ def upload_to_frigate(jobs: list[dict]) -> None: effective_count -= 1 min_quality_score_for_slot = None if using_fscore else candidate_score else: - logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement") + logger.warning("Failed to delete %s for %s, skipping replacement", target_frigate_file, name) failed_deletes.add(target_frigate_file) progress.advance(upload_task) continue @@ -582,7 +509,7 @@ def upload_to_frigate(jobs: list[dict]) -> None: if resp.status_code == 400: progress.console.print(f" [dim]{error_detail}[/dim]") else: - logger.debug(f"{fname} HTTP {resp.status_code}: {error_detail}") + logger.debug("%s HTTP %s: %s", fname, resp.status_code, error_detail) if resp.status_code == 400 and "face" in full_body.lower(): asset_id = asset_map.get(fname) if asset_id: @@ -626,8 +553,8 @@ def upload_to_frigate(jobs: list[dict]) -> None: ) # Batch-map Frigate filenames to asset IDs now that all uploads are done. - if actually_uploaded: - _reconcile_frigate_mappings(name, known_frigate_files_at_start, actually_uploaded) + if actually_uploaded and not _skip_reconcile: + reconcile_frigate_mappings(name, known_frigate_files_at_start, actually_uploaded) # Per-person summary if person_failed == 0: diff --git a/winnow/frigate_api.py b/winnow/frigate_api.py index 3d3b6fd..7d2297a 100644 --- a/winnow/frigate_api.py +++ b/winnow/frigate_api.py @@ -8,6 +8,24 @@ import requests logger = logging.getLogger(__name__) +def get_frigate_version() -> str | None: + """Fetch Frigate's version string from GET /api/version. + + Returns the version string (e.g. "0.16.0-beta4") or None if FRIGATE_URL + is unset, the endpoint is unreachable, or the response is not parseable. + """ + frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/") + if not frigate_url: + return None + try: + resp = requests.get(f"{frigate_url}/api/version", timeout=5) + if resp.ok: + return resp.text.strip().strip('"') + return None + except Exception: + return None + + def _get_faces_data() -> dict | None: """Fetch raw GET /api/faces response. Returns None if unavailable.""" frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/") @@ -18,7 +36,7 @@ def _get_faces_data() -> dict | None: resp.raise_for_status() return resp.json() except Exception as e: - logger.warning(f"Could not query Frigate faces API: {e}") + logger.warning("Could not query Frigate faces API: %s", e) return None @@ -33,6 +51,10 @@ def get_all_frigate_person_files() -> dict[str, list[str]] | None: return None # Response: {person_name: [file, ...], "train": [...], ...} # "train" is a flat pending list, not a person — skip it. + # TODO(frigate-api): "train" is the only known special key as of Frigate v0.16. + # If Frigate adds other top-level non-person keys, they'll be silently treated + # as person names here. Switch to an allowlist or a typed schema when Frigate + # documents its response contract. return { name: files for name, files in data.items() @@ -75,6 +97,16 @@ def recognize_face(file_path: str) -> tuple[str | None, float] | None: Returns None if FRIGATE_URL is unset, the API is unreachable, no face is detected, or face recognition is not enabled in Frigate. + + LIMITATION — mean embedding comparison: the score reflects similarity to + the arithmetic mean of all training embeddings, not to individual ones. + A bimodal training set (e.g. frontals + profiles) has a mean that sits + between both clusters, making candidates from either cluster look more + novel than they are. Winnow could add redundant frontals while the score + suggests novelty, because the mean is pulled toward profiles. + TODO(frigate-api): if Frigate exposes per-file embeddings via the API, + replace mean-comparison with nearest-neighbour distance across individual + training embeddings for accurate coverage detection. """ frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/") if not frigate_url: @@ -93,7 +125,7 @@ def recognize_face(file_path: str) -> tuple[str | None, float] | None: return (data.get("face_name"), round(float(data["score"]), 4)) return None except Exception as e: - logger.debug(f"Frigate recognize failed for {file_path}: {e}") + logger.debug("Frigate recognize failed for %s: %s", file_path, e) return None @@ -115,15 +147,15 @@ def delete_frigate_person_files(person_name: str, filenames: list[str]) -> bool: timeout=10, ) if resp.ok: - logger.debug(f"Deleted {len(filenames)} Frigate file(s) for {person_name}") + logger.debug("Deleted %s Frigate file(s) for %s", len(filenames), person_name) return True if resp.status_code == 404: # File already absent — stale tracker entry. Return True so the caller # removes it from the tracker and frees the slot cleanly. - logger.warning(f"Frigate file(s) not found for {person_name} (stale tracker entry?): {filenames}") + logger.warning("Frigate file(s) not found for %s (stale tracker entry?): %s", person_name, filenames) return True - logger.warning(f"Frigate delete returned {resp.status_code} for {person_name}") + logger.warning("Frigate delete returned %s for %s", resp.status_code, person_name) return False except Exception as e: - logger.warning(f"Failed to delete Frigate files for {person_name}: {e}") + logger.warning("Failed to delete Frigate files for %s: %s", person_name, e) return False diff --git a/winnow/image_processing.py b/winnow/image_processing.py index cc08bbe..04d21af 100644 --- a/winnow/image_processing.py +++ b/winnow/image_processing.py @@ -37,7 +37,7 @@ def align_face(img: Image.Image, landmarks: list[list[float]] | np.ndarray) -> I img_np = np.asarray(img) lm = np.array(landmarks, dtype=np.float32) if lm.shape != (5, 2): - logger.debug(f"Invalid landmark shape: {lm.shape}, expected (5, 2)") + logger.debug("Invalid landmark shape: %s, expected (5, 2)", lm.shape) return None aligned = norm_crop(img_np, lm) return Image.fromarray(aligned) @@ -45,7 +45,7 @@ def align_face(img: Image.Image, landmarks: list[list[float]] | np.ndarray) -> I logger.debug("InsightFace not available for face alignment") return None except Exception as e: - logger.debug(f"Face alignment failed: {e}") + logger.debug("Face alignment failed: %s", e) return None @@ -79,7 +79,7 @@ def process_face_mode( break if not face_info: - logger.debug(f"No face info for {person.get('name')} in asset {asset.get('id')}") + logger.debug("No face info for %s in asset %s", person.get("name"), asset.get("id")) return None