0.10 only removed burst shots (distance 0.01-0.05). Same-event photos with
similar pose and lighting sit at 0.10-0.20 and were passing through,
producing visually similar training images especially for people with small
datasets. 0.20 removes these while still preserving genuinely different
poses, expressions, and lighting conditions.
os.path.join silently discards the base when the second arg is absolute,
and '../..' sequences escape the output tree. _safe_person_dir() resolves
both paths with realpath and rejects any name that lands outside the output
directory, logging an error and skipping the job rather than touching an
unintended path.
- RESET_PERSON=* resets every tracked person (deletes their Frigate files
and clears the tracker). Any other value resets that specific person by
name, including someone literally named 'all'.
- Near-duplicate removal pass added before diversity clustering: greedily
drops candidates within 0.10 cosine distance of a higher-quality image,
eliminating burst-shot duplicates that FPS would otherwise pass through.
Burst shots produce embeddings that differ slightly (~0.01-0.05 cosine
distance) due to JPEG noise and minor lighting variation, so FPS does not
filter them. Add a greedy dedup pass after embedding collection: sort
candidates by quality score descending, then drop any candidate within
0.10 cosine distance of an already-kept image. The best frame from each
near-identical group survives; the rest are dropped before clustering.
- HTTP 500 errors from Frigate no longer echo the response body to the user
(Frigate's generic message says 'Try restarting Frigate' which is wrong —
500s on upload are almost always image-specific, not a health issue). The
detail is now logged at debug level. HTTP 400 detail is still shown since
'No face was detected' is genuinely useful.
- np.median on empty upper triangle (n=1 after quality filtering) no longer
emits RuntimeWarning; _compute_adaptive_threshold returns the floor (0.05)
immediately when there are no pairwise distances to sample.
- k-medoids cluster count floor raised to 1 (was 0 when n < 3), preventing
k=0 being passed to _kmedoids.
When Immich has multiple person records sharing a name (e.g. unmerged
face clusters), winnow would previously run separate jobs for each,
with the second job wiping the first job's output directory — resulting
in far fewer training images than expected.
New behaviour:
- At startup, duplicate names are detected and a warning is printed
showing asset counts for each duplicate.
- By default (MERGE_DUPLICATE_PEOPLE=false), only the person with the
most assets is processed; smaller duplicates are skipped cleanly.
- With MERGE_DUPLICATE_PEOPLE=true, the duplicates are permanently
merged inside Immich via PUT /api/people/{id}/merge (keeps the
largest), then the people list is re-fetched before jobs run.
Also adds an explicit comment in executor.py confirming that replacement
targets come exclusively from tracker-mapped files, so manually-added
Frigate training images are never selected for deletion.
Immich's /api/faces endpoint only returns bounding boxes, not facial
landmarks. This meant align_face() never fired and all crops fell back
to a plain bbox rectangle — producing partial crops (forehead-only,
off-angle faces) when Immich's detection was slightly off.
Now, when ENABLE_FACE_ALIGNMENT is true and InsightFace is loaded,
execute_jobs() passes the app to process_face_mode(). For each face,
it expands the Immich bbox by 50%, crops that search region, runs
InsightFace detection within it, and aligns the nearest face to the
standard ArcFace 112×112 format using norm_crop(). Falls back to bbox
crop if InsightFace finds no face in the search region.
The InsightFace model is already in GPU memory from the diversity/
embedding phase, so the singleton lookup adds no load cost.
CUDA 13.3 requires driver >= 575 but the host only has 570 (error 804).
CUDA 12.8.1 is the highest version supported by driver 570 and works
correctly with the NVIDIA Container Toolkit.
Add scripts/benchmark.py to measure InsightFace + SigLIP latency and
throughput across GPU and CPU modes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
release.yml now calls docker-publish.yml via workflow_call instead of
re-running all four image builds independently. docker-publish.yml gains
workflow_call inputs (tag, version) for release context; branch trigger
is narrowed to dev only (main changes only land via tagged releases).
