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import torch
import os
import tqdm
import glob
import multiprocessing
from functools import partial
from time_r1.utils.clip_service import SiglipClient
from time_r1.utils.qwen_vl_utils import fetch_video
from time_r1.utils.io import load_jsonl
import os
SIGLIP_URL = os.environ.get("SIGLIP_URL", "grpc://127.0.0.1:51000")
clip_model = SiglipClient(base_url=SIGLIP_URL)
def process_single_video(video_path):
# ele = {
# "video": video_path,
# "fps": fps,
# "max_frames": max_frames,
# "total_pixels": max_frames * 1024 * 28 * 28,
# }
# video, sample_fps = fetch_video(ele, return_video_sample_fps=True)
# print(video_path)
try:
video = torch.load(video_path + ".frame_cache")["frame_tensor"]
features = clip_model.encode_images(video)
print(features.shape, video.shape)
# save features
torch.save(features, video_path + ".feature_cache")
except Exception as e:
print(f'{e}, {video_path}')
def prepare_feature_cache(video_root, dataset_path=None, num_workers=8, overwrite=False):
if dataset_path is not None:
# 修改这里：直接使用json.load而不是load_jsonl
import json
with open(dataset_path, 'r', encoding='utf-8') as f:
video_data = json.load(f) # 这是JSON数组
# 提取video_path字段，并去重（同一个视频可能有多条记录）
video_paths = set() # 使用set去重
for v in video_data:
if "video_path" in v:
video_paths.add(v["video_path"])
elif "video" in v:
# 如果有video字段，可能需要拼接路径
video_path = os.path.join(video_root, v["video"])
video_paths.add(video_path)
video_list = list(video_paths)
if not video_list:
print(f"No MP4 videos found in {video_root}")
return
if not overwrite:
print("skipping videos that already have feature cache")
num_total = len(video_list)
video_list = [v for v in video_list if not os.path.exists(v + ".feature_cache")]
num_skipped = num_total - len(video_list)
print(f"skipped {num_skipped} videos")
if num_workers is None:
num_workers = multiprocessing.cpu_count() # Default to using all available CPU cores
print(f"Found {len(video_list)} videos. Starting processing with {num_workers} workers...")
# Use a multiprocessing Pool to process videos in parallel
with multiprocessing.Pool(processes=num_workers) as pool:
# Using tqdm with pool.imap_unordered for progress bar and efficient iteration
# We wrap process_single_video if it needs more arguments or if we want to handle results
# For this case, process_single_video only takes video_path
list(tqdm.tqdm(pool.imap_unordered(process_single_video, video_list), total=len(video_list)))
print("All videos processed.")
if __name__ == "__main__":
import fire
fire.Fire(prepare_feature_cache)
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