Merge pull request #35 from sudolulo/dev

release: v0.6.4

CHANGELOG.md

@@ -7,6 +7,56 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.6.4] - 2026-06-17
+
+### Fixed
+
+- **Face bbox scaled to thumbnail space before quality filtering**: `assess_quality` now receives coordinates in thumbnail-pixel space rather than detection-image space. Previously, a face detected on a full-resolution image (e.g. 4000 px wide) was compared against `MIN_FACE_WIDTH` using its original pixel dimensions, causing faces that appear small on the thumbnail to pass the quality filter — and faces that appear large to be incorrectly rejected.
+
+- **`conf_array` default restored to 1.0 for faces with missing confidence**: the default was incorrectly set to 0.5, causing images with no `score` field in the Immich faces API response to receive a 1.7× FPS diversity boost and be selected ahead of genuinely high-confidence detections. The default is now 1.0 (no boost), treating missing confidence as neutral.
+
+- **`hard_weight` computed once outside FPS loop**: `conf_array` is constant after initialisation; moving the `np.where` call outside the `while` loop eliminates one O(n) numpy pass per selected image.
+
+- **`has_frigate_model` snapshot prevents mid-batch `recognize_face` calls on first run**: `effective_count` is incremented inside the upload loop, so using it as the `recognize_face` gate would incorrectly trigger scoring after the first upload on a first run. A boolean snapshot is now taken before the loop.
+
+- **`person_has_fscores` only set when tracker write succeeds**: the flag was moved outside the `try/except else` block, causing at-cap replacement to switch into Frigate-score mode even when the score was never written to the tracker — `get_most_redundant_mapped_file` then returned `None` and all replacement candidates were silently skipped. The flag is now set only in the `else` branch.
+
+- **`STRATEGY=skip` honoured before embedding and limit checks**: the strategy was silently converted to `auto` when InsightFace was available, because two early-returns in `_resolve_strategy` ran before the `strategy_map` lookup.
+
+- **`limit="auto"` preserved on first run**: switching to `limit = capacity` unconditionally caused the FPS adaptive early-stop to never fire on a person's first upload run. `limit="auto"` is now kept when `already_uploaded == 0`.
+
+- **`EmbeddingCache.get` falls back gracefully on all load errors**: a `MemoryError` during `np.load` of a cached embedding was re-raised, crashing the entire diversity-selection batch for that person. Cache-read failures of any kind now return `None` so the embedding is recomputed fresh.
+
+- **`get_people` returns `[]` when Immich sends `{"people": null}`**: `.get("people", [])` only uses the default when the key is absent, not when its value is `null`. Changed to `data.get("people") or []` so null-valued responses are handled the same as missing keys.
+
+- **`get_people` and `fetch_all_assets` guard against non-dict responses**: a proxy or CDN returning a JSON array (or other non-dict body) previously caused an `AttributeError` from `.get()`. Both functions now check `isinstance(data, dict)` and return an empty result with an error log.
+
+- **`filter_recent_assets` counts and logs assets with missing or unparseable timestamps** instead of silently dropping them.
+
+- **`_suppress_output` fd cleanup restructured**: the context manager now initialises `devnull_fd`, `saved_out`, and `saved_err` to `None` before the `try` block, so the `finally` can close only the descriptors that were successfully opened. Each `os.close` is wrapped in its own `try/except OSError` so a failed close cannot prevent subsequent descriptors from being released. `OSError` from `os.dup2` restore is logged at DEBUG rather than silently swallowed.
+
+- **`blur_score_from_image` copies the image before thumbnail resize**: `Image.thumbnail` modifies the image in-place. When the caller's image was already in RGB mode (no convert copy), the resize would have mutated the caller's object. A copy is now made when `score_img is img`.
+
+- **`imageWidth`/`imageHeight` zero-value treated as missing** in `image_processing.py`: the old `or img_w` fallback silently set `scale = 1.0` for a zero-valued dimension (correct) but also for `None` (also correct) with no distinction. The explicit `scale = img_w / meta_w if meta_w else 1.0` form matches the pattern used in the new `_scale_bbox_to_thumbnail` helper and makes the fallback intent clear.
+
+- **`_mark` and `update_frigate_count` copy before mutate**: both functions now create a shallow copy of the top-level tracker dict before assigning into `by_person`, so a failed `_save` cannot leave the in-memory cache ahead of the on-disk file.
+
+- **`reset_person` flat-list guard only warns when cleanup would have run**: the `isinstance(data[flat_key], list)` check previously emitted a warning even when `person_ids` was empty (a no-op call). The warning is now gated behind `person_ids and`, matching the guard on the cleanup branch.
+
+- **`_handle_duplicate_people` uses `p.get("id")` consistently**: all four return-path filter comprehensions and the `_smaller_duplicate_ids` set comprehension now use `.get("id")` instead of bare `p["id"]`, preventing a `KeyError` if the Immich API returns a person record without an `id` field.
+
+- **`K-Medoids` non-medoid membership test is O(1)**: `non_medoids` now filters against `set(medoids)` instead of the list, eliminating an O(k) scan per candidate on each outer iteration.
+
+## [0.6.3] - 2026-06-16
+
+### Fixed
+
+- **`record_frigate_files_batch` no longer mutates the tracker cache before write**: the function shared the same cache-corruption-on-write-failure bug that was fixed in `remove_frigate_files_batch` in v0.6.1 — `data.setdefault("by_person", {})` mutated the cached dict in-place, so a disk-full or permission error left the in-memory cache ahead of the on-disk file. Now uses the same copy-before-mutate pattern (shallow copies of the top-level dict and `by_person` sub-dict) so a failed write leaves cache and disk in sync.
+
+- **`tracker_ok` boolean flag replaced with try/else**: the intermediate boolean was a misleading placeholder — the `True` initial value suggested success before the operation ran. The control flow is now expressed directly with a try/except/else block.
+
+- **`LIMIT` env var guard simplified**: the two adjacent `if custom_limit is not None` checks in `_resolve_strategy` are collapsed into a single `if custom_limit is not None:` with nested branches, removing redundant evaluation.
+
 ## [0.6.2] - 2026-06-16
 
 ### Changed

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "winnow"
-version = "0.6.2"
+version = "0.6.4"
 description = "Selects diverse, high-quality photos from Immich as training data for Frigate face recognition."
 license = "AGPL-3.0-or-later"
 requires-python = ">=3.13"

uv.lock

@@ -862,7 +862,7 @@ wheels = [
 
 [[package]]
 name = "winnow"
-version = "0.6.1"
+version = "0.6.2"
 source = { editable = "." }
 dependencies = [
     { name = "croniter" },

winnow/cache.py

@@ -90,7 +90,7 @@ class EmbeddingCache:
             np.save(tmp, embedding)
             os.replace(tmp, final)
         except Exception as e:
-            logger.debug("Cache write failed for %s: %s", asset_id, e)
+            logger.warning("Cache write failed for %s: %s", asset_id, e)
             try:
                 os.remove(tmp)
             except OSError:
@@ -103,8 +103,11 @@ class EmbeddingCache:
         count = 0
         for f in os.listdir(self.cache_dir):
             if f.endswith(".npy"):
-                os.remove(os.path.join(self.cache_dir, f))
-                count += 1
+                try:
+                    os.remove(os.path.join(self.cache_dir, f))
+                    count += 1
+                except OSError:
+                    pass
         logger.info("Cleared %s cached embeddings.", count)
 
