Oh my god. It's full of code!


Create an Alexa skill in Node.Js and host it on Heroku

Ok, here we go.

So for the last couple weeks I’ve been totally enamored with working with Alexa. It’s really fun to have a programming project that can actually ‘interact’ with the real world. Until now I’ve just been using my hacked together web server with If This Then That commands to trigger events. After posting some of my work on Reddit, I got some encouragement to try and develop and actual Alexa skill, instead of having to piggyback off IFTTT. With some sample code, an awesome guy willing to help a bit along the way and more than a little caffeine I decided to give it a shot.

The Approach: Since I already have a decent handle on Node.Js and I was informed there are libraries for working with Alexa, I decided Node would be my language of choice for accomplishing this. I’ll be using alexa-app-server and alexa-app Node.Js libraries. I’ll be using 2 github repos, and hosting the end result on Heroku.

How it’s done: I’ll be honest, this is a reasonably complex project with about a million steps, so I’ll do my best to outline them all but forgive me if I gloss over a few things. We will be hosting our server in github, creating a repo for the server, making a sub module for the skill, and deploying it all to Heroku. Lets get started. First off, go and get yourself a github account and a Heroku account. If you haven’t used git yet, get that installed. Also install the heroku toolbelt (which may come with git, I can’t quite remember). Of course you’ll also need node.js and Node Package Manager (NPM) which odds are you already have.

If you don’t want to create all the code and such from scratch and want to just start with a functioning app, feel free to clone my test app https://github.com/Kenji776/AlexaServer1.git

Create a new directory for your project on your local machine for your project. Then head on over to github and create yourself a new project/repo. Call it AlexaServer or something similar. Go ahead and make it a public repo. Do not initialize it with a readme at this time. This is the repo where the core server code will live. It’s important to think of the server code as a separate component from each individual skill, they are distinct things. You should see this screen.


Open a command prompt and change to the directory you created for your project. Enter the commands show in the first section for creating a new repository. Once those commands are entered if you refresh the screen you shoud see your readme file there with the contents shown like this.


Okay, now we are ready to get the Alexa-Server app, https://www.npmjs.com/package/alexa-app-server is what you are looking for. In your project directory type in

“npm install alexa-app-server”

This will take a few moments but should complete without any problems. In your project folder you’ll want to create a folder called apps. This is where each individual skill will live. We will cover that later. Now you’ll need to create your actual server file. Create a file called server.js. Put this in there.

'use strict';

var AlexaAppServer = require( 'alexa-app-server' );

var server = new AlexaAppServer( {
	httpsEnabled: false,
	port: process.env.PORT || 80
} );


Pretty simple code overall. The weird bit of code in the port section is for Heroku (they give your app a port to use when hosted). If not on Heroku then it will default to using port 80. Now you need to create your Procfile. This is going to tell Heroku what to do when it tries to run your program. It should be a file named Procfile in the same directory with no file extension. The contents of which are simply

“web: node server.js”

without quotes. We will also want to create a package.json file. So again in your project directory run the command

npm init

This will run a script and it will ask you a few questions. Answer them and your package.json file should get generated. Go ahead and push this all into Github using the following command sequence.

git add .
git commit -m “added server.js and Procfile, along with alexa-app-server-dependency”
git push origin master

If you view your Github repo online you should see all your files there. It should look something like this.

first repo push


You can see all of our files got pushed in. Now with the server setup, it’s time to create our skill. Create another GitHub repo. Call this whatever you like, hopefully something descriptive of the skill you are making. In your command prompt get into the apps directory within your main project. In there create another folder with a name same or similar to your new GitHub repo. Follow the same steps as before to initialize the repo and do the initial commit/push. Now we are going to indicate to GitHub that the apps/test-skill folder is actually a sub module so any dependencies and such will be maintained within itself and not within the project root. To do this navigate to the root project folder and enter.

git submodule add https://github.com/Kenji776/AlexaTestSkill.git apps/test-skill

Replacing the github project url with the one for your skill, and the apps/test-skill with apps/whatever-your-skill-is-named. Now Git knows that this folder is a submodule, but NPM doesn’t know that. If you try and install anything using NPM for this skill it’s going to toss it into the root directory of the project. So we generate a package.json file for this skill and then NPM knows that this skill is a stand alone thing. So run

npm init

Again and go through all the questions again. This should generate a package.json file for your skill. Now we are ready to install the actual alexa-app package. So run…

npm install alexa-app –save

and you should see that the skill now has it’s own node_modules folder, in which is contained the alexa-app dependency. After this you’ll have to regenerate your package.json file again because you’ve now added a new dependency. Now it’s time to make our skill DO something. In your skill folder create a file called index.js. Just to get started as a ‘hello world’ kind of app, plug this into that file.

module.change_code = 1;
'use strict';

var alexa = require( 'alexa-app' );
var app = new alexa.app( 'test-skill' );

app.launch( function( request, response ) {
	response.say( 'Welcome to your test skill' ).reprompt( 'Way to go. You got it to run. Bad ass.' ).shouldEndSession( false );
} );

app.error = function( exception, request, response ) {
	response.say( 'Sorry an error occured ' + error.message);

		"say the number {1-100|number}",
		"give me the number {1-100|number}",
		"tell me the number {1-100|number}",
		"I want to hear you say the number {1-100|number}"]
  function(request,response) {
    var number = request.slot('number');
    response.say("You asked for the number "+number);

module.exports = app;

Now, if you aren’t familiar with how Alexa works, I’ll try and break it down for you real quick to explain what’s happening with this code. Every action a skill can perform is called an intent. It is called this because ideally there are many ways a person might trigger that function. The might say “say the number {number}” or they might say “give me the number {number}” or many other variations, but they all have the same intent. So hence the name. Your intent should account for most of the common ways a user a might try to invoke your functions. You do this by creating utterances. Each utterance represents something a user might say to make that function run. Slots are variables. You define the potential variables using slots, then use them in your utterances. There are different types of data a slot can contain, or you can create your own. Check out https://developer.amazon.com/public/solutions/alexa/alexa-skills-kit/docs/alexa-skills-kit-interaction-model-reference for more information on slots types. I’m still figuring them out myself a bit. So the intent is triggered by saying one of the utterances. The slot is populated with the number the person says, and after reading the variable from the request, it is read back to the user.

So now, believe it or not, your skill is usable. You can test it by heading by starting your server. Again in your command line shell within the root project directory type

node server.js

A console window should show up that looks like this.

skill running

Now in your browser you should be able to bring up an emulator/debugger page be heading to

http://yourserver/alexa/your-skill-name       (EX: http://localhost/alexa/test-skill)

You should get a page that looks like this.


Holy cow, we did it! We have a functioning skill. The only problem now is that there is no way for Alexa to ‘get to it’. As in, Alexa doesn’t know about this skill, so there is no way for echo to route requests to it (also, I find it mildly creepy that when referring to Alexa, I keep accidentally wanting to type ‘her’. Ugh). So now we have to put this skill somewhere were it’s accessible. That is where Heroku is going to come in. Sure we could host this on our local machine, setup port forwarding, handle the SSL certs ourselves, but who wants to have their skill always running on their local box? I’ll do a bit on how to do that later since it requires creating a self signed SSL certificate using openSSL and such but for now I’m going to focus on getting this sucker into a cloud host.

