tools: read REMAINING OB off the HUD, so the scan can follow the counter

Both hand attempts at this address managed exactly one filter and then lost the
counter, because every reading of the HUD costs a human round trip. ob_read.py
does it by template correlation over three fixed digit cells, and ob_hunt.py
uses that to run the whole method unattended: confirm the HUD on both sides of
the 0.9 s scan, then filter on each following transition, labelling the first as
the one it selected on and the rest as verification.

Two things measured rather than assumed:

* Normalisation is the point, not a nicety. The plate is translucent, so an
  explosion turns it orange - on the 008 reference frame a cyan-stroke mask finds
  6 of ~70 stroke columns and would silently read nothing. Per-cell mean/std
  normalisation reads it correctly at 0.843.
* The accept rule is two-sided because the margin is narrow: over 12 cells from
  four frames of known value the correct digit scores 0.843..1.000 and the best
  WRONG digit reaches 0.789 (0 and 8 are similar outlines). So a floor of 0.80
  AND a 0.05 margin over the runner-up, against a smallest observed correct
  margin of 0.093. A bare threshold fitted between those two numbers would be
  fitted to twelve samples.

Templates exist for 0 1 2 4 8 - the digits actually seen. Anything else reads as
"?" and callers must treat a "?" as unknown, never as a value. Rejects both
negatives tested: GAME OVER scores 0.05-0.18, the main menu 0.07-0.14.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PMRJjbxLqZtsb5Vb7KunPE

tools/re-capture/ob_read.py

@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""Read the HUD's `REMAINING OB` counter off a screenshot.
+
+Finding the counter in RAM needs the HUD value at the moment of every scan and
+every filter, and the corpus's rule needs it again at a *later* transition. Doing
+that by eye costs a round trip per reading, which is why the last two attempts
+each managed one filter and then missed the next step of the counter.
+
+Three digits, fixed cells, matched against templates by **normalised** cross-
+correlation. The normalisation is the point rather than a nicety: the plate is
+translucent, so an explosion turns the whole thing red and a nearby tracer washes
+it out. A hue mask keyed on the digits' cyan silently fails on exactly those
+frames — measured: the `008` reference frame has an orange flash across it and a
+cyan mask finds 6 of the ~70 stroke columns. Subtracting each cell's own mean and
+dividing by its own standard deviation makes the match indifferent to both.
+
+The cells and the template strip were measured from the cyan-stroke column
+profile of clean frames: strokes at x 1109-1132, 1139-1162, 1169-1191 and rows
+236-269, so the cells are those boxes with a small margin.
+
+**Templates exist for 0 1 2 4 8 only** — those are the digits the counter has
+actually shown in captured frames. Anything else reads as `?`, and a reading with
+a `?` in it is not a number: callers must treat it as "unknown", never as a
+value. Add a digit by pasting a new 29x39 cell into `ob_digits.png` and extending
+DIGITS; the file is a plain grey strip in DIGITS order.
+
+Usage: ob_read.py <png> [--json]     -> "004" | "0?2" | "none"
+"""
+import json
+import os
+import sys
+
+import numpy as np
+from PIL import Image
+
+CELLS = [(1106, 233, 1135, 272), (1136, 233, 1165, 272), (1166, 233, 1195, 272)]
+DIGITS = "01248"
+STRIP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "ob_digits.png")
+# MEASURED over 12 cells from four frames whose value is known by eye, rather
+# than guessed: the correct digit scores 0.843 .. 1.000 and the best WRONG digit
+# scores up to 0.789 — these are outline glyphs on a translucent plate, so 0 and
+# 8 look a lot alike to a correlator. A bare threshold between 0.789 and 0.843
+# would be fitted to twelve samples, so accept on TWO conditions instead: a floor
+# well below the worst correct match, AND a margin over the runner-up. The
+# smallest correct-to-runner-up margin observed is 0.093.
+THRESHOLD = 0.80
+MARGIN = 0.05
+
+
+def _norm(a):
+    a = a.astype(np.float64)
+    s = a.std()
+    return (a - a.mean()) / s if s > 1e-6 else a * 0.0
+
+
+def _templates():
+    strip = np.asarray(Image.open(STRIP).convert("L"))
+    w = strip.shape[1] // len(DIGITS)
+    return {d: _norm(strip[:, i * w:(i + 1) * w]) for i, d in enumerate(DIGITS)}
+
+
+def read(path):
+    """Return (text, per-digit best scores)."""
+    im = Image.open(path).convert("L")
+    tmpl = _templates()
+    out, scores = "", []
+    for box in CELLS:
+        c = _norm(np.asarray(im.crop(box)))
+        ranked = sorted(((float((c * t).mean()), d) for d, t in tmpl.items()
+                         if t.shape == c.shape), reverse=True)
+        best, bd = ranked[0]
+        runner = ranked[1][0] if len(ranked) > 1 else -2.0
+        scores.append((round(best, 3), round(best - runner, 3)))
+        out += bd if best >= THRESHOLD and best - runner >= MARGIN else "?"
+    return out, scores
+
+
+if __name__ == "__main__":
+    txt, sc = read(sys.argv[1])
+    if "--json" in sys.argv:
+        print(json.dumps({"text": txt, "scores": sc,
+                          "value": int(txt) if txt.isdigit() else None}))
+    else:
+        print(f"{txt}  scores={sc}")
+    sys.exit(0 if txt.isdigit() else 1)