mirror of
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refactor(webui): 重构视频脚本生成-目录结果
- 将视频脚本生成相关代码从 script_settings.py 移动到新的 generate_script_docu.py 文件 - 新增 base.py 文件，提取公共工具函数 - 优化代码结构，提高可维护性和可读性- 重构函数名称，更清晰地反映功能
This commit is contained in:
linyq
parent
65d5a681ac
commit
d2f724217c
@ -1,120 +1,13 @@
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import os
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import ssl
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import glob
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import json
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import time
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import asyncio
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import traceback
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from requests.adapters import HTTPAdapter
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from urllib3.util.retry import Retry
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import requests
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import streamlit as st
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from loguru import logger
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from app.config import config
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from app.models.schema import VideoClipParams
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from app.utils.script_generator import ScriptProcessor
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from app.utils import utils, check_script, gemini_analyzer, video_processor, video_processor_v2, qwenvl_analyzer
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from webui.utils import file_utils
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def get_batch_timestamps(batch_files, prev_batch_files=None):
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"""
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解析一批文件的时间戳范围,支持毫秒级精度
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Args:
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batch_files: 当前批次的文件列表
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prev_batch_files: 上一个批次的文件列表,用于处理单张图片的情况
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Returns:
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tuple: (first_timestamp, last_timestamp, timestamp_range)
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时间戳格式: HH:MM:SS,mmm (时:分:秒,毫秒)
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例如: 00:00:50,100 表示50秒100毫秒
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示例文件名格式:
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keyframe_001253_000050100.jpg
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其中 000050100 表示 00:00:50,100 (50秒100毫秒)
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"""
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if not batch_files:
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logger.warning("Empty batch files")
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return "00:00:00,000", "00:00:00,000", "00:00:00,000-00:00:00,000"
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def get_frame_files():
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"""获取首帧和尾帧文件名"""
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if len(batch_files) == 1 and prev_batch_files and prev_batch_files:
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# 单张图片情况:使用上一批次最后一帧作为首帧
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first = os.path.basename(prev_batch_files[-1])
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last = os.path.basename(batch_files[0])
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logger.debug(f"单张图片批次,使用上一批次最后一帧作为首帧: {first}")
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else:
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first = os.path.basename(batch_files[0])
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last = os.path.basename(batch_files[-1])
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return first, last
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def extract_time(filename):
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"""从文件名提取时间信息"""
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try:
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# 提取类似 000050100 的时间戳部分
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time_str = filename.split('_')[2].replace('.jpg', '')
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if len(time_str) < 9: # 处理旧格式
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time_str = time_str.ljust(9, '0')
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return time_str
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except (IndexError, AttributeError) as e:
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logger.warning(f"Invalid filename format: {filename}, error: {e}")
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return "000000000"
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def format_timestamp(time_str):
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"""
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将时间字符串转换为 HH:MM:SS,mmm 格式
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Args:
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time_str: 9位数字字符串,格式为 HHMMSSMMM
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例如: 000010000 表示 00时00分10秒000毫秒
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000043039 表示 00时00分43秒039毫秒
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Returns:
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str: HH:MM:SS,mmm 格式的时间戳
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"""
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try:
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if len(time_str) < 9:
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logger.warning(f"Invalid timestamp format: {time_str}")
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return "00:00:00,000"
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# 从时间戳中提取时、分、秒和毫秒
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hours = int(time_str[0:2]) # 前2位作为小时
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minutes = int(time_str[2:4]) # 第3-4位作为分钟
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seconds = int(time_str[4:6]) # 第5-6位作为秒数
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milliseconds = int(time_str[6:]) # 最后3位作为毫秒
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return f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}"
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except ValueError as e:
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logger.warning(f"时间戳格式转换失败: {time_str}, error: {e}")
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return "00:00:00,000"
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# 获取首帧和尾帧文件名
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first_frame, last_frame = get_frame_files()
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# 从文件名中提取时间信息
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first_time = extract_time(first_frame)
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last_time = extract_time(last_frame)
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# 转换为标准时间戳格式
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first_timestamp = format_timestamp(first_time)
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last_timestamp = format_timestamp(last_time)
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timestamp_range = f"{first_timestamp}-{last_timestamp}"
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# logger.debug(f"解析时间戳: {first_frame} -> {first_timestamp}, {last_frame} -> {last_timestamp}")
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return first_timestamp, last_timestamp, timestamp_range
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def get_batch_files(keyframe_files, result, batch_size=5):
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"""
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获取当前批次的图片文件
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"""
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batch_start = result['batch_index'] * batch_size
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batch_end = min(batch_start + batch_size, len(keyframe_files))
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return keyframe_files[batch_start:batch_end]
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from app.utils import utils, check_script
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from webui.tools.generate_script_docu import generate_script_docu
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def render_script_panel(tr):
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@ -330,7 +223,7 @@ def render_script_buttons(tr, params):
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if st.button(button_name, key="script_action", disabled=not script_path):
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if script_path == "auto":
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generate_script(tr, params)
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generate_script_docu(tr, params)
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else:
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load_script(tr, script_path)
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@ -385,280 +278,6 @@ def load_script(tr, script_path):
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st.error(f"{tr('Failed to load script')}: {str(e)}")
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def generate_script(tr, params):
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"""生成视频脚本"""
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progress_bar = st.progress(0)
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status_text = st.empty()
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def update_progress(progress: float, message: str = ""):
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progress_bar.progress(progress)
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if message:
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status_text.text(f"{progress}% - {message}")
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else:
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status_text.text(f"进度: {progress}%")
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try:
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with st.spinner("正在生成脚本..."):
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if not params.video_origin_path:
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st.error("请先选择视频文件")
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return
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# ===================提取键帧===================
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update_progress(10, "正在提取关键帧...")
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# 创建临时目录用于存储关键帧
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keyframes_dir = os.path.join(utils.temp_dir(), "keyframes")
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video_hash = utils.md5(params.video_origin_path + str(os.path.getmtime(params.video_origin_path)))
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video_keyframes_dir = os.path.join(keyframes_dir, video_hash)
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# 检查是否已经提取过关键帧
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keyframe_files = []
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if os.path.exists(video_keyframes_dir):
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# 取已有的关键帧文件
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for filename in sorted(os.listdir(video_keyframes_dir)):
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if filename.endswith('.jpg'):
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keyframe_files.append(os.path.join(video_keyframes_dir, filename))
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if keyframe_files:
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logger.info(f"使用已缓存的关键帧: {video_keyframes_dir}")
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st.info(f"使用已缓存的关键帧，如需重新提取请删除目录: {video_keyframes_dir}")
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update_progress(20, f"使用已缓存关键帧，共 {len(keyframe_files)} 帧")
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# 如果没有缓存的关键帧，则进行提取
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if not keyframe_files:
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try:
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# 确保目录存在
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os.makedirs(video_keyframes_dir, exist_ok=True)
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# 初始化视频处理器
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if config.frames.get("version") == "v2":
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processor = video_processor_v2.VideoProcessor(params.video_origin_path)
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# 处理视频并提取关键帧
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processor.process_video_pipeline(
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output_dir=video_keyframes_dir,
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skip_seconds=st.session_state.get('skip_seconds'),
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threshold=st.session_state.get('threshold')
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)
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else:
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processor = video_processor.VideoProcessor(params.video_origin_path)
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# 处理视频并提取关键帧
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processor.process_video(
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output_dir=video_keyframes_dir,
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skip_seconds=0
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)
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# 获取所有关键文件路径
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for filename in sorted(os.listdir(video_keyframes_dir)):
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if filename.endswith('.jpg'):
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keyframe_files.append(os.path.join(video_keyframes_dir, filename))
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if not keyframe_files:
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raise Exception("未提取到任何关键帧")
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update_progress(20, f"关键帧提取完成，共 {len(keyframe_files)} 帧")
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except Exception as e:
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# 如果提取失败，清理创建的目录
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try:
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if os.path.exists(video_keyframes_dir):
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import shutil
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shutil.rmtree(video_keyframes_dir)
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except Exception as cleanup_err:
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logger.error(f"清理失败的关键帧目录时出错: {cleanup_err}")
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raise Exception(f"关键帧提取失败: {str(e)}")
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# 根据不同的 LLM 提供商处理
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vision_llm_provider = st.session_state.get('vision_llm_providers').lower()
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logger.debug(f"Vision LLM 提供商: {vision_llm_provider}")
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try:
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# ===================初始化视觉分析器===================
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update_progress(30, "正在初始化视觉分析器...")