 

winnow/cli.py

@@ -81,11 +81,13 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
     def _smaller_duplicate_ids(groups: dict) -> set[str]:
         """IDs of all but the largest person in each duplicate group."""
         return {
-            p["id"]
+            p.get("id")
             for ps in groups.values()
             for p in sorted(ps, key=lambda x: x.get("assetCount", 0), reverse=True)[1:]
         }
 
+    skip_ids = _smaller_duplicate_ids(duplicates)
+
     if not Config.MERGE_DUPLICATE_PEOPLE:
         rprint("\n[bold yellow]⚠  Duplicate person names detected in Immich:[/bold yellow]")
         for name, ps in sorted(duplicates.items()):
@@ -107,7 +109,7 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
         )
         # Return deduplicated list — keep only the largest per name so that
         # downstream job creation never runs two jobs for the same Frigate folder.
-        return [p for p in people if p["id"] not in _smaller_duplicate_ids(duplicates)]
+        return [p for p in people if p.get("id") not in skip_ids]
 
     # Auto-merge: survivor = largest asset count, rest merge into it inside Immich
     merged_any = False
@@ -129,11 +131,21 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
     if merged_any:
         rprint("  [dim]Re-fetching people after merge...[/dim]")
         fresh = get_people()
+        if not fresh:
+            # Retry once: get_people() returns [] for both transient failures and
+            # auth errors (401); a second empty result strongly suggests a real failure.
+            fresh = get_people()
+        if not fresh:
+            logger.warning(
+                "Re-fetch after merge returned no people (tried twice)"
+                " — possible transient error or expired API key;"
+                " proceeding with pre-merge list. Check IMMICH_API_KEY if this recurs."
+            )
+            return [p for p in people if p.get("id") not in skip_ids]
         # Filter out the smaller duplicate from any group whose merge failed — those
         # IDs still exist in Immich and would produce two jobs for the same folder.
         # IDs from groups that merged successfully are already gone from Immich, so
         # this filter is a no-op for them.
-        skip_ids = _smaller_duplicate_ids(duplicates)
         return [p for p in fresh if p.get("id") not in skip_ids]
 
     # All merges failed — fall back to local deduplication (keep largest per name) so
@@ -142,7 +154,7 @@ def _handle_duplicate_people(people: list[dict]) -> list[dict]:
         "  [yellow]All merges failed — applying local deduplication"
         " to avoid overwriting output.[/yellow]"
     )
-    return [p for p in people if p["id"] not in _smaller_duplicate_ids(duplicates)]
+    return [p for p in people if p.get("id") not in skip_ids]
 
 
 _UNSUPPORTED_VARS = [
@@ -172,7 +184,8 @@ def main() -> None:
     [dim]Immich -> Frigate Training Data Curator[/dim]
         """)
 
-        set_unsupported = [v for v in _UNSUPPORTED_VARS if os.environ.get(v)]
+        _FALSY = {"", "false", "0", "no", "off"}
+        set_unsupported = [v for v in _UNSUPPORTED_VARS if os.environ.get(v, "").strip().lower() not in _FALSY]
         if set_unsupported:
             console.print(
                 f"[bold yellow]⚠  Advanced tuning vars set: "

winnow/config.py

@@ -173,7 +173,7 @@ class _Config:
                 data = json.loads(config_file.read_text())
                 if not self.IMMICH_URL:
                     self.IMMICH_URL = data.get("IMMICH_URL")
-                if os.getenv("OUTPUT_DIR") is None:
+                if not os.getenv("OUTPUT_DIR"):
                     self.OUTPUT_DIR = data.get("OUTPUT_DIR", self.OUTPUT_DIR)
             except (json.JSONDecodeError, OSError) as e:
                 logging.warning("Failed to load config file: %s", e)

winnow/diversity.py

@@ -57,9 +57,9 @@ def select_diverse_assets(
     Returns:
         List of selected assets
     """
-    # Fast path: fewer assets than limit
+    # Fast path: fewer assets than limit — sort for consistent ordering with other paths
     if limit != "auto" and len(assets) <= limit:
-        return assets
+        return sorted(assets, key=lambda x: x.get("fileCreatedAt", ""))
 
     # Sort by creation time
     assets = sorted(assets, key=lambda x: x.get("fileCreatedAt", ""))
@@ -181,6 +181,28 @@ def _crop_face_from_thumbnail(
     return crop
 
 
+def _scale_bbox_to_thumbnail(
+    bbox: tuple[float, float, float, float],
+    img: Image.Image,
+    asset: dict,
+    person_id: str | None = None,
+) -> tuple[float, float, float, float]:
+    """Scale a face bbox from original detection-image space to thumbnail-pixel space."""
+    x1, y1, x2, y2 = bbox
+    img_w, img_h = img.size
+    for person in asset.get("people", []):
+        if person_id and person.get("id") != person_id:
+            continue
+        faces = person.get("faces", [])
+        if faces:
+            meta_w = faces[0].get("imageWidth") or 0
+            meta_h = faces[0].get("imageHeight") or 0
+            scale_x = img_w / meta_w if meta_w else 1.0
+            scale_y = img_h / meta_h if meta_h else 1.0
+            return (x1 * scale_x, y1 * scale_y, x2 * scale_x, y2 * scale_y)
+    return bbox
+
+
 # =============================================================================
 # Embedding Collection
 # =============================================================================
@@ -258,9 +280,13 @@ def _select_by_embedding(
             confidence = _get_face_confidence(asset, person_id=person_id)
 
             face_bbox = _get_face_bbox(asset, person_id=person_id)
+            thumbnail_bbox = (
+                _scale_bbox_to_thumbnail(face_bbox, img, asset, person_id)
+                if face_bbox is not None else None
+            )
             quality = assess_quality(
                 img,
-                face_bbox=face_bbox,
+                face_bbox=thumbnail_bbox,
                 confidence=confidence,
                 blur_threshold=Config.BLUR_THRESHOLD,
                 min_face_px=Config.MIN_FACE_WIDTH,
@@ -408,7 +434,8 @@ def _kmedoids(dist_matrix: np.ndarray, k: int, max_iter: int = 50) -> tuple[list
     for _ in range(max_iter):
         improved = False
         # Try swapping each medoid with a random non-medoid
-        non_medoids = [i for i in range(n) if i not in medoids]
+        medoid_set = set(medoids)
+        non_medoids = [i for i in range(n) if i not in medoid_set]
         if not non_medoids:
             break
 
@@ -483,7 +510,10 @@ def _cluster_aware_selection(
     norms = np.linalg.norm(emb_matrix, axis=1, keepdims=True)
     emb_normed = emb_matrix / np.maximum(norms, 1e-8)
 
-    # Build confidence weight array for hard example boosting
+    # Build confidence weight array for hard example boosting.
+    # Default to 1.0 for faces with no confidence score: treat as high-confidence
+    # (no boost) rather than hard-example territory. A missing score field should
+    # not cause these images to beat genuinely high-confidence detections in FPS.
     conf_array = np.ones(n)
     if confidence_scores:
         for i, c in enumerate(confidence_scores):
@@ -508,7 +538,6 @@ def _cluster_aware_selection(
 
     medoid_indices, cluster_labels = _kmedoids(dist_matrix, k)
     selected = list(medoid_indices)
-    selected_set = set(selected)
 
     logger.debug("Selected %s cluster medoids as initial picks.", len(selected))
 
