File size: 5,266 Bytes
d8a0e2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import argparse, csv, hashlib, json, os, re
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
META = os.path.join(ROOT, "meta")
csv.field_size_limit(10**9)
# Only the "a person is talking at the camera" cases: those clips can carry a high
# motion score (lip/hand movement) while nothing in the scene actually changes.
TALKING = re.compile(r"\b(talk|talks|talking|interview|interviews|interviewed|"
r"podcast|podcasts)\b", re.I)
p = argparse.ArgumentParser()
p.add_argument("--n-frames", type=int, default=10)
p.add_argument("--step", type=float, default=0.5)
p.add_argument("--max-windows", type=int, default=2,
help="hard cap on sequences taken from one clip")
p.add_argument("--margin", type=float, default=0.25,
help="seconds of slack required past the last frame of a window")
p.add_argument("--motion-lo", type=float, default=5.27)
p.add_argument("--motion-hi", type=float, default=60.0)
p.add_argument("--min-aesthetic", type=float, default=5.0)
p.add_argument("--keep-talking", action="store_true")
p.add_argument("--order", choices=["efficiency", "shuffle", "quality"], default="efficiency")
p.add_argument("--out", default=os.path.join(META, "manifest.csv"))
args = p.parse_args()
SPAN = (args.n_frames - 1) * args.step # first frame -> last frame
SLOT = args.n_frames * args.step # start-to-start distance between windows
def n_windows(sec):
"""How many non-overlapping windows fit, capped at --max-windows."""
w = 0
while w < args.max_windows and sec >= w * SLOT + SPAN + args.margin:
w += 1
return w
print("loading part index ...", flush=True)
idx = {}
with open(os.path.join(META, "part_index.jsonl")) as f:
for line in f:
d = json.loads(line)
idx[d["clip"]] = (d["part"], d["member"], d["bytes"])
print(f" {len(idx):,} clips indexed", flush=True)
print("loading OpenVidHD.csv ...", flush=True)
hd = set()
with open(os.path.join(META, "OpenVidHD.csv"), newline="") as f:
for r in csv.DictReader(f):
hd.add(r["video"])
print(f" {len(hd):,} HD clips", flush=True)
print("scanning OpenVid-1M.csv ...", flush=True)
rows = []
stats = dict(total=0, prefix=0, short=0, motion=0, aesth=0, talking=0, noidx=0, bad=0)
with open(os.path.join(META, "OpenVid-1M.csv"), newline="") as f:
for r in csv.DictReader(f):
stats["total"] += 1
try:
v = r["video"]
if v.startswith(("celebv_", "pixabay_")): # 512x512 face crops / 2.67s stock
stats["prefix"] += 1; continue
sec, mot = float(r["seconds"]), float(r["motion score"])
nw = n_windows(sec)
if nw == 0:
stats["short"] += 1; continue
if not (args.motion_lo <= mot <= args.motion_hi):
stats["motion"] += 1; continue
if float(r["aesthetic score"]) < args.min_aesthetic:
stats["aesth"] += 1; continue
if not args.keep_talking and TALKING.search(r["caption"]):
stats["talking"] += 1; continue
if v not in idx:
stats["noidx"] += 1; continue
part, member, nbytes = idx[v]
rows.append(dict(video=v, part=part, member=member, bytes=nbytes,
windows=nw, seconds=sec, fps=float(r["fps"]), motion=mot,
aesthetic=float(r["aesthetic score"]),
camera=r["camera motion"], hd=int(v in hd),
caption=r["caption"]))
except Exception:
stats["bad"] += 1
print(json.dumps(stats, indent=2), flush=True)
print(f"kept: {len(rows):,} clips", flush=True)
# Sort order == download priority: any prefix of the manifest is a usable dataset.
if args.order == "efficiency": # most sequences per downloaded GB
rows.sort(key=lambda d: d["bytes"] / d["windows"])
elif args.order == "shuffle": # unbiased sample at any cut point
rows.sort(key=lambda d: hashlib.md5(("openvid" + d["video"]).encode()).hexdigest())
else:
rows.sort(key=lambda d: -(d["aesthetic"] + 2*d["hd"] - abs(d["motion"] - 12)/20))
cum = 0
fields = ["rank","video","part","member","bytes","cum_gb","windows",
"seconds","fps","motion","aesthetic","camera","hd","caption"]
with open(args.out, "w", newline="") as f:
w = csv.DictWriter(f, fieldnames=fields)
w.writeheader()
for i, d in enumerate(rows):
cum += d["bytes"]
w.writerow({"rank": i, "cum_gb": round(cum/1e9, 4), **d})
tot = sum(d["windows"] for d in rows)
print(f"\nmanifest -> {args.out}", flush=True)
print(f" {args.n_frames} frames x {args.step}s (span {SPAN}s), <= {args.max_windows} windows/clip, "
f"motion in [{args.motion_lo}, {args.motion_hi}]", flush=True)
print(f" order={args.order} {len(rows):,} clips / {tot:,} sequences / "
f"{tot*(args.n_frames-1):,} pairs / {cum/1e9:,.1f} GB", flush=True)
for b in (200, 400, 600, 800, 1000):
c = s = 0; acc = 0
for d in rows:
acc += d["bytes"]
if acc/1e9 > b: break
c += 1; s += d["windows"]
print(f" budget {b:>5} GB -> {c:>7,} clips / {s:>7,} seq / {s*(args.n_frames-1):>9,} pairs", flush=True)
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