img_w, img_h = img.size @@ -95,7 +95,7 @@ def process_face_mode( face_w, face_h = x2 - x1, y2 - y1 if face_w < min_width or face_h < min_width: - logger.debug(f"Face too small ({face_w:.1f}x{face_h:.1f})") + logger.debug("Face too small (%.1fx%.1f)", face_w, face_h) return None # Re-detect face with InsightFace for landmark-based alignment. @@ -131,7 +131,7 @@ def process_face_mode( _save_jpeg(aligned, os.path.join(output_dir, f"{count}.jpg")) return aligned.size except Exception as e: - logger.debug(f"InsightFace re-detection failed for {asset.get('id')}: {e}") + logger.debug("InsightFace re-detection failed for %s: %s", asset.get("id"), e) # Landmark alignment from Immich metadata (Immich does not currently # expose landmarks, so this path is a future-proofing fallback) diff --git a/winnow/immich_api.py b/winnow/immich_api.py index d3332be..96ce3ff 100644 --- a/winnow/immich_api.py +++ b/winnow/immich_api.py @@ -26,6 +26,25 @@ class FaceData: image_height: int +def get_immich_version() -> tuple[int, int, int] | None: + """Fetch Immich server version from GET /api/server/version. + + Returns (major, minor, patch) or None if unreachable or unparseable. + """ + try: + resp = requests.get( + f"{Config.IMMICH_URL}/api/server/version", + headers=get_headers(), + timeout=5, + ) + if resp.ok: + data = resp.json() + return (int(data["major"]), int(data["minor"]), int(data["patch"])) + return None + except Exception: + return None + + def get_people() -> list[dict]: """Fetch all people from Immich.""" try: @@ -40,7 +59,7 @@ def get_people() -> list[dict]: resp.raise_for_status() return resp.json().get("people", []) except (requests.RequestException, ValueError) as e: - logger.error(f"Failed to fetch people from Immich: {e}") + logger.error("Failed to fetch people from Immich: %s", e) return [] @@ -60,7 +79,7 @@ def merge_people(survivor_id: str, merge_ids: list[str]) -> bool: resp.raise_for_status() return True except requests.RequestException as e: - logger.error(f"Failed to merge people into {survivor_id}: {e}") + logger.error("Failed to merge people into %s: %s", survivor_id, e) return False @@ -71,7 +90,7 @@ def fetch_all_assets(person: dict) -> list[dict]: url = f"{Config.IMMICH_URL}/api/search/metadata" page_size = 1000 - logger.debug(f"Fetching assets for {name}...") + logger.debug("Fetching assets for %s...", name) assets = [] for page in range(1, MAX_PAGES + 1): @@ -84,7 +103,7 @@ def fetch_all_assets(person: dict) -> list[dict]: ) if not resp.ok: - logger.error(f"Error fetching assets for {name} (page {page}): {resp.status_code}") + logger.error("Error fetching assets for %s (page %s): %s", name, page, resp.status_code) break page_assets = resp.json().get("assets", []) @@ -95,13 +114,13 @@ def fetch_all_assets(person: dict) -> list[dict]: break assets.extend(a for a in page_assets if isinstance(a, dict)) - logger.debug(f"Fetched page {page}, total: {len(assets)}") + logger.debug("Fetched page %s, total: %s", page, len(assets)) if len(page_assets) < page_size or len(assets) >= _MAX_ASSETS_PER_PERSON: break except (requests.RequestException, ValueError) as e: - logger.error(f"Exception fetching assets for {name}: {e}") + logger.error("Exception fetching assets for %s: %s", name, e) break return assets @@ -129,7 +148,7 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N ) if not resp.ok: - logger.debug(f"Face data endpoint returned {resp.status_code} for {asset_id}") + logger.debug("Face data endpoint returned %s for %s", resp.status_code, asset_id) return None faces = resp.json() @@ -162,10 +181,10 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N ) except requests.RequestException as e: - logger.debug(f"Failed to fetch face data for {asset_id}: {e}") + logger.debug("Failed to fetch face data for %s: %s", asset_id, e) return None except (AttributeError, KeyError, TypeError, ValueError) as e: - logger.debug(f"Failed to parse face data for {asset_id}: {e}") + logger.debug("Failed to parse face data for %s: %s", asset_id, e) return None @@ -186,9 +205,9 @@ def fetch_full_image(asset_id: str, timeout: int = 60) -> Image.Image | None: try: return ImageOps.exif_transpose(Image.open(BytesIO(resp.content))) except Exception: - logger.debug(f"PIL can't open original for {asset_id}, falling back to preview") + logger.debug("PIL can't open original for %s, falling back to preview", asset_id) except requests.RequestException: - logger.debug(f"Original request failed for {asset_id}, falling back to preview") + logger.debug("Original request failed for %s, falling back to preview", asset_id) # Fall back to preview thumbnail (always JPEG) try: @@ -200,7 +219,7 @@ def fetch_full_image(asset_id: str, timeout: int = 60) -> Image.Image | None: if resp.ok: return ImageOps.exif_transpose(Image.open(BytesIO(resp.content))) except Exception as e: - logger.error(f"Failed to fetch image {asset_id}: {e}") + logger.error("Failed to fetch image %s: %s", asset_id, e) return None @@ -210,7 +229,7 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d years = years or Config.YEARS_FILTER cutoff = datetime.now(timezone.utc) - timedelta(days=365 * years) - logger.debug(f"Filtering assets older than {years} years ({cutoff})") + logger.debug("Filtering assets older than %s years (%s)", years, cutoff) recent, skipped = [], 0 for asset in assets: @@ -228,6 +247,6 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d except ValueError: continue - logger.debug(f"Retained {len(recent)} assets (filtered {skipped} old assets).") + logger.debug("Retained %s assets (filtered %s old assets).", len(recent), skipped) return recent diff --git a/winnow/jobs.py b/winnow/jobs.py index 9d3a309..bd5db89 100644 --- a/winnow/jobs.py +++ b/winnow/jobs.py @@ -120,14 +120,33 @@ def _perform_selection( return selected +def _build_job( + person: dict, + assets: list, + limit: int | str, + selection_mode: str, + quality_replacement: bool = False, +) -> dict | None: + """Select from pre-filtered assets and build a job dict. No terminal I/O.""" + if not assets: + return None + name = person["name"] + selected = _perform_selection(assets, limit, name, selection_mode, person_id=person["id"]) + if not selected: + return None + return { + "person": person, + "assets": selected, + "limit": len(selected), + "config": {"name": name, "quality_replacement": quality_replacement}, + } + + def _configure_person(person: dict, people: list[dict]) -> dict | None: """Configure training for a single person. Returns job dict or None.""" name = person["name"] console.print(f"\nSelected: [bold green]{name}[/bold green]") - config = {"name": name, "quality_replacement": Config.QUALITY_REPLACEMENT} - - # Fetch and filter assets years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER) console.print(f"Scanning for {name}...") @@ -137,11 +156,10 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None: rprint(f" Found [bold]{len(all_assets)}[/bold] total, [bold]{len(recent_assets)}[/bold] in range ({years} years).") - # Filter out assets already uploaded to Frigate. - # In interactive mode, ask — use the env var only as the default so it can - # still be pre-set (e.g. RETRY_REJECTED=true) without forcing the answer. + # Ask before strategy so the post-dedup count can inform the choice retry_env = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes") retry_rejected = Confirm.ask("Include previously rejected images?", default=retry_env) + before_dedup = len(recent_assets) new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected)) recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids] @@ -153,19 +171,19 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None: rprint(" [dim]Skipping (0 new images after dedup).[/dim]") return None - # Strategy selection has_embedding = is_embedding_available() rprint(f"\n[bold cyan]Select Training Strategy for {name}:[/bold cyan]") - limit, selection_mode = _get_strategy_choice(has_embedding) if selection_mode == "skip": return None - # Perform selection - selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"]) + job = _build_job(person, recent_assets, limit, selection_mode, quality_replacement=Config.QUALITY_REPLACEMENT) + if job is None: + rprint(" [dim]Skipping (0 images selected).[/dim]") + return None - rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]") - return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config} + rprint(f" [green]Queued {job['limit']} images for {name}.[/green]") + return job def interactive_configure(people: list[dict]) -> list[dict]: @@ -240,26 +258,12 @@ def auto_configure(people: list[dict]) -> list[dict]: jobs = [] for person in valid_people: name = person["name"] - config = {"name": name} all_assets = fetch_all_assets(person) recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER) rprint(f" {name}: {len(all_assets)} total, {len(recent_assets)} recent") - # Filter out assets already uploaded to Frigate - retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes") - before_dedup = len(recent_assets) - new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected)) - recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids] - skipped = before_dedup - len(recent_assets) - if skipped: - rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]") - - if not recent_assets: - rprint(f" [dim]Skipping {name} (0 new images after dedup).[/dim]") - continue - # Enforce MAX_AUTO_IMAGES against the tracked file count only. # Manually-added Frigate files are invisible to this cap so users can # curate their own files without shrinking winnow's managed quota. @@ -281,31 +285,45 @@ def auto_configure(people: list[dict]) -> list[dict]: else: quality_replacement_only = False - config["quality_replacement"] = quality_replacement_only or Config.QUALITY_REPLACEMENT + quality_replacement = quality_replacement_only or Config.QUALITY_REPLACEMENT has_embedding = is_embedding_available() limit, selection_mode = _resolve_strategy(strategy, has_embedding) # Cap selection to remaining capacity (no cap when replacement-only — executor # decides per-image whether to swap; any candidate could be an improvement). - auto_cap = None if not quality_replacement_only: if limit == "auto": + # Switch from open-ended auto to a fixed budget at remaining capacity + # so the diversity selector itself stops at the right count instead of + # selecting MAX_AUTO_IMAGES and then discarding the excess by position. if already_uploaded > 0: - auto_cap = capacity + limit = capacity else: limit = min(limit, capacity) if selection_mode == "skip": continue - selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"]) - if auto_cap is not None: - selected_assets = selected_assets[:auto_cap] + retry_rejected = os.environ.get("RETRY_REJECTED", "false").lower() in ("true", "1", "yes") + before_dedup = len(recent_assets) + new_asset_ids = set(filter_already_uploaded([a["id"] for a in recent_assets], retry_rejected=retry_rejected)) + recent_assets = [a for a in recent_assets if a["id"] in new_asset_ids] + skipped = before_dedup - len(recent_assets) + if skipped: + rprint(f" [dim]Skipped {skipped} assets already uploaded to Frigate.[/dim]") - if selected_assets: - rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]") - jobs.append({"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config}) + if not recent_assets: + rprint(f" [dim]Skipping {name} (0 new images after dedup).[/dim]") + continue + + job = _build_job(person, recent_assets, limit, selection_mode, quality_replacement=quality_replacement) + if job is None: + rprint(f" [dim]Skipping {name} (0 images selected).[/dim]") + continue + + rprint(f" [green]Queued {job['limit']} images for {name}.[/green]") + jobs.append(job) return jobs diff --git a/winnow/reconcile.py b/winnow/reconcile.py new file mode 100644 index 0000000..20d1639 --- /dev/null +++ b/winnow/reconcile.py @@ -0,0 +1,126 @@ +"""Frigate upload post-processing: reconciliation and asset enrichment.""" + +import logging +import time + +from .frigate_api import get_frigate_person_files +from .immich_api import fetch_face_data +from .upload_tracker import record_frigate_files_batch + +logger = logging.getLogger(__name__) + + +def reconcile_frigate_mappings( + person_name: str, + known_files_before: set[str], + uploaded: list[tuple[str, str | None]], +) -> None: + """Map Frigate filenames to asset IDs after a batch of uploads. + + Polls until all expected new files appear in the Frigate API, then maps + them to asset IDs by filename timestamp order (Frigate processes the + upload queue in FIFO order, so earlier uploads get earlier timestamps). + + KNOWN LIMITATION — race condition with external uploads: + If another client uploads a face file for this person concurrently, the + count of new files will exceed `len(uploaded)` and