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// Copyright (c) 2018 ml5
//
// This software is released under the MIT License.
// https://opensource.org/licenses/MIT
/* ===
ml5 Example
Creating a regression extracting features of MobileNet. Build with p5js.
=== */
let featureExtractor;
let regressor;
let video;
let loss;
let slider;
let samples = 0;
let rectSize = 50;
let lerpedResult = 0.5;
function setup() {
createCanvas(640,480);
// Create a video element
video = createCapture(VIDEO);
video.size(640,480);
video.hide();
// Extract the features from MobileNet
featureExtractor = ml5.featureExtractor('MobileNet', modelReady);
// Create a new regressor using those features and give the video we want to use
regressor = featureExtractor.regression(video, videoReady);
// Create the UI buttons
setupButtons();
rectMode(CENTER);
}
function draw() {
image(video, 0, 0, width, height);
noStroke();
fill(255, 0, 0,100);
rectSize = slider.value()*400;
rectSize = constrain(rectSize, 0, 400);
rect(width/2, height/2, rectSize, rectSize);
textSize(slider.value()*100);
//text("Hello", 50, 100);
}
// A function to be called when the model has been loaded
function modelReady() {
select('#modelStatus').html('Model loaded!');
}
// A function to be called when the video has loaded
function videoReady() {
select('#videoStatus').html('Video ready!');
}
// Classify the current frame.
function predict() {
regressor.predict(gotResults);
}
// A util function to create UI buttons
function setupButtons() {
slider = select('#slider');
select('#addSample').mousePressed(function() {
regressor.addImage(slider.value());
select('#amountOfSamples').html(samples++);
});
// Train Button
select('#train').mousePressed(function() {
regressor.train(function(lossValue) {
if (lossValue) {
loss = lossValue;
select('#loss').html('Loss: ' + loss);
} else {
select('#loss').html('Done Training! Final Loss: ' + loss);
}
});
});
// Predict Button
select('#buttonPredict').mousePressed(predict);
}
// Show the results
function gotResults(err, result) {
if (err) {
console.error(err);
}
lerpedResult = lerp(lerpedResult, result, 0.50);
slider.value(lerpedResult);
predict();
}