//
// Shinobi - Tensorflow Plugin
// Copyright (C) 2016-2025 Moe Alam, moeiscool
//
// # Donate
//
// If you like what I am doing here and want me to continue please consider donating :)
// PayPal : paypal@m03.ca
//
// Base Init >>
var fs = require('fs');
var config = require('./conf.json')
var dotenv = require('dotenv').config()
var s
const {
  workerData
} = require('worker_threads');
if(workerData && workerData.ok === true){
    try{
        s = require('../pluginWorkerBase.js')(__dirname,config)
    }catch(err){
        console.log(err)
        try{
            s = require('./pluginWorkerBase.js')(__dirname,config)
        }catch(err){
            console.log(err)
            return console.log(config.plug,'WORKER : Plugin start has failed. pluginBase.js was not found.')
        }
    }
}else{
    try{
        s = require('../pluginBase.js')(__dirname,config)
    }catch(err){
        console.log(err)
        try{
            s = require('./pluginBase.js')(__dirname,config)
        }catch(err){
            console.log(err)
            return console.log(config.plug,'Plugin start has failed. pluginBase.js was not found.')
        }
    }
    const {
        haltMessage,
        checkStartTime,
        setStartTime,
    } = require('../pluginCheck.js')
    if(!checkStartTime()){
        console.log(haltMessage,new Date())
        s.disconnectWebSocket()
        return
    }
    setStartTime()
}
// Base Init />>

const ObjectDetectors = require('./ObjectDetectors.js')(config);

s.detectObject = function(buffer,d,tx,frameLocation,callback){
    new ObjectDetectors(buffer).process().then((resp)=>{
        var results = resp.data
        if(results[0]){
            var mats = []
            results.forEach(function(v){
                mats.push({
                    x: v.bbox[0],
                    y: v.bbox[1],
                    width: v.bbox[2],
                    height: v.bbox[3],
                    tag: v.class,
                    confidence: v.score,
                })
            })
            var isObjectDetectionSeparate = d.mon.detector_pam === '1' && d.mon.detector_use_detect_object === '1'
            var width = parseFloat(isObjectDetectionSeparate  && d.mon.detector_scale_y_object ? d.mon.detector_scale_y_object : d.mon.detector_scale_y)
            var height = parseFloat(isObjectDetectionSeparate  && d.mon.detector_scale_x_object ? d.mon.detector_scale_x_object : d.mon.detector_scale_x)
            tx({
                f:'trigger',
                id:d.id,
                ke:d.ke,
                details:{
                    plug:config.plug,
                    name:'Tensorflow',
                    reason:'object',
                    matrices:mats,
                    imgHeight:width,
                    imgWidth:height,
                    time: resp.time
                }
            })
        }
        callback()
    })
}