How fast is your Tensorflow COCO SSD

General
16

The code inside the function node is a bit specialized towards my specific setup, I hope it can be understood. At least it could be a point of reference for discussion, everything inside is pretty straight forward I think (and I think I can also clarify any unclear details). Here it is:

Please note that I have assigned a unique number to all my cameras. Like '51' or '11'. So when an image is captured from a camera it is published (by another separate process) to an MQTT broker at a camera specific topic. Like resized/22
In this way I can identify from which camera the image is captured. To be able to set the correct labeling, check the required score etc etc

let conf_lev = {
    'XX':0.50,
    '51':0.50,
    '52':0.50,
    '41':0.40,
    '42':0.35,
    '31':0.65,
    '32':0.45,
    '21':0.70,
    '22':0.45,
    '11':0.35,
    '12':0.35
};

let min_area = {
    'XX':700,
    '51':700,
    '52':700,
    '41':8600,
    '42':700,
    '31':4900,
    '32':5000,
    '21':3000,
    '22':4000,
    '11':1500,
    '12':3700
};

let ratios = {
    'XX':0.65,
    '51':0.65,
    '52':0.65,
    '41':0.65,
    '42':0.65,
    '31':0.65,
    '32':0.65,
    '21':0.65,
    '22':0.65,
    '11':0.70,
    '12':0.70
};

let ar = context.get("ar") || {
    'XX':0,
    '51':0,
    '52':0,
    '41':0,
    '42':0,
    '31':0,
    '32':0,
    '21':0,
    '22':0,
    '11':0,
    '12':0
};

let tmrs = context.get("tmrs") || {
    'XX':null,
    '51':null,
    '52':null,
    '41':null,
    '42':null,
    '31':null,
    '32':null,
    '21':null,
    '22':null,
    '11':null,
    '12':null
}

let labels = {
    'XX':"Just a demo nbr XX",
    '51':"Just a demo nbr 51",
    '52':"Just a demo nbr 52",
    '41':"At the front entrance door",
    '42':"Mobile webcam",
    '31':"In front of the carport",
    '32':"In the carport",
    '22':"Around the washroom entrance",
    '21':"In the garden",
    '11':"Near the front entrance door",
    '12':"Walking towards the carport"
};

let cam_pos = {
    'XX':'MotionXX:detect_now1',
    '51':'Motion5:detect_now1',
    '52':'Motion5:detect_now2',
    '41':'Motion4:detect_now1',
    '42':'Motion4:detect_now2',
    '31':'Motion3:detect_now1',
    '32':'Motion3:detect_now2',
    '21':'Motion2:detect_now1',
    '22':'Motion2:detect_now2',
    '11':'Motion1:detect_now1',
    '12':'Motion1:detect_now2'
};

function f(cam) {
    ar[cam] = 0;
    context.set("ar", ar);
    tmrs[cam] = null;
    context.set("tmrs", tmrs);
//    node.warn("Timer triggered: "+cam);
}


let detections = msg.payload;
let cam = msg.topic.split('/')[1];
let score = 0;
let clss = '';
let tclss = '';
let h = 0;
let w = 0;
for (var det in detections) {
//    node.warn(detections[det]);
    sc = detections[det]['score'];
//    node.warn(sc);
    clss = detections[det]['class'];
    w = detections[det]['bbox']['2'];
    h = detections[det]['bbox']['3'];
//    node.warn(w*h);
//    node.warn(w/h);
    if (clss === 'person'){
        tclss = clss;
        if (w*h > min_area[cam] && w/h < ratios[cam]){
            if (sc > score){
                score = parseFloat(sc.toPrecision(2));
//                node.warn(score);
                sc = 0;
            }     
        }
    }
}

if (tmrs[cam] == null) {
    tmrs[cam] = setTimeout( f, 60000, cam );
    context.set("tmrs", tmrs);
}


if ((tclss === 'person' && score > conf_lev[cam] && w*h > ar[cam]) || cam === 'XX'){
    ar[cam] = w*h;
    context.set("ar", ar);
    msg.payload = msg.image;
    delete msg.image;
    msg.filename = '/home/pi/pics/captured'+cam+'.jpg';
    let label = labels[cam];
    msg.caption = label+' '+tclss+' '+score
    msg.itr = 'Intruder detected: '+cam_pos[cam];
    return msg;
}