code-MIM/animal_data at master · OhyunHighSchoolML/code-MIM · GitHub

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<!DOCTYPE html>
<!-- 12분 30초 -->
<html lang="en">
<head>
  <link href = "https://fonts.googleapis.com/css2? family = Noto + Sans + KR : wght @ 100 & display = swap"rel = "stylesheet">
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta http-equiv="X-UA-Compatible" content="ie=edge">
  <title>Lost Pet Finder</title>
  <style>
  *{
    font-family : 'Noto Sans KR', sans-serif;
    list-style: none;
    text-decoration: none;
    border-collapse: collapse;
    margin: 0px;
    padding: 0px;
    color: #000;
  }

  .intro_bg{
    background-image: url("image/intro.png");
    width: 100%;
    height: 718px;
  }

  .header{
    display: flex;
    width: 1280;
    margin:auto;
    background: red;
    height: 86px;
  }

  .searchArea{
    width: 300px;
    height: 40px;
    background: green;
    border-radius: 5px;
    margin-top: 24px;
  }

  .nav{
    display: flex;
    justify-content: flex-end;
    line-height: 86px;
    width: calc(1280px - 300px);
    background: blue;
  }

  .nav>li{
    margin-left: 84px;
  }
  </style>
</head>
<body>
  <div class="wrap">
    <div class="intro_bg">
      <div class="header">
        <div class="searchArea">
          <form>
            <input type="search" placeholder="Search">
            <span>검색</span>
          </form>
        </div>
        <ul class="nav">
          <li><a href="">HOME</a></li>
          <li><a href="">OWNER</a></li>
          <li><a href="">FINDER</a></li>
          <li><a href="">REWARD</a></li>
        </ul>
        <img src="" alt="">
      </div>
    </div>
  </div>
</body>
</html>


<!--
<div>Teachable Machine Image Model</div>
<button type="button" onclick="init()">Start</button>
<div id="webcam-container"></div>
<div id="label-container"></div>
<script src="[https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js](https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js)"></script>
<script src="[https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js](https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js)"></script>
<script type="text/javascript">
// More API functions here:
// [https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image](https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image)


// the link to your model provided by Teachable Machine export panel
const URL = "./my_model/";

let model, webcam, labelContainer, maxPredictions;

// Load the image model and setup the webcam
async function init() {
    const modelURL = URL + "model.json";
    const metadataURL = URL + "metadata.json";

    // load the model and metadata
    // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
    // or files from your local hard drive
    // Note: the pose library adds "tmImage" object to your window (window.tmImage)
    model = await tmImage.load(modelURL, metadataURL);
    maxPredictions = model.getTotalClasses();

    // Convenience function to setup a webcam
    const flip = true; // whether to flip the webcam
    webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip
    await webcam.setup(); // request access to the webcam
    await webcam.play();
    window.requestAnimationFrame(loop);

    // append elements to the DOM
    document.getElementById("webcam-container").appendChild(webcam.canvas);
    labelContainer = document.getElementById("label-container");
    for (let i = 0; i < maxPredictions; i++) { // and class labels
        labelContainer.appendChild(document.createElement("div"));
    }
}

async function loop() {
    webcam.update(); // update the webcam frame
    await predict();
    window.requestAnimationFrame(loop);
}

// run the webcam image through the image model
async function predict() {
    // predict can take in an image, video or canvas html element
    const prediction = await model.predict(webcam.canvas);
    for (let i = 0; i < maxPredictions; i++) {
        const classPrediction =
            prediction[i].className + ": " + prediction[i].probability.toFixed(2);
        labelContainer.childNodes[i].innerHTML = classPrediction;
    }
}

```

</script>
 -->


<!--
<div>Teachable Machine Image Model</div>
<button type="button" onclick="init()">Start</button>
<div id="webcam-container"></div>
<div id="label-container"></div>
<script src="[https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js](https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js)"></script>
<script src="[https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js](https://cdn.jsdelivr.net/npm/@teachablemachine/image@0.8/dist/teachablemachine-image.min.js)"></script>
<script type="text/javascript">
// More API functions here:
// [https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image](https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image)

```
// the link to your model provided by Teachable Machine export panel
const URL = "./my_model/";

let model, webcam, labelContainer, maxPredictions;

// Load the image model and setup the webcam
async function init() {
    const modelURL = URL + "model.json";
    const metadataURL = URL + "metadata.json";

    // load the model and metadata
    // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
    // or files from your local hard drive
    // Note: the pose library adds "tmImage" object to your window (window.tmImage)
    model = await tmImage.load(modelURL, metadataURL);
    maxPredictions = model.getTotalClasses();

    // Convenience function to setup a webcam
    const flip = true; // whether to flip the webcam
    webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip
    await webcam.setup(); // request access to the webcam
    await webcam.play();
    window.requestAnimationFrame(loop);

    // append elements to the DOM
    document.getElementById("webcam-container").appendChild(webcam.canvas);
    labelContainer = document.getElementById("label-container");
    for (let i = 0; i < maxPredictions; i++) { // and class labels
        labelContainer.appendChild(document.createElement("div"));
    }
}

async function loop() {
    webcam.update(); // update the webcam frame
    await predict();
    window.requestAnimationFrame(loop);
}

// run the webcam image through the image model
async function predict() {
    // predict can take in an image, video or canvas html element
    const prediction = await model.predict(webcam.canvas);
    for (let i = 0; i < maxPredictions; i++) {
        const classPrediction =
            prediction[i].className + ": " + prediction[i].probability.toFixed(2);
        labelContainer.childNodes[i].innerHTML = classPrediction;
    }
}

```

</script>
 -->