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<title>Code coverage report for objectDetection.js</title>
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<h1><a href="index.html">All files</a> objectDetection.js</h1>
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<div class='fl pad1y space-right2'>
<span class="strong">67.39% </span>
<span class="quiet">Statements</span>
<span class='fraction'>93/138</span>
</div>
<div class='fl pad1y space-right2'>
<span class="strong">48.21% </span>
<span class="quiet">Branches</span>
<span class='fraction'>27/56</span>
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<div class='fl pad1y space-right2'>
<span class="strong">94.11% </span>
<span class="quiet">Functions</span>
<span class='fraction'>16/17</span>
</div>
<div class='fl pad1y space-right2'>
<span class="strong">67.96% </span>
<span class="quiet">Lines</span>
<span class='fraction'>87/128</span>
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<span class="cline-any cline-neutral">&nbsp;</span></td><td class="text"><pre class="prettyprint lang-js">/**
* Object Detection Service
* Uses ONNX Runtime for inference with YOLO-based models
*/
&nbsp;
let ort;
try {
ort = require('onnxruntime-node');
} catch {
console.warn('onnxruntime-node not available, using fallback mode');
}
const sharp = require('sharp');
const path = require('path');
const fs = require('fs').promises;
const winston = require('winston');
&nbsp;
const logger = winston.createLogger({
level: 'info',
format: winston.format.simple(),
transports: [new winston.transports.Console()]
});
&nbsp;
// Component definitions for different object types
const COMPONENT_DEFINITIONS = {
atm: [
{ id: 'card_reader', name: 'Card Reader', critical: true },
{ id: 'pin_pad', name: 'PIN Pad', critical: true },
{ id: 'display', name: 'Display', critical: false },
{ id: 'cash_dispenser', name: 'Cash Dispenser', critical: true },
{ id: 'nfc_reader', name: 'NFC Reader', critical: false },
{ id: 'receipt_printer', name: 'Receipt Printer', critical: false },
{ id: 'camera', name: 'Camera', critical: false },
{ id: 'speaker', name: 'Speaker', critical: false },
{ id: 'skimmer', name: 'Skimmer Device', critical: true, anomaly: true },
{ id: 'extra_device', name: 'Extra Device', critical: true, anomaly: true }
],
charging_station: [
{ id: 'display', name: 'Display', critical: false },
{ id: 'cable', name: 'Cable', critical: true },
{ id: 'connector', name: 'Connector', critical: true },
{ id: 'payment_terminal', name: 'Payment Terminal', critical: true },
{ id: 'status_lights', name: 'Status Lights', critical: false },
{ id: 'emergency_stop', name: 'Emergency Stop', critical: true }
],
parking_meter: [
{ id: 'display', name: 'Display', critical: false },
{ id: 'coin_slot', name: 'Coin Slot', critical: true },
{ id: 'card_reader', name: 'Card Reader', critical: true },
{ id: 'receipt_printer', name: 'Receipt Printer', critical: false },
{ id: 'keypad', name: 'Keypad', critical: true },
{ id: 'qr_scanner', name: 'QR Scanner', critical: false }
],
defibrillator: [
{ id: 'cabinet', name: 'Cabinet', critical: true },
{ id: 'status_indicator', name: 'Status Indicator', critical: true },
{ id: 'access_handle', name: 'Access Handle', critical: true },
{ id: 'instructions', name: 'Instructions', critical: false },
{ id: 'speaker', name: 'Speaker', critical: false }
]
};
&nbsp;
class ObjectDetectionService {
constructor() {
this.sessions = new Map();
this.modelPath = process.env.MODEL_PATH || './data/models';
}
&nbsp;
async loadModel(objectType) {
const modelFile = path.join(this.modelPath, `${objectType}_detection.onnx`);
try {
await fs.access(modelFile);
} catch {
logger.warn(`Model not found for ${objectType}, using generic`);
// Fallback to generic model
return null;
}
&nbsp;
<span class="cstat-no" title="statement not covered" > try {</span>
<span class="cstat-no" title="statement not covered" > if (!ort) <span class="cstat-no" title="statement not covered" >throw new Error('ONNX not available');</span></span>
const session = <span class="cstat-no" title="statement not covered" >await ort.InferenceSession.create(modelFile);</span>
<span class="cstat-no" title="statement not covered" > this.sessions.set(objectType, session);</span>
<span class="cstat-no" title="statement not covered" > logger.info(`Loaded model for ${objectType}`);</span>
<span class="cstat-no" title="statement not covered" > return session;</span>
} catch (error) {
<span class="cstat-no" title="statement not covered" > logger.error(`Failed to load model for ${objectType}:`, error);</span>
<span class="cstat-no" title="statement not covered" > return null;</span>
}
}
&nbsp;
async detect(imageBuffer, objectType, options = {}) {
const startTime = Date.now();
// Get or load model
