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1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 | /** * Quad Detection Library * * A general-purpose quadrilateral detection system using OpenCV.js. * Designed for document scanning, but works for any rectangular object detection. * * Features: * - Lazy loads OpenCV.js (~8MB) only when first used * - Single-frame detection for static images * - Multi-frame tracking for camera feeds with stability scoring * - Perspective transform extraction * - Auto-rotation based on content analysis * * Usage: * * // Static image detection * const detector = await QuadDetector.load() * const result = detector.detect(canvas) * if (result.bestQuad) { * const extracted = detector.extract(canvas, result.bestQuad.corners) * } * * // Camera feed with tracking * const detector = await QuadDetector.load() * const tracker = new QuadTracker(detector) * // In animation loop: * const frame = captureVideoFrame(video) * const result = tracker.processFrame(frame) * tracker.drawOverlay(overlayCanvas, result) */ // ============================================================================= // Types // ============================================================================= export interface Corner { x: number y: number } export interface DetectedQuad { /** Ordered corners: top-left, top-right, bottom-right, bottom-left */ corners: Corner[] /** Area in pixels */ area: number /** Aspect ratio (width/height or height/width, whichever is larger) */ aspectRatio: number /** Unique ID based on center position (for tracking) */ centerId: string } export interface TrackedQuad extends DetectedQuad { /** Unique tracking ID */ id: string /** Number of frames this quad has been seen */ frameCount: number /** Last frame number when this quad was seen */ lastSeenFrame: number /** Stability score (0-1) based on corner consistency */ stabilityScore: number /** History of corner positions */ cornerHistory: Corner[][] } export interface QuadDetectionOptions { /** Minimum area as ratio of frame (default: 0.15) */ minAreaRatio?: number /** Maximum area as ratio of frame (default: 0.95) */ maxAreaRatio?: number /** Aspect ratio tolerance (default: 0.3) */ aspectRatioTolerance?: number /** Expected aspect ratios to accept (default: letter, A4, square) */ expectedAspectRatios?: number[] /** Canny edge detection thresholds (default: [50, 150]) */ cannyThresholds?: [number, number] /** Gaussian blur kernel size (default: 5) */ blurSize?: number } export interface QuadDetectionResult { /** Whether any valid quads were detected */ detected: boolean /** All detected quads, sorted by area (largest first) */ quads: DetectedQuad[] /** The best (largest) quad, or null if none detected */ bestQuad: DetectedQuad | null } export interface TrackedQuadResult extends QuadDetectionResult { /** All tracked quads with history */ trackedQuads: TrackedQuad[] /** Best tracked quad with stability info */ bestTrackedQuad: TrackedQuad | null /** Whether detection is stable (good time to capture) */ isStable: boolean /** Whether detection is locked (very stable, ideal to capture) */ isLocked: boolean } // ============================================================================= // OpenCV Types (minimal interface) // ============================================================================= interface CVMat { delete: () => void data32S: Int32Array data: ArrayBuffer rows: number cols: number } interface CVMatVector { size: () => number get: (i: number) => CVMat delete: () => void } interface CVSize { width: number height: number } interface CVPoint { x: number y: number } interface CV { Mat: new () => CVMat MatVector: new () => CVMatVector Size: new (w: number, h: number) => CVSize Scalar: new (r?: number, g?: number, b?: number, a?: number) => unknown imread: (canvas: HTMLCanvasElement) => CVMat imshow: (canvas: HTMLCanvasElement, mat: CVMat) => void cvtColor: (src: CVMat, dst: CVMat, code: number) => void GaussianBlur: ( src: CVMat, dst: CVMat, size: CVSize, sigmaX: number, sigmaY: number, borderType: number ) => void Canny: (src: CVMat, dst: CVMat, t1: number, t2: number) => void dilate: (src: CVMat, dst: CVMat, kernel: CVMat, anchor: CVPoint, iterations: number) => void findContours: ( src: CVMat, contours: CVMatVector, hierarchy: CVMat, mode: number, method: number ) => void contourArea: (contour: CVMat) => number arcLength: (contour: CVMat, closed: boolean) => number approxPolyDP: (contour: CVMat, approx: CVMat, epsilon: number, closed: boolean) => void getPerspectiveTransform: (src: CVMat, dst: CVMat) => CVMat warpPerspective: ( src: CVMat, dst: CVMat, M: CVMat, size: CVSize, flags: number, borderMode: number, borderValue: unknown ) => void rotate: (src: CVMat, dst: CVMat, rotateCode: number) => void matFromArray: (rows: number, cols: number, type: number, data: number[]) => CVMat COLOR_RGBA2GRAY: number BORDER_DEFAULT: number RETR_LIST: number CHAIN_APPROX_SIMPLE: number CV_32FC2: number INTER_LINEAR: number BORDER_CONSTANT: number ROTATE_90_CLOCKWISE: number ROTATE_180: number ROTATE_90_COUNTERCLOCKWISE: number } // ============================================================================= // Constants // ============================================================================= const DEFAULT_OPTIONS: Required<QuadDetectionOptions> = { minAreaRatio: 0.15, maxAreaRatio: 0.95, aspectRatioTolerance: 0.3, expectedAspectRatios: [ 8.5 / 11, // US Letter portrait 11 / 8.5, // US Letter landscape 1 / Math.sqrt(2), // A4 portrait Math.sqrt(2), // A4 landscape 1, // Square ], cannyThresholds: [50, 150], blurSize: 5, } // Tracking constants const HISTORY_LENGTH = 10 const MIN_FRAMES_FOR_STABLE = 3 const LOCKED_FRAME_COUNT = 5 const QUAD_MATCH_THRESHOLD = 0.08 // ============================================================================= // Utility Functions // ============================================================================= /** Calculate Euclidean distance between two points */ export function distance(p1: Corner, p2: Corner): number { return Math.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2) } /** Order corners consistently: top-left, top-right, bottom-right, bottom-left */ export function orderCorners(corners: Corner[]): Corner[] { if (corners.length !== 4) return corners // Find centroid const cx = corners.reduce((s, c) => s + c.x, 0) / 4 const cy = corners.reduce((s, c) => s + c.y, 0) / 4 // Sort by angle from centroid const sorted = [...corners].sort((a, b) => { const angleA = Math.atan2(a.y - cy, a.x - cx) const angleB = Math.atan2(b.y - cy, b.x - cx) return angleA - angleB }) // Find top-left (smallest x+y) let topLeftIdx = 0 let minSum = Infinity for (let i = 0; i < 4; i++) { const sum = sorted[i].x + sorted[i].y if (sum < minSum) { minSum = sum topLeftIdx = i } } // Rotate array so top-left is first const ordered: Corner[] = [] for (let i = 0; i < 4; i++) { ordered.push(sorted[(topLeftIdx + i) % 4]) } return ordered } /** Load an image file into a canvas */ export async function loadImageToCanvas( source: File | Blob | string ): Promise<HTMLCanvasElement | null> { return new Promise((resolve) => { const img = new Image() const url = typeof source === 'string' ? source : URL.createObjectURL(source) const shouldRevoke = typeof source !== 'string' img.onload = () => { if (shouldRevoke) URL.revokeObjectURL(url) const canvas = document.createElement('canvas') canvas.width = img.naturalWidth canvas.height = img.naturalHeight const ctx = canvas.getContext('2d') if (!ctx) { resolve(null) return } ctx.drawImage(img, 0, 0) resolve(canvas) } img.onerror = () => { if (shouldRevoke) URL.revokeObjectURL(url) resolve(null) } img.src = url }) } /** Capture a video element's current frame to a canvas */ export function captureVideoFrame(video: HTMLVideoElement): HTMLCanvasElement | null { if (!video.videoWidth || !video.videoHeight) return null const canvas = document.createElement('canvas') canvas.width = video.videoWidth canvas.height = video.videoHeight const ctx = canvas.getContext('2d') if (!ctx) return null ctx.drawImage(video, 0, 0) return canvas } /** Get fallback corners (full image bounds) */ export function getFallbackCorners(width: number, height: number): Corner[] { return [ { x: 0, y: 0 }, { x: width, y: 0 }, { x: width, y: height }, { x: 0, y: height }, ] } // ============================================================================= // QuadDetector Class // ============================================================================= /** * Core quad detection using OpenCV.js * * Singleton pattern - use QuadDetector.load() to get an instance. * OpenCV is lazy-loaded only when first needed. */ export class QuadDetector { private static instance: QuadDetector | null = null private static loadPromise: Promise<QuadDetector> | null = null private cv: CV private constructor(cv: CV) { this.cv = cv } /** * Load OpenCV and get a QuadDetector instance. * Safe to call multiple times - returns same instance after first load. */ static async load(): Promise<QuadDetector> { if (QuadDetector.instance) return QuadDetector.instance if (QuadDetector.loadPromise) { return QuadDetector.loadPromise } QuadDetector.loadPromise = (async () => { const cv = await loadOpenCV() QuadDetector.instance = new QuadDetector(cv) return QuadDetector.instance })() return QuadDetector.loadPromise } /** Check if OpenCV is loaded */ static isLoaded(): boolean { return QuadDetector.instance !== null } /** Get the loaded instance (or null if not loaded) */ static getInstance(): QuadDetector | null { return QuadDetector.instance } /** Get the OpenCV reference (for advanced use cases) */ getCV(): CV { return this.cv } /** * Detect quadrilaterals in an image. * * @param source - Canvas containing the image to analyze * @param options - Detection options (thresholds, expected aspect ratios, etc.) * @returns Detection result with all found quads and the best one */ detect(source: HTMLCanvasElement, options?: QuadDetectionOptions): QuadDetectionResult { const opts = { ...DEFAULT_OPTIONS, ...options } const quads = this.findAllQuads(source, opts) return { detected: quads.length > 0, quads, bestQuad: quads[0] ?? null, } } /** * Extract a quadrilateral region using perspective transform. * * @param source - Source canvas * @param corners - Four corners defining the quad to extract * @returns New canvas with the extracted, perspective-corrected region */ extract(source: HTMLCanvasElement, corners: Corner[]): HTMLCanvasElement { const cv = this.cv // Calculate output dimensions const width1 = distance(corners[0], corners[1]) const width2 = distance(corners[3], corners[2]) const height1 = distance(corners[0], corners[3]) const height2 = distance(corners[1], corners[2]) const outputWidth = Math.round((width1 + width2) / 2) const outputHeight = Math.round((height1 + height2) / 2) // Create transform matrices const srcPts = cv.matFromArray(4, 1, cv.CV_32FC2, [ corners[0].x, corners[0].y, corners[1].x, corners[1].y, corners[2].x, corners[2].y, corners[3].x, corners[3].y, ]) const dstPts = cv.matFromArray(4, 1, cv.CV_32FC2, [ 0, 0, outputWidth, 0, outputWidth, outputHeight, 0, outputHeight, ]) const M = cv.getPerspectiveTransform(srcPts, dstPts) const src = cv.imread(source) const dst = new cv.Mat() cv.warpPerspective( src, dst, M, new cv.Size(outputWidth, outputHeight), cv.INTER_LINEAR, cv.BORDER_CONSTANT, new cv.Scalar() ) // Create output canvas const outputCanvas = document.createElement('canvas') outputCanvas.width = outputWidth outputCanvas.height = outputHeight cv.imshow(outputCanvas, dst) // Clean up srcPts.delete() dstPts.delete() M.delete() src.delete() dst.delete() return outputCanvas } /** * Analyze document orientation and determine rotation needed. * * Uses edge detection to find dominant text line direction * and content density to detect upside-down orientation. * * @param source - Canvas to analyze * @returns Degrees to rotate (0, 90, 180, or 270) */ analyzeOrientation(source: HTMLCanvasElement): 0 | 90 | 180 | 270 { const cv = this.cv let src: CVMat | null = null let gray: CVMat | null = null let edges: CVMat | null = null try { src = cv.imread(source) gray = new cv.Mat() edges = new cv.Mat() cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY) cv.Canny(gray, edges, 50, 150) const width = edges.cols const height = edges.rows // Sample middle section (avoid margins) const marginX = Math.floor(width * 0.1) const marginY = Math.floor(height * 0.1) const sampleHeight = height - 2 * marginY const edgeData = new Uint8Array(edges.data) // Count horizontal vs vertical edge continuity let horizontalEdges = 0 let verticalEdges = 0 // Horizontal scan for (let y = marginY; y < height - marginY; y += 5) { let runLength = 0 for (let x = marginX; x < width - marginX; x++) { if (edgeData[y * width + x] > 0) { runLength++ } else { if (runLength > 10) horizontalEdges += runLength runLength = 0 } } if (runLength > 10) horizontalEdges += runLength } // Vertical scan for (let x = marginX; x < width - marginX; x += 5) { let runLength = 0 for (let y = marginY; y < height - marginY; y++) { if (edgeData[y * width + x] > 0) { runLength++ } else { if (runLength > 10) verticalEdges += runLength runLength = 0 } } if (runLength > 10) verticalEdges += runLength } // Determine if 90° rotation needed const ratio = horizontalEdges / (verticalEdges + 1) let rotation: 0 | 90 | 180 | 270 = 0 if (ratio < 0.5) { rotation = 90 // Vertical edges dominate - text is sideways } // Check for upside-down by comparing top/bottom content density const topThird = Math.floor(sampleHeight / 3) let topDensity = 0 let bottomDensity = 0 for (let y = marginY; y < marginY + topThird; y++) { for (let x = marginX; x < width - marginX; x += 3) { if (edgeData[y * width + x] > 0) topDensity++ } } for (let y = height - marginY - topThird; y < height - marginY; y++) { for (let x = marginX; x < width - marginX; x += 3) { if (edgeData[y * width + x] > 0) bottomDensity++ } } // If bottom has significantly more content, probably upside down if (bottomDensity > topDensity * 1.5) { rotation = rotation === 0 ? 180 : rotation === 90 ? 270 : rotation } return rotation } catch (err) { console.warn('Orientation analysis failed:', err) return 0 } finally { src?.delete() gray?.delete() edges?.delete() } } /** * Rotate a canvas by the specified degrees. * * @param source - Canvas to rotate * @param degrees - Rotation amount (0, 90, 180, or 270) * @returns New rotated canvas (or same canvas if degrees is 0) */ rotate(source: HTMLCanvasElement, degrees: 0 | 90 | 180 | 270): HTMLCanvasElement { if (degrees === 0) return source const cv = this.cv let src: CVMat | null = null let dst: CVMat | null = null try { src = cv.imread(source) dst = new cv.Mat() const rotateCode = degrees === 90 ? cv.ROTATE_90_CLOCKWISE : degrees === 180 ? cv.ROTATE_180 : cv.ROTATE_90_COUNTERCLOCKWISE cv.rotate(src, dst, rotateCode) const outputCanvas = document.createElement('canvas') if (degrees === 90 || degrees === 270) { outputCanvas.width = source.height outputCanvas.height = source.width } else { outputCanvas.width = source.width outputCanvas.height = source.height } cv.imshow(outputCanvas, dst) return outputCanvas } catch (err) { console.warn('Canvas rotation failed:', err) return source } finally { src?.delete() dst?.delete() } } // --------------------------------------------------------------------------- // Private methods // --------------------------------------------------------------------------- private findAllQuads( canvas: HTMLCanvasElement, opts: Required<QuadDetectionOptions> ): DetectedQuad[] { const cv = this.cv const quads: DetectedQuad[] = [] const frameArea = canvas.width * canvas.height let src: CVMat | null = null let gray: CVMat | null = null let blurred: CVMat | null = null let edges: CVMat | null = null let contours: CVMatVector | null = null let hierarchy: CVMat | null = null try { src = cv.imread(canvas) gray = new cv.Mat() blurred = new cv.Mat() edges = new cv.Mat() // Convert to grayscale cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY) // Blur to reduce noise cv.GaussianBlur( gray, blurred, new cv.Size(opts.blurSize, opts.blurSize), 0, 0, cv.BORDER_DEFAULT ) // Edge detection cv.Canny(blurred, edges, opts.cannyThresholds[0], opts.cannyThresholds[1]) // Dilate edges to connect gaps const kernel = new cv.Mat() cv.dilate(edges, edges, kernel, { x: -1, y: -1 } as CVPoint, 1) kernel.delete() // Find contours contours = new cv.MatVector() hierarchy = new cv.Mat() cv.findContours(edges, contours, hierarchy, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE) // Process each contour for (let i = 0; i < contours.size(); i++) { const contour = contours.get(i) const area = cv.contourArea(contour) const areaRatio = area / frameArea // Skip if too small or too large if (areaRatio < opts.minAreaRatio || areaRatio > opts.maxAreaRatio) { continue } // Approximate to polygon const approx = new cv.Mat() const perimeter = cv.arcLength(contour, true) cv.approxPolyDP(contour, approx, 0.02 * perimeter, true) // Check if it's a quadrilateral if (approx.rows === 4) { // Extract corners const corners: Corner[] = [] for (let j = 0; j < 4; j++) { corners.push({ x: approx.data32S[j * 2], y: approx.data32S[j * 2 + 1], }) } // Order corners consistently const orderedCorners = orderCorners(corners) // Calculate aspect ratio const width = distance(orderedCorners[0], orderedCorners[1]) const height = distance(orderedCorners[1], orderedCorners[2]) const aspectRatio = Math.max(width, height) / Math.min(width, height) // Check if aspect ratio is acceptable const isValidAspectRatio = opts.expectedAspectRatios.some( (expected) => Math.abs(aspectRatio - expected) < opts.aspectRatioTolerance ) if (isValidAspectRatio) { quads.push({ corners: orderedCorners, area, aspectRatio, centerId: this.getQuadCenterId(orderedCorners, canvas.width, canvas.height), }) } } approx.delete() } } finally { src?.delete() gray?.delete() blurred?.delete() edges?.delete() contours?.delete() hierarchy?.delete() } // Sort by area (largest first) quads.sort((a, b) => b.area - a.area) return quads } private getQuadCenterId(corners: Corner[], frameWidth: number, frameHeight: number): string { const cx = corners.reduce((s, c) => s + c.x, 0) / 4 const cy = corners.reduce((s, c) => s + c.y, 0) / 4 const gridX = Math.floor((cx / frameWidth) * 10) const gridY = Math.floor((cy / frameHeight) * 10) return `${gridX},${gridY}` } } // ============================================================================= // QuadTracker Class // ============================================================================= /** * Multi-frame quad tracker for camera feeds. * * Tracks detected quads across frames and provides stability scoring. * Use this when you need smooth, stable detection from a video stream. */ export class QuadTracker { private detector: QuadDetector private trackedQuads: Map<string, TrackedQuad> = new Map() private frameCount = 0 private bestQuad: TrackedQuad | null = null private lastStableFrame: HTMLCanvasElement | null = null private options: Required<QuadDetectionOptions> constructor(detector: QuadDetector, options?: QuadDetectionOptions) { this.detector = detector this.options = { ...DEFAULT_OPTIONS, ...options } } /** * Process a video frame and update tracking. * * @param frame - Canvas containing the current video frame * @returns Tracking result with stability information */ processFrame(frame: HTMLCanvasElement): TrackedQuadResult { const result = this.detector.detect(frame, this.options) const bestTrackedQuad = this.updateTracking(result.quads, frame.width, frame.height) // Save stable frame if (bestTrackedQuad && this.isQuadLocked(bestTrackedQuad)) { if (!this.lastStableFrame) { this.lastStableFrame = document.createElement('canvas') } this.lastStableFrame.width = frame.width this.lastStableFrame.height = frame.height const ctx = this.lastStableFrame.getContext('2d') ctx?.drawImage(frame, 0, 0) } return { detected: result.detected, quads: result.quads, bestQuad: result.bestQuad, trackedQuads: Array.from(this.trackedQuads.values()), bestTrackedQuad, isStable: bestTrackedQuad ? bestTrackedQuad.frameCount >= MIN_FRAMES_FOR_STABLE : false, isLocked: bestTrackedQuad ? this.isQuadLocked(bestTrackedQuad) : false, } } /** * Draw detection overlay on a canvas. * * @param overlayCanvas - Canvas to draw on (will be resized to match frame) * @param result - Tracking result from processFrame * @param frameWidth - Width to resize canvas to (optional) * @param frameHeight - Height to resize canvas to (optional) */ drawOverlay( overlayCanvas: HTMLCanvasElement, result: TrackedQuadResult, frameWidth?: number, frameHeight?: number ): void { if (frameWidth && frameHeight) { if (overlayCanvas.width !== frameWidth || overlayCanvas.height !== frameHeight) { overlayCanvas.width = frameWidth overlayCanvas.height = frameHeight } } const ctx = overlayCanvas.getContext('2d') if (!ctx) return ctx.clearRect(0, 0, overlayCanvas.width, overlayCanvas.height) // Draw all detected quads (faded) for (const quad of result.quads) { if (result.bestTrackedQuad && quad.centerId === result.bestTrackedQuad.id) continue this.drawQuad(ctx, quad.corners, 'rgba(100, 100, 100, 0.3)', 2) } // Draw best quad with color based on stability if (result.bestTrackedQuad) { const { color, lineWidth } = this.getQuadStyle(result.bestTrackedQuad) this.drawQuad(ctx, result.bestTrackedQuad.corners, color, lineWidth) } } /** Reset tracking state */ reset(): void { this.trackedQuads.clear() this.frameCount = 0 this.bestQuad = null this.lastStableFrame = null } /** Get current best quad corners (or null if none) */ getBestCorners(): Corner[] | null { return this.bestQuad ? [...this.bestQuad.corners] : null } /** Get the last stable frame (when detection was locked) */ getLastStableFrame(): HTMLCanvasElement | null { return this.lastStableFrame } /** Update detection options */ setOptions(options: QuadDetectionOptions): void { this.options = { ...DEFAULT_OPTIONS, ...options } } // --------------------------------------------------------------------------- // Private methods // --------------------------------------------------------------------------- private updateTracking( detectedQuads: DetectedQuad[], frameWidth: number, frameHeight: number ): TrackedQuad | null { const currentFrame = this.frameCount++ const frameDiagonal = Math.sqrt(frameWidth ** 2 + frameHeight ** 2) const seenIds = new Set<string>() // Match detected quads to tracked quads for (const detected of detectedQuads) { let matched = false for (const [id, tracked] of this.trackedQuads) { if (!seenIds.has(id) && this.quadsMatch(detected.corners, tracked.corners, frameDiagonal)) { // Update existing tracked quad tracked.corners = detected.corners tracked.area = detected.area tracked.aspectRatio = detected.aspectRatio tracked.frameCount++ tracked.lastSeenFrame = currentFrame tracked.cornerHistory.push(detected.corners) if (tracked.cornerHistory.length > HISTORY_LENGTH) { tracked.cornerHistory.shift() } tracked.stabilityScore = this.calculateStability(tracked.cornerHistory) seenIds.add(id) matched = true break } } if (!matched) { // New quad - start tracking const newId = `quad_${currentFrame}_${Math.random().toString(36).slice(2, 8)}` this.trackedQuads.set(newId, { ...detected, id: newId, frameCount: 1, lastSeenFrame: currentFrame, stabilityScore: 0, cornerHistory: [detected.corners], }) seenIds.add(newId) } } // Remove quads not seen recently for (const [id, tracked] of this.trackedQuads) { if (currentFrame - tracked.lastSeenFrame > 3) { this.trackedQuads.delete(id) } } // Find best quad let bestQuad: TrackedQuad | null = null let bestScore = 0 for (const tracked of this.trackedQuads.values()) { if (currentFrame - tracked.lastSeenFrame > 2) continue const score = tracked.frameCount * (0.5 + tracked.stabilityScore) * Math.sqrt(tracked.area) if (score > bestScore) { bestScore = score bestQuad = tracked } } this.bestQuad = bestQuad return bestQuad } private quadsMatch(q1: Corner[], q2: Corner[], frameDiagonal: number): boolean { const threshold = frameDiagonal * QUAD_MATCH_THRESHOLD let totalDist = 0 for (let i = 0; i < 4; i++) { totalDist += distance(q1[i], q2[i]) } return totalDist / 4 < threshold } private calculateStability(history: Corner[][]): number { if (history.length < 2) return 0 let totalVariance = 0 for (let corner = 0; corner < 4; corner++) { const xs = history.map((h) => h[corner].x) const ys = history.map((h) => h[corner].y) const meanX = xs.reduce((a, b) => a + b, 0) / xs.length const meanY = ys.reduce((a, b) => a + b, 0) / ys.length const varX = xs.reduce((a, b) => a + (b - meanX) ** 2, 0) / xs.length const varY = ys.reduce((a, b) => a + (b - meanY) ** 2, 0) / ys.length totalVariance += Math.sqrt(varX + varY) } const avgVariance = totalVariance / 4 return Math.max(0, 1 - avgVariance / 50) } private isQuadLocked(quad: TrackedQuad): boolean { return quad.frameCount >= LOCKED_FRAME_COUNT && quad.stabilityScore > 0.5 } private getQuadStyle(quad: TrackedQuad): { color: string lineWidth: number } { const isStable = quad.frameCount >= MIN_FRAMES_FOR_STABLE const isLocked = this.isQuadLocked(quad) if (isLocked) { return { color: 'rgba(0, 255, 100, 0.95)', lineWidth: 6 } } else if (isStable) { return { color: 'rgba(100, 255, 100, 0.85)', lineWidth: 5 } } else { return { color: 'rgba(255, 200, 0, 0.8)', lineWidth: 4 } } } private drawQuad( ctx: CanvasRenderingContext2D, corners: Corner[], color: string, lineWidth: number ): void { ctx.strokeStyle = color ctx.lineWidth = lineWidth ctx.lineCap = 'round' ctx.lineJoin = 'round' ctx.beginPath() ctx.moveTo(corners[0].x, corners[0].y) for (let i = 1; i < corners.length; i++) { ctx.lineTo(corners[i].x, corners[i].y) } ctx.closePath() ctx.stroke() // Draw corner circles ctx.fillStyle = color for (const corner of corners) { ctx.beginPath() ctx.arc(corner.x, corner.y, lineWidth * 2, 0, Math.PI * 2) ctx.fill() } } } // ============================================================================= // OpenCV Loader // ============================================================================= let loadPromise: Promise<CV> | null = null async function loadOpenCV(): Promise<CV> { if (loadPromise) return loadPromise loadPromise = new Promise((resolve, reject) => { // Check if already loaded const existingCV = (window as unknown as { cv?: CV & { imread?: unknown } }).cv if (existingCV && typeof existingCV.imread === 'function') { resolve(existingCV as CV) return } // Check for existing script const existingScript = document.querySelector('script[src="/opencv.js"]') if (existingScript) { waitForOpenCV(resolve, reject) return } // Load script const script = document.createElement('script') script.src = '/opencv.js' script.async = true script.onload = () => waitForOpenCV(resolve, reject) script.onerror = () => reject(new Error('Failed to load OpenCV.js')) document.head.appendChild(script) }) return loadPromise } function waitForOpenCV(resolve: (cv: CV) => void, reject: (err: Error) => void): void { const maxWait = 30000 const startTime = Date.now() const checkReady = () => { const cv = (window as unknown as { cv?: CV & { imread?: unknown } }).cv if (cv && typeof cv.imread === 'function') { resolve(cv as CV) return } if (Date.now() - startTime > maxWait) { reject(new Error('OpenCV.js loading timed out')) return } const windowCV = (window as unknown as { cv?: { onRuntimeInitialized?: () => void } }).cv if (windowCV) { const previousCallback = windowCV.onRuntimeInitialized windowCV.onRuntimeInitialized = () => { previousCallback?.() resolve(windowCV as unknown as CV) } } else { setTimeout(checkReady, 100) } } checkReady() } |