retrotoon-studio/server/videoProcessor.ts

/**
 * Video Processor Service
 * Handles video ingestion: frame extraction, audio isolation, scene detection
 * 
 * NOTE: In the deployed environment, heavy video processing would be delegated
 * to an external service. This module provides the API interface and simulation
 * for the web application layer.
 */

import { invokeLLM } from "./_core/llm";

export interface VideoMetadata {
  fps: number;
  totalFrames: number;
  width: number;
  height: number;
  duration: number; // ms
  codec: string;
}

export interface SceneCut {
  frameIndex: number;
  confidence: number;
  type: "hard_cut" | "dissolve" | "fade";
}

/**
 * Simulate video metadata extraction
 * In production, this would use FFprobe or a media analysis service
 */
export async function extractVideoMetadata(videoUrl: string): Promise<VideoMetadata> {
  // Simulated metadata - in production would call FFprobe via external service
  return {
    fps: 24,
    totalFrames: 576, // ~24s at 24fps
    width: 720,
    height: 480,
    duration: 24000,
    codec: "h264",
  };
}

/**
 * Simulate frame extraction from video
 * In production, this would use FFmpeg via an external processing service
 * Returns URLs to extracted frame images stored in S3
 */
export async function extractFrames(
  videoUrl: string,
  startFrame: number,
  endFrame: number,
  outputPrefix: string
): Promise<string[]> {
  // Simulate frame extraction - returns placeholder URLs
  const frameUrls: string[] = [];
  for (let i = startFrame; i <= endFrame; i++) {
    frameUrls.push(`/manus-storage/frames/${outputPrefix}/frame_${String(i).padStart(6, "0")}.png`);
  }
  return frameUrls;
}

/**
 * Simulate audio extraction from video
 * In production, this would use FFmpeg to extract audio track
 */
export async function extractAudio(videoUrl: string, outputKey: string): Promise<string> {
  // Returns placeholder URL for extracted audio
  return `/manus-storage/audio/${outputKey}.wav`;
}

/**
 * Detect scene cuts using frame difference analysis
 * Uses LLM vision to analyze frame transitions when available
 */
export async function detectSceneCuts(
  projectId: number,
  totalFrames: number,
  fps: number
): Promise<SceneCut[]> {
  // Simulate scene detection based on typical animation patterns
  // In production, this would analyze actual frame histograms and pixel differences
  const cuts: SceneCut[] = [];
  const avgSceneLength = fps * 3; // Average 3 seconds per scene in animation
  
  let currentFrame = 0;
  while (currentFrame < totalFrames) {
    // Add some randomness to simulate real scene detection
    const sceneLength = Math.floor(avgSceneLength * (0.5 + Math.random() * 1.5));
    currentFrame += sceneLength;
    
    if (currentFrame < totalFrames) {
      cuts.push({
        frameIndex: currentFrame,
        confidence: 0.85 + Math.random() * 0.15,
        type: Math.random() > 0.8 ? "dissolve" : "hard_cut",
      });
    }
  }
  
  return cuts;
}

/**
 * Analyze a frame using LLM vision to determine:
 * - Whether the background is static
 * - What characters/objects are present
 * - Quality score for background reference selection
 */
export async function analyzeFrame(frameUrl: string, context: string): Promise<{
  isStaticBackground: boolean;
  characters: string[];
  objects: string[];
  qualityScore: number;
  description: string;
}> {
  try {
    const response = await invokeLLM({
      messages: [
        {
          role: "system",
          content: `Tu es un analyste d'animation professionnelle. Analyse cette frame de dessin animé et fournis:
1. Si l'arrière-plan semble statique (typique des dessins animés des années 80)
2. Les personnages visibles
3. Les objets en mouvement
4. Un score de qualité (0-100) pour utiliser cette frame comme référence de fond
Contexte: ${context}
Réponds en JSON.`,
        },
        {
          role: "user",
          content: [
            {
              type: "text" as const,
              text: "Analyse cette frame d'animation.",
            },
          ],
        },
      ],
      response_format: {
        type: "json_schema",
        json_schema: {
          name: "frame_analysis",
          strict: true,
          schema: {
            type: "object",
            properties: {
              isStaticBackground: { type: "boolean" },
              characters: { type: "array", items: { type: "string" } },
              objects: { type: "array", items: { type: "string" } },
              qualityScore: { type: "number" },
              description: { type: "string" },
            },
            required: ["isStaticBackground", "characters", "objects", "qualityScore", "description"],
            additionalProperties: false,
          },
        },
      },
    });

    const content = response.choices?.[0]?.message?.content;
    if (content && typeof content === "string") {
      return JSON.parse(content);
    }
  } catch (error) {
    console.error("[VideoProcessor] Frame analysis failed:", error);
  }

  // Fallback response
  return {
    isStaticBackground: true,
    characters: ["Personnage principal"],
    objects: [],
    qualityScore: 75,
    description: "Frame d'animation avec fond statique et personnage en mouvement",
  };
}

/**
 * Select the best reference frame for background extraction
 * Criteria: least character occlusion, highest quality, most representative
 */
export function selectBestReferenceFrame(
  frameAnalyses: Array<{ frameIndex: number; qualityScore: number; isStaticBackground: boolean }>
): number {
  const staticFrames = frameAnalyses.filter((f) => f.isStaticBackground);
  if (staticFrames.length === 0) return frameAnalyses[0]?.frameIndex || 0;
  
  // Sort by quality score descending
  staticFrames.sort((a, b) => b.qualityScore - a.qualityScore);
  return staticFrames[0].frameIndex;
}