内容简介：First of all, simply include the scriptOr you can install it via npm for use in a TypeScript / ES6 projectTo stream your webcam into the browser, I utilize the npm JavaScript module
Implementation
# Step 1 : Include handtrack.js
First of all, simply include the script handtrack.js
in the <head> section of the html file.
<script src="https://cdn.jsdelivr.net/npm/handtrackjs/dist/handtrack.min.js"> </script>
Or you can install it via npm for use in a TypeScript / ES6 project
npm install --save handtrackjs
# Step 2 : Stream webcam to browser
To stream your webcam into the browser, I utilize the npm JavaScript module webcam-easy.js
, which provides an easy to use module that can access webcam and take a photo. To find out more details about that, please refer to my previous blog :
# Step 3 : Load HandTrack Model
In order to perform hand tracking, we first need to load the pre-trained HandTrack model, by calling the API of handTrack.load(modelParams)
. HandTrack comes with a few optional parameters of the model:
- flipHorizontal — default value: True
flip e.g for video
- imageScaleFactor — default value: 0.7
reduce input image size for gains in speed
- maxNumBoxes — default value: 20
maximum number of boxes to detect
- iouThreshold — default value: 0.5
ioU threshold for non-max suppression
- scoreThreshold — default value: 0.99
confidence threshold for predictions
const modelParams = {
flipHorizontal: true,
maxNumBoxes: 20,
iouThreshold: 0.5,
scoreThreshold: 0.8
}handTrack.load(modelParams).then(mdl => {
model = mdl;
console.log("model loaded");
});
# Step 4 : Hand detection
Next, we start to feed the webcam stream through the HandTrack model to perform hand detection, by calling the API of model.detect(video)
. It takes an input image element (can be an img
, video
, canvas
tag) and returns an array of bounding boxes with class name and confidence level.
model.detect(webcamElement).then(predictions => {
console.log("Predictions: ", predictions);
showFire(predictions);
});
Return of predictions would look like:
[{
bbox: [x, y, width, height],
class: "hand",
score: 0.8380282521247864
}, {
bbox: [x, y, width, height],
class: "hand",
score: 0.74644153267145157
}]
# Step 5 : Show magic fire
In the above function, we get the bounding box of the hand position, now we can use it to show the fire GIF image in your hand.
HTML
Overlay the canvas
layer on top of the webcam
element
<video id="webcam" autoplay playsinline width="640" height="480"></video><div id="canvas" width="640" height="480"></div>
JavaScript
Set the size and position of the fireElement
, and append it to the canvas
layer.
function showFire(predictions){
if(handCount != predictions.length){
$("#canvas").empty();
fireElements = [];
}
handCount = predictions.length;
for (let i = 0; i < predictions.length; i++) {
if (fireElements.length > i) {
fireElement = fireElements[i];
}else{
fireElement = $("<div class='fire_in_hand'></div>");
fireElements.push(fireElement);
fireElement.appendTo($("#canvas"));
}
var fireSizeWidth = fireElement.css("width").replace("px","");
var fireSizeHeight = fireElement.css("height").replace("px","");
var firePositionTop = hand_center_point[0]- fireSizeHeight;
var firePositionLeft = hand_center_point[1] - fireSizeWidth/2;
fireElement.css({top: firePositionTop, left: firePositionLeft, position:'absolute'});
}
}
CSS
set the background-image
to be the fire.gif
image
.fire_in_hand {
width: 300px;
height: 300px;
background-image: url(../images/fire.gif);
background-position: center center;
background-repeat: no-repeat;
background-size: cover;
}
That’s pretty much for the code! Now you should be good to start showing the magic fire in your hands!
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