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# 从配置中获取相关配置
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if vision_llm_provider == 'gemini':
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vision_api_key = st.session_state.get('vision_gemini_api_key')
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vision_model = st.session_state.get('vision_gemini_model_name')
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vision_base_url = st.session_state.get('vision_gemini_base_url')
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elif vision_llm_provider == 'qwenvl':
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vision_api_key = st.session_state.get('vision_qwenvl_api_key')
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vision_model = st.session_state.get('vision_qwenvl_model_name', 'qwen-vl-max-latest')
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vision_base_url = st.session_state.get('vision_qwenvl_base_url', 'https://dashscope.aliyuncs.com/compatible-mode/v1')
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else:
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raise ValueError(f"不支持的视觉分析提供商: {vision_llm_provider}")
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# 创建视觉分析器实例
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analyzer = create_vision_analyzer(
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provider=vision_llm_provider,
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api_key=vision_api_key,
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model=vision_model,
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base_url=vision_base_url
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)
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update_progress(40, "正在分析关键帧...")
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# ===================创建异步事件循环===================
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# 执行异步分析
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vision_batch_size = st.session_state.get('vision_batch_size') or config.frames.get("vision_batch_size")
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results = loop.run_until_complete(
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analyzer.analyze_images(
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images=keyframe_files,
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prompt=config.app.get('vision_analysis_prompt'),
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batch_size=vision_batch_size
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)
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)
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loop.close()
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# ===================处理分析结果===================
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update_progress(60, "正在整理分析结果...")
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# 合并所有批次的析结果
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frame_analysis = ""
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prev_batch_files = None
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for result in results:
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if 'error' in result:
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logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}")
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# 获取当前批次的文件列表 keyframe_001136_000045.jpg 将 000045 精度提升到 毫秒
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batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
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logger.debug(f"批次 {result['batch_index']} 处理完成，共 {len(batch_files)} 张图片")
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# logger.debug(batch_files)
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first_timestamp, last_timestamp, _ = get_batch_timestamps(batch_files, prev_batch_files)
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logger.debug(f"处理时间戳: {first_timestamp}-{last_timestamp}")
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# 添加带时间戳的分析结果
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frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
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frame_analysis += result['response']
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frame_analysis += "\n"
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# 更新上一个批次的文件
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prev_batch_files = batch_files
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if not frame_analysis.strip():
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raise Exception("未能生成有效的帧分析结果")
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# 保存分析结果
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analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
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with open(analysis_path, 'w', encoding='utf-8') as f:
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f.write(frame_analysis)
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update_progress(70, "正在生成脚本...")
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# 从配置中获取文本生成相关配置
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text_provider = config.app.get('text_llm_provider', 'gemini').lower()
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text_api_key = config.app.get(f'text_{text_provider}_api_key')
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text_model = config.app.get(f'text_{text_provider}_model_name')
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text_base_url = config.app.get(f'text_{text_provider}_base_url')
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# 构建帧内容列表
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frame_content_list = []
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prev_batch_files = None
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for i, result in enumerate(results):
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if 'error' in result:
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continue
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batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
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_, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
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frame_content = {
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"timestamp": timestamp_range,
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"picture": result['response'],
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"narration": "",
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"OST": 2
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}
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frame_content_list.append(frame_content)
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logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
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# 更新上一个批次的文件
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prev_batch_files = batch_files
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if not frame_content_list:
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raise Exception("没有有效的帧内容可以处理")
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# ===================开始生成文案===================
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update_progress(80, "正在生成文案...")
