Store (width, height) of each face crop in the tracker at upload time
alongside the existing blur score. Expose TRACE_CROP_SIZE=<px> to look
up which Immich asset produced a crop with that pixel dimension, making
it straightforward to trace unexpected or low-quality images visible in
Frigate back to their source.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- quality.py:122: split long tuple line to satisfy E501 (146 → ≤120)
- update-lockfile.yml: add branches filter so tag pushes don't trigger
the workflow (tag checkout is detached HEAD; git push has no target)
- release.yml: split four Docker build steps into parallel jobs (build-gpu,
build-cpu, build-rocm, build-intel), each with its own runner; previously
all four ran in one job and exhausted disk after GPU+CPU builds, leaving
ROCm and Intel cancelled
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
C1/C4: Cap blur-score computation at 1440 px before calling assess_quality
so scores are always on the same Laplacian scale as the embedding path
(which operates on Immich preview thumbnails). Also converts the image to
RGB before scoring and stores 0.0 on assess_quality failure so files
uploaded without a score remain eligible for future quality replacement
instead of occupying a slot permanently.
C2: Fall back to the tracker's mapped-filename set as the pre-upload
baseline when the Frigate GET /api/faces endpoint is unreachable at upload
start. Previously, uploads that succeeded during a partial API outage were
never mapped in frigate_files, leaving get_tracked_frigate_file_count
permanently under-counting those files and allowing Frigate to exceed
MAX_AUTO_IMAGES over time.
C3: Track min_quality_score_for_slot when a quality-replacement delete
succeeds but the subsequent upload fails. This ensures the freed slot can
only be filled by a candidate that beats the deleted file's score, not just
the next file in iteration order (which could be lower quality than what
was deleted).
Add get_tracked_frigate_filenames() to upload_tracker and expand tracker
tests to cover the new function and exclude-parameter behaviour.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously, effective_count and the jobs.py cap check used the total
Frigate file count (including manually-added files), so any file a user
curated by hand ate into winnow's managed quota. Now:
- get_tracked_frigate_file_count() returns len(frigate_files) from the
tracker — only files winnow uploaded and reconciled
- effective_count in the upload loop uses this tracker count so
manually-added files are invisible to the cap
- jobs.py capacity check uses len(frigate_files) instead of the live
Frigate API count or cached frigate_count
- Frigate API call for known_frigate_files_at_start is now only used
for the post-upload reconciliation diff, not for cap enforcement
Side-effect: fixes audit bug #1 — an unreachable Frigate GET no longer
zeroes effective_count and bypasses the cap, because the cap is now
read from the always-available local tracker.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When no mapped files exist for a person, worst is None and the old
f-string tried to subscript it before the conditional was evaluated.
Extracted worst_score_str as a local variable to avoid the crash.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Track laplacian blur score through quality filtering pipeline
(quality.py: blur_score on QualityResult; diversity.py: store on asset;
executor.py: read via quality_score key)
- Replace per-file polling with post-person batch reconciliation:
after all uploads for a person complete, poll Frigate (up to 15s)
until the expected number of new files appear, then map by filename
timestamp order (Frigate FIFO queue = upload order = timestamp order)
- Document race condition limitation: concurrent external uploads cause
the batch to be skipped entirely (safe but files go unmapped); noted
in code as requiring a Frigate API fix (return filename on upload)
- Add two assess_quality integration tests for blur_score
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Delete failure retry loop: when delete_frigate_person_files() fails,
remove the file from the tracker so the next candidate targets a
different worst file rather than re-attempting the same failed delete.
- Interactive mode quality replacement: _configure_person() never set
config["quality_replacement"], causing the executor to always default
to False and silently skip all uploads for at-cap interactive jobs.
Now mirrors auto_configure by reading Config.QUALITY_REPLACEMENT.
- Silent mapping loss on API flap: after a successful upload, if the
post-upload GET /api/faces returns None (transient API failure),
the file was silently left unmapped. Now logs a warning so users
know quality replacement won't target that file.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When a person is at MAX_AUTO_IMAGES, winnow now replaces the
lowest-quality mapped training image in Frigate if a higher-confidence
candidate is available, keeping the training set always optimised.
Only files winnow uploaded (tracked via frigate_files mapping) are ever
replaced — manually added Frigate training images are never touched.
