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>
Config tests now verify QUALITY_REPLACEMENT defaults to True and
respects the QUALITY_REPLACEMENT=false env override.
CI: replace minimal disk cleanup with more aggressive removal
(Android SDK ~14GB, Swift, CodeQL, docker system prune) so the
NVIDIA GPU image build no longer exhausts runner disk space.
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>
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>
- 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>
flanandClaude Sonnet 4.6