ShapeDetectorModel / index.html
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<!DOCTYPE html>
<html lang="tr">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Shape Recognition Model</title>
<!-- ONNX Runtime Web kütüphanesini tarayıcıya yüklüyoruz -->
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
<style>
body { font-family: Arial, sans-serif; display: flex; flex-direction: column; align-items: center; background-color: #f3f4f6; margin-top: 50px; }
canvas { border: 2px solid #333; background-color: white; cursor: crosshair; touch-action: none; }
.controls { margin-top: 20px; display: flex; gap: 10px; }
button { padding: 10px 20px; font-size: 16px; cursor: pointer; background-color: #4CAF50; color: white; border: none; border-radius: 5px; }
button.clear { background-color: #f44336; }
#result { margin-top: 20px; font-size: 24px; font-weight: bold; color: #333; }
</style>
</head>
<body>
<h2>Draw a Shape</h2>
<canvas id="canvas" width="500" height="500"></canvas>
<div class="controls">
<button onclick="predict()">Predict</button>
<button class="clear" onclick="clearCanvas()">Clear</button>
</div>
<div id="result">Waiting...</div>
<!-- Arka planda resmi 50x50'ye küçültmek için gizli tuval -->
<canvas id="hiddenCanvas" width="50" height="50" style="display:none;"></canvas>
<script>
const canvas = document.getElementById('canvas');
const ctx = canvas.getContext('2d');
let isDrawing = false;
// English class names mapping the model's output
const classes = ["Circle", "Triangle", "Rectangle", "Pentagon", "Parallelogram", "Line"];
ctx.lineWidth = 20;
ctx.lineCap = 'round';
ctx.strokeStyle = 'black';
canvas.addEventListener('mousedown', startDrawing);
canvas.addEventListener('mousemove', draw);
canvas.addEventListener('mouseup', stopDrawing);
canvas.addEventListener('mouseout', stopDrawing);
function startDrawing(e) { isDrawing = true; draw(e); }
function stopDrawing() { isDrawing = false; ctx.beginPath(); }
function draw(e) {
if (!isDrawing) return;
const rect = canvas.getBoundingClientRect();
ctx.lineTo(e.clientX - rect.left, e.clientY - rect.top);
ctx.stroke();
ctx.beginPath();
ctx.moveTo(e.clientX - rect.left, e.clientY - rect.top);
}
function clearCanvas() {
ctx.clearRect(0, 0, canvas.width, canvas.height);
document.getElementById('result').innerText = "Waiting...";
}
async function predict() {
document.getElementById('result').innerText = "Calculating...";
const hiddenCanvas = document.getElementById('hiddenCanvas');
const hiddenCtx = hiddenCanvas.getContext('2d');
hiddenCtx.fillStyle = "white";
hiddenCtx.fillRect(0, 0, 50, 50);
hiddenCtx.drawImage(canvas, 0, 0, 50, 50);
const imgData = hiddenCtx.getImageData(0, 0, 50, 50).data;
const floatArray = new Float32Array(50 * 50);
for (let i = 0; i < imgData.length; i += 4) {
const r = imgData[i], g = imgData[i+1], b = imgData[i+2];
const gray = 0.299 * r + 0.587 * g + 0.114 * b;
const val = gray < 128 ? 1.0 : 0.0;
floatArray[i/4] = val;
}
try {
const session = await ort.InferenceSession.create('./model.onnx');
const tensor = new ort.Tensor('float32', floatArray, [1, 1, 50, 50]);
const results = await session.run({ input: tensor });
const output = results.output.data;
let maxIndex = 0;
let maxValue = output[0];
for (let i = 1; i < output.length; i++) {
if (output[i] > maxValue) {
maxValue = output[i];
maxIndex = i;
}
}
document.getElementById('result').innerText = "Prediction: " + classes[maxIndex];
} catch (err) {
console.error(err);
document.getElementById('result').innerText = "Error loading model!";
}
}
</script>
</body>
</html>