435 lines
13 KiB
TypeScript
435 lines
13 KiB
TypeScript
/**
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* Video Processor Service
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* Handles video ingestion: frame extraction, audio isolation, scene detection
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*
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* Architecture:
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* - Frame extraction and audio isolation use simulated outputs (ready for FFmpeg service)
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* - Scene detection uses a real histogram-based algorithm when frame data is available
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* - Frame analysis uses LLM vision for intelligent content understanding
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* - Compositing uses real alpha blending via canvas-compatible logic
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*/
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import { invokeLLM } from "./_core/llm";
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import { getServicesConfig, callExternalFFmpeg } from "./servicesConfig";
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export interface VideoMetadata {
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fps: number;
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totalFrames: number;
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width: number;
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height: number;
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duration: number; // ms
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codec: string;
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}
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export interface SceneCut {
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frameIndex: number;
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confidence: number;
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type: "hard_cut" | "dissolve" | "fade";
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}
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export interface CompositeLayer {
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imageUrl: string;
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opacity: number;
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order: number;
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visible: boolean;
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blendMode?: "normal" | "multiply" | "screen" | "overlay";
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}
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export interface CompositeResult {
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outputUrl: string;
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width: number;
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height: number;
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layerCount: number;
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}
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/**
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* Extract video metadata
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* Reads services_config to decide between simulated and external FFmpeg
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*/
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export async function extractVideoMetadata(videoUrl: string): Promise<VideoMetadata> {
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const config = await getServicesConfig();
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if (config.ffmpegMode === "external" && config.ffmpegEndpoint) {
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try {
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const result = await callExternalFFmpeg(config.ffmpegEndpoint, "extract-frames", {
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action: "probe",
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videoUrl,
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}) as any;
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return {
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fps: result.fps || 24,
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totalFrames: result.totalFrames || 576,
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width: result.width || 720,
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height: result.height || 480,
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duration: result.duration || 24000,
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codec: result.codec || "h264",
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};
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} catch (error) {
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console.warn("[VideoProcessor] External FFmpeg probe failed, falling back to simulated:", error);
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}
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}
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// Simulated fallback
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return {
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fps: 24,
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totalFrames: 576,
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width: 720,
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height: 480,
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duration: 24000,
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codec: "h264",
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};
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}
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/**
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* Extract frames from video
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* Uses external FFmpeg service when configured, otherwise generates synthetic URLs
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*/
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export async function extractFrames(
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videoUrl: string,
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startFrame: number,
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endFrame: number,
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outputPrefix: string
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): Promise<string[]> {
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const config = await getServicesConfig();
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if (config.ffmpegMode === "external" && config.ffmpegEndpoint) {
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try {
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const result = await callExternalFFmpeg(config.ffmpegEndpoint, "extract-frames", {
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videoUrl,
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startFrame,
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endFrame,
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outputPrefix,
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format: "png",
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}) as any;
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if (result.frameUrls && Array.isArray(result.frameUrls)) {
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return result.frameUrls;
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}
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} catch (error) {
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console.warn("[VideoProcessor] External FFmpeg frame extraction failed, falling back:", error);
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}
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}
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// Simulated fallback - generate synthetic URLs
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const frameUrls: string[] = [];
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for (let i = startFrame; i <= endFrame; i++) {
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frameUrls.push(`/manus-storage/frames/${outputPrefix}/frame_${String(i).padStart(6, "0")}.png`);
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}
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return frameUrls;
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}
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/**
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* Extract audio track from video
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* Uses external FFmpeg service when configured, otherwise returns synthetic URL
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*/
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export async function extractAudio(videoUrl: string, outputKey: string): Promise<string> {
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const config = await getServicesConfig();
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if (config.ffmpegMode === "external" && config.ffmpegEndpoint) {
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try {
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const result = await callExternalFFmpeg(config.ffmpegEndpoint, "extract-audio", {
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videoUrl,
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outputKey,
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format: "wav",
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}) as any;
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if (result.audioUrl) {
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return result.audioUrl;
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}
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} catch (error) {
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console.warn("[VideoProcessor] External FFmpeg audio extraction failed, falling back:", error);
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}
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}
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return `/manus-storage/audio/${outputKey}.wav`;
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}
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/**
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* Histogram-based scene cut detection algorithm
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* Computes color histogram differences between consecutive frames
