Best of New Zealand

View of Auckland from Mount Eden Summit

View New Zealand Album

Best of Bora Bora, Tahiti

Over-water bungalows at Four Seasons Resort, Bora Bora

View Bora Bora Album

Underwater video of me swimming amongst the fish in the coral reefs

Underwater video of me swimming with sharks

I just finished reading these two books. They were very easy to understand and quite inspiring. I recommend them to anyone who is interested in learning more about investing. Start with the first, then read the second.

How I Made $2,000,000 in the stock market by Nicolas Darvas
How to Make Money in Stocks: A Winning System in Good Times and Bad by William O’Neil

The reason I particularly like these books is that they go against the whole notion of listening to Wall Street analysts. Darvas’ book is mostly a story about how he made it rich on the market in the 50s and 60s. O’Neil is widely regarded as one of the best investors of the last century, and he’s the founder of www.investors.com. His book is extremely focussed on the math behind a stock’s rise. He encourages you to pay attention to charts, graphs and numbers and less on analysts, news and rumours.

Thought I’d share a few mockups that I designed. I’m not sure if we’ll be using these, and they may go through some modifications but anyway, here they are!

A lot of people think I’m a designer but I’m not. I can make some nice-looking designs but at the end of a day, I’m an engineer.

Peekaboo is an iOS app that lets you find out more about people around you. It accomplishes this by using facial detection and recognition. Simply snap a picture and Peekaboo tries to find out who the people in it are. You can login to the app using Facebook, and it goes through your tagged Facebook photos to create a model of your face. Don’t have or don’t want to connect with Facebook? Peekaboo can train you using your phone’s front-facing camera.

Watch the short video

I thought a video would explain it better than words can. This was taken on my iPhone 4S and recorded using AirPlay. It’s a slightly old build.

The idea

We started with the idea of wanting to know more about people in university. If you think about it, there are 10,000 like-minded students in a university campus but I only know 0.01% of them, due to a random moment that made me meet them. That 0.01% is responsible for most of the joy and friendships that I have created in the last 4 years. So what if this 0.01% was larger? We felt that there should be a better way of knowing more about people around you, so that you can meet more like-minded individuals. Is Peekaboo the right approach? I’m not totally sure, and it certainly has polarizing aspects, but it sure was fun to make.

The technology

I mentioned how I designed Peekaboo’s system architecture in an earlier post. Some things have changed since then. We found face.com provided us improved facial recognition and had a faster execution time, so we went with that. This allowed us to simplify a lot of the code on the C++ side, effectively making the system a combination of the iOS app and a Node.js server on Heroku that acted as an API that linked to the various services, Parse and Face.com.

I was responsible for coding the majority of the Node.js server and the iOS app. My goal throughout this process was to become proficient with Objective-C and the iOS SDK and I think I’ve got close.

Some pictures

Here are some screenshots and Instagrams of Peekaboo and the design symposium.

Something I found very interesting was Ive’s response to the question,”When you are coming up with product ideas such as the iPod, do you try to solve a problem?”

His reply:

There are different approaches – sometimes things can irritate you so you become aware of a problem, which is a very pragmatic approach and the least challenging.

What is more difficult is when you are intrigued by an opportunity. That, I think, really exercises the skills of a designer. It’s not a problem you’re aware of, nobody has articulated a need. But you start asking questions, what if we do this, combine it with that, would that be useful? This creates opportunities that could replace entire categories of device, rather than tactically responding to an individual problem. That’s the real challenge, and that’s what is exciting.

Now this is fascinating for me, because we are taught to always ask the question, “What’s the problem that this is trying to solve“, when designing or developing something new. Ive says revolutionary products come when you ask “What can we do with this?“.

To sum it up, you can create an NSDictionary through the following:

dict = @{ k1 : o1, k2 : o2, k3 : o3 };

Similarly for an NSArray:

array = @[ a, b, c ];

and for NSNumber:

NSNumber *number;
number = @'X';
number = @12345;
number = @12345ul;
number = @12345ll;
number = @123.45f;
number = @123.45;
number = @YES;


Find the entire article at CocoaHeads • Objective-C literals for NSDictionary, NSArray, and NSNumber.

If your popover is being instantiated from a UIViewController with a UITableView in it that does not fill up the whole window, then you may have some issues getting the popover to align properly.

To get the alignment to work, you need to get the UITableViewCell‘s absolute position on screen.

You can achieve that through the following code:


CGRect rectInTableView = [tableView rectForRowAtIndexPath:indexPath];
CGRect rectInSuperview = [tableView convertRect:rectInTableView toView:[tableView superview]];


And then…

Thanks to Nick Weaver at this StackOverflow question for explaining this. Took some finding.

Here’s a post I’ve been meaning to write for a while, but just never got around to it.

As a Waterloo Engineering student, we have to complete a final year design project under the guidance of a supervisor in groups of three.

What have I been making?

My project is called Peekaboo. It uses facial detection and recognition to allow a user to find out more about people around them.

I’ve been working on this for about 5 months now with my group members. We’ve made sufficient progress and are now at a stage where I can talk about our systems architecture and some of the design decisions that we made.

System Overview

Peekaboo is comprised of 3 components, shown in the diagram below.

System Overview

iOS App + Face Detection + Parse

We have a native iOS app that uses Parse for it’s mobile backend. We’ve been very happy with Parse, and it was painless to set up. Face detection is performed on the device itself through Face.com’s API. We went with Face.com over OpenCV’s Face detection due to higher accuracy and the availability of more data. Face.com is able to give us JSON back with eye positions, mouth posiitions, head tilt and yaw, gender, and more.

I strongly believe user experience should be important to any project. We use a lot of great open source iOS UI components available at Cocoa Controls.

Web Server on Heroku

We wrote a web server in Node.js deployed over Heroku to act as an API. Once again, Heroku makes this painless to set up and deploy. The web server is the mediator used by the iOS app and the desktop C++ code. The web server talks to Parse to get information and feeds it to the C++ code (and vice versa). It uses standard authentication.

The web server has a MySQL database which we usually query instead of directly querying Parse. This way, we stay below the rate limits. Parse is only queried when there is a face that needs recognition.

• We found Sequelize to be a good ORM for MySQL (available as an npm module)
• We found Request to be a useful module for simplifying HTTP Requests.

Facial Recognition Algorithms

I won’t lie, I’m not the best at C++. I find web languages and Objective-C a lot easier to code in. Nonetheless, when doing Face Recognition, we had to use C++. It’s mainstream and fast. Of course, we went with OpenCV, one of the most popular computer vision libraries out there (See my post on how to set up OpenCV on Mac).

Like I said, we don’t use OpenCV for Face detection. We use it to work with images, do some basic image processing (histogram equalization) and use OpenCV’s Eigenfaces implementation. We have also implemented another Facial Recognition algorithm known as LDA (Linear Discriminant Analysis). This was done mostly through reading research papers and contacting people smarter than us. Google Scholar is a useful resource. LDA gives us a 10-15% improved recognition rate over EigenFaces.

Communication Layer

System diagram of how facial recognition works

Final Thoughts

Some parts of our code are a little hacked together. This is partly due to the time crunch we are under and partly due to the fact that we are still learning. However, I am pretty proud of what we have achieved so far. We can accurately (over 70%) recognize a person who has been trained by our system (database size is 70 people currently). We’ve used $0 on this project, which is a testament to how open and amazing our software engineering industry is. We have learned a lot through out this process. I’ll put another post up with a live demo sometime soon.

If you have questions, ask them below!