#!/usr/bin/env python3
"""Find the worst sustained-motion window in a stream, cheaply.

STATUS lists "a sustained action sequence is the one thing that could still
break the bitrate" as the open risk, and every clip measured so far has been
1.2-1.7 s. Picking a hot clip by eye is how you get a comfortable answer, so
this scans the whole stream instead.

Proxy: mean absolute inter-frame difference at 96x72, decimated to the target
12 fps.  It is a proxy, not a bitrate -- but the codec's cost is dominated by
how many blocks fail SKIP, and that is what frame difference measures.  The
window it picks then gets encoded for real.

Usage: python3 tools/analysis/07_motion_survey.py 00223 [window_seconds]
"""
import subprocess, sys
import numpy as np

STREAM_DIR = "/media/reala-misaki/BDROM/BDMV/STREAM"
W, H, FPS = 96, 72, 12

def frames(stream):
    src = f"{STREAM_DIR}/{stream}.m2ts"
    vf = f"fps={FPS},crop=1440:1080:240:0,scale={W}:{H}:flags=bilinear"
    p = subprocess.Popen(["ffmpeg", "-v", "error", "-i", src, "-vf", vf,
                          "-f", "rawvideo", "-pix_fmt", "gray", "-"],
                         stdout=subprocess.PIPE)
    buf = p.stdout.read()
    p.wait()
    n = len(buf) // (W * H)
    return np.frombuffer(buf[:n*W*H], np.uint8).reshape(n, H, W).astype(np.int16)

def main():
    stream = sys.argv[1]
    win_s  = float(sys.argv[2]) if len(sys.argv) > 2 else 10.0
    f = frames(stream)
    d = np.abs(np.diff(f, axis=0)).mean(axis=(1, 2))      # per-frame motion energy
    print(f"{stream}: {len(f)} frames @ {FPS}fps = {len(f)/FPS:.1f}s")
    print(f"  motion energy  mean {d.mean():.2f}  median {np.median(d):.2f}  "
          f"p90 {np.percentile(d,90):.2f}  max {d.max():.2f}")

    w = int(win_s * FPS)
    if len(d) < w:
        print("stream shorter than the window"); return
    # sustained = highest mean over a sliding window, not the single hottest frame
    k = np.convolve(d, np.ones(w) / w, mode="valid")
    best = int(np.argmax(k))
    print(f"  hottest sustained {win_s:.0f}s window: t = {best/FPS:.1f}s "
          f"(mean {k[best]:.2f}, {k[best]/d.mean():.2f}x stream mean)")
    quiet = int(np.argmin(k))
    print(f"  quietest {win_s:.0f}s window:          t = {quiet/FPS:.1f}s "
          f"(mean {k[quiet]:.2f}, {k[quiet]/d.mean():.2f}x stream mean)")
    np.save(f"tmp/motion_{stream}.npy", d)
    print(f"  per-frame energy -> tmp/motion_{stream}.npy")

if __name__ == "__main__":
    main()