Add LPR test button in annotation UI
New POST /event/{id}/test-lpr endpoint runs PlateRecognizer on the full
frame and local OCR on the drawn bbox region (with perspective correction
when possible). Annotation UI shows results inline with a one-click
"Utiliser" button to copy the best plate into the input field.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
perco
co-authored by
Claude Sonnet 4.6
Claude Sonnet 4.6
parent
ea4118ffcd
commit
c43652d187
+81
@@ -316,6 +316,87 @@ async def whitelist_remove(plate: str, back: str = Form("")):
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return RedirectResponse(back or "/whitelist", status_code=303)
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@app.post("/event/{event_id}/test-lpr")
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async def test_lpr(event_id: str, frame_path: str = Form(""), bbox: str = Form("")):
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import json, base64, cv2, numpy as np
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frame_abs = os.path.join("/data", frame_path) if frame_path else None
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if not frame_abs or not os.path.exists(frame_abs):
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raise HTTPException(404, "Frame introuvable")
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def _run():
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frame = cv2.imread(frame_abs)
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if frame is None:
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return {"error": "Impossible de lire l'image"}
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result: dict = {"has_pr": bool(PLATERECOGNIZER_KEY)}
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if PLATERECOGNIZER_KEY:
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from lpr import call_platerecognizer
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from watcher import _normalize_plate
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p, c = call_platerecognizer(frame, PLATERECOGNIZER_KEY)
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if p:
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result["platerecognizer"] = {
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"plate": _normalize_plate(p) or p,
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"raw": p,
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"conf": round(c, 3),
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}
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analyzer = watcher._analyzer
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if bbox and analyzer:
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try:
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box = json.loads(bbox)
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h, w = frame.shape[:2]
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cx2, cy2 = box["cx"] * w, box["cy"] * h
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bw2, bh2 = box["w"] * w, box["h"] * h
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x1 = max(0, int(cx2 - bw2 / 2))
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y1 = max(0, int(cy2 - bh2 / 2))
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x2 = min(w, int(cx2 + bw2 / 2))
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y2 = min(h, int(cy2 + bh2 / 2))
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source = "raw_crop"
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ocr_img = None
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quad = analyzer._find_plate_quad(frame, x1, y1, x2, y2)
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if quad is not None:
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corrected = analyzer._perspective_correct(frame, quad)
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if corrected is not None and corrected.size > 0:
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ocr_img = analyzer._enhance_crop(corrected)
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source = "perspective_corrected"
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if ocr_img is None:
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crop = frame[y1:y2, x1:x2]
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ocr_img = analyzer._enhance_crop(crop) if crop.size > 0 else None
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if ocr_img is not None:
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text, conf = analyzer._ocr_paddle(ocr_img)
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if not text:
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text, conf = analyzer._ocr_tesseract(ocr_img)
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oh, ow = ocr_img.shape[:2]
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if ow > 0 and ow < 300:
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scale = 300 / ow
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ocr_display = cv2.resize(ocr_img, (300, int(oh * scale)), interpolation=cv2.INTER_CUBIC)
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else:
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ocr_display = ocr_img
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_, buf = cv2.imencode(".jpg", ocr_display, [cv2.IMWRITE_JPEG_QUALITY, 90])
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from watcher import _normalize_plate
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result["local"] = {
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"plate": _normalize_plate(text) or text,
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"raw": text,
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"conf": round(conf, 3),
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"img_b64": base64.b64encode(buf).decode(),
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"source": source,
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}
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except Exception as e:
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result["local_error"] = str(e)
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return result
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(None, _run)
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@app.get("/health")
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async def health():
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return {"status": "ok"}
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@@ -102,12 +102,20 @@
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style="background:#166534;color:#4ade80;border:1px solid #16a34a;">
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✓ Sauvegarder l'annotation
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</button>
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<button type="button" id="btn-test-lpr" onclick="testLPR()"
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class="px-4 py-2 rounded font-medium"
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style="background:#1e293b;color:#60a5fa;border:1px solid #2563eb;">
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🔍 Tester LPR
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</button>
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<span id="save-warning" class="text-yellow-400 text-sm hidden">⚠ Dessine d'abord le rectangle autour de la plaque</span>
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<span id="save-ok" class="text-green-400 text-sm hidden">✓ Rectangle défini — prêt à sauvegarder</span>
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</div>
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</div>
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</form>
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<!-- LPR test results -->
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<div id="lpr-result" class="card p-4 hidden"></div>
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</div>
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</div>
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@@ -442,6 +450,109 @@
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redraw();
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};
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// ── LPR test ───────────────────────────────────────────────────────────
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async function testLPR() {
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const framePathEl = document.getElementById('input-frame');
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const bboxEl = document.getElementById('input-bbox');
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const resultEl = document.getElementById('lpr-result');
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const btn = document.getElementById('btn-test-lpr');