let session = this.sessions.get(objectType);
<span class="missing-if-branch" title="else path not taken" >E</span>if (!session) {
session = await this.loadModel(objectType);
}
&nbsp;
// Preprocess image
const { tensor, originalSize } = await this.preprocess(imageBuffer);
let detections = [];
<span class="missing-if-branch" title="if path not taken" >I</span>if (session) {
// Run ONNX inference
const feeds = <span class="cstat-no" title="statement not covered" >{ input: tensor };</span>
const results = <span class="cstat-no" title="statement not covered" >await session.run(feeds);</span>
<span class="cstat-no" title="statement not covered" > detections = this.postprocess(results, originalSize, objectType, options);</span>
} else {
// Fallback: Simulate detection for testing
detections = this.simulateDetection(objectType, options);
}
&nbsp;
const duration = Date.now() - startTime;
return {
detections,
modelVersion: 'v1.0.0',
inferenceTime: duration,
imageSize: originalSize
};
}
&nbsp;
async preprocess(imageBuffer) {
// Resize to model input size (typically 640x640 for YOLO)
const processed = await sharp(imageBuffer)
.resize(640, 640, { fit: 'contain', background: { r: 114, g: 114, b: 114 } })
.raw()
.toBuffer({ resolveWithObject: true });
&nbsp;
const { data, info } = processed;
// Convert to float32 and normalize
const floatData = new Float32Array(640 * 640 * 3);
for (let i = 0; i &lt; data.length; i += 3) {
floatData[i / 3 * 3] = data[i] / 255.0;
floatData[i / 3 * 3 + 1] = data[i + 1] / 255.0;
floatData[i / 3 * 3 + 2] = data[i + 2] / 255.0;
}
&nbsp;
// Create tensor [1, 3, 640, 640] if ONNX is available
let tensor = null;
<span class="missing-if-branch" title="if path not taken" >I</span>if (ort) {
<span class="cstat-no" title="statement not covered" > try {</span>
<span class="cstat-no" title="statement not covered" > tensor = new ort.Tensor('float32', floatData, [1, 3, 640, 640]);</span>
} catch (e) {
// Fallback if Tensor creation fails
}
}
&nbsp;
return {
tensor,
originalSize: { width: info.width, height: info.height }
};
}
&nbsp;
<span class="fstat-no" title="function not covered" > po</span>stprocess(results, originalSize, objectType, options) {
const { confidenceThreshold = <span class="branch-0 cbranch-no" title="branch not covered" >0.5 </span>} = <span class="cstat-no" title="statement not covered" >options;</span>
const components = <span class="cstat-no" title="statement not covered" >COMPONENT_DEFINITIONS[objectType] || [];</span>
// Parse YOLO output format
// This is a simplified version - real implementation depends on model format
const output = <span class="cstat-no" title="statement not covered" >results.output || results['output0'];</span>
<span class="cstat-no" title="statement not covered" > if (!output) <span class="cstat-no" title="statement not covered" >return [];</span></span>
&nbsp;
const detections = <span class="cstat-no" title="statement not covered" >[];</span>
const data = <span class="cstat-no" title="statement not covered" >output.data;</span>
const dims = <span class="cstat-no" title="statement not covered" >output.dims;</span>
// YOLOv8 output: [batch, 84, 8400] where 84 = 4 bbox + 80 classes
const numDetections = <span class="cstat-no" title="statement not covered" >dims[2];</span>
const numClasses = <span class="cstat-no" title="statement not covered" >dims[1] - 4;</span>
<span class="cstat-no" title="statement not covered" > for (let i = <span class="cstat-no" title="statement not covered" >0;</span> i &lt; numDetections; i++) {</span>
const offset = <span class="cstat-no" title="statement not covered" >i * (4 + numClasses);</span>
const x = <span class="cstat-no" title="statement not covered" >data[offset];</span>
const y = <span class="cstat-no" title="statement not covered" >data[offset + 1];</span>
const w = <span class="cstat-no" title="statement not covered" >data[offset + 2];</span>
const h = <span class="cstat-no" title="statement not covered" >data[offset + 3];</span>
// Find best class
let bestScore = <span class="cstat-no" title="statement not covered" >0;</span>
let bestClass = <span class="cstat-no" title="statement not covered" >-1;</span>
<span class="cstat-no" title="statement not covered" > for (let c = <span class="cstat-no" title="statement not covered" >0;</span> c &lt; numClasses &amp;&amp; c &lt; components.length; c++) {</span>
const score = <span class="cstat-no" title="statement not covered" >data[offset + 4 + c];</span>
<span class="cstat-no" title="statement not covered" > if (score &gt; bestScore) {</span>
<span class="cstat-no" title="statement not covered" > bestScore = score;</span>
<span class="cstat-no" title="statement not covered" > bestClass = c;</span>
}
}
<span class="cstat-no" title="statement not covered" > if (bestScore &gt; confidenceThreshold &amp;&amp; bestClass &gt;= 0) {</span>