Oh by the way, once everything is working smoothly, you should commit and push it all into github. Remember, you’ll have to do a separate commit and push for your server, and for the skill submodule since they are officially different things as far as github is concerned, even though the skill is a sub directory of your server. Also, I’m still learning exactly how submodules work, but if for some reason it doesn’t seem like your submodule is updating properly you can navigate to the project root folder and try this series of commands.

git submodule foreach git pull origin master
git add .
git commit -m “updated submodule”
git push origin master

with some luck that should do it. You’ll know it’s all working right when you open up your server repo in github, click the apps folder and you see a greyish folder icon for the name of your skill. Clicking it should show you the skill with all the files in there. Like this.

submoduleNow, on to Heroku. This part is pretty easy comparatively. Head on over to Heroku and create a new app. As usual, name it whatever you like, preferably something descriptive. Next you’ll be in the deploy area. You’ll be able to specify where the code comes from. Since we are smart, and we used github you can actually just link this heroku project straight to your github project.

hreoku connect
Just like that! However, this automatic integration does not seem to include sub modules, so we will have to add our skill submodule from the command line. So again navigate to your project folder from the shell, and run this command to link your directory with the heroku project.

heroku git:remote -a alexatestapp

Of course replacing alexatestapp with whatever the name of your heroku app is. You should get back a response saying that the heroku remote has been added. Now we need to push it all into Heroku (which in retrospect may have made the above linking done from the website un-needed, but whatever. It doesn’t seem to hurt anything). So now run

git push heroku master

You should be treated with a whole bunch of console output about compressing, creating runtimes, resolving dependencies, blah blah blah. Now with some luck your app should work via your hosted Heroku app. Just like when hosted locally we can access the emulator/debugger by going to


Replacing the domain with your heroku app, and the skill with whatever your skill is named. If all has gone well it should operate just like it did on your local machine. We are now finally ready to let Amazon and Echo know about our new skill. So head on over to https://developer.amazon.com/edw/home.html#/skills/list (you’ll need to sign up for a developer account if you do not have one) and click the add new skill button.

app create 1

On the next page it’s going to ask you for your intent schema. This is just a JSON representation of your intents and their slots. Thankfully that handy debug page generates that for you. So it’s just copy paste the intent schema from the debug page into that box. It will ask you about custom slot types, and odds are if you are making a more complicated app you are going to want to make your own, but really all that is is creating a name for your data type, common values for it (no it doesn’t have to be every value) and saving it. Spend some time reading the docs on custom slot types if you need to. It’s also going to want sample utterances, which again that debug page generates for you, so again, just copy paste. You might get a few odd errors here about formatting, or wrong slot types. Just do your best to work through them.



Next up is setting up the HTTPS. Thankfully Heroku handles that for us, so we don’t have to worry about it! Just select

“My development endpoint is a subdomain of a domain that has a wildcard certificate from a certificate authority”

Next up us account linking. I haven’t figured that one out yet, still working on it, so for now we just have to say no. Next up is the test screen. If everything is gone well up to this point, it should work just peachy for ya.


Next you’ll be asked to input some meta data about your app. Description, keywords, logo, icon, etc. Just fill it out and hit next. Finally you’ll be asked about privacy info. For now you have just say no to everything, and come back to update that when you’ve got your million dollar app ready to go. Hit save and you should be just about ready to test through your Echo. If you open your Alexa app on your phone and look for your skill by name, you should find it and it should be automatically enabled. You should now be able to go to your echo, use the wake word, speak the invocation name and ask for a number. If all has gone well you’ll hear the wonderful sound of Alexa reading back your number to you. For example

“Alexa Ask Test Get My Number”

Alexa should respond with what you defined as the app start message in your skill. You can now say

“Say the number 10”

Which should then be repeated back to you. Congrats. You just created a cloud hosted, Node.Js Alexa Skill that is now ready for you to make do something awesome. Get out there and blow my mind. Good luck!

Making Amazon Echo/Alexa order me a pizza

UPDATE! I’ve started a github project for my server software. Still very alpha but you can check it out here: https://github.com/Kenji776/AlexaHomeHub

If you follow my blog, you might have caught my post yesterday about how I bought an Amazon Echo device, and have begun creating my own custom actions for it. I started small, simply making it call out to my web server by using If This Then That (IFTTT – a website that allows you to connect and integrate different services). I got it to connect to my door lock and unlock service I had written, and even got it to chromecast specific pre-setup videos to one of my TV’s using a command line tool. Feeling somewhat confident I decided it was time to take on something a little more in depth, but oh so worth it. I was going going to make Alexa order me a pizza.

If you are an Echo/Alexa user you might know that there is already support for ordering a pizza but only from Dominos. It uses some system of having a saved order, then tweeting a specific bit of info at the Dominos twitter account that is tied to your order which then places it. This has a few drawbacks. Primarily being that it orders you a Dominos pizza (sorry guys, in all fairness Dominos has gotten a lot better in recent years). Also it requires twitter integration, and as far as I know only supports one order that you have saved (I could be wrong). The using a saved order was a good idea as it streamlines and simplifies the ordering process quite nicely. I wanted to do something like this, but instead I wanted Sarpinos pizza, and I wanted to be able to pick from several different pre-created orders. Using my knowledge of browser automation that I picked up from my door lock/unlock project and my existing web server, I got to work.

First off, I had to figure out all the things that needed to happen. Off the bat I knew I’d be using their online ordering interface. They don’t have an API, so I knew I’d have to be automating browser interactions. Next I had to break down the process of ordering the pizza online step by step, all the HTML elements involved and how to interact with them. Then I would be able to automate those interactions using the Selenium library. So I went through the process like a normal person and created this list. At each step I inspected the HTML elements involved and recorded them so I could figure out how to identify them and interact with them later. I created an order and saved it as a favorite so next time I’d come back in I would be prompted if I’d like to order that again. From there I was able to create the following list of things I knew needed to get done.

Order Steps:

1) Invoke: https://order.gosarpinos.com/Login/

2) Wait For Load

3) On load populate credential fields:
	- <input class="text-box single-line valid" data-val="true" data-val-required="Email is required" id="Email" name="Email" type="email" value="" >
	- <input class="text-box single-line password valid" data-val="true" data-val-required="Password is required" id="Password" name="Password" type="password" value="" >

4) Click Login button
	- <input type="submit" value="Login" class="ui-button ui-widget ui-state-default ui-corner-all" role="button" aria-disabled="false">
5) Wait For Page Load

6) Find button with provided favourite id (428388)
	- <button class="wcFavoriteSelectFavoriteButton ui-button ui-widget ui-state-default ui-corner-all ui-button-text-only" data-favorite-id="428388" id="wiSelectFavorite428388" type="button" role="button" aria-disabled="false">

7) For For Delivery Popup to load


A fair amount of steps, but none of them super complicated. I knew I’d have to learn a bit more about Selenium as this interaction was definitely more complicated than the door lock code, and that one was already seemingly over complicated. Thankfully I did, and found out that in my previous attempt I had been mixing synchronous and async methods unknowingly hence leading to perceived complexity (I thought driver.wait() was a async method and you put everything that depended on it inside. Turns out it’s synchronous and once the condition inside is true the program continues. No wonder it was acting a little funny). I knew also since I was going to be passing in a fair amount of data (username, password, order id, credit card info, etc) that I should probably define an object which would have all the required properties, then just pass JSON into my web service that mirrored that object. This is what I came up with.



Obviously the sensitive values are blacked out, but you get the jist. My webserver is already primed to look for post requests that have a JSON payload. The ‘router’ code looks at the ‘action’ attribute to figure out what function to send the payload to. I created a new ‘pizza’ action and related function. Here is that function.

function orderPizza(orderObject,callback)
	var orderResult = new Object();
	//create instance of selenium web driver
	var driver = new webdriver.Builder().

	//request the login page with the locks page as the return url
	//find and click submit button

	console.log('Logged in as: ' + orderObject.username);

	//wait for order page to load
	driver.wait(function() {
		return driver.getTitle();
	console.log('Attempting To Choose Favorite Order With Id: ' + orderObject.orderId);
	//have to wait until the proper order button appears since it's in a dialog. If it isn't found after 5 seconds, fail. Otherwise click the corresponding order button
	//the favorite order button has an attribute 'type' of 'button' and a 'data-favorite-id' attribute with the id of that order
	driver.wait(function () {
		return driver.findElement(webdriver.By.css("div[aria-describedby='wiFavoriteListDialog']")).isDisplayed();
	}, 5000);
	driver.wait(function () {
		return driver.findElement(webdriver.By.css("button[data-favorite-id='"+orderObject.orderId+"']")).isDisplayed();
	}, 5000);
	//after the button above is clicked, that dialog closes and another one opens. This one asks the user to select delivery or pickup. We want delivery
	//the delivery button has an attribute with a 'data-type' of 'WBD' and an attribute 'role' of 'button'
	driver.wait(function () {
		return driver.findElement(webdriver.By.css("button[data-type='WBD'][role='button']")).isDisplayed();
	}, 5000);
	//ensure the checkout button is visible
	driver.wait(function () {
		return driver.findElement(webdriver.By.css("button[id='wiLayoutColumnGuestcheckCheckoutBottom'][role='button']")).isDisplayed();
	}, 5000);
	//hacky method to ensure that the modal dialog should now be gone and we can click the checkout button
	//then the browser will move to the order screen. Once it loads we have to populate the order field data

	//wait for payment page to load
	driver.wait(function() {
		return driver.getTitle();

	//wait until the pay by credit card button shows up.
	driver.wait(function () {
		return driver.findElement(webdriver.By.id("wiCheckoutPaymentCreditCard")).isDisplayed();
	}, 5000);

	//check the pay by credit card radio button
	//wait until the credit card number box shows up
	driver.wait(function () {
		return driver.findElement(webdriver.By.id("Payment_CCNumber")).isDisplayed();
	}, 5000);
	//populate the form fields

	//stupid jQuery ui selects are impossible to set with normal selenium since the original select is hidden. So use an execute script to set em.