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# 校验配置
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api_params = {
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"vision_api_key": vision_api_key,
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"vision_model_name": vision_model,
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"vision_base_url": vision_base_url or "",
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"text_api_key": text_api_key,
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"text_model_name": text_model,
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"text_base_url": text_base_url or ""
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}
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headers = {
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'accept': 'application/json',
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'Content-Type': 'application/json'
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}
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session = requests.Session()
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retry_strategy = Retry(
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total=3,
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backoff_factor=1,
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status_forcelist=[500, 502, 503, 504]
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)
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adapter = HTTPAdapter(max_retries=retry_strategy)
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session.mount("https://", adapter)
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try:
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response = session.post(
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f"{config.app.get('narrato_api_url')}/video/config",
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headers=headers,
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json=api_params,
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timeout=30,
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verify=True
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)
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except Exception as e:
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pass
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custom_prompt = st.session_state.get('custom_prompt', '')
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processor = ScriptProcessor(
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model_name=text_model,
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api_key=text_api_key,
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prompt=custom_prompt,
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base_url=text_base_url or "",
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video_theme=st.session_state.get('video_theme', '')
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)
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# 处理帧内容生成脚本
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script_result = processor.process_frames(frame_content_list)
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# 结果转换为JSON字符串
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script = json.dumps(script_result, ensure_ascii=False, indent=2)
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except Exception as e:
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logger.exception(f"大模型处理过程中发生错误\n{traceback.format_exc()}")
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raise Exception(f"分析失败: {str(e)}")
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if script is None:
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st.error("生成脚本失败，请检查日志")
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st.stop()
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logger.info(f"脚本生成完成")
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if isinstance(script, list):
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st.session_state['video_clip_json'] = script
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elif isinstance(script, str):
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st.session_state['video_clip_json'] = json.loads(script)
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update_progress(80, "脚本生成完成")
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time.sleep(0.1)
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progress_bar.progress(100)
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status_text.text("脚本生成完成！")
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st.success("视频脚本生成成功！")
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except Exception as err:
