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254
python_core/scene_detection/workflows/batch_workflow_nodes.py
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254
python_core/scene_detection/workflows/batch_workflow_nodes.py
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#!/usr/bin/env python3
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"""
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Batch Workflow Nodes
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批量工作流节点
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"""
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import time
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import asyncio
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from pathlib import Path
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from typing import Dict, Any, List
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from python_core.utils.logger import logger
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from python_core.utils.jsonrpc_enhanced import ProgressLevel
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from ..types.batch_workflow_state import BatchSceneDetectionWorkflowState, BatchVideoTask
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from ..types.enums import DetectorType, OutputFormat
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from ..services.detector_service import SceneDetectorService
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from ..services.video_info_service import VideoInfoService
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from ..services.ai_analysis_service import AIAnalysisService
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from python_core.services.ffmpeg_slice_service import FfmpegSliceService, SliceOptions
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from ..utils.result_saver import ResultSaver
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class BatchWorkflowNodes:
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"""批量工作流节点"""
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def __init__(self):
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# 初始化服务
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self.detector_service = SceneDetectorService()
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self.video_info_service = VideoInfoService()
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self.ai_analysis_service = AIAnalysisService()
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self.splitter_service = FfmpegSliceService()
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self.result_saver = ResultSaver()
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def validate_batch_input(self, state: BatchSceneDetectionWorkflowState) -> Dict[str, Any]:
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"""验证批量输入"""
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state.current_stage = "validation"
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state.send_progress("validate_input", "验证批量输入参数", ProgressLevel.INFO)
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errors = []
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# 检查视频文件
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valid_videos = []
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for video_path in state.video_paths:
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if not video_path.exists():
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errors.append(f"视频文件不存在: {video_path}")
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continue
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if video_path.suffix.lower() not in {'.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm', '.m4v'}:
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errors.append(f"不支持的视频格式: {video_path}")
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continue
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valid_videos.append(video_path)
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if not valid_videos:
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errors.append("没有有效的视频文件")
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# 检查输出目录
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if state.output_base_dir:
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try:
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state.output_base_dir.mkdir(parents=True, exist_ok=True)
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except Exception as e:
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errors.append(f"无法创建输出目录: {e}")
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# 检查FFmpeg（如果需要切分）
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if state.enable_video_splitting:
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if not self.splitter_service.check_ffmpeg_available():
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errors.append("FFmpeg不可用，无法进行视频切分")
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if errors:
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state.global_errors.extend(errors)
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return {
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"workflow_state": "failed",
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"errors": errors,
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"valid_videos": valid_videos
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}
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# 更新有效视频列表
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state.video_paths = valid_videos
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state.tasks = [
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BatchVideoTask(
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video_path=video_path,
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output_dir=state.output_base_dir / video_path.stem if state.output_base_dir else None
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)
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for video_path in valid_videos
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]
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state.total_tasks = len(state.tasks)
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state.send_progress("validate_input", f"验证完成，找到 {len(valid_videos)} 个有效视频", ProgressLevel.SUCCESS)
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return {
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"workflow_state": "validated",
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"valid_videos": valid_videos,
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"total_tasks": state.total_tasks
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}
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def process_videos_batch(self, state: BatchSceneDetectionWorkflowState) -> Dict[str, Any]:
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"""批量处理视频"""
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state.current_stage = "batch_processing"
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state.send_progress("batch_process", "开始批量处理视频", ProgressLevel.INFO)
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# 使用线程池进行并发处理
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with ThreadPoolExecutor(max_workers=state.max_concurrent) as executor:
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# 提交所有任务
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future_to_index = {}
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for i, task in enumerate(state.tasks):
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future = executor.submit(self._process_single_video, state, i, task)
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future_to_index[future] = i
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# 处理完成的任务
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for future in as_completed(future_to_index):
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task_index = future_to_index[future]
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try:
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result = future.result()
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if result["success"]:
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state.mark_task_completed(
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task_index,
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result["detection_result"],
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result.get("split_results", [])
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)
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state.send_progress(
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"task_completed",
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f"任务 {task_index + 1}/{state.total_tasks} 完成: {state.tasks[task_index].video_path.name}",
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ProgressLevel.SUCCESS,
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{"task_index": task_index + 1, "video_name": state.tasks[task_index].video_path.name}
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)
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else:
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state.mark_task_failed(task_index, result["error"])
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state.send_progress(
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"task_failed",
