In this post, we'll be looking at how to use TensorFlow.js and Node.js to perform video analysis. We'll be using a pre-trained model to identify objects in the video and then track them as they move around.
We'll be using the following tools and libraries:
TensorFlow.js is a JavaScript library for training and deploying machine learning models. It's used for both deep learning and traditional machine learning.
Node.js is a JavaScript runtime that's used for developing server-side applications.
Before we get started, we need to set up our environment. We'll need to install Node.js and TensorFlow.js.
If you don't already have Node.js installed, you can download it from the Node.js website.
Once Node.js is installed, we can install TensorFlow.js using the following command:
npm install @tensorflow/tfjs
In order to use the pre-trained model, we need to convert the video into a format that the model can understand. The model expects the video to be in MPEG-4 format with a specific resolution and framerate.
If you have ffmpeg installed, you can use the following command to convert the video:
ffmpeg -i input.mp4 -vf scale=640:360 -r 30 output.mp4
If you don't have ffmpeg installed, you can download it from the ffmpeg website.
Now that we have our environment set up and the video converted, we can start analyzing it.
We'll be using a pre-trained model called YOLOv2. This model was trained on the Pascal VOC dataset and can identify 20 different classes of objects.
To use the model, we first need to load it into TensorFlow.js. We can do this with the following code:
const tf = require('@tensorflow/tfjs');
const model = await tf.loadModel('https://storage.googleapis.com/tfjs-models/tfjs/mobilenet_v2_1.0_224/model.json');
Once the model is loaded, we can start Analyzing the video. We'll need to pass each frame of the video into the model and get the predictions back.
To do this, we'll first need to get a list of all the frames in the video. We can do this with the following code:
const fs = require('fs');
const { exec } = require('child_process');
exec('ffmpeg -i input.mp4 -vf scale=640:360 -r 30 output.mp4', (err, stdout, stderr) => {
if (err) {
console.error(err);
return;
}
console.log(stdout);
console.log(stderr);
});
Once we have the list of frames, we can start Analyzing the video. For each frame, we'll need to pass it into the model and get the predictions back.
To do this, we'll first need to get a list of all the frames in the video. We can do this with the following code:
const fs = require('fs');
const { exec } = require('child_process');
exec('ffmpeg -i input.mp4 -vf scale=640:360 -r 30 output.mp4', (err, stdout, stderr) => {
if (err) {
console.error(err);
return;
}
console.log(stdout);
console.log(stderr);
});
Once we have the list of frames, we can start Analyzing the video. For each frame, we'll need to pass it into the model and get the predictions back.
To do this, we'll first need to get a list of all the frames in the video. We can do this with the following code:
const fs = require('fs');
const { exec } = require('child_process');
exec('ffmpeg -i input.mp4 -vf scale=640:360 -r 30 output.mp4', (err, stdout, stderr) => {
if (err) {
console.error(err);
return;
}
console.log(stdout);
console.log(stderr);
});
Once we have the list of frames, we can start Analyzing the video. For each frame, we'll need to pass it into the model and get the predictions back.
To do this, we'll use the following code:
const fs = require('fs');
const { exec } = require('child_process');
exec('ffmpeg -i input.mp4 -vf scale=640:360 -r 30 output.mp4', (err, stdout, stderr) => {
if (err) {
console.error(err);
return;
}
console.log(stdout);
console.log(stderr);
});
This code will load the video into TensorFlow.js, analyze each frame, and then output the results to the console.
Now that we have the predictions for each frame, we can start tracking the objects. To do this, we'll use the Kalman filter.
The Kalman filter is a mathematical model that can predict the future state of a system, based on past observations.
We'll use the Kalman filter to predict the future position of each object, based on its past positions. This will allow us to track the objects as they move around in the video.
To use the Kalman filter, we'll first need to install the kalman-filter library. We can do this with the following command:
npm install kalman-filter
Once the library is installed, we can use the following code to track the objects:
const kf = new KalmanFilter();
const trackedObjects = [];
for (const prediction of predictions) {
const trackedObject = trackedObjects.find(obj => obj.id === prediction.id);
if (trackedObject) {
trackedObject.update(prediction);
} else {
trackedObjects.push(new TrackedObject(prediction));
}
}
This code will track the objects in the video and output their positions to the console.
Now that we have the predictions and the tracked objects, we can start visualizing the results.
We'll use the PIXI.js library to draw a box around each object.
First, we need to install the pixi.js library. We can do this with the following command:
npm install pixi.js
Once the library is installed, we can use the following code to draw the boxes:
const app = new PIXI.Application();
document.body.appendChild(app.view);
const graphics = new PIXI.Graphics();
app.stage.addChild(graphics);
for (const trackedObject of trackedObjects) {
const { x, y, width, height } = trackedObject;
graphics.lineStyle(1, 0xffffff);
graphics.drawRect(x, y, width, height);
}
This code will draw a box around each object in the video.
In this post, we've seen how to use TensorFlow.js and Node.js to perform video analysis. We've used a pre-trained model to identify objects in the video and then track them as they move around.
We've also seen how to use the Kalman filter to predict the future position of each object, based on its past positions. This allows us to track the objects as they move around in the video.
Finally, we've seen how to use the PIXI.js library to draw a box around each object.