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st.error(f"生成过程中发生错误: {str(err)}")
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logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
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finally:
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time.sleep(2)
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progress_bar.empty()
|
||||
status_text.empty()
|
||||
|
||||
|
||||
def save_script(tr, video_clip_json_details):
|
||||
"""保存视频脚本"""
|
||||
if not video_clip_json_details:
|
||||
@ -713,23 +332,3 @@ def crop_video(tr, params):
|
||||
time.sleep(2)
|
||||
progress_bar.empty()
|
||||
status_text.empty()
|
||||
|
||||
|
||||
def get_script_params():
|
||||
"""获取脚本参数"""
|
||||
return {
|
||||
'video_language': st.session_state.get('video_language', ''),
|
||||
'video_clip_json_path': st.session_state.get('video_clip_json_path', ''),
|
||||
'video_origin_path': st.session_state.get('video_origin_path', ''),
|
||||
'video_name': st.session_state.get('video_name', ''),
|
||||
'video_plot': st.session_state.get('video_plot', '')
|
||||
}
|
||||
|
||||
|
||||
def create_vision_analyzer(provider, api_key, model, base_url):
|
||||
if provider == 'gemini':
|
||||
return gemini_analyzer.VisionAnalyzer(model_name=model, api_key=api_key)
|
||||
elif provider == 'qwenvl':
|
||||
return qwenvl_analyzer.QwenAnalyzer(model_name=model, api_key=api_key)
|
||||
else:
|
||||
raise ValueError(f"不支持的视觉分析提供商: {provider}")
|
||||
|
||||
124
webui/tools/base.py
Normal file
124
webui/tools/base.py
Normal file
@ -0,0 +1,124 @@
|
||||
import os
|
||||
import streamlit as st
|
||||
from loguru import logger
|
||||
|
||||
from app.utils import gemini_analyzer, qwenvl_analyzer
|
||||
|
||||
|
||||
def create_vision_analyzer(provider, api_key, model, base_url):
|
||||
if provider == 'gemini':
|
||||
return gemini_analyzer.VisionAnalyzer(model_name=model, api_key=api_key)
|
||||
elif provider == 'qwenvl':
|
||||
return qwenvl_analyzer.QwenAnalyzer(model_name=model, api_key=api_key)
|
||||
else:
|
||||
raise ValueError(f"不支持的视觉分析提供商: {provider}")
|
||||
|
||||
|
||||
def get_script_params():
|
||||
"""获取脚本参数"""
|
||||
return {
|
||||
'video_language': st.session_state.get('video_language', ''),
|
||||
'video_clip_json_path': st.session_state.get('video_clip_json_path', ''),
|
||||
'video_origin_path': st.session_state.get('video_origin_path', ''),
|
||||
'video_name': st.session_state.get('video_name', ''),
|
||||
'video_plot': st.session_state.get('video_plot', '')
|
||||
}
|
||||
|
||||
|
||||
def get_batch_timestamps(batch_files, prev_batch_files=None):
|
||||
"""
|
||||
解析一批文件的时间戳范围,支持毫秒级精度
|
||||
|
||||
Args:
|
||||
batch_files: 当前批次的文件列表
|
||||
prev_batch_files: 上一个批次的文件列表,用于处理单张图片的情况
|
||||
|
||||
Returns:
|
||||
tuple: (first_timestamp, last_timestamp, timestamp_range)
|
||||
时间戳格式: HH:MM:SS,mmm (时:分:秒,毫秒)
|
||||
例如: 00:00:50,100 表示50秒100毫秒
|
||||
|
||||
示例文件名格式:
|
||||
keyframe_001253_000050100.jpg
|
||||
其中 000050100 表示 00:00:50,100 (50秒100毫秒)
|
||||
"""
|
||||
if not batch_files:
|
||||
logger.warning("Empty batch files")
|
||||
return "00:00:00,000", "00:00:00,000", "00:00:00,000-00:00:00,000"
|
||||
|
||||
def get_frame_files():
|
||||
"""获取首帧和尾帧文件名"""
|
||||
if len(batch_files) == 1 and prev_batch_files and prev_batch_files:
|
||||
# 单张图片情况:使用上一批次最后一帧作为首帧
|
||||
first = os.path.basename(prev_batch_files[-1])
|
||||
last = os.path.basename(batch_files[0])
|
||||
logger.debug(f"单张图片批次,使用上一批次最后一帧作为首帧: {first}")
|
||||
else:
|
||||
first = os.path.basename(batch_files[0])
|
||||
last = os.path.basename(batch_files[-1])
|
||||
return first, last
|
||||
|
||||
def extract_time(filename):
|
||||
"""从文件名提取时间信息"""
|
||||
try:
|
||||
# 提取类似 000050100 的时间戳部分
|
||||
time_str = filename.split('_')[2].replace('.jpg', '')
|
||||
if len(time_str) < 9: # 处理旧格式
|
||||
time_str = time_str.ljust(9, '0')
|
||||
return time_str
|
||||
except (IndexError, AttributeError) as e:
|
||||
logger.warning(f"Invalid filename format: {filename}, error: {e}")
|
||||
return "000000000"
|
||||
|
||||
def format_timestamp(time_str):
|
||||
"""
|
||||
将时间字符串转换为 HH:MM:SS,mmm 格式
|
||||
|
||||
Args:
|
||||
time_str: 9位数字字符串,格式为 HHMMSSMMM
|
||||
例如: 000010000 表示 00时00分10秒000毫秒
|
||||
000043039 表示 00时00分43秒039毫秒
|
||||
|
||||
Returns:
|
||||
str: HH:MM:SS,mmm 格式的时间戳
|
||||
"""
|
||||
try:
|
||||
if len(time_str) < 9:
|
||||
logger.warning(f"Invalid timestamp format: {time_str}")
|
||||
return "00:00:00,000"
|
||||
|
||||
# 从时间戳中提取时、分、秒和毫秒
|
||||
hours = int(time_str[0:2]) # 前2位作为小时
|
||||
minutes = int(time_str[2:4]) # 第3-4位作为分钟
|
||||
seconds = int(time_str[4:6]) # 第5-6位作为秒数
|
||||
milliseconds = int(time_str[6:]) # 最后3位作为毫秒
|
||||
|
||||
return f"{hours:02d}:{minutes:02d}:{seconds:02d},{milliseconds:03d}"
|
||||
|
||||
except ValueError as e:
