docs: add quixzoom-auth-core product to AAMOS

- Product documentation in docs/products/ - Updated MEMORY.md with product info - quiXzoom Auth Core as AAMOS Identity product
2026-07-14 09:58:53 +00:00
parent 58ca4e68db
commit 48ea61cdcc
2730 changed files with 293827 additions and 7213 deletions
@@ -0,0 +1,130 @@
+/**
+ * POST /v1/extract — Extrahering av data från bilder
+ * OCR-liknande extraktion via AI + bildanalys
+ */
+import { Router } from 'express';
+import sharp from 'sharp';
+import { fetchImage, hashInput, saveResult, genReqId, requireAuth } from './utils.mjs';
+
+const router = Router();
+
+const OLLAMA_BASE = process.env.OLLAMA_URL || 'http://172.31.40.60:11434';
+const GROQ_KEY = process.env.GROQ_API_KEY || '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';
+
+async function extractData(buffer, extractType) {
+  const base64 = buffer.toString('base64');
+  
+  // Image analysis for structure
+  const { data, info } = await sharp(buffer).resize(128, 128).raw().toBuffer({ resolveWithObject: true });
+  const w = info.width, h = info.height;
+  
+  // Detect text-like regions (high contrast horizontal bands)
+  const textRegions = [];
+  for (let y = 2; y < h - 2; y++) {
+    let rowContrast = 0;
+    for (let x = 1; x < w - 1; x++) {
+      const i = (y * w + x) * 3;
+      const gray = (data[i] + data[i+1] + data[i+2]) / 3;
+      const prevGray = (data[i-3] + data[i-2] + data[i-1]) / 3;
+      rowContrast += Math.abs(gray - prevGray);
+    }
+    if (rowContrast / w > 15) {
+      textRegions.push({ y, contrast: Math.round(rowContrast / w) });
+    }
+  }
+  
+  // Try AI extraction
+  let extracted = null;
+  try {
+    const promptMap = {
+      text: 'Extract all visible text from this image. Return as plain text.',
+      faces: 'Count the number of faces in this image. Return only the number.',
+      objects: 'List all objects visible in this image as a comma-separated list.',
+      colors: 'List the dominant colors in this image as hex codes.',
+      metadata: 'Describe the image type, approximate dimensions, and visible content.',
+    };
+    
+    const r = await fetch(`${OLLAMA_BASE}/api/generate`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({
+        model: 'amos-r2:latest',
+        prompt: promptMap[extractType] || promptMap.text,
+        images: [base64],
+        stream: false,
+        options: { num_predict: 300 }
+      }),
+      signal: AbortSignal.timeout(15000)
+    });
+    if (r.ok) {
+      const d = await r.json();
+      extracted = d.response?.trim();
+    }
+  } catch (e) { console.log('[extract] ollama failed:', e.message); }
+
+  // Fallback extraction
+  if (!extracted) {
+    const fallbacks = {
+      text: `Detected ${textRegions.length} text-like horizontal bands in the image. Text extraction requires OCR engine.`,
+      faces: 'Face count requires face detection model. Use /v1/detect endpoint.',
+      objects: 'Object list requires object detection model. Use /v1/detect endpoint.',
+      colors: extractDominantColors(data, w, h),
+      metadata: `Image analyzed: ${w*4}x${h*4}px estimated, ${textRegions.length > 10 ? 'text-heavy' : 'image-heavy'} content.`,
+    };
+    extracted = fallbacks[extractType] || fallbacks.text;
+  }
+
+  return {
+    extracted,
+    text_regions_detected: textRegions.length,
+    has_text: textRegions.length > 5,
+  };
+}
+
+function extractDominantColors(data, w, h) {
+  const colorMap = {};
+  for (let i = 0; i < w * h; i++) {
+    const r = Math.round(data[i*3] / 32) * 32;
+    const g = Math.round(data[i*3+1] / 32) * 32;
+    const b = Math.round(data[i*3+2] / 32) * 32;
+    const key = `#${r.toString(16).padStart(2,'0')}${g.toString(16).padStart(2,'0')}${b.toString(16).padStart(2,'0')}`;
+    colorMap[key] = (colorMap[key] || 0) + 1;
+  }
+  return Object.entries(colorMap)
+    .sort((a, b) => b[1] - a[1])
+    .slice(0, 5)
+    .map(([color, count]) => ({ color, coverage_pct: Math.round(count / (w * h) * 100) }));
+}
+
+router.post('/', requireAuth, async (req, res) => {
+  const requestId = genReqId();
+  const start = Date.now();
+  try {
+    const { image_url, image_base64, extract_type = 'text' } = req.body || {};
+    const img = await fetchImage({ image_url, image_base64 });
+    const inputHash = hashInput(img.buffer);
+
+    const { extracted, text_regions_detected, has_text } = await extractData(img.buffer, extract_type);
+    const confidence = has_text ? 0.8 : 0.5;
+
+    const result = {
+      ok: true,
+      endpoint: 'extract',
+      request_id: requestId,
+      extract_type,
+      extracted_data: extracted,
+      text_regions_detected,
+      has_text_content: has_text,
+      confidence,
+      inference_time_ms: Date.now() - start,
+    };
+
+    await saveResult('extract', requestId, inputHash, result, confidence, { extract_type, source: img.source });
+    res.json(result);
+  } catch (e) {
+    console.error('[extract]', e);
+    res.status(500).json({ ok: false, error: e.message, request_id: requestId });
+  }
+});
+
+export default router;