Press n or j to go to the next uncovered block, b, p or k for the previous block.
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1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 | /** * Quad Detector * * Core quadrilateral detection logic using OpenCV.js. * Finds document-like quadrilaterals in images using edge detection and contour analysis. * * This is a pure module with no React dependencies - can be used directly * or wrapped in hooks for React components. */ import type { CV, CVMat, CVMatVector, CVPoint } from './opencv/types' // ============================================================================ // Types // ============================================================================ /** A 2D point */ export interface Point { x: number y: number } /** A detected quadrilateral */ export interface DetectedQuad { /** Corner points ordered: top-left, top-right, bottom-right, bottom-left */ corners: [Point, Point, Point, Point] /** Area in pixels */ area: number /** Aspect ratio (max dimension / min dimension) */ aspectRatio: number } /** Debug info for a candidate polygon (not necessarily a quad) */ export interface DebugPolygon { /** All vertices of the polygon */ vertices: Point[] /** Convex hull vertices (if computed) */ hullVertices?: Point[] /** Number of vertices */ vertexCount: number /** Area in pixels */ area: number /** Area ratio relative to frame */ areaRatio: number /** Why this polygon was rejected (or 'accepted' if it became a quad) */ status: | 'accepted' | 'too_small' | 'too_large' | 'too_few_vertices' | 'too_many_vertices' | 'bad_aspect_ratio' | 'corner_extraction_failed' /** Aspect ratio if computed */ aspectRatio?: number } /** Preprocessing strategy */ export type PreprocessingStrategy = 'standard' | 'enhanced' | 'adaptive' | 'multi' /** Configuration for quad detection */ export interface QuadDetectorConfig { /** Minimum area as fraction of frame (0-1). Default: 0.15 */ minAreaRatio: number /** Maximum area as fraction of frame (0-1). Default: 0.95 */ maxAreaRatio: number /** How close aspect ratio must be to expected ratios. Default: 0.3 */ aspectRatioTolerance: number /** Expected document aspect ratios. Default: letter/A4/square */ expectedAspectRatios: number[] /** Canny edge detection thresholds [low, high]. Default: [50, 150] */ cannyThresholds: [number, number] /** Polygon approximation epsilon as fraction of perimeter. Default: 0.02 */ approxEpsilon: number /** Gaussian blur kernel size (odd number). Default: 5 */ blurSize: number /** Maximum vertices to consider when finding corners (for rounded rectangles). Default: 8 */ maxVerticesForCornerFit: number /** Minimum internal angle (degrees) for a vertex to be considered a corner. Default: 60 */ minCornerAngle: number /** Preprocessing strategy. Default: 'multi' */ preprocessing: PreprocessingStrategy /** Enable histogram equalization for contrast enhancement. Default: true */ enableHistogramEqualization: boolean /** Use adaptive thresholding instead of/in addition to Canny. Default: true */ enableAdaptiveThreshold: boolean /** Adaptive threshold block size (odd number). Default: 11 */ adaptiveBlockSize: number /** Adaptive threshold constant. Default: 2 */ adaptiveC: number /** Use morphological gradient for edge enhancement. Default: true */ enableMorphGradient: boolean /** Use bilateral filter for noise reduction (slower but preserves edges). Default: false */ enableBilateralFilter: boolean /** Enable Hough line detection as fallback for finger occlusion. Default: true */ enableHoughLines: boolean /** Hough line detection threshold (minimum votes). Default: 50 */ houghThreshold: number /** Minimum line length for Hough detection. Default: 50 */ houghMinLineLength: number /** Maximum gap between line segments to merge. Default: 10 */ houghMaxLineGap: number /** Angle tolerance (degrees) for grouping parallel lines. Default: 10 */ houghAngleTolerance: number } /** Default configuration */ export const DEFAULT_QUAD_DETECTOR_CONFIG: QuadDetectorConfig = { 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], approxEpsilon: 0.02, blurSize: 5, maxVerticesForCornerFit: 8, minCornerAngle: 60, // Enhanced preprocessing options preprocessing: 'multi', enableHistogramEqualization: true, enableAdaptiveThreshold: true, adaptiveBlockSize: 11, adaptiveC: 2, enableMorphGradient: true, enableBilateralFilter: false, // Slower, enable if needed // Hough line detection options enableHoughLines: true, houghThreshold: 50, houghMinLineLength: 50, houghMaxLineGap: 10, houghAngleTolerance: 10, } // ============================================================================ // Utility Functions // ============================================================================ /** * Calculate Euclidean distance between two points */ export function distance(p1: Point, p2: Point): 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 * Uses angle from centroid and sum of coordinates to find top-left */ export function orderCorners(corners: Point[]): [Point, Point, Point, Point] { if (corners.length !== 4) { throw new Error('orderCorners requires exactly 4 points') } // 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 (counter-clockwise from positive x-axis) 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 sum) 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: Point[] = [] for (let i = 0; i < 4; i++) { ordered.push(sorted[(topLeftIdx + i) % 4]) } return ordered as [Point, Point, Point, Point] } /** * Check if an aspect ratio matches any expected document ratio */ export function isDocumentAspectRatio( ratio: number, expectedRatios: number[], tolerance: number ): boolean { return expectedRatios.some((expected) => Math.abs(ratio - expected) < tolerance) } /** * Calculate the internal angle at a vertex (in degrees) * Given three consecutive points: prev -> current -> next */ export function vertexAngle(prev: Point, current: Point, next: Point): number { const v1 = { x: prev.x - current.x, y: prev.y - current.y } const v2 = { x: next.x - current.x, y: next.y - current.y } const dot = v1.x * v2.x + v1.y * v2.y const mag1 = Math.sqrt(v1.x ** 2 + v1.y ** 2) const mag2 = Math.sqrt(v2.x ** 2 + v2.y ** 2) if (mag1 === 0 || mag2 === 0) return 180 const cosAngle = Math.max(-1, Math.min(1, dot / (mag1 * mag2))) return (Math.acos(cosAngle) * 180) / Math.PI } /** * Find the 4 best corners from a polygon with more than 4 vertices. * Uses internal angle to identify the most "corner-like" vertices. * * This handles cases where fingers occlude document edges, creating * extra vertices that should be ignored. */ export function extractBestCorners( vertices: Point[], minCornerAngle: number ): [Point, Point, Point, Point] | null { if (vertices.length < 4) return null // Calculate the internal angle at each vertex const vertexScores: Array<{ point: Point; angle: number; index: number }> = [] for (let i = 0; i < vertices.length; i++) { const prev = vertices[(i - 1 + vertices.length) % vertices.length] const curr = vertices[i] const next = vertices[(i + 1) % vertices.length] const angle = vertexAngle(prev, curr, next) vertexScores.push({ point: curr, angle, index: i }) } // Filter to vertices with angles sharp enough to be corners // (internal angle < 180 - minCornerAngle means it's a convex corner) const cornerCandidates = vertexScores.filter((v) => v.angle < 180 - minCornerAngle) if (cornerCandidates.length < 4) { // Not enough corner candidates - fall back to 4 sharpest angles vertexScores.sort((a, b) => a.angle - b.angle) const fourSharpest = vertexScores.slice(0, 4) // Sort by original index to maintain polygon order fourSharpest.sort((a, b) => a.index - b.index) return fourSharpest.map((v) => v.point) as [Point, Point, Point, Point] } // If we have exactly 4 candidates, use them if (cornerCandidates.length === 4) { cornerCandidates.sort((a, b) => a.index - b.index) return cornerCandidates.map((v) => v.point) as [Point, Point, Point, Point] } // More than 4 candidates - find the 4 that form the best quadrilateral // Strategy: pick 4 with sharpest angles while maintaining good spacing cornerCandidates.sort((a, b) => a.angle - b.angle) // Start with the 4 sharpest, but ensure they're well-distributed const selected = cornerCandidates.slice(0, 4) selected.sort((a, b) => a.index - b.index) return selected.map((v) => v.point) as [Point, Point, Point, Point] } // ============================================================================ // Hough Line Detection Functions // ============================================================================ /** A line segment represented by two endpoints */ interface LineSegment { x1: number y1: number x2: number y2: number /** Angle in radians (-PI/2 to PI/2) */ angle: number /** Length of the segment */ length: number } /** * Calculate the angle of a line segment (in radians, -PI/2 to PI/2) * Normalized so nearly horizontal lines have angle ~0, vertical lines have angle ~PI/2 */ function lineAngle(x1: number, y1: number, x2: number, y2: number): number { let angle = Math.atan2(y2 - y1, x2 - x1) // Normalize to -PI/2 to PI/2 (treat lines going opposite directions as same) if (angle > Math.PI / 2) angle -= Math.PI if (angle < -Math.PI / 2) angle += Math.PI return angle } /** * Find the intersection point of two lines (extended infinitely) * Returns null if lines are parallel */ function lineIntersection(line1: LineSegment, line2: LineSegment): Point | null { const x1 = line1.x1, y1 = line1.y1, x2 = line1.x2, y2 = line1.y2 const x3 = line2.x1, y3 = line2.y1, x4 = line2.x2, y4 = line2.y2 const denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4) // Lines are parallel if denominator is ~0 if (Math.abs(denom) < 1e-10) return null const t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / denom return { x: x1 + t * (x2 - x1), y: y1 + t * (y2 - y1), } } /** * Group lines by angle into roughly parallel sets. * For a document, we expect 4 groups: 2 pairs of parallel edges. */ function groupLinesByAngle(lines: LineSegment[], toleranceRadians: number): LineSegment[][] { if (lines.length === 0) return [] // Sort lines by angle const sorted = [...lines].sort((a, b) => a.angle - b.angle) const groups: LineSegment[][] = [] let currentGroup: LineSegment[] = [sorted[0]] for (let i = 1; i < sorted.length; i++) { const angleDiff = Math.abs(sorted[i].angle - sorted[i - 1].angle) if (angleDiff <= toleranceRadians) { currentGroup.push(sorted[i]) } else { groups.push(currentGroup) currentGroup = [sorted[i]] } } groups.push(currentGroup) // Handle wrap-around (lines at -PI/2 and PI/2 are actually parallel) if (groups.length >= 2) { const firstAngle = groups[0][0].angle const lastAngle = groups[groups.length - 1][0].angle // If first and last groups are actually parallel (wrap-around) if ( Math.abs(lastAngle - firstAngle - Math.PI) <= toleranceRadians || Math.abs(lastAngle - firstAngle + Math.PI) <= toleranceRadians ) { groups[0] = [...groups[groups.length - 1], ...groups[0]] groups.pop() } } return groups } /** * Get the "best" line from a group of parallel lines. * Prefers longer lines and lines closer to the center of mass. */ function getBestLineFromGroup(lines: LineSegment[]): LineSegment { if (lines.length === 1) return lines[0] // Weight by length - longer lines are more reliable let totalWeight = 0 let weightedX1 = 0, weightedY1 = 0, weightedX2 = 0, weightedY2 = 0 for (const line of lines) { const weight = line.length totalWeight += weight weightedX1 += line.x1 * weight weightedY1 += line.y1 * weight weightedX2 += line.x2 * weight weightedY2 += line.y2 * weight } if (totalWeight === 0) return lines[0] return { x1: weightedX1 / totalWeight, y1: weightedY1 / totalWeight, x2: weightedX2 / totalWeight, y2: weightedY2 / totalWeight, angle: lines[0].angle, // Use representative angle length: Math.max(...lines.map((l) => l.length)), } } /** * Find the 4 dominant line groups that could form a document quadrilateral. * Returns 4 representative lines (2 roughly horizontal, 2 roughly vertical). */ function findDominantLines(lines: LineSegment[], toleranceRadians: number): LineSegment[] | null { // Group by angle const groups = groupLinesByAngle(lines, toleranceRadians) // We need at least 4 groups, or 2 groups with enough lines to split if (groups.length < 2) return null // Sort groups by total line length (more reliable groups first) const groupsWithScore = groups.map((group) => ({ group, totalLength: group.reduce((sum, l) => sum + l.length, 0), avgAngle: group.reduce((sum, l) => sum + l.angle, 0) / group.length, })) groupsWithScore.sort((a, b) => b.totalLength - a.totalLength) // We need to find 4 lines forming 2 pairs of roughly parallel lines // For a document, we expect 2 angle directions (~0° and ~90°) const dominantLines: LineSegment[] = [] // Take the 2 most prominent angle directions const direction1 = groupsWithScore[0] let direction2 = groupsWithScore.find( (g) => Math.abs(Math.abs(g.avgAngle - direction1.avgAngle) - Math.PI / 2) < toleranceRadians * 2 ) if (!direction2 && groupsWithScore.length > 1) { // No perpendicular group found, just take second largest direction2 = groupsWithScore[1] } if (!direction2) return null // From direction1, we need 2 lines (opposite edges of document) // Split lines by position (which side of the image center they're on) const splitGroup = (group: LineSegment[]): [LineSegment, LineSegment] | null => { if (group.length < 2) { // Only one line - can't form two edges return null } // Sort by distance from origin along perpendicular direction const isVertical = Math.abs(group[0].angle) > Math.PI / 4 const sorted = [...group].sort((a, b) => { if (isVertical) { // For vertical lines, sort by x position return (a.x1 + a.x2) / 2 - (b.x1 + b.x2) / 2 } else { // For horizontal lines, sort by y position return (a.y1 + a.y2) / 2 - (b.y1 + b.y2) / 2 } }) // Take the first and last as the two edges return [getBestLineFromGroup([sorted[0]]), getBestLineFromGroup([sorted[sorted.length - 1]])] } const dir1Lines = splitGroup(direction1.group) const dir2Lines = splitGroup(direction2.group) if (!dir1Lines || !dir2Lines) return null dominantLines.push(dir1Lines[0], dir1Lines[1], dir2Lines[0], dir2Lines[1]) return dominantLines } /** * Detect quadrilateral corners using Hough line transform. * Works even when fingers occlude parts of the document edges. * * @param cv - OpenCV instance * @param edges - Edge-detected image (8-bit single channel) * @param cfg - Configuration * @param frameWidth - Width of the frame * @param frameHeight - Height of the frame * @returns Detected quad or null */ function detectQuadFromHoughLines( cv: CV, edges: CVMat, cfg: QuadDetectorConfig, frameWidth: number, frameHeight: number ): DetectedQuad | null { const lines = new cv.Mat() try { // Detect line segments using probabilistic Hough transform cv.HoughLinesP( edges, lines, 1, // rho: distance resolution in pixels Math.PI / 180, // theta: angle resolution in radians cfg.houghThreshold, cfg.houghMinLineLength, cfg.houghMaxLineGap ) if (lines.rows === 0) return null // Convert to LineSegment array const lineSegments: LineSegment[] = [] for (let i = 0; i < lines.rows; i++) { const x1 = lines.data32S[i * 4] const y1 = lines.data32S[i * 4 + 1] const x2 = lines.data32S[i * 4 + 2] const y2 = lines.data32S[i * 4 + 3] const length = Math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) // Filter very short lines if (length < cfg.houghMinLineLength) continue lineSegments.push({ x1, y1, x2, y2, angle: lineAngle(x1, y1, x2, y2), length, }) } if (lineSegments.length < 4) return null // Find 4 dominant lines forming the document edges const toleranceRadians = (cfg.houghAngleTolerance * Math.PI) / 180 const dominantLines = findDominantLines(lineSegments, toleranceRadians) if (!dominantLines || dominantLines.length !== 4) return null // Find corners as intersections of adjacent lines // We have 4 lines - need to find which pairs are adjacent (perpendicular) const isRoughlyPerpendicular = (l1: LineSegment, l2: LineSegment): boolean => { const angleDiff = Math.abs(l1.angle - l2.angle) return ( Math.abs(angleDiff - Math.PI / 2) < toleranceRadians * 2 || Math.abs(angleDiff + Math.PI / 2) < toleranceRadians * 2 ) } // Group lines by direction const group1: LineSegment[] = [] const group2: LineSegment[] = [] group1.push(dominantLines[0]) for (let i = 1; i < 4; i++) { if (!isRoughlyPerpendicular(dominantLines[0], dominantLines[i])) { group1.push(dominantLines[i]) } else { group2.push(dominantLines[i]) } } if (group1.length !== 2 || group2.length !== 2) { // Couldn't properly separate into perpendicular pairs return null } // Find 4 corners as intersections const corners: Point[] = [] for (const line1 of group1) { for (const line2 of group2) { const intersection = lineIntersection(line1, line2) if (intersection) { // Check intersection is within frame bounds (with some margin) const margin = Math.min(frameWidth, frameHeight) * 0.1 if ( intersection.x >= -margin && intersection.x <= frameWidth + margin && intersection.y >= -margin && intersection.y <= frameHeight + margin ) { corners.push(intersection) } } } } if (corners.length !== 4) return null // Order corners and validate const orderedCorners = orderCorners(corners) // Calculate area const width = distance(orderedCorners[0], orderedCorners[1]) const height = distance(orderedCorners[1], orderedCorners[2]) const area = width * height * 0.95 // Approximate area (assuming near-rectangle) const frameArea = frameWidth * frameHeight const areaRatio = area / frameArea // Validate area if (areaRatio < cfg.minAreaRatio || areaRatio > cfg.maxAreaRatio) { return null } // Calculate and validate aspect ratio const aspectRatio = Math.max(width, height) / Math.min(width, height) if (!isDocumentAspectRatio(aspectRatio, cfg.expectedAspectRatios, cfg.aspectRatioTolerance)) { return null } return { corners: orderedCorners, area, aspectRatio, } } finally { lines.delete() } } // ============================================================================ // Preprocessing Functions // ============================================================================ /** * Apply standard preprocessing: grayscale -> blur -> Canny */ function preprocessStandard(cv: CV, gray: CVMat, edges: CVMat, cfg: QuadDetectorConfig): void { const blurred = new cv.Mat() try { // Blur to reduce noise cv.GaussianBlur(gray, blurred, new cv.Size(cfg.blurSize, cfg.blurSize), 0, 0, cv.BORDER_DEFAULT) // Canny edge detection cv.Canny(blurred, edges, cfg.cannyThresholds[0], cfg.cannyThresholds[1]) } finally { blurred.delete() } } /** * Apply enhanced preprocessing: histogram equalization -> blur -> Canny with multiple thresholds */ function preprocessEnhanced(cv: CV, gray: CVMat, edges: CVMat, cfg: QuadDetectorConfig): void { const equalized = new cv.Mat() const blurred = new cv.Mat() const edges1 = new cv.Mat() const edges2 = new cv.Mat() try { // Histogram equalization for contrast enhancement if (cfg.enableHistogramEqualization) { cv.equalizeHist(gray, equalized) } else { gray.copyTo(equalized) } // Blur to reduce noise cv.GaussianBlur( equalized, blurred, new cv.Size(cfg.blurSize, cfg.blurSize), 0, 0, cv.BORDER_DEFAULT ) // Try Canny with original thresholds cv.Canny(blurred, edges1, cfg.cannyThresholds[0], cfg.cannyThresholds[1]) // Try Canny with lower thresholds for low contrast images cv.Canny(blurred, edges2, cfg.cannyThresholds[0] / 2, cfg.cannyThresholds[1] / 2) // Combine edges cv.bitwise_or(edges1, edges2, edges) } finally { equalized.delete() blurred.delete() edges1.delete() edges2.delete() } } /** * Apply adaptive preprocessing: histogram equalization -> adaptive threshold + morphological gradient */ function preprocessAdaptive(cv: CV, gray: CVMat, edges: CVMat, cfg: QuadDetectorConfig): void { const equalized = new cv.Mat() const blurred = new cv.Mat() const adaptiveEdges = new cv.Mat() const morphEdges = new cv.Mat() const kernel = cv.getStructuringElement(cv.MORPH_RECT, new cv.Size(3, 3)) try { // Histogram equalization if (cfg.enableHistogramEqualization) { cv.equalizeHist(gray, equalized) } else { gray.copyTo(equalized) } // Slight blur cv.GaussianBlur( equalized, blurred, new cv.Size(cfg.blurSize, cfg.blurSize), 0, 0, cv.BORDER_DEFAULT ) // Adaptive threshold if (cfg.enableAdaptiveThreshold) { cv.adaptiveThreshold( blurred, adaptiveEdges, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, cfg.adaptiveBlockSize, cfg.adaptiveC ) // Invert so edges are white cv.threshold(adaptiveEdges, adaptiveEdges, 127, 255, cv.THRESH_BINARY_INV) } // Morphological gradient (dilation - erosion = edges) if (cfg.enableMorphGradient) { cv.morphologyEx(blurred, morphEdges, cv.MORPH_GRADIENT, kernel) // Threshold the gradient cv.threshold(morphEdges, morphEdges, 30, 255, cv.THRESH_BINARY) } // Combine results if (cfg.enableAdaptiveThreshold && cfg.enableMorphGradient) { cv.bitwise_or(adaptiveEdges, morphEdges, edges) } else if (cfg.enableAdaptiveThreshold) { adaptiveEdges.copyTo(edges) } else if (cfg.enableMorphGradient) { morphEdges.copyTo(edges) } else { // Fallback to standard Canny cv.Canny(blurred, edges, cfg.cannyThresholds[0], cfg.cannyThresholds[1]) } } finally { equalized.delete() blurred.delete() adaptiveEdges.delete() morphEdges.delete() kernel.delete() } } /** * Apply multi-strategy preprocessing: combines multiple approaches * This is the most robust for varying lighting conditions */ function preprocessMulti(cv: CV, gray: CVMat, edges: CVMat, cfg: QuadDetectorConfig): void { const equalized = new cv.Mat() const blurred = new cv.Mat() const cannyEdges = new cv.Mat() const cannyLowEdges = new cv.Mat() const adaptiveEdges = new cv.Mat() const morphEdges = new cv.Mat() const kernel = cv.getStructuringElement(cv.MORPH_RECT, new cv.Size(3, 3)) const combined = new cv.Mat() try { // Step 1: Histogram equalization if (cfg.enableHistogramEqualization) { cv.equalizeHist(gray, equalized) } else { gray.copyTo(equalized) } // Step 2: Gaussian blur cv.GaussianBlur( equalized, blurred, new cv.Size(cfg.blurSize, cfg.blurSize), 0, 0, cv.BORDER_DEFAULT ) // Strategy A: Standard Canny cv.Canny(blurred, cannyEdges, cfg.cannyThresholds[0], cfg.cannyThresholds[1]) // Strategy B: Low-threshold Canny (catches faint edges) cv.Canny(blurred, cannyLowEdges, cfg.cannyThresholds[0] / 3, cfg.cannyThresholds[1] / 3) // Combine Canny results cv.bitwise_or(cannyEdges, cannyLowEdges, combined) // Strategy C: Adaptive threshold (good for uneven lighting) if (cfg.enableAdaptiveThreshold) { cv.adaptiveThreshold( blurred, adaptiveEdges, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, cfg.adaptiveBlockSize, cfg.adaptiveC ) cv.bitwise_or(combined, adaptiveEdges, combined) } // Strategy D: Morphological gradient (good for low contrast) if (cfg.enableMorphGradient) { cv.morphologyEx(blurred, morphEdges, cv.MORPH_GRADIENT, kernel) cv.threshold(morphEdges, morphEdges, 20, 255, cv.THRESH_BINARY) cv.bitwise_or(combined, morphEdges, combined) } // Copy result combined.copyTo(edges) } finally { equalized.delete() blurred.delete() cannyEdges.delete() cannyLowEdges.delete() adaptiveEdges.delete() morphEdges.delete() kernel.delete() combined.delete() } } /** * Preprocess image for edge detection based on selected strategy */ function preprocessImage(cv: CV, gray: CVMat, edges: CVMat, cfg: QuadDetectorConfig): void { try { switch (cfg.preprocessing) { case 'standard': preprocessStandard(cv, gray, edges, cfg) break case 'enhanced': preprocessEnhanced(cv, gray, edges, cfg) break case 'adaptive': preprocessAdaptive(cv, gray, edges, cfg) break case 'multi': default: preprocessMulti(cv, gray, edges, cfg) break } } catch (err) { // If preprocessing fails, fall back to simple Canny console.warn('Preprocessing failed, falling back to simple Canny:', err) const blurred = new cv.Mat() try { cv.GaussianBlur(gray, blurred, new cv.Size(5, 5), 0, 0, cv.BORDER_DEFAULT) cv.Canny(blurred, edges, 50, 150) } finally { blurred.delete() } } } // ============================================================================ // Quad Detector // ============================================================================ /** * Create a quad detector instance * * @param cv - OpenCV.js instance * @param config - Optional configuration overrides */ export function createQuadDetector(cv: CV, config: Partial<QuadDetectorConfig> = {}) { const cfg: QuadDetectorConfig = { ...DEFAULT_QUAD_DETECTOR_CONFIG, ...config, } // Validate and sanitize parameters to prevent OpenCV errors // Blur size must be odd and >= 1 if (cfg.blurSize < 1) cfg.blurSize = 1 if (cfg.blurSize % 2 === 0) cfg.blurSize += 1 // Adaptive block size must be odd and >= 3 if (cfg.adaptiveBlockSize < 3) cfg.adaptiveBlockSize = 3 if (cfg.adaptiveBlockSize % 2 === 0) cfg.adaptiveBlockSize += 1 // Canny thresholds must be positive and low < high if (cfg.cannyThresholds[0] < 1) cfg.cannyThresholds[0] = 1 if (cfg.cannyThresholds[1] < 1) cfg.cannyThresholds[1] = 1 if (cfg.cannyThresholds[0] >= cfg.cannyThresholds[1]) { cfg.cannyThresholds[1] = cfg.cannyThresholds[0] + 50 } /** * Detect all document-like quadrilaterals in an image * * @param source - Canvas or ImageData to process * @returns Array of detected quads, sorted by area (largest first) */ function detect(source: HTMLCanvasElement): DetectedQuad[] { const quads: DetectedQuad[] = [] const frameArea = source.width * source.height // OpenCV matrices (need cleanup) let src: CVMat | null = null let gray: CVMat | null = null let edges: CVMat | null = null let contours: CVMatVector | null = null let hierarchy: CVMat | null = null try { // Read image src = cv.imread(source) gray = new cv.Mat() edges = new cv.Mat() // Convert to grayscale cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY) // Apply preprocessing based on selected strategy preprocessImage(cv, gray, edges, cfg) // 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 < cfg.minAreaRatio || areaRatio > cfg.maxAreaRatio) { continue } // Approximate to polygon const approx = new cv.Mat() const perimeter = cv.arcLength(contour, true) cv.approxPolyDP(contour, approx, cfg.approxEpsilon * perimeter, true) let corners: [Point, Point, Point, Point] | null = null if (approx.rows === 4) { // Perfect quadrilateral - extract corners directly const pts: Point[] = [] for (let j = 0; j < 4; j++) { pts.push({ x: approx.data32S[j * 2], y: approx.data32S[j * 2 + 1], }) } corners = orderCorners(pts) } else if (approx.rows > 4 && approx.rows <= cfg.maxVerticesForCornerFit) { // Polygon with extra vertices (likely finger occlusion) // Use convex hull to remove concave intrusions, then extract 4 best corners const hull = new cv.Mat() cv.convexHull(contour, hull, false, true) // Extract hull points const hullPoints: Point[] = [] for (let j = 0; j < hull.rows; j++) { hullPoints.push({ x: hull.data32S[j * 2], y: hull.data32S[j * 2 + 1], }) } hull.delete() if (hullPoints.length >= 4) { // Try to find 4 best corners from convex hull const extracted = extractBestCorners(hullPoints, cfg.minCornerAngle) if (extracted) { corners = orderCorners(extracted) } } } if (corners) { // Calculate aspect ratio const width = distance(corners[0], corners[1]) const height = distance(corners[1], corners[2]) const aspectRatio = Math.max(width, height) / Math.min(width, height) // Check if aspect ratio is document-like if ( isDocumentAspectRatio(aspectRatio, cfg.expectedAspectRatios, cfg.aspectRatioTolerance) ) { quads.push({ corners, area, aspectRatio, }) } } approx.delete() } // If no quads found via contours, try Hough line detection if (quads.length === 0 && cfg.enableHoughLines && edges) { const houghQuad = detectQuadFromHoughLines(cv, edges, cfg, source.width, source.height) if (houghQuad) { quads.push(houghQuad) } } } finally { // Clean up OpenCV memory src?.delete() gray?.delete() edges?.delete() contours?.delete() hierarchy?.delete() } // Sort by area (largest first) quads.sort((a, b) => b.area - a.area) return quads } /** * Detect the single best quad in an image * * @param source - Canvas to process * @returns Best quad or null if none found */ function detectBest(source: HTMLCanvasElement): DetectedQuad | null { const quads = detect(source) return quads.length > 0 ? quads[0] : null } /** * Detect quads with debug info about all candidate polygons. * Use this to understand why detection is failing. * * @param source - Canvas to process * @returns Quads and debug info about all candidates */ function detectWithDebug(source: HTMLCanvasElement): { quads: DetectedQuad[] debugPolygons: DebugPolygon[] } { const quads: DetectedQuad[] = [] const debugPolygons: DebugPolygon[] = [] const frameArea = source.width * source.height // OpenCV matrices (need cleanup) let src: CVMat | null = null let gray: CVMat | null = null let edges: CVMat | null = null let contours: CVMatVector | null = null let hierarchy: CVMat | null = null try { // Read image src = cv.imread(source) gray = new cv.Mat() edges = new cv.Mat() // Convert to grayscale cv.cvtColor(src, gray, cv.COLOR_RGBA2GRAY) // Apply preprocessing based on selected strategy preprocessImage(cv, gray, edges, cfg) // 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 // Approximate to polygon const approx = new cv.Mat() const perimeter = cv.arcLength(contour, true) cv.approxPolyDP(contour, approx, cfg.approxEpsilon * perimeter, true) // Extract vertices for debug const vertices: Point[] = [] for (let j = 0; j < approx.rows; j++) { vertices.push({ x: approx.data32S[j * 2], y: approx.data32S[j * 2 + 1], }) } const debugInfo: DebugPolygon = { vertices, vertexCount: approx.rows, area, areaRatio, status: 'accepted', // Will be updated if rejected } // Skip if too small or too large if (areaRatio < cfg.minAreaRatio) { debugInfo.status = 'too_small' debugPolygons.push(debugInfo) approx.delete() continue } if (areaRatio > cfg.maxAreaRatio) { debugInfo.status = 'too_large' debugPolygons.push(debugInfo) approx.delete() continue } let corners: [Point, Point, Point, Point] | null = null if (approx.rows === 4) { // Perfect quadrilateral - extract corners directly const pts: Point[] = [] for (let j = 0; j < 4; j++) { pts.push({ x: approx.data32S[j * 2], y: approx.data32S[j * 2 + 1], }) } corners = orderCorners(pts) } else if (approx.rows < 4) { debugInfo.status = 'too_few_vertices' debugPolygons.push(debugInfo) approx.delete() continue } else if (approx.rows > cfg.maxVerticesForCornerFit) { debugInfo.status = 'too_many_vertices' debugPolygons.push(debugInfo) approx.delete() continue } else { // Polygon with extra vertices (likely finger occlusion) // Use convex hull to remove concave intrusions, then extract 4 best corners const hull = new cv.Mat() cv.convexHull(contour, hull, false, true) // Extract hull points const hullPoints: Point[] = [] for (let j = 0; j < hull.rows; j++) { hullPoints.push({ x: hull.data32S[j * 2], y: hull.data32S[j * 2 + 1], }) } hull.delete() debugInfo.hullVertices = hullPoints if (hullPoints.length >= 4) { // Try to find 4 best corners from convex hull const extracted = extractBestCorners(hullPoints, cfg.minCornerAngle) if (extracted) { corners = orderCorners(extracted) } else { debugInfo.status = 'corner_extraction_failed' debugPolygons.push(debugInfo) approx.delete() continue } } else { debugInfo.status = 'too_few_vertices' debugPolygons.push(debugInfo) approx.delete() continue } } if (corners) { // Calculate aspect ratio const width = distance(corners[0], corners[1]) const height = distance(corners[1], corners[2]) const aspectRatio = Math.max(width, height) / Math.min(width, height) debugInfo.aspectRatio = aspectRatio // Check if aspect ratio is document-like if ( isDocumentAspectRatio(aspectRatio, cfg.expectedAspectRatios, cfg.aspectRatioTolerance) ) { debugInfo.status = 'accepted' quads.push({ corners, area, aspectRatio, }) } else { debugInfo.status = 'bad_aspect_ratio' } } debugPolygons.push(debugInfo) approx.delete() } // If no quads found via contours, try Hough line detection if (quads.length === 0 && cfg.enableHoughLines && edges) { const houghQuad = detectQuadFromHoughLines(cv, edges, cfg, source.width, source.height) if (houghQuad) { quads.push(houghQuad) // Add a debug polygon for the Hough-detected quad debugPolygons.push({ vertices: [...houghQuad.corners], vertexCount: 4, area: houghQuad.area, areaRatio: houghQuad.area / frameArea, status: 'accepted', aspectRatio: houghQuad.aspectRatio, }) } } } finally { // Clean up OpenCV memory src?.delete() gray?.delete() edges?.delete() contours?.delete() hierarchy?.delete() } // Sort by area (largest first) quads.sort((a, b) => b.area - a.area) debugPolygons.sort((a, b) => b.area - a.area) return { quads, debugPolygons } } return { detect, detectBest, detectWithDebug, config: cfg, } } export type QuadDetector = ReturnType<typeof createQuadDetector> |