🔸 imagelib

A basic image handling library to process images. Allowing basic image resize and get image dimensions

//Include the library

ImageLib functions

imagelib.getImageDimension("user:/Desktop/test.jpg"); //return [width, height]
imagelib.resizeImage("user:/Desktop/input.png", "user:/Desktop/output.png", 500, 300); //Resize input.png to 500 x 300 pixal and write to output.png
imagelib.loadThumbString("user:/Desktop/test.jpg"); //Load the given file's thumbnail as base64 string, return false if failed
imagelib.cropImage("user:/Desktop/test.jpg", "user:/Desktop/out.jpg",100,100,200,200)); 
imagelib.classify("tmp:/classify.jpg", "yolo3"); //Classify an image using neural network, since v1.119

Crop Image Options

Crop the given image with the following arguemnts: 

1) Input file (virtual path)
2) Output file (virtual path, will be overwritten if exists)
3) Position X
4) Position Y
5) Crop With
6) Crop Height

return true if success, false if failed

AI Classifier Options (since v1.119)

ImageLib AI Classifier requires darknet to operate normally. If your ArozOS is a trim down version or using a host architecture that ArozOS did not ship with a valid darknet binary executable in system/neuralnet/ folder, this will always returnfalse.

Classify allow the following classifier options

1) default / darknet19
2) yolo3

The output of the classifier will output the followings

Name (string, the name of object detected)
Percentage (float, the confidence of detection)
Positions (integer array, the pixel location of the detected object in left, top, width, height sequence)

Here is an example code for parsing the output, or you can also directly pass it into the JSON stringify and process it on frontend

//Get image classification, will take a bit time 
var results = imagelib.classify("tmp:/classify.jpg"); 
    var responses = [];
    for (var i = 0; i < results.length; i++){
            "object": results[i].Name,
            "confidence": results[i].Percentage,
            "position_x": results[i].Positions[0],
            "position_y": results[i].Positions[1],
            "width": results[i].Positions[2],
            "height": results[i].Positions[3]