|
||||
logger.warning(f"时间戳格式转换失败: {time_str}, error: {e}")
|
||||
return "00:00:00,000"
|
||||
|
||||
# 获取首帧和尾帧文件名
|
||||
first_frame, last_frame = get_frame_files()
|
||||
|
||||
# 从文件名中提取时间信息
|
||||
first_time = extract_time(first_frame)
|
||||
last_time = extract_time(last_frame)
|
||||
|
||||
# 转换为标准时间戳格式
|
||||
first_timestamp = format_timestamp(first_time)
|
||||
last_timestamp = format_timestamp(last_time)
|
||||
timestamp_range = f"{first_timestamp}-{last_timestamp}"
|
||||
|
||||
# logger.debug(f"解析时间戳: {first_frame} -> {first_timestamp}, {last_frame} -> {last_timestamp}")
|
||||
return first_timestamp, last_timestamp, timestamp_range
|
||||
|
||||
|
||||
def get_batch_files(keyframe_files, result, batch_size=5):
|
||||
"""
|
||||
获取当前批次的图片文件
|
||||
"""
|
||||
batch_start = result['batch_index'] * batch_size
|
||||
batch_end = min(batch_start + batch_size, len(keyframe_files))
|
||||
return keyframe_files[batch_start:batch_end]
|
||||
293
webui/tools/generate_script_docu.py
Normal file
293
webui/tools/generate_script_docu.py
Normal file
@ -0,0 +1,293 @@
|
||||
# 纪录片脚本生成
|
||||
import os
|
||||
import json
|
||||
import time
|
||||
import asyncio
|
||||
import traceback
|
||||
import requests
|
||||
import streamlit as st
|
||||
from loguru import logger
|
||||
from requests.adapters import HTTPAdapter
|
||||
from urllib3.util.retry import Retry
|
||||
|
||||
from app.config import config
|
||||
from app.utils.script_generator import ScriptProcessor
|
||||
from app.utils import utils, video_processor, video_processor_v2, qwenvl_analyzer
|
||||
from webui.tools.base import create_vision_analyzer, get_batch_files, get_batch_timestamps
|
||||
|
||||
|
||||
def generate_script_docu(tr, params):
|
||||
"""
|
||||
生成 纪录片 视频脚本
|
||||
"""
|
||||
progress_bar = st.progress(0)
|
||||
status_text = st.empty()
|
||||
|
||||
def update_progress(progress: float, message: str = ""):
|
||||
progress_bar.progress(progress)
|
||||
if message:
|
||||
status_text.text(f"{progress}% - {message}")
|
||||
else:
|
||||
status_text.text(f"进度: {progress}%")
|
||||
|
||||
try:
|
||||
with st.spinner("正在生成脚本..."):
|
||||
if not params.video_origin_path:
|
||||
st.error("请先选择视频文件")
|
||||
return
|
||||
|
||||
# ===================提取键帧===================
|
||||
update_progress(10, "正在提取关键帧...")
|
||||
|
||||
# 创建临时目录用于存储关键帧
|
||||
keyframes_dir = os.path.join(utils.temp_dir(), "keyframes")
|
||||
video_hash = utils.md5(params.video_origin_path + str(os.path.getmtime(params.video_origin_path)))
|
||||
video_keyframes_dir = os.path.join(keyframes_dir, video_hash)
|
||||
|
||||
# 检查是否已经提取过关键帧
|
||||
keyframe_files = []
|
||||
if os.path.exists(video_keyframes_dir):
|
||||
# 取已有的关键帧文件
|
||||
for filename in sorted(os.listdir(video_keyframes_dir)):
|
||||
if filename.endswith('.jpg'):
|
||||
keyframe_files.append(os.path.join(video_keyframes_dir, filename))
|
||||
|
||||
if keyframe_files:
|
||||
logger.info(f"使用已缓存的关键帧: {video_keyframes_dir}")
|
||||
st.info(f"使用已缓存的关键帧，如需重新提取请删除目录: {video_keyframes_dir}")
|
||||
update_progress(20, f"使用已缓存关键帧，共 {len(keyframe_files)} 帧")
|
||||
|
||||
# 如果没有缓存的关键帧，则进行提取
|
||||
if not keyframe_files:
|
||||
try:
|
||||
# 确保目录存在
|
||||
os.makedirs(video_keyframes_dir, exist_ok=True)
|
||||
|
||||
# 初始化视频处理器
|
||||
if config.frames.get("version") == "v2":
|
||||
processor = video_processor_v2.VideoProcessor(params.video_origin_path)
|
||||
# 处理视频并提取关键帧
|
||||
processor.process_video_pipeline(
|
||||
output_dir=video_keyframes_dir,
|
||||
skip_seconds=st.session_state.get('skip_seconds'),
|
||||
threshold=st.session_state.get('threshold')
|
||||
)
|
||||
else:
|
||||
processor = video_processor.VideoProcessor(params.video_origin_path)
|
||||
# 处理视频并提取关键帧
|
||||
processor.process_video(
|
||||
output_dir=video_keyframes_dir,
|
||||
skip_seconds=0
|
||||
)
|
||||
|
||||
# 获取所有关键文件路径
|
||||
for filename in sorted(os.listdir(video_keyframes_dir)):
|
||||
if filename.endswith('.jpg'):
|
||||
keyframe_files.append(os.path.join(video_keyframes_dir, filename))
|
||||
|
||||
if not keyframe_files:
|
||||
raise Exception("未提取到任何关键帧")
|
||||
|
||||
update_progress(20, f"关键帧提取完成，共 {len(keyframe_files)} 帧")
|
||||
|
||||
except Exception as e:
|
||||
# 如果提取失败，清理创建的目录
|
||||
try:
|
||||
if os.path.exists(video_keyframes_dir):
|
||||
import shutil
|
||||
shutil.rmtree(video_keyframes_dir)
|
||||
except Exception as cleanup_err:
|
||||
logger.error(f"清理失败的关键帧目录时出错: {cleanup_err}")
|
||||
|
||||
raise Exception(f"关键帧提取失败: {str(e)}")
|
||||
|
||||
# 根据不同的 LLM 提供商处理
|
||||
vision_llm_provider = st.session_state.get('vision_llm_providers').lower()
|
||||
logger.debug(f"Vision LLM 提供商: {vision_llm_provider}")
|
||||
|
||||
try:
|
||||
# ===================初始化视觉分析器===================
|
||||
update_progress(30, "正在初始化视觉分析器...")