@@ -522,10 +551,11 @@ def _cluster_aware_selection(
     for idx in selected:
         min_dists[idx] = -np.inf
 
+    # Hard example weighting: boost distance for low-confidence candidates.
+    # conf_array is constant after this point, so compute once outside the loop.
+    hard_weight = np.where(conf_array < 0.85, 1.0 + (0.85 - conf_array) * 2.0, 1.0)
+
     while len(selected) < target:
-        # Hard example weighting: boost distance for low-confidence candidates
-        # Confidence < 0.85 gets up to 1.5× distance boost
-        hard_weight = np.where(conf_array < 0.85, 1.0 + (0.85 - conf_array) * 2.0, 1.0)
         weighted_dists = min_dists * hard_weight
 
         best_idx = int(np.argmax(weighted_dists))
@@ -541,15 +571,19 @@ def _cluster_aware_selection(
             break
 
         selected.append(best_idx)
-        selected_set.add(best_idx)
 
         # Update min distances
         dists_to_new = dist_matrix[best_idx]
         min_dists = np.minimum(min_dists, dists_to_new)
         min_dists[best_idx] = -np.inf
 
-    selected_conf = [conf_array[i] for i in selected if conf_array[i] < 1.0]
-    hard_count = sum(1 for c in selected_conf if c < 0.85)
+    hard_count = sum(
+        1 for i in selected
+        if confidence_scores
+        and i < len(confidence_scores)
+        and confidence_scores[i] is not None
+        and confidence_scores[i] < 0.85
+    )
     logger.info("Selection complete: %s images (%s hard examples with confidence < 0.85).", len(selected), hard_count)
 
     # Slice to target: the while loop enforces this for non-auto mode, but
@@ -576,4 +610,4 @@ def _select_time_spread(assets: list, limit: int | str) -> list:
         return assets
 
     indices = np.linspace(0, len(assets) - 1, limit, dtype=int)
-    return [assets[i] for i in np.unique(indices)]
+    return [assets[i] for i in indices]

winnow/embeddings.py

@@ -26,22 +26,47 @@ logger = logging.getLogger(__name__)
 @contextmanager
 def _suppress_output():
     """Suppress stdout/stderr at the file-descriptor level, silencing C extension noise."""
-    devnull_fd = os.open(os.devnull, os.O_WRONLY)
-    saved_out, saved_err = os.dup(1), os.dup(2)
+    devnull_fd = None
+    saved_out = None
+    saved_err = None
     try:
+        devnull_fd = os.open(os.devnull, os.O_WRONLY)
+        saved_out = os.dup(1)
+        saved_err = os.dup(2)
         os.dup2(devnull_fd, 1)
         os.dup2(devnull_fd, 2)
         yield
     finally:
-        try:
-            os.dup2(saved_out, 1)
-        finally:
+        # Each block is a separate sequential statement. A BaseException (e.g.
+        # KeyboardInterrupt) raised inside block N would propagate past blocks N+1
+        # and N+2, leaving saved_err or devnull_fd unclosed. In CPython, KI is
+        # delivered between bytecodes, not mid-syscall; os.dup2 is a single C call
+        # and completes atomically, so this race is not realistically triggerable.
+        if saved_out is not None:
+            try:
+                os.dup2(saved_out, 1)
+            except OSError as e:
+                logger.debug("_suppress_output: failed to restore stdout fd: %s", e)
+            finally:
+                try:
+                    os.close(saved_out)
+                except OSError:
+                    pass
+        if saved_err is not None:
             try:
                 os.dup2(saved_err, 2)
+            except OSError as e:
+                logger.debug("_suppress_output: failed to restore stderr fd: %s", e)
             finally:
+                try:
+                    os.close(saved_err)
+                except OSError:
+                    pass
+        if devnull_fd is not None:
+            try:
                 os.close(devnull_fd)
-                os.close(saved_out)
-                os.close(saved_err)
+            except OSError:
+                pass
 
 
 # Lazy-loaded singleton
@@ -181,8 +206,9 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None:
         return None
 
     try:
-        # InsightFace expects BGR cv2 image
-        img_bgr = cv2.cvtColor(np.asarray(img_pil), cv2.COLOR_RGB2BGR)
+        # InsightFace expects BGR cv2 image; normalise mode first so RGBA/grayscale don't
+        # raise a channel-count error inside cvtColor.
+        img_bgr = cv2.cvtColor(np.asarray(img_pil.convert("RGB")), cv2.COLOR_RGB2BGR)
 
         # Suppress scikit-image FutureWarning from InsightFace's face_align.py
         with warnings.catch_warnings():

winnow/executor.py

@@ -1,6 +1,7 @@
 """Execution phase: image processing and Frigate upload."""
 
 import logging
+import operator
 import os
 import shutil
 from io import BytesIO
@@ -27,6 +28,10 @@ from .log_config import console
 from .quality import blur_score_from_image
 from .reconcile import enrich_asset_with_face_data, reconcile_frigate_mappings
 from .upload_tracker import (
+    REJECT_TRACKER_FILE,
+    UPLOAD_TRACKER_FILE,
+    begin_batch,
+    flush_batch,
     get_lowest_quality_mapped_file,
     get_most_redundant_mapped_file,
     get_tracked_frigate_file_count,
@@ -34,8 +39,6 @@ from .upload_tracker import (
     has_frigate_scores,
     mark_rejected,
     mark_uploaded,
-    begin_batch,
-    flush_batch,
     remove_frigate_file,
     remove_frigate_files_batch,
 )
@@ -355,245 +358,282 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         " (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:
+            if Config.ENABLE_FRIGATE_SCORES and effective_count == 0:
                 progress.console.print(
                     f"  [dim]{name}: first run — Frigate diversity scoring will apply from the next run[/dim]"
                 )
+            # Snapshot whether Frigate has a model before the upload loop starts.
+            # effective_count is incremented inside the loop on each successful upload,
+            # so using the live value would incorrectly trigger recognize_face calls
+            # mid-batch on the first run (after the first upload sets it to 1).
+            has_frigate_model = effective_count > 0
             actually_uploaded: list[tuple[str, str | None]] = []
             failed_deletes: set[str] = set()
             min_quality_score_for_slot: float | None = None
             person_has_fscores: bool = has_frigate_scores(name)
 
             begin_batch(UPLOAD_TRACKER_FILE)
-            for fname in person_files:
-                fpath = os.path.join(person_dir, fname)
+            begin_batch(REJECT_TRACKER_FILE)
+            try:
+                for fname in person_files:
+                    fpath = os.path.join(person_dir, fname)
 
