face-sets/README.md

# face-sets

Sort photos by similar face using InsightFace embeddings + agglomerative clustering, refine into per-identity sets, and export ready-to-drop bundles for face-swap tooling (roop-unleashed, etc.).

## Pipeline

`sort_faces.py` is a single-file CLI with six subcommands:

| step        | what it does                                                                                                |
|-------------|-------------------------------------------------------------------------------------------------------------|
| embed       | Recursively scan a source tree, detect + embed every face, write `.npz` cache. Resumable; sha256-dedup.    |
| cluster     | Raw agglomerative clustering of the cache into `person_NNN/` / `_singletons/` / `_noface/` with manifest.  |
| refine      | Initial cluster → centroid merge → quality gate → outlier rejection → size filter → `faceset_NNN/`.        |
| dedup       | Post-hoc near-duplicate report: byte-identical + visual near-dupe groups → `<cache>.duplicates.json`.      |
| extend      | Fold new embeddings into an existing raw/refine output via nearest person-centroid without renumbering.    |
| enrich      | Re-detect each cached face to persist landmark_2d_106, landmark_3d_68, pose (pitch/yaw/roll) into cache.   |
| export-swap | Per-identity export: tight outlier gate + visual-dupe collapse + composite quality rank + single-face PNG crops + `.fsz` bundles (top-N and full) ready for roop-unleashed. Optional singleton rescue into `_candidates/`. |

### Design principles

- **embed is resumable and incremental.** It loads any existing cache at the target path and only hashes / embeds files it has not seen. Atomic flush every 50 new files so a mid-run crash loses at most ~50 embeddings.
- **Byte-identical duplicates are sha256-grouped at listing time.** The canonical file is embedded once; other paths with the same hash become `path_aliases` in the cache. Every alias is materialized by `cluster` / `refine` / `export-swap`, so each on-disk location is represented.
- **`safe_dst_name` always flattens the absolute path.** This keeps output filenames stable across runs even as `src_root` changes between embed / extend / export invocations.
- **Caches and outputs stay out of git** via `.gitignore`; defaults live under `work/`.

## Typical end-to-end run

```bash
SRC=/mnt/x/src/nl
CACHE=work/cache/nl_full.npz
OUT=/mnt/e/temp_things/fcswp/nl_sorted

# 1. Embed (CPU; InsightFace buffalo_l). Resumable on re-run.
python sort_faces.py embed "$SRC" "$CACHE"

# 2. Raw clusters (one person_NNN/ per multi-face cluster).
python sort_faces.py cluster "$CACHE" "$OUT/raw_full"

# 3. Refined facesets (quality-gated per-identity sets).
python sort_faces.py refine  "$CACHE" "$OUT/facesets_full"

# 4. Near-duplicate report (byte + visual).
python sort_faces.py dedup   "$CACHE"

# 5. Enrich the cache with landmarks + pose (needed by export-swap).
python sort_faces.py enrich  "$CACHE"

# 6. Export roop-unleashed-ready bundles.
python sort_faces.py export-swap "$CACHE" \
  "$OUT/facesets_full/refine_manifest.json" "$OUT/facesets_swap_ready" \
  --raw-manifest "$OUT/raw_full/manifest.json" --candidates
```

### Merging a new source into an existing result

```bash
# Embed new source into the same cache (resume from existing embeddings + aliases).
python sort_faces.py embed /mnt/x/src/lzbkp_red "$CACHE"

# Fold new faces into raw_full + facesets_full without renumbering.
python sort_faces.py extend "$CACHE" "$OUT/raw_full" --refine-out "$OUT/facesets_full"

# Refresh the swap-ready export to reflect the merge.
python sort_faces.py enrich "$CACHE"
python sort_faces.py export-swap "$CACHE" \
  "$OUT/facesets_full/refine_manifest.json" "$OUT/facesets_swap_ready" \
  --raw-manifest "$OUT/raw_full/manifest.json" --candidates
```

### Importing hand-sorted folders as identities

When source folders are already hand-sorted by person (one folder per identity), the
clustering path is the wrong tool — the identity is asserted, not inferred. The
orchestration script `work/build_folders.py` covers this case:

- For each trusted folder, it filters cache records that fall under it, builds an
  identity centroid via two-pass outlier rejection (cos-dist 0.55 → 0.45) so
  bystanders in group photos drop out, and writes a synthetic `refine_manifest.json`.
- It then routes each face record from a *mixed* folder (e.g. `osrc/`) into every
  identity centroid within a tight cosine cutoff (default 0.45). A multi-identity
  photo lands in multiple facesets; `export-swap`'s per-bbox outlier filter ensures
  each faceset crops only its matching face.
- Finally it invokes `cmd_export_swap` against the synthetic manifest, renames the
  emitted `.fsz` bundles after the source folder, drops a `<label>.txt` marker, and
  merges the new entries into the canonical `facesets_swap_ready/manifest.json`
  (existing facesets are left untouched).

```bash
# Embed each hand-sorted folder + the mixed bucket; cache deduplicates by sha256.
for d in k m mi mir s sab t osrc; do
  python sort_faces.py embed "/mnt/x/src/$d" "$CACHE"
done

# Bring landmarks/pose + visual-dupe report in sync with the new records.
python sort_faces.py enrich "$CACHE"
python sort_faces.py dedup  "$CACHE"

# Build per-folder identities + osrc routing -> facesets_swap_ready/faceset_NNN/.
python work/build_folders.py
```

The script's config block (`TRUSTED`, `START_NNN`, `OSRC_THRESHOLD`, `TOP_N`, etc.)
is the only thing to edit when adding more hand-sorted folders later.

