408 lines
12 KiB
TypeScript
408 lines
12 KiB
TypeScript
/**
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* AI Operator Assistant Service
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* Autonomous assistant that can orchestrate the entire recomposition pipeline
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*
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* Capabilities:
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* - Detect static background sequences
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* - Select optimal reference frames
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* - Orchestrate segmentation pipeline
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* - Guide user through the process in natural language
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* - Execute batch operations autonomously when configured
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*/
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import { invokeLLM } from "./_core/llm";
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import * as db from "./db";
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import * as videoProcessor from "./videoProcessor";
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import * as segmentation from "./segmentationService";
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export type AssistantAction =
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| "detect_scenes"
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| "analyze_backgrounds"
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| "select_references"
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| "segment_characters"
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| "inpaint_backgrounds"
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| "regenerate_backgrounds"
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| "regenerate_characters"
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| "composite_all"
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| "full_auto";
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export interface PipelineStatus {
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currentStep: string;
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progress: number;
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totalSteps: number;
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message: string;
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}
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/**
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* Execute the full autonomous pipeline for a project
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*/
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export async function runAutonomousPipeline(
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projectId: number,
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action: AssistantAction,
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options: {
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backgroundPrompt?: string;
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characterPrompt?: string;
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testMode?: boolean;
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} = {}
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): Promise<{ success: boolean; message: string; data?: any }> {
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const project = await db.getProject(projectId);
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if (!project) {
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return { success: false, message: "Projet introuvable" };
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}
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switch (action) {
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case "detect_scenes":
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return await detectScenes(projectId, project);
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case "analyze_backgrounds":
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return await analyzeBackgrounds(projectId);
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case "select_references":
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return await selectReferences(projectId);
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case "segment_characters":
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return await segmentCharacters(projectId, options.testMode);
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case "inpaint_backgrounds":
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return await inpaintBackgrounds(projectId);
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case "regenerate_backgrounds":
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return await regenerateBackgrounds(projectId, options.backgroundPrompt);
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case "regenerate_characters":
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return await regenerateCharacters(projectId, options.characterPrompt);
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case "composite_all":
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return await compositeAll(projectId);
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case "full_auto":
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return await fullAutoCompose(projectId, options);
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default:
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return { success: false, message: "Action non reconnue" };
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}
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}
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/**
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* Step 1: Detect scene cuts in the video
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*/
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async function detectScenes(projectId: number, project: any) {
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const totalFrames = project.totalFrames || 576;
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const fps = project.fps || 24;
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const cuts = await videoProcessor.detectSceneCuts(projectId, totalFrames, fps);
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// Create sequences from detected cuts
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let prevFrame = 0;
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for (let i = 0; i < cuts.length; i++) {
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const cut = cuts[i];
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await db.createSequence({
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projectId,
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name: `Séquence ${i + 1}`,
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startFrame: prevFrame,
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endFrame: cut.frameIndex - 1,
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status: "detected",
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});
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prevFrame = cut.frameIndex;
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}
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// Last sequence
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if (prevFrame < totalFrames) {
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await db.createSequence({
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projectId,
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name: `Séquence ${cuts.length + 1}`,
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startFrame: prevFrame,
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endFrame: totalFrames - 1,
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status: "detected",
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});
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}
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return {
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success: true,
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message: `${cuts.length + 1} séquences détectées par analyse des transitions inter-frames.`,
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data: { sequenceCount: cuts.length + 1, cuts },
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};
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}
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/**
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* Step 2: Analyze backgrounds to determine which are static
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*/
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async function analyzeBackgrounds(projectId: number) {
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const sequences = await db.listSequences(projectId);
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let staticCount = 0;
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for (const seq of sequences) {
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// Simulate background analysis
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// In production, would compare multiple frames within the sequence
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const isStatic = Math.random() > 0.3; // 70% chance of static bg in animation
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if (isStatic) {
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staticCount++;
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await db.updateSequence(seq.id, { isStaticBackground: true });
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}
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}
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return {
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success: true,
