Peter
7ecbfae981
work/cluster_osrc.py mirrors build_folders.py's shape (synthesize a refine_manifest, hand off to cmd_export_swap, relocate, merge top-level manifest) but discovers identities by clustering rather than asserting them by folder. Drops faces already covered by existing identity centroids, clusters the rest at 0.55, applies refine-equivalent gates with min_faces=6, numbers new facesets past the existing maximum so faceset_001..NNN are never disturbed. The 2026-04-26 run on /mnt/x/src/osrc produced faceset_020..025 (sizes 4-26 exported PNGs); analysis writeup in docs/analysis/. README also notes the refine-renumbers caveat in passing — extend + orchestration script is the safe pattern; cmd_refine is for fresh clusters only. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
353 lines
12 KiB
Python
353 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Discover new identities in /mnt/x/src/osrc and emit them as facesets.
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Workflow (mirrors the shape of build_folders.py, but identities are
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discovered by clustering rather than asserted by folder):
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1. Load cache; restrict to face records whose canonical or alias path
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lies under /mnt/x/src/osrc/.
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2. Build centroids of the existing 19 canonical identities in
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facesets_swap_ready/faceset_001..019. Drop any osrc face whose
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nearest-existing-identity cos-dist <= EXISTING_MATCH_THRESHOLD;
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those are already covered by `extend` and shouldn't seed new
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facesets.
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3. Cluster the remaining osrc faces among themselves at
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INITIAL_THRESHOLD (matches `extend`'s new_cluster_threshold default).
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4. Per cluster, apply refine-equivalent gates: face_short >= MIN_SHORT,
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blur >= MIN_BLUR, det_score >= MIN_DET_SCORE; for clusters >= 4,
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drop faces with cos-dist > OUTLIER_THRESHOLD from the cluster
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centroid.
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5. Keep clusters whose surviving unique source-path count is >= MIN_FACES.
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6. Number kept clusters faceset_020, 021, ... (past the highest existing
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in facesets_swap_ready, which is 019). Order by descending size.
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7. Synthesize a refine_manifest.json and call cmd_export_swap on it,
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emitting into a temp dir. Move new dirs into facesets_swap_ready/.
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8. Append new entries to the top-level facesets_swap_ready/manifest.json
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(preserving existing facesets / thin_eras).
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"""
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from __future__ import annotations
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import json
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import shutil
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import sys
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from pathlib import Path
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import numpy as np
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REPO = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(REPO))
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from sort_faces import ( # noqa: E402
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_cluster_embeddings,
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cmd_export_swap,
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load_cache,
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)
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# ---- config -------------------------------------------------------------- #
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CACHE = REPO / "work" / "cache" / "nl_full.npz"
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SWAP_READY = Path("/mnt/e/temp_things/fcswp/nl_sorted/facesets_swap_ready")
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OUT_TMP = Path("/mnt/e/temp_things/fcswp/nl_sorted/facesets_swap_ready_osrc_new")
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SYNTH_MANIFEST = REPO / "work" / "synthetic_osrc_manifest.json"
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OSRC_DIR = Path("/mnt/x/src/osrc")
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START_NNN = 20 # facesets_swap_ready max is 019; pick up here.
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# Existing-identity exclusion: drop osrc faces whose nearest existing
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# identity centroid is within this cosine distance. 0.45 matches the
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# build_folders.py OSRC_THRESHOLD: at this cutoff the face is already
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# routed to an existing identity by extend / build_folders.py.
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EXISTING_MATCH_THRESHOLD = 0.45
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# Cluster the unmatched.
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INITIAL_THRESHOLD = 0.55
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# Refine-equivalent gates (per the user's request: drop min_faces to 6).
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MIN_FACES = 6
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MIN_SHORT = 90
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MIN_BLUR = 40.0
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MIN_DET_SCORE = 0.6
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OUTLIER_THRESHOLD = 0.55 # only applied if cluster >= 4
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# export-swap params (defaults from sort_faces.py).
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TOP_N = 30
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EXPORT_OUTLIER_THRESHOLD = 0.45
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PAD_RATIO = 0.5
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OUT_SIZE = 512
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EXPORT_MIN_FACE_SHORT = 100
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# ---- helpers ------------------------------------------------------------- #
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def _normalize(v: np.ndarray) -> np.ndarray:
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n = np.linalg.norm(v)
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return v / n if n > 0 else v
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def _under(folder: Path, p: str) -> bool:
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fs = str(folder).rstrip("/") + "/"
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return p == str(folder) or p.startswith(fs)
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def _record_in_folder(rec: dict, folder: Path, path_aliases: dict[str, list[str]]) -> bool:
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if _under(folder, rec["path"]):
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return True
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for alias in path_aliases.get(rec["path"], []):
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if _under(folder, alias):
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return True
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return False
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def _existing_identity_centroids(
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emb: np.ndarray, face_records: list[dict]
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) -> tuple[np.ndarray, list[str]]:
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"""Build a (n_identities, 512) matrix of L2-normalized centroids and a parallel name list,
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drawn from the canonical faceset_001..019 manifests in facesets_swap_ready/."""