A concurrent-upload race condition is detected per-file: if N>1 new
files appear after one upload, the mapping is skipped rather than
guessed, logging at INFO level. The per-file snapshot approach is
retained over a batch approach because wrong mappings (which a batch
approach risks on race) are worse than no mapping.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New image variants:
- :rocm — InsightFace via ROCmExecutionProvider, SigLIP via PyTorch ROCm 6.3
- :intel — InsightFace via OpenVINOExecutionProvider (onnxruntime-openvino);
Intel GPU compute runtime auto-installed from Intel graphics repo;
OPENVINO_DEVICE=GPU opts into Arc/iGPU inference (default: CPU)
Also adds:
- pyproject-rocm.toml + uv-rocm.lock, pyproject-intel.toml + uv-intel.lock
- compose.yml device passthrough snippets for AMD and Intel
- CI: build-rocm and build-intel jobs in docker-publish.yml; all four
variants built and tagged in release.yml
- README reworked: cleaner structure, GPU variant quick-start examples,
OPENVINO_DEVICE env var documented
- CHANGELOG entry and version bump to 0.2.12
Fix: IntPrompt in dict literal was eagerly evaluated in the no-embedding
fallback path of _get_strategy_choice, prompting users for a custom count
regardless of which strategy they picked.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two small correctness fixes from post-commit code review:
- except Exception: continue swallowed network/auth errors silently; add
logger.debug so systematic failures are diagnosable in winnow.log
- batch_images.pop() regressed duplicate-asset-ID handling: if Immich
returns the same asset ID twice within the same 32-item batch window
(pagination edge case), the second occurrence got None and its embedding
was silently dropped. Switching back to .get() matches the old
thumbnail_map.get() behaviour. Peak memory is still bounded to _BATCH
images because batch_images goes out of scope between batches.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously all candidate thumbnails (up to 3000) were loaded into a
single dict before any processing started. At ~5 MB per decoded preview
image, 472 candidates = ~2.4 GB of thumbnail data alone, easily
exhausting a 4 GB container memory limit on CPU.
Now thumbnails are downloaded and processed in batches of 32. Each
image is pop()'d from the batch dict immediately after embedding so the
decoder memory is released before the next batch starts. Peak in-flight
thumbnail memory is now bounded to ~32 × 8 MB = ~256 MB regardless of
candidate pool size. GPU users benefit too — faster first results and
lower host RAM pressure during large runs.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- immich_api: `score or confidence` treated 0.0 score as falsy; use explicit None check
- diversity: same falsy-zero fix in _get_face_confidence
- diversity: _crop_face_from_thumbnail scale loop now filters by person_id (was
using first person's imageWidth/imageHeight regardless of target in group photos)
- jobs: partially-trained auto mode kept limit="auto" for adaptive stopping, then
caps result to remaining capacity (was converting to int, silently disabling FPS
adaptive threshold and early-stop)
- embeddings: _suppress_output finally block wraps first dup2 in try/finally so
stderr is always restored even if stdout restore raises OSError
- Dockerfile: ldconfig find uses python3.* glob instead of hardcoded python3.13
- scheduler: sleep until next_run instead of fixed 60s; eliminates late-fire jitter
and unnecessary wakeups on long schedules
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Frigate /api/faces response has person names as top-level keys with
lists of filenames — {person: [file, ...], "train": [...]}. The old
code incorrectly looked inside data["train"] as if it were a dict of
persons, causing 'list object has no attribute items' on every run.
Immich get_people() now checks for 401 before raise_for_status() and
logs a clear "API key invalid or expired" message instead of the raw
requests exception string.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The log file already captures DEBUG unconditionally. This env var wires
the same to the Rich console handler for troubleshooting without needing
to read the log file.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bugs fixed (from high-effort review):
- InsightFace CPU fallback now passes providers=['CPUExecutionProvider'] and
wraps in _suppress_output() so broken GPU drivers don't cause fallback to
try the same broken provider again, and C-extension noise stays suppressed
- scheduler.py: BaseException → Exception (KeyboardInterrupt already re-raised;
winnow has no sys.exit() calls, so SystemExit would not occur, but Exception
is the correct scope)
- compose.yml: fix inverted AUTO_MODE comment (docker run -it enables
interactive mode via TTY, not non-interactive)
Model loading logging (embeddings.py):
- InsightFace: disk cache check, "not cached — downloading now (~300 MB)",
"loading into memory on GPU/CPU...", "ready on GPU/CPU (Xs)"
- SigLIP: same treatment; cache path derived dynamically from model_name
via HuggingFace slug convention (models--org--model) so it stays correct
if the model variant ever changes
Logging level audit (INFO/DEBUG/WARNING/ERROR):
- diversity.py: internal algo steps (clustering, medoids, adaptive threshold,
auto-stop decision) → DEBUG; final selection summaries stay INFO
- immich_api.py: "Fetching assets" and "Retained N assets" → DEBUG (callers
already print this to the console via rprint)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
embeddings.py: face embedding cache used 'immich' for lookup but
'insightface' for storage, so the cache was never hit for locally-
computed embeddings. Unified to 'insightface'/'siglip' throughout.
