ker00sama-dev/index.html

## index.html
<!-- Copyright 2023 The MediaPipe Authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. -->
<link href="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.css" rel="stylesheet">
<script src="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands/hands.js" crossorigin="anonymous"></script>

<body>
  <h1>Hand landmark detection using the MediaPipe HandLandmarker task</h1>

  <section id="demos" class="invisible">
    <h2>Demo: Detecting Images</h2>
    <p><b>Click on an image below</b> to see the key landmarks of the hands.</p>

    <div class="detectOnClick">
      <img src="https://assets.codepen.io/9177687/hand-ge4ca13f5d_1920.jpg" width="100%" crossorigin="anonymous" title="Click to get detection!" />
    </div>
    <div class="detectOnClick">
      <img src="https://assets.codepen.io/9177687/couple-gb7cb5db4c_1920.jpg" width="100%" crossorigin="anonymous" title="Click to get detection!" />
    </div>

    <h2>Demo: Webcam continuous hands landmarks detection</h2>
    <p>Hold your hand in front of your webcam to get real-time hand landmarker detection.</br>Click <b>enable webcam</b> below and grant access to the webcam if prompted.</p>

    <div id="liveView" class="videoView">
      <button id="webcamButton" class="mdc-button mdc-button--raised">
        <span class="mdc-button__ripple"></span>
        <span class="mdc-button__label">ENABLE WEBCAM</span>
      </button>
      <div style="position: relative;">
        <video id="webcam" style="position: abso" autoplay playsinline></video>
        <canvas class="output_canvas" id="output_canvas" style="position: absolute; left: 0px; top: 0px;"></canvas>
      </div>
    </div>
  </section>

## mediapipe-handlandmarker-task-for-web.markdown

      
    Raw
  

              mediapipe-handlandmarker-task-for-web.markdown
            
          
    MediaPipe HandLandmarker Task for web

A Pen by MediaPipe Preview on CodePen.
License.

  
## script.typescript
// Copyright 2023 The MediaPipe Authors.

// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at

//      http://www.apache.org/licenses/LICENSE-2.0

// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

import {
  HandLandmarker,
  FilesetResolver
} from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.0";

const demosSection = document.getElementById("demos");

let handLandmarker = undefined;
let runningMode = "IMAGE";
let enableWebcamButton: HTMLButtonElement;
let webcamRunning: Boolean = false;

// Before we can use HandLandmarker class we must wait for it to finish
// loading. Machine Learning models can be large and take a moment to
// get everything needed to run.
const createHandLandmarker = async () => {
  const vision = await FilesetResolver.forVisionTasks(
    "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.0/wasm"
  );
  handLandmarker = await HandLandmarker.createFromOptions(vision, {
    baseOptions: {
      modelAssetPath: `https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task`,
      delegate: "GPU"
    },
    runningMode: runningMode,
    numHands: 2
  });
  demosSection.classList.remove("invisible");
};
createHandLandmarker();

/********************************************************************
// Demo 1: Grab a bunch of images from the page and detection them
// upon click.
********************************************************************/

// In this demo, we have put all our clickable images in divs with the
// CSS class 'detectionOnClick'. Lets get all the elements that have
// this class.
const imageContainers = document.getElementsByClassName("detectOnClick");

// Now let's go through all of these and add a click event listener.
for (let i = 0; i < imageContainers.length; i++) {
  // Add event listener to the child element whichis the img element.
  imageContainers[i].children[0].addEventListener("click", handleClick);
}

// When an image is clicked, let's detect it and display results!
async function handleClick(event) {
  if (!handLandmarker) {
    console.log("Wait for handLandmarker to load before clicking!");
    return;
  }

  if (runningMode === "VIDEO") {
    runningMode = "IMAGE";
    await handLandmarker.setOptions({ runningMode: "IMAGE" });
  }
  // Remove all landmarks drawed before
  const allCanvas = event.target.parentNode.getElementsByClassName("canvas");
  for (var i = allCanvas.length - 1; i >= 0; i--) {
    const n = allCanvas[i];
    n.parentNode.removeChild(n);
  }