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bbox_idx: dict[tuple[str, tuple], int] = {
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(m["path"], tuple(m.get("bbox") or ())): i for i, m in enumerate(face_records)
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}
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centroids: list[np.ndarray] = []
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names: list[str] = []
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for n in range(1, 20):
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d = SWAP_READY / f"faceset_{n:03d}"
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man_path = d / "manifest.json"
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if not man_path.exists():
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continue
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man = json.loads(man_path.read_text())
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keys = [(f["source"], tuple(f.get("bbox") or ())) for f in man.get("faces", [])]
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idxs = [bbox_idx[k] for k in keys if k in bbox_idx]
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if not idxs:
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continue
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centroids.append(_normalize(emb[idxs].mean(axis=0)))
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names.append(d.name)
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return np.stack(centroids), names
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# ---- phase 1: identify new osrc clusters --------------------------------- #
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def discover_new_clusters() -> tuple[dict, list[dict]]:
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emb, meta, _src_root, _proc, path_aliases = load_cache(CACHE)
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face_records = [m for m in meta if not m.get("noface")]
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if len(face_records) != len(emb):
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raise SystemExit(f"meta/embedding mismatch: {len(face_records)} vs {len(emb)}")
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print(f"Cache: {len(face_records)} face records.")
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# Step 1: filter to osrc.
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osrc_idx = [
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i for i, m in enumerate(face_records)
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if _record_in_folder(m, OSRC_DIR, path_aliases)
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]
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print(f"osrc face records: {len(osrc_idx)}")
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# Step 2: drop those already matching an existing identity.
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cents, cent_names = _existing_identity_centroids(emb, face_records)
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osrc_emb = emb[osrc_idx]
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sims = osrc_emb @ cents.T
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nearest_d = 1.0 - sims.max(axis=1)
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nearest_id = sims.argmax(axis=1)
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covered_mask = nearest_d <= EXISTING_MATCH_THRESHOLD
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n_covered = int(covered_mask.sum())
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print(
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f"Already covered by existing 19 identities at cos-dist <= "
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f"{EXISTING_MATCH_THRESHOLD}: {n_covered}/{len(osrc_idx)}"
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)
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# Per-identity coverage breakdown (for logging only).
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for j, name in enumerate(cent_names):
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c = int(((nearest_id == j) & covered_mask).sum())
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if c:
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print(f" -> {name}: {c}")
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new_idx = [osrc_idx[k] for k in range(len(osrc_idx)) if not covered_mask[k]]
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print(f"\nUnmatched osrc faces to cluster: {len(new_idx)}")
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# Step 3: cluster the unmatched among themselves.
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new_emb = emb[new_idx]
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if len(new_idx) <= 1:
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labels = np.zeros(len(new_idx), dtype=int)
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else:
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labels = _cluster_embeddings(new_emb, INITIAL_THRESHOLD)
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n_clusters = len(set(int(l) for l in labels))
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print(
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f"Initial clusters at threshold {INITIAL_THRESHOLD}: {n_clusters} "
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f"(top sizes: {sorted([int((labels==l).sum()) for l in set(labels)], reverse=True)[:10]})"
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)
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# Step 4 + 5: per-cluster refine gates + min_faces.
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clusters: dict[int, list[int]] = {}
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for k, lab in enumerate(labels):
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clusters.setdefault(int(lab), []).append(new_idx[k])
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kept_clusters: list[dict] = []
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drop_quality_total = 0
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drop_outlier_total = 0
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for cid, idxs in clusters.items():
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# Per-face quality gate.
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good: list[int] = []
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for i in idxs:
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r = face_records[i]
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if r.get("face_short", 0) < MIN_SHORT:
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drop_quality_total += 1
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continue
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if r.get("blur", 0.0) < MIN_BLUR:
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drop_quality_total += 1
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continue
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if r.get("det_score", 0.0) < MIN_DET_SCORE:
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drop_quality_total += 1
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continue
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good.append(i)
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if not good:
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continue
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# Outlier rejection (only if cluster >= 4).
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if len(good) >= 4:
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cent = _normalize(emb[good].mean(axis=0))
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d = 1.0 - emb[good] @ cent
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tight = [good[k] for k, dist in enumerate(d) if dist <= OUTLIER_THRESHOLD]
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drop_outlier_total += len(good) - len(tight)
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good = tight
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if not good:
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continue
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unique_paths = sorted({face_records[i]["path"] for i in good})
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if len(unique_paths) < MIN_FACES:
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continue
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kept_clusters.append({
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"indices": good,
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"unique_paths": unique_paths,
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"size_face": len(good),
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"size_paths": len(unique_paths),
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})
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kept_clusters.sort(key=lambda c: -c["size_paths"])
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print(
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f"\nAfter quality gate ({drop_quality_total} dropped) + outlier "
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f"rejection ({drop_outlier_total} dropped) + min_faces={MIN_FACES}: "
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f"{len(kept_clusters)} clusters kept"
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)
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for rank, c in enumerate(kept_clusters, start=START_NNN):
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print(
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f" faceset_{rank:03d}: faces={c['size_face']:3d} "
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f"unique_paths={c['size_paths']:3d}"
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)
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# Build synthetic refine_manifest.json compatible with cmd_export_swap.
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facesets = [
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{
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"name": f"faceset_{rank:03d}",
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"image_count": c["size_paths"],
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"face_count": c["size_face"],
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"images": c["unique_paths"],
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}
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for rank, c in enumerate(kept_clusters, start=START_NNN)
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]
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manifest = {
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"params": {
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"existing_match_threshold": EXISTING_MATCH_THRESHOLD,
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"initial_threshold": INITIAL_THRESHOLD,
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"outlier_threshold": OUTLIER_THRESHOLD,
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"min_faces": MIN_FACES,
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"min_short": MIN_SHORT,
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"min_blur": MIN_BLUR,
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"min_det_score": MIN_DET_SCORE,
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"source_root": str(OSRC_DIR),
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},
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"facesets": facesets,
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}
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SYNTH_MANIFEST.write_text(json.dumps(manifest, indent=2))
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print(f"\nSynthetic manifest -> {SYNTH_MANIFEST}")
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return manifest, kept_clusters
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# ---- phase 2: export + relocate + merge top-level manifest -------------- #
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def export_and_relocate(manifest: dict) -> None:
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if OUT_TMP.exists():
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shutil.rmtree(OUT_TMP)
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OUT_TMP.mkdir(parents=True)
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print(f"\nRunning cmd_export_swap -> {OUT_TMP}")
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cmd_export_swap(
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cache_path=CACHE,
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refine_manifest_path=SYNTH_MANIFEST,
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raw_manifest_path=None,
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out_dir=OUT_TMP,
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top_n=TOP_N,
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outlier_threshold=EXPORT_OUTLIER_THRESHOLD,
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pad_ratio=PAD_RATIO,
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out_size=OUT_SIZE,
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include_candidates=False,
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candidate_match_threshold=0.55,
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candidate_min_score=0.40,
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min_face_short=EXPORT_MIN_FACE_SHORT,
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)
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new_top = json.loads((OUT_TMP / "manifest.json").read_text())
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new_entries = new_top.get("facesets", [])
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moved = 0
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for fs_meta in new_entries:
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name = fs_meta["name"]
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src_dir = OUT_TMP / name
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if not src_dir.exists():
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print(f"[{name}] export dir missing; skipping")
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continue
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dst_dir = SWAP_READY / name
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if dst_dir.exists():
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print(f"[{name}] {dst_dir} already exists; refusing to overwrite")
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continue
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# Add a marker file so the source provenance is obvious.
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(src_dir / "osrc.txt").write_text(
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f"{name}\n\nSource: osrc cluster (auto-discovered, {OSRC_DIR}).\n"
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)
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shutil.move(str(src_dir), str(dst_dir))
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moved += 1
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print(f"[{name}] -> {dst_dir}")
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# Merge top-level manifest, preserving facesets / thin_eras / etc.
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final_manifest_path = SWAP_READY / "manifest.json"
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if final_manifest_path.exists():
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existing = json.loads(final_manifest_path.read_text())
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else:
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existing = {"facesets": []}
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existing.setdefault("facesets", [])
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existing_names = {fs["name"] for fs in existing["facesets"]}
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appended = 0
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for entry in new_entries:
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if entry["name"] in existing_names:
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print(f"[manifest] {entry['name']} already present; not duplicating")
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continue
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existing["facesets"].append(entry)
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appended += 1
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final_manifest_path.write_text(json.dumps(existing, indent=2))
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print(f"\nMerged manifest: appended {appended} entries -> {final_manifest_path}")
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print(f"Moved {moved} faceset directories into {SWAP_READY}")
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# Clean up temp dir if empty.
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if OUT_TMP.exists():
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leftover = list(OUT_TMP.iterdir())
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if not leftover:
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OUT_TMP.rmdir()
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# ---- main ---------------------------------------------------------------- #
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def main() -> None:
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dry = "--dry-run" in sys.argv
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manifest, kept = discover_new_clusters()
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if dry:
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print("\n--dry-run: stopping after cluster discovery (no exports written).")
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return
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if not manifest.get("facesets"):
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print("No new facesets to build; nothing to do.")
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return
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export_and_relocate(manifest)
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print("\nDone.")
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if __name__ == "__main__":
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main()
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