Method Profiling in Android

I've recently been using the Android implementation of OpenCV for real-time computer vision on mobile devices. Computer vision is computationally expensive - especially when you're working with a camera stream in real-time. In trying to speed up my object tracking algorithm I used Android's method profiler to analyse the time spent in each function, hoping to identify potential areas for optimisation. This makes an interesting little case study and example of how to use Android's profiling tools.

How do I enable profiling?
Traceview is part of the Eclipse ADT. Whilst in the DDMS perspective, method profiling can be enabled by selecting a debuggable process and clicking the button circled below. To stop profiling, click the button again. After the profiler is stopped, a Traceview window will appear.

Interpreting Traceview output

The image above was my first method trace, capturing around seven seconds of execution and thousands of method invocations. Each row in the trace corresponds to a method (ordered by CPU usage by default). Selecting a row expands that method, showing all methods invoked from within that method. Again, these are ordered by their CPU usage.

Optimisation using profile data
Using the above example, we can see that my object tracking algorithm spends most of its time waiting for four methods to return: Imgproc.pyrDown, MainActivity.blobUpdate, Imgproc.cvtColor and VideoCapture.retrieve. The pyrDown method downsamples an image matrix whilst applying a Gaussian blur filter. The blobUpdate method is a callback I use to give updates on a tracked object. The cvtColor method converts the values in a matrix to those of another colour space. The retrieve method captures a frame from the device camera.

The latter two methods are crucial to my object tracking algorithm, as I need to call retrieve to get images from the camera and cvtColor is used to convert from RGB to HSV colour space, as it is better to perform colour thresholding this way. The former two, however, can potentially be optimised.

From this trace I've already identified a redundant yet expensive method call: pyrDown. 30% of the time spent in the processFrame method is spent waiting for pyrDown to return. I was using this function to downsample an image from the camera to 240x320, as a smaller image can be processed faster. Instead, this call can be eliminated by requesting 240x320 images from the camera.

In the blobUpdate method I send updates about the location of the tracked object and its size. I maintain a short history of these readings and use dynamic time warping to detect gesture input. By expanding the trace for this method I see that my gesture classification function is taking the most time to execute. As dynamic time warping, by design, finds alignments between sequences of different lengths, I can reduce the frequency of checking for gestures. By only checking for gestures in every second call of blobUpdate, I effectively half the amount of time spent checking for gestures. This still maintains a high recognition rate by virtue of dynamic time warping's resilience to differences in alignment length.

Conclusion
The case study in this post demonstrates how method profiling can be used to identify potential areas for optimisation; something which can be particularly beneficial in a computationally expensive application. By profiling a few seconds of execution of a computer vision algorithm I was able to capture data about thousands of method invocations. From the trace data I identified a redundant method call which accounted for 30% of my algorithm's execution time and identified an optimisation to the second most expensive method call.

"Feelable" touchscreens revisited

Tactus have gotten quite a lot of attention recently after demonstrating their new touchscreen technology (pictured above; image source). Their "Tactile Layer" technology raises bubbles on the touchscreen, creating, essentially, physical objects on the touchscreen. I suppose I've taken quite an interest in this since it's similar to something I wrote about 6 months ago: feelable touchscreens.

Here are two amazing and innovative technologies, each taking a different approach towards creating tactile sensations from a touchscreen. Senseg use small electric currents to stimulate the skin, creating edges and feelings of texture, while Tactus actually create something physical.

To the best of my understanding, Tactus' technology allows bubbles (I'm reluctant to call them buttons; who knows what else interaction designers could do with this!) in pre-determined locations, configured during manufacture. Different configurations are possible, apparently, but from what I've read it seems that these are decided at manufacture. Whilst this allows some fundamental improvements to the touchscreen experience (e.g. providing a configuration for a keyboard), it lacks some flexibility as manufacture determines where bubbles can be used.

Senseg's tech, however, is more flexible and appears to be truly dynamic; application developers can control the precise location where feelings can be experienced rather than this being decided during manufacture.

Having dabbled with Microsoft Surface over the past year I'm pleased to see that both of these technologies apparently scale well to larger displays. Interactive tabletops suffer from the same loss of tactile feedback that touchscreen mobile devices do although this is perhaps less apparent on a large scale device where widgets aren't crammed into such a small space.

I don't think it's fair to ask which of these technologies is better, because they can't fairly be compared. Although the flexibility of Senseg vs the physical tactility of Tactus is an interesting comparison, I feel that a better question is could these concepts be somehow combined? Imagine a touchscreen which offers complete configuration flexibility, a richer tactile experience like Senseg claim to offer (e.g. feeling texture, not just the presence of something) and the benefits of feeling something physical on the touchscreen. Now that would be awesome.