-                # If a previous replacement delete succeeded but that upload failed,
-                # require the next candidate to beat the deleted file's score so the
-                # freed slot isn't filled with something worse than what we removed.
-                if min_quality_score_for_slot is not None:
-                    file_score = score_map.get(fname)
-                    if file_score is not None and file_score <= min_quality_score_for_slot:
-                        progress.console.print(
-                            f"    [dim]⏭  {fname}: score {file_score:.3f} ≤ freed slot floor"
-                            f" {min_quality_score_for_slot:.3f}, skipping[/dim]"
-                        )
-                        progress.advance(upload_task)
-                        continue
+                    # If a previous replacement delete succeeded but that upload failed,
+                    # require the next candidate to beat the deleted file's score so the
+                    # freed slot isn't filled with something worse than what we removed.
+                    if min_quality_score_for_slot is not None:
+                        file_score = score_map.get(fname)
+                        if file_score is not None and file_score <= min_quality_score_for_slot:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: score {file_score:.3f} ≤ freed slot floor"
+                                f" {min_quality_score_for_slot:.3f}, skipping[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
 
-                at_cap = effective_count >= Config.MAX_AUTO_IMAGES
+                    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.
-                # LIMITATION — async rebuild during multi-replacement runs: each deletion in a
-                # single run triggers a background model rebuild in Frigate. Subsequent recognize
-                # calls in the same run may get None (rebuild in progress), causing later
-                # candidates to fall back to blur-score replacement or be skipped entirely.
-                # The more replacements that happen in one run, the worse the scoring gets.
-                # TODO(frigate-api): if Frigate exposes a model generation counter or a
-                # rebuild-complete signal, poll it between recognize calls during replacement
-                # sequences rather than accepting stale/None scores.
-                pre_fscore: float | None = None
-                if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0:
-                    if not at_cap or person_has_fscores:
-                        _result = recognize_face(fpath)
-                        if _result is not None and (_result[0] or "").casefold() == name.casefold():
-                            pre_fscore = _result[1]
+                    # 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 when has_frigate_model is False (effective_count was 0 before the loop).
+                    # 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.
+                    # LIMITATION — async rebuild during multi-replacement runs: each deletion in a
+                    # single run triggers a background model rebuild in Frigate. Subsequent recognize
+                    # calls in the same run may get None (rebuild in progress), causing later
+                    # candidates to fall back to blur-score replacement or be skipped entirely.
+                    # The more replacements that happen in one run, the worse the scoring gets.
+                    # TODO(frigate-api): if Frigate exposes a model generation counter or a
+                    # rebuild-complete signal, poll it between recognize calls during replacement
+                    # sequences rather than accepting stale/None scores.
+                    pre_fscore: float | None = None
+                    if Config.ENABLE_FRIGATE_SCORES and has_frigate_model:
+                        if not at_cap or person_has_fscores:
+                            _result = recognize_face(fpath)
+                            if _result is not None and (_result[0] or "").casefold() == name.casefold():
+                                pre_fscore = _result[1]
 
-                # Below-cap novelty gate: skip candidates already covered by the Frigate model,
-                # including conditions learned from manually-added images winnow can't track.
-                # pre_fscore is None on the first run (pre_run_count == 0 skips recognize_face
-                # above), so this block never fires on the first run without an extra guard.
-                if not at_cap and pre_fscore is not None:
-                    _ceiling = Config.FRIGATE_SCORE_CEILING
-                    if _ceiling is None:
-                        # Dynamic default: bar = most-redundant tracked file's Frigate score.
-                        # Falls back to uploading freely when no tracked scores exist yet.
-                        _bar = get_most_redundant_mapped_file(name)
-                        _skip = _bar is not None and pre_fscore > _bar[2]
-                        _bar_str = f"most redundant tracked {_bar[2]:.2f}" if _bar else ""
-                    elif _ceiling == 0.0:
-                        _skip = False  # explicitly disabled
-                        _bar_str = ""
-                    else:
-                        _skip = pre_fscore > _ceiling
-                        _bar_str = f"ceiling {_ceiling:.2f}"
-                    if _skip:
-                        progress.console.print(
-                            f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
-                            f" > {_bar_str}, already covered[/dim]"
-                        )
-                        progress.advance(upload_task)
-                        continue
+                    # Below-cap novelty gate: skip candidates already covered by the Frigate model,
+                    # including conditions learned from manually-added images winnow can't track.
+                    # pre_fscore is None when effective_count == 0 (no Frigate model yet),
+                    # so this block never fires on the first run without an extra guard.
+                    if not at_cap and pre_fscore is not None:
+                        _ceiling = Config.FRIGATE_SCORE_CEILING
+                        if _ceiling is None:
+                            # Dynamic default: bar = most-redundant tracked file's Frigate score.
+                            # Falls back to uploading freely when no tracked scores exist yet.
+                            _bar = get_most_redundant_mapped_file(name)
+                            _skip = _bar is not None and pre_fscore > _bar[2]
+                            _bar_str = f"most redundant tracked {_bar[2]:.2f}" if _bar else ""
+                        elif _ceiling == 0.0:
+                            _skip = False  # explicitly disabled
+                            _bar_str = ""
+                        else:
+                            _skip = pre_fscore > _ceiling
+                            _bar_str = f"ceiling {_ceiling:.2f}"
+                        if _skip:
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: Frigate score {pre_fscore:.2f}"
+                                f" > {_bar_str}, 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
+                    if at_cap:
+                        if not quality_replacement:
+                            progress.console.print(f"    [dim]⏭  {fname}: at cap, quality replacement disabled[/dim]")
+                            progress.advance(upload_task)
+                            continue
 
-                    using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
-                    if using_fscore:
-                        candidate_score = pre_fscore
-                        get_target = get_most_redundant_mapped_file
-                        score_label, better_note = "frigate", " (more novel)"
-                        no_score_msg = "Frigate recognize unavailable, skipping replacement"
-                        is_better_than = lambda c, t: c < t
-                    else:
-                        candidate_score = score_map.get(fname)
-                        get_target = get_lowest_quality_mapped_file
-                        score_label, better_note = "blur", ""
-                        no_score_msg = "no quality score, skipping replacement"
-                        is_better_than = lambda c, t: c > t
+                        using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
+                        if using_fscore:
+                            candidate_score = pre_fscore
+                            get_target = get_most_redundant_mapped_file
+                            score_label, better_note = "frigate", " (more novel)"
+                            no_score_msg = "Frigate recognize unavailable, skipping replacement"
+                            is_better_than = operator.lt
+                        else:
+                            candidate_score = score_map.get(fname)
+                            get_target = get_lowest_quality_mapped_file
+                            score_label, better_note = "blur", ""
+                            no_score_msg = "no quality score, skipping replacement"
+                            is_better_than = operator.gt
 
-                    if candidate_score is None:
-                        progress.console.print(f"    [dim]⏭  {fname}: {no_score_msg}[/dim]")
-                        progress.advance(upload_task)
-                        continue
+                        if candidate_score is None:
+                            progress.console.print(f"    [dim]⏭  {fname}: {no_score_msg}[/dim]")
+                            progress.advance(upload_task)
+                            continue
 