	//wait for confirmation page to load.
	driver.wait(function() {
		return driver.getTitle();


	driver.wait(function() {
		return driver.getTitle();
		console.log('Ordering complete!');
		orderResult.success = true;
		orderResult.message = 'Order Placed Successfully';



Now if that code seems a little dense or confusing, don’t feel bad. It took me several hours of trial and error to figure it out, especially then it came to setting the select list values, and getting the script to wait while various elements where created and destroyed by the page. Selenium has this super fun behavior where if you ever try and reference an element that doesn’t exist, the whole script goes down in flames. In response to that I made my code very ‘defensive’ checking to make sure elements that are required frequently before attempting to interact with them.

With the script created and integrated into my web server ‘router’ I was ready to get IFTTT to invoke it. Once again it was as simple as creating a new recipe with Alexa as the If and the Maker make a web request feature as the do.

pizza 1pizza2

You can see that with the combination of specific phrases and the fact that you can have multiple saved orders, it would be easy to setup many different possibilities. My roommate is even going to create his own IFTTT account and link it to my Alexa. Then he can create his own orders, specify his own credit card information in the JSON payload, and order whenever he wants using the same device but have his own information. The next step I think is to encrypt the JSON payloads which contain the credit card info and then decrypt them when the arrive at my server. That way I’m not storing my CC info in plain text anywhere which right now is a bit of a concern. This was mostly just proof of concept stuff last night, but I was too excited not to share this as soon as I could, so some of the ‘polish’ features are missing but overall I think it’s a damn good start. Now if you’ll excuse me, I’m going to get myself a pizza.

Update: Adding encryption was pretty easy. First I got some encrypt and decrypt functions set up. Like this.

// Nodejs encryption with CTR
var crypto = require('crypto'),
    algorithm = 'aes-256-ctr',
    password = 'xxxxxxxxxxxxxxxxxxxxxxxxxx';

function encrypt(text){
  var cipher = crypto.createCipher(algorithm,password)
  var crypted = cipher.update(text,'utf8','hex')
  crypted += cipher.final('hex');
  return crypted;
function decrypt(text){
  var decipher = crypto.createDecipher(algorithm,password)
  var dec = decipher.update(text,'hex','utf8')
  dec += decipher.final('utf8');
  return dec;

Then update my incoming data object so that the encrypted data was in its own property so I could still tell what kind of request it was without having to decrypt the payload first (since my webserver supports other, unencrypted calls).

"data":"f613f8f479bad299bdfedf [rest of encrypted string omitted]",


Then just had to change my ‘router’ to decrypt the incoming data if encryption was detected.

else if(action == 'pizza')
	//pizza request contains encrypted info. Decrypt and send to function
	var pizzaRequestData = new Object();
		console.log('Encryped Payload Detected. Decrypting Containted Data');
		pizzaRequestData = JSON.parse(decrypt(parsedContent.data));
		console.log('Decryption complete');

		responseObject.message = 'Ordering Pizza!';
		//because order pizza is async the result data comes in a callback
			responseObject.pizzaRequest = data;
		console.log('Un-encrypted pizza order detected. Skipping');

After that I just had to use the encrypt function to generate an encrypted version of my pizza request data, update the IFTTT recipe with the new request and that’s it! Now my CC information is safely encrypted and I don’t really have to worry about it getting intercepted. Yay security.

Even more Automation with Tasker and Geofencing

So if you saw my last blog, you know I spent some time writing a small application that would log into my home security providers website, then lock and unlock my door on a timed schedule. If you haven’t read it, check it out at https://iwritecrappycode.wordpress.com/2015/10/21/automating-things-that-should-already-be-automated-with-selenium-and-node-js to get some context. While that was great and all, I figured I could do more with it. I mean timed scheduled are great, but I do occasionally leave the house so you know what would be really cool? If my door automatically locked when I left, and unlocked itself again when I returned home. I figured it should be a reasonably simple process to make my script respond to remote commands, and trigger my phone to send such commands when leaving and entering an area. Turns out I was mostly right. At it’s core its pretty easy, but there are a lot of moving parts and places to make mistakes, which I did make plenty of (it’s crazy how often as a programmer I type the wrong numbers in somewhere).

So obviously the first part of this challenge was going to be to setup my script to respond somehow to external requests. Being written in Node.js it made sense that it respond to web requests. I figured the requester should provide the username and password to login to the security website with, as well as a desired action of either lock or unlock. I decided to make it respond to POST requests just to reduce the possibility of some web spider hitting it and by some fluke making it do something (I also put the service on a different port, but we’ll cover that later). So the service listens for post requests with some JSON encoded data in the request body and then makes the request to alarm.com. The code for that method is as follows.

//create a server
server = http.createServer( function(request, response) {

    console.log('Request received from ' + request.connection.remoteAddress);

	var responseObject = new Object();
	responseObject.message = 'Waiting';
	responseObject.status = 'OK';

	//listen for POST requests
    if (request.method == 'POST') {

		console.log("POST Request Received");

		//when we get the data from the post reqest
        request.on('data', function (data)
				console.log('Raw Data: ' + data);
				//parse content in the body of the request. It should be JSON
				var parsedContent = JSON.parse(data);
				//read some variables from the parsed json
				var action = parsedContent['action'];
				var password = parsedContent['password'];
				var username = parsedContent['username'];

				responseObject.action = action;

				if(action == 'lock')
					responseObject.message = 'Sent Lock Request';
					console.log('Locking Door!');
					//because toggledoor is async the result data comes in a callback
						responseObject.lockRequestData = data;

				else if(action == 'unlock')
					responseObject.message = 'Send Unlock Request';
					console.log('Unlocking Door!');
					//because toggledoor is async the result data comes in a callback
						responseObject.lockRequestData = data;

					console.log('Invalid Post Acton ' + action);
					responseObject.message = 'No method defined with name: ' + action;
				responseObject.message = 'Error: ' + exception.message;
		console.log('Non post request. Ignoring.');
		responseObject.message = 'Please use post request';

}).listen(PORT, function(){
    //Callback triggered when server is successfully listening. Hurray!
    console.log("Server listening on: http://localhost:%s", PORT);

So now with a server that listens, I need to allow requests from the outside world to come in and actually get to my server. This is where my old days of networking came in handy. First I decided I’d like to have a domain name to reach my server instead of just an IP address (maybe I can setup dynamic DNS later so if my home IP address changes, my registar it automatically updated with the new one and will change my zone file records). I know this is more of a programming blog, but just a basic bit of networking is required. First I headed over to GoDaddy and grabbed myself a cheap domain name. Then changed the DNS settings to point the @ record at my home IP address.

Simple DNS Config

Now going to my domain would send traffic to my home, but of course it would be stopped at my firewall. So I had to setup port forwarding in my router. This is where I was able to change from using the web traffic port 80 to my custom port to help obscure the fact that I am running a web server (as well as having to change the listening port in the Node script). Of course I decided to turn off DHCP on the machine hosting this and give it a static IP address. I would have used a reservation but my crappy router doesn’t have that option. Lame.

port config

After some fiddling with settings for a bit my service was now responding to outside traffic. The only bummer part is that my domain name does not work within my network because again my router is crappy and doesn’t support NAT loopback, so it doesn’t know how to route the request to the right machine internally. I could modify my hosts file or setup my own DNS server but it’s not worth it seeing as this service is only useful when I’m outside my own network anyway.

