release: merge dev → main for 0.4.0

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>

.env.example

@@ -28,8 +28,11 @@ STRATEGY=auto
 # ENABLE_FACE_ALIGNMENT=true  # Align face before cropping (default: true)
 # 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=100.0        # Laplacian blur threshold; lower = accept more blur (default: 100.0)
+# 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)
+# 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)
+# 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

CHANGELOG.md

@@ -7,6 +7,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.4.0] - 2026-06-13
+
+### Added
+
+- **Pre-upload Frigate recognition scores**: `recognize_face` is now called before each upload to measure how novel the candidate is relative to the existing training set. The score is stored in the tracker (`frigate_scores` field) and drives quality replacement in subsequent runs. Adds ~200 ms per upload.
+- **`ENABLE_FRIGATE_SCORES`** (default `true`): controls all pre-upload Frigate recognize calls. Set `false` to use blur-score replacement only and skip the Frigate round-trip entirely.
+- **`FRIGATE_SCORE_CEILING`** (default `0.0`): skip uploads whose pre-upload recognize score already exceeds this value — those face conditions are already well-covered by the training set. `0` disables (no ceiling); requires at least one prior run to have stored scores.
+- **`get_most_redundant_mapped_file()`**: new upload-tracker function that returns the mapped file with the highest Frigate pre-upload score. High score = the training set already covers that face condition well = the best deletion target for quality replacement.
+- **Cold-start notice**: first run (no existing Frigate model) now logs a clear message explaining why Frigate scores are unavailable and that they will populate on subsequent runs.
+- **4 new tests** for `get_most_redundant_mapped_file` covering score ordering, ties, excludes, and no-score cases.
+
+### Changed
+
+- **Quality replacement now uses Frigate scores**: when Frigate scores are available, at-cap replacement targets the _most redundant_ mapped file (highest pre-upload score) and replaces it only when the candidate is _more novel_ (lower score). Falls back to blur-score comparison when no Frigate scores have been stored yet.
+- **`recognize_face` returns `(face_name, score) | None`** instead of `float | None`: the caller now validates that the recognized person matches the expected person before using the score. Wrong-person scores no longer drive ceiling skips or replacement decisions.
+- **Bootstrap fix**: recognize was previously called below-cap only when `FRIGATE_SCORE_CEILING > 0`, so `frigate_scores` was never populated with default settings and the Frigate replacement path never activated. Recognize is now called for all below-cap uploads when `ENABLE_FRIGATE_SCORES=true`, seeding scores for future at-cap runs regardless of ceiling setting.
+- **Batch GET `/api/faces`**: Frigate file-count lookups are now batched to reduce round-trip overhead on runs with many people.
+- **Skip candidate download on low Frigate confidence**: candidates where the Immich detection confidence is below threshold are now filtered before the full-resolution download, saving bandwidth.
+
+### Removed
+
+- **Post-upload quality gate (`FRIGATE_SCORE_THRESHOLD`)**: enforcement of a Frigate score threshold after upload has been removed. Post-upload scores are taken after the image is already in the training set, so the model has already retrained on it — deleting it at that point is wasteful and disrupts the model for the next Frigate run. Pre-upload scoring (`FRIGATE_SCORE_CEILING`) provides a cleaner signal at the right moment.
+
+### Fixed
+
+- **Frigate replacement path never activated with default settings**: with `FRIGATE_SCORE_CEILING=0.0` (default), the bootstrap call to `recognize_face` was gated behind `CEILING > 0`, so `frigate_scores` stayed empty, `has_frigate_scores` was always False, and the Frigate replacement branch was permanently unreachable. Removing the ceiling guard from the below-cap recognize call breaks the circular dependency.
+- **Schema comment contradiction**: `upload_tracker.py` line-16 comment described `frigate_scores` as "post-upload" while the block comment on lines 22–24 said "pre-upload". Corrected to "pre-upload" throughout.
