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Config input into sequencial data to fake temporal data
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| <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.10.2/p5.js"></script> | ||
| <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.10.2/addons/p5.sound.min.js"></script> | ||
| <script src="https://unpkg.com/[email protected]/dist/ml5.min.js"></script> | ||
| <link rel="stylesheet" type="text/css" href="style.css"> | ||
| <meta charset="utf-8" /> | ||
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| </head> | ||
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| <body> | ||
| <script src="sketch.js"></script> | ||
| <script src="/collect/sketch.js"></script> | ||
| </body> | ||
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| </html> | ||
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| let brain; | ||
| let state = 'waiting'; | ||
| let targetLabel; // Déclarer targetLabel à la portée globale | ||
| let targetLabel; | ||
| let rawData = []; // Stocke les données brutes de l'accéléromètre | ||
| let sequenceLength = 10; // La longueur de la séquence pour chaque axe | ||
| let sequenceCounter = 0; // Compteur pour le nombre de séquences ajoutées | ||
| let maxSequences = 40; // Nombre maximal de séquences à collecter | ||
| let collectionInterval; // Pour stocker l'identifiant de l'intervalle de collecte | ||
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| // Variables pour les données d'accélération | ||
| let accelerationX = 0; | ||
| let accelerationY = 0; | ||
| let accelerationZ = 0; | ||
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| // Charger un son pour le bip (remplacer 'bip.mp3' par le chemin de votre fichier son) | ||
| // Charger un son pour le bip | ||
| let bipSound; | ||
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| function preload() { | ||
| bipSound = loadSound('../sound/bip.mp3'); // Assurez-vous que le fichier 'bip.mp3' est à la racine du projet | ||
| bipSound = loadSound('../sound/bip.mp3'); | ||
| } | ||
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| function setup() { | ||
| createCanvas(640, 480); | ||
| background(255); | ||
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| // Écouter les événements d'accéléromètre | ||
| if (window.DeviceMotionEvent) { | ||
| window.addEventListener('devicemotion', (event) => { | ||
| accelerationX = event.accelerationIncludingGravity.x; | ||
| accelerationY = event.accelerationIncludingGravity.y; | ||
| accelerationZ = event.accelerationIncludingGravity.z; | ||
| }); | ||
| } else { | ||
| console.log("DeviceMotionEvent is not supported"); | ||
| // Configuration du réseau de neurones | ||
| let inputs = []; | ||
| for (let i = 0; i < sequenceLength; i++) { | ||
| inputs.push('x' + i); | ||
| inputs.push('y' + i); | ||
| inputs.push('z' + i); | ||
| } | ||
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| let options = { | ||
| inputs: 3, | ||
| outputs: 4, | ||
| const options = { | ||
| inputs: inputs, | ||
| outputs: 3, // Nombre de labels de classification | ||
| task: 'classification', | ||
| debug: true | ||
| }; | ||
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| console.log(options) | ||
| brain = ml5.neuralNetwork(options); | ||
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| // Création et configuration des boutons de collecte pour différents labels | ||
| createCollectButton('Ligne', 'line'); | ||
| createCollectButton('Carré', 'square'); | ||
| createCollectButton('Infini', 'infinite'); | ||
| createCollectButton('Rien', 'nothing'); | ||
| // Boutons pour la collecte de données | ||
| createCollectButton('Ligne', 'line', 1); | ||
| createCollectButton('Circle', 'circle', 2); | ||
| createCollectButton('Rien', 'nothing', 3); | ||
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| // Bouton pour télécharger les données | ||
| let downloadButton = createButton('Télécharger les données'); | ||
| downloadButton.position(19, 19); | ||
| downloadButton.mousePressed(() => { | ||
| brain.saveData('collectedData'); | ||
| }); | ||
| } | ||
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| function createCollectButton(buttonText, label) { | ||
| function collectData(x, y, z) { | ||
| // Arrondir les valeurs à deux décimales | ||
| let roundedX = parseFloat(x.toFixed(3)); | ||
| let roundedY = parseFloat(y.toFixed(3)); | ||
| let roundedZ = parseFloat(z.toFixed(3)); | ||
