Reconstruct repo from Claude Code + codex transcripts

Working tree (including .git) was lost to an rm. Rebuilt by replaying Write/Edit/
Read/attachment events from 25 Claude sessions and 22 successful codex apply_patch
blocks into one timestamp-ordered timeline.

Verified against ground truth recorded in the transcripts: wc -l on 10 files and
ls -l on 5 files at 2026-07-18T13:13:44Z both match exactly; 18 files are
byte-identical to their newest ~/.claude/file-history blob.

See HANDOFF.md for sources, gaps, and how to rebuild .venv.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

face_detect.py

@@ -0,0 +1,74 @@
+# face_detect.py — anime face detector (YOLOv8, ONNX) -> real character-face boxes for set-of-mark
+# speaker attribution. The vision DETECT stage returns gemma-guessed bboxes which are too imprecise to
+# draw a mark on; this gives grounded boxes the same way bubble_detect gives grounded text regions.
+# Model: deepghs/anime_face_detection face_detect_v1.4_s (single class "face"). CPU EP -> no GPU contention.
+# ponytail: face boxes only (not full body). Attribution just needs "which face said this"; add a
+#   person head only if off-panel/back-turned speakers need body grounding.
+import os
+import numpy as np
+import cv2
+
+MODEL = os.environ.get("FACE_MODEL",
+                       os.path.join(os.path.dirname(__file__), "models", "anime_face_v1.4_s.onnx"))
+CONF = float(os.environ.get("FACE_CONF", "0.30"))
+_sess = None
+
+
+def _load():
+    global _sess
+    if _sess is None:
+        import onnxruntime as ort
+        _sess = ort.InferenceSession(MODEL, providers=["CPUExecutionProvider"])
+    return _sess
+
+
+def _letterbox(img, sz=640):
+    h, w = img.shape[:2]
+    r = min(sz / h, sz / w)
+    nh, nw = int(round(h * r)), int(round(w * r))
+    canvas = np.full((sz, sz, 3), 114, np.uint8)
+    py, px = (sz - nh) // 2, (sz - nw) // 2
+    canvas[py:py + nh, px:px + nw] = cv2.resize(img, (nw, nh))
+    return canvas, r, px, py
+
+
+def detect_faces(img, conf: float = None) -> list:
+    """img: BGR ndarray. -> [{"bbox":[x1,y1,x2,y2], "conf":float}] in ORIGINAL pixels, sorted
+    reading order. Empty on no faces. Lazy load so import never touches the model."""
+    conf = CONF if conf is None else conf
+    h, w = img.shape[:2]
+    canvas, r, px, py = _letterbox(img)
+    x = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB).astype(np.float32).transpose(2, 0, 1)[None] / 255.0
+    out = _load().run(None, {"images": x})[0][0].T   # (8400,5) = cx,cy,w,h,conf in 640 space
+    m = out[:, 4] > conf
+    if not m.any():
+        return []
+    b = out[m]
+    rects = [[float(cx - bw / 2), float(cy - bh / 2), float(bw), float(bh)] for cx, cy, bw, bh in b[:, :4]]
+    idx = cv2.dnn.NMSBoxes(rects, b[:, 4].tolist(), conf, 0.45)
+    if len(idx) == 0:
+        return []
+    res = []
+    for i in np.array(idx).flatten():
+        cx, cy, bw, bh = b[i, :4]
+        x1, y1 = max(0, int((cx - bw / 2 - px) / r)), max(0, int((cy - bh / 2 - py) / r))
+        x2, y2 = min(w, int((cx + bw / 2 - px) / r)), min(h, int((cy + bh / 2 - py) / r))
+        if x2 > x1 and y2 > y1:
+            res.append({"bbox": [x1, y1, x2, y2], "conf": round(float(b[i, 4]), 3)})
+    res.sort(key=lambda d: (d["bbox"][1], d["bbox"][0]))
+    return res
+
+
+if __name__ == "__main__":
+    # self-check needs a real anime face; use a page from the sample series if present, else skip loudly.
+    import glob
+    pages = glob.glob("/mnt/server/mnt/hdd2/manga-raw/*/*.png")
+    if not pages:
+        print("face_detect: no sample page available, skipping live check")
+    else:
+        faces = detect_faces(cv2.imread(sorted(pages)[13]))
+        assert faces, "no face detected on a page that should have characters"
+        for f in faces:
+            x1, y1, x2, y2 = f["bbox"]
+            assert x2 > x1 and y2 > y1 and f["conf"] >= CONF
+        print(f"face_detect self-check ok ({len(faces)} face(s), top conf {faces[0]['conf']})")