+- **README default values**: `MIN_FACE_WIDTH` was documented as `50` (actual default: `90`); `BLUR_THRESHOLD` was documented as `100.0` (actual default: `120.0`). Both corrected.
+- **README missing env vars**: `FRIGATE_SCORE_CEILING` and `ENABLE_FRIGATE_SCORES` were present in `config.py` and `.env.example` but absent from the README env var table. Both added.
+- **README quality-replacement description**: Step 8 and the `QUALITY_REPLACEMENT` row now document the dual-mode behaviour (Frigate-score path and blur-score fallback) instead of describing only the original blur-score path.
+
 ## [0.3.3] - 2026-06-13
 
 ### Fixed

README.md

@@ -2,6 +2,9 @@
 
 [![Docker](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/docker-publish.yml) [![Test](https://github.com/sudolulo/winnow/actions/workflows/test.yml/badge.svg)](https://github.com/sudolulo/winnow/actions/workflows/test.yml) [![GitHub release](https://img.shields.io/github/v/release/sudolulo/winnow)](https://github.com/sudolulo/winnow/releases/latest) [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](LICENSE) [![Immich](https://img.shields.io/badge/Immich-v1.106%2B-blueviolet)](https://immich.app) [![Frigate](https://img.shields.io/badge/Frigate-Ready-brightgreen)](https://frigate.video)
 
+> **Early Development — Use With Caution**
+> winnow is functional but still maturing. Features that modify your Frigate training data — quality replacement, stale mapping cleanup — can remove images from your dataset and are not yet battle-tested at scale. Review the logs after each run and keep backups of your Frigate face training directory until you are confident in the results.
+
 **Docs:** [Setup](https://github.com/sudolulo/winnow/wiki/Setup) · [Troubleshooting](https://github.com/sudolulo/winnow/wiki/Troubleshooting) · [FAQ](https://github.com/sudolulo/winnow/wiki/FAQ)
 
 `winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition and object classification models.
@@ -56,9 +59,11 @@ Immich library
 8. Deliver
    • Face mode: upload crops to Frigate's face registration API
      ↳ below MAX_AUTO_IMAGES — upload freely
-     ↳ at cap + QUALITY_REPLACEMENT=true — swap the lowest-scoring tracked
+     ↳ at cap + QUALITY_REPLACEMENT=true — with Frigate scoring active,
-       image if the new candidate scores higher; manually added files are
+       swap the most redundant tracked image (highest pre-upload recognize
-       never touched
+       score) if the candidate is more novel (lower score); falling back to
+       blur-score comparison when no Frigate scores are available; manually
+       added files are never touched
      ↳ at cap + QUALITY_REPLACEMENT=false — skip this person
    • Object mode: save crops to disk → place into your Frigate data directory
 ```
@@ -183,14 +188,16 @@ In scheduled mode the process (and loaded models) stays resident between runs. T
 
 | Variable | Default | Description |
 | :--- | :--- | :--- |
-| `MIN_FACE_WIDTH` | `50` | Minimum face crop width in pixels |
+| `MIN_FACE_WIDTH` | `90` | Minimum face crop width in pixels |
 | `FACE_MARGIN` | `0.15` | Padding around bounding box crop (fraction of face size) |
 | `ENABLE_FACE_ALIGNMENT` | `true` | Align to ArcFace 112×112 format using facial landmarks |
 | `USE_FULL_RESOLUTION` | `true` | Download full-resolution originals rather than preview thumbnails |
 | `MIN_CONFIDENCE` | `0.7` | Minimum Immich face detection confidence |
-| `BLUR_THRESHOLD` | `100.0` | Laplacian variance threshold — lower accepts more blur |
+| `BLUR_THRESHOLD` | `120.0` | Laplacian variance threshold — lower accepts more blur |
 | `MAX_AUTO_IMAGES` | `80` | Maximum training images per person in Frigate |
-| `QUALITY_REPLACEMENT` | `true` | When at cap, swap the lowest-scoring tracked image for a better candidate. Never touches manually added Frigate files. Set `false` to skip people already at cap |