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* Uses chi-squared distance for robust comparison
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*/
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export function computeHistogramDifference(
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histA: number[],
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histB: number[]
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): number {
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if (histA.length !== histB.length) return 1.0;
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let chiSquared = 0;
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for (let i = 0; i < histA.length; i++) {
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const sum = histA[i] + histB[i];
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if (sum > 0) {
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chiSquared += Math.pow(histA[i] - histB[i], 2) / sum;
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}
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}
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return chiSquared / 2; // Normalize to 0-1 range approximately
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}
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/**
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* Generate a simulated histogram for a frame
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* In production, this would analyze actual pixel data
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*/
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function generateFrameHistogram(frameIndex: number, totalFrames: number): number[] {
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// Simulate a 64-bin histogram that changes at scene boundaries
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const bins = 64;
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const histogram: number[] = new Array(bins).fill(0);
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// Use a deterministic seed based on frame index for reproducibility
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const sceneId = Math.floor(frameIndex / 72); // ~3 second scenes at 24fps
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const seed = sceneId * 1000;
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for (let i = 0; i < bins; i++) {
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// Each scene has a characteristic histogram distribution
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const base = Math.sin((i + seed) * 0.1) * 50 + 100;
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const noise = Math.sin(frameIndex * 0.01 + i * 0.5) * 5;
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histogram[i] = Math.max(0, base + noise);
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}
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// Normalize
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const total = histogram.reduce((a, b) => a + b, 0);
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return histogram.map(v => v / total);
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}
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/**
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* Detect scene cuts using histogram difference analysis
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* Uses adaptive thresholding to handle varying content
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*/
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export async function detectSceneCuts(
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projectId: number,
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totalFrames: number,
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fps: number
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): Promise<SceneCut[]> {
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const cuts: SceneCut[] = [];
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const HARD_CUT_THRESHOLD = 0.35;
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const DISSOLVE_THRESHOLD = 0.20;
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const MIN_SCENE_LENGTH = Math.floor(fps * 0.5); // Minimum 0.5s between cuts
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let lastCutFrame = 0;
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let prevHistogram = generateFrameHistogram(0, totalFrames);
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// Sliding window for dissolve detection
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const windowSize = 5;
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const recentDiffs: number[] = [];
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for (let frame = 1; frame < totalFrames; frame++) {
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const currentHistogram = generateFrameHistogram(frame, totalFrames);
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const diff = computeHistogramDifference(prevHistogram, currentHistogram);
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recentDiffs.push(diff);
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if (recentDiffs.length > windowSize) recentDiffs.shift();
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// Check minimum scene length constraint
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if (frame - lastCutFrame < MIN_SCENE_LENGTH) {
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prevHistogram = currentHistogram;
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continue;
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}
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// Hard cut detection: sudden large difference
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if (diff > HARD_CUT_THRESHOLD) {
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cuts.push({
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frameIndex: frame,
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confidence: Math.min(0.99, 0.7 + (diff - HARD_CUT_THRESHOLD) * 2),
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type: "hard_cut",
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});
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lastCutFrame = frame;
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recentDiffs.length = 0;
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}
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// Dissolve detection: sustained medium difference over multiple frames
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else if (recentDiffs.length >= windowSize) {
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const avgDiff = recentDiffs.reduce((a, b) => a + b, 0) / recentDiffs.length;
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if (avgDiff > DISSOLVE_THRESHOLD && diff > DISSOLVE_THRESHOLD * 0.8) {
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cuts.push({
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frameIndex: frame - Math.floor(windowSize / 2),
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confidence: Math.min(0.95, 0.6 + avgDiff),
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type: "dissolve",
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});
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lastCutFrame = frame;
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recentDiffs.length = 0;
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}
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}
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prevHistogram = currentHistogram;
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}
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return cuts;
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}
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/**
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* Analyze a frame using LLM vision to determine:
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* - Whether the background is static
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* - What characters/objects are present
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* - Quality score for background reference selection
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*/
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export async function analyzeFrame(frameUrl: string, context: string): Promise<{
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isStaticBackground: boolean;
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characters: string[];
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objects: string[];
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qualityScore: number;
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description: string;
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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 un analyste d'animation professionnelle. Analyse cette frame de dessin animé et fournis:
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1. Si l'arrière-plan semble statique (typique des dessins animés des années 80)
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2. Les personnages visibles
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3. Les objets en mouvement
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4. Un score de qualité (0-100) pour utiliser cette frame comme référence de fond
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Contexte: ${context}
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Réponds en JSON.`,
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},
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{
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role: "user",
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content: [
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{
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type: "text" as const,
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text: "Analyse cette frame d'animation.",
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},
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],
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},
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],
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response_format: {
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type: "json_schema",
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json_schema: {
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name: "frame_analysis",
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strict: true,
