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.

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!

The best way to get it through a single request is using the following FQL query.

SELECT cover_object_id from album where aid IN (SELECT aid, name FROM album WHERE owner=me() AND name="Cover Photos")

Note that you will need an access token to use this query. It definitely works for me, but I’m still testing with permissions to see what’s needed to make it work with other users. Will post an update if anything changes.

Update: Yep, this works. Have it running on my iPhone right now. Remember to change “me()” with the Facebook ID of the user. “me()” will only work from the FQL console.

I am making an iOS app as part of my 4th year design project, and we are leveraging Facebook for it. Basically, our app allows you to take a picture of someone’s face and we use facial recognition to pull up their Facebook profile.

Anyway, I was fooling around with the Facebook iOS SDK and one of the things I’ve been wanting to do is to group one’s likes by category. Facebook seems to just return a large array of likes, with no grouping. This little method below groups them so I can have a table view with headings like “Favorite Books”, “Favorite Movies”, etc. Check it out.

Update: We got featured on GigaOM too.

“Oh, are you the guys that made TopForty? I use that all the time when I’m studying!”

At Demo Day, someone came up and said this to us – great feeling!