we bail out entirely + (the "> target" branch). That's safe — we never record a wrong mapping — + but those uploads become permanently unmapped (they won't be eligible for + quality replacement). The right fix is a Frigate API that returns the + filename in the upload response, removing the need for any post-upload + diffing. Until then, the external-upload guard keeps mappings correct at + the cost of occasionally missing them when another client is active. + """ + target = len(uploaded) + current_files: set[str] = set() + + for delay in (1, 2, 4, 8): + time.sleep(delay) + fresh = get_frigate_person_files(person_name) + if fresh is None: + logger.warning( + "%s: Frigate API unreachable during mapping reconciliation" + " — quality replacement won't target these files", + person_name, + ) + return + current_files = set(fresh) + new_count = len(current_files - known_files_before) + if new_count == target: + break + if new_count > target: + break # external upload already visible — no point polling further + + new_files = current_files - known_files_before + + if len(new_files) == target: + def _ts(fname: str) -> float: + try: + return float(fname.rsplit("_", 1)[-1].replace(".webp", "")) + except (ValueError, IndexError): + return 0.0 + + mappings = { + frigate_file: asset_id + for (_, asset_id), frigate_file in zip(uploaded, sorted(new_files, key=_ts)) + if asset_id + } + record_frigate_files_batch(person_name, mappings) + elif len(new_files) > target: + logger.info( + "%s: %s new Frigate files for %s uploads" + " (external upload detected) — skipping file mapping", + person_name, + len(new_files), + target, + ) + else: + logger.warning( + "%s: only %s of %s expected Frigate files" + " appeared after reconciliation — mapping skipped", + person_name, + len(new_files), + target, + ) + + +def enrich_asset_with_face_data(asset: dict, person: dict) -> dict: + """Enrich an asset dict with face bounding box data from the Immich faces API. + + The search/metadata endpoint does not include face bounding box data, + so we fetch it from GET /api/faces?id={asset_id} and inject it into + the asset's "people" field so process_face_mode can find it. + + Returns the enriched asset dict (modifies in place and returns it). + """ + person_id = person["id"] + face_data = fetch_face_data(asset["id"], person_id=person_id) + + if face_data is None: + logger.debug("No face data returned for %s in asset %s", person.get("name"), asset.get("id")) + # Clean any None entries from the people list (can come from Immich API) + if "people" in asset: + asset["people"] = [p for p in asset["people"] if p is not None] + return asset + + # Skip zero-area bounding boxes (face detection failed or no face found) + if face_data.bbox == (0, 0, 0, 0): + logger.debug("Zero-area bounding box for %s in asset %s", person.get("name"), asset.get("id")) + # Clean any None entries from the people list (can come from Immich API) + if "people" in asset: + asset["people"] = [p for p in asset["people"] if p is not None] + return asset + + face_info = { + "boundingBoxX1": face_data.bbox[0], + "boundingBoxY1": face_data.bbox[1], + "boundingBoxX2": face_data.bbox[2], + "boundingBoxY2": face_data.bbox[3], + "imageWidth": face_data.image_width, + "imageHeight": face_data.image_height, + } + + # Inject into asset so process_face_mode can find it via asset["people"] + asset["people"] = [{"id": person_id, "faces": [face_info]}] + asset["face_confidence"] = face_data.confidence + return asset diff --git a/winnow/upload_tracker.py b/winnow/upload_tracker.py index a435027..4e6e55a 100644 --- a/winnow/upload_tracker.py +++ b/winnow/upload_tracker.py @@ -1,146 +1,213 @@ -"""Persistent tracker for Immich asset IDs already uploaded/rejected by Frigate. +"""Persistent tracker for Immich asset IDs uploaded/rejected by Frigate. -Two separate JSON files in CACHE_DIR: - frigate_uploaded_ids.json — successfully uploaded assets - frigate_rejected_ids.json — assets Frigate rejected (e.g. no face detected) +Uses a local SQLite database (frigate_tracker.db) in CACHE_DIR. -Both are excluded from future candidate pools. To reset: - - All: delete both files - - One person: call reset_person("Name") or set RESET_PERSON=Name - - Rejects only: delete frigate_rejected_ids.json, or set RETRY_REJECTED=true +Schema +------ +tracked_assets — one row per (asset_id, status) pair +frigate_files — Frigate filename → Immich asset_id mapping +person_metadata — last-known Frigate training image count per person -by_person schema (frigate_uploaded_ids.json): - { - "asset_ids": ["immich-id-1", ...], # all assets we attempted to upload - "scores": {"immich-id-1": 450.3}, # Laplacian blur variance at upload time - "frigate_scores": {"immich-id-1": 0.87}, # Frigate recognition confidence (0-1) pre-upload - "frigate_files": {"PersonName-123.webp": "immich-id-1"}, # Frigate filename → asset ID - "crop_dims": {"immich-id-1": [640, 480]}, # crop pixel dimensions at upload time - "frigate_count": 42 # last known Frigate training image count - } - -frigate_scores stores pre-upload recognize scores (0-1 sigmoid-mapped cosine -similarity). High score = the existing training set already covers this face -condition well. Low score = a gap — novel/diverse for the training set. - -frigate_files only contains files winnow uploaded — files added manually through -Frigate's UI are never mapped here and are never touched by quality replacement. +Migration +--------- +On first open, if the old JSON files exist and the tables are empty, their +data is migrated automatically. The JSON files are then renamed to .json.bak. """ import json import logging import os +import sqlite3 from pathlib import Path from .frigate_api import delete_frigate_person_files logger = logging.getLogger(__name__) -UPLOAD_TRACKER_FILE = "frigate_uploaded_ids.json" -REJECT_TRACKER_FILE = "frigate_rejected_ids.json" +# Legacy JSON filenames (for migration) +_UPLOAD_JSON = "frigate_uploaded_ids.json" +_REJECT_JSON = "frigate_rejected_ids.json" +_DB_NAME = "frigate_tracker.db" -# 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. -# Keyed by full path so tests with isolated tmp dirs never share entries. -_cache: dict[str, dict] = {} +_DDL = """ +CREATE TABLE IF NOT EXISTS tracked_assets ( + asset_id TEXT NOT NULL, + person_name TEXT, + status TEXT NOT NULL CHECK(status IN ('uploaded', 'rejected')), + blur_score