File size: 3,165 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 | import argparse, collections, json, os, random, statistics
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
ap = argparse.ArgumentParser()
ap.add_argument("jsonl", nargs="?", default=os.path.join(ROOT, "sequences.jsonl"))
ap.add_argument("--full", action="store_true", help="stat every frame path (else sample 2000 seqs)")
ap.add_argument("--max-windows", type=int, default=2)
ap.add_argument("--motion-lo", type=float, default=5.27)
A = ap.parse_args()
seqs = nframes = 0
bad, ids = [], set()
per_video = collections.Counter()
nf_seen, step_seen = collections.Counter(), collections.Counter()
motions, sample = [], []
for line in open(A.jsonl):
d = json.loads(line)
seqs += 1
ids.add(d["id"])
per_video[d["video"]] += 1
nf_seen[d["n_frames"]] += 1
step_seen[d["step_sec"]] += 1
motions.append(float(d["meta"]["motion"]))
fr = d["frames"]
nframes += len(fr)
if len(fr) != d["n_frames"] or len(d["timestamps"]) != len(fr):
bad.append((d["id"], "len mismatch"))
ts = d["timestamps"]
if any(round(ts[i+1] - ts[i], 3) != d["step_sec"] for i in range(len(ts)-1)):
bad.append((d["id"], "irregular timestamps"))
if A.full:
for p in fr:
if not os.path.exists(os.path.join(ROOT, p)):
bad.append((d["id"], f"missing {p}")); break
else:
sample.append((d["id"], fr))
if not A.full and sample:
for sid, fr in random.Random(0).sample(sample, min(2000, len(sample))):
for p in fr:
if not os.path.exists(os.path.join(ROOT, p)):
bad.append((sid, f"missing {p}")); break
over = [v for v, c in per_video.items() if c > A.max_windows]
low = [m for m in motions if m < A.motion_lo]
print(f"sequences : {seqs:,}")
print(f"unique ids : {len(ids):,} (dupes: {seqs - len(ids)})")
print(f"source clips : {len(per_video):,} (mean {seqs/max(1,len(per_video)):.2f} windows/clip)")
print(f"windows/clip : {dict(sorted(collections.Counter(per_video.values()).items()))}"
f" over cap({A.max_windows}): {len(over)}")
print(f"n_frames : {dict(nf_seen)}")
print(f"step_sec : {dict(step_seen)}")
print(f"frames listed : {nframes:,}")
print(f"adjacent pairs : {nframes - seqs:,} <- VLM caption calls")
if motions:
ms = sorted(motions)
print(f"motion : min {ms[0]:.2f} med {statistics.median(ms):.2f} "
f"max {ms[-1]:.2f} below {A.motion_lo}: {len(low)}")
print(f"path check : {'FULL' if A.full else f'sampled {min(2000,len(sample))} seqs'} "
f"-> {len(bad)} problems")
for b in bad[:10]:
print(" ", b)
fs = []
for dp, _, fn in os.walk(os.path.join(ROOT, "frames")):
fs += [os.path.getsize(os.path.join(dp, f)) for f in fn if f.endswith(".jpg")]
if fs:
print(f"jpgs on disk : {len(fs):,} ({sum(fs)/1e9:.1f} GB, mean {statistics.mean(fs)/1024:.0f} KB)")
print(f"orphan jpgs : {len(fs) - nframes:,}")
vd = os.path.join(ROOT, "videos")
vids = [os.path.getsize(os.path.join(vd, f)) for f in os.listdir(vd) if f.endswith(".mp4")]
print(f"videos on disk : {len(vids):,} ({sum(vids)/1e9:.1f} GB)")
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