### Splitting an identity by era (age sub-clustering)

Long-running source corpora produce identities that span 10+ years. The 2009 face
and the 2024 face of the same person sit in the same cluster (correctly — same
identity), but a single averaged embedding pulled from that cluster blurs across
ages. For face-swap output that should target a specific period, the identity
needs to be split by era *after* the identity is established.

`work/age_split_001.py` is a worked example for `faceset_001` and a template for
any other identity. The pipeline is:

- **Probe first** with `work/check_faceset001_age.py` — report intra-cluster
  pairwise cos-dist histogram, sub-cluster sizes at thresholds 0.30..0.50, and
  EXIF-year distribution per sub-cluster. If sub-clusters at 0.35 align with
  distinct year ranges, the identity is age-sortable.
- **Seed centroid** from the curated `facesets_swap_ready/faceset_001/`
  (manifest provides face keys → cache rows).
- **Wide recovery** at cos-dist ≤ 0.55 against the seed under the original
  source roots, then quality-gate (`face_short`, `blur`, `det_score`) and one
  re-centroid + tighten pass at 0.50 to absorb new faces without drift.
- **Sub-cluster** the survivors at cos-dist 0.35 (precomputed-distance
  agglomerative, average linkage).
- **Anchor-based fragment assignment** (not transitive merge — that caused
  year-drift): sub-clusters with size ≥ 20 are *anchors*; smaller fragments
  attach to the single nearest anchor only if both the centroid distance ≤ 0.40
  AND the dominant EXIF year is within ±5 years. Fragments with no qualifying
  anchor remain standalone (and end up THIN-tagged downstream).
- **EXIF year per source path** with on-disk caching at
  `work/cache/age_split_exif.json` — the Windows-mount EXIF read is the
  slowest step, so re-runs after a parameter tweak are nearly instant.
- **Per-era export** mirrors `export-swap`: composite-quality rank, single-face
  square PNG crops, top-N + `_all` `.fsz` bundles, per-era `manifest.json`,
  human-readable `<era>.txt` marker. Eras with < 20 face records also drop a
  `THIN.txt` marker so they can be quarantined.
- **Top-level manifest merge**: era buckets are appended to
  `facesets_swap_ready/manifest.json`. Operationally the THIN buckets should be
  moved into `_thin/` (and the manifest split into `facesets` + `thin_eras`),
  leaving only the substantive era buckets at the top level.

```bash
# 1. Confirm the identity is age-sortable.
python work/check_faceset001_age.py

# 2. Split it. Re-runs are cheap thanks to the EXIF cache.
python work/age_split_001.py
```

For the `faceset_001` run on 5260-face `nl_full.npz`, this produced 6 substantive
era buckets (2005–10, 2010–13, 2011, 2014–17, 2018–19, 2018–20; sizes 43–282)
plus 68 thin/fragment buckets quarantined under `_thin/`.

## Key defaults

`refine`:

| flag                    | default | meaning |
|-------------------------|--------:|---------|
| `--initial-threshold`   | 0.55    | cosine distance for stage-1 clustering |
| `--merge-threshold`     | 0.40    | centroid-level merge of over-split clusters |
| `--outlier-threshold`   | 0.55    | drop face if cosine dist from centroid exceeds (only if cluster ≥ 4) |
| `--min-faces`           | 15      | minimum unique images per faceset |
| `--min-short`           | 90      | minimum short-edge pixels of face bbox |
| `--min-blur`            | 40.0    | Laplacian-variance blur gate |
| `--min-det-score`       | 0.6     | InsightFace detector score gate |

`export-swap`:

| flag                          | default | meaning |
|-------------------------------|--------:|---------|
| `--top-n`                     | 30      | size of the `<faceset>_topN.fsz` bundle |
| `--outlier-threshold`         | 0.45    | tighter than refine; trims cluster boundary for averaging |
| `--pad-ratio`                 | 0.5     | padding around face bbox for PNG crop |
| `--out-size`                  | 512     | PNG output is square `out_size × out_size` |
| `--min-face-short`            | 100     | export gate; stricter than refine's 90 |
| `--candidates`                | off     | rescue `_singletons/` into `_candidates/` for manual review |
| `--candidate-match-threshold` | 0.55    | cos-dist cutoff for singleton → existing faceset |
| `--candidate-min-score`       | 0.40    | composite-quality floor for candidates |

The composite quality score in `export-swap` is `0.30·frontality + 0.20·det_score + 0.20·landmark_symmetry + 0.15·face_size + 0.15·sharpness`, each normalized to `[0, 1]`.

## Downstream: roop-unleashed

The `.fsz` bundles emitted by `export-swap` drop straight into roop-unleashed's Face Swap tab. Each PNG inside is already a clean single-face crop — critical, because the roop-unleashed loader appends every face it re-detects in each PNG to the averaged identity embedding.

Highly recommended at swap time: enable **Select post-processing = GFPGAN** with the **Original/Enhanced image blend ratio = 0.85** (default is 0.65 which is conservative). See `docs/analysis/facesets-downstream-refinement-evaluation.md` for the full evaluation.

## Layout

```
/opt/face-sets/
├─ README.md                                     (this file)
├─ sort_faces.py                                 (the tool)
├─ docs/
│  └─ analysis/
│     └─ facesets-downstream-refinement-evaluation.md
└─ work/                                         (gitignored except force-tracked .py)
   ├─ build_folders.py                           (hand-sorted-folder orchestration)
   ├─ check_faceset001_age.py                    (age-split readiness probe)
   ├─ age_split_001.py                           (age-split orchestration; faceset_001)
   ├─ synthetic_refine_manifest.json             (last build_folders.py output)
   ├─ cache/
   │  ├─ nl_full.npz                             (canonical cache + duplicates.json)
   │  └─ age_split_exif.json                     (path → EXIF-year cache)
   └─ logs/
      └─ *.log                                   (every long step writes here)
```