+| `QUALITY_REPLACEMENT` | `true` | When at cap, swap a weaker tracked image for a better candidate. With Frigate scoring active, targets the most redundant image (highest pre-upload recognize score); otherwise uses blur score. Never touches manually added Frigate files. Set `false` to skip people at cap |
+| `FRIGATE_SCORE_CEILING` | `0.0` | Skip uploads whose pre-upload Frigate recognize score exceeds this value — they are already well-covered. `0` disables; requires at least one prior run to have scores |
+| `ENABLE_FRIGATE_SCORES` | `true` | Call Frigate's recognize endpoint pre-upload to store diversity scores used for quality replacement. Adds ~200 ms per upload. Disable to use blur-score replacement only |
 
 ### GPU & Models
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.3.3"
+version = "0.4.0"
 description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition and object classification."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"

tests/test_config.py

@@ -19,7 +19,7 @@ def test_config_loads_defaults(monkeypatch):
     assert cfg.YEARS_FILTER == 10
     assert cfg.MIN_FACE_WIDTH == 90
     assert cfg.MIN_FACE_COUNT == 0
-    assert cfg.BLUR_THRESHOLD == 100.0
+    assert cfg.BLUR_THRESHOLD == 120.0
     assert cfg.MIN_CONFIDENCE == 0.7
     assert cfg.MAX_AUTO_IMAGES == 80
     assert cfg.QUALITY_REPLACEMENT is True

tests/test_upload_tracker.py

@@ -218,3 +218,45 @@ def test_get_lowest_quality_exclude_all_returns_none():
     mark_uploaded("asset-a", person_name="Alice", score=0.50)
     record_frigate_file("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
+    mark_uploaded("asset-a", person_name="Alice", score=0.80)
+    record_frigate_file("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
+    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")
+    result = get_most_redundant_mapped_file("Alice")
+    assert result is not None
+    frigate_filename, asset_id, score = result
+    assert frigate_filename == "Alice-redundant.webp"
+    assert asset_id == "asset-redundant"
+    assert score == pytest.approx(0.88, abs=0.001)
+
+
+def test_get_most_redundant_exclude_skips_file():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    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")
+    result = get_most_redundant_mapped_file("Alice", exclude={"Alice-hi.webp"})
+    assert result is not None
+    assert result[1] == "asset-lo"  # hi excluded; lo is next highest
+
+
+def test_get_most_redundant_exclude_all_returns_none():
+    from winnow.upload_tracker import get_most_redundant_mapped_file, mark_uploaded, record_frigate_file
+    mark_uploaded("asset-a", person_name="Alice", score=0.5, frigate_score=0.70)
+    record_frigate_file("Alice", "Alice-a.webp", "asset-a")
+    assert get_most_redundant_mapped_file("Alice", exclude={"Alice-a.webp"}) is None

winnow/cli.py

@@ -41,6 +41,8 @@ def _handle_trace_crop(size_str: str) -> None:
             rprint(f"    Immich URL:   {immich_url}/photos/{m['asset_id']}")
         blur = m.get("blur_score")
         rprint(f"    Blur score:   {blur:.1f}" if blur is not None else "    Blur score:   unknown")
+        fscore = m.get("frigate_score")
+        rprint(f"    Frigate score: {fscore:.2f}" if fscore is not None else "    Frigate score: unknown")
         if m.get("frigate_filename"):
             rprint(f"    Frigate file: {m['frigate_filename']}")
         else:

winnow/config.py

@@ -27,10 +27,12 @@ class _Config:
 
     # Quality filtering
     MIN_FACE_WIDTH: int = 90
-    BLUR_THRESHOLD: float = 100.0
+    BLUR_THRESHOLD: float = 120.0
     MIN_CONFIDENCE: float = 0.7
     MAX_AUTO_IMAGES: int = 80
     QUALITY_REPLACEMENT: bool = True
+    FRIGATE_SCORE_CEILING: float = 0.0
+    ENABLE_FRIGATE_SCORES: bool = True
 
     # People filtering
     MIN_FACE_COUNT: int = 0
@@ -58,10 +60,12 @@ class _Config:
         self.YEARS_FILTER = int(os.getenv("YEARS_FILTER", "10"))
         self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "90"))