Now the trickiest part, I needed to find a way to make my phone detect when I entered or left a given geographic region and when it detected that, send the specially crafted POST request to my server. I knew of two options off the top of my head that I had heard of but never played with. The first one I tried was a service called ‘If this then that’ or IFTTT for short. While the website and app are very sleek and they make configuration of these rules very easy, there was one problem. It just didn’t work. No matter what I tried, what recipes I configured nothing worked. Not even when triggered manually would it send the POST request. After playing with that for a bit, I decided to give up and give the other app I had heard of a shot. Tasker.

So if IFTTT is the mac of task automation (sleek, easy, unable to do the most basic things). Then Tasker is Linux. It’s extremely powerful, flexible, a bit difficult to understand and not much to look at. It does however have all the features I needed to finish my project. I ended up buying both Tasker (its like 3 bucks) and a plugin for it called AutoLocation. You see Tasker by itself is fairly powerful but it allowes for additional plugins to perform other actions and gather other kinds of data. AutoLocation allowed me to easy configure a ‘geofence’ basically a geographic barrier upon passing through which you can trigger actions. So I configured my geofence and then imported that config data into Tasker. Then it was simple a matter of created two profiles. One for entering the area and one for leaving it.

GeoFence Takser Profiles

I also added a simple vibrate rule so that my phone will buzz when either of the rules trigger so I know the command was sent. Later that night when I headed off to the store, that little buzz was the sweet vibration of success. I may or may not have yelled for joy in my car and frightened other motorists. I hope perhaps this post might inspire you to create own you crazy automation service. With the combination of selenium, node, tasker, and a bit of networking know how it’s possible to create all kinds of cool things.  If you’d like to download the source code for my auto lock program you can grab it below (I don’t really feel like making a git project for something so small. Also I realize its not the best quality code in the world, it was meant to be a simple script not a portfolio demo).

Download AutoLock Source

Till next time!

Automating Things That Should Already Be Automated With Selenium and Node.js

So I had invited my parents over a while back, and it came up that I don’t often lock my front door. Of course being good parents they chided me about it saying that I should really do so. I really have no excuse because I even have an app that allows me to to it remotely (yay home automation) but the be honest I’m just forgetful when it comes to things like that. However I decided to heed their warning and do something about it. I decided if I was going to get diligent about locking my door, I couldn’t be the one in charge of actually doing it, I’d have to make a computer do it. The problem is, that while my home security provider does offer a web app, and a phone app for locking the door and a very basic ‘rule’ system (arm the panel when the door is locked, vice versa) there are no time based controls, so I’d still have to actually do something. Totally unacceptable.

After some investigation I found as I had figured that my security provider does not offer any sort of API. Nor would it be easy to try and replicate the post request that is send from the app to trigger the lock door command due to numerous session variables and cookies and things include unique to each login session. Nope, if I was going to automate this it seemed like I’d actually have to interact with the browser as much as the thought displeased me (I’m all for dirty hacks, but c’mon). At first I looked at python for a solution, but as seems to often be the case with python every discussion of a solution was disjointed with no clear path and generally unsatisfactory (sorry python). Instead I turned to Node to see what potential solutions awaited me there. After a bit of looking around I found Selenium for Node. While it’s obvious and stated focus was on automated web app testing, not intentional browser automation scripts I could see no reason it wouldn’t work.

Quickly I spun up a new Node project and used NPM to grab the Selenium package (even after many uses NPM still feels like some kind of magic after manually handling javascript libraries for so long). Followed the guide to getting the Selenium web drivers to work, which at first seemed a bit odd having to install executable on my system for a javascript package but it makes perfect sense in retrospect. After finding a basic Selenium tutorial I was ready to attempt to get my script to login to alarm.com’s web page. First I had to get the names of the inputs I wanted Selenum to fill. Of course chrome makes this easy, just right click, inspect element and snag the names of the inputs.

Finding the required name property is easy.

Finding the required name property is easy.


Then simply tell Selenium to populate the boxes and click the login button.


var driver = new webdriver.Builder().

Thankfully just through dumb luck when creating this my session had timed out and I found out the login page accepted a return url parameter that it would direct the browser to after successful login. So now, if the login goes smoothly, the browser should be at the screen where I can control the locks. A different button is used to lock or unlock the door and only one is visible at a time. So writing my function in such a way that it accepted boolean ‘lock’ (where false would be unlock) param and then failing if it’s not able to find the button is a safe way to ensure I don’t unlock the door when I mean to lock it and vice versa.

driver.wait(function() {
	return driver.getTitle().then(function(title) {
		console.log('Toggling Door Status');
			lockResult.message = 'Lock request sent!';
			lockResult.message = 'UnLock request sent!';					
		lockResult.success = true;


		return lockResult;
}, 1000);


I don’t know Selenium super well yet, but it seems that after the login button is clicked the driver waits until it can retrieve the title of the page (which is a easy way to tell the page has at least somewhat loaded) and when it has then run the inner logic for clicking the proper button.  Honestly I’m not totally sure, but it works and that’s the important thing 😛

I was actually a bit shocked when my little function worked. Calling it with the proper username and password actually made the door lock a few moments later, much to my dogs surprise as he sat napping in the living room (the little motor on that lock is kind of loud). Now the next peice of this puzzle was to invoke that function on a timer system. Locking the door at say, 11:00pm and unlocking at 8:00am. This turned out to require only your regular every day javascript, nothing fancy.

var lockHour = 23;
var unlockHour = 8;
var beenLockedToday = false;
var beenUnlockedToday = false;

var date = new Date();
var current_hour = date.getHours(); 
console.log('Checking current hour for lock status checks. Current hour is ' + current_hour  + ' Will automatically lock at ' + lockHour + ' and unlock at ' + unlockHour + ' listening on port ' + port);
function monitorLoop() {
	date = new Date();
	current_hour = date.getHours();       
	console.log('Checking local hour. It is ' + current_hour);
	if(current_hour >= lockHour && !beenLockedToday) 
		console.log('Lock hour hit or passed and door has not been locked. Locking!!');
		beenLockedToday = true;
	if(current_hour == unlockHour && !beenUnlockedToday) 
		console.log('Un-Lock hour hit!');
		beenUnlockedToday = true;
	if(current_hour == 0)
		console.log('Resetting lock status variables');
		beenLockedToday = false;
		beenUnlockedToday = false;		




It’s just a function that is called via setInterval every hour. It checks the current hour against my two predefined lock and unlock times. I used a couple variables to track if the door has been locked or unlocked so if I reduce the time on the event loop it’s not attempting to lock/unlock the door every few minutes and wearing out the batteries on the motor. Obviously omitted from this is my alarm_username and alarm_password variables stored higher up in the script. With this event loop and the Selenium automation function I now have one less thing to worry about. Now if I could just find a Node.Js host that supported Selenium (damn you Heroku). So if anything, I’d say this is the take away: Don’t do manually what you can automate, and browser automation with Selenium is crazy easy. So easy that when it all worked I was almost disappointed that it seemed like I didn’t do anything.

Glorious event loop in action

Glorious event loop in action

Till next time!

Also, be sure to check out the addendum to this project in my next blog post where I added automatic operations with a geofence. https://iwritecrappycode.wordpress.com/2015/10/21/automating-things-that-should-already-be-automated-with-selenium-and-node-js/

Saltybot – A descent into salty, salty madness

*Article below was written pertaining to Saltybat as it was in September 2013, I have no idea the current standing of the website or if the techniques below are still applicable/required (looking now it seems that the fighter names are available right next to the bet buttons, so the whole needing an OCR scanner to extract the names is no longer required. Bummer that made me feel so smart too)


I don’t even care about the bucks, it’s about solving the problem. I know that somewhere in the chaos there is data that will allow me to get every bet correct and ‘solve’ the problem. But wait, let me back up a step. First, what the hell is Saltybet? It’s an online video feed of your favorite characters new and old fighting it out controlled by computer AI. You bet on the matches and get ‘saltybucks’ if you win based on a weighted odds system. Before they duke it out both characters stand on screen for about 45 seconds allowing you to consider whom to bet on. After that window betting closes the screen blanks out for a moment, then the fight begins. At that point all you can do is sit and watch (and bitch in the chat window). No you can’t do anything with the money and there is very little rhythm or reason to what makes a good character (one of the best, if not the best currently is an interpretation of Ronald McDonald). It’s pretty addictive to watch and I’ve been having a good time just keeping it on my spare monitor during my work day.