Kinect Disconnect

If Microsoft's announcements at E3 are anything to go by then they obviously see Kinect, their hands-free motion-sensing input, as a centrepiece in the Xbox ecosystem. Kinect is also being used increasingly more outside of gaming. There's plenty of interesting examples of novel interaction design centred around Kinect and it's becoming almost ubiquitous in hands-free interaction research. Still, there's one thing that Kinect lacks: tactile response. I feel that this has more of an impact in games than other uses, so that's what I'll discuss here.

Lack of tactile feedback is obvious; if I'm providing input to a game by waving my hands about, I'm not going to feel anything in response to my actions. Actually, that's not technically true. There's some really cool research happening at the University of Tokyo combining Kinect and ultrasound so that you feel like you're manipulating a mid-air object with your hands but this technology (so far) isn't really suited to games. Limitations with their current technology means that it wouldn't scale to the typical Kinect gaming scenario: a user on the opposite side of the living room from the television, moving around a large space.

To recap: Kinect is pretty cool, but it lacks tactile response. Why does this matter in gaming? Interaction in gaming benefits from a closed feedback loop between player and game. As the player provides some input (e.g. mouse click, button press, gesture), they receive some response from the game. This is a continuous loop where the feedback provided (visual, audible, tactile) communicates what effect that action had and allows the player to adjust their actions if appropriate.

I feel that tactile feedback is an important part of this feedback loop: in action games it can confirm success of an action (e.g. you "feel" a punch connect) or inform you some event (e.g. you "feel" your character take a hit). Tactile feedback can also be rewarding. A well designed game combines the aesthetics of the game, good sound design and the feedback from the controller to make a satisfying experience.

With devices such as Kinect you lose this tactile feedback. Touchscreen devices can also suffer from this problem; tablets often lack the rotational motors that mobile phones have to provide low fidelity feedback. Not only does this remove a way of communicating in-game state, but also removes some all-important precision of control. In some games precision is key. Actually being able to hold and feel an input device allows a similar tactile feedback as discussed previously, except this time it's the physical characteristics of the controller which the player feels. Being able to feel the controller and how it responds permits a greater amount of control and precision. A few months ago I wrote briefly about "feelable" touchscreens which could be one way to enrich the mobile gaming experience for touchscreens, but that's another discussion. Back to Kinect.

Kinect is an amazing piece of technology and it, and similar devices, have a great potential in interaction design. I can't help but feel that its use in gaming, however, suffers from the loss of a modality. It potentially detracts from two key aspects of games: communication of state and input precision. Where am I going with this? I'm not really sure. Should Kinect be written off for games? Absolutely not: it does have its uses in more casual games. But what is the future of Kinect in more "serious" games (a term I detest but can't think of an alternative)? Should we strive to develop haptic technologies which make Kinect viable for these games or just continue to treat it as a bit of a gimmick?

Looks like I'm graduating

For the past seven weeks I've been studying intensely, preparing for my end of year exams. I've sat 6 so far and have 3 remaining. After some good news last week I now know that these will be the last exams I ever sit as an undergraduate. Having been unconditionally accepted to start as a PhD student this autumn, I'm now graduating early with a bachelors degree, rather than the masters degree I always intended to aim for. It's a big step, but I'm excited about it. I enjoy research and now I'll get to do even more; there won't even be pesky lectures and coursework assignments getting in the way of it!

After my last exam a week on Friday I'll have three weeks of a holiday before I start work for summer. I'm going to make the most of it and try to relax; something I've hardly had the chance to do because of exams and working on my dissertation. It's tough (despite what Allie says...) but it always pays off in the end. In that time I'll also be writing a paper on my final year project. After an unsuccessful CHI submission, I'm really hoping that it'll get in to ITS. Getting a publication out of my project work would be a rewarding bonus to what has been an intense, but hopefully successful, project.

Amazon Hackathon

This weekend was the Amazon Hackathon in Glasgow, the first of its kind in the UK. The idea was simple: get 50 students to show up in teams, give them 20 hours to innovate and create, and provide them with $50 of AWS credit and a stack of pizzas to power their ideas. What followed was anything but simple. A 20 hour frenzy of brainstorming and prototyping to create something which we would eventually present to our peers and the "Amazonians". Some amazing ideas were demonstrated, with one team netting $1,000 in AWS credit to help kickstart their idea of a gifting-based social network.