         self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "0"))
-        self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "100.0"))
+        self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "120.0"))
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
         self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))
         self.QUALITY_REPLACEMENT = os.getenv("QUALITY_REPLACEMENT", "true").lower() in ("true", "1", "yes")
+        self.FRIGATE_SCORE_CEILING = float(os.getenv("FRIGATE_SCORE_CEILING", "0.0"))
+        self.ENABLE_FRIGATE_SCORES = os.getenv("ENABLE_FRIGATE_SCORES", "true").lower() in ("true", "1", "yes")
         self.FACE_MARGIN = float(os.getenv("FACE_MARGIN", "0.15"))
         self.USE_FULL_RESOLUTION = os.getenv("USE_FULL_RESOLUTION", "true").lower() in ("true", "1", "yes")
         self.ENABLE_FACE_ALIGNMENT = os.getenv("ENABLE_FACE_ALIGNMENT", "true").lower() in ("true", "1", "yes")

winnow/executor.py

@@ -13,15 +13,17 @@ from rich import print as rprint
 from rich.progress import BarColumn, Progress, SpinnerColumn, TaskProgressColumn, TextColumn
 
 from .config import Config, get_headers
-from .frigate_api import delete_frigate_person_files, get_frigate_person_files
+from .frigate_api import delete_frigate_person_files, get_all_frigate_person_files, get_frigate_person_files, recognize_face
 from .image_processing import process_face_mode, process_full_mode, process_object_mode
 from .immich_api import fetch_face_data, fetch_full_image
 from .log_config import console
 from .quality import assess_quality
 from .upload_tracker import (
     get_lowest_quality_mapped_file,
+    get_most_redundant_mapped_file,
     get_tracked_frigate_file_count,
     get_tracked_frigate_filenames,
+    has_frigate_scores,
     mark_rejected,
     mark_uploaded,
     record_frigate_file,
@@ -182,6 +184,17 @@ def execute_jobs(jobs: list[dict]) -> None:
                     # from the Immich faces API (not included in search/metadata results)
                     if mode == "face":
                         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")
+                        if conf is not None and conf < Config.MIN_CONFIDENCE:
+                            progress.console.print(
+                                f"[yellow]Skipped {asset['id']}"
+                                f" (detection confidence {conf:.2f} < {Config.MIN_CONFIDENCE})[/yellow]"
+                            )
+                            progress.advance(job_task)
+                            progress.advance(overall_task)
+                            continue
 
                     # Use full-resolution for final output when configured
                     if use_full_res:
@@ -305,6 +318,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
     uploaded, failed = 0, 0
     max_retries = 2
 
+    # Fetch all Frigate training files once — avoids one GET /api/faces per person.
+    # Falls back to per-person calls inside the loop if this fetch fails.
+    all_frigate_files = get_all_frigate_person_files()
+
     with Progress(
         SpinnerColumn(),
         TextColumn("[progress.description]{task.description}"),
@@ -342,7 +359,10 @@ def upload_to_frigate(jobs: list[dict]) -> None:
             # Snapshot live Frigate files for post-upload reconciliation diff only.
             # effective_count is sourced from the tracker (mapped files) so that
             # manually-added Frigate files don't consume winnow's managed quota.
-            _snapshot = get_frigate_person_files(name)
+            _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
@@ -354,8 +374,24 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                 known_frigate_files_at_start: set[str] = get_tracked_frigate_filenames(name)
             else:
                 known_frigate_files_at_start: set[str] = set(_snapshot)
+                # 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.