const component = <span class="cstat-no" title="statement not covered" >components[bestClass];</span>
<span class="cstat-no" title="statement not covered" > if (component) {</span>
<span class="cstat-no" title="statement not covered" > detections.push({</span>
componentType: component.id,
componentName: component.name,
confidence: bestScore,
boundingBox: {
x: (x - w / 2) / 640,
y: (y - h / 2) / 640,
width: w / 640,
height: h / 640
},
critical: component.critical || false
});
}
}
}
&nbsp;
// Apply NMS (Non-Maximum Suppression)
<span class="cstat-no" title="statement not covered" > return this.applyNMS(detections, 0.45);</span>
}
&nbsp;
applyNMS(detections, iouThreshold) {
// Sort by confidence
detections.sort((a, b) =&gt; b.confidence - a.confidence);
const kept = [];
for (const det of detections) {
let shouldKeep = true;
for (const keptDet of kept) {
if (det.componentType === keptDet.componentType) {
const iou = this.calculateIoU(det.boundingBox, keptDet.boundingBox);
<span class="missing-if-branch" title="else path not taken" >E</span>if (iou &gt; iouThreshold) {
shouldKeep = false;
break;
}
}
}
if (shouldKeep) {
kept.push(det);
}
}
return kept;
}
&nbsp;
calculateIoU(box1, box2) {
const x1 = Math.max(box1.x, box2.x);
const y1 = Math.max(box1.y, box2.y);
const x2 = Math.min(box1.x + box1.width, box2.x + box2.width);
const y2 = Math.min(box1.y + box1.height, box2.y + box2.height);
const intersection = Math.max(0, x2 - x1) * Math.max(0, y2 - y1);
const area1 = box1.width * box1.height;
const area2 = box2.width * box2.height;
const union = area1 + area2 - intersection;
return union &gt; 0 ? intersection / union : <span class="branch-1 cbranch-no" title="branch not covered" >0;</span>
}
&nbsp;
simulateDetection(objectType, options) {
// For testing without a real model
const components = COMPONENT_DEFINITIONS[objectType] || [];
return components.map(comp =&gt; ({
componentType: comp.id,
componentName: comp.name,
confidence: 0.85 + Math.random() * 0.14,
boundingBox: {
x: Math.random() * 0.7,
y: Math.random() * 0.7,
width: 0.1 + Math.random() * 0.2,
height: 0.1 + Math.random() * 0.2
},
critical: comp.critical || false
}));
}
&nbsp;
async detectAnomalies(detections, referenceDetections, objectType) {
const anomalies = [];
const riskRules = this.getRiskRules(objectType);
// Check for new components
const currentTypes = new Set(detections.map(d =&gt; d.componentType));
const referenceTypes = new Set(referenceDetections.map(d =&gt; d.componentType));
for (const det of detections) {
if (!referenceTypes.has(det.componentType)) {
// New component detected
const riskLevel = riskRules[`new_${det.componentType}`] || 'yellow';
anomalies.push({
type: 'new_object',
component: det.componentType,
confidence: det.confidence,
severity: this.riskToSeverity(riskLevel),
description: `New component detected: ${det.componentName}`
});
}
}
// Check for missing components
for (const ref of referenceDetections) {
if (!currentTypes.has(ref.componentType)) {
anomalies.push({
type: 'missing_component',
component: ref.componentType,
confidence: 0.9,
severity: 'high',
description: `Missing component: ${ref.componentName}`
});
}
}
// Check for position changes in critical components
for (const det of detections) {
const ref = referenceDetections.find(r =&gt; r.componentType === det.componentType);
if (ref) {
const distance = this.calculateDistance(det.boundingBox, ref.boundingBox);
<span class="missing-if-branch" title="if path not taken" >I</span>if (distance &gt; 0.1) { // 10% of image size
<span class="cstat-no" title="statement not covered" > anomalies.push({</span>
type: 'position_shift',
component: det.componentType,
confidence: 0.8,
severity: 'medium',
description: `Component position changed: ${det.componentName}`
});
}
}
}
return anomalies;
}
&nbsp;
getRiskRules(objectType) {
const rules = {
atm: {
new_object_near_card_reader: 'orange',
extra_plastic_on_card_reader: 'red',
pin_pad_modified: 'red',
display_changed: 'yellow',
camera_blocked: 'orange'
}
};
return rules[objectType] || <span class="branch-1 cbranch-no" title="branch not covered" >{};</span>
}
&nbsp;
riskToSeverity(risk) {
const map = {
green: 'low',
yellow: 'medium',
orange: 'high',
red: 'critical'
};
return map[risk] || <span class="branch-1 cbranch-no" title="branch not covered" >'medium';</span>
}
&nbsp;
calculateDistance(box1, box2) {
const cx1 = box1.x + box1.width / 2;
const cy1 = box1.y + box1.height / 2;
const cx2 = box2.x + box2.width / 2;
const cy2 = box2.y + box2.height / 2;
return Math.sqrt(Math.pow(cx2 - cx1, 2) + Math.pow(cy2 - cy1, 2));
}
}
&nbsp;
module.exports = { ObjectDetectionService };
&nbsp;</pre></td></tr></table></pre>
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