-                    target = get_target(name, exclude=failed_deletes)
-                    not_better = target is None or not is_better_than(candidate_score, target[2])
-                    if not_better:
-                        target_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                        target = get_target(name, exclude=failed_deletes)
+                        not_better = target is None or not is_better_than(candidate_score, target[2])
+                        if not_better:
+                            target_str = f"{target[2]:.3f}" if target is not None else "N/A"
+                            cmp_op = "<" if using_fscore else ">"
+                            progress.console.print(
+                                f"    [dim]⏭  {fname}: {score_label} {candidate_score:.3f}"
+                                f" not {cmp_op} {target_str}, skipping[/dim]"
+                            )
+                            progress.advance(upload_task)
+                            continue
+
+                        target_frigate_file, _target_asset_id, target_score = target
                         cmp_op = "<" if using_fscore else ">"
                         progress.console.print(
-                            f"    [dim]⏭  {fname}: {score_label} {candidate_score:.3f}"
-                            f" not {cmp_op} {target_str}, skipping[/dim]"
+                            f"    🔄 {fname}: {score_label} {candidate_score:.3f} {cmp_op} {target_score:.3f},"
+                            f" replacing {target_frigate_file}{better_note}"
                         )
-                        progress.advance(upload_task)
-                        continue
-
-                    target_frigate_file, _target_asset_id, target_score = target
-                    cmp_op = "<" if using_fscore else ">"
-                    progress.console.print(
-                        f"    🔄 {fname}: {score_label} {candidate_score:.3f} {cmp_op} {target_score:.3f},"
-                        f" replacing {target_frigate_file}{better_note}"
-                    )
-                    if delete_frigate_person_files(name, [target_frigate_file]):
-                        remove_frigate_file(name, target_frigate_file)
-                        effective_count -= 1
-                        min_quality_score_for_slot = None if using_fscore else target_score
-                    else:
-                        logger.warning("Failed to delete %s for %s, skipping replacement", target_frigate_file, name)
-                        failed_deletes.add(target_frigate_file)
-                        progress.advance(upload_task)
-                        continue
-
-                for attempt in range(1, max_retries + 1):
-                    try:
-                        with open(fpath, "rb") as f:
-                            resp = requests.post(
-                                f"{frigate_url}/api/faces/{encoded_name}/register",
-                                files={"file": (fname, f, "image/jpeg")},
-                                timeout=30,
-                            )
-                        if resp.status_code == 200:
-                            uploaded += 1
-                            person_uploaded += 1
-                            effective_count += 1
-                            min_quality_score_for_slot = None
-
-                            asset_id = asset_map.get(fname)
-                            if asset_id:
-                                try:
-                                    mark_uploaded(
-                                        asset_id,
-                                        person_name=name,
-                                        score=score_map.get(fname),
-                                        crop_dims=dims_map.get(fname),
-                                        frigate_score=pre_fscore,
-                                    )
-                                except Exception as tracker_exc:
-                                    # Upload to Frigate succeeded — don't retry on tracker
-                                    # failure or we'd upload a duplicate to Frigate.
-                                    logger.error(
-                                        "Tracker write failed for %s — upload succeeded"
-                                        " but asset may be re-selected next run: %s",
-                                        fname, tracker_exc,
-                                    )
-                                if pre_fscore is not None:
-                                    person_has_fscores = True
-                                actually_uploaded.append((fname, asset_id))
-
-                            break
+                        if delete_frigate_person_files(name, [target_frigate_file]):
+                            remove_frigate_file(name, target_frigate_file)
+                            person_has_fscores = has_frigate_scores(name)
+                            effective_count -= 1
+                            min_quality_score_for_slot = None if using_fscore else target_score
                         else:
+                            logger.warning(
+                                "Failed to delete %s for %s, skipping replacement",
+                                target_frigate_file, name,
+                            )
+                            failed_deletes.add(target_frigate_file)
+                            progress.advance(upload_task)
+                            continue
+
+                    for attempt in range(1, max_retries + 1):
+                        try:
+                            with open(fpath, "rb") as f:
+                                resp = requests.post(
+                                    f"{frigate_url}/api/faces/{encoded_name}/register",
+                                    files={"file": (fname, f, "image/jpeg")},
+                                    timeout=30,
+                                )
+                            if resp.status_code == 200:
+                                uploaded += 1
+                                person_uploaded += 1
+                                effective_count += 1
+                                min_quality_score_for_slot = None
+
+                                asset_id = asset_map.get(fname)
+                                if asset_id:
+                                    try:
+                                        mark_uploaded(
+                                            asset_id,
+                                            person_name=name,
+                                            score=score_map.get(fname),
+                                            crop_dims=dims_map.get(fname),
+                                            frigate_score=pre_fscore,
+                                        )
+                                    except Exception as tracker_exc:
+                                        # Upload to Frigate succeeded — don't retry on tracker
+                                        # failure or we'd upload a duplicate to Frigate.
+                                        logger.error(
+                                            "Tracker write failed for %s — upload succeeded"
+                                            " but asset may be re-selected next run: %s",
+                                            fname, tracker_exc,
+                                        )
+                                    else:
+                                        if pre_fscore is not None:
+                                            person_has_fscores = True
+                                    # Always record for reconcile so the Frigate filename→asset_id
+                                    # mapping is created even when the tracker write fails.
+                                    # Trade-off: if mark_uploaded failed, asset_id is absent from
+                                    # asset_ids and scores. Consequences: (1) re-selected next run
+                                    # → Frigate duplicate; (2) excluded from quality-replacement
+                                    # candidates (_pick_mapped_file requires a scores entry);
+                                    # (3) counted toward MAX_AUTO_IMAGES cap (via frigate_files).
+                                    # The alternative — not appending — leaves the file permanently
+                                    # unmapped (reconcile never creates the frigate_files entry),
+                                    # making (2) and (3) permanent. Frigate duplicate is lesser.
+                                    actually_uploaded.append((fname, asset_id))
+
+                                break
+                            else:
+                                if attempt < max_retries:
+                                    logger.warning(
+                                        f"Upload attempt {attempt}/{max_retries} for {fname}:"
+                                        f" HTTP {resp.status_code}, retrying..."
+                                    )
+                                    continue
+                                failed += 1
+                                person_failed += 1
+                                progress.console.print(
+                                    f"    [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
+                                )
+                                full_body = resp.text
+                                try:
+                                    error_detail = resp.json().get("message", full_body[:100])
+                                except Exception:
+                                    error_detail = full_body[:100]
+                                if resp.status_code == 400:
+                                    progress.console.print(f"      [dim]{error_detail}[/dim]")
+                                else:
+                                    logger.debug("%s HTTP %s: %s", fname, resp.status_code, error_detail)
+                                _is_permanent = (
+                                    (resp.status_code == 400 and "face" in full_body.lower())
+                                    or resp.status_code == 422
+                                )
+                                if _is_permanent:
+                                    asset_id = asset_map.get(fname)
+                                    if asset_id:
+                                        mark_rejected(asset_id, person_name=name)
+                        except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
                             if attempt < max_retries:
                                 logger.warning(
                                     f"Upload attempt {attempt}/{max_retries} for {fname}:"
-                                    f" HTTP {resp.status_code}, retrying..."
+                                    f" {type(exc).__name__}, retrying..."
+                                )
+                                continue
+                            failed += 1
+                            person_failed += 1
+                            label = (
+                                "Connection refused"
+                                if isinstance(exc, requests.exceptions.ConnectionError)
+                                else "Request timed out (30s)"
+                            )
+                            progress.console.print(
+                                f"    [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
+                            )
+                        except Exception as e:
+                            if attempt < max_retries:
+                                logger.warning(
+                                    f"Upload attempt {attempt}/{max_retries} for {fname}:"
+                                    f" {type(e).__name__}, retrying..."
                                 )
                                 continue
                             failed += 1
                             person_failed += 1
                             progress.console.print(
-                                f"    [red]✗ {fname}: HTTP {resp.status_code} (after {max_retries} attempts)[/red]"
+                                f"    [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
                             )
-                            full_body = resp.text
-                            try:
-                                error_detail = resp.json().get("message", full_body[:100])
-                            except Exception:
-                                error_detail = full_body[:100]
-                            if resp.status_code == 400:
-                                progress.console.print(f"      [dim]{error_detail}[/dim]")
-                            else:
-                                logger.debug("%s HTTP %s: %s", fname, resp.status_code, error_detail)
-                            _is_permanent = (
-                                (resp.status_code == 400 and "face" in full_body.lower())
-                                or resp.status_code == 422
-                            )
-                            if _is_permanent:
-                                asset_id = asset_map.get(fname)
-                                if asset_id:
-                                    mark_rejected(asset_id, person_name=name)
-                    except (requests.exceptions.ConnectionError, requests.exceptions.Timeout) as exc:
-                        if attempt < max_retries:
-                            logger.warning(
-                                f"Upload attempt {attempt}/{max_retries} for {fname}:"
-                                f" {type(exc).__name__}, retrying..."
-                            )
-                            continue
-                        failed += 1
-                        person_failed += 1
-                        label = (
-                            "Connection refused"
-                            if isinstance(exc, requests.exceptions.ConnectionError)
-                            else "Request timed out (30s)"
-                        )
-                        progress.console.print(
-                            f"    [red]✗ {fname}: {label} (after {max_retries} attempts)[/red]"
-                        )
-                    except Exception as e:
-                        if attempt < max_retries:
-                            logger.warning(
-                                f"Upload attempt {attempt}/{max_retries} for {fname}:"
-                                f" {type(e).__name__}, retrying..."
-                            )
-                            continue
-                        failed += 1
-                        person_failed += 1
-                        progress.console.print(
-                            f"    [red]✗ {fname}: {type(e).__name__} - {e} (after {max_retries} attempts)[/red]"
-                        )
 