This affects all users since ENABLE_CACHE now defaults to true.
scheduler.py: INSIGHTFACE_HOME=/models/.insightface was having
'.insightface' appended again, making buffalo_l check always report
'will download'. Also catch BaseException (not just Exception) so a
SystemExit from a library call can't silently kill all future runs.
log_config.py: handlers.clear() abandoned open FileHandler fds on
each scheduled main() call. Close each handler properly before removal.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
RETRY_REJECTED was silently read from the environment in _configure_person,
bypassing user control in interactive sessions. Now prompts the user with
the env var value as the default, so the setting is visible and overridable.
All other env vars in the interactive path are already correct:
YEARS_FILTER is a prompt default, ONLY_PEOPLE/SKIP_PEOPLE/MIN_FACE_COUNT
are auto_configure-only, and TRAINING_MODE/STRATEGY/OBJECT_CLASS are
always prompted.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The primary use case is headless Docker, so auto mode is now the default
whenever stdin has no TTY. Interactive mode activates when a terminal is
present (docker run -it, local shell). AUTO_MODE=true remains as an
explicit override for scripting with a pseudo-TTY.
Removes AUTO_MODE=true, stdin_open, tty, and FORCE_CPU=false from
compose.yml — none are needed for headless operation. Updates README
and the interactive-mode hint in the CLI.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The static LD_LIBRARY_PATH only covered cudnn and cuda_runtime — missing
cublas, cufft, curand, cusolver, cusparse, nvjitlink, etc. onnxruntime-gpu
needs libcublasLt.so at minimum, so GPU mode silently fell back to CPU.
Replace with a one-shot ldconfig call over every nvidia site-packages lib/
dir, which covers all packages regardless of what gets installed.
Also: remove the ambiguous directory="" from preload_dlls (use auto-search
default) and add a clear warning when CUDAExecutionProvider is absent so
the user sees actionable guidance instead of silent CPU fallback.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When stdin has no TTY (Docker without -it), IntPrompt/Confirm raise
EOFError and crash the container into a restart loop. Treat a non-TTY
stdin the same as AUTO_MODE=true so headless runs work without any env
var configuration.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Dockerfile: ENV INSIGHTFACE_HOME=/models → /models/.insightface to
match compose.yml and .env.example; the old value caused InsightFace
to store models at /models/models/buffalo_l (double-appended subdir)
- entrypoint.sh: use /app/.venv/bin/winnow (installed entry point)
instead of python -m winnow.cli
- config.py: ENABLE_CACHE default false → true; embedding cache is
always beneficial in practice; users can opt out with ENABLE_CACHE=false
- compose.yml: comment out CRON_SCHEDULE so scheduling is opt-in;
flip ENABLE_CACHE to commented opt-out to reflect new default
- README.md: update ENABLE_CACHE default documentation to true
- tests/test_config.py: update default assertion to match
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- __init__.py: derive __version__ from importlib.metadata instead of
a hardcoded "0.1.0" that was six releases out of date
- scheduler.py: move winnow.cli import to module top (no more noqa);
clean up redundant bool variables in check_models
- tests/test_quality.py: 17 tests covering all five quality check
functions individually plus assess_quality integration cases
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- winnow/logging.py → winnow/log_config.py: avoids shadowing the stdlib
logging module; log file renamed from immich_export.log to winnow.log
- scheduler.py: run main() in-process instead of subprocess.run so
InsightFace and SigLIP models stay resident in memory across scheduled
runs (hundreds of MB load, previously reloaded every run)
- .gitignore: replaced 200-line boilerplate with ~30 project-relevant
patterns; removed Django/Flask/Redis/RabbitMQ/Scrapy/etc. noise
- .python-version: untracked (redundant with requires-python in
pyproject.toml; kept in .gitignore for local pyenv users)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- embeddings: initialize ctx_id=-1 before try block so the except
handler cannot NameError; move insightface_home out of try for the
same reason
- embeddings: replace contextlib.redirect_stdout/stderr (Python-level
only) with fd-level dup2 suppression — actually silences C extension
noise from InsightFace during model loading
- jobs: fix frigate_count==0 falling through `or` chain; use explicit
`is not None` check so a real zero is not treated as missing data
- diversity: thread person_id through select_diverse_assets →
_select_by_embedding → _get_face_bbox / _get_face_confidence /
_crop_face_from_thumbnail so group-photo assets embed the target
person's face rather than whichever person is listed first
- config: replace type() hack for ConfigManager with a proper class
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Store Immich face confidence scores per asset in frigate_uploaded_ids.json
- Add frigate_api.py: query GET /api/faces to count trained images per person
- Record Frigate training count as frigate_count in tracker for offline fallback
- MAX_AUTO_IMAGES cap now uses live Frigate count → cached frigate_count → local uploaded count
- Startup summary shows last known Frigate training count per person
- Migrate by_person entries from flat list to {asset_ids, scores, frigate_count} dict
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
flanandClaude Sonnet 4.6