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f"任务 {task_index + 1}/{state.total_tasks} 失败: {result['error']}",
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ProgressLevel.ERROR,
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{"task_index": task_index + 1, "error": result["error"]}
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)
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except Exception as e:
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state.mark_task_failed(task_index, str(e))
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state.send_progress(
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"task_error",
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f"任务 {task_index + 1}/{state.total_tasks} 异常: {str(e)}",
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ProgressLevel.ERROR,
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{"task_index": task_index + 1, "error": str(e)}
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)
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# 生成批量处理结果
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summary = state.get_summary()
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state.send_progress(
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"batch_complete",
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f"批量处理完成: {state.completed_tasks}/{state.total_tasks} 成功",
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ProgressLevel.SUCCESS,
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summary
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)
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return {
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"workflow_state": "completed",
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"summary": summary,
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"completed_tasks": state.completed_tasks,
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"failed_tasks": state.failed_tasks,
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"total_tasks": state.total_tasks
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}
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def _process_single_video(self, state: BatchSceneDetectionWorkflowState,
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task_index: int, task: BatchVideoTask) -> Dict[str, Any]:
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"""处理单个视频"""
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try:
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task.status = "processing"
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task.start_time = time.time()
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logger.info(f"🎬 处理视频 {task_index + 1}/{state.total_tasks}: {task.video_path.name}")
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# 1. 场景检测
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detection_result = self.detector_service.detect_scenes(
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task.video_path,
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DetectorType(state.detector_type),
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state.threshold,
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state.min_scene_length
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)
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if not detection_result.success:
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return {
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"success": False,
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"error": f"场景检测失败: {detection_result.error}"
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}
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# 2. 保存检测结果
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if task.output_dir:
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task.output_dir.mkdir(parents=True, exist_ok=True)
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result_file = task.output_dir / f"scenes.{state.output_format}"
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self.result_saver.save_results(
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detection_result,
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result_file,
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OutputFormat(state.output_format)
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)
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# 3. 视频切分（如果启用）
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split_results = []
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if state.enable_video_splitting and task.output_dir:
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try:
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# 创建切分选项
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slice_options = SliceOptions(
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crf=state.split_quality,
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preset=state.split_preset
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)
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split_results_raw = self.splitter_service.split_video_by_scenes(
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task.video_path,
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detection_result.scenes,
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task.output_dir / "scenes",
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use_advanced=state.use_advanced_split,
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options=slice_options
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)
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# 转换为字典格式
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split_results = [
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{
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"scene_index": r.scene_index + 1,
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"output_path": str(r.output_path),
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"start_time": r.start_time,
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"end_time": r.end_time,
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"duration": r.duration,
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"file_size": r.file_size,
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"success": r.success,
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"error": r.error
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}
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for r in split_results_raw
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]
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# 保存切分摘要
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split_summary = self.splitter_service.create_split_summary(
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task.video_path, split_results_raw
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)
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summary_file = task.output_dir / "split_summary.json"
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import json
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with open(summary_file, 'w', encoding='utf-8') as f:
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json.dump(split_summary, f, indent=2, ensure_ascii=False)
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except Exception as e:
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logger.error(f"视频切分失败: {e}")
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# 切分失败不影响整体任务成功
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task.end_time = time.time()
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return {
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"success": True,
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"detection_result": detection_result,
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"split_results": split_results
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}
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except Exception as e:
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task.end_time = time.time()
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return {
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"success": False,
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"error": str(e)
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}
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@@ -6,12 +6,14 @@ Workflow Manager
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import time
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from pathlib import Path
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from typing import Dict, Any, Optional, Literal
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from typing import Dict, Any, Optional, List, Literal
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from python_core.utils.logger import logger
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from python_core.utils.jsonrpc_enhanced import EnhancedJSONRPCResponse
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from ..types import SceneDetectionWorkflowState, DetectorType, OutputFormat
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from .workflow_nodes import WorkflowNodes
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from .batch_workflow_nodes import BatchWorkflowNodes
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from ..types.batch_workflow_state import BatchSceneDetectionWorkflowState