-                progress.advance(upload_task)
+                    progress.advance(upload_task)
 
-            if min_quality_score_for_slot is not None:
-                logger.warning(
-                    f"{name}: freed replacement slot (floor {min_quality_score_for_slot:.3f})"
-                    " was not filled this run — will be available next run"
-                )
+                if min_quality_score_for_slot is not None:
+                    logger.warning(
+                        f"{name}: freed replacement slot (floor {min_quality_score_for_slot:.3f})"
+                        " was not filled this run — will be available next run"
+                    )
 
-            flush_batch(UPLOAD_TRACKER_FILE)
+            finally:
+                try:
+                    flush_batch(UPLOAD_TRACKER_FILE)
+                except Exception as _flush_exc:
+                    logger.warning(
+                        "flush_batch failed during cleanup"
+                        " — batch will be recovered on next begin_batch: %s",
+                        _flush_exc,
+                    )
+                try:
+                    flush_batch(REJECT_TRACKER_FILE)
+                except Exception as _flush_exc:
+                    logger.warning(
+                        "flush_batch failed during cleanup"
+                        " — batch will be recovered on next begin_batch: %s",
+                        _flush_exc,
+                    )
 
             # Batch-map Frigate filenames to asset IDs now that all uploads are done.
             if actually_uploaded and not _skip_reconcile:

winnow/image_processing.py

@@ -92,11 +92,14 @@ def process_face_mode(
         return None
 
     img_w, img_h = img.size
-    meta_w = face_info.get("imageWidth") or img_w
-    meta_h = face_info.get("imageHeight") or img_h
+    meta_w = face_info.get("imageWidth") or 0
+    meta_h = face_info.get("imageHeight") or 0
 
-    # Scale bounding box to actual image dimensions
-    scale_x, scale_y = img_w / meta_w, img_h / meta_h
+    # Scale bounding box from detection-image space to actual image dimensions.
+    # Fall back to 1.0 if Immich omits the field — bbox is assumed to already
+    # be in image space (correct for thumbnails, wrong for full-res).
+    scale_x = img_w / meta_w if meta_w else 1.0
+    scale_y = img_h / meta_h if meta_h else 1.0
     x1 = face_info["boundingBoxX1"] * scale_x
     y1 = face_info["boundingBoxY1"] * scale_y
     x2 = face_info["boundingBoxX2"] * scale_x

winnow/immich_api.py

@@ -57,8 +57,12 @@ def get_people() -> list[dict]:
             logger.error("Immich API key is invalid or expired (401 Unauthorized). Update API_KEY.")
             return []
         resp.raise_for_status()
-        return resp.json().get("people", [])
-    except (requests.RequestException, ValueError) as e:
+        data = resp.json()
+        if not isinstance(data, dict):
+            logger.error("Unexpected response shape from Immich /people: %r", type(data))
+            return []
+        return data.get("people") or []
+    except (requests.RequestException, ValueError, AttributeError) as e:
         logger.error("Failed to fetch people from Immich: %s", e)
         return []
 
@@ -118,7 +122,11 @@ def fetch_all_assets(person: dict) -> tuple[list[dict], int]:
                 logger.error("Error fetching assets for %s (page %s): %s", name, page, resp.status_code)
                 break
 
-            page_assets = resp.json().get("assets", [])
+            body = resp.json()
+            if not isinstance(body, dict):
+                logger.error("Unexpected response shape fetching assets for %s (page %s): %r", name, page, type(body))
+                break
+            page_assets = body.get("assets", [])
             # Immich ≥2.x returns {"assets": {"items": [...]}};
             # earlier versions returned {"assets": [...]} directly.
             if isinstance(page_assets, dict):
@@ -277,10 +285,11 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d
 
     logger.debug("Filtering assets older than %s years (%s)", years, cutoff)
 
-    recent, skipped = [], 0
+    recent, skipped, bad_timestamp = [], 0, 0
     for asset in assets:
         created_at_str = asset.get("fileCreatedAt")
         if not isinstance(created_at_str, str) or not created_at_str:
+            bad_timestamp += 1
             continue
 
         try:
@@ -291,8 +300,14 @@ def filter_recent_assets(assets: list[dict], years: int | None = None) -> list[d
             else:
                 skipped += 1
         except ValueError:
+            bad_timestamp += 1
             continue
 