REAL, + crop_width INTEGER, + crop_height INTEGER, + frigate_score REAL, + PRIMARY KEY (asset_id, status) +); + +CREATE TABLE IF NOT EXISTS frigate_files ( + frigate_filename TEXT PRIMARY KEY, + person_name TEXT NOT NULL, + asset_id TEXT NOT NULL +); + +CREATE TABLE IF NOT EXISTS person_metadata ( + person_name TEXT PRIMARY KEY, + frigate_count INTEGER +); +""" + +# Module-level connection state — re-opened when CACHE_DIR changes (test isolation) +_conn: sqlite3.Connection | None = None +_conn_path: str | None = None -def _tracker_path(filename: str) -> Path: - try: - from .config import Config - return Path(Config.CACHE_DIR) / filename - except (ImportError, AttributeError): - return Path(filename) +def _get_conn() -> sqlite3.Connection: + """Return (or create) the module-level SQLite connection. + Re-opens the connection when Config.CACHE_DIR has changed — this provides + test isolation when the isolated_cache fixture sets a new tmp directory and + calls _Config.reset(). + """ + global _conn, _conn_path -def _load(filename: str) -> dict: - path = _tracker_path(filename) - key = str(path) - if key in _cache: - return _cache[key] - data: dict = {} - if path.exists(): + from .config import Config + cache_dir = Config.CACHE_DIR + db_path = str(Path(cache_dir) / _DB_NAME) + + if _conn is not None and _conn_path != db_path: try: - with open(path) as f: - data = json.load(f) - except (json.JSONDecodeError, OSError) as e: - logger.warning(f"Could not load tracker {filename}: {e}") - _cache[key] = data - return data + _conn.close() + except Exception: + pass + _conn = None + + if _conn is None: + Path(cache_dir).mkdir(parents=True, exist_ok=True) + _conn = sqlite3.connect(db_path, check_same_thread=False) + _conn.row_factory = sqlite3.Row + _conn.execute("PRAGMA journal_mode=WAL") + _conn.execute("PRAGMA foreign_keys=ON") + _conn.executescript(_DDL) + _conn.commit() + _conn_path = db_path + _maybe_migrate(cache_dir, _conn) + + return _conn -def _save(filename: str, data: dict) -> None: - path = _tracker_path(filename) - _cache[str(path)] = data # keep cache consistent with what we write - path.parent.mkdir(parents=True, exist_ok=True) - with open(path, "w") as f: - json.dump(data, f, indent=2) +# --------------------------------------------------------------------------- +# JSON → SQLite migration +# --------------------------------------------------------------------------- + +def _maybe_migrate(cache_dir: str, conn: sqlite3.Connection) -> None: + """If the old JSON files exist and DB is empty, migrate and rename them.""" + base = Path(cache_dir) + upload_json = base / _UPLOAD_JSON + reject_json = base / _REJECT_JSON + + if not upload_json.exists() and not reject_json.exists(): + return + + # No row-count guard here: INSERT OR IGNORE makes migration idempotent, so it + # is safe to re-run if a previous attempt renamed one file but not the other + # (e.g. a PermissionError on the second rename would have left the first file's + # data committed but the second file un-renamed and un-migrated). + + logger.info("Migrating JSON tracker files to SQLite in %s", cache_dir) + + try: + with conn: + if upload_json.exists(): + _migrate_json_data(conn, json.loads(upload_json.read_text()), "uploaded") + if reject_json.exists(): + _migrate_json_data(conn, json.loads(reject_json.read_text()), "rejected") + except Exception as exc: + logger.warning("JSON migration failed, will retry next run: %s", exc) + return + + # Rename each file independently so a failure on one does not prevent the + # other from being marked complete on this run. + for json_path in (upload_json, reject_json): + if json_path.exists(): + try: + json_path.rename(json_path.with_suffix(".json.bak")) + except OSError as exc: + logger.warning("Could not rename %s after migration: %s", json_path, exc) + + logger.info("JSON → SQLite migration complete") -def _flat_key(filename: str) -> str: - return "uploaded_asset_ids" if "uploaded" in filename else "rejected_asset_ids" +def _migrate_json_data(conn: sqlite3.Connection, data: dict, status: str) -> None: + """Insert one JSON tracker file's data into SQLite tables.""" + flat_key = "uploaded_asset_ids" if status == "uploaded" else "rejected_asset_ids" + flat_ids: set[str] = set(data.get(flat_key, [])) + person_covered: set[str] = set() + + for person_name, raw_entry in data.get("by_person", {}).items(): + if isinstance(raw_entry, list): + entry: dict = {"asset_ids": raw_entry, "scores": {}, "frigate_scores": {}, + "frigate_files": {}, "crop_dims": {}} + else: + entry = { + "asset_ids": raw_entry.get("asset_ids", []), + "scores": raw_entry.get("scores", {}), + "frigate_scores": raw_entry.get("frigate_scores", {}), + "frigate_files": raw_entry.get("frigate_files", {}), + "crop_dims": raw_entry.get("crop_dims", {}), + "frigate_count": raw_entry.get("frigate_count"), + } + + for asset_id in entry["asset_ids"]: + person_covered.add(asset_id) + dims = entry.get("crop_dims", {}).get(asset_id) + conn.execute( + """INSERT OR IGNORE INTO tracked_assets + (asset_id, person_name, status, blur_score, + crop_width, crop_height, frigate_score) + VALUES (?, ?, ?, ?, ?, ?, ?)""", + ( + asset_id, + person_name, + status, + entry.get("scores", {}).get(asset_id), + dims[0] if dims else None, + dims[1] if dims else None, + entry.get("frigate_scores", {}).get(asset_id) if status == "uploaded" else None, + ), + ) + + if status == "uploaded": + for ff, aid in entry.get("frigate_files", {}).items(): + conn.execute( + "INSERT OR IGNORE INTO frigate_files (frigate_filename, person_name, asset_id) VALUES (?, ?, ?)", + (ff, person_name, aid), + ) + fc = entry.get("frigate_count") + if fc is not None: + conn.execute( + "INSERT OR REPLACE INTO person_metadata (person_name, frigate_count) VALUES (?, ?)", + (person_name, fc), + ) + + # Flat IDs not covered by any by_person entry → insert with NULL person + for asset_id in flat_ids - person_covered: + conn.execute( + "INSERT OR IGNORE INTO tracked_assets (asset_id, person_name, status) VALUES (?, NULL, ?)", + (asset_id, status), + ) -def _load_flat(filename: str) -> set[str]: - return set(_load(filename).get(_flat_key(filename), [])) - - -def _get_ids(entry: list | dict) -> list[str]: - """Extract asset_ids from either the old list format or the new dict format.""" - if isinstance(entry, list): - return entry - return entry.get("asset_ids", []) - - -def _migrate_entry(entry: list | dict) -> dict: - """Ensure by_person entry is in the current dict format.""" - if isinstance(entry, list): - return {"asset_ids": sorted(entry), "scores": {}, "frigate_scores": {}, "frigate_files": {}, "crop_dims": {}} - entry.setdefault("asset_ids", []) - entry.setdefault("scores", {}) - entry.setdefault("frigate_scores", {}) - entry.setdefault("frigate_files", {}) - entry.setdefault("crop_dims", {}) - return entry - - -def _mark( - filename: str, - asset_id: str, - person_name: str | None, - score: float | None = None, - crop_dims: tuple[int, int] | None = None, - frigate_score: float | None = None, -) -> None: - data = _load(filename) - flat_key = _flat_key(filename) - flat = set(data.get(flat_key, [])) - flat.add(asset_id) - data[flat_key] = sorted(flat) - if person_name: - by_person = data.setdefault("by_person", {}) - entry = _migrate_entry(by_person.get(person_name, {})) - ids = set(entry["asset_ids"]) - ids.add(asset_id) - entry["asset_ids"] = sorted(ids) - if score is not None: - entry["scores"][asset_id] = round(score, 4) - if crop_dims is not None: - entry["crop_dims"][asset_id] = [crop_dims[0], crop_dims[1]] - if frigate_score is not None: - entry["frigate_scores"][asset_id] = round(frigate_score, 4) - by_person[person_name] = entry - _save(filename, data) - - -# ── Public API ──────────────────────────────────────────────────────────────── +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- def load_uploaded_ids() -> set[str]: - return _load_flat(UPLOAD_TRACKER_FILE) + conn = _get_conn() + rows = conn.execute("SELECT asset_id FROM tracked_assets WHERE status='uploaded'").fetchall() + return {r[0] for r in rows} def load_rejected_ids() -> set[str]: - return _load_flat(REJECT_TRACKER_FILE) + conn = _get_conn() + rows = conn.execute("SELECT asset_id FROM tracked_assets WHERE status='rejected'").fetchall() + return {r[0] for r in rows} def mark_uploaded( @@ -150,233 +217,269 @@ def mark_uploaded( crop_dims: tuple[int, int] | None = None, frigate_score: float | None = None, ) -> None: - _mark(UPLOAD_TRACKER_FILE, asset_id, person_name, score=score, crop_dims=crop_dims, frigate_score=frigate_score) - logger.debug(f"Marked {asset_id} as uploaded ({person_name})") + conn = _get_conn() + with conn: + conn.execute( + """INSERT OR REPLACE INTO tracked_assets + (asset_id, person_name, status, blur_score, crop_width, crop_height, frigate_score) + VALUES (?, ?, 'uploaded', ?, ?, ?, ?)""", + ( + asset_id, + person_name, + round(score, 4) if score is not None else None, + crop_dims[0] if crop_dims else None, + crop_dims[1] if crop_dims else None, + round(frigate_score, 4) if frigate_score is not None else None, + ), + ) + logger.debug("Marked %s as uploaded (%s)", asset_id, person_name) def mark_rejected(asset_id: str, person_name: str | None = None) -> None: - _mark(REJECT_TRACKER_FILE, asset_id, person_name) - logger.debug(f"Marked {asset_id} as rejected ({person_name})") - - - -def record_frigate_file(person_name: str, frigate_filename: str, asset_id: str) -> None: - """Record the mapping from a Frigate training filename to an Immich asset ID.""" - data = _load(UPLOAD_TRACKER_FILE) - by_person = data.setdefault("by_person", {}) - entry = _migrate_entry(by_person.get(person_name, {})) - entry["frigate_files"][frigate_filename] = asset_id - by_person[person_name] = entry - _save(UPLOAD_TRACKER_FILE, data) - logger.debug(f"Mapped Frigate file {frigate_filename} → {asset_id} ({person_name})") + conn = _get_conn() + with conn: + conn.execute( + "INSERT OR IGNORE INTO tracked_assets (asset_id, person_name, status) VALUES (?, ?, 'rejected')", + (asset_id, person_name), + ) + logger.debug("Marked %s as rejected (%s)", asset_id, person_name) def record_frigate_files_batch(person_name: str, mappings: dict[str, str]) -> None: - """Record multiple Frigate filename → asset_id mappings in a single load/save.""" + """Record multiple Frigate filename → asset_id mappings in a single transaction.""" if not mappings: return - data = _load(UPLOAD_TRACKER_FILE) - by_person = data.setdefault("by_person", {}) - entry = _migrate_entry(by_person.get(person_name, {})) - entry["frigate_files"].update(mappings) - by_person[person_name] = entry - _save(UPLOAD_TRACKER_FILE, data) - logger.debug(f"Batch-mapped {len(mappings)} Frigate file(s) for {person_name}") + conn = _get_conn() + with conn: + conn.executemany( + "INSERT OR REPLACE INTO frigate_files (frigate_filename, person_name, asset_id) VALUES (?, ?, ?)", + [(ff, person_name, aid) for ff, aid in mappings.items()], + ) + logger.debug("Batch-mapped %s Frigate file(s) for %s", len(mappings), person_name) def remove_frigate_file(person_name: str, frigate_filename: str) -> None: - """Remove a Frigate filename from the mapping after it has been deleted. + """Remove a Frigate filename mapping and clear its asset's frigate_score. - Does NOT unmark the source asset_id — the deletion was deliberate and - we don't want to re-upload the inferior image on the next run. + Does NOT unmark the source asset_id — the deletion was deliberate. """ - data = _load(UPLOAD_TRACKER_FILE) - by_person = data.get("by_person", {}) - entry = _migrate_entry(by_person.get(person_name, {})) - asset_id = entry["frigate_files"].pop(frigate_filename, None) - if asset_id: - entry["frigate_scores"].pop(asset_id, None) - by_person[person_name] = entry - _save(UPLOAD_TRACKER_FILE, data) - logger.debug(f"Removed Frigate file mapping {frigate_filename} ({person_name})") + conn = _get_conn() + with conn: + row = conn.execute( + "SELECT asset_id FROM frigate_files WHERE frigate_filename=? AND person_name=?", + (frigate_filename, person_name), + ).fetchone() + conn.execute( + "DELETE FROM frigate_files WHERE frigate_filename=? AND person_name=?", + (frigate_filename, person_name), + ) + if row: + conn.execute( + "UPDATE tracked_assets SET frigate_score=NULL WHERE asset_id=? AND person_name=?", + (row["asset_id"], person_name), + ) + logger.debug("Removed Frigate file mapping %s (%s)", frigate_filename, person_name) def get_tracked_frigate_file_count(person_name: str) -> int: - """Return the number of Frigate training files winnow has mapped for this person. - - Used as the cap baseline so that manually-added Frigate files do not - consume slots from winnow's managed quota. - """ - data = _load(UPLOAD_TRACKER_FILE) - entry = _migrate_entry(data.get("by_person", {}).get(person_name, {})) - return len(entry["frigate_files"]) + """Return the number of Frigate training files winnow has mapped for this person.""" + conn = _get_conn() + row = conn.execute( + "SELECT COUNT(*) FROM frigate_files WHERE person_name=?", (person_name,) + ).fetchone() + return row[0] def get_tracked_frigate_filenames(person_name: str) -> set[str]: - """Return the set of Frigate filenames currently mapped in the tracker for a person. - - Used as a pre-upload baseline when the Frigate GET API is unreachable at - upload start, so reconciliation can still identify newly uploaded files. - """ - data = _load(UPLOAD_TRACKER_FILE) - entry = _migrate_entry(data.get("by_person", {}).get(person_name, {})) - return set(entry["frigate_files"].keys()) + """Return the set of Frigate filenames currently mapped for a person.""" + conn = _get_conn() + rows = conn.execute( + "SELECT frigate_filename FROM frigate_files WHERE person_name=?", (person_name,) + ).fetchall() + return {r[0] for r in rows} def has_frigate_scores(person_name: str) -> bool: """Return True if any mapped file for this person has a stored Frigate recognition score.""" - data = _load(UPLOAD_TRACKER_FILE) - entry = _migrate_entry(data.get("by_person", {}).get(person_name, {})) - frigate_files = entry.get("frigate_files", {}) - frigate_scores = entry.get("frigate_scores", {}) - return any(asset_id in frigate_scores for asset_id in frigate_files.values()) + conn = _get_conn() + row = conn.execute( + """SELECT COUNT(*) FROM frigate_files ff + JOIN tracked_assets ta ON ta.asset_id=ff.asset_id AND ta.person_name=ff.person_name + WHERE ff.person_name=? AND ta.frigate_score IS NOT NULL""", + (person_name,), + ).fetchone() + return row[0] > 0 + + +_VALID_SCORE_COLS = frozenset({"blur_score", "frigate_score"}) def _pick_mapped_file( - person_name: str, score_key: str, *, highest: bool, exclude: set[str] | None = None + person_name: str, score_col: str, *, highest: bool, exclude: set[str] | None = None ) -> tuple[str, str, float] | None: - data = _load(UPLOAD_TRACKER_FILE) - entry = _migrate_entry(data.get("by_person", {}).get(person_name, {})) - scores = entry.get(score_key, {}) - candidates = [ - (ff, asset_id, scores[asset_id]) - for ff, asset_id in entry.get("frigate_files", {}).items() - if (exclude is None or ff not in exclude) and asset_id in scores - ] - if not candidates: - return None - return max(candidates, key=lambda x: x[2]) if highest else min(candidates, key=lambda x: x[2]) + if score_col not in _VALID_SCORE_COLS: + raise ValueError(f"Invalid score column: {score_col!r}") + conn = _get_conn() + order = "DESC" if highest else "ASC" + rows = conn.execute( + f"""SELECT ff.frigate_filename, ff.asset_id, ta.{score_col} + FROM frigate_files ff + JOIN tracked_assets ta ON ta.asset_id=ff.asset_id AND ta.person_name=ff.person_name + WHERE ff.person_name=? AND ta.{score_col} IS NOT NULL + ORDER BY ta.{score_col} {order}""", + (person_name,), + ).fetchall() + for row in rows: + if exclude is None or row[0] not in exclude: + return (row[0], row[1], row[2]) + return None def get_lowest_quality_mapped_file( person_name: str, exclude: set[str] | None = None ) -> tuple[str, str, float] | None: - """Return (frigate_filename, asset_id, score) for the mapped file with the lowest - blur score, or None if no mapped files with known scores exist. - - Used for quality replacement when no Frigate scores are available. - Pass `exclude` to skip files that failed to delete this run. - """ - return _pick_mapped_file(person_name, "scores", highest=False, exclude=exclude) + """Return (frigate_filename, asset_id, score) for the mapped file with the lowest blur score.""" + return _pick_mapped_file(person_name, "blur_score", highest=False, exclude=exclude) def get_most_redundant_mapped_file( person_name: str, exclude: set[str] | None = None ) -> tuple[str, str, float] | None: - """Return (frigate_filename, asset_id, score) for the mapped file with the highest - Frigate recognition score, or None if no mapped files with Frigate scores exist. - - High Frigate score = the training set already covers this face condition well - = the most redundant file and therefore the best replacement target. - Pass `exclude` to skip files that failed to delete this run. - """ - return _pick_mapped_file(person_name, "frigate_scores", highest=True, exclude=exclude) + """Return (frigate_filename, asset_id, score) for the mapped file with the highest Frigate score.""" + return _pick_mapped_file(person_name, "frigate_score", highest=True, exclude=exclude) def get_frigate_filename_for_asset(person_name: str, asset_id: str) -> str | None: """Return the Frigate training filename mapped to this asset ID, or None.""" - data = _load(UPLOAD_TRACKER_FILE) - entry = _migrate_entry(data.get("by_person", {}).get(person_name, {})) - for frigate_filename, aid in entry["frigate_files"].items(): - if aid == asset_id: - return frigate_filename - return None + conn = _get_conn() + row = conn.execute( + "SELECT frigate_filename FROM frigate_files WHERE person_name=? AND asset_id=?", + (person_name, asset_id), + ).fetchone() + return row[0] if row else None def find_by_crop_dimension(size: int) -> list[dict]: """Return all tracked crops whose width or height matches `size` pixels. - Returns a list of dicts: {person, asset_id, width, height, blur_score, frigate_filename}. - frigate_filename is None when the Frigate mapping was lost to a reconciliation race. + Returns a list of dicts: {person, asset_id, width, height, blur_score, frigate_score, frigate_filename}. """ - data = _load(UPLOAD_TRACKER_FILE) - results = [] - for person_name, raw_entry in data.get("by_person", {}).items(): - entry = _migrate_entry(raw_entry) - scores = entry.get("scores", {}) - frigate_files = entry.get("frigate_files", {}) - asset_to_frigate = {v: k for k, v in frigate_files.items()} - frigate_scores = entry.get("frigate_scores", {}) - for asset_id, dims in entry.get("crop_dims", {}).items(): - w, h = dims[0], dims[1] - if w == size or h == size: - results.append({ - "person": person_name, - "asset_id": asset_id, - "width": w, - "height": h, - "blur_score": scores.get(asset_id), - "frigate_score": frigate_scores.get(asset_id), - "frigate_filename": asset_to_frigate.get(asset_id), - }) - return results + conn = _get_conn() + rows = conn.execute( + """SELECT