I could write a small program, just a little javascript bookmarklet that could take the names of the fighters and using past data tell me who is most likely to win. Hell maybe I could even automate some of it. Overcome with excitement I pushed the major technical challenges out of my mind so not to kill my motivation. I just wanted to try to see what I could do. So my last weeks obsession began.

The Bot

First off, I just got this working no more than like 45 minutes ago so I’m pretty excited about it. That does mean that though some of the code I show you and approaches I use may not be the best, as they are mostly just POC to see if my architecture works. I don’t intent to release this final product so it may never get totally cleaned up. That aside, please find below my decent into madness as I write one of the most complicated cobbled` together things I’ve ever even heard of.

I needed data. A lot of it. For an analytic engine you need data points. Thankfully if you are willing to pitch in a few bucks for server costs you get access to all the characters win loss record and access to the results of every match you have bet on. So not only could you calculate win percentage if you know who various characters won and lost against you could implement a rating system like they use in chess (ELO) or maybe even Glicko. The issue here though was that of course that data was not presented in any kind of API, it was just an HTML table. So the first challenge was trying to get that HTML data into some kind of actual data structure. After a little bit of ajax and jQuery magic I was able to convert the table into a javascript object keyed by the fighters name. I ran their name though a simple regex to remove spaces, make it lower case to make matching easier. I was making progress, now typing in the two fighters name would net me their win loss percents, which are powerful numbers but of course don’t tell the whole story (a person with a 1-0 record might have a 100% win loss, but that isn’t nearly as good someone with a 30-5 record even though they’d have a lower win percent).

Next up was trying to implement a real rating system. I know there are systems out there that could tell you the true strength of a player based on their previous matches and who they won and lost against. Chess ratings implement this kind of system as well as various competitive online games. I knew that something like that could go a long way toward getting me to real odds. The problem is that you can only see the record of who a fighter won and lost against if you bet on that match. There was no way I’d have time to sit around and bet on every match, nor did I want to. I knew I’d have to setup some kind of automated betting to just put in small bets for me to gather data while I was doing other things. First though, I wanted to get the rating system in place so as data came in I could see it affect the results of the equation. After some research and consideration I decided on the ELO system (mostly because I am a bit familiar with it, and its fairly easy to implement and I found some sample code so I didn’t have to write it myself XD ). The basic idea is that every character starts at a rating of 1200, then based on who they win or lose against their rating changes. The more matches they go through the higher the confidence of the result. The simple function for calculating the change in score looks like this.

function CalculateEloRatingChange(player1Score,player2Score,result,gamesPlayed) 

	var Elo1 = player1Score; 
	var Elo2 = player2Score; 
	var K = Math.round(800/gamesPlayed); 
	var EloDifference = Elo2 - Elo1; 
	var percentage = 1 / ( 1 + Math.pow( 10, EloDifference / 400 ) ); 
	var win = Math.round( K * ( 1 - percentage ) ); 
	var draw = Math.round( K * ( .5 - percentage ) ); 
	if (win > 0 ) win = "+" + win; 
	if (draw > 0 ) draw = "+" + draw; 

	if(result == 'win')
		return parseInt(win,10);
		return parseInt(Math.round( K * ( 0 - percentage ) ),10); 

Like I said this was pulled from another source, so I’m not 100% certain on the logic used, but it seems solid and it’s been returning what seem to be reasonable numbers for me. So now all I had to do is pull the betting history table and iterate over it back start to finish, each time feeding the result of the match into this function and recording the new ELO score for that character. Of course since I only have access to the data for matches I have bet on, this is not perfect or absolute, but it is better than nothing and as I keep gathering more data it will just get more and more accurate (addendum: later on I started farming results from other players. Offering them access to the tool in exchange for their betting history which I could feed into my engine).

Now I was able to get a win percent and an ELO score. I was well on the way to having some meaningful data that could point me in the right direction. Both these facts left out something that I thought was pretty crucial though. If this EXACT matchup has happened before the results of that are likely to be repeated and should definitely be taken into consideration. So In the betting history I also decided to look to see if this same match had happened before. If so I initially just printed out a warning to my utility to let me know. I knew that that should have it’s own numerical meaning as well but I couldn’t find any formula like that online so i decided to brew my own. I really don’t have much a background in probability and stats or anything like that so I am really not sure about the weights that I assigned the various outcomes. Maybe someone with those skills could help me tweak this. Overall my scoring formula looks like this

function calculateProjectedWinner(player1Name,player2Name)
	//find players rating difference and record
	fighterCareers[player1Name].ratingDiff = fighterCareers[player1Name].eloScore - fighterCareers[player2Name].eloScore;
	fighterCareers[player2Name].ratingDiff = fighterCareers[player2Name].eloScore - fighterCareers[player1Name].eloScore;

	//calculate their win probabilities. The Elo system has it's own function for calculating win probability
	//based on scores, so I just use that as my 'baseline' probabilities. Then I modify it using my other data later on.
	fighterCareers[player1Name].eloWinProbability = parseInt(calculateEloWinOddsPercent(fighterCareers[player1Name].ratingDiff) * 100,10);
	fighterCareers[player2Name].eloWinProbability = parseInt(calculateEloWinOddsPercent(fighterCareers[player2Name].ratingDiff) * 100,10);

	//calculate custom their win probabilities starting at ELO
	fighterCareers[player1Name].computedWinProbability = fighterCareers[player1Name].eloWinProbability;
	fighterCareers[player2Name].computedWinProbability = fighterCareers[player2Name].eloWinProbability;

	//now we need to see if these two players have had any previous matches together. If so we iterate over them
	//and modify their win probabilities accordingly.
	var prevMatches = findPreviousMatch(player1Name,player2Name)		

	for(var i = 0; i < prevMatches.length; i++)
		var winner = prevMatches[i].winner;
		var loser = prevMatches[i].loser;

		//we don't want to make their probability much higher than 95 because we can never be that sure and also
		//anything over 100 is totally meaningless. I decided a factor of 8 percent per win seems about decent. Maybe
		//it should be a little more? I don't know it's still something I'm kind of playing with. 
		if(fighterCareers[winner].computedWinProbability < .92)
			fighterCareers[winner].computedWinProbability = fighterCareers[winner].computedWinProbability + 0.08;
		if(fighterCareers[loser].computedWinProbability > .08)
			fighterCareers[loser].computedWinProbability = fighterCareers[loser].computedWinProbability - 0.08;

	//their win loss percent can be a good statistic if it is composed of enough data points to be meaningful.
	//here is where I wish I had more prob and stats background because I really don't know how many matches it would
	//take for this percent to be actually significant. I'm guessing at 10, so I decided to go with that. If both chars
	//have more than 10 matches under their belt, then lets include their win loss percents in our calculation.
	if(fighterCareers[player1Name].total >= 10 && fighterCareers[player2Name].total >= 10)
		//get the difference between the two win percents. So if we had p1 with 50 and p2 with 75 the difference is 25
		//yes I know ternaries are hard to read, but its cleaner than a stupid one line if statment. Just know that this
		//will return a positive amount that is the difference in win percent between the two.
		var winPercentDifference = fighterCareers[player1Name].winPercent > fighterCareers[player2Name].winPercent ? fighterCareers[player1Name].winPercent - fighterCareers[player2Name].winPercent : fighterCareers[player2Name].winPercent - fighterCareers[player1Name].winPercent;

		//multiple that difference by how confident we are (total number of matches) topping out at. So a number from 20 to 100
		var confidenceScore = fighterCareers[player1Name].total + fighterCareers[player2Name].total > 100 ? 100 : fighterCareers[player1Name].total + fighterCareers[player2Name].total;

		var adjustment = Math.round((winPercentDifference) * (confidenceScore/100)/2);

		//make the actual adjustments to the players probabilities
		console.log('Proposed modifying win perceny by +/- '+ adjustment);
		if(fighterCareers[player1Name].winPercent > fighterCareers[player2Name].winPercent)
			fighterCareers[player1Name].computedWinProbability += adjustment;
			fighterCareers[player2Name].computedWinProbability += adjustment*-1;	
			fighterCareers[player1Name].computedWinProbability += adjustment*-1;
			fighterCareers[player2Name].computedWinProbability += adjustment;			

	//find the winner name
	var projWinner = fighterCareers[player1Name].computedWinProbability > fighterCareers[player2Name].computedWinProbability ? player1Name : player2Name;

