#!/usr/bin/env python3 """ Lab 自建数据集预处理：将 features/lab 下的 mp4 + text.json 转为 QVHighlights 风格标注， 并提取 CLIP 视频特征与 LLaMA 文本特征，便于用 FlashVTG 做实验。 目录约定： - features/lab/*.mp4 原始视频（如 1.mp4, 2.mp4, 3.mp4） - features/lab/text.json 每行一个样本：{"textN": "query", "gt:s1,s2,..."}，gt 单位为秒 - data/lab_val.jsonl 输出：QV 风格标注（qid, query, duration, vid, relevant_windows 等） - features/lab/clip_features CLIP 视频特征（{vid}.npz, key "features", shape (T, 768)） - features/lab/llama_text_feature LLaMA 文本特征（qid{qid}.npz, key "last_hidden_state"） 用法： # 仅生成标注（并获取视频时长） python scripts/prepare_lab_dataset.py --lab_dir features/lab --out_jsonl data/lab_val.jsonl # 生成标注 + 提取 CLIP + 提取 LLaMA（需 GPU） python scripts/prepare_lab_dataset.py --lab_dir features/lab --out_jsonl data/lab_val.jsonl --extract_clip --extract_llama """ from __future__ import annotations import argparse import json import os import re from pathlib import Path import numpy as np def get_video_duration_sec(video_path: str) -> float: """获取视频时长（秒）。优先 decord，否则 opencv。""" try: from decord import VideoReader, cpu vr = VideoReader(video_path, ctx=cpu(0)) n = len(vr) fps = vr.get_avg_fps() if fps and fps > 0: return n / fps return max(0.0, n / 30.0) except Exception: pass try: import cv2 cap = cv2.VideoCapture(video_path) n = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) fps = cap.get(cv2.CAP_PROP_FPS) or 30.0 cap.release() return n / fps if fps > 0 else 0.0 except Exception: return 0.0 def parse_text_json(lab_dir: str) -> list[dict]: """ 解析 features/lab/text.json。 每行格式：{"text1": "query text", "gt:9,10,11"} 或 {"text1": "query", "gt": "9,10,11"} 返回：[{"vid": "1", "query": "...", "gt_seconds": [9,10,11]}, ...] """ path = Path(lab_dir) / "text.json" if not path.exists(): raise FileNotFoundError(f"Not found: {path}") samples = [] with open(path, "r", encoding="utf-8") as f: for line in f: line = line.strip() if not line: continue # 兼容 "gt:9,10,11"}（键无引号、值无引号）-> 改成 "gt": "9,10,11"} if '"gt:' in line: line = re.sub(r'"gt:([^"]*?)"\s*}', r'"gt": "\1"}', line) try: d = json.loads(line) except json.JSONDecodeError: continue # 取 text1 / text2 / text3 query = None vid = None for k, v in d.items(): if k.startswith("text") and isinstance(v, str): num = k.replace("text", "").strip() vid = num if num else "1" query = v break if query is None: continue # gt 列表（秒） gt_raw = d.get("gt", "") if isinstance(gt_raw, list): gt_seconds = [float(x) for x in gt_raw] else: gt_seconds = [float(x.strip()) for x in str(gt_raw).split(",") if x.strip()] if not gt_seconds: continue samples.append({"vid": vid, "query": query, "gt_seconds": gt_seconds}) return samples def seconds_to_relevant_windows(gt_seconds: list[float]) -> list[list[float]]: """将 gt 秒数列表转为 QV 的 relevant_windows [[start, end], ...]。""" if not gt_seconds: return [] gt_seconds = sorted(gt_seconds) return [[min(gt_seconds), max(gt_seconds)]] def seconds_to_clip_ids(gt_seconds: list[float], clip_length: float = 2.0) -> list[int]: """与 QV 一致：每 clip_length 秒一个 clip，返回与 gt 重叠的 clip 索引。""" if not gt_seconds: return [] t_min, t_max = min(gt_seconds), max(gt_seconds) id_min = max(0, int(t_min / clip_length)) id_max = int(t_max / clip_length) return list(range(id_min, id_max + 1)) def build_lab_jsonl( lab_dir: str, out_jsonl: str, clip_length: float = 2.0, ) -> list[dict]: """ 根据 text.json 和 lab 下 mp4 生成 QV 风格 jsonl。 返回写入的每条记录列表（便于后续提取特征时用）。 """ lab_path = Path(lab_dir) samples = parse_text_json(lab_dir) if not samples: raise ValueError("text.json 中未解析到有效样本") records = [] for idx, s in enumerate(samples): vid = s["vid"] mp4 = lab_path / f"{vid}.mp4" if not mp4.exists(): # 尝试 .mp4 已在 vid 中 if not (lab_path / f"{vid}.mp4").exists(): raise FileNotFoundError(f"Video not found: {mp4}") duration = get_video_duration_sec(str(mp4)) if duration <= 0: duration = 30.0 # fallback relevant_windows = seconds_to_relevant_windows(s["gt_seconds"]) relevant_clip_ids = seconds_to_clip_ids(s["gt_seconds"], clip_length) # QV: saliency_scores 长度 = 该 segment 内 clip 数；这里用与 relevant_clip_ids 等长的占位 saliency_scores = [[1, 1, 1]] * len(relevant_clip_ids) if relevant_clip_ids else [[1, 1, 1]] qid = idx + 1 rec = { "qid": qid, "query": s["query"], "duration": round(duration, 