"""
Visualize 100 samples from new_policy7w_v2_reformat dataset.
Output: /mnt/bn/bohanzhainas1/jiashuo/viz/
- samples/sample_NNN/ -> video frames + info.json
- index.html -> browsable HTML overview
"""
import os, json, base64, glob
import pandas as pd
from pathlib import Path
DATA_DIR = "/mnt/hdfs/byte_tt_data_cu_vagcp/haogeng.liu/new_policy7w_v2_reformat"
OUT_DIR = Path("/mnt/bn/bohanzhainas1/jiashuo/viz")
N_SAMPLES = 100
PREVIEW_FRAMES = [0, 4, 8, 12, 15] # frames shown in HTML (out of 16)
# ── helpers ──────────────────────────────────────────────────────────────────
def save_frames(frames_b64: list, out_dir: Path, prefix: str) -> list[str]:
"""Save all frames to disk, return relative paths."""
paths = []
for i, b64 in enumerate(frames_b64):
raw = base64.b64decode(b64)
rel = f"{prefix}_frame_{i:02d}.jpg"
(out_dir / rel).write_bytes(raw)
paths.append(rel)
return paths
def parse_row(row) -> dict:
msgs = json.loads(row["messages"])
extra = json.loads(row["extra_info"])
user_content = msgs[0]["content"] # list of {type, video/text, ...}
asst_content = msgs[1]["content"] # list of {type, text}
# extract videos and texts from user turn
videos, user_texts = [], []
for item in user_content:
if item["type"] == "video":
videos.append(item["video"]) # list of 16 base64 strings
elif item["type"] == "text":
user_texts.append(item["text"])
elif item["type"] == "image":
videos.append([item["image"]]) # treat single-image as 1-frame video
assistant_text = " ".join(
c["text"] for c in asst_content if c.get("type") == "text"
)
return {
"videos": videos,
"user_texts": user_texts,
"assistant_text": assistant_text,
"extra_info": extra,
}
def build_sample_dir(idx: int, parsed: dict) -> dict:
"""Save frames + info.json, return metadata for HTML."""
sdir = OUT_DIR / "samples" / f"sample_{idx:03d}"
sdir.mkdir(parents=True, exist_ok=True)
frame_paths = {} # video_idx -> [relative paths]
for vi, frames in enumerate(parsed["videos"]):
paths = save_frames(frames, sdir, f"video{vi+1}")
frame_paths[vi] = paths
info = {
"user_texts": parsed["user_texts"],
"assistant": parsed["assistant_text"],
"extra_info": parsed["extra_info"],
"num_videos": len(parsed["videos"]),
"frames_per_video": [len(v) for v in parsed["videos"]],
}
(sdir / "info.json").write_text(json.dumps(info, ensure_ascii=False, indent=2))
return {"sdir": sdir, "frame_paths": frame_paths, "info": info}
# ── HTML generation ───────────────────────────────────────────────────────────
def img_tag(sdir: Path, rel_path: str, width=160) -> str:
abs_path = sdir / rel_path
b64 = base64.b64encode(abs_path.read_bytes()).decode()
return f''
def json_block(obj) -> str:
if isinstance(obj, str):
try:
obj = json.loads(obj)
except Exception:
pass
text = json.dumps(obj, ensure_ascii=False, indent=2) if not isinstance(obj, str) else obj
return f'
{text}'
def render_sample_html(idx: int, meta: dict) -> str:
sdir = meta["sdir"]
frame_paths = meta["frame_paths"]
info = meta["info"]
# video strips (only PREVIEW_FRAMES)
video_html = ""
for vi, paths in frame_paths.items():
preview = [paths[i] for i in PREVIEW_FRAMES if i < len(paths)]
imgs = "".join(img_tag(sdir, p) for p in preview)
video_html += f"""
{extra[key]}Showing {count} samples |
Each sample: 2 videos (16 frames each) + metadata texts + assistant label
Full frames + info.json saved under samples/sample_NNN/