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First download and install GraphicsMagick. In Mac OS X, you can simply use Homebrew and do:
brew install graphicsmagick
Then download the Recognizejs using npm:
npm i recognizejs
Import Recognizejs into your project:
const Recognizejs = require('recognizejs');
Recognizejs and initialize it:const myModel = new Recognizejs();
// initialize it
// The init function returns a Promise object
await myModel.init();
PS: Model initialization may take up to 1-2 minutes (depending on the performance of your device), so please be patient. 
const fs = require('fs');
const myImgBuffer = fs.readFileSync(myImagePath);
recognize function and pass the image buffer as a parameter:// The recognize function will return a Promise object, we recommend that you use await statement to get the return value.
const results = await myModel.recognize(myImgBuffer);
/*
[
{
className: ['className1', 'className2', 'className...'],
probability: 0.9
},
{
className: ['className1', 'className2', 'className...'],
probability: 0.599
}
]
*/
console.log(results);
The code for this example can be found in the examples folder.
new Recognizejs(config?);
Args: config is an optional parameter and has the following attributes:
{
cocoSsd?: {
// base: Controls the base cnn model, can be 'mobilenet_v1', 'mobilenet_v2' or 'lite_mobilenet_v2'. Defaults to 'lite_mobilenet_v2'. lite_mobilenet_v2 is smallest in size, and fastest in inference speed. mobilenet_v2 has the highest classification accuracy.
base?: ObjectDetectionBaseModel,
// An optional string that specifies custom url of the model. This is useful for area/countries that don't have access to the model hosted on GCP.
modelUrl?: string
},
mobileNet?: {
// The MobileNet version number. Use 1 for MobileNetV1, and 2 for MobileNetV2. Defaults to 1.
version: 1,
// Controls the width of the network, trading accuracy for performance. A smaller alpha decreases accuracy and increases performance. 0.25 is only available for V1. Defaults to 1.0.
alpha?: 0.25 | .50 | .75 | 1.0,
// Optional param for specifying the custom model url or tf.io.IOHandler object. Returns a model object.
// If you are in mainland China, please change modelUrl to the link of the model on https://hub.tensorflow.google.cn
modelUrl?: string
// Optional param specifying the pixel value range expected by the trained model hosted at the modelUrl. This is typically [0, 1] or [-1, 1].
inputRange?: [number, number]
}
}
cocoSsd and mobileNet are different neural networks. cocoSsd is used to identify and classify multiple objects in an image, while mobileNet is used to accurately identify an object.
model.init(modelType?);
The init function returns a Promise object, you can use await statement to handle it.
Args: modelType can be a string or an array. You can set the model to be loaded here to avoid loading the model that is not needed. [If you don't set modelType, it will load both cocoSsd and mobileNet models]
Example:
model.init();
// or
model.init(['cocoSsd', 'mobileNet']);
// or
model.init('cocoSsd');
// or
model.init('mobileNet');
If you don't use the init function to load the model, the model will load automatically when you need to use them, but it may take a long time to load the model, so please choose the loading method as appropriate.
model.recognize(buf);
The recognize function returns a Promise object, you can use await statement to get its return value.
Args: The buf parameter requires you to pass a buffer type of image data. You can read the image through the fs module.
Return value:
[
{
className: [
'giant panda',
'panda',
'panda bear',
'coon bear',
'Ailuropoda melanoleuca'
],
probability: 0.9819085597991943
},
{
className: [ 'Chihuahua' ],
probability: 0.006128392647951841
},
{
className: [ 'French bulldog' ],
probability: 0.0026271280366927385
}
]
Example:
const myImgBuf = require('fs').readFileSync(myImgPath);
model.recognize(myImgBuf);
model.detect(buf)
The detect function returns a Promise object, you can use await statement to get its return value.
Args: The buf parameter requires you to pass a buffer type of image data. You can read the image through the fs module.
Return value:
[
{
bbox: {
x: 66.92952662706375,
y: 158.30181241035461,
width: 157.67111629247665,
height: 165.00252485275269
},
class: 'bear',
score: 0.9642460346221924
},
{
bbox: {
x: 180.56899309158325,
y: -0.32786130905151367,
width: 246.6680407524109,
height: 308.3251893520355
},
class: 'bear',
score: 0.9133073091506958
}
]
Example:
const myImgBuf = require('fs').readFileSync(myImgPath);
model.detect(myImgBuf);
model.detectAndRecognize(buf);
The detectAndRecognize function returns a Promise object, you can use await statement to get its return value.
Args: The buf parameter requires you to pass a buffer type of image data. You can read the image through the fs module.
Return value:
[
recognizeObject,
recognizeObject,
recognizeObject
]
Example:
const myImgBuf = require('fs').readFileSync(myImgPath);
model.detectAndRecognize(myImgBuf);
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