ta.person_name, ta.asset_id, ta.crop_width, ta.crop_height, + ta.blur_score, ta.frigate_score, ff.frigate_filename + FROM tracked_assets ta + LEFT JOIN frigate_files ff ON ff.asset_id=ta.asset_id AND ff.person_name=ta.person_name + WHERE ta.status='uploaded' AND (ta.crop_width=? OR ta.crop_height=?)""", + (size, size), + ).fetchall() + return [ + { + "person": r["person_name"], + "asset_id": r["asset_id"], + "width": r["crop_width"], + "height": r["crop_height"], + "blur_score": r["blur_score"], + "frigate_score": r["frigate_score"], + "frigate_filename": r["frigate_filename"], + } + for r in rows + ] def update_frigate_count(person_name: str, count: int) -> None: """Record Frigate's authoritative training image count for a person.""" - data = _load(UPLOAD_TRACKER_FILE) - by_person = data.setdefault("by_person", {}) - entry = _migrate_entry(by_person.get(person_name, {})) - entry["frigate_count"] = count - by_person[person_name] = entry - _save(UPLOAD_TRACKER_FILE, data) + conn = _get_conn() + with conn: + conn.execute( + "INSERT OR REPLACE INTO person_metadata (person_name, frigate_count) VALUES (?, ?)", + (person_name, count), + ) def reset_person(person_name: str) -> None: """Remove all uploaded and rejected records for a given person. Also deletes winnow-managed Frigate training files so the next run starts - clean rather than uploading on top of orphaned files. Manually-added Frigate - files (not in frigate_files) are never touched. Proceeds with tracker reset - even if Frigate is unreachable. + clean rather than uploading on top of orphaned files. """ - 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()) + conn = _get_conn() + + # Collect Frigate filenames before deleting + frigate_filenames = list(get_tracked_frigate_filenames(person_name)) if frigate_filenames: if not os.environ.get("FRIGATE_URL", "").strip(): - logger.info(f"FRIGATE_URL not set — skipping Frigate file deletion for {person_name}") + logger.info("FRIGATE_URL not set — skipping Frigate file deletion for %s", person_name) elif delete_frigate_person_files(person_name, frigate_filenames): - logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}") + logger.info("Deleted %s Frigate file(s) for %s", len(frigate_filenames), person_name) else: - logger.warning(f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway") + logger.warning( + "Could not delete Frigate files for %s — tracker reset proceeding anyway", person_name + ) - changed = False - tracker_files = ((UPLOAD_TRACKER_FILE, upload_data), (REJECT_TRACKER_FILE, _load(REJECT_TRACKER_FILE))) - for filename, data in tracker_files: - flat_key = _flat_key(filename) - by_person = data.get("by_person", {}) - tracker_entry = by_person.pop(person_name, None) - if tracker_entry is not None: - person_ids = set(_get_ids(tracker_entry)) - flat = set(data.get(flat_key, [])) - person_ids - data[flat_key] = sorted(flat) - data["by_person"] = by_person - _save(filename, data) - changed = True - if changed: - logger.info(f"Reset tracking data for {person_name}") - else: - logger.debug(f"reset_person: no tracking data found for {person_name}") + with conn: + conn.execute("DELETE FROM frigate_files WHERE person_name=?", (person_name,)) + conn.execute("DELETE FROM tracked_assets WHERE person_name=?", (person_name,)) + conn.execute("DELETE FROM person_metadata WHERE person_name=?", (person_name,)) + + logger.info("Reset tracking data for %s", person_name) def get_person_summary() -> dict[str, dict]: """Return {person_name: {uploaded, rejected, frigate_count, scores, frigate_files}} for display/capacity.""" - uploaded_data = _load(UPLOAD_TRACKER_FILE).get("by_person", {}) - rejected_data = _load(REJECT_TRACKER_FILE).get("by_person", {}) - names = set(uploaded_data) | set(rejected_data) - result = {} - for name in sorted(names): - u_entry = uploaded_data.get(name, {}) - r_entry = rejected_data.get(name, {}) - result[name] = { - "uploaded": len(_get_ids(u_entry)), - "rejected": len(_get_ids(r_entry)), - "frigate_count": u_entry.get("frigate_count") if isinstance(u_entry, dict) else None, - "scores": u_entry.get("scores", {}) if isinstance(u_entry, dict) else {}, - "frigate_files": u_entry.get("frigate_files", {}) if isinstance(u_entry, dict) else {}, - } - return result + conn = _get_conn() + + # Counts per person per status + rows = conn.execute( + """SELECT person_name, status, COUNT(*) AS cnt + FROM tracked_assets WHERE person_name IS NOT NULL + GROUP BY person_name, status""" + ).fetchall() + + summary: dict[str, dict] = {} + for r in rows: + name = r["person_name"] + if name not in summary: + summary[name] = {"uploaded": 0, "rejected": 0, "frigate_count": None, + "scores": {}, "frigate_files": {}} + summary[name][r["status"]] = r["cnt"] + + # Scores for uploaded assets + score_rows = conn.execute( + """SELECT person_name, asset_id, blur_score + FROM tracked_assets + WHERE status='uploaded' AND person_name IS NOT NULL AND blur_score IS NOT NULL""" + ).fetchall() + for r in score_rows: + name = r["person_name"] + if name not in summary: + summary[name] = {"uploaded": 0, "rejected": 0, "frigate_count": None, + "scores": {}, "frigate_files": {}} + summary[name]["scores"][r["asset_id"]] = r["blur_score"] + + # Frigate file mappings + ff_rows = conn.execute( + "SELECT person_name, frigate_filename, asset_id FROM frigate_files" + ).fetchall() + for r in ff_rows: + name = r["person_name"] + if name not in summary: + summary[name] = {"uploaded": 0, "rejected": 0, "frigate_count": None, + "scores": {}, "frigate_files": {}} + summary[name]["frigate_files"][r["frigate_filename"]] = r["asset_id"] + + # Frigate counts + meta_rows = conn.execute( + "SELECT person_name, frigate_count FROM person_metadata" + ).fetchall() + for r in meta_rows: + name = r["person_name"] + if name not in summary: + summary[name] = {"uploaded": 0, "rejected": 0, "frigate_count": None, + "scores": {}, "frigate_files": {}} + summary[name]["frigate_count"] = r["frigate_count"] + + return dict(sorted(summary.items())) def filter_already_uploaded( @@ -390,5 +493,5 @@ def filter_already_uploaded( new_ids = [aid for aid in asset_ids if aid not in exclude] skipped = len(asset_ids) - len(new_ids) if skipped: - logger.info(f"Skipping {skipped} assets already uploaded or rejected by Frigate") + logger.info("Skipping %s assets already uploaded or rejected by Frigate", skipped) return new_ids