	//dream mode is 'intelligently making the stupid bet'. Because long shot bets have such high payouts they can be worth betting on 
	//if you have nothing to lose. Since you are always given 'bailout' cash if you end up with 0 or in the hole, it makes sense to 
	//bet on super long shots. If they win you get a TON of cash. If they lose you are just right back to where you started. Of course
	//that's up to the player though if they want to use that mentality so I made it optional. Also most players would only want to make stupid bets
	//if they have under a certain amount to keep from losing their fortune, and because at higher dollar values you can bet a large enough
	//percent of the total pot to still make good returns.
	if(dreamMode && saltyBucks < dreamModeDisabledAtAmount)
		var winPercentDifference = fighterCareers[player1Name].computedWinProbability > fighterCareers[player2Name].computedWinProbability ? fighterCareers[player1Name].computedWinProbability - fighterCareers[player2Name].computedWinProbability : fighterCareers[player2Name].computedWinProbability - fighterCareers[player1Name].computedWinProbability;
		if(winPercentDifference > dreamModePercentThreshold)
			$('#statusDiv').html('Bet on the dream!');
			 projWinner = fighterCareers[player1Name].computedWinProbability < fighterCareers[player2Name].computedWinProbability ? player1Name : player2Name;
	$('#statusDiv').html('Projected winner is ' + projWinner);
	return projWinner;

Great, now I had pretty confidently who was going to win and lose. But I was still short on data and betting manually all the time was getting to be a pain. My bot could auto bet, but not know who was fighting, or I could manually bet and have to actually enter names to do it. At this point you are probably saying, ‘well just extract the character names from somewhere, feed them into the formula and be done with it!’. I wish it was that simple. The stream of the fight is an embedded flash object and the names of the characters do not appear anywhere. The names are simply not available by any conventional means. It seriously seemed like the author went out of his way to make the names not available to prevent this kind of thing. I knew I’d have to solve that problem but, for the time being I needed to collect data. I settled on just having a stupid bot bet small amounts on someone at random so I could harvest that sweet sweet result data.

Even with that decision it wasn’t totally easy. Because it’s an embedded flash object how would I know when the betting window is open? I’ve only got about 45 seconds from when betting opens to when it closes, so whatever I do has to be reasonably quick. I then realized that the status text below the video changes to ‘Betting is Now open’ when you can bet. I simply told my bot to keep on a DOM transform onchange function to that. When that element changes evaluate the text and figure out if it says betting is now open. If so, wait about 40 seconds (so I have time to enter a manual bet if I want to) and then if no bet has been placed enter one. Using that same technique I know when the fight starts, ends, and payouts have been distributed. That ended up working out pretty well, though occasionally there seemed to be some sever delays that prevented entering a bet if I was too close to the deadline.

What my javascript bookmarklet looks like

What my javascript bookmarklet looks like

Using the same kind of trick I was able to extract the players current saltybuck total so i could deduce how to bet a small percent amount of their total, instead of just some static amount. Things were coming together well. I could just leave the bot on all night and it would bet for me. There were one or two mornings i came back and it had won me over 100K (randomly of course, it had no idea who it was betting on at this point). I build a nice little interface using jQuery UI that could be launched via my bookmarklet and if i entered the names I could get some decent odds data. I even rigged up an auto complete on the fighter names based on all the known fighters from the win/loss totals table. I added a few more fun little features, a hotkey combination to show and hide the window. I even added a sound effect ‘Oh yeah‘ if the bot wins a big amount of money (currently defined as over 10K, though I should probably make it to something like over 200% of your current total topping out at like 50K or something). When I actually paying attention and betting I was doing well, and if I walked away the bot would take over and place small bets to keep that sweet data stream coming in.

I knew that this was about as far as I could take the bot running as just a javascript thing bookmarklet thing. If I wanted more (centralized data so ELO and such didn’t have to be recalculated every fight), potentially to actually know who is fighting, I’d have to step out and really tread into unknown territory. I was going to need to somehow get a screenshot of who was fighting during the betting time. I was going to need to extract the names from the image. I’d have to feed that into some kind of optical character recognition engine (OCR). Then I’d have to take those results and make them available via a web service. I’d have to modify the bot to reach out to that webservice to trigger the reading and get the names. This couldn’t be done in the browser so I was going to need to develop some kind of server mechanism. I’d also need about 5 pots of coffee.

The Server

I decided I’d tackle what I considered to the easier part first to keep my spirits up and keep me from quitting when I reached the part which i knew would be most difficult (the OCR). The server had a fairly simple job to do in my mind. I needed to listen for a call from the client (since the client knew when the betting screen was open, it could make the callout, where as the server would have no idea because that monitoring functionally was still built into the client part. I’d have to refactor this later). When it got the request it would need to take a screenshot of the browser window which would also have to be running on the server. Ideally it would extract just name of the fighters, and save those images. It would then trigger the OCR engine to read the files. When that was done it would then read out the resulting data back to the requester (huh, now that I type that out it sound kind of hard, but regardless it wasn’t really too bad). I decided the easiest and lightest weight answer for a server would be a node js instance. I have some experience with node and it’s quick to get running so it seemed like the natural candidate.

After a bit of reading to get back up to speed on how to setup node and getting my basic hello world up and running I found a library that would allow node to execute commands on the server (yeah I know that’s dangerous, but this is all local, so whatever). I just rigged it up to listen for a specific page request, and once it got that it would run a batch file which would handle the screenshot, image processing and OCR work. Once it got word the batch file had run it would read the contents of the two text files that were hosted on the server as well that would hold the names of the current fighters. Here is the node code.

var express = require('express');
var sh = require('execSync');
var app = express.createServer() ;
var fs = require('fs');

var port = process.env.PORT || 80;
//configure static content route blah
  app.use(express.static(__dirname + '/public'));
    dumpExceptions: true, 
    showStack: true

app.listen(port, function() {
  console.log('Listening on ' + port);

app.get('/getFighters', function(request, response){

	console.log('Request made to get fighter data');
	var result = sh.exec('cmd /C screenshot.bat');

	console.log('Command ran ' + result.stdout);
 	fs.readFile( 'public\\fighter1Name.txt', "utf-8", function (err, fighter1) {
		if (err) console.log( err );
		fs.readFile( 'public\\fighter2Name.txt', "utf-8", function (err, fighter2) {
		  if (err) console.log( err );
		  var fighters = new Object();
		  fighters.fighter1 = fighter1.trim();
		  fighters.fighter2 = fighter2.trim();


Not too bad eh? As you can see the results are wrapped using a JSONP style callback system so this can be invoked from anywhere. Once that was up and running now I had to write the batch file that actually did all the hard work.

The Bat File

The node server pretty much has a black box kind of process. It just calls some batch file and expects results. Not that it really matters, but the execute process is async and so the server didn’t know when that process had completed (ended up having to have a loop that attempts to read the contents until it succeeds, shitty I know). It has no idea of course how the bat file does what it does, and honestly neither did I when i first started building it. I knew the bat file would have to take a screenshot, extract the names of the fighters from that screenshot, and invoke the OCR engine. At this point i knew I was at least going to use Tesseract for my OCR engine, and that ImageMagick (a suite a command line tools for image processing) where likely going to be how I did the image processing. For capturing the screenshot I found a simple utility on google code called screenshot-cmd that would take a screenshot of the primary monitor. I figured then I could use imagemagick  to crop down the un-needed stuff (since the video is in the exact same place on my screen every time I could use coordinate based cropping). Then with the images cleaned up I could forward them onto Tesseract.