I entered as part of Team Giraffa Cakes, a team of four from the University of Glasgow. Disappointingly, three of our team were the only 4th year students from Glasgow to enter. Our idea was to crowd-source information to help inform product comparisons. Users would be able to search for two products on Amazon and our web service would then gather relevant content from sources such as Twitter, Blogger and Youtube to help inform decisions. We implemented a system which did just that, performing sentiment analysis on the tweets and blog posts to give an overview of how people feel about those products. At a glance users would be able to tell if opinion about each product was generally negative, neutral or positive. Information from the web service was made available to both an Android app which I implemented and a website frantically thrown together in record time by James.

Our Android app, showing the product overview.

The experience was an interesting one and was lots of fun, despite the intense desire for sleep that kicked in around 4am. To stay sane as the night went on we found ourselves increasingly taking breaks just to get away from the computer. Cold pizza and instant coffee from a kettle of questionable hygiene proved to be a welcome respite and every hour or so we went outside for a short break. As appreciated as the fresh air was, it was probably the darkness which was most welcome; a break for our eyes. I think the location of the event may have contributed to the drowsiness of everyone. The lab it was held in is notoriously hot and stuffy; in retrospect our team probably would've been more comfortable downstairs in our own, gloriously air conditioned lab. Although then we'd have to walk further for cold pizza.

Overall it was a blast. We had a lot of fun, created something we were all proud of and the icing on the cake was that our fellow hackers voted us the People's Choice. It was great to see so many ideas brought to completion in a single night - it's not uncommon to see university coursework that doesn't work after several weeks of work! Thanks for all the pizza and fresh fruit, Amazon... same time next year?

A picture says 15,000 words

My honours year dissertation as a word cloud.

Deadlines, workshops and twisted ankles

I've not written much here lately; it's been a hectic few weeks with university coursework deadlines. Thankfully most of them have now passed and I can get back to focussing on my project. My dissertation for my honours project is due three weeks tomorrow. I'm not too worried about it, having started in September last year! I've been doing most of the writing before actually doing the work (an approach I very much prefer for research), as I always feel more confident going forward with a planned and reasoned about methodology.

January was a really good month for running, and February started really positively. Alas, for the last two weeks I've been unable to run because of a dodgy ankle. I'm avoiding running with it until it's recovered a bit more - I don't want to risk damaging it further and waiting even longer for it to heal.

A few weeks ago we ran the Glasgow CompSoc Android workshop (which I first mentioned in November last year). It was a fun experience. I've never really stood up and taught something to a group of people before, so that was quite exciting. I'd quite like to do some similar lectures/workshops next year for CompSoc.

My goals for March are to get my project out of the way, and hopefully pick up running again once my ankle recovers some more. Looking forward to wrapping up my honours project. It's been fun, and I'm sure there's a lot I'll be able to write about in a paper afterwards, but it'll be nice to take a break from everything. I love doing research, it's just a shame that lectures and deadlines have to interfere with it so much as an undergraduate!

Multimodal Android Development Part 1

This post is the first of two which gives a brief introduction to creating multimodal interactions in Android applications. I'll briefly cover some of the SDK features available to you as an Android developer which you can use to create richer interactions in your apps. Example code will be quite concise because I assume you have at least a basic knowledge of Android development. Feel free to leave any comments suggesting how I can better explain these concepts, or to let me know if I've made any mistakes or omissions.

What is "multimodal" interaction?

Multimodal interaction, put simply, is interaction involving more than one modality (e.g. multiple senses). For example, an application may provide a combination of visual and haptic (touch) feedback. These types of interaction design provide a number of benefits, for example allowing those with sensory impairment to interact using other senses, or allowing interaction in contexts where one sense may be otherwise occupied.

One of the most ubiquitous examples of a multimodal interaction is the way in which mobile phones combine visual, audible and haptic feedback to inform users of a new text, phone call, etc. This combination of modalities is particularly useful when your phone is, say, in your pocket. Obviously you can't see the phone, but you will probably feel the phone vibrate or hear your ringtone as new notifications appear.

Haptic feedback in Android

Most handheld Android devices have some sort of rotation motor in them allowing simple haptic feedback. Although not common in tablets (largely due to size constraints), all modern Android phones will have tactile feedback available. You can control the phone vibrator through the Vibrator class. Note that in order to use this, your Manifest must request the following permission: android.permission.VIBRATE

/* Request the device's vibrator service. Remember to check
 * for null return value, in case this isn't available. */
Vibrator vibrator = (Vibrator) getSystemService(Context.VIBRATOR_SERVICE);

/* Two ways to control the vibrator:
 *  1. Turn on for a specific time
 *  2. Provide a vibration pattern */

/* 1. Vibrate for 200ms */
vibrator.vibrate(200);

/* 2. Vibrate for 200ms, pause for 100ms, vibrate for 300ms. */
long[] pattern = new long[] {0, 200, 100, 300};

/* Perform this pattern once only (repeat := -1). */
vibrator.vibrate(pattern, -1);

/* Vibrate for 200ms, followed by indefinite repeat of
 * 100ms pause followed by 300ms vibrate. Setting
 * repeat := 2 tells the vibrator to repeat at offset
 * 2 into the vibration pattern. */
vibrator.vibrate(pattern, 2);

Touchscreen gestures

Using touchscreen gestures to interact with applications can be fun, efficient and useful when users may be unable to select a particular action on the screen. For example, it can be difficult to select a button on-screen when running or walking. A touch gesture, however, is a lot easier and requires less precision from the user. The disadvantage with touch gestures is that if not used sparingly, there may be too much for the user to remember!

Creating a set of gestures for your application is simple: create a gesture library on an Android Virtual Device using the Gesture Builder application (available on the AVD by default) and add a GestureOverlayView to your activity layout. In your activity, you just have to load the gesture library from your resources and implement an OnGesturePerformedListener.

private GestureLibrary mLibrary;

public void onCreate(Bundle savedInstanceState) {
  ...
  /* 1. Load gesture library from the res/raw/gestures file */
  mLibrary = GestureLibraries.fromRawResource(this, R.raw.gestures);

  if (!mLibrary.load())
    /* Error: unable to load from resources! */
    ...

  /* 2. Find reference to the gesture overlay view */
  GestureOverlayView gov = (GestureOverlayView) findViewById(R.id.gestureOverlay);

  /* 3. Register callback for gesture input */
  gov.addOnGesturePerformedListener(this);
}

The callback method for gesture performance receives a Gesture as an argument. This can be used to obtain a list of predictions: which gestures in your library that Android thought the gesture was. With these predictions, you can use the prediction score (or contextual information) to determine which gesture the user was most likely to have performed. I find it useful to define a threshold for gesture acceptance, so that you can reject erroneous or inaccurate gestures. The best way to choose this threshold value is through trial and error: see what works for you and your gestures.

private static final double ACCEPTANCE_THRESHOLD = 10.0;

public void onGesturePerformed(GestureOverlayView overlay, Gesture gesture) {
  /* 1. Get list of gesture predictions */
  ArrayList predictions = mLibrary.recognize(gesture);

  if (predictions.size() > 0) {
    /* 2. Find highest scoring prediction */
    Prediction bestPrediction = predictions.get(0);

    for (int i = 1; i < predictions.size(); i++) {
      Prediction p = predictions.get(i);
      if (p.score > bestPrediction.score)
        bestPrediction = p;
    }

    /* 3. Decide if we'll accept this gesture */
    if (bestPrediction.score > ACCEPTANCE_THRESHOLD)
      gestureAccepted(bestPrediction.name);
  }
}

private void gestureAccepted(String gestureName) {
  /* Respond appropriately to the gesture name */
  ...
}

Saving map images in Android

Recently I've been working on a little Android project and wanted to save thumbnail images of a map within the application. This post is just sharing how to do exactly that. Nothing too complicated.

public class MyMapActivity extends MapActivity {
    private MapView mapView;

    ...

    private Bitmap getMapImage() {
        /* Position map for output */
        MapController mc = mapView.getController();
        mc.setCenter(SOME_POINT);
        mc.setZoom(16);

        /* Capture drawing cache as bitmap */
        mapView.setDrawingCacheEnabled(true);
        Bitmap bmp = Bitmap.createBitmap(mapView.getDrawingCache());
        mapView.setDrawingCacheEnabled(false);

        return bmp;
    }

    private void saveMapImage() {
        String filename = "foo.png";
        File f = new File(getExternalFilesDir(null), filename);
        FileOutputStream out = new FileOutputStream(f);
    
        Bitmap bmp = getMapImage();
    
        bmp.compress(Bitmap.CompressFormat.PNG, 100, out);
    
        out.close();
    }
}

In the getMapImage method, we're telling the map controller to move to a particular point (this may not matter to you, you may just want to take the image as it appears) and zooming in to show a sufficient level of detail. Then a Bitmap is created from the map view's drawing cache. The saveMapImage method is just an example of how you may want to save an image to the application's external file directory.