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if (!framePathEl.value) {
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resultEl.innerHTML = '<p class="text-yellow-400 text-sm">Aucune frame sélectionnée.</p>';
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resultEl.classList.remove('hidden');
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return;
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}
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btn.disabled = true;
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btn.textContent = '⏳ En cours…';
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resultEl.innerHTML = '<p class="text-slate-500 text-sm">Requête en cours…</p>';
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resultEl.classList.remove('hidden');
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const fd = new FormData();
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fd.append('frame_path', framePathEl.value);
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fd.append('bbox', bboxEl.value);
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try {
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const resp = await fetch('/event/{{ ev.id }}/test-lpr', { method: 'POST', body: fd });
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const data = await resp.json();
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renderLPRResult(data);
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} catch(e) {
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resultEl.innerHTML = `<p class="text-red-400 text-sm">Erreur: ${e.message}</p>`;
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} finally {
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btn.disabled = false;
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btn.textContent = '🔍 Tester LPR';
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}
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}
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function usePlate(plate) {
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document.getElementById('input-plate').value = plate;
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}
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function renderLPRResult(data) {
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const resultEl = document.getElementById('lpr-result');
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if (data.error) {
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resultEl.innerHTML = `<p class="text-red-400 text-sm">${data.error}</p>`;
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return;
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}
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let html = '<div class="space-y-4">';
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html += '<label class="block">Résultats LPR</label>';
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// PlateRecognizer
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if (data.platerecognizer) {
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const pr = data.platerecognizer;
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const pct = Math.round(pr.conf * 100);
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const col = pr.conf >= 0.7 ? '#4ade80' : pr.conf >= 0.4 ? '#fbbf24' : '#f87171';
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html += `<div>
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<div class="text-xs text-slate-500 mb-1 uppercase tracking-wide font-semibold">PlateRecognizer API</div>
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<div class="flex items-center gap-3 flex-wrap">
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<span class="font-mono text-lg font-bold px-4 py-1 rounded" style="background:#1e3a5f;color:#60a5fa;border:1px solid #2563eb;">${pr.plate || '—'}</span>
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<span class="text-sm font-mono" style="color:${col};">${pct}%</span>
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${pr.plate ? `<button type="button" onclick="usePlate('${pr.plate}')" class="btn-ghost text-xs py-1 px-2 text-blue-400">↑ Utiliser</button>` : ''}
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</div>
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${pr.raw && pr.raw.replace(/-/g,'') !== pr.plate.replace(/-/g,'') ? `<p class="text-slate-500 text-xs mt-1">Brut API: ${pr.raw}</p>` : ''}
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</div>`;
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} else if (data.has_pr) {
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html += `<div>
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<div class="text-xs text-slate-500 mb-1 uppercase tracking-wide font-semibold">PlateRecognizer API</div>
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<p class="text-slate-500 text-sm">Aucune plaque détectée</p>
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</div>`;
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}
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// Local OCR
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if (data.local) {
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const loc = data.local;
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const pct = Math.round(loc.conf * 100);
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const col = loc.conf >= 0.7 ? '#4ade80' : loc.conf >= 0.4 ? '#fbbf24' : '#f87171';
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const src = loc.source === 'perspective_corrected' ? 'correction perspective ✓' : 'recadrage direct';
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html += `<div>
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<div class="text-xs text-slate-500 mb-1 uppercase tracking-wide font-semibold">OCR local — ${src}</div>
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<div class="flex items-center gap-3 flex-wrap mb-2">
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<span class="font-mono text-lg font-bold px-4 py-1 rounded" style="background:#1e3a5f;color:#60a5fa;border:1px solid #2563eb;">${loc.plate || '—'}</span>
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<span class="text-sm font-mono" style="color:${col};">${pct}%</span>
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${loc.plate ? `<button type="button" onclick="usePlate('${loc.plate}')" class="btn-ghost text-xs py-1 px-2 text-blue-400">↑ Utiliser</button>` : ''}
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</div>
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${loc.raw && loc.raw !== loc.plate ? `<p class="text-slate-500 text-xs mb-2">Brut OCR: "${loc.raw}"</p>` : ''}
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${loc.img_b64 ? `<img src="data:image/jpeg;base64,${loc.img_b64}" class="rounded border border-slate-600" style="image-rendering:pixelated;max-height:56px;" title="Image envoyée à l'OCR">` : ''}
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</div>`;
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} else if (data.local_error) {
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html += `<div>
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<div class="text-xs text-slate-500 mb-1 uppercase tracking-wide font-semibold">OCR local</div>
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<p class="text-red-400 text-sm">${data.local_error}</p>
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</div>`;
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} else if (document.getElementById('input-bbox').value) {
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html += `<div>
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<div class="text-xs text-slate-500 mb-1 uppercase tracking-wide font-semibold">OCR local</div>
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<p class="text-slate-500 text-sm">Aucun résultat</p>
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</div>`;
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} else {
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html += `<p class="text-slate-500 text-sm italic">Dessine un rectangle autour de la plaque pour tester l'OCR local.</p>`;
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}
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html += '</div>';
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resultEl.innerHTML = html;
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}
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// ── Form validation ────────────────────────────────────────────────────
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document.getElementById('ann-form').addEventListener('submit', function(e) {
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if (!document.getElementById('input-bbox').value) {
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