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| rawData.push({ x: roundedX, y: roundedY, z: roundedZ }); | ||
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| if (rawData.length === sequenceLength) { | ||
| let dataObject = {}; | ||
| for (let i = 0; i < sequenceLength; i++) { | ||
| dataObject['x' + i] = rawData[i].x; | ||
| dataObject['y' + i] = rawData[i].y; | ||
| dataObject['z' + i] = rawData[i].z; | ||
| } | ||
| console.log(dataObject) | ||
| brain.addData(dataObject, { label: targetLabel }); | ||
| sequenceCounter++; | ||
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| rawData = []; // Réinitialiser pour la prochaine séquence | ||
| if (sequenceCounter >= maxSequences) { | ||
| clearInterval(collectionInterval); // Arrêtez la collecte | ||
| state = 'waiting'; | ||
| sequenceCounter = 0; // Réinitialiser pour la prochaine fois | ||
| bipSound.play(); // Jouer le son pour indiquer la fin | ||
| } | ||
| } | ||
| } | ||
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| function createCollectButton(buttonText, label, index) { | ||
| let button = createButton(buttonText); | ||
| button.position(19, (19 + index * 40)); | ||
| button.mousePressed(() => { | ||
| prepareToCollect(label); | ||
| }); | ||
| } | ||
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| function prepareToCollect(label) { | ||
| if (state === 'waiting') { | ||
| if (state === 'waiting' && sequenceCounter < maxSequences) { | ||
| targetLabel = label; | ||
| state = 'preparing'; | ||
| console.log(`Preparing to collect for label: ${label}`); | ||
| setTimeout(() => startCollecting(label), 3000); // Commence la collecte après un délai de 3 secondes | ||
| setTimeout(() => startCollecting(), 1500); | ||
| } | ||
| } | ||
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| function startCollecting(targetLabel) { | ||
| bipSound.play(); // Jouer le bip sonore | ||
| console.log(`Collecting for label: ${targetLabel}`); | ||
| state = 'collecting'; | ||
| setTimeout(stopCollecting, 15000); // Arrête la collecte après 15 secondes | ||
| } | ||
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| function stopCollecting() { | ||
| bipSound.play(); // Jouer le bip sonore | ||
| state = 'waiting'; | ||
| console.log('Collection stopped'); | ||
| brain.saveData('collectedData.json'); // Sauvegarde les données collectées | ||
| function startCollecting() { | ||
| if (state === 'preparing') { | ||
| bipSound.play(); | ||
| state = 'collecting'; | ||
| collectionInterval = setInterval(() => { | ||
| if (window.DeviceMotionEvent && window.latestDeviceMotionEvent) { | ||
| let event = window.latestDeviceMotionEvent; | ||
| collectData( | ||
| event.accelerationIncludingGravity.x, | ||
| event.accelerationIncludingGravity.y, | ||
| event.accelerationIncludingGravity.z | ||
| ); | ||
| } | ||
| }, 100); | ||
| } | ||
| } | ||
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| function draw() { | ||
| background(255); | ||
| fill(0); | ||
| noStroke(); | ||
| textSize(32); | ||
| textSize(16); | ||
| textAlign(LEFT, TOP); | ||
| text(`X: ${accelerationX.toFixed(2)}`, 10, 40); | ||
| text(`Y: ${accelerationY.toFixed(2)}`, 10, 80); | ||
| text(`Z: ${accelerationZ.toFixed(2)}`, 10, 120); | ||
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| if (state === 'collecting') { | ||
| let inputs = [accelerationX, accelerationY, accelerationZ]; | ||
| let target = [targetLabel]; | ||
| brain.addData(inputs, target); | ||
| } | ||
| text('Collecte: ' + state, 50, 50); | ||
| text('Label: ' + targetLabel, 50, 70); | ||
| text('Séquences collectées: ' + sequenceCounter, 50, 90); | ||
| } | ||
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| // Gestionnaire d'événements pour l'accéléromètre | ||
| if (window.DeviceMotionEvent) { | ||
| window.addEventListener('devicemotion', (event) => { | ||
| window.latestDeviceMotionEvent = event; | ||
| }); | ||
| } |
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| {"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,3],"dtype":"float32"}},{"class_name":"Dense","config":{"units":4,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[3,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,4],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[4],"dtype":"float32"}]}]} | ||
| {"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,30],"dtype":"float32"}},{"class_name":"Dense","config":{"units":3,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[30,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,3],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[3],"dtype":"float32"}]}]} |
