In the world of web development, Node.js is a popular server-side JavaScript runtime environment. It's used by major companies like Netflix, Walmart, and eBay. And it's not just for web development. Node.js is also popular for building desktop applications and for embedded systems development.
One area where Node.js is seeing a lot of adoption is in computer vision. In this article, we'll take a look at what computer vision is and how you can use Node.js to build hands-on solutions.
Computer vision is a field of artificial intelligence that deals with how computers can be made to understand digital images. This involves tasks like image recognition, object detection, and image classification.
Computer vision is a broad field with many different applications. It's used in self-driving cars, for facial recognition, and in medical image analysis.
There are a few different ways to use Node.js for computer vision. The two most popular are the open source library OpenCV and the commercial library Sightengine.
OpenCV is the most popular computer vision library and has bindings for many programming languages, including Node.js. It's used in a wide range of applications, including security, robotics, and augmented reality.
Sightengine is a commercial computer vision library with a focus on image recognition. It offers a free trial and has a Node.js client library.
In this article, we'll focus on OpenCV, as it's the more popular of the two libraries.
OpenCV is a open source library, so it can be installed for free. The easiest way to install it is using the Node.js package manager, npm.
npm install opencv
This will install the latest version of OpenCV.
Once OpenCV is installed, you can require it in your Node.js code.
const cv = require('opencv');
The first step in any computer vision project is reading in an image. OpenCV makes this easy with the readImage
function.
cv.readImage('image.jpg', (err, img) => {
if (err) {
console.error(err);
} else {
// Do something with the image
}
});
This will read in the image image.jpg
and provide it as an img
object.
OpenCV supports a wide range of image formats, including JPEG, PNG, and TIFF. However, not all formats are supported by all functions. For example, the threshold
function only supports grayscale images.
To convert an image to grayscale, you can use the cvtColor
function.
cv.cvtColor(img, cv.COLOR_BGR2GRAY, (err, grayImg) => {
if (err) {
console.error(err);
} else {
// Do something with the grayscale image
}
});
This will convert the image img
to grayscale and provide it as grayImg
.
Thresholding is a common operation in computer vision. It's used to convert an image into a binary image, where each pixel is either black or white.
OpenCV provides a threshold
function that takes an image and a threshold value. Pixels with a intensity greater than the threshold are converted to white, and pixels with a intensity less than the threshold are converted to black.
cv.threshold(grayImg, 127, 255, cv.THRESH_BINARY, (err, binImg) => {
if (err) {
console.error(err);
} else {
// Do something with the binary image
}
});
This will threshold the image grayImg
with a threshold value of 127. The resulting image will be provided as binImg
.
In some cases, it might be useful to invert an image. This can be done with the bitwise_not
function.
cv.bitwise_not(binImg, (err, invImg) => {
if (err) {
console.error(err);
} else {
// Do something with the inverted image
}
});
This will invert the image binImg
and provide the result as invImg
.
Once you've processed an image, you'll probably want to save the results. OpenCV provides the saveImage
function for this.
cv.saveImage('output.jpg', invImg);
This will save the image invImg
as output.jpg
.
In this article, we've looked at what computer vision is and how you can use Node.js to build hands-on solutions. We've covered how to install OpenCV, how to read and convert images, how to threshold images, and how to save images.
This is just the tip of the iceberg when it comes to computer vision. If you're interested in learning more, I recommend checking out the resources below.