|
||||
|
||||
# 从配置中获取相关配置
|
||||
if vision_llm_provider == 'gemini':
|
||||
vision_api_key = st.session_state.get('vision_gemini_api_key')
|
||||
vision_model = st.session_state.get('vision_gemini_model_name')
|
||||
vision_base_url = st.session_state.get('vision_gemini_base_url')
|
||||
elif vision_llm_provider == 'qwenvl':
|
||||
vision_api_key = st.session_state.get('vision_qwenvl_api_key')
|
||||
vision_model = st.session_state.get('vision_qwenvl_model_name', 'qwen-vl-max-latest')
|
||||
vision_base_url = st.session_state.get('vision_qwenvl_base_url',
|
||||
'https://dashscope.aliyuncs.com/compatible-mode/v1')
|
||||
else:
|
||||
raise ValueError(f"不支持的视觉分析提供商: {vision_llm_provider}")
|
||||
|
||||
# 创建视觉分析器实例
|
||||
analyzer = create_vision_analyzer(
|
||||
provider=vision_llm_provider,
|
||||
api_key=vision_api_key,
|
||||
model=vision_model,
|
||||
base_url=vision_base_url
|
||||
)
|
||||
|
||||
update_progress(40, "正在分析关键帧...")
|
||||
|
||||
# ===================创建异步事件循环===================
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
|
||||
# 执行异步分析
|
||||
vision_batch_size = st.session_state.get('vision_batch_size') or config.frames.get("vision_batch_size")
|
||||
results = loop.run_until_complete(
|
||||
analyzer.analyze_images(
|
||||
images=keyframe_files,
|
||||
prompt=config.app.get('vision_analysis_prompt'),
|
||||
batch_size=vision_batch_size
|
||||
)
|
||||
)
|
||||
loop.close()
|
||||
|
||||
# ===================处理分析结果===================
|
||||
update_progress(60, "正在整理分析结果...")
|
||||
|
||||
# 合并所有批次的析结果
|
||||
frame_analysis = ""
|
||||
prev_batch_files = None
|
||||
|
||||
for result in results:
|
||||
if 'error' in result:
|
||||
logger.warning(f"批次 {result['batch_index']} 处理出现警告: {result['error']}")
|
||||
|
||||
# 获取当前批次的文件列表 keyframe_001136_000045.jpg 将 000045 精度提升到 毫秒
|
||||
batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
|
||||
logger.debug(f"批次 {result['batch_index']} 处理完成，共 {len(batch_files)} 张图片")
|
||||
# logger.debug(batch_files)
|
||||
|
||||
first_timestamp, last_timestamp, _ = get_batch_timestamps(batch_files, prev_batch_files)
|
||||
logger.debug(f"处理时间戳: {first_timestamp}-{last_timestamp}")
|
||||
|
||||
# 添加带时间戳的分析结果
|
||||
frame_analysis += f"\n=== {first_timestamp}-{last_timestamp} ===\n"
|
||||
frame_analysis += result['response']
|
||||
frame_analysis += "\n"
|
||||
|
||||
# 更新上一个批次的文件
|
||||
prev_batch_files = batch_files
|
||||
|
||||
if not frame_analysis.strip():
|
||||
raise Exception("未能生成有效的帧分析结果")
|
||||
|
||||
# 保存分析结果
|
||||
analysis_path = os.path.join(utils.temp_dir(), "frame_analysis.txt")
|
||||
with open(analysis_path, 'w', encoding='utf-8') as f:
|
||||
f.write(frame_analysis)
|
||||
|
||||
update_progress(70, "正在生成脚本...")
