Camera Capture: Manual Controls 

Session 508 WWDC 2014

Learn about using manual controls to manipulate focus, exposure, and white balance. Take control of the camera’s focus position, ISO, shutter speed, temperature, tint, and more. Learn how to use the new iOS 8 exposure bracketing API to enable powerful creative and computational photography applications. Discover view-level services for capturing audio and video on OS X.

Good morning and welcome to session 508.

I’m Brad Ford.

I’m an Engineer in the Camera Software Group.

And hopefully you’re not here to learn about Swift, because we’re going to talk about cameras today, specifically camera developments on Yosemite and iOS 8.

If you want to hear about that you’ve come to the right place.

If you’re new to Camera Capture in general on OS X or iOS we invite you to review our past sessions from WWDC.

They’re available right within your WWDC app or online at

They provide great background information for today’s talk and they also show you the progression of our API set over the last four years.

And seeing as we’re so close to lunchtime we thought we’d present you a little menu of our own.

We’ll begin with a light appetizer of Yosemite camera developments and iOS updates followed by our manual camera controls main course and then we’ll finish things off with some tasty bracketed capture for dessert.

There is a lot to digest here so let’s get going.

First up is Capture in AVKit.

AVKit is sort of like AppKit except it’s for audio and video thus the name AVKit.

It is to AV Foundation as AppKit is to Foundation.

So, it provides view classes and standard UI for common media operations like media playback and now for capture as well.

Here’s a first look at what AVCaptureView looks like on OS X.

It’s a standardized UI for capture and it’s built on top of AV Foundation’s capