import { useEffect, useState } from 'react' import type { GridCell } from '@/app/(admin)/ai/ocr-overlay/types' export interface WordPosition { xPct: number wPct: number yPct: number hPct: number text: string fontRatio: number } /** * "Slide from left" positioning using OCR word bounding boxes. * * TEXT comes from cell.text (cleaned, IPA-corrected). * POSITIONS come from word_boxes (exact OCR coordinates). * * Tokens from cell.text are matched 1:1 (in order) to word_boxes * sorted left-to-right. This guarantees: * - ALL words from cell.text appear (no dropping) * - Words preserve their reading order * - Each word lands on its correct black-text position * - No red words overlap each other * * If token count != box count, extra tokens get estimated positions * (spread across remaining space). * * Fallback: pixel-projection slide if no word_boxes available. */ export function useSlideWordPositions( imageUrl: string, cells: GridCell[], active: boolean, rotation: 0 | 180 = 0, ): Map { const [result, setResult] = useState>(new Map()) useEffect(() => { if (!active || cells.length === 0 || !imageUrl) return const img = new Image() img.crossOrigin = 'anonymous' img.onload = () => { const imgW = img.naturalWidth const imgH = img.naturalHeight const hasWordBoxes = cells.some(c => c.word_boxes && c.word_boxes.length > 0) if (hasWordBoxes) { // --- WORD-BOX PATH: use OCR positions directly --- // Each word_box already has exact coordinates from OCR. // Use them as-is — no fuzzy matching needed. const positions = new Map() for (const cell of cells) { if (!cell.bbox_pct || !cell.text) continue const boxes = (cell.word_boxes || []) .filter(wb => wb.text.trim()) .sort((a, b) => a.left - b.left) if (boxes.length === 0) { // No word_boxes — spread tokens evenly across cell const tokens = cell.text.split(/\s+/).filter(Boolean) if (tokens.length === 0) continue const fallbackW = cell.bbox_pct.w / tokens.length const wordPos = tokens.map((t, i) => ({ xPct: cell.bbox_pct.x + i * fallbackW, wPct: fallbackW, yPct: cell.bbox_pct.y, hPct: cell.bbox_pct.h, text: t, fontRatio: 1.0, })) positions.set(cell.cell_id, wordPos) continue } // Use each word_box directly with its OCR coordinates const wordPos: WordPosition[] = boxes.map(box => ({ xPct: (box.left / imgW) * 100, wPct: (box.width / imgW) * 100, yPct: (box.top / imgH) * 100, hPct: (box.height / imgH) * 100, text: box.text, fontRatio: 1.0, })) if (wordPos.length > 0) { positions.set(cell.cell_id, wordPos) } } setResult(positions) return } // --- FALLBACK: pixel-projection slide (no word_boxes) --- const canvas = document.createElement('canvas') canvas.width = imgW canvas.height = imgH const ctx = canvas.getContext('2d') if (!ctx) return if (rotation === 180) { ctx.translate(imgW, imgH) ctx.rotate(Math.PI) ctx.drawImage(img, 0, 0) ctx.setTransform(1, 0, 0, 1, 0, 0) } else { ctx.drawImage(img, 0, 0) } const refFontSize = 40 const fontFam = "'Liberation Sans', Arial, sans-serif" ctx.font = `${refFontSize}px ${fontFam}` const cellHeights = cells .filter(c => c.bbox_pct && c.bbox_pct.h > 0) .map(c => Math.round(c.bbox_pct.h / 100 * imgH)) .sort((a, b) => a - b) const medianCh = cellHeights.length > 0 ? cellHeights[Math.floor(cellHeights.length / 2)] : 30 const renderedFontImgPx = medianCh * 0.7 const measureScale = renderedFontImgPx / refFontSize const spaceWidthPx = Math.max(2, Math.round(ctx.measureText(' ').width * measureScale)) const positions = new Map() for (const cell of cells) { if (!cell.bbox_pct || !cell.text) continue let cx: number, cy: number const cw = Math.round(cell.bbox_pct.w / 100 * imgW) const ch = Math.round(cell.bbox_pct.h / 100 * imgH) if (rotation === 180) { cx = Math.round((100 - cell.bbox_pct.x - cell.bbox_pct.w) / 100 * imgW) cy = Math.round((100 - cell.bbox_pct.y - cell.bbox_pct.h) / 100 * imgH) } else { cx = Math.round(cell.bbox_pct.x / 100 * imgW) cy = Math.round(cell.bbox_pct.y / 100 * imgH) } if (cw <= 0 || ch <= 0) continue if (cx < 0) cx = 0 if (cy < 0) cy = 0 if (cx + cw > imgW || cy + ch > imgH) continue const imageData = ctx.getImageData(cx, cy, cw, ch) const proj = new Float32Array(cw) for (let y = 0; y < ch; y++) { for (let x = 0; x < cw; x++) { const idx = (y * cw + x) * 4 const lum = 0.299 * imageData.data[idx] + 0.587 * imageData.data[idx + 1] + 0.114 * imageData.data[idx + 2] if (lum < 128) proj[x]++ } } const threshold = Math.max(1, ch * 0.03) const ink = new Uint8Array(cw) for (let x = 0; x < cw; x++) { ink[x] = proj[x] >= threshold ? 1 : 0 } if (rotation === 180) { ink.reverse() } const tokens = cell.text.split(/\s+/).filter(Boolean) if (tokens.length === 0) continue const tokenWidthsPx = tokens.map(t => Math.max(4, Math.round(ctx.measureText(t).width * measureScale)) ) const wordPos: WordPosition[] = [] let cursor = 0 for (let ti = 0; ti < tokens.length; ti++) { const tokenW = tokenWidthsPx[ti] const coverageNeeded = Math.max(1, Math.round(tokenW * 0.15)) let bestX = cursor const searchLimit = Math.max(cursor, cw - tokenW) for (let x = cursor; x <= searchLimit; x++) { let inkCount = 0 const spanEnd = Math.min(x + tokenW, cw) for (let dx = 0; dx < spanEnd - x; dx++) { inkCount += ink[x + dx] } if (inkCount >= coverageNeeded) { bestX = x break } if (x > cursor + cw * 0.3 && ti > 0) { bestX = cursor break } } if (bestX + tokenW > cw) { bestX = Math.max(0, cw - tokenW) } wordPos.push({ xPct: cell.bbox_pct.x + (bestX / cw) * cell.bbox_pct.w, wPct: (tokenW / cw) * cell.bbox_pct.w, yPct: cell.bbox_pct.y, hPct: cell.bbox_pct.h, text: tokens[ti], fontRatio: 1.0, }) cursor = bestX + tokenW + spaceWidthPx } if (wordPos.length > 0) { positions.set(cell.cell_id, wordPos) } } setResult(positions) } img.src = imageUrl }, [active, cells, imageUrl, rotation]) return result }