After a bit of messing around I managed to get the screenshot and get ImageMagick to extract just the names of the fighters from the betting screen image. Later on I had a sudden moment of clarity and realized I could remove the background from the names if I just deleted everything that wasn’t the red color for the player 1 name, and the blue color for the player 2 name (since they are always exactly the same color). Also I decided to archive the old captures so I’d have them to help train the OCR engine. The final batch script looks like this


FOR %%I IN ("public\screens\*.png") DO (
  SET lmdate=%%~tI
  SETLOCAL EnableDelayedExpansion
  SET lmdate=!lmdate:~6,4!-!lmdate:~3,2!-!lmdate:~0,2! !lmdate:~11,2!-!lmdate:~14,2!
  MOVE "%%I" "public\screens\old\!lmdate!-%%~nxI"

::Take screenshot of primary monitor at full resolution
screenshot-cmd 0 0 1920 1080 -o public\screens\fighters.png

::ImageMagick shave off the left 478 pixels and the top 135 pixels to cleanup the image
convert -shave 478x135  public\screens\fighters.png public\screens\fighters.png

::ImageMagick remove the bottom and right borders
convert public\screens\fighters.png -gravity South  -chop  0x150  public\screens\fighters.png

::Now we have a screenshot with just the fighters. Now we have to extract the names of the fighters and put them in separate files

::Extract fighter1 name by cropping out an 800px X 40px swatch from the top of the image
convert public\screens\fighters.png -crop 800x40+60+0 public\screens\name1.png

::Remove all colors except for the red used by the font
convert public\screens\name1.png -matte ( +clone -fuzz 4600 -transparent #e3522d ) -compose DstOut -composite public\screens\name1.png

::Extract fighter1 name by cropping out an 800px X 40px swatch from the bottom of the image
convert public\screens\fighters.png -crop 800x40+200+618 public\screens\name2.png

::Remove all colors except for the red used by the font
convert public\screens\name2.png -matte ( +clone -fuzz 4600 -transparent #2798ff ) -compose DstOut -composite public\screens\name2.png

::Feed the player names into tesseract for OCR scanning.Write results to two different text files. One for each fighter
tesseract public\screens\name1.png public\fighter1Name -l salty
tesseract public\screens\name2.png public\fighter2Name -l salty

The commands took a bit of time to get just right (what with having to find just the right offsets and messing with the color removing fuzz factor). The final output is pretty damn good actually. Check this out.

All things considered I’d say those are some damn fine extractions from a screenshot of a flash video. Now all that was left is the final part, tackling the Tesseract OCR training process to teach it about this strange font.

Tesseract OCR

Tesseract is pretty much the premier freeware OCR engine. There really isn’t anything else that competes with it. It’s hard as hell to figure out and takes a ton of time to get setup properly for new languages but I had heard when it works, it works pretty damn well. I know next to nothing of OCR, so I knew tackling this was going to be a challenge. The basic outline breaks out like this

1) Gather samples of your new font. At least a few occurrences of every possible character.

2) Create a ‘box’ file which is basically just a coordinate mapping of where each character starts and stops and what it represents. (finding a functional tool for this part took forever, because it turns out I was using a bad image that caused them all to have problems or act very slowly. Pro tip, when saving your TIF file to feed into a box editor, if using photoshop discard the layer data. It makes the file way too big and slow to use).

3) Train Tesseract using the box file

4) Generate the rest of the weird files it needs that I don’t know what do.

5) Package all the files and see if your new language works.

eng.salty.exp0The shortcut method here is create your training image with all your chars, use jTessBoxEditor to do your modifications to the box file. Then use SerakTesseractTrainer to do the training and create the files. Honestly if i had known about those two things right off the bat, my life would have been a lot easier. Over half my battle was just trying to find what tools to use and getting them to work right.

Also retraining it after I was able to remove the backgrounds from the names made it about a billion times more accurate. I would highly recommend that approach if you have the ability to. Good training data makes all the difference. Trying to train it with crummy data with backgrounds and weird shit going on makes it next to impossible. On the right you can see what my training data looked like and it ended up working out pretty well. It’s still lacking some numeric characters, but I’ll have to add those in later.

I was amazed to find it actually worked. The names were being read properly and written to the file. The node server was grabbing the contents of the file and returning it to the requesting bot. The bot took the names and fed them into the scoring system and placed bets accordingly. It was a beautiful symphony built from a total clusterfuck. I am almost sad now because I have solved my project. Sure i can make a little better, implement a database, maybe tweak the scoring engine some, but overall it’s been solved. All that’s left to do now is sit back and watch the salt roll in. Later on I did a bit of re-factoring, moving the calculation onto the server and out of the client (where it belongs). I also created an extension just for the server that would invoke the screen reading process instead of accepting the request for the normal client (since I figured I may end up distributing the code I didn’t want everyone’s clients telling my server to constantly try to re-read the screen and such). Eventually the client got dumbed down to just polling the server when it detected that bets were open until it got back the fight odds and it then could set a suggested bet amount for the player. I also ended up adding a few other features to the client like ‘dream mode’ where in if the odds against a character were so insane as to make payout on the favorite nominal but the payouts for underdog amazing, bet on the underdog in hopes of a huge payout. You could set some variables like always bet in dream mode until you reached a certain threshold. You could also bet all in mode which would automatically bet all your money until a certain threshold since payouts at lower levels of betting were always so minimal. This is what the ‘final’ version of the client ended up looking like.

saltyclient final

As a postscript to this story to gather more data I ended up offering a trade to other players. If they could provide my their betting history data and enough of it was unique (I didn’t already have the results of that fight, which I identified by timestamp I would give them access to the tool). With their betting data added onto mine I ended up having an accuracy rate of around 85% which isn’t too bad. The overall results were somewhat disappointing though because for whatever reason the SaltyBet community was really good at guessing as well and the odds would end up so heavily staked in the winners favor that usually my payouts were pretty small.

Right now the Saltybot server isn’t running and the data is probably badly out of date, but hey if you want to download the source and get it running again, knock yourself out. You can download the source here


Update: I’ve also stored the code on github now like a real developer. https://github.com/Kenji776/SaltyBot

I don’t remember the exact setup steps, but I believe you’ll want to drop all the server files in a directory on your machine. Spin up a node.js console and launch core.js. Open up saltybet.com and keep it fullscreen. Then on your server install the saltyBotServerExtension into chrome. That should watch for fight changes and do the OCR process and put the results into the public folder. You’ll want to setup a web server where the public folder is available for your client to get at. Then install the client extension in your machine you intend to use as your ‘betting’ machine and point it at your webserver (yeah you’ll probably have to modify the source, thankfully in chrome you can just modify the source and load the unpacked extension). That should get you pretty close. If you have questions, feel free to ask, I’ll do what I can to help. I am interested in seeing where this goes, I’m just too lazy right now to do much with it myself. If there is interest maybe I’ll try and get it running again.

Node.js, Socket.io, and Force.com canvas and you

So I got back from Dreamforce a week ago, and my head hasn’t stopped spinning. So many cool technologies and possibilities, I’ve been coding all week playing with all this new stuff (canvas, node.js, socket.io, nforce, heroku, streaming api, etc). Cloudspokes conveniently also had a challenge asking us to write a force.com canvas application that used a node.js back end. I wanted to take this chance to see if I could put what I had learned into practice. Turns out, it’s not too hard once you actually get your dev environment set up, and get things actually uploading and all the ‘paper work’ done. I wanted to leverage the Salesforce.com streaming API, Node.Js, and Socket.io to build a real time data streaming app. I also wanted to use Force.com canvas to get rid of having to worry about authentication (honestly the best part about canvas, by a wide margin). You can see the end result here:

http://www.screencast.com/t/Qfen94pl (Note the animations don’t show too well in the video due to framerate issues with the video capture software).

You can also grab my source project from here

Demo Source

Getting Set Up

First off, huge word of warning. All this process is just what I’ve found to work from trial and error and reading a ton of shit. I have no idea if this is the recommended process or even a good way to do things. It did/does however work. This was my first ever node.js application, as well as my first time using canvas and only my second time using Heroku. So ya know, definitely not pro level here but it’s at least functional. Also the actual idea for this application was inspired by Kevin O’Hara (@kevinohara80) and Shoby Abdi (@shobyabdi) from their streaming API session in dreamforce. They are also the authors of the kick ass nforce library, without which this app would not be possible, so thanks guys!

So how can you get started doing the same? Well first of course get yourself setup with Heroku. Heroku is where we are going to store our project, it’s a free hosting platform where you can host node.js, python and java applications. So if you don’t have a Heroku account, go get one, it’s free.

You’ll also want to download the Heroku toolbelt. This is going to get us our tools for dealing with Heroku apps (heroku client), as well as testing our stuff locally (foreman), and working with git. You can grab it here. https://toolbelt.heroku.com/. On that page it walks you through creating your project. For a more in depth guide, check out https://devcenter.heroku.com/articles/nodejs. Get a node.js project created and move onto the next step.