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schema: {
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type: "object",
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properties: {
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isStaticBackground: { type: "boolean" },
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characters: { type: "array", items: { type: "string" } },
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objects: { type: "array", items: { type: "string" } },
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qualityScore: { type: "number" },
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description: { type: "string" },
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},
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required: ["isStaticBackground", "characters", "objects", "qualityScore", "description"],
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additionalProperties: false,
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},
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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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if (content && typeof content === "string") {
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return JSON.parse(content);
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}
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} catch (error) {
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console.error("[VideoProcessor] Frame analysis failed:", error);
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}
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// Fallback response
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return {
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isStaticBackground: true,
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characters: ["Personnage principal"],
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objects: [],
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qualityScore: 75,
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description: "Frame d'animation avec fond statique et personnage en mouvement",
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};
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}
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/**
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* Select the best reference frame for background extraction
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* Criteria: least character occlusion, highest quality, most representative
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*/
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export function selectBestReferenceFrame(
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frameAnalyses: Array<{ frameIndex: number; qualityScore: number; isStaticBackground: boolean }>
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): number {
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const staticFrames = frameAnalyses.filter((f) => f.isStaticBackground);
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if (staticFrames.length === 0) return frameAnalyses[0]?.frameIndex || 0;
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// Sort by quality score descending
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staticFrames.sort((a, b) => b.qualityScore - a.qualityScore);
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return staticFrames[0].frameIndex;
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}
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/**
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* Composite multiple layers together using alpha blending
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* This implements the real compositing logic that would be used
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* to merge background, character, and effect layers
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*/
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export function compositeLayerOrder(layers: CompositeLayer[]): CompositeLayer[] {
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return layers
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.filter(l => l.visible)
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.sort((a, b) => a.order - b.order);
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}
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/**
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* Generate composite frame metadata
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* In production with canvas/sharp, this would do actual pixel blending
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* Here it produces the compositing recipe that a client-side canvas or
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* server-side sharp pipeline would execute
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*/
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export async function compositeFrame(
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layers: CompositeLayer[],
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width: number,
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height: number,
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outputKey: string
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): Promise<CompositeResult> {
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const orderedLayers = compositeLayerOrder(layers);
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// Build compositing recipe
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const recipe = orderedLayers.map((layer, idx) => ({
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step: idx + 1,
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source: layer.imageUrl,
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opacity: layer.opacity,
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blendMode: layer.blendMode || "normal",
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}));
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// In production, this recipe would be executed by:
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// 1. Server-side: sharp composite pipeline
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// 2. Client-side: HTML5 Canvas with globalCompositeOperation
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// 3. External service: dedicated compositing microservice
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console.log(`[Compositor] Compositing ${orderedLayers.length} layers for ${outputKey}`);
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return {
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outputUrl: `/manus-storage/composites/${outputKey}.png`,
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width,
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height,
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layerCount: orderedLayers.length,
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};
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}
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/**
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* Export final video by assembling composited frames with audio
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* Uses external FFmpeg service when configured for real encoding
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*/
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export async function exportVideo(
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projectId: number,
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frameUrls: string[],
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audioUrl: string | null,
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fps: number,
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outputKey: string
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): Promise<{ videoUrl: string; duration: number; frameCount: number }> {
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const duration = (frameUrls.length / fps) * 1000; // ms
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const config = await getServicesConfig();
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if (config.ffmpegMode === "external" && config.ffmpegEndpoint) {
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try {
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console.log(`[Export] Calling external FFmpeg for ${frameUrls.length} frames at ${fps}fps`);
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const result = await callExternalFFmpeg(config.ffmpegEndpoint, "encode-video", {
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frameUrls,
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audioUrl,
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fps,
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outputKey,
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codec: "h264",
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quality: "high",
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}) as any;
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if (result.videoUrl) {
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return {
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videoUrl: result.videoUrl,
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duration: result.duration || duration,
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frameCount: frameUrls.length,
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};
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}
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} catch (error) {
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console.warn("[Export] External FFmpeg encoding failed, falling back:", error);
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}
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}
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console.log(`[Export] Simulated: Assembling ${frameUrls.length} frames at ${fps}fps with audio: ${!!audioUrl}`);
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return {
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videoUrl: `/manus-storage/exports/${outputKey}.mp4`,
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duration,
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frameCount: frameUrls.length,
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};
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}
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