tomAIto/yolov8_pytorch_python-main/index.html
2023-12-28 18:40:41 +01:00

70 lines
2.3 KiB
HTML

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>YOLOv8 Object Detection</title>
<style>
canvas {
display:block;
border: 1px solid black;
margin-top:10px;
}
</style>
</head>
<body>
<input id="uploadInput" type="file"/>
<canvas></canvas>
<script>
/**
* "Upload" button onClick handler: uploads selected
* image file to backend, receives an array of
* detected objects and draws them on top of image
*/
const input = document.getElementById("uploadInput");
input.addEventListener("change",async(event) => {
const file = event.target.files[0];
const data = new FormData();
data.append("image_file",file,"image_file");
const response = await fetch("/detect",{
method:"post",
body:data
});
const boxes = await response.json();
draw_image_and_boxes(file,boxes);
})
/**
* Function draws the image from provided file
* and bounding boxes of detected objects on
* top of the image
* @param file Uploaded file object
* @param boxes Array of bounding boxes in format
[[x1,y1,x2,y2,object_type,probability],...]
*/
function draw_image_and_boxes(file,boxes) {
const img = new Image()
img.src = URL.createObjectURL(file);
img.onload = () => {
const canvas = document.querySelector("canvas");
canvas.width = img.width;
canvas.height = img.height;
const ctx = canvas.getContext("2d");
ctx.drawImage(img,0,0);
ctx.strokeStyle = "#00FF00";
ctx.lineWidth = 5;
ctx.font = "20px serif";
boxes.forEach(([x1,y1,x2,y2,object_type, prob]) => {
const label = `${object_type} ${prob.toFixed(2)}`;
ctx.strokeRect(x1,y1,x2-x1,y2-y1);
ctx.fillStyle = "#00ff00";
const width = ctx.measureText(label).width;
ctx.fillRect(x1,y1,width+10,25);
ctx.fillStyle = "#000000";
ctx.fillText(label,x1,y1+18);
});
}
}
</script>
</body>
</html>