  // We can call handLandmarker.detect as many times as we like with
  // different image data each time. This returns a promise
  // which we wait to complete and then call a function to
  // print out the results of the prediction.
  const handLandmarkerResult = handLandmarker.detect(event.target);
  console.log(handLandmarkerResult.handednesses[0][0]);
  const canvas = document.createElement("canvas");
  canvas.setAttribute("class", "canvas");
  canvas.setAttribute("width", event.target.naturalWidth + "px");
  canvas.setAttribute("height", event.target.naturalHeight + "px");
  canvas.style =
    "left: 0px;" +
    "top: 0px;" +
    "width: " +
    event.target.width +
    "px;" +
    "height: " +
    event.target.height +
    "px;";

  event.target.parentNode.appendChild(canvas);
  const cxt = canvas.getContext("2d");
  for (const landmarks of handLandmarkerResult.landmarks) {
    drawConnectors(cxt, landmarks, HAND_CONNECTIONS, {
      color: "#00FF00",
      lineWidth: 5
    });
    drawLandmarks(cxt, landmarks, { color: "#FF0000", lineWidth: 1 });
  }
}

/********************************************************************
// Demo 2: Continuously grab image from webcam stream and detect it.
********************************************************************/

const video = document.getElementById("webcam") as HTMLVideoElement;
const canvasElement = document.getElementById(
  "output_canvas"
) as HTMLCanvasElement;
const canvasCtx = canvasElement.getContext("2d");

// Check if webcam access is supported.
const hasGetUserMedia = () => !!navigator.mediaDevices?.getUserMedia;

// If webcam supported, add event listener to button for when user
// wants to activate it.
if (hasGetUserMedia()) {
  enableWebcamButton = document.getElementById("webcamButton");
  enableWebcamButton.addEventListener("click", enableCam);
} else {
  console.warn("getUserMedia() is not supported by your browser");
}

// Enable the live webcam view and start detection.
function enableCam(event) {
  if (!handLandmarker) {
    console.log("Wait! objectDetector not loaded yet.");
    return;
  }

  if (webcamRunning === true) {
    webcamRunning = false;
    enableWebcamButton.innerText = "ENABLE PREDICTIONS";
  } else {
    webcamRunning = true;
    enableWebcamButton.innerText = "DISABLE PREDICTIONS";
  }

  // getUsermedia parameters.
  const constraints = {
    video: true
  };

  // Activate the webcam stream.
  navigator.mediaDevices.getUserMedia(constraints).then((stream) => {
    video.srcObject = stream;
    video.addEventListener("loadeddata", predictWebcam);
  });
}

let lastVideoTime = -1;
let results = undefined;
console.log(video);
async function predictWebcam() {
  canvasElement.style.width = video.videoWidth;;
  canvasElement.style.height = video.videoHeight;
  canvasElement.width = video.videoWidth;
  canvasElement.height = video.videoHeight;

  // Now let's start detecting the stream.
  if (runningMode === "IMAGE") {
    runningMode = "VIDEO";
    await handLandmarker.setOptions({ runningMode: "VIDEO" });
  }
  let startTimeMs = performance.now();
  if (lastVideoTime !== video.currentTime) {
    lastVideoTime = video.currentTime;
    results = handLandmarker.detectForVideo(video, startTimeMs);
  }
  canvasCtx.save();
  canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
  if (results.landmarks) {
    for (const landmarks of results.landmarks) {
      drawConnectors(canvasCtx, landmarks, HAND_CONNECTIONS, {
        color: "#00FF00",
        lineWidth: 5
      });
      drawLandmarks(canvasCtx, landmarks, { color: "#FF0000", lineWidth: 2 });
    }
  }
  canvasCtx.restore();

  // Call this function again to keep predicting when the browser is ready.
  if (webcamRunning === true) {
    window.requestAnimationFrame(predictWebcam);
  }
}

## style.css
/* Copyright 2023 The MediaPipe Authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

     http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

@use "@material";
body {
  font-family: roboto;
  margin: 2em;
  color: #3d3d3d;
  --mdc-theme-primary: #007f8b;
  --mdc-theme-on-primary: #f1f3f4;
}

h1 {
  color: #007f8b;
}

h2 {
  clear: both;
}

em {
  font-weight: bold;
}

video {
  clear: both;
  display: block;
  transform: rotateY(180deg);
  -webkit-transform: rotateY(180deg);
  -moz-transform: rotateY(180deg);
}

section {
  opacity: 1;
  transition: opacity 500ms ease-in-out;
}

header,
footer {
  clear: both;
}

.removed {
  display: none;
}

.invisible {
  opacity: 0.2;
}

.note {
  font-style: italic;
  font-size: 130%;
}

.videoView,
.detectOnClick {
  position: relative;
  float: left;
  width: 48%;
  margin: 2% 1%;
  cursor: pointer;
}

.videoView p,
.detectOnClick p {
  position: absolute;
  padding: 5px;
  background-color: #007f8b;
  color: #fff;
  border: 1px dashed rgba(255, 255, 255, 0.7);
  z-index: 2;
  font-size: 12px;
  margin: 0;
}

.highlighter {
  background: rgba(0, 255, 0, 0.25);
  border: 1px dashed #fff;
  z-index: 1;
  position: absolute;
}

.canvas {
  z-index: 1;
  position: absolute;
  pointer-events: none;
}

.output_canvas {
  transform: rotateY(180deg);
  -webkit-transform: rotateY(180deg);
  -moz-transform: rotateY(180deg);
}

.detectOnClick {
  z-index: 0;
}

.detectOnClick img {
  width: 100%;
}
	<!-- Copyright 2023 The MediaPipe Authors.

	Licensed under the Apache License, Version 2.0 (the "License");
	you may not use this file except in compliance with the License.
	You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License. -->
	<link href="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.css" rel="stylesheet">
	<script src="https://unpkg.com/material-components-web@latest/dist/material-components-web.min.js"></script>
	<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
	<script src="https://cdn.jsdelivr.net/npm/@mediapipe/hands/hands.js" crossorigin="anonymous"></script>

	<body>
	<h1>Hand landmark detection using the MediaPipe HandLandmarker task</h1>

	<section id="demos" class="invisible">
	<h2>Demo: Detecting Images</h2>
	<p><b>Click on an image below</b> to see the key landmarks of the hands.</p>

	<div class="detectOnClick">
	<img src="https://assets.codepen.io/9177687/hand-ge4ca13f5d_1920.jpg" width="100%" crossorigin="anonymous" title="Click to get detection!" />
	</div>
	<div class="detectOnClick">
	<img src="https://assets.codepen.io/9177687/couple-gb7cb5db4c_1920.jpg" width="100%" crossorigin="anonymous" title="Click to get detection!" />
	</div>

	<h2>Demo: Webcam continuous hands landmarks detection</h2>
	<p>Hold your hand in front of your webcam to get real-time hand landmarker detection.</br>Click <b>enable webcam</b> below and grant access to the webcam if prompted.</p>

	<div id="liveView" class="videoView">
	<button id="webcamButton" class="mdc-button mdc-button--raised">
	<span class="mdc-button__ripple"></span>
	<span class="mdc-button__label">ENABLE WEBCAM</span>
	</button>
	<div style="position: relative;">
	<video id="webcam" style="position: abso" autoplay playsinline></video>
	<canvas class="output_canvas" id="output_canvas" style="position: absolute; left: 0px; top: 0px;"></canvas>
	</div>
	</div>
	</section>
	// Copyright 2023 The MediaPipe Authors.

	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at

	// http://www.apache.org/licenses/LICENSE-2.0

	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.

	import {
	HandLandmarker,
	FilesetResolver
	} from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.0";

	const demosSection = document.getElementById("demos");

	let handLandmarker = undefined;
	let runningMode = "IMAGE";
	let enableWebcamButton: HTMLButtonElement;
	let webcamRunning: Boolean = false;

	// Before we can use HandLandmarker class we must wait for it to finish
	// loading. Machine Learning models can be large and take a moment to
	// get everything needed to run.
	const createHandLandmarker = async () => {
	const vision = await FilesetResolver.forVisionTasks(
	"https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.0/wasm"
	);
	handLandmarker = await HandLandmarker.createFromOptions(vision, {
	baseOptions: {
	modelAssetPath: `https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task`,
	delegate: "GPU"
	},
	runningMode: runningMode,
	numHands: 2
	});
	demosSection.classList.remove("invisible");
	};
	createHandLandmarker();

	/********************************************************************
	// Demo 1: Grab a bunch of images from the page and detection them
	// upon click.
	********************************************************************/

	// In this demo, we have put all our clickable images in divs with the
	// CSS class 'detectionOnClick'. Lets get all the elements that have
	// this class.
	const imageContainers = document.getElementsByClassName("detectOnClick");

	// Now let's go through all of these and add a click event listener.
	for (let i = 0; i < imageContainers.length; i++) {
	// Add event listener to the child element whichis the img element.
	imageContainers[i].children[0].addEventListener("click", handleClick);
	}

	// When an image is clicked, let's detect it and display results!
	async function handleClick(event) {
	if (!handLandmarker) {
	console.log("Wait for handLandmarker to load before clicking!");
	return;
	}

	if (runningMode === "VIDEO") {
	runningMode = "IMAGE";
	await handLandmarker.setOptions({ runningMode: "IMAGE" });
	}
	// Remove all landmarks drawed before
	const allCanvas = event.target.parentNode.getElementsByClassName("canvas");
	for (var i = allCanvas.length - 1; i >= 0; i--) {
	const n = allCanvas[i];
	n.parentNode.removeChild(n);
	}

	// We can call handLandmarker.detect as many times as we like with
	// different image data each time. This returns a promise
	// which we wait to complete and then call a function to
	// print out the results of the prediction.
	const handLandmarkerResult = handLandmarker.detect(event.target);
	console.log(handLandmarkerResult.handednesses[0][0]);
	const canvas = document.createElement("canvas");
	canvas.setAttribute("class", "canvas");
	canvas.setAttribute("width", event.target.naturalWidth + "px");
	canvas.setAttribute("height", event.target.naturalHeight + "px");
	canvas.style =
	"left: 0px;" +
	"top: 0px;" +
	"width: " +
	event.target.width +
	"px;" +
	"height: " +
	event.target.height +
	"px;";

	event.target.parentNode.appendChild(canvas);
	const cxt = canvas.getContext("2d");
	for (const landmarks of handLandmarkerResult.landmarks) {
	drawConnectors(cxt, landmarks, HAND_CONNECTIONS, {
	color: "#00FF00",
	lineWidth: 5
	});
	drawLandmarks(cxt, landmarks, { color: "#FF0000", lineWidth: 1 });
	}
	}

	/********************************************************************
	// Demo 2: Continuously grab image from webcam stream and detect it.
	********************************************************************/

	const video = document.getElementById("webcam") as HTMLVideoElement;
	const canvasElement = document.getElementById(
	"output_canvas"
	) as HTMLCanvasElement;
	const canvasCtx = canvasElement.getContext("2d");

	// Check if webcam access is supported.
	const hasGetUserMedia = () => !!navigator.mediaDevices?.getUserMedia;

	// If webcam supported, add event listener to button for when user
	// wants to activate it.
	if (hasGetUserMedia()) {
	enableWebcamButton = document.getElementById("webcamButton");
	enableWebcamButton.addEventListener("click", enableCam);
	} else {
	console.warn("getUserMedia() is not supported by your browser");
	}

	// Enable the live webcam view and start detection.
	function enableCam(event) {
	if (!handLandmarker) {
	console.log("Wait! objectDetector not loaded yet.");
	return;
	}

	if (webcamRunning === true) {
	webcamRunning = false;
	enableWebcamButton.innerText = "ENABLE PREDICTIONS";
	} else {
	webcamRunning = true;
	enableWebcamButton.innerText = "DISABLE PREDICTIONS";
	}

	// getUsermedia parameters.
	const constraints = {
	video: true
	};

	// Activate the webcam stream.
	navigator.mediaDevices.getUserMedia(constraints).then((stream) => {
	video.srcObject = stream;
	video.addEventListener("loadeddata", predictWebcam);
	});
	}

	let lastVideoTime = -1;
	let results = undefined;
	console.log(video);
	async function predictWebcam() {
	canvasElement.style.width = video.videoWidth;;
	canvasElement.style.height = video.videoHeight;
	canvasElement.width = video.videoWidth;
	canvasElement.height = video.videoHeight;

	// Now let's start detecting the stream.
	if (runningMode === "IMAGE") {
	runningMode = "VIDEO";
	await handLandmarker.setOptions({ runningMode: "VIDEO" });
	}
	let startTimeMs = performance.now();
	if (lastVideoTime !== video.currentTime) {
	lastVideoTime = video.currentTime;
	results = handLandmarker.detectForVideo(video, startTimeMs);
	}
	canvasCtx.save();
	canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
	if (results.landmarks) {
	for (const landmarks of results.landmarks) {
	drawConnectors(canvasCtx, landmarks, HAND_CONNECTIONS, {
	color: "#00FF00",
	lineWidth: 5
	});
	drawLandmarks(canvasCtx, landmarks, { color: "#FF0000", lineWidth: 2 });
	}
	}
	canvasCtx.restore();

	// Call this function again to keep predicting when the browser is ready.
	if (webcamRunning === true) {
	window.requestAnimationFrame(predictWebcam);
	}
	}
	/* Copyright 2023 The MediaPipe Authors.

	Licensed under the Apache License, Version 2.0 (the "License");
	you may not use this file except in compliance with the License.
	You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License. */

	@use "@material";
	body {
	font-family: roboto;
	margin: 2em;
	color: #3d3d3d;
	--mdc-theme-primary: #007f8b;
	--mdc-theme-on-primary: #f1f3f4;
	}

	h1 {
	color: #007f8b;
	}

	h2 {
	clear: both;
	}

	em {
	font-weight: bold;
	}

	video {
	clear: both;
	display: block;
	transform: rotateY(180deg);
	-webkit-transform: rotateY(180deg);
	-moz-transform: rotateY(180deg);
	}

	section {
	opacity: 1;
	transition: opacity 500ms ease-in-out;
	}

	header,
	footer {
	clear: both;
	}

	.removed {
	display: none;
	}

	.invisible {
	opacity: 0.2;
	}

	.note {
	font-style: italic;
	font-size: 130%;
	}

	.videoView,
	.detectOnClick {
	position: relative;
	float: left;
	width: 48%;
	margin: 2% 1%;
	cursor: pointer;
	}

	.videoView p,
	.detectOnClick p {
	position: absolute;
	padding: 5px;
	background-color: #007f8b;
	color: #fff;
	border: 1px dashed rgba(255, 255, 255, 0.7);
	z-index: 2;
	font-size: 12px;
	margin: 0;
	}

	.highlighter {
	background: rgba(0, 255, 0, 0.25);
	border: 1px dashed #fff;
	z-index: 1;
	position: absolute;
	}

	.canvas {
	z-index: 1;
	position: absolute;
	pointer-events: none;
	}

	.output_canvas {
	transform: rotateY(180deg);
	-webkit-transform: rotateY(180deg);
	-moz-transform: rotateY(180deg);
	}

	.detectOnClick {
	z-index: 0;
	}

	.detectOnClick img {
	width: 100%;
	}