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import numpy as np
import cv2
from PIL import ImageGrab

fourcc = cv2.cv.CV_FOURCC(*'XVID') # or cv2.VideoWriter_fourcc('X','V','I','D') if you use opencv 3.0
vid = cv2.VideoWriter('record.avi', fourcc, 8, (500,490))
while(True):
    img = ImageGrab.grab(bbox=(100, 10, 600, 500)) #x, y, w, h
    img_np = np.array(img)
    #frame = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)

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var anchors = {}
anchors["minimap"] = [];
anchors["center"] = [];
anchors["botright"] = [];

function ProgressBar(data)
{
  this.data = data;
  this.value = data.value;
  this.disp_value = data.value;

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RegisterServerEvent('vRP:payment')
RegisterServerEvent('vRP:salary')

AddEventHandler('vRP:payment',function(player, amount)
  local user_id = vRP.getUserId(player)
  if user_id ~= nil then
  	vRP.giveMoney(user_id, amount)
  end
end)

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var tmi = require('tmi.js')

var options = {
  options: {
    debug: true
  },
  connection: {
    reconnect: true
  },
  identity: {

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var tmi = require('tmi.js');
var config = require('./config.js');
 
var client = new tmi.client(config.tmi);
client.connect();
 
 
client.on("subscription", function (channel, username, method, message, userstate) {
    if (method.prime === true) {
        console.log("Prime Sub");

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var myArray = ['Bobby', 'Jack', 'Danny'];
var favCities = ['Atlanta', 'Miami'];

function listFirst(array) {
  return array[0];
}

console.log(listFirst(myArray));

console.log(listFirst(favCities));

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DQN Setup Steps Cloney

1. init dqn in setup_play()
2. ?????? enter_train_mode() (WHAT IS DIFFERENCE BETWEEN RUN OBSERVER TRAIN)
3. next_step()?????????
4. build_frame_stack() and then / or update_frame_stack()??
5. pick_action(RANDOM or PREDICTED at % 20) how to format action mapping
6. now observe = next_step()?????
7. append_to_replay_memory() how to use reward parameter????
8. calculate_target_error() how to use observation parameter from previous step???

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    # =============================
    # -----------DQN TODO ---------
    # =============================
    def setup_play_ddqn(self):
        self.plugin_path = offshoot.config["file_paths"]["plugins"]

        context_classifier_path = f"{self.plugin_path}/SerpentCloneyGameAgentPlugin/files/ml_models/cloney_context_classifier.model"

        context_classifier = CNNInceptionV3ContextClassifier(input_shape=(288, 512, 3))
        context_classifier.prepare_generators()

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