SYNCNET/run_syncnet.py

#!/usr/bin/python
#-*- coding: utf-8 -*-

import time, pdb, argparse, subprocess, pickle, os, gzip, glob
import tempfile
import uuid
import shutil

from SyncNetInstance import *


# ==================== SYNCNET FUNCTION ====================

def run_syncnet(video_path, 
                model_path="data/syncnet_v2.model",
                batch_size=20,
                vshift=15,
                tmp_dir=None,
                cleanup=True):
    """
    运行SyncNet评估视频的音频-视频同步
    
    参数:
        video_path (str): 输入视频文件路径
        model_path (str): SyncNet模型路径，默认为 "data/syncnet_v2.model"
        batch_size (int): 批次大小，默认为 20
        vshift (int): 视频偏移量，默认为 15
        tmp_dir (str): 临时目录路径，如果为None则使用系统临时目录
        cleanup (bool): 是否在完成后清理临时文件，默认为 True
    
    返回:
        dict: 包含以下键的字典
            - 'offset': 音频-视频偏移量 (numpy array)
            - 'confidence': 置信度 (numpy array)
            - 'dists': 距离矩阵 (numpy array)
    """
    # 设置临时目录
    if tmp_dir is None:
        tmp_dir = os.path.join(tempfile.gettempdir(), 'syncnet_' + str(uuid.uuid4())[:8])
    
    reference = 'temp_ref'
    work_tmp_dir = os.path.join(tmp_dir, reference)
    
    # 创建opt对象
    class Opt:
        pass
    
    opt = Opt()
    opt.tmp_dir = tmp_dir
    opt.reference = reference
    opt.batch_size = batch_size
    opt.vshift = vshift
    
    try:
        # 初始化SyncNet实例
        s = SyncNetInstance()
        
        # 加载模型
        if not os.path.exists(model_path):
            raise FileNotFoundError(f"模型文件不存在: {model_path}")
        s.loadParameters(model_path)
        print(f"Model {model_path} loaded.")
        
        # 运行评估 (evaluate方法会自动创建和清理临时目录)
        offset, conf, dists = s.evaluate(opt, videofile=video_path)
        
        # 返回结果
        result = {
            'offset': offset,
            'confidence': conf,
            'dists': dists
        }
        
        return result
        
    finally:
        # 清理临时文件
        if cleanup and os.path.exists(work_tmp_dir):
            shutil.rmtree(work_tmp_dir)
            print(f"Cleaned up temporary directory: {work_tmp_dir}")

# ==================== PARSE ARGUMENT ====================

parser = argparse.ArgumentParser(description = "SyncNet");
parser.add_argument('--initial_model', type=str, default="syncnet_models/syncnet_v2.model", help='');
parser.add_argument('--batch_size', type=int, default='20', help='');
parser.add_argument('--vshift', type=int, default='15', help='');
parser.add_argument('--data_dir', type=str, default='data/work', help='');
parser.add_argument('--videofile', type=str, default='/share/zhaohu_workspace/light-video-gen-v2/test_videos/man_2.mp4', help='');
parser.add_argument('--reference', type=str, default='', help='');
opt = parser.parse_args();

setattr(opt,'avi_dir',os.path.join(opt.data_dir,'pyavi'))
setattr(opt,'tmp_dir',os.path.join(opt.data_dir,'pytmp'))
setattr(opt,'work_dir',os.path.join(opt.data_dir,'pywork'))
setattr(opt,'crop_dir',os.path.join(opt.data_dir,'pycrop'))


s = SyncNetInstance();

s.loadParameters(opt.initial_model)
print("Model %s loaded."%opt.initial_model)

flist = glob.glob(os.path.join(opt.crop_dir,opt.reference,'0*.avi'))
flist.sort()

# ==================== GET OFFSETS ====================

dists = []
for idx, fname in enumerate(flist):
    print(f"fname is {fname}")
    offset, conf, dist = s.evaluate(opt,videofile=fname)
    dists.append(dist)
      
# ==================== PRINT RESULTS TO FILE ====================

with open(os.path.join(opt.work_dir,opt.reference,'activesd.pckl'), 'wb') as fil:
    pickle.dump(dists, fil)


# 自定义参数
# result = run_syncnet(
#     video_path="/share/zhaohu_workspace/benchmarks/outputs_benchmark_3/latent_sync/case_0000_RD_Radio1_000_0_80__RD_Radio11_001_648_728.mp4",
#     model_path="syncnet_models/syncnet_v2.model",
#     batch_size=20,
#     vshift=15
# )

# print(f"result: {result}")