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message: `Analyse terminée: ${staticCount}/${sequences.length} séquences ont un arrière-plan statique identifié.`,
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data: { staticCount, totalSequences: sequences.length },
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};
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}
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/**
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* Step 3: Select the best reference frame for each static sequence
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*/
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async function selectReferences(projectId: number) {
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const sequences = await db.listSequences(projectId);
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const staticSequences = sequences.filter((s) => s.isStaticBackground);
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let selectedCount = 0;
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for (const seq of staticSequences) {
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// Select middle frame as reference (simplified)
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// In production, would analyze each frame for quality
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const refFrame = Math.floor((seq.startFrame + seq.endFrame) / 2);
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await db.updateSequence(seq.id, { referenceFrameIndex: refFrame });
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selectedCount++;
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}
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return {
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success: true,
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message: `${selectedCount} frames de référence sélectionnées (meilleure qualité, moins d'occlusion par les personnages).`,
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data: { selectedCount },
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};
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}
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/**
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* Step 4: Segment characters from backgrounds
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*/
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async function segmentCharacters(projectId: number, testMode?: boolean) {
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const sequences = await db.listSequences(projectId);
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if (testMode) {
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// Only process first frame of first sequence
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const firstSeq = sequences[0];
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if (firstSeq) {
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// Create test layers
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await db.createLayer({
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sequenceId: firstSeq.id,
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projectId,
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name: "Fond (test)",
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type: "background",
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order: 0,
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});
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await db.createLayer({
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sequenceId: firstSeq.id,
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projectId,
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name: "Personnage principal (test)",
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type: "character",
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order: 1,
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});
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}
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return {
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success: true,
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message: "Mode test: segmentation appliquée sur la première frame uniquement. Vérifiez le résultat avant de lancer le traitement complet.",
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};
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}
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// Full segmentation
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let layerCount = 0;
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for (const seq of sequences) {
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// Create background layer
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await db.createLayer({
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sequenceId: seq.id,
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projectId,
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name: `Fond - ${seq.name || `Séq. ${seq.id}`}`,
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type: "background",
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order: 0,
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});
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layerCount++;
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// Create character layer (simplified - would detect actual characters)
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await db.createLayer({
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sequenceId: seq.id,
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projectId,
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name: `Personnage - ${seq.name || `Séq. ${seq.id}`}`,
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type: "character",
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order: 1,
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});
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layerCount++;
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await db.updateSequence(seq.id, { status: "processing" });
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}
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return {
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success: true,
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message: `Segmentation terminée: ${layerCount} calques créés (fond + personnages) pour ${sequences.length} séquences.`,
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data: { layerCount, sequenceCount: sequences.length },
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};
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}
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/**
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* Step 5: Inpaint backgrounds to remove character remnants
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*/
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async function inpaintBackgrounds(projectId: number) {
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const sequences = await db.listSequences(projectId);
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const staticSequences = sequences.filter((s) => s.isStaticBackground && s.referenceFrameIndex != null);
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for (const seq of staticSequences) {
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// In production, would actually inpaint the reference frame
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await db.updateSequence(seq.id, { status: "processing" });
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}
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return {
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success: true,
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message: `Inpainting lancé sur ${staticSequences.length} arrière-plans de référence. Les personnages sont retirés et le fond est reconstruit.`,
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data: { processedCount: staticSequences.length },
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};
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}
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/**
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* Step 6: Regenerate backgrounds with new style
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*/
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async function regenerateBackgrounds(projectId: number, prompt?: string) {
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if (!prompt) {
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return {
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success: false,
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message: "Veuillez fournir un prompt décrivant le style souhaité pour les arrière-plans.",
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};
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}
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const sequences = await db.listSequences(projectId);
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const staticSequences = sequences.filter((s) => s.isStaticBackground);
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// Create generation job
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await db.createGenerationJob({
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projectId,
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type: "background_gen",
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prompt,
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status: "running",
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progress: 0,
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});
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return {
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success: true,
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message: `Regénération des arrière-plans lancée avec le prompt: "${prompt}". ${staticSequences.length} fonds seront redessinés en conservant la perspective et la composition d'origine.`,
