chore: bump version to 0.2.0, write full changelog

- pyproject.toml: version 0.1.0 → 0.2.0
- CHANGELOG.md: full [0.2.0] entry covering all changes since the fork —
  headless operation, Docker/scheduling, object mode, people filtering,
  quality controls, CI/CD, tests, docs, and all bug fixes
- uv.lock: regenerated after version bump

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

CHANGELOG.md

@@ -5,20 +5,100 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.1.0] - 2026-03-03
+## [Unreleased]
+
+## [0.2.0] - 2026-06-12
+
+First release of winnow. Forked from [if-curator](https://github.com/ds-sebastian/if_curator) by Sebastian and rewritten for headless Docker deployment.
 
 ### Added
-- Initial release of `winnow` — Immich to Frigate training set curator
-- **Face recognition prep**: InsightFace (ArcFace/Buffalo_L) embeddings on face crops for Frigate face recognition training
-- **Object/state classification prep**: SigLIP (Vision Transformer) embeddings with YOLOv9c object detection
-- **Smart diversity selection**: K-Medoids clustering + Farthest Point Sampling (FPS) with hard-example weighting
-- **Adaptive auto-threshold**: Automatically stops selection when adding more images becomes redundant (capped at 80)
-- **Quality filtering**: Automatic rejection of blurry, grayscale/IR, over/underexposed, low-confidence, and too-small images
-- **Face alignment**: Align faces to ArcFace 112×112 format via InsightFace landmarks
-- **Full-resolution downloads**: Downloads originals for final crops (falls back to JPEG preview for HEIC/RAW)
-- **Concurrent downloads**: 8 parallel thumbnail workers for performance
-- **Embedding cache**: Optional disk-based cache for faster re-runs
-- **Multi-person batch mode**: Process multiple people in one session
-- **Interactive CLI**: Rich terminal UI with preview summary table before downloading
-- **GPU support**: Optional CUDA/ROCm/MPS acceleration via `onnxruntime-gpu` extra
-- **Environment variable configuration**: Full control via `.env` or shell environment
+
+**Headless operation**
+- `AUTO_MODE` env var — runs without any interactive prompts; required for Docker/cron use
+- `DRY_RUN` env var — previews selection without downloading, cropping, or uploading anything
+- `RETRY_REJECTED` env var — re-attempts assets previously rejected by Frigate's face API
+- `RESET_PERSON` env var — clears upload and rejection history for one named person
+
+**Docker and scheduling**
+- `Dockerfile` — multi-stage build (CUDA 12.9 on amd64, plain Ubuntu on arm64); runtime stage excludes build tools (g++, python3.12-dev, curl, gnupg)
+- `compose.yml` — fully annotated with inline comments grouped by concern; TrueNAS volume paths in the example
+- `entrypoint.sh` — runs the tool once on startup, then hands off to the scheduler if `CRON_SCHEDULE` is set
+- `scheduler.py` — in-process cron scheduler that keeps the container (and loaded models) alive between runs
+- `CRON_SCHEDULE` env var — standard cron expression for recurring runs; unset exits after first run
+- `.dockerignore` — keeps `.venv`, `__pycache__`, test files, and logs out of the image context
+- Multi-arch image: `linux/amd64` and `linux/arm64` built and merged into a single manifest on GHCR
+- `tini` as PID 1 init process for correct signal handling
+- Non-root container user (`appuser`, uid 568) matching TrueNAS default app UID
+- `HEALTHCHECK` in Dockerfile
+
+**Object mode**
+- `TRAINING_MODE=object` — runs YOLOv9c detection on full images, crops each detected instance of a target class, and saves crops to the output volume (Frigate has no training API for objects — crops are placed manually)
+- `OBJECT_CLASS` env var — target YOLO class label (e.g. `dog`, `cat`, `car`); defaults to `dog`
+
+**People filtering**
+- `ONLY_PEOPLE` env var — comma-separated whitelist; only these people are processed
+- `SKIP_PEOPLE` env var — comma-separated list; these people are skipped
+- `MIN_FACE_COUNT` env var — skip people with fewer than N tagged assets in Immich
+- `YEARS_FILTER` env var — ignore assets older than N years (default: 10)
+
+**Image quality controls** (previously hardcoded)
+- `BLUR_THRESHOLD` env var — Laplacian variance threshold for blur rejection
+- `MIN_CONFIDENCE` env var — minimum Immich face detection confidence
+- `MAX_AUTO_IMAGES` env var — hard cap on auto-diversity selection
+- `FACE_MARGIN` env var — padding around bounding box crops as a fraction of face size
+- `USE_FULL_RESOLUTION` env var — download full-res originals vs preview thumbnails
+- `ENABLE_FACE_ALIGNMENT` env var — align to ArcFace 112×112 format via InsightFace landmarks
+