So now I assume you have a basic node.js project on Heroku. Now to actually make it do stuff, we’ll need to install some libraries using NPM. Open a command prompt and navigate to your local project folder. Install express (npm install express), socket.io (npm install socket.io) and nforce (npm install nforce). This should add all the required libraries to your project and modify the package.json file that tells Heroku the shit it needs to include.

You’ll also need a winter 13 enabled salesforce org to start building canvas apps. So go sign up for one here (https://www.salesforce.com/form/signup/prerelease-winter13.jsp) and get signed up. Depending when you are reading this you may not need a prerelease org, winter 13 may just be standard issue. Whatever the case, you need at least winter 13 to create canvas apps. As soon as you get your org, you’ll also probably want to create a namespace. Only orgs with namespaces and publish canvas apps, which you may want to do later. Also having a namespace is just a good idea, so navigate to the setup->develop->packages section, and register one for yourself.

In your org, you’ll need to configure your push topic. This is the query that will provide the live streaming data to your application.Open a console or execute anonymous window, and run this:

PushTopic pushTopic = new PushTopic();
pushTopic.ApiVersion = 23.0;
pushTopic.Name = 'NewContacts';
pushtopic.Query = 'Select firstname, lastname, email, id from contact;
insert pushTopic;
System.debug('Created new PushTopic: '+ pushTopic.Id); 

This will create a live streaming push topic in your org of all the new incoming contacts. You could change the query to whatever you want of course but for the purpose of this example, lets keep it simple.

Next, you’ll want to configure your canvas application. In your org, go to setup->create->apps. There should be a section called connected apps. Create a new one. Give it all the information for your Heroku hosted application. Permissions and callbacks here are a bit un-needed (since canvas will be taking care of the auth for us via a signed request) but should be set properly anyway. The callback url can be just the url of your application on Heroku. Remember only https is accepted here, but that’s okay because Heroku supports https without you having to do anything. Pretty sweet. Set your canvas app url to the url of your Heroku application and set access method to post signed request. That means when your app is called by canvas, it’s going to be as a post request, and in the post body is going to be a encoded signed request that contains an oAuth key we can use to make calls on behalf of the user. Save your canvas application.

The actual code (there isn’t much of it)
So we have everything configured now, but no real code. Our app exists, but it doesn’t do shit. Lets make it do something cool. Open up node server file (it’s probably called like web.js, or maybe app.js if you followed the guide above. It’s going to be whatever file is specified in your Procfile in your project). Paste this code. You’ll need to modify the clientId and clientSecret values from your canvas application. They are the consumer key and consumer secret respectively. I honestly don’t know if you’d need to provide your client secret here since the app is already getting passed a valid oAuth token, but whatever, it can’t hurt.

var express = require('express');
var nforce = require('nforce');
var app = express.createServer() , io = require('socket.io').listen(app);

var port = process.env.PORT || 3000;
//configure static content route blah
  app.use(express.static(__dirname + '/public'));
    dumpExceptions: true, 
    showStack: true

app.listen(port, function() {
  console.log('Listening on ' + port);

io.configure(function () { 
  io.set("transports", ["xhr-polling"]); 
  io.set("polling duration", 10); 

var oauth;

var org = nforce.createConnection({
      redirectUri: 'http://localhost:' + port + '/oauth/_callback',
      apiVersion: 'v24.0',  // optional, defaults to v24.0
      environment: 'production'  // optional, sandbox or production, production default

//on post to the base url of our application
app.post('/', function(request, response){
    //get at the signed_request field in the post body
    var reqBody = request.body.signed_request;   

    //split the request body at any encountered period (the data has two sections, separated by a .)
    var requestSegments = reqBody.split('.'); 

    //the second part of the request segment is base64 encoded json. So decode it, and parse it to JSON
    //to get a javascript object with all the oAuth and user info we need. It actually contains a lot of 
    //data so feel free to do a console.log here and check out what's in it. Remember console.log statments 
    //in node run server side, so you'll need to check the server logs to see it, most likely using the eclipse plugin.   
    var requestContext = JSON.parse(new Buffer(requestSegments[1], 'base64').toString('ascii'));
    //create an object with the passed in oAuth data for nForce to use later to subscribe to the push topic
    oauth = new Object();
    oauth.access_token = requestContext.oauthToken;
    oauth.instance_url = requestContext.instanceUrl;
    //send the index file down to the client


//when a new socket.io connection gets established
io.sockets.on('connection', function (socket) {
      //create connection to the NewContacts push topic.
      var str = org.stream('NewContacts', oauth);
      //on connection, log it.
      str.on('connect', function(){
        console.log('connected to pushtopic');
      str.on('error', function(error) {
      //as soon as our query has new data, emit it to any connected client using socket.emit.
      str.on('data', function(data) {
         socket.emit('news', data);

Now you’ll also need the index.html file that the server will send to the client when it connects (as specified by the response.sendfile(‘index.html’); line). Create a file called index.html, and put this in there.

<!DOCTYPE html>
        <!-- - does a commit work remotely as well? -->
        <title>New Contacts</title>
        <meta name="apple-mobile-web-app-capable" content="yes" />
        <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent" />
        <link href='http://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
        <link rel="stylesheet" href="/reveal/css/main.css">
        <link rel="stylesheet" href="/reveal/css/theme/default.css" id="theme">    
        <script type="text/javascript" src="//ajax.googleapis.com/ajax/libs/jquery/1.4.2/jquery.min.js"></script>
        <script src="/socket.io/socket.io.js"></script>
          var socket = io.connect();

          socket.on('news', function (data) {
            jQuery('.slides').append('<section><h2><a href="https://na2.salesforce.com/'+data.sobject.Id+'">'+data.sobject.FirstName+' '+data.sobject.LastName+'</a></h2><br>'+data.sobject.Email+'<br/></section>');


        <div class="state-background"></div>
        <div class="reveal">
            <div class="slides"> 
                <section>New Contacts Live Feed</section>

            <!-- The navigational controls UI -->
            <aside class="controls">
                <a class="left" href="#">◄</a>
                <a class="right" href="#">►</a>
                <a class="up" href="#">▲</a>
                <a class="down" href="#">▼</a>

            <!-- Presentation progress bar -->
            <div class="progress"><span></span></div>                

            <script src="/reveal/lib/js/head.min.js"></script>
            <script src="/reveal/js/reveal.min.js"></script>    
                // Full list of configuration options available here:
                // https://github.com/hakimel/reveal.js#configuration
                    controls: true,
                    progress: true,
                    history: true,
                    mouseWheel: true,
                    rollingLinks: true,
                    overview: true,
                    keyboard: true,
                    theme: Reveal.getQueryHash().theme || 'default', // available themes are in /css/theme
                    transition: Reveal.getQueryHash().transition || 'cube', // default/cube/page/concave/linear(2d)


We are also going to need the CSS reveal framework to create the awesome slideshow. Grab it https://github.com/hakimel/reveal.js. In your Heroku project create a folder called public. In there create a folder called reveal. In that folder dump the css, js, lib and plugin folders from reveal. So it should be like root_folder->public->reveal->js->reveal.js for example. There is probably a more ‘git’ way to include the reveal library, but I don’t know what it is. So for now, moving folders around should work.

Now use git to push this all up to Heroku. I’d really highly recommend using the Heroku plugin for eclipse to make life easier. There is an install guide for it here https://devcenter.heroku.com/articles/getting-started-with-heroku-eclipse. However you do it, either from eclipse or command line, you gotta push your project up to Heroku. If you are using command line, I think it’s something like “git add .” then “git commit” then “git push heroku master” or something like that. Just use the damn eclipse plugin honestly (right click on your project and click team->commit, then right click on the root of your project and click team->push upstream).

If your app pushes successfully and doesn’t crash, it should run when called from Canvas now. Canvas will call your Heroku application using a post request. The post request contains the signed request data including an oAuth token. We use that oAuth token and store it in our node.js app for making subsequent api calls. Node.js returns the index.html file to the client. The client uses socket.io to connect back to the server. The server has a handler that says upon a new socket.io connection, create a connection to the push topic newContacts in salesforce, using the oAuth token we got before. When a new event comes from that connection, use Socket.IO to push it down to the client. The client handler says when a new socket.io event happens, create a new slide, and change to that slide. That’s it! It’s a ton of moving parts and about a million integration, but very little actual code.

Enjoy and have fun!