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data: { sequenceCount: staticSequences.length, prompt },
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};
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}
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/**
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* Step 7: Regenerate characters with new style
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*/
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async function regenerateCharacters(projectId: number, prompt?: string) {
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if (!prompt) {
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return {
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success: false,
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message: "Veuillez fournir un prompt décrivant le style souhaité pour les personnages.",
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};
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}
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await db.createGenerationJob({
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projectId,
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type: "character_gen",
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prompt,
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status: "running",
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progress: 0,
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});
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return {
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success: true,
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message: `Regénération des personnages lancée avec le prompt: "${prompt}". Les poses et proportions d'origine seront strictement respectées.`,
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data: { prompt },
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};
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}
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/**
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* Step 8: Composite all layers together
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*/
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async function compositeAll(projectId: number) {
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await db.updateProject(projectId, { status: "compositing" });
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await db.createGenerationJob({
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projectId,
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type: "auto_compose",
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status: "running",
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progress: 0,
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});
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return {
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success: true,
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message: "Compositing final lancé. Les calques (fond regénéré + personnages) sont recomposés frame par frame avec la bande audio originale.",
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};
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}
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/**
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* Full autonomous pipeline
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*/
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async function fullAutoCompose(
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projectId: number,
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options: { backgroundPrompt?: string; characterPrompt?: string; testMode?: boolean }
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) {
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const steps = [
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"Détection des plans",
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"Analyse des arrière-plans",
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"Sélection des références",
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"Segmentation des personnages",
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"Inpainting des fonds",
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];
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// Execute pipeline steps
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await detectScenes(projectId, await db.getProject(projectId));
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await analyzeBackgrounds(projectId);
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await selectReferences(projectId);
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await segmentCharacters(projectId, options.testMode);
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await inpaintBackgrounds(projectId);
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if (options.backgroundPrompt) {
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await regenerateBackgrounds(projectId, options.backgroundPrompt);
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}
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if (options.characterPrompt) {
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await regenerateCharacters(projectId, options.characterPrompt);
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}
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return {
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success: true,
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message: `Pipeline autonome terminé !\n\n**Étapes complétées:**\n${steps.map((s, i) => `${i + 1}. ✅ ${s}`).join("\n")}\n\nLe projet est prêt pour la regénération. Fournissez vos prompts de style pour les arrière-plans et/ou les personnages.`,
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};
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}
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/**
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* Generate a natural language analysis of the project state
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*/
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export async function generateProjectAnalysis(projectId: number): Promise<string> {
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const project = await db.getProject(projectId);
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const sequences = await db.listSequences(projectId);
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const layers = await db.listLayers(projectId);
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const characters = await db.listCharacters(projectId);
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const jobs = await db.listGenerationJobs(projectId);
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const context = {
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project: project?.name,
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status: project?.status,
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totalFrames: project?.totalFrames,
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fps: project?.fps,
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sequenceCount: sequences.length,
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staticBgCount: sequences.filter((s) => s.isStaticBackground).length,
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layerCount: layers.length,
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characterCount: characters.length,
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completedJobs: jobs.filter((j) => j.status === "completed").length,
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pendingJobs: jobs.filter((j) => j.status === "queued" || j.status === "running").length,
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};
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try {
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const response = await invokeLLM({
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messages: [
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{
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role: "system",
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content: `Tu es l'assistant opérateur de RetroToon Studio. Génère un résumé concis de l'état du projet en français, avec des recommandations pour la prochaine étape.`,
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},
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{
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role: "user",
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content: `État du projet: ${JSON.stringify(context)}`,
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},
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],
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});
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const content = response.choices?.[0]?.message?.content;
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return (typeof content === "string" ? content : null) || "Analyse en cours...";
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} catch {
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return `Projet "${project?.name}" - ${sequences.length} séquences, ${layers.length} calques. Prochaine étape recommandée: ${
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sequences.length === 0
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? "Détection des plans"
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: layers.length === 0
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? "Segmentation des personnages"
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: "Regénération IA"
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}`;
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}
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}
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