2), "vid": vid, "relevant_clip_ids": relevant_clip_ids, "saliency_scores": saliency_scores, "relevant_windows": relevant_windows, } records.append(rec) os.makedirs(os.path.dirname(os.path.abspath(out_jsonl)) or ".", exist_ok=True) with open(out_jsonl, "w", encoding="utf-8") as f: for rec in records: f.write(json.dumps(rec, ensure_ascii=False) + "\n") print(f"Wrote {len(records)} samples to {out_jsonl}") return records def main(): import sys import importlib.util parser = argparse.ArgumentParser(description="Lab dataset: build QV-style jsonl + optional CLIP/LLaMA extraction") parser.add_argument("--lab_dir", default="features/lab", help="Directory with mp4 and text.json") parser.add_argument("--out_jsonl", default="data/lab_val.jsonl", help="Output QV-style jsonl path") parser.add_argument("--clip_length", type=float, default=2.0, help="Seconds per clip (QV default 2)") parser.add_argument("--extract_clip", action="store_true", help="Extract CLIP video features into features/lab/clip_features") parser.add_argument("--extract_llama", action="store_true", help="Extract LLaMA text features into features/lab/llama_text_feature") try: import torch device_default = "cuda" if torch.cuda.is_available() else "cpu" except Exception: device_default = "cpu" parser.add_argument("--device", default=device_default) args = parser.parse_args() lab_dir = os.path.abspath(args.lab_dir) out_jsonl = os.path.abspath(args.out_jsonl) script_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(script_dir) if project_root not in sys.path: sys.path.insert(0, project_root) # 1) 生成 QV 风格 jsonl build_lab_jsonl(lab_dir, out_jsonl, clip_length=args.clip_length) # 2) 可选：CLIP 视频特征（复用 extract_vidstg_clip_features） if args.extract_clip: clip_out = os.path.join(lab_dir, "clip_features") try: spec = importlib.util.spec_from_file_location( "extract_clip_mod", os.path.join(script_dir, "extract_vidstg_clip_features.py"), ) mod = importlib.util.module_from_spec(spec) spec.loader.exec_module(mod) extract_clip_features = mod.extract_clip_features import open_clip lab_path = Path(lab_dir) mp4s = sorted(lab_path.glob("*.mp4")) model, _, preprocess = open_clip.create_model_and_transforms("ViT-L-14", pretrained="openai") model = model.to(args.device).eval() os.makedirs(clip_out, exist_ok=True) for mp4 in mp4s: vid = mp4.stem out_path = os.path.join(clip_out, f"{vid}.npz") if os.path.exists(out_path): print(f"Skip CLIP {vid} (exists)") continue feats = extract_clip_features(str(mp4), model, preprocess, args.device, sample_fps=0.5, batch_size=8) if feats.ndim == 1: feats = feats[None, :] np.savez_compressed(out_path, features=feats.astype(np.float32)) print(f"CLIP {vid} -> {feats.shape[0]} frames") except Exception as e: print("CLIP extraction failed:", e) # 3) 可选：LLaMA 文本特征（复用 extract_vidstg_llama_features） if args.extract_llama: llama_out = os.path.join(lab_dir, "llama_text_feature") try: spec = importlib.util.spec_from_file_location( "extract_llama_mod", os.path.join(script_dir, "extract_vidstg_llama_features.py"), ) mod = importlib.util.module_from_spec(spec) spec.loader.exec_module(mod) extract_llama_features = mod.extract_llama_features from transformers import AutoModelForCausalLM, AutoTokenizer import torch qid2query = {} with open(out_jsonl) as f: for line in f: d = json.loads(line) qid2query[d["qid"]] = d["query"] tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( "openlm-research/open_llama_7b", torch_dtype=torch.float32, device_map="auto" if args.device == "cuda" else None, trust_remote_code=True, ) model.eval() os.makedirs(llama_out, exist_ok=True) for qid, query in qid2query.items(): out_path = os.path.join(llama_out, f"qid{qid}.npz") if os.path.exists(out_path): print(f"Skip LLaMA qid{qid} (exists)") continue feats = extract_llama_features(model, tokenizer, query, args.device, max_length=40) np.savez_compressed(out_path, last_hidden_state=feats.astype(np.float32)) print(f"LLaMA qid{qid} -> {feats.shape}") except Exception as e: print("LLaMA extraction failed:", e) print("Done. Use data/lab_val.jsonl and features/lab/clip_features, features/lab/llama_text_feature for inference.") if __name__ == "__main__": main()