+**GPU and model configuration**
+- `FORCE_CPU` env var — disable GPU; fall back to CPU for embedding computation
+- `INSIGHTFACE_HOME` env var — controls model persistence for Buffalo_L
+- `HF_HOME` env var — HuggingFace model cache path for SigLIP
+- `LD_LIBRARY_PATH` set in the image to expose CUDA and cuDNN pip libraries so `onnxruntime-gpu` can find them at runtime
+
+**Caching and upload tracking**
+- `ENABLE_CACHE` / `CACHE_DIR` env vars — opt-in embedding cache to skip recomputation on reruns
+- Per-person upload tracker persisted as JSON; prevents the same asset from being uploaded twice across runs weeks apart, even if the container is recreated
+- Startup summary showing uploaded and rejected counts per person
+- `LIMIT` env var — exact image count overriding `STRATEGY` preset
+
+**CI/CD**
+- `docker-publish.yml` — builds multi-arch image and pushes to GHCR on push to `main` (`:latest`) or `dev` (`:dev`)
+- `release.yml` — triggered by `v*` tags or `workflow_dispatch`; creates a GitHub Release, extracts changelog notes, builds and pushes versioned image to GHCR
+- `lint.yml` — runs Ruff on push/PR to `main` and `dev`
+- `test.yml` — runs pytest on push/PR to `main` and `dev`
+- `update-lockfile.yml` — regenerates `uv.lock` and commits it when `pyproject.toml` changes
+- Dependabot: weekly grouped PRs for Python dependencies (uv ecosystem) and GitHub Actions versions
+
+**Testing**
+- 24 unit tests across four modules: `test_config`, `test_immich_api`, `test_jobs`, `test_upload_tracker`
+
+**Documentation**
+- `docs/setup.md` — step-by-step install guide with TrueNAS and bare-Docker sections
+- `docs/troubleshooting.md` — common failure modes with fixes
+- `docs/faq.md` — answers to questions new users will ask
+- `.env.example` — copy-paste starting point with every env var and inline comments
+- README rewritten: pipeline diagram, env var reference tables, scheduling behaviour, requirements
+
+### Changed
+
+- `cli.py` split into three focused modules — `cli.py` (entry point), `jobs.py` (configuration and strategy resolution), `executor.py` (download, crop, upload)
+- Dependency management replaced with [`uv`](https://astral.sh/uv); `uv.lock` pins the full transitive graph for reproducible builds
+- `compose.yml` fully annotated; all env vars documented with inline comments
+- `LD_LIBRARY_PATH` extended to include both cuDNN and CUDA runtime libraries
+
+### Fixed
+
+- **EXIF orientation**: PIL opens JPEGs without applying rotation metadata; Immich computes face bounding boxes on orientation-corrected images, so portrait photos produced misaligned crops. `ImageOps.exif_transpose()` now normalizes orientation before any coordinate math.
+- **Model persistence**: `FaceAnalysis` was initialized with `root="~/.insightface"` (hardcoded), ignoring `INSIGHTFACE_HOME`. Buffalo_L was re-downloaded into the container on every run instead of persisting to the mounted volume.
+- **Upload deduplication**: `upload_to_frigate()` scanned the output directory with `os.listdir()`, picking up leftover files from previous runs and re-uploading them. Now only files created in the current run are uploaded.
+- **RGBA images**: Images in RGBA mode raised an error when encoding to JPEG. All images are now converted to RGB before saving.
+- **Object mode uploads**: Object mode incorrectly called the Frigate face registration API. Frigate has no API for object training data — object mode now only saves crops to disk.
+- **Stale output files**: The output directory was not cleaned between runs, causing crops to accumulate. Now wiped at the start of each face-mode run.
+- **JSON decode errors**: `get_people()` and `fetch_all_assets()` only caught `RequestException`, leaving `JSONDecodeError` unhandled on non-JSON 200 responses from Immich.
+- **Spaces in names**: People names with spaces caused downstream errors.
+- **Inconsistent headers**: Some API calls used a raw header dict instead of `get_headers()`.
+- **Docker layer caching**: `uv sync` was placed after `COPY if_curator/`, so any source change invalidated the 800 MB dependency cache. Dependencies are now installed before source is copied.
+
+### Security
+
+- CUDA base image bumped from `nvidia/cuda:12.6.3` to `nvidia/cuda:12.9.2-cudnn-runtime-ubuntu22.04`, picking up Ubuntu security patches flagged by Dependabot.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.1.0"
+version = "0.2.0"
 description = "Immich to Frigate training sets"
 license = "MIT"
 requires-python = ">=3.12"

uv.lock

@@ -2289,7 +2289,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.1.0"
+version = "0.2.0"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },