abd72a2c24
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
86 lines
3.6 KiB
Python
Executable File
86 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Read the HUD's `REMAINING OB` counter off a screenshot.
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Finding the counter in RAM needs the HUD value at the moment of every scan and
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every filter, and the corpus's rule needs it again at a *later* transition. Doing
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that by eye costs a round trip per reading, which is why the last two attempts
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each managed one filter and then missed the next step of the counter.
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Three digits, fixed cells, matched against templates by **normalised** cross-
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correlation. The normalisation is the point rather than a nicety: the plate is
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translucent, so an explosion turns the whole thing red and a nearby tracer washes
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it out. A hue mask keyed on the digits' cyan silently fails on exactly those
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frames — measured: the `008` reference frame has an orange flash across it and a
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cyan mask finds 6 of the ~70 stroke columns. Subtracting each cell's own mean and
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dividing by its own standard deviation makes the match indifferent to both.
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The cells and the template strip were measured from the cyan-stroke column
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profile of clean frames: strokes at x 1109-1132, 1139-1162, 1169-1191 and rows
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236-269, so the cells are those boxes with a small margin.
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**Templates exist for 0 1 2 4 8 only** — those are the digits the counter has
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actually shown in captured frames. Anything else reads as `?`, and a reading with
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a `?` in it is not a number: callers must treat it as "unknown", never as a
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value. Add a digit by pasting a new 29x39 cell into `ob_digits.png` and extending
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DIGITS; the file is a plain grey strip in DIGITS order.
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Usage: ob_read.py <png> [--json] -> "004" | "0?2" | "none"
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"""
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import json
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import os
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import sys
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import numpy as np
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from PIL import Image
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CELLS = [(1106, 233, 1135, 272), (1136, 233, 1165, 272), (1166, 233, 1195, 272)]
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DIGITS = "01248"
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STRIP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "ob_digits.png")
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# MEASURED over 12 cells from four frames whose value is known by eye, rather
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# than guessed: the correct digit scores 0.843 .. 1.000 and the best WRONG digit
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# scores up to 0.789 — these are outline glyphs on a translucent plate, so 0 and
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# 8 look a lot alike to a correlator. A bare threshold between 0.789 and 0.843
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# would be fitted to twelve samples, so accept on TWO conditions instead: a floor
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# well below the worst correct match, AND a margin over the runner-up. The
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# smallest correct-to-runner-up margin observed is 0.093.
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THRESHOLD = 0.80
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MARGIN = 0.05
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def _norm(a):
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a = a.astype(np.float64)
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s = a.std()
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return (a - a.mean()) / s if s > 1e-6 else a * 0.0
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def _templates():
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strip = np.asarray(Image.open(STRIP).convert("L"))
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w = strip.shape[1] // len(DIGITS)
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return {d: _norm(strip[:, i * w:(i + 1) * w]) for i, d in enumerate(DIGITS)}
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def read(path):
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"""Return (text, per-digit best scores)."""
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im = Image.open(path).convert("L")
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tmpl = _templates()
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out, scores = "", []
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for box in CELLS:
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c = _norm(np.asarray(im.crop(box)))
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ranked = sorted(((float((c * t).mean()), d) for d, t in tmpl.items()
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if t.shape == c.shape), reverse=True)
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best, bd = ranked[0]
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runner = ranked[1][0] if len(ranked) > 1 else -2.0
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scores.append((round(best, 3), round(best - runner, 3)))
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out += bd if best >= THRESHOLD and best - runner >= MARGIN else "?"
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return out, scores
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if __name__ == "__main__":
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txt, sc = read(sys.argv[1])
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if "--json" in sys.argv:
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print(json.dumps({"text": txt, "scores": sc,
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"value": int(txt) if txt.isdigit() else None}))
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else:
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print(f"{txt} scores={sc}")
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sys.exit(0 if txt.isdigit() else 1)
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