Object-Detection.html

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Enhanced Object Detection</title>
<link href="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.css" rel="stylesheet">
<style>
  body { font-family: 'Roboto', sans-serif; margin: 2em; color: #3d3d3d; background: #f0f2f5; }
  h1 { color: #007f8b; text-align: center; }
  .container { max-width: 1200px; margin: 0 auto; background: white; padding: 20px; border-radius: 12px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); }
  
  /* Controls Section */
  .controls { display: flex; gap: 20px; flex-wrap: wrap; margin-bottom: 20px; padding: 15px; background: #e6fcfd; border-radius: 8px; align-items: center; }
  .control-group { display: flex; flex-direction: column; min-width: 200px; }
  label { font-weight: bold; font-size: 0.9em; margin-bottom: 5px; color: #007f8b; }
  input[type=range] { width: 100%; }
  .upload-btn { background: #007f8b; color: white; padding: 10px 20px; border-radius: 25px; cursor: pointer; display: inline-block; font-weight: bold; text-align: center; }
  .upload-btn:hover { background: #006069; }
  input[type="file"] { display: none; }

  /* Image Grid */
/* Replace the existing .image-grid and .detect-card styles */

.image-grid {
  /* Disable Grid, use Columns instead */
  display: block; 
  column-count: 3; /* Creates 3 columns like Pinterest */
  column-gap: 20px;
}

/* Responsive: 2 columns on smaller screens */
@media (max-width: 900px) {
  .image-grid { column-count: 2; }
}
@media (max-width: 600px) {
  .image-grid { column-count: 1; }
}

.detect-card { 
  position: relative; 
  border-radius: 8px; 
  overflow: hidden; 
  box-shadow: 0 2px 4px rgba(0,0,0,0.1); 
  background: #000; 
  cursor: pointer; 
  transition: transform 0.2s; 
  z-index: 1; 
  
  /* NEW: Prevents card from splitting across columns */
  break-inside: avoid-column; 
  margin-bottom: 20px; 
}

  .detect-card:hover { transform: scale(1.01); }
  .detect-card img { display: block; width: 100%; height: auto; transition: opacity 0.3s; }
  
  /* Processing State */
  .detect-card.processing { pointer-events: none; } /* Prevent double clicks */
  .detect-card.processing img { opacity: 0.6; filter: grayscale(50%); }

  /* --- NEW: Inference Loading Bar --- */
  .inference-panel {
    position: absolute;
    bottom: 0;
    left: 0;
    width: 100%;
    background: rgba(255, 255, 255, 0.95);
    padding: 15px;
    box-sizing: border-box;
    transform: translateY(100%); /* Hidden by default */
    transition: transform 0.3s cubic-bezier(0.4, 0.0, 0.2, 1);
    z-index: 50;
    border-top: 3px solid #007f8b;
  }
  
  /* Show panel when processing */
  .detect-card.processing .inference-panel {
    transform: translateY(0);
  }

  .inference-status {
    display: flex;
    justify-content: space-between;
    font-weight: bold;
    color: #007f8b;
    margin-bottom: 8px;
    font-size: 0.9rem;
  }

  .progress-track {
    width: 100%;
    height: 6px;
    background: #e0e0e0;
    border-radius: 3px;
    overflow: hidden;
  }

  .progress-bar {
    height: 100%;
    background: #007f8b;
    width: 30%;
    border-radius: 3px;
    animation: loading 1.5s infinite ease-in-out;
  }

  @keyframes loading {
    0% { transform: translateX(-100%); }
    100% { transform: translateX(400%); }
  }
  /* ---------------------------------- */
  
  /* Bounding Boxes */
  .highlighter { position: absolute; border: 2px solid; border-radius: 4px; z-index: 10; pointer-events: none; }
  .label-tag { position: absolute; padding: 2px 6px; color: white; font-size: 11px; font-weight: bold; border-radius: 4px; pointer-events: none; z-index: 11; white-space: nowrap; box-shadow: 0 1px 2px rgba(0,0,0,0.2); }
  
  /* Loading Spinner (Initial Model Load) */
  #loader { position: fixed; top: 50%; left: 50%; transform: translate(-50%, -50%); padding: 20px; background: white; border-radius: 8px; box-shadow: 0 0 20px rgba(0,0,0,0.2); display: none; z-index: 1000; font-weight: bold; }
</style>
</head>
<body>

<div class="container">
  <h1>Smart Object Recognition</h1>
  
  <div class="controls">
    <div class="control-group">
      <label for="imageUpload" class="upload-btn">📂 Upload Image</label>
      <input type="file" id="imageUpload" accept="image/*">
    </div>
    
    <div class="control-group">
      <label>Confidence Threshold: <span id="confValue">50</span>%</label>
      <input type="range" id="confidenceSlider" min="10" max="90" value="50">
      <small>Increase to remove weak guesses.</small>
    </div>

    <div class="control-group">
      <label>Overlap Fix (NMS): <span id="overlapValue">30</span>%</label>
      <input type="range" id="overlapSlider" min="0" max="100" value="30">
      <small>Lower value = Fewer overlapping boxes.</small>
    </div>
  </div>

  <div id="loader">Loading AI Model...</div>

  <div class="image-grid" id="imageContainer">
    <!-- Image 1 -->
    <div class="detect-card">
      <img src="https://assets.codepen.io/9177687/coupledog.jpeg" crossorigin="anonymous" />
    </div>
    
    <!-- Image 2 -->
    <div class="detect-card">
      <img src="https://assets.codepen.io/9177687/doggo.jpeg" crossorigin="anonymous" />
    </div>

    <!-- Image 3 (FIXED: Added wrapper div) -->
    <div class="detect-card">
      <img src="https://tse3.mm.bing.net/th/id/OIP.mIJJ36cXpVujF1wnZnd4VQHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 4 -->
    <div class="detect-card">
      <img src="https://images.pexels.com/photos/23409055/pexels-photo-23409055/free-photo-of-cars-on-street-in-town.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1" crossorigin="anonymous" />
    </div>

    <!-- Image 5 -->
    <div class="detect-card">
      <img src="https://tse4.mm.bing.net/th/id/OIP.bWwaHeR-aoBb3esBRaAEEgHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 6 -->
    <div class="detect-card">
      <img src="https://tse4.mm.bing.net/th/id/OIP.vs_d1C-7n4PoNv0GVlaVDwHaFj?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 7 -->
    <div class="detect-card">
      <img src="https://tse4.mm.bing.net/th/id/OIP.V1zVa5IUI22o0i6gG4or2QHaLH?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 8 -->
    <div class="detect-card">
      <img src="https://th.bing.com/th/id/R.2be55af1ab4a38df1a9b54bf6b68a8bd?rik=f7wiKdc8N42mgA&pid=ImgRaw&r=0" crossorigin="anonymous" />
    </div>

    <!-- Image 9 -->
    <div class="detect-card">
      <img src="https://images.pexels.com/photos/20625972/pexels-photo-20625972.jpeg?cs=srgb&dl=pexels-saturnus99-20625972.jpg&fm=jpg" crossorigin="anonymous" />
    </div>

    <!-- Image 10 -->
    <div class="detect-card">
      <img src="https://tse4.mm.bing.net/th/id/OIP.ZMnNqw1GVTa9HpHvsTYcjQAAAA?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 11 -->
    <div class="detect-card">
      <img src="https://bestbackpacklab.com/wp-content/uploads/2021/05/children-1536x864.jpg" crossorigin="anonymous" />
    </div>

    <!-- Image 12 -->
    <div class="detect-card">
      <img src="https://tse1.explicit.bing.net/th/id/OIP.za2l0WGKXbR4Qkj8phu2UwHaE8?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 13 -->
    <div class="detect-card">
      <img src="https://tse2.mm.bing.net/th/id/OIP.bcOP7ZTpLAyyl3tKpdk5gAHaFB?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 14 -->
    <div class="detect-card">
      <img src="https://tse3.mm.bing.net/th/id/OIP.PC6Fr2mEuGUsEiaNfCSOaAHaE7?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 15 -->
    <div class="detect-card">
      <img src="https://tse3.mm.bing.net/th/id/OIF.EABwKojMHBX0uEfpxor95w?rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>

    <!-- Image 16 -->
    <div class="detect-card">
      <img src="https://tse4.mm.bing.net/th/id/OIP.qliYrfiREN-ydW4DxWYfSgHaE7?w=626&h=417&rs=1&pid=ImgDetMain&o=7&rm=3" crossorigin="anonymous" />
    </div>
  </div>
</div>

<script type="module">
import { ObjectDetector, FilesetResolver } from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.2";

const loader = document.getElementById("loader");
let objectDetector;
let runningMode = "IMAGE";

// SETTINGS
let confidenceThreshold = 0.5;
let overlapThreshold = 0.3; 

// 1. Initialize MediaPipe
const initializeObjectDetector = async () => {
  loader.style.display = "block";
  const vision = await FilesetResolver.forVisionTasks("https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.2/wasm");
  
  objectDetector = await ObjectDetector.createFromOptions(vision, {
    baseOptions: {
      modelAssetPath: `https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite2/float16/1/efficientdet_lite2.tflite`,
      delegate: "GPU"
    },
    scoreThreshold: 0.2, 
    runningMode: runningMode
  });
  
  loader.style.display = "none";
  console.log("Model Loaded: EfficientDet-Lite2");
};

initializeObjectDetector();

// 2. NMS Filter Function
function filterDetections(detections, iouLimit) {
  detections.sort((a, b) => b.categories[0].score - a.categories[0].score);
  
  const selected = [];
  const active = new Array(detections.length).fill(true);

  for (let i = 0; i < detections.length; i++) {
    if (!active[i]) continue;
    if (detections[i].categories[0].score < confidenceThreshold) continue;

    selected.push(detections[i]);
    const boxA = detections[i].boundingBox;

    for (let j = i + 1; j < detections.length; j++) {
      if (!active[j]) continue;
      
      const boxB = detections[j].boundingBox;
      
      const x1 = Math.max(boxA.originX, boxB.originX);
      const y1 = Math.max(boxA.originY, boxB.originY);
      const x2 = Math.min(boxA.originX + boxA.width, boxB.originX + boxB.width);
      const y2 = Math.min(boxA.originY + boxA.height, boxB.originY + boxB.height);

      if (x2 < x1 || y2 < y1) continue; 

      const intersection = (x2 - x1) * (y2 - y1);
      const areaA = boxA.width * boxA.height;
      const areaB = boxB.width * boxB.height;
      const union = areaA + areaB - intersection;
      
      const iou = intersection / union;

      if (iou > iouLimit) {
        active[j] = false; 
      }
    }
  }
  return selected;
}

// 3. Handle Clicks & Draw
async function handleClick(event) {
  if (!objectDetector) return;
  
  const img = event.target;
  const card = img.parentNode;

  // -- NEW: Inject/Show Inference Bar --
  let infoPanel = card.querySelector('.inference-panel');
  if (!infoPanel) {
    infoPanel = document.createElement('div');
    infoPanel.className = 'inference-panel';
    infoPanel.innerHTML = `
      <div class="inference-status">
        <span>Running Inference...</span>
        <span>Please wait</span>
      </div>
      <div class="progress-track">
        <div class="progress-bar"></div>
      </div>
    `;
    card.appendChild(infoPanel);
  }

  // 1. Show Loading State
  card.classList.add('processing');
  // Clear old boxes immediately so the user sees a "reset"
  card.querySelectorAll('.highlighter, .label-tag').forEach(el => el.remove());

  // 2. Force a tiny delay so the browser renders the loading bar 
  // before the heavy synchronous AI detection freezes the thread.
  await new Promise(resolve => requestAnimationFrame(() => setTimeout(resolve, 50)));
  
  try {
    // 3. Run Detection
    const predictions = objectDetector.detect(img);
    const filteredDetections = filterDetections(predictions.detections, overlapThreshold);
    displayDetections(filteredDetections, img);
  } catch(e) {
    console.error(e);
    alert("Error running model");
  } finally {
    // 4. Hide Loading State
    card.classList.remove('processing');
  }
}

function displayDetections(detections, img) {
  const ratioX = img.width / img.naturalWidth;
  const ratioY = img.height / img.naturalHeight; 
  
  detections.forEach(detection => {
    const box = detection.boundingBox;
    const category = detection.categories[0];
    const score = Math.round(category.score * 100);
    const color = getColorForLabel(category.categoryName);

    const highlighter = document.createElement("div");
    highlighter.className = "highlighter";
    highlighter.style.left = `${box.originX * ratioX}px`;
    highlighter.style.top = `${box.originY * ratioY}px`;
    highlighter.style.width = `${box.width * ratioX}px`;
    highlighter.style.height = `${box.height * ratioY}px`;
    highlighter.style.borderColor = color;
    highlighter.style.backgroundColor = color + "20"; 

    const label = document.createElement("div");
    label.className = "label-tag";
    label.innerText = `${category.categoryName} ${score}%`;
    label.style.backgroundColor = color;
    
    const topPos = (box.originY * ratioY) - 25;
    label.style.left = `${box.originX * ratioX}px`;
    label.style.top = `${topPos > 0 ? topPos : (box.originY * ratioY)}px`;

    img.parentNode.appendChild(highlighter);
    img.parentNode.appendChild(label);
  });
}

function getColorForLabel(label) {
  let hash = 0;
  for (let i = 0; i < label.length; i++) {
    hash = label.charCodeAt(i) + ((hash << 5) - hash);
  }
  const c = (hash & 0x00FFFFFF).toString(16).toUpperCase();
  return "#" + "00000".substring(0, 6 - c.length) + c;
}

// 4. Initialization & Event Listeners
const imageContainer = document.getElementById("imageContainer");

imageContainer.addEventListener('click', (e) => {
  if (e.target.tagName === 'IMG') handleClick(e);
});

document.getElementById('imageUpload').addEventListener('change', (e) => {
  const file = e.target.files[0];
  if (!file) return;
  
  const reader = new FileReader();
  reader.onload = (event) => {
    const div = document.createElement('div');
    div.className = 'detect-card';
    const img = document.createElement('img');
    img.src = event.target.result;
    div.appendChild(img);
    imageContainer.insertBefore(div, imageContainer.firstChild); 
  };
  reader.readAsDataURL(file);
});

document.getElementById('confidenceSlider').addEventListener('input', (e) => {
  confidenceThreshold = e.target.value / 100;
  document.getElementById('confValue').innerText = e.target.value;
});

document.getElementById('overlapSlider').addEventListener('input', (e) => {
  overlapThreshold = e.target.value / 100;
  document.getElementById('overlapValue').innerText = e.target.value;
});
</script>

</body>
</html>