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| {"inputUnits":[3],"outputUnits":4,"inputs":{"0":{"dtype":"number","min":-11.3,"max":18.900000000000002},"1":{"dtype":"number","min":-10.600000000000001,"max":4.4},"2":{"dtype":"number","min":-0.30000000000000004,"max":25}},"outputs":{"0":{"dtype":"string","min":0,"max":1,"uniqueValues":["nothing","line","square","infinite"],"legend":{"nothing":[1,0,0,0],"line":[0,1,0,0],"square":[0,0,1,0],"infinite":[0,0,0,1]}}},"isNormalized":true} | ||
| {"inputUnits":[30],"outputUnits":3,"inputs":{"x0":{"dtype":"number","min":-6.2,"max":7.8},"y0":{"dtype":"number","min":-3.9,"max":3.2},"z0":{"dtype":"number","min":4.7,"max":14},"x1":{"dtype":"number","min":-7.9,"max":6.7},"y1":{"dtype":"number","min":-3.7,"max":3.4},"z1":{"dtype":"number","min":2.1,"max":14.4},"x2":{"dtype":"number","min":-7.8,"max":6.4},"y2":{"dtype":"number","min":-4.1,"max":3.7},"z2":{"dtype":"number","min":2.6,"max":13.2},"x3":{"dtype":"number","min":-6.4,"max":6.9},"y3":{"dtype":"number","min":-3.4,"max":3.6},"z3":{"dtype":"number","min":3.9,"max":14.1},"x4":{"dtype":"number","min":-6.4,"max":8.7},"y4":{"dtype":"number","min":-4.4,"max":3.7},"z4":{"dtype":"number","min":4.1,"max":14.1},"x5":{"dtype":"number","min":-6.5,"max":6.7},"y5":{"dtype":"number","min":-4,"max":3.7},"z5":{"dtype":"number","min":2.7,"max":15.1},"x6":{"dtype":"number","min":-7.2,"max":7.6},"y6":{"dtype":"number","min":-3.9,"max":4.1},"z6":{"dtype":"number","min":6.5,"max":15.9},"x7":{"dtype":"number","min":-6.9,"max":6.9},"y7":{"dtype":"number","min":-3.9,"max":4.3},"z7":{"dtype":"number","min":4.8,"max":15.9},"x8":{"dtype":"number","min":-6.6,"max":7.4},"y8":{"dtype":"number","min":-3.5,"max":4},"z8":{"dtype":"number","min":4,"max":14.3},"x9":{"dtype":"number","min":-7,"max":7.5},"y9":{"dtype":"number","min":-3.4,"max":4.3},"z9":{"dtype":"number","min":5.4,"max":13.8}},"outputs":{"label":{"dtype":"string","min":0,"max":1,"uniqueValues":["nothing","circle","line"],"legend":{"nothing":[1,0,0],"circle":[0,1,0],"line":[0,0,1]}}},"isNormalized":true} |
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| @@ -0,0 +1 @@ | ||
| {"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,30],"dtype":"float32"}},{"class_name":"Dense","config":{"units":3,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[30,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,3],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[3],"dtype":"float32"}]}]} |
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| {"inputUnits":[30],"outputUnits":3,"inputs":{"x0":{"dtype":"number","min":-6.2,"max":7.8},"y0":{"dtype":"number","min":-3.9,"max":3.2},"z0":{"dtype":"number","min":4.7,"max":14},"x1":{"dtype":"number","min":-7.9,"max":6.7},"y1":{"dtype":"number","min":-3.7,"max":3.4},"z1":{"dtype":"number","min":2.1,"max":14.4},"x2":{"dtype":"number","min":-7.8,"max":6.4},"y2":{"dtype":"number","min":-4.1,"max":3.7},"z2":{"dtype":"number","min":2.6,"max":13.2},"x3":{"dtype":"number","min":-6.4,"max":6.9},"y3":{"dtype":"number","min":-3.4,"max":3.6},"z3":{"dtype":"number","min":3.9,"max":14.1},"x4":{"dtype":"number","min":-6.4,"max":8.7},"y4":{"dtype":"number","min":-4.4,"max":3.7},"z4":{"dtype":"number","min":4.1,"max":14.1},"x5":{"dtype":"number","min":-6.5,"max":6.7},"y5":{"dtype":"number","min":-4,"max":3.7},"z5":{"dtype":"number","min":2.7,"max":15.1},"x6":{"dtype":"number","min":-7.2,"max":7.6},"y6":{"dtype":"number","min":-3.9,"max":4.1},"z6":{"dtype":"number","min":6.5,"max":15.9},"x7":{"dtype":"number","min":-6.9,"max":6.9},"y7":{"dtype":"number","min":-3.9,"max":4.3},"z7":{"dtype":"number","min":4.8,"max":15.9},"x8":{"dtype":"number","min":-6.6,"max":7.4},"y8":{"dtype":"number","min":-3.5,"max":4},"z8":{"dtype":"number","min":4,"max":14.3},"x9":{"dtype":"number","min":-7,"max":7.5},"y9":{"dtype":"number","min":-3.4,"max":4.3},"z9":{"dtype":"number","min":5.4,"max":13.8}},"outputs":{"label":{"dtype":"string","min":0,"max":1,"uniqueValues":["nothing","circle","line"],"legend":{"nothing":[1,0,0],"circle":[0,1,0],"line":[0,0,1]}}},"isNormalized":true} |
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| </head> | ||
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| <body> | ||
| <script src="run.js"></script> | ||
| <script src="/run/sketch.js"></script> | ||
| </body> | ||
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| </html> | ||
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