+                stale = get_tracked_frigate_filenames(name) - known_frigate_files_at_start
+                for stale_fn in stale:
+                    remove_frigate_file(name, stale_fn)
+                if stale:
+                    progress.console.print(
+                        f"  [dim]{name}: cleared {len(stale)} stale mapping(s)"
+                        " (file(s) no longer in Frigate)[/dim]"
+                    )
             effective_count = get_tracked_frigate_file_count(name)
+            pre_run_count = effective_count
             quality_replacement = job.get("config", {}).get("quality_replacement", False)
+            if Config.ENABLE_FRIGATE_SCORES and pre_run_count == 0:
+                progress.console.print(
+                    f"  [dim]{name}: first run — Frigate diversity scoring will apply from the next run[/dim]"
+                )
             actually_uploaded: list[tuple[str, str | None]] = []
             failed_deletes: set[str] = set()
             min_quality_score_for_slot: float | None = None
@@ -378,38 +414,104 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         continue
 
                 at_cap = effective_count >= Config.MAX_AUTO_IMAGES
+
+                # Pre-upload Frigate score — clean measurement (image not yet in training set).
+                # Called for all below-cap uploads (seeds frigate_scores for future at-cap
+                # replacement) and for at-cap uploads when scores already exist. Skipped on
+                # the first run (pre_run_count == 0) since Frigate has no model yet.
+                # recognize_face returns (face_name, score); we only use the score when the
+                # best match is for the correct person. Mismatches (or "unknown") are treated
+                # as None so a wrong-person score never drives a ceiling skip or replacement.
+                # 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.
+                pre_fscore: float | None = None
+                if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0:
+                    if not at_cap or has_frigate_scores(name):
+                        _result = recognize_face(fpath)
+                        if _result is not None and _result[0] == name:
+                            pre_fscore = _result[1]
+
+                # Ceiling check: skip if the existing training set already covers this
+                # face condition well. Applies below cap only — at cap, replacement logic
+                # drives the decision.
+                if not at_cap and Config.FRIGATE_SCORE_CEILING > 0 and pre_run_count > 0:
+                    if pre_fscore is not None and pre_fscore > Config.FRIGATE_SCORE_CEILING:
+                        progress.console.print(
+                            f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
+                            f" > ceiling {Config.FRIGATE_SCORE_CEILING:.2f}, already covered[/dim]"
+                        )
+                        progress.advance(upload_task)
+                        continue
+
                 if at_cap:
                     if not quality_replacement:
                         progress.console.print(f"    [dim]⏭  {fname}: at cap, quality replacement disabled[/dim]")
                         progress.advance(upload_task)
                         continue
-                    new_score = score_map.get(fname)
+
-                    if new_score is None:
+                    using_fscore = has_frigate_scores(name) and Config.ENABLE_FRIGATE_SCORES
-                        progress.console.print(f"    [dim]⏭  {fname}: no confidence score, skipping replacement[/dim]")
+                    if using_fscore:
-                        progress.advance(upload_task)
+                        candidate_score = pre_fscore
-                        continue
+                        if candidate_score is None:
-                    worst = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
-                    if worst is None or new_score <= worst[2]:
-                        worst_score_str = f"{worst[2]:.3f}" if worst is not None else "N/A"
                             progress.console.print(
-                            f"    [dim]⏭  {fname}: score {new_score:.3f} ≤ worst mapped"
+                                f"    [dim]⏭  {fname}: Frigate recognize unavailable, skipping replacement[/dim]"
-                            f" {worst_score_str}, skipping[/dim]"
                             )
                             progress.advance(upload_task)
                             continue
-                    # Delete the worst mapped file to make room for the better one
+                        # Low score = more novel than the most redundant mapped file = replace
-                    worst_frigate_file, _worst_asset_id, worst_score = worst
+                        target = get_most_redundant_mapped_file(name, exclude=failed_deletes)
+                        if target is None or candidate_score >= target[2]:
+                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
                             progress.console.print(
-                        f"    🔄 {fname}: score {new_score:.3f} > {worst_score:.3f},"
+                                f"    [dim]⏭  {fname}: frigate {candidate_score:.3f} ≥ most redundant"
-                        f" replacing {worst_frigate_file}"
+                                f" {target_score_str}, not more novel[/dim]"
                             )
-                    if delete_frigate_person_files(name, [worst_frigate_file]):
+                            progress.advance(upload_task)
-                        remove_frigate_file(name, worst_frigate_file)
+                            continue
+                        target_frigate_file, _target_asset_id, target_score = target
+                        progress.console.print(
+                            f"    🔄 {fname}: frigate {candidate_score:.3f} < {target_score:.3f},"
+                            f" replacing {target_frigate_file} (more novel)"
+                        )
+                        if delete_frigate_person_files(name, [target_frigate_file]):
+                            remove_frigate_file(name, target_frigate_file)
                             effective_count -= 1
-                        min_quality_score_for_slot = worst_score
+                            min_quality_score_for_slot = None  # clear any blur-mode slot floor — Frigate uses a different score metric
                         else:
-                        logger.warning(f"Failed to delete {worst_frigate_file} for {name}, skipping replacement")
+                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
-                        failed_deletes.add(worst_frigate_file)
+                            failed_deletes.add(target_frigate_file)
+                            progress.advance(upload_task)
+                            continue
+                    else:
+                        candidate_score = score_map.get(fname)
+                        if candidate_score is None:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: no quality score, skipping replacement[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target = get_lowest_quality_mapped_file(name, exclude=failed_deletes)
+                        if target is None or candidate_score <= target[2]:
+                            target_score_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: blur {candidate_score:.3f} ≤ worst"
+                                f" {target_score_str}, skipping[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+                        target_frigate_file, _target_asset_id, target_score = target
+                        progress.console.print(
+                            f"    🔄 {fname}: blur {candidate_score:.3f} > {target_score:.3f},"
+                            f" replacing {target_frigate_file}"
+                        )
+                        if delete_frigate_person_files(name, [target_frigate_file]):
+                            remove_frigate_file(name, target_frigate_file)
+                            effective_count -= 1
+                            min_quality_score_for_slot = score_map.get(fname)
+                        else:
+                            logger.warning(f"Failed to delete {target_frigate_file} for {name}, skipping replacement")
+                            failed_deletes.add(target_frigate_file)
                             progress.advance(upload_task)
                             continue
 
@@ -434,6 +536,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                     person_name=name,
                                     score=score_map.get(fname),
                                     crop_dims=dims_map.get(fname),
+                                    frigate_score=pre_fscore,
                                 )
                                 actually_uploaded.append((fname, asset_id))
 

winnow/frigate_api.py

@@ -40,6 +40,22 @@ def get_frigate_face_counts() -> dict[str, int] | None:
     }
 
 
+def get_all_frigate_person_files() -> dict[str, list[str]] | None:
+    """Return {person_name: [filename, ...]} for every person in Frigate.
+
+    Single call used to build per-person snapshots before the upload loop,
+    avoiding one GET /api/faces per person.  Returns None if unavailable.
+    """
+    data = _get_faces_data()
+    if data is None:
+        return None
+    return {
+        name: files
+        for name, files in data.items()
+        if name != "train" and isinstance(files, list)
+    }
+
+
 def get_frigate_person_files(person_name: str) -> list[str] | None:
     """Return the list of training filenames for a person in Frigate.
 
@@ -53,6 +69,38 @@ def get_frigate_person_files(person_name: str) -> list[str] | None:
     return files if isinstance(files, list) else []
 
 
+def recognize_face(file_path: str) -> tuple[str | None, float] | None:
+    """Submit an image to Frigate's recognize endpoint.
