Files
Dragon-s-Lair-X68k/tools/analysis/08_mode_map.py
T
prosolis 7d365b3ff5 Drop SASI on capacity, then find the budget never had the disk in it
USER DECISION: drop the `sasi` profile. Not on bandwidth -- on capacity. A SASI
volume is 40 MB, and the 22.8 min of unique scene footage on the source Blu-ray
(streams 00000-00201, measured, not recalled) is 146 MiB at the LOWEST rate this
codec makes -- more than the machine's whole 4-unit SASI space. `scsi` is the
only profile now. FINDINGS 32.

Then the user asked whether we were drawing the wrong conclusions about PIO vs
DMA, and we were, more broadly than the question implied. Every CPU figure in
FINDINGS 24-34 is scored against the full 833,333 cycles/frame with nothing
subtracted for moving the bitstream off disk. Debiting the HD63450 cycle-steal
at the long-standing 8 clk/word ESTIMATE, "1 frame of 120 misses" becomes 84 of
120, median 112.4%. PIO at the span rate is 99.8% of the machine. Spans buy
cycles by spending bandwidth and the bandwidth returns as steal, so 31.6's "fits
completely" becomes a worst frame of 114.3%. 10 fps absorbs it: median 93.7%,
1/120. FINDINGS 35. `11_cpu_budget.py` takes --io dma|pio|none, defaults to dma,
and warns if asked for none.

Also landed:
- item 1 done: the cost model checked against the 68000 on a cost-aware
  container, -3.07% to +0.01%, whole-window mean -1.22%. FINDINGS 34.
- item 4 done: the container carries its own 4-byte record alignment (DLX2).
  94/120 record starts were on odd addresses -- an address error, not a slow
  read -- now 0/120 for 16 B/s. Re-encoding reproduces 31.1 exactly. FINDINGS 33.
- a `scsi` window does not fit the 2 MB machine the rig emulates (2.84 MB of
  stream past a 0x200000 ceiling). The gate now verifies 80 of 120 frames and
  SAYS so, and fails loudly when the pass does not complete, instead of
  reporting a phantom 49,005-pixel diff. FINDINGS 36.

Three near-misses this session had one shape: an unobservable run nearly
produced a false finding. stdbuf -oL on any MAME job that prints progress -- a
file is block-buffered too, and a run that is merely finishing looks exactly
like one that is wedged.

check.sh ALL GREEN.

Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6
2026-08-23 17:09:47 -07:00