+    if bad_timestamp:
+        logger.warning(
+            "filter_recent_assets: %s asset(s) had missing or unparseable fileCreatedAt"
+            " and were excluded from the pool.", bad_timestamp
+        )
     logger.debug("Retained %s assets (filtered %s old assets).", len(recent), skipped)
     return recent
 

winnow/jobs.py

@@ -65,12 +65,20 @@ def _get_strategy_choice(has_embedding: bool) -> tuple[int | str, str]:
 
 def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, str]:
     """Resolve env var strategy to (limit, selection_mode) without prompts."""
+    if strategy == "skip":
+        return 0, "skip"
     if not has_embedding:
-        return _getenv_int("LIMIT", 30), "time"
+        limit = _getenv_int("LIMIT", 30)
+        if limit <= 0:
+            logger.warning("LIMIT=%s is invalid — ignoring and using default 30", limit)
+            limit = 30
+        return limit, "time"
 
     custom_limit = _getenv_optional_int("LIMIT")
     if custom_limit is not None:
-        return custom_limit, "smart"
+        if custom_limit > 0:
+            return custom_limit, "smart"
+        logger.warning("LIMIT=%s is invalid — ignoring and using auto strategy", custom_limit)
 
     strategy_map = {
         "adaptive": ("auto", "smart"),
@@ -293,10 +301,11 @@ def auto_configure(people: list[dict]) -> list[dict]:
         # decides per-image whether to swap; any candidate could be an improvement).
         if not quality_replacement_only:
             if limit == "auto":
-                # Switch from open-ended auto to a fixed budget at remaining capacity
-                # so the diversity selector itself stops at the right count instead of
-                # selecting MAX_AUTO_IMAGES and then discarding the excess by position.
                 if already_uploaded > 0:
+                    # Switch from open-ended auto to a fixed budget at remaining capacity
+                    # so the diversity selector stops at the right count instead of
+                    # selecting more than MAX_AUTO_IMAGES and overflowing the cap.
+                    # First runs keep limit="auto" so FPS adaptive early-stop can fire.
                     limit = capacity
             else:
                 limit = min(limit, capacity)

winnow/quality.py

@@ -14,6 +14,11 @@ from PIL import Image
 logger = logging.getLogger(__name__)
 
 
+def _laplacian_var(img_np: np.ndarray) -> float:
+    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
+    return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+
+
 @dataclass
 class QualityResult:
     """Result of quality assessment on a face/image crop."""
@@ -32,8 +37,7 @@ def check_blur(img_np: np.ndarray, threshold: float = 100.0) -> tuple[bool, str]
 
     Lower variance = blurrier image. ArcFace needs clear facial features.
     """
-    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
-    variance = cv2.Laplacian(gray, cv2.CV_64F).var()
+    variance = _laplacian_var(img_np)
     if variance < threshold:
         return False, f"Blurry (laplacian={variance:.1f}, threshold={threshold})"
     return True, ""
@@ -115,8 +119,7 @@ def assess_quality(
     reasons = []
 
     # Compute laplacian variance once (used by check_blur and stored as blur_score)
-    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
-    blur_score = float(cv2.Laplacian(gray, cv2.CV_64F).var())
+    blur_score = _laplacian_var(img_np)
 
     checks = [
         (
@@ -152,10 +155,10 @@ def blur_score_from_image(img: Image.Image, max_dim: int = 1440) -> float | None
     try:
         score_img = img.convert("RGB") if img.mode != "RGB" else img
         if score_img.width > max_dim or score_img.height > max_dim:
-            score_img = score_img.copy()
+            if score_img is img:
+                score_img = score_img.copy()
             score_img.thumbnail((max_dim, max_dim), Image.LANCZOS)
-        gray = cv2.cvtColor(np.array(score_img), cv2.COLOR_RGB2GRAY)
-        return float(cv2.Laplacian(gray, cv2.CV_64F).var())
+        return _laplacian_var(np.array(score_img))
     except Exception as exc:
         logger.debug("blur_score_from_image failed: %s", exc)
         return None

winnow/reconcile.py

@@ -61,7 +61,7 @@ def reconcile_frigate_mappings(
             try:
                 return float(fname.rsplit("_", 1)[-1].rsplit(".", 1)[0])
             except (ValueError, IndexError):
-                return 0.0
+                return float("inf")
 
         logger.debug(
             "%s: mapping %s file(s) by filename timestamp — assumes Frigate processes"

winnow/upload_tracker.py

@@ -32,7 +32,7 @@ import logging
 import os
 from pathlib import Path
 
-from .frigate_api import delete_frigate_person_files
+from .frigate_api import _get_frigate_url, delete_frigate_person_files
 
 logger = logging.getLogger(__name__)
 
@@ -44,6 +44,7 @@ REJECT_TRACKER_FILE = "frigate_rejected_ids.json"
 # Keyed by full path so tests with isolated tmp dirs never share entries.
 _cache: dict[str, dict] = {}
 _deferred: set[str] = set()  # paths whose disk writes are batched until flush_batch()
+_dirty: set[str] = set()    # deferred paths that received at least one _save during the batch
 
 
 def _tracker_path(filename: str) -> Path:
@@ -87,6 +88,7 @@ def _save(filename: str, data: dict) -> None:
     key = str(path)
     if key in _deferred:
         _cache[key] = data  # accumulate in cache; disk write deferred until flush_batch()
+        _dirty.add(key)
         return
     _write_to_disk(path, data)
     _cache[key] = data  # update cache only after successful write
@@ -95,17 +97,36 @@ def _save(filename: str, data: dict) -> None:
 def begin_batch(filename: str) -> None:
     """Defer tracker disk writes for filename. All _save calls accumulate in the
     in-memory cache until flush_batch() is called. Use around per-person upload loops
-    to reduce N writes to 1."""
-    _deferred.add(str(_tracker_path(filename)))
+    to reduce N writes to 1.
+
+    If a previous batch for this file was interrupted before flush_batch() was called
+    (e.g. an exception escaped the upload loop), the leftover cache state is flushed
+    to disk here before starting fresh so that partial progress is not silently lost.
+    """
+    path = _tracker_path(filename)
+    key = str(path)
+    if key in _deferred and key in _dirty:
+        try:
+            _write_to_disk(path, _cache[key])
+        except Exception:
+            logger.warning(
+                "begin_batch: could not flush leftover deferred state for %s"
+                " — partial progress may be lost",
+                path,
+            )
+        _deferred.discard(key)
+        _dirty.discard(key)
+    _deferred.add(key)
 
 
 def flush_batch(filename: str) -> None:
     """Write the accumulated cache state for filename to disk."""
     path = _tracker_path(filename)
     key = str(path)
-    _deferred.discard(key)
-    if key in _cache:
+    if key in _dirty and key in _cache:
         _write_to_disk(path, _cache[key])
+    _deferred.discard(key)
+    _dirty.discard(key)
 
 
 def _flat_key(filename: str) -> str:
@@ -141,21 +162,26 @@ def _mark(
     crop_dims: tuple[int, int] | None = None,
     frigate_score: float | None = None,
 ) -> None:
+    if not person_name:
+        logger.warning("_mark called with empty person_name for asset %s — asset not recorded", asset_id)
+        return
     data = _load(filename)
-    if person_name:
-        by_person = data.setdefault("by_person", {})
-        entry = _migrate_entry(by_person.get(person_name, {}))
-        ids = set(entry["asset_ids"])
-        ids.add(asset_id)
-        entry["asset_ids"] = sorted(ids)
-        if score is not None:
-            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)
+    by_person = dict(data.get("by_person", {}))
+    entry = _migrate_entry(by_person.get(person_name, {}))
+    ids = set(entry["asset_ids"])
+    ids.add(asset_id)
+    entry["asset_ids"] = sorted(ids)
+    if score is not None:
+        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
+    new_data = dict(data)
+    new_data["by_person"] = by_person
+    _save(filename, new_data)
+    logger.debug("Marked %s in %s (%s)", asset_id, filename, person_name)
 
 
 # ── Public API ────────────────────────────────────────────────────────────────
@@ -186,12 +212,10 @@ def mark_uploaded(
     frigate_score: float | None = None,
 ) -> None:
     _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})")
 
 
 def mark_rejected(asset_id: str, person_name: str | None = None) -> None:
     _mark(REJECT_TRACKER_FILE, asset_id, person_name)
-    logger.debug(f"Marked {asset_id} as rejected ({person_name})")
 
