forked from 77media/video-flow
334 lines
9.6 KiB
TypeScript
334 lines
9.6 KiB
TypeScript
/**
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* AI剪辑适配器 - 将OpenCut的AI剪辑逻辑适配到video-flow-b
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* 文件路径: video-flow-b/components/pages/work-flow/ai-editing-adapter.ts
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*/
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// 从OpenCut项目导入的类型定义
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interface AIEditingPlan {
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version_name: string;
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version_summary: string;
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timeline_clips: AIClipData[];
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}
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interface AIClipData {
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sequence_clip_id: string;
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source_clip_id: string;
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video_url: string;
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source_in_timecode: string;
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source_out_timecode: string;
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sequence_start_timecode: string;
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clip_duration_in_sequence: string;
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corresponding_script_scene_id?: string;
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}
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interface AIEditingData {
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editing_plan: {
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editing_sequence_plans: AIEditingPlan[];
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};
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subtitle_data?: {
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final_dialogue_segments?: any[];
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final_srt_content?: string;
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};
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}
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interface ExportOptions {
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quality: 'low' | 'standard' | 'high';
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format: 'mp4' | 'webm' | 'mov';
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fps?: number;
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}
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/**
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* video-flow-b项目的任务对象类型
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*/
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interface VideoFlowTaskObject {
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title: string;
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currentStage: string;
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videos: {
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data: Array<{
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urls?: string[];
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video_id: string;
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video_status: number;
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}>;
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total_count: number;
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};
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final: {
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url: string;
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note: string;
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};
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}
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/**
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* AI剪辑适配器类
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* 负责将video-flow-b的视频数据转换为OpenCut的AI剪辑格式，并执行自动化剪辑流程
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*/
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export class AIEditingAdapter {
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private projectId: string;
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private taskObject: VideoFlowTaskObject;
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private onProgress?: (progress: number, message: string) => void;
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private onComplete?: (finalVideoUrl: string) => void;
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private onError?: (error: string) => void;
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constructor(
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projectId: string,
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taskObject: VideoFlowTaskObject,
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callbacks?: {
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onProgress?: (progress: number, message: string) => void;
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onComplete?: (finalVideoUrl: string) => void;
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onError?: (error: string) => void;
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}
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) {
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this.projectId = projectId;
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this.taskObject = taskObject;
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this.onProgress = callbacks?.onProgress;
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this.onComplete = callbacks?.onComplete;
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this.onError = callbacks?.onError;
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}
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/**
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* 将video-flow-b的视频数据转换为AI剪辑计划格式
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*/
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private convertToAIEditingPlan(): AIEditingPlan {
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const clips: AIClipData[] = [];
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// 遍历video-flow-b的视频数据，转换为AI剪辑格式
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this.taskObject.videos.data.forEach((video, index) => {
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if (video.video_status === 1 && video.urls && video.urls.length > 0) {
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// 只处理生成成功的视频
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const clip: AIClipData = {
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sequence_clip_id: `clip_${index + 1}`,
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source_clip_id: video.video_id,
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video_url: video.urls[0], // 取第一个URL
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source_in_timecode: "00:00:00:00",
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source_out_timecode: this.estimateClipDuration(index),
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sequence_start_timecode: this.calculateSequenceStart(index),
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clip_duration_in_sequence: this.estimateClipDuration(index),
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corresponding_script_scene_id: `scene_${index + 1}`
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};
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clips.push(clip);
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}
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});
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return {
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version_name: `${this.taskObject.title} - AI剪辑版本`,
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version_summary: `基于${clips.length}个视频片段的智能剪辑，自动优化节奏和转场效果`,
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timeline_clips: clips
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};
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}
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/**
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* 估算视频片段时长（默认5秒，实际项目中应该获取真实时长）
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*/
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private estimateClipDuration(index: number): string {
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const duration = 5; // 默认5秒
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const frames = Math.floor(duration * 30); // 30fps
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return `00:00:0${duration}:${frames.toString().padStart(2, '0')}`;
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}
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/**
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* 计算序列开始时间码
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*/
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private calculateSequenceStart(index: number): string {
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const startSeconds = index * 5; // 每个片段5秒
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const minutes = Math.floor(startSeconds / 60);
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const seconds = startSeconds % 60;
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return `00:${minutes.toString().padStart(2, '0')}:${seconds.toString().padStart(2, '0')}:00`;
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}
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/**
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* 执行自动化AI剪辑流程
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* 这是核心方法，整合了OpenCut的一键剪辑逻辑
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*/
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public async executeAutoAIEditing(): Promise<string> {
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try {
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this.onProgress?.(10, "初始化AI剪辑系统...");
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// 第一步：生成AI剪辑计划
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const aiEditingPlan = this.convertToAIEditingPlan();
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if (aiEditingPlan.timeline_clips.length === 0) {
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throw new Error("没有可用的视频片段进行剪辑");
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}
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this.onProgress?.(25, `已识别${aiEditingPlan.timeline_clips.length}个视频片段`);
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// 第二步：构建导出请求数据
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const exportRequest = {
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clips: aiEditingPlan.timeline_clips,