117 lines
5.5 KiB
Python

#!/usr/bin/env python3
"""Render what the codec is actually DOING, per block, per frame.
Three panels at 12 fps: the palettised source (the real quality ceiling, not
1080p -- FINDINGS 11), the decoded output, and a block-mode map.
The mode map is not decoration. FINDINGS 24.5 makes the per-frame non-SKIP
fraction the number that selects the decoder's inner loop, and a percentile
cannot show you that the non-SKIP blocks are CLUSTERED (a moving character on a
held background) rather than scattered. Clustering is what a run-length over
the mode headers would exploit.
SKIP left as the previous frame, costs the 68000 nothing
V1 one 4x4 codeword, 1 byte
V4 four 2x2 codewords, 4 bytes
RAW 16 literal palette indices -- the escape that makes lam=0 pixel-exact
Usage: python3 tools/analysis/08_mode_map.py <frames_dir> <out.webm>
[--profile scsi] [--scale N] [--lossless] [--fixed-lam]
--fixed-lam renders the pre-session-6 encoder (no rate control) instead.
Output format follows the extension. Prefer .webm: GIF re-quantises to 256
colours, which is a poor fit for output whose subject is colour fidelity.
"""
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "encoder"))
import numpy as np
from PIL import Image
import vq as VQ, vq_hybrid as H, ratectl as RC
MODE_RGB = np.array([[ 20, 22, 30], # SKIP - near black, costs nothing
[ 60, 150, 230], # V1 - blue
[ 80, 200, 120], # V4 - green
[235, 90, 70]], # RAW - red, the expensive escape
dtype=np.uint8)
LABEL = ["SKIP", "V1", "V4", "RAW"]
SCALE = 1
LOSSLESS = False
def main():
global SCALE, LOSSLESS
LOSSLESS = "--lossless" in sys.argv
src, out = sys.argv[1], sys.argv[2]
if "--scale" in sys.argv:
SCALE = int(sys.argv[sys.argv.index("--scale")+1])
prof = RC.PROFILES[sys.argv[sys.argv.index("--profile")+1]
if "--profile" in sys.argv else "scsi"]
m = H.build(src, k1=prof["k1"], k4=prof["k4"])
# Rate-controlled by default, so the map shows the mode decisions that
# actually ship. --fixed-lam renders the pre-session-6 encoder instead;
# the difference is visible as V4/RAW collapsing to V1/SKIP on peak frames.
if "--fixed-lam" in sys.argv:
enc = H.encode(m, lam=prof["lam"])
else:
enc = RC.encode_rate_controlled(m, prof["kbps"],
lam_lo=prof["lam"])
pal, H_, W_ = m["pal"], m["H"], m["W"]
nbx, nby = W_ // 4, H_ // 4
frames, stats = [], []
for f, (rec, mode) in enumerate(zip(enc["recon"], enc["modes"])):
srcp = pal[m["idx"][f]]
decp = pal[rec]
mmap = MODE_RGB[mode.reshape(nby, nbx)].repeat(4, 0).repeat(4, 1)
# tint the mode map with the decoded luma so the action stays legible
luma = decp.mean(2, keepdims=True) / 255.0
mmap = (mmap * (0.45 + 0.55 * luma)).astype(np.uint8)
gap = np.full((H_, 3, 3), 60, np.uint8)
panel = np.hstack([srcp, gap, decp, gap, mmap])
im = Image.fromarray(panel)
if SCALE != 1:
im = im.resize((panel.shape[1]*SCALE, H_*SCALE), Image.NEAREST)
frames.append(im)
stats.append([(mode == i).mean() for i in range(4)])
if out.endswith(".webm") or out.endswith(".mp4"):
# Preferred. GIF would impose its own 256-colour palette on top of
# output whose entire subject is colour fidelity, and costs ~4x the
# bytes doing it. -lossless keeps the panels pixel-exact.
import subprocess
w, h = frames[0].size
cmd = ["ffmpeg", "-v", "error", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
"-s", f"{w}x{h}", "-r", "12", "-i", "-"]
# yuv444p, not 420: the mode map is flat saturated colour on a 4-pixel
# grid, and chroma subsampling smears exactly those edges. Lossless is
# available but runs larger than the GIF on this content; crf 18 in 444
# is visually clean at a quarter the size.
if out.endswith(".webm"):
cmd += ["-c:v", "libvpx-vp9", "-pix_fmt", "yuv444p", "-row-mt", "1"]
cmd += ["-lossless", "1"] if LOSSLESS else ["-crf", "18", "-b:v", "0"]
else:
cmd += ["-c:v", "libx264", "-crf", "12", "-pix_fmt", "yuv444p"]
p = subprocess.Popen(cmd + [out], stdin=subprocess.PIPE)
for f in frames:
p.stdin.write(np.asarray(f.convert("RGB")).tobytes())
p.stdin.close(); p.wait()
else:
# One shared adaptive palette: per-frame palettes are what make a naive
# GIF of this enormous, and a stable palette also stops the mode-map
# colours shimmering between frames.
shared = frames[0].quantize(colors=192, method=Image.MEDIANCUT)
q = [f.quantize(palette=shared, dither=Image.NONE) for f in frames]
q[0].save(out, save_all=True, append_images=q[1:],
duration=1000//12, loop=0, optimize=True)
st = np.array(stats)
print(f"{len(frames)} frames -> {out} ({os.path.getsize(out)/1024:.0f} KB)")
print(" panels: palettised source | decoded | block mode map")
for i, n in enumerate(LABEL):
print(f" {n:4s} mean {100*st[:,i].mean():5.1f}% "
f"per-frame range {100*st[:,i].min():5.1f}% .. {100*st[:,i].max():5.1f}%")
ns = 100 * (1 - st[:, 0])
print(f" non-SKIP: median {np.median(ns):.1f}% p90 {np.percentile(ns,90):.1f}%")
if __name__ == "__main__":
main()