Spaces:
Running
Running
File size: 17,025 Bytes
351b503 9769f61 351b503 9769f61 351b503 9582f8f 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 9769f61 351b503 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle YOLOv8 Rust/WASM</title>
</head>
<body></body>
</html>
<!doctype html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
html,
body {
font-family: "Source Sans 3", sans-serif;
}
code,
output,
select,
pre {
font-family: "Source Code Pro", monospace;
}
</style>
<script src="https://cdn.tailwindcss.com"></script>
<script
src="https://cdn.jsdelivr.net/gh/huggingface/hub-js-utils/share-canvas.js"
type="module"
></script>
<script type="module">
const MODEL_BASEURL =
"https://huggingface.co/lmz/candle-yolo-v8/resolve/main/";
const MODELS = {
yolov8n: {
model_size: "n",
url: "yolov8n.safetensors",
},
yolov8s: {
model_size: "s",
url: "yolov8s.safetensors",
},
yolov8m: {
model_size: "m",
url: "yolov8m.safetensors",
},
yolov8l: {
model_size: "l",
url: "yolov8l.safetensors",
},
yolov8x: {
model_size: "x",
url: "yolov8x.safetensors",
},
yolov8n_pose: {
model_size: "n",
url: "yolov8n-pose.safetensors",
},
yolov8s_pose: {
model_size: "s",
url: "yolov8s-pose.safetensors",
},
yolov8m_pose: {
model_size: "m",
url: "yolov8m-pose.safetensors",
},
yolov8l_pose: {
model_size: "l",
url: "yolov8l-pose.safetensors",
},
yolov8x_pose: {
model_size: "x",
url: "yolov8x-pose.safetensors",
},
};
const COCO_PERSON_SKELETON = [
[4, 0], // head
[3, 0],
[16, 14], // left lower leg
[14, 12], // left upper leg
[6, 12], // left torso
[6, 5], // top torso
[6, 8], // upper arm
[8, 10], // lower arm
[1, 2], // head
[1, 3], // right head
[2, 4], // left head
[3, 5], // right neck
[4, 6], // left neck
[5, 7], // right upper arm
[7, 9], // right lower arm
[5, 11], // right torso
[11, 12], // bottom torso
[11, 13], // right upper leg
[13, 15], // right lower leg
];
// init web worker
const yoloWorker = new Worker("./yoloWorker.js", { type: "module" });
let hasImage = false;
//add event listener to image examples
document.querySelector("#image-select").addEventListener("click", (e) => {
const target = e.target;
if (target.nodeName === "IMG") {
const href = target.src;
drawImageCanvas(href);
}
});
//add event listener to file input
document.querySelector("#file-upload").addEventListener("change", (e) => {
const target = e.target;
if (target.files.length > 0) {
const href = URL.createObjectURL(target.files[0]);
drawImageCanvas(href);
}
});
// add event listener to drop-area
const dropArea = document.querySelector("#drop-area");
dropArea.addEventListener("dragenter", (e) => {
e.preventDefault();
dropArea.classList.add("border-blue-700");
});
dropArea.addEventListener("dragleave", (e) => {
e.preventDefault();
dropArea.classList.remove("border-blue-700");
});
dropArea.addEventListener("dragover", (e) => {
e.preventDefault();
});
dropArea.addEventListener("drop", (e) => {
e.preventDefault();
dropArea.classList.remove("border-blue-700");
const url = e.dataTransfer.getData("text/uri-list");
const files = e.dataTransfer.files;
if (files.length > 0) {
const href = URL.createObjectURL(files[0]);
drawImageCanvas(href);
} else if (url) {
drawImageCanvas(url);
}
});
function drawImageCanvas(imgURL) {
const canvas = document.querySelector("#canvas");
const canvasResult = document.querySelector("#canvas-result");
canvasResult
.getContext("2d")
.clearRect(0, 0, canvas.width, canvas.height);
const ctx = canvas.getContext("2d");
ctx.clearRect(0, 0, canvas.width, canvas.height);
document.querySelector("#share-btn").hidden = true;
const img = new Image();
img.crossOrigin = "anonymous";
img.onload = () => {
canvas.width = img.width;
canvas.height = img.height;
ctx.drawImage(img, 0, 0);
canvas.parentElement.style.height = canvas.offsetHeight + "px";
hasImage = true;
document.querySelector("#detect").disabled = false;
};
img.src = imgURL;
}
async function classifyImage(
imageURL, // URL of image to classify
modelID, // ID of model to use
modelURL, // URL to model file
modelSize, // size of model
confidence, // confidence threshold
iou_threshold, // IoU threshold
updateStatus // function receives status updates
) {
return new Promise((resolve, reject) => {
yoloWorker.postMessage({
imageURL,
modelID,
modelURL,
modelSize,
confidence,
iou_threshold,
});
yoloWorker.addEventListener("message", (event) => {
if ("status" in event.data) {
updateStatus(event.data.status);
}
if ("error" in event.data) {
reject(new Error(event.data.error));
}
if (event.data.status === "complete") {
resolve(event.data);
}
});
});
}
// add event listener to detect button
document.querySelector("#detect").addEventListener("click", async () => {
if (!hasImage) {
return;
}
const modelID = document.querySelector("#model").value;
const modelURL = MODEL_BASEURL + MODELS[modelID].url;
const modelSize = MODELS[modelID].model_size;
const confidence = parseFloat(
document.querySelector("#confidence").value
);
const iou_threshold = parseFloat(
document.querySelector("#iou_threshold").value
);
const canvasInput = document.querySelector("#canvas");
const canvas = document.querySelector("#canvas-result");
canvas.width = canvasInput.width;
canvas.height = canvasInput.height;
const scale = canvas.width / canvas.offsetWidth;
const ctx = canvas.getContext("2d");
ctx.drawImage(canvasInput, 0, 0);
const imageURL = canvas.toDataURL();
const results = await await classifyImage(
imageURL,
modelID,
modelURL,
modelSize,
confidence,
iou_threshold,
updateStatus
);
const { output } = results;
ctx.lineWidth = 1 + 2 * scale;
ctx.strokeStyle = "#3c8566";
ctx.fillStyle = "#0dff9a";
const fontSize = 14 * scale;
ctx.font = `${fontSize}px sans-serif`;
for (const detection of output) {
// check keypoint for pose model data
let xmin, xmax, ymin, ymax, label, confidence, keypoints;
if ("keypoints" in detection) {
xmin = detection.xmin;
xmax = detection.xmax;
ymin = detection.ymin;
ymax = detection.ymax;
confidence = detection.confidence;
keypoints = detection.keypoints;
} else {
const [_label, bbox] = detection;
label = _label;
xmin = bbox.xmin;
xmax = bbox.xmax;
ymin = bbox.ymin;
ymax = bbox.ymax;
confidence = bbox.confidence;
}
const [x, y, w, h] = [xmin, ymin, xmax - xmin, ymax - ymin];
const text = `${label ? label + " " : ""}${confidence.toFixed(2)}`;
const width = ctx.measureText(text).width;
ctx.fillStyle = "#3c8566";
ctx.fillRect(x - 2, y - fontSize, width + 4, fontSize);
ctx.fillStyle = "#e3fff3";
ctx.strokeRect(x, y, w, h);
ctx.fillText(text, x, y - 2);
if (keypoints) {
ctx.save();
ctx.fillStyle = "magenta";
ctx.strokeStyle = "yellow";
for (const keypoint of keypoints) {
const { x, y } = keypoint;
ctx.beginPath();
ctx.arc(x, y, 3, 0, 2 * Math.PI);
ctx.fill();
}
ctx.beginPath();
for (const [xid, yid] of COCO_PERSON_SKELETON) {
//draw line between skeleton keypoitns
if (keypoints[xid] && keypoints[yid]) {
ctx.moveTo(keypoints[xid].x, keypoints[xid].y);
ctx.lineTo(keypoints[yid].x, keypoints[yid].y);
}
}
ctx.stroke();
ctx.restore();
}
}
});
function updateStatus(statusMessage) {
const button = document.querySelector("#detect");
if (statusMessage === "detecting") {
button.disabled = true;
button.classList.add("bg-blue-700");
button.classList.remove("bg-blue-950");
button.textContent = "Predicting...";
} else if (statusMessage === "complete") {
button.disabled = false;
button.classList.add("bg-blue-950");
button.classList.remove("bg-blue-700");
button.textContent = "Predict";
document.querySelector("#share-btn").hidden = false;
}
}
document.querySelector("#share-btn").addEventListener("click", () => {
shareToCommunity(
"lmz/candle-yolo",
"Candle + YOLOv8",
"YOLOv8 with [Candle](https://github.com/huggingface/candle)",
"canvas-result",
"share-btn"
);
});
</script>
</head>
<body class="container max-w-4xl mx-auto p-4">
<main class="grid grid-cols-1 gap-8 relative">
<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
<div>
<h1 class="text-5xl font-bold">Candle YOLOv8</h1>
<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
<p class="max-w-lg">
This demo showcases object detection and pose estimation models in
your browser using Rust/WASM. It utilizes
<a
href="https://huggingface.co/lmz/candle-yolo-v8"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>
safetensor's YOLOv8 models
</a>
and a WASM runtime built with
<a
href="https://github.com/huggingface/candle/"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>Candle </a
>.
</p>
<p>
To run pose estimation, select a yolo pose model from the dropdown
</p>
</div>
<div>
<label for="model" class="font-medium">Models Options: </label>
<select
id="model"
class="border-2 border-gray-500 rounded-md font-light"
>
<option value="yolov8n" selected>yolov8n (6.37 MB)</option>
<option value="yolov8s">yolov8s (22.4 MB)</option>
<option value="yolov8m">yolov8m (51.9 MB)</option>
<option value="yolov8l">yolov8l (87.5 MB)</option>
<option value="yolov8x">yolov8x (137 MB)</option>
<!-- Pose models -->
<option value="yolov8n_pose">yolov8n_pose (6.65 MB)</option>
<option value="yolov8s_pose">yolov8s_pose (23.3 MB)</option>
<option value="yolov8m_pose">yolov8m_pose (53 MB)</option>
<option value="yolov8l_pose">yolov8l_pose (89.1 MB)</option>
<option value="yolov8x_pose">yolov8x_pose (139 MB)</option>
</select>
</div>
<!-- drag and drop area -->
<div class="relative">
<div
id="drop-area"
class="flex flex-col items-center justify-center border-2 border-gray-300 border-dashed rounded-xl relative aspect-video w-full overflow-hidden"
>
<div
class="flex flex-col items-center justify-center space-y-1 text-center"
>
<svg
width="25"
height="25"
viewBox="0 0 25 25"
fill="none"
xmlns="http://www.w3.org/2000/svg"
>
<path
d="M3.5 24.3a3 3 0 0 1-1.9-.8c-.5-.5-.8-1.2-.8-1.9V2.9c0-.7.3-1.3.8-1.9.6-.5 1.2-.7 2-.7h18.6c.7 0 1.3.2 1.9.7.5.6.7 1.2.7 2v18.6c0 .7-.2 1.4-.7 1.9a3 3 0 0 1-2 .8H3.6Zm0-2.7h18.7V2.9H3.5v18.7Zm2.7-2.7h13.3c.3 0 .5 0 .6-.3v-.7l-3.7-5a.6.6 0 0 0-.6-.2c-.2 0-.4 0-.5.3l-3.5 4.6-2.4-3.3a.6.6 0 0 0-.6-.3c-.2 0-.4.1-.5.3l-2.7 3.6c-.1.2-.2.4 0 .7.1.2.3.3.6.3Z"
fill="#000"
/>
</svg>
<div class="flex text-sm text-gray-600">
<label
for="file-upload"
class="relative cursor-pointer bg-white rounded-md font-medium text-blue-950 hover:text-blue-700"
>
<span>Drag and drop your image here</span>
<span class="block text-xs">or</span>
<span class="block text-xs">Click to upload</span>
</label>
</div>
<input
id="file-upload"
name="file-upload"
type="file"
class="sr-only"
/>
</div>
<canvas
id="canvas"
class="absolute pointer-events-none w-full"
></canvas>
<canvas
id="canvas-result"
class="absolute pointer-events-none w-full"
></canvas>
</div>
<div class="text-right py-2">
<button
id="share-btn"
hidden
class="bg-white rounded-md hover:outline outline-orange-200 disabled:opacity-50"
>
<img
src="https://huggingface.co/datasets/huggingface/badges/raw/main/share-to-community-sm.svg"
/>
</button>
</div>
</div>
<div>
<div class="flex gap-3 items-center" id="image-select">
<h3 class="font-medium">Examples:</h3>
<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/sf.jpg"
class="cursor-pointer w-24 h-24 object-cover"
/>
<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/bike.jpeg"
class="cursor-pointer w-24 h-24 object-cover"
/>
<img
src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/000000000077.jpg"
class="cursor-pointer w-24 h-24 object-cover"
/>
</div>
</div>
<div>
<div class="grid grid-cols-3 max-w-md items-center gap-3">
<label class="text-sm font-medium" for="confidence"
>Confidence Threshold</label
>
<input
type="range"
id="confidence"
name="confidence"
min="0"
max="1"
step="0.01"
value="0.25"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
/>
<output
class="text-xs font-light px-1 py-1 border border-gray-700 rounded-md w-min"
>0.25</output
>
<label class="text-sm font-medium" for="iou_threshold"
>IoU Threshold</label
>
<input
type="range"
id="iou_threshold"
name="iou_threshold"
min="0"
max="1"
step="0.01"
value="0.45"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
/>
<output
class="font-extralight text-xs px-1 py-1 border border-gray-700 rounded-md w-min"
>0.45</output
>
</div>
</div>
<div>
<button
id="detect"
disabled
class="bg-blue-950 hover:bg-blue-700 text-white font-normal py-2 px-4 rounded disabled:opacity-75 disabled:hover:bg-blue-950"
>
Predict
</button>
</div>
</main>
</body>
</html>
|