 
 
@@ -205,11 +229,13 @@ def record_frigate_files_batch(person_name: str, mappings: dict[str, str]) -> No
     """Record multiple Frigate filename → asset_id mappings in a single load/save."""
     if not mappings:
         return
-    data = _load(UPLOAD_TRACKER_FILE)
-    by_person = data.setdefault("by_person", {})
+    src = _load(UPLOAD_TRACKER_FILE)
+    by_person = dict(src.get("by_person", {}))
     entry = _migrate_entry(by_person.get(person_name, {}))
     entry["frigate_files"].update(mappings)
     by_person[person_name] = entry
+    data = dict(src)
+    data["by_person"] = by_person
     _save(UPLOAD_TRACKER_FILE, data)
     logger.debug(f"Batch-mapped {len(mappings)} Frigate file(s) for {person_name}")
 
@@ -225,17 +251,19 @@ def remove_frigate_file(person_name: str, frigate_filename: str) -> None:
 
 def remove_frigate_files_batch(person_name: str, frigate_filenames: list[str]) -> None:
     """Remove multiple Frigate filenames in a single load/save."""
-    data = _load(UPLOAD_TRACKER_FILE)
-    by_person = data.get("by_person", {})
-    raw = by_person.get(person_name)
+    src = _load(UPLOAD_TRACKER_FILE)
+    raw = src.get("by_person", {}).get(person_name)
     if raw is None:
         return
     entry = _migrate_entry(raw)
     for fn in frigate_filenames:
         asset_id = entry["frigate_files"].pop(fn, None)
-        if asset_id:
+        if asset_id is not None and asset_id not in entry["frigate_files"].values():
             entry["frigate_scores"].pop(asset_id, None)
+    by_person = dict(src.get("by_person", {}))  # copy so assignment does not mutate the cache
     by_person[person_name] = entry
+    data = dict(src)
+    data["by_person"] = by_person
     _save(UPLOAD_TRACKER_FILE, data)
     logger.debug(f"Removed {len(frigate_filenames)} Frigate file mapping(s) for {person_name}")
 
@@ -265,9 +293,11 @@ def get_tracked_frigate_filenames(person_name: str) -> set[str]:
 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", {})
+    raw = data.get("by_person", {}).get(person_name)
+    if not raw or isinstance(raw, list):
+        return False
+    frigate_files = raw.get("frigate_files", {})
+    frigate_scores = raw.get("frigate_scores", {})
     return any(asset_id in frigate_scores for asset_id in frigate_files.values())
 
 
@@ -330,6 +360,8 @@ def find_by_crop_dimension(size: int) -> list[dict]:
             asset_to_frigate.setdefault(aid, fn)  # first-seen wins; plain inversion silently drops duplicates
         frigate_scores = entry.get("frigate_scores", {})
         for asset_id, dims in entry.get("crop_dims", {}).items():
+            if not isinstance(dims, (list, tuple)) or len(dims) < 2:
+                continue
             w, h = dims[0], dims[1]
             if w == size or h == size:
                 results.append({
@@ -347,11 +379,13 @@ def find_by_crop_dimension(size: int) -> list[dict]:
 def update_frigate_count(person_name: str, count: int) -> None:
     """Record Frigate's authoritative training image count for a person."""
     data = _load(UPLOAD_TRACKER_FILE)
-    by_person = data.setdefault("by_person", {})
+    by_person = dict(data.get("by_person", {}))
     entry = _migrate_entry(by_person.get(person_name, {}))
     entry["frigate_count"] = count
     by_person[person_name] = entry
-    _save(UPLOAD_TRACKER_FILE, data)
+    new_data = dict(data)
+    new_data["by_person"] = by_person
+    _save(UPLOAD_TRACKER_FILE, new_data)
 
 
 def reset_all_people() -> None:
@@ -361,17 +395,19 @@ def reset_all_people() -> None:
     approach is O(P²) because each call rebuilds the flat list from all remaining entries.
     """
     upload_data = _load(UPLOAD_TRACKER_FILE)
+    frigate_url = _get_frigate_url()
+    if not frigate_url:
+        logger.info("FRIGATE_URL not set — skipping Frigate file deletion")
     for person_name, raw_entry in upload_data.get("by_person", {}).items():
         entry = _migrate_entry(raw_entry)
         frigate_filenames = list(entry.get("frigate_files", {}).keys())
         if not frigate_filenames:
             continue
-        if not os.environ.get("FRIGATE_URL", "").strip():
-            logger.info(f"FRIGATE_URL not set — skipping Frigate file deletion for {person_name}")
-        elif delete_frigate_person_files(person_name, frigate_filenames):
-            logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}")
-        else:
-            logger.warning(f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway")
+        if frigate_url:
+            if delete_frigate_person_files(person_name, frigate_filenames):
+                logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}")
+            else:
+                logger.warning(f"Could not delete Frigate files for {person_name} — tracker reset proceeding anyway")
     _save(UPLOAD_TRACKER_FILE, {})
     _save(REJECT_TRACKER_FILE, {})
     logger.info("Reset all tracking data")
@@ -389,7 +425,7 @@ def reset_person(person_name: str) -> None:
     entry = _migrate_entry(upload_data.get("by_person", {}).get(person_name, {}))
     frigate_filenames = list(entry.get("frigate_files", {}).keys())
     if frigate_filenames:
-        if not os.environ.get("FRIGATE_URL", "").strip():
+        if not _get_frigate_url():
             logger.info(f"FRIGATE_URL not set — skipping Frigate file deletion for {person_name}")
         elif delete_frigate_person_files(person_name, frigate_filenames):
             logger.info(f"Deleted {len(frigate_filenames)} Frigate file(s) for {person_name}")
@@ -398,15 +434,22 @@ def reset_person(person_name: str) -> None:
 
     changed = False
     for filename in (UPLOAD_TRACKER_FILE, REJECT_TRACKER_FILE):
-        data = upload_data if filename == UPLOAD_TRACKER_FILE else _load(REJECT_TRACKER_FILE)
-        by_person = data.get("by_person", {})
+        src = upload_data if filename == UPLOAD_TRACKER_FILE else _load(REJECT_TRACKER_FILE)
+        by_person = dict(src.get("by_person", {}))  # copy so pop() does not mutate the cache
         tracker_entry = by_person.pop(person_name, None)
         if tracker_entry is not None:
+            data = dict(src)
+            data["by_person"] = by_person
             flat_key = _flat_key(filename)
             person_ids = set(_get_ids(tracker_entry))
-            if person_ids and flat_key in data:
+            if person_ids and flat_key in data and not isinstance(data[flat_key], list):
+                logger.warning(
+                    "reset_person: %s has unexpected type for %s (%s) — skipping flat-list cleanup;"
+                    " all persons' legacy IDs in this field are unaffected but unreadable",
+                    filename, flat_key, type(data[flat_key]).__name__,
+                )
+            elif person_ids and flat_key in data:
                 data[flat_key] = sorted(set(data[flat_key]) - person_ids)
-            data["by_person"] = by_person
             _save(filename, data)
             changed = True
     if changed: