NeuralNetworkClassifier

Last updated 29 days ago

Time to make NeuralNetworkClassifier watcher classify fruits.

Let's create an initialization function for the NNC watcher.

const initializeNNC = () => {};

Retrieve the elements we'll be working with, along with the bounding rect of the element defining the watcher's contour.

const initializeNNC = () => {
// Get the elements we'll be working with...
const nncElement = document.getElementsByClassName('nnc')[0];
const nncRecognizedClassElement = document.getElementsByClassName('nnc-recognized-class')[0];
// ...along with the bounding rect that defines the watcher size
const nncBounds = nncElement.getBoundingClientRect();
};

Define the watcher data structure.

// ...
const nncFruitsWatcher = {
name: 'NeuralNetworkClassifier',
shape: lampix.helpers.rectangle(
nncBounds.left,
nncBounds.top,
nncBounds.width,
nncBounds.height
),
params: {
neural_network_name: 'fruits'
}
};

In case you're wondering, the above lampix.helpers.rectangle could be replaced with:

{
type: 'rectangle',
data: {
posX: nncBounds.left,
posY: nncBounds.top,
width: nncBounds.width,
height: nncBounds.height
}
}

The watcher data structure above is almost complete, but it's missing one key component: what to actually do when classification is triggered.

// ...
// All Lampix classifiers return a list of recognized objects
// NNClassifier only recognizes one at a time, hence expecting
// an array with one element and destructuring it
const nncCallback = ([recognizedObject]) => {
nncRecognizedClassElement.textContent = `Recognized: ${recognizedObject.classTag}`;
if (Number(recognizedObject.classTag) === 1) {
// Change border color on each new detection
nncElement.style.borderColor = randomColor();
} else {
// Go back to white if object no longer there
nncElement.style.borderColor = '#FFFFFF';
}
};
const nncFruitsWatcher = {
name: 'NeuralNetworkClassifier',
shape: lampix.helpers.rectangle(
nncBounds.left,
nncBounds.top,
nncBounds.width,
nncBounds.height
),
params: {
neural_network_name: 'fruits'
},
onClassification: nncCallback
};

All that's left now is to inform Lampix of its existence, by adding the following to the end of the initializeNNC function.

// ...
lampix.watchers.add(nncFruitsWatcher);

Now, initializeNNC should look like this:

const initializeNNC = () => {
// Get the elements we'll be working with...
const nncElement = document.getElementsByClassName('nnc')[0];
const nncRecognizedClassElement = document.getElementsByClassName('nnc-recognized-class')[0];
// ...along with the bounding rect that defines the watcher size
const nncBounds = nncElement.getBoundingClientRect();
// All Lampix classifiers return a list of recognized objects
// NNClassifier only recognizes one at a time, hence expecting
// an array with one element and destructuring it
const nncCallback = ([recognizedObject]) => {
nncRecognizedClassElement.textContent = `Recognized: ${recognizedObject.classTag}`;
if (Number(recognizedObject.classTag) === 1) {
nncElement.style.borderColor = '#FF0000';
} else {
// Go back to white if object no longer there
nncElement.style.borderColor = '#FFFFFF';
}
};
const nncFruitsWatcher = {
name: 'NeuralNetworkClassifier',
shape: lampix.helpers.rectangle(
nncBounds.left,
nncBounds.top,
nncBounds.width,
nncBounds.height
),
params: {
neural_network_name: 'fruits'
},
onClassification: nncCallback
};
lampix.watchers.add(nncFruitsWatcher);
};