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index.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Perros vs. Gatos</title>
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet"
integrity="sha384-EVSTQN3/azprG1Anm3QDgpJLIm9Nao0Yz1ztcQTwFspd3yD65VohhpuuCOmLASjC" crossorigin="anonymous" />
<style>
#resultado {
font-weight: bold;
font-size: 6rem;
text-align: center;
}
</style>
</head>
<body>
<main>
<div class="px-4 py-2 my-1 text-center border-bottom">
<img class="d-block mx-auto mb-2" src="./img/portada-readme.png" alt="" width="180" height="80" />
<h1 class="display-5 fw-bold">Perros y gatos</h1>
<div class="col-lg-6 mx-auto">
<p class="lead mb-0">
Clasificación de imágenes (Perro o Gato) usando la
cámara web utilizando Tensorflow.js, puede tardar unos segundos en cargar el modelo neuronal.
</p>
<p class="lead mb-4">2021</p>
</div>
</div>
<div class="b-example-divider"></div>
<div class="container mt-5">
<div class="row">
<div class="col-8 col-md-4 offset-md-4 text-center">
<video id="video" playsinline autoplay style="width: 1px"></video>
<button class="btn btn-primary mb-2" id="cambiar-camara" onclick="cambiarCamara();">
Cambiar camara
</button>
<canvas id="canvas" width="400" height="400" style="max-width: 100%"></canvas>
<span>A continuación, se transforma la imagen a 100x100 pixeles
(dimensión de la red neuronal), aunque el canvas sea de 150x150 para su mejor visualización</span>
</div>
<div class="col-4 col-md-4 offset-md-4 text-center">
<canvas clas="justify-content-center" id="otrocanvas" width="150" height="150"></canvas>
</div>
<div class="row">
<h3 class="text-center" id="resultado"></h3>
</div>
</div>
</div>
<div class="b-example-divider"></div>
<div class="bg-dark text-secondary mt-5 px-4 py-2 text-center">
<div class="py-5">
<h1 class="display-5 fw-bold text-white">Subido en</h1>
<div class="col-lg-6 mx-auto">
<p class="fs-5 mb-4">2021</p>
</div>
</div>
</div>
<div class="b-example-divider mb-0"></div>
</main>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"
integrity="sha384-MrcW6ZMFYlzcLA8Nl+NtUVF0sA7MsXsP1UyJoMp4YLEuNSfAP+JcXn/tWtIaxVXM"
crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script type="text/javascript">
var tamano = 400;
var video = document.getElementById("video");
var canvas = document.getElementById("canvas");
var otrocanvas = document.getElementById("otrocanvas");
var ctx = canvas.getContext("2d");
var currentStream = null;
var facingMode = "user";
var modelo = null;
(async () => {
console.log("Cargando modelo...");
modelo = await tf.loadLayersModel("./modelo-red-neuronal/model.json");
console.log("Modelo cargado");
})();
window.onload = function () {
mostrarCamara();
};
function mostrarCamara() {
var opciones = {
audio: false,
video: {
width: tamano,
height: tamano,
},
};
if (navigator.mediaDevices.getUserMedia) {
navigator.mediaDevices
.getUserMedia(opciones)
.then(function (stream) {
currentStream = stream;
video.srcObject = currentStream;
procesarCamara();
predecir();
})
.catch(function (err) {
alert("No se pudo utilizar la camara :(");
console.log(err);
alert(err);
});
} else {
alert("No existe la funcion getUserMedia");
}
}
function cambiarCamara() {
if (currentStream) {
currentStream.getTracks().forEach((track) => {
track.stop();
});
}
facingMode = facingMode == "user" ? "environment" : "user";
var opciones = {
audio: false,
video: {
facingMode: facingMode,
width: tamano,
height: tamano,
},
};
navigator.mediaDevices
.getUserMedia(opciones)
.then(function (stream) {
currentStream = stream;
video.srcObject = currentStream;
})
.catch(function (err) {
console.log("Oops, hubo un error", err);
});
}
function procesarCamara() {
ctx.drawImage(video, 0, 0, tamano, tamano, 0, 0, tamano, tamano);
setTimeout(procesarCamara, 20);
}
function predecir() {
if (modelo != null) {
resample_single(canvas, 100, 100, otrocanvas);
//Hacer la predicción
var ctx2 = otrocanvas.getContext("2d");
var imgData = ctx2.getImageData(0, 0, 100, 100);
var arr = [];
var arr100 = [];
// El siguiente for tiene aumento p+=4 porque las posiciones 0,1,2 corresponden a colores R,G,B y la posición 3 corresponde a Alfa del mismo pixel
//Al cambiar cada 4 se asegura de evaluar un nuevo pixel
for (var p = 0; p < imgData.data.length; p += 4) {
var rojo = imgData.data[p] / 255;
var verde = imgData.data[p + 1] / 255;
var azul = imgData.data[p + 2] / 255;
var gris = (rojo + verde + azul) / 3;
arr100.push([gris]);
if (arr100.length == 100) {
arr.push(arr100);
arr100 = [];
}
}
arr = [arr];
var tensor = tf.tensor4d(arr);
var resultado = modelo.predict(tensor).dataSync();
var respuesta;
if (resultado <= 0.5) {
respuesta = "Resultado: Gato";
} else {
respuesta = "Resultado: Perro";
}
document.getElementById("resultado").innerHTML = respuesta;
}
setTimeout(predecir, 150);
}
/**
* Hermite resize - fast image resize/resample using Hermite filter. 1 cpu version!
*
* @param {HtmlElement} canvas
* @param {int} width
* @param {int} height
* @param {boolean} resize_canvas if true, canvas will be resized. Optional.
* Cambiado por RT, resize canvas ahora es donde se pone el chiqitillllllo
*/
function resample_single(canvas, width, height, resize_canvas) {
var width_source = canvas.width;
var height_source = canvas.height;
width = Math.round(width);
height = Math.round(height);
var ratio_w = width_source / width;
var ratio_h = height_source / height;
var ratio_w_half = Math.ceil(ratio_w / 2);
var ratio_h_half = Math.ceil(ratio_h / 2);
var ctx = canvas.getContext("2d");
var ctx2 = resize_canvas.getContext("2d");
var img = ctx.getImageData(0, 0, width_source, height_source);
var img2 = ctx2.createImageData(width, height);
var data = img.data;
var data2 = img2.data;
for (var j = 0; j < height; j++) {
for (var i = 0; i < width; i++) {
var x2 = (i + j * width) * 4;
var weight = 0;
var weights = 0;
var weights_alpha = 0;
var gx_r = 0;
var gx_g = 0;
var gx_b = 0;
var gx_a = 0;
var center_y = (j + 0.5) * ratio_h;
var yy_start = Math.floor(j * ratio_h);
var yy_stop = Math.ceil((j + 1) * ratio_h);
for (var yy = yy_start; yy < yy_stop; yy++) {
var dy = Math.abs(center_y - (yy + 0.5)) / ratio_h_half;
var center_x = (i + 0.5) * ratio_w;
var w0 = dy * dy; //pre-calc part of w
var xx_start = Math.floor(i * ratio_w);
var xx_stop = Math.ceil((i + 1) * ratio_w);
for (var xx = xx_start; xx < xx_stop; xx++) {
var dx = Math.abs(center_x - (xx + 0.5)) / ratio_w_half;
var w = Math.sqrt(w0 + dx * dx);
if (w >= 1) {
//pixel too far
continue;
}
//hermite filter
weight = 2 * w * w * w - 3 * w * w + 1;
var pos_x = 4 * (xx + yy * width_source);
//alpha
gx_a += weight * data[pos_x + 3];
weights_alpha += weight;
//colors
if (data[pos_x + 3] < 255)
weight = (weight * data[pos_x + 3]) / 250;
gx_r += weight * data[pos_x];
gx_g += weight * data[pos_x + 1];
gx_b += weight * data[pos_x + 2];
weights += weight;
}
}
data2[x2] = gx_r / weights;
data2[x2 + 1] = gx_g / weights;
data2[x2 + 2] = gx_b / weights;
data2[x2 + 3] = gx_a / weights_alpha;
}
}
ctx2.putImageData(img2, 0, 0);
}
</script>
</body>
</html>