Each release previously built all four variants twice (~90 min) — once on
merge to main, once on tag push. Now it builds once.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
CUDA 13.3 requires driver >= 575 but the host only has 570 (error 804).
CUDA 12.8.1 is the highest version supported by driver 570 and works
correctly with the NVIDIA Container Toolkit.
Add scripts/benchmark.py to measure InsightFace + SigLIP latency and
throughput across GPU and CPU modes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
release.yml now calls docker-publish.yml via workflow_call instead of
re-running all four image builds independently. docker-publish.yml gains
workflow_call inputs (tag, version) for release context; branch trigger
is narrowed to dev only (main changes only land via tagged releases).
Each release previously built all four variants twice (~90 min) — once on
merge to main, once on tag push. Now it builds once.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously reset_person wiped the local tracker but left existing Frigate
training files as orphans, causing the next run to upload a full new batch
on top of them. Now deletes all winnow-managed files from Frigate first so
the next run starts truly clean. Manually-added Frigate files are never
touched.
Also fixes a spurious warning when FRIGATE_URL is unset: the deletion step
is now skipped at info level rather than logging a misleading error. Moves
the deferred import to top-level and eliminates a double disk read.
Bumps to 0.4.1. Also fixes ruff lint violations in executor.py (import
sort, line length) and promotes the "winnow only touches files it uploaded"
callout to the README intro.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- upload_tracker: extract _pick_mapped_file() private helper; get_lowest_quality_mapped_file
and get_most_redundant_mapped_file are now one-liners over the same body
- upload_tracker: remove_frigate_file now also prunes the corresponding frigate_scores entry,
preventing unbounded accumulation of orphaned score entries across replacement cycles
- frigate_api: get_frigate_face_counts delegates to get_all_frigate_person_files, eliminating
the duplicated "name != 'train' and isinstance(files, list)" filter body
- executor: cache has_frigate_scores(name) as person_has_fscores before the per-file loop;
refresh it after each remove_frigate_file call and after each scored upload, eliminating
two redundant disk reads per at-cap file iteration
- executor: casefold() both sides of the recognize_face person-name comparison so a Frigate
casing normalization or manual-registration casing mismatch does not silently suppress scoring
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Frigate pre-upload scoring, quality replacement inversion, bootstrap fix,
FRIGATE_SCORE_CEILING / ENABLE_FRIGATE_SCORES, removal of post-upload gate.
See CHANGELOG.md [0.4.0] for the full list.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Finalizes the 0.4.0 release:
- Version bumped to 0.4.0 in pyproject.toml
- CHANGELOG.md: add [0.4.0] section covering Frigate pre-upload scoring,
quality replacement inversion, bootstrap fix, FRIGATE_SCORE_CEILING,
ENABLE_FRIGATE_SCORES, removal of post-upload quality gate, and all
doc/default corrections
- README.md: step 8 updated for dual-mode replacement, FRIGATE_SCORE_CEILING
and ENABLE_FRIGATE_SCORES added to env var table, MIN_FACE_WIDTH and
BLUR_THRESHOLD defaults corrected (50→90, 100→120)
- .env.example: FRIGATE_SCORE_THRESHOLD replaced with FRIGATE_SCORE_CEILING;
QUALITY_REPLACEMENT line added; comments updated to match current semantics
- winnow/executor.py: bootstrap fix — recognize now called for all below-cap
uploads when ENABLE_FRIGATE_SCORES=true (was gated on CEILING > 0)
- winnow/upload_tracker.py: frigate_scores schema comment corrected to
pre-upload; get_most_redundant_mapped_file() added
- winnow/frigate_api.py: recognize_face returns (face_name, score)|None tuple
so wrong-person scores never drive replacement or ceiling decisions
- winnow/config.py: FRIGATE_SCORE_THRESHOLD renamed to FRIGATE_SCORE_CEILING;
ENABLE_FRIGATE_SCORES added
- tests/test_upload_tracker.py: 4 new tests for get_most_redundant_mapped_file
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Fetch all Frigate training files once before the upload loop instead of once
per person — for N people this reduces GET /api/faces calls from N to 1.