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class SceneDetectionWorkflowManager:
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@@ -19,6 +21,7 @@ class SceneDetectionWorkflowManager:
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def __init__(self):
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self.nodes = WorkflowNodes()
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self.batch_nodes = BatchWorkflowNodes()
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self.workflow = None
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def create_detection_workflow(self):
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@@ -174,3 +177,111 @@ class SceneDetectionWorkflowManager:
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# 非JSON-RPC模式：抛出异常
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logger.error(f"❌ {error_msg}")
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raise
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def batch_detect_and_split(self, video_paths: List[Path], output_base_dir: Optional[Path] = None,
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detector_type: DetectorType = DetectorType.CONTENT, threshold: float = 30.0,
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min_scene_length: float = 1.0, output_format: OutputFormat = OutputFormat.JSON,
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enable_ai_analysis: bool = False, enable_video_splitting: bool = True,
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max_concurrent: int = 2, continue_on_error: bool = True,
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use_advanced_split: bool = True, split_quality: int = 23, split_preset: str = "fast",
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request_id: Optional[str] = None) -> Dict[str, Any]:
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"""批量场景检测和视频切分"""
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# 创建批量工作流状态
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state = BatchSceneDetectionWorkflowState(
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video_paths=video_paths,
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output_base_dir=output_base_dir,
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detector_type=detector_type.value,
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threshold=threshold,
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min_scene_length=min_scene_length,
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output_format=output_format.value,
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enable_ai_analysis=enable_ai_analysis,
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enable_video_splitting=enable_video_splitting,
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use_advanced_split=use_advanced_split,
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split_quality=split_quality,
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split_preset=split_preset,
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max_concurrent=max_concurrent,
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continue_on_error=continue_on_error,
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request_id=request_id,
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enable_jsonrpc=request_id is not None
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)
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try:
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logger.info(f"🚀 开始批量场景检测和切分")
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logger.info(f"📁 视频数量: {len(video_paths)}")
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logger.info(f"📂 输出目录: {output_base_dir}")
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logger.info(f"🎯 检测器: {detector_type.value}, 阈值: {threshold}")
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logger.info(f"✂️ 视频切分: {'启用' if enable_video_splitting else '禁用'}")
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logger.info(f"🔄 并发数: {max_concurrent}")
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# 1. 验证输入
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validation_result = self.batch_nodes.validate_batch_input(state)
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if validation_result["workflow_state"] == "failed":
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error_msg = f"批量输入验证失败: {'; '.join(validation_result['errors'])}"
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if request_id:
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state.send_error_result(-32602, error_msg, validation_result["errors"])
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return {
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"jsonrpc_mode": True,
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"request_id": request_id,
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"error_sent": True,
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"error": error_msg
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}
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else:
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raise ValueError(error_msg)
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# 2. 批量处理
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processing_result = self.batch_nodes.process_videos_batch(state)
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# 3. 构建最终结果
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final_result = {
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"workflow_state": processing_result["workflow_state"],
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"summary": processing_result["summary"],
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"batch_results": {
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"total_videos": state.total_tasks,
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"completed_videos": state.completed_tasks,
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"failed_videos": state.failed_tasks,
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"success_rate": (state.completed_tasks / state.total_tasks * 100) if state.total_tasks > 0 else 0,
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"output_base_dir": str(output_base_dir) if output_base_dir else None,
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"enable_video_splitting": enable_video_splitting,
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"tasks": [
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{
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"video_path": str(task.video_path),
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"status": task.status,
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"output_dir": str(task.output_dir) if task.output_dir else None,
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"total_scenes": task.detection_result.total_scenes if task.detection_result else 0,
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"detection_time": task.detection_result.detection_time if task.detection_result else 0,
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"split_count": len(task.split_results) if task.split_results else 0,
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"error": task.error,
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"processing_time": (task.end_time - task.start_time) if task.start_time and task.end_time else 0
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}
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for task in state.tasks
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]
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}
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}
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if request_id:
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state.send_final_result(final_result)
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return {
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"jsonrpc_mode": True,
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"request_id": request_id,
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"result_sent": True,
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"result": final_result
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}
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else:
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return final_result
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except Exception as e:
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error_msg = f"批量工作流执行失败: {str(e)}"
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logger.error(error_msg)
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if request_id:
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state.send_error_result(-32603, error_msg, str(e))
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return {
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"jsonrpc_mode": True,
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"request_id": request_id,
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"error_sent": True,
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"error": error_msg
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}
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else:
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logger.error(f"❌ {error_msg}")
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raise
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