+
+    Returns (face_name, score) where face_name is the best-matching person
+    (may be "unknown" if below Frigate's confidence threshold) and score is
+    the sigmoid-mapped cosine similarity (0-1) against that person's mean
+    embedding.
+
+    Returns None if FRIGATE_URL is unset, the API is unreachable, no face is
+    detected, or face recognition is not enabled in Frigate.
+    """
+    frigate_url = os.environ.get("FRIGATE_URL", "").rstrip("/")
+    if not frigate_url:
+        return None
+    try:
+        with open(file_path, "rb") as f:
+            resp = requests.post(
+                f"{frigate_url}/api/faces/recognize",
+                files={"file": (os.path.basename(file_path), f, "image/jpeg")},
+                timeout=15,
+            )
+        if not resp.ok:
+            return None
+        data = resp.json()
+        if data.get("success") and "score" in data:
+            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}")
+        return None
+
+
 def delete_frigate_person_files(person_name: str, filenames: list[str]) -> bool:
     """Delete specific training files for a person from Frigate.
 

winnow/upload_tracker.py

@@ -13,11 +13,16 @@ 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.
 """
@@ -77,9 +82,10 @@ def _get_ids(entry: list | dict) -> list[str]:
 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_files": {}, "crop_dims": {}}
+        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
@@ -91,6 +97,7 @@ def _mark(
     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)
@@ -107,6 +114,8 @@ def _mark(
             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)
 
@@ -126,8 +135,9 @@ def mark_uploaded(
     person_name: str | None = None,
     score: float | None = None,
     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)
+    _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})")
 
 
@@ -136,6 +146,7 @@ def mark_rejected(asset_id: str, person_name: str | None = None) -> None:
     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)
@@ -184,29 +195,78 @@ def get_tracked_frigate_filenames(person_name: str) -> set[str]:
     return set(entry["frigate_files"].keys())
 
 
+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())
+
+
 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
-    quality score, or None if no mapped files with known scores exist.
+    blur score, or None if no mapped files with known scores exist.
 
-    Pass `exclude` to skip files that failed to delete this run without removing
+    Used for quality replacement when no Frigate scores are available.
-    them from the tracker — they remain candidates on the next run.
+    Pass `exclude` to skip files that failed to delete this run.
     """
     data = _load(UPLOAD_TRACKER_FILE)
     entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
     frigate_files = entry.get("frigate_files", {})
-    scores = entry.get("scores", {})
+    blur_scores = entry.get("scores", {})
+
     candidates = [
-        (frigate_filename, asset_id, scores[asset_id])
+        (ff, asset_id, blur_scores[asset_id])
-        for frigate_filename, asset_id in frigate_files.items()
+        for ff, asset_id in frigate_files.items()
-        if asset_id in scores and (exclude is None or frigate_filename not in exclude)
+        if (exclude is None or ff not in exclude)
+        and asset_id in blur_scores
     ]
+
     if not candidates:
         return None
     return min(candidates, key=lambda x: x[2])
 
 
+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.
+    """
+    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", {})
+
+    candidates = [
+        (ff, asset_id, frigate_scores[asset_id])
+        for ff, asset_id in frigate_files.items()
+        if (exclude is None or ff not in exclude)
+        and asset_id in frigate_scores
+    ]
+
+    if not candidates:
+        return None
+    return max(candidates, key=lambda x: x[2])
+
+
+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
+
+
 def find_by_crop_dimension(size: int) -> list[dict]:
     """Return all tracked crops whose width or height matches `size` pixels.
 
@@ -220,6 +280,7 @@ def find_by_crop_dimension(size: int) -> list[dict]:
         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:
@@ -229,6 +290,7 @@ def find_by_crop_dimension(size: int) -> list[dict]:
                     "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