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subtitles: "", // 暂时不处理字幕
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totalDuration: this.calculateTotalDuration(aiEditingPlan.timeline_clips),
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options: {
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quality: 'standard' as const,
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format: 'mp4' as const,
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fps: 30
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}
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};
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this.onProgress?.(40, "开始智能剪辑处理...");
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// 第三步：调用OpenCut的导出API
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const finalVideoUrl = await this.callExportAPI(exportRequest);
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this.onProgress?.(90, "剪辑完成，正在生成最终视频...");
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// 第四步：更新video-flow-b项目状态
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await this.updateVideoFlowProject(finalVideoUrl);
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this.onProgress?.(100, "AI剪辑流程完成！");
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this.onComplete?.(finalVideoUrl);
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return finalVideoUrl;
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} catch (error) {
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const errorMessage = error instanceof Error ? error.message : "AI剪辑过程中发生未知错误";
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console.error("AI剪辑失败:", error);
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this.onError?.(errorMessage);
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throw error;
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}
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}
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/**
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* 计算总时长
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*/
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private calculateTotalDuration(clips: AIClipData[]): number {
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// 简单估算：每个片段5秒
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return clips.length * 5;
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}
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/**
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* 调用video-flow-b的导出API
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* 复用OpenCut项目中的 /api/export/ai-clips 逻辑
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*/
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private async callExportAPI(exportRequest: any): Promise<string> {
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const response = await fetch('/api/export/ai-clips', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify(exportRequest)
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});
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if (!response.ok) {
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throw new Error(`导出API调用失败: ${response.statusText}`);
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}
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// 处理流式响应
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const reader = response.body?.getReader();
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if (!reader) {
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throw new Error("无法读取响应流");
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}
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let finalVideoUrl = "";
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const decoder = new TextDecoder();
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try {
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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const chunk = decoder.decode(value);
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const lines = chunk.split('\n');
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for (const line of lines) {
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if (line.startsWith('data: ')) {
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try {
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const data = JSON.parse(line.slice(6));
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// 更新进度
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if (data.type === 'progress') {
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const progress = 40 + (data.progress * 40); // 40-80% 区间
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this.onProgress?.(progress, data.message || "处理中...");
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}
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// 获取最终结果
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if (data.type === 'completed' && data.downloadUrl) {
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finalVideoUrl = data.downloadUrl;
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}
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} catch (e) {
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console.warn("解析响应数据失败:", e);
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}
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}
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}
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}
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} finally {
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reader.releaseLock();
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}
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if (!finalVideoUrl) {
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throw new Error("未能获取最终视频URL");
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}
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return finalVideoUrl;
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}
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/**
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* 更新video-flow-b项目状态
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* 将AI剪辑结果更新到video-flow-b的数据结构中
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*/
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private async updateVideoFlowProject(finalVideoUrl: string): Promise<void> {
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try {
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// 构建任务结果数据，模拟video-flow-b的粗剪接口调用
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const taskResult = {
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task_result: JSON.stringify({
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video: finalVideoUrl
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}),
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task_name: "generate_final_simple_video",
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project_id: this.projectId
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};
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console.log('🎬 更新video-flow-b项目状态:');
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console.log(' - 项目ID:', this.projectId);
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console.log(' - 最终视频URL:', finalVideoUrl);
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console.log(' - 任务结果:', taskResult);
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// 这里可以调用video-flow-b的API来更新项目状态
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// 或者直接更新本地状态（取决于具体需求）
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} catch (error) {
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console.warn("更新video-flow-b项目状态失败:", error);
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// 不抛出错误，避免影响主流程
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}
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}
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/**
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* 静态方法：检查是否可以执行AI剪辑
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*/
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public static canExecuteAIEditing(taskObject: VideoFlowTaskObject): boolean {
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// 检查是否有可用的视频片段
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const availableVideos = taskObject.videos.data.filter(
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video => video.video_status === 1 && video.urls && video.urls.length > 0
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);
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return availableVideos.length > 0;
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}
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/**
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* 静态方法：获取可用视频数量
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*/
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public static getAvailableVideoCount(taskObject: VideoFlowTaskObject): number {
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return taskObject.videos.data.filter(
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video => video.video_status === 1 && video.urls && video.urls.length > 0
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).length;
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}
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}
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/**
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* 导出工厂函数，方便在组件中使用
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*/
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export function createAIEditingAdapter(
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projectId: string,
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taskObject: VideoFlowTaskObject,
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callbacks?: {
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onProgress?: (progress: number, message: string) => void;
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onComplete?: (finalVideoUrl: string) => void;
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onError?: (error: string) => void;
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}
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): AIEditingAdapter {
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return new AIEditingAdapter(projectId, taskObject, callbacks);
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}
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/**
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* 导出类型定义供其他文件使用
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*/
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export type { VideoFlowTaskObject, AIEditingPlan, AIClipData, ExportOptions };
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