Falls back to per-person calls if the pre-fetch fails.
Check InsightFace detection confidence immediately after face enrichment,
before fetching the full-resolution image. Assets that fail MIN_CONFIDENCE
are skipped without downloading, saving potentially large image downloads.
Collapse the gate removal tracker writes from 3×N file ops into 2 total
via remove_and_reclassify_batch: one write to the uploaded tracker (remove
file mappings + remove from flat set) and one write to the rejected tracker.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Dynamic floor now always active once Frigate scores exist — new images must
score at least as well as the weakest image already in the set, with no
config required. FRIGATE_SCORE_THRESHOLD adds an explicit absolute floor on
top. Gate active state is surfaced in normal output for both cases.
Quality replacement now pre-checks the gate threshold before deleting the
worst image. If the candidate would fail the gate, replacement is skipped
entirely rather than creating a net slot loss.
Gate-failed assets are reclassified as rejected (moved from uploaded_asset_ids
to rejected_asset_ids) so they are excluded from future runs without wasting
API calls on re-upload. RESET_PERSON still clears rejected records for a true
full reset. RETRY_REJECTED can recover them if the threshold is later lowered.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When the dynamic threshold (min stored Frigate score) raises the effective
gate floor above the configured FRIGATE_SCORE_THRESHOLD, print it as a dim
info line rather than only logging at DEBUG/VERBOSE level.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- At upload start, diff tracker vs live Frigate file list and remove any
mappings for files no longer present; corrects effective_count so manually
deleted files don't permanently consume quota slots
- Add ENABLE_FRIGATE_SCORES config (default true); when false, skips all
recognize_face calls and falls back to blur scores for quality replacement
- Print a dim notice when FRIGATE_SCORE_THRESHOLD is set but pre_run_count
is zero, so users know the gate is deferred to the next run
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- FRIGATE_SCORE_THRESHOLD=0.0 now fully disables the quality gate including the
dynamic floor; a positive value is required to activate either
- Post-reconcile gate deletions are batched into one API call per person instead
of one call per file
- Per-person summary reports gate removals and net uploaded count when the gate
fires; grand summary includes total removed across all people
- .env.example comment updated to match the corrected opt-in behaviour
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
At the start of each person's upload phase, compute the minimum stored
Frigate recognition score across all currently mapped files. Use
max(config_threshold, dynamic_min) as the effective gate threshold so
new uploads must score at least as well as the weakest image already
in the training set.
Prevents overtraining well-recognised people: if all 80 images score
≥0.85, the dynamic threshold becomes ~0.85 and new additions that
score below that are removed rather than diluting a good training set.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When FRIGATE_SCORE_THRESHOLD > 0, images that score below the threshold
after upload are deleted from Frigate and removed from the tracker.
Skipped when pre_run_count == 0 (cold start — no class mean to compare
against yet). Deletion happens after reconciliation so the Frigate
filename is known. Disabled by default (0.0).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
After each successful upload, call POST /api/faces/recognize to get
Frigate's own confidence score (0-1) for the uploaded crop. Store it
in the tracker as frigate_scores alongside the existing blur score.
When quality replacement activates and frigate_scores are present,
use them for the replacement comparison instead of blur scores — an
image Frigate recognizes poorly is a worse training image than one it
recognizes well, regardless of sharpness. Falls back to blur scores
on first run before any frigate_scores are populated.
Also surfaces frigate_score in TRACE_CROP_SIZE output.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Frigate classifies images with Laplacian variance < 120 as "very blurry"
and its own docs recommend avoiding blurry training data. Winnow was
accepting images in the 100-120 range that Frigate considers too blurry.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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