|
||||
|
||||
# 从配置中获取文本生成相关配置
|
||||
text_provider = config.app.get('text_llm_provider', 'gemini').lower()
|
||||
text_api_key = config.app.get(f'text_{text_provider}_api_key')
|
||||
text_model = config.app.get(f'text_{text_provider}_model_name')
|
||||
text_base_url = config.app.get(f'text_{text_provider}_base_url')
|
||||
|
||||
# 构建帧内容列表
|
||||
frame_content_list = []
|
||||
prev_batch_files = None
|
||||
|
||||
for i, result in enumerate(results):
|
||||
if 'error' in result:
|
||||
continue
|
||||
|
||||
batch_files = get_batch_files(keyframe_files, result, vision_batch_size)
|
||||
_, _, timestamp_range = get_batch_timestamps(batch_files, prev_batch_files)
|
||||
|
||||
frame_content = {
|
||||
"timestamp": timestamp_range,
|
||||
"picture": result['response'],
|
||||
"narration": "",
|
||||
"OST": 2
|
||||
}
|
||||
frame_content_list.append(frame_content)
|
||||
|
||||
logger.debug(f"添加帧内容: 时间范围={timestamp_range}, 分析结果长度={len(result['response'])}")
|
||||
|
||||
# 更新上一个批次的文件
|
||||
prev_batch_files = batch_files
|
||||
|
||||
if not frame_content_list:
|
||||
raise Exception("没有有效的帧内容可以处理")
|
||||
|
||||
# ===================开始生成文案===================
|
||||
update_progress(80, "正在生成文案...")
|
||||
# 校验配置
|
||||
api_params = {
|
||||
"vision_api_key": vision_api_key,
|
||||
"vision_model_name": vision_model,
|
||||
"vision_base_url": vision_base_url or "",
|
||||
"text_api_key": text_api_key,
|
||||
"text_model_name": text_model,
|
||||
"text_base_url": text_base_url or ""
|
||||
}
|
||||
headers = {
|
||||
'accept': 'application/json',
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
session = requests.Session()
|
||||
retry_strategy = Retry(
|
||||
total=3,
|
||||
backoff_factor=1,
|
||||
status_forcelist=[500, 502, 503, 504]
|
||||
)
|
||||
adapter = HTTPAdapter(max_retries=retry_strategy)
|
||||
session.mount("https://", adapter)
|
||||
try:
|
||||
response = session.post(
|
||||
f"{config.app.get('narrato_api_url')}/video/config",
|
||||
headers=headers,
|
||||
json=api_params,
|
||||
timeout=30,
|
||||
verify=True
|
||||
)
|
||||
except Exception as e:
|
||||
pass
|
||||
custom_prompt = st.session_state.get('custom_prompt', '')
|
||||
processor = ScriptProcessor(
|
||||
model_name=text_model,
|
||||
api_key=text_api_key,
|
||||
prompt=custom_prompt,
|
||||
base_url=text_base_url or "",
|
||||
video_theme=st.session_state.get('video_theme', '')
|
||||
)
|
||||
|
||||
# 处理帧内容生成脚本
|
||||
script_result = processor.process_frames(frame_content_list)
|
||||
|
||||
# 结果转换为JSON字符串
|
||||
script = json.dumps(script_result, ensure_ascii=False, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
logger.exception(f"大模型处理过程中发生错误\n{traceback.format_exc()}")
|
||||
raise Exception(f"分析失败: {str(e)}")
|
||||
|
||||
if script is None:
|
||||
st.error("生成脚本失败，请检查日志")
|
||||
st.stop()
|
||||
logger.info(f"脚本生成完成")
|
||||
if isinstance(script, list):
|
||||
st.session_state['video_clip_json'] = script
|
||||
elif isinstance(script, str):
|
||||
st.session_state['video_clip_json'] = json.loads(script)
|
||||
update_progress(80, "脚本生成完成")
|
||||
|
||||
time.sleep(0.1)
|
||||
progress_bar.progress(100)
|
||||
status_text.text("脚本生成完成！")
|
||||
st.success("视频脚本生成成功！")
|
||||
|
||||
except Exception as err:
|
||||
st.error(f"生成过程中发生错误: {str(err)}")
|
||||
logger.exception(f"生成脚本时发生错误\n{traceback.format_exc()}")
|
||||
finally:
|
||||
time.sleep(2)
|
||||
progress_bar.empty()
|
||||
status_text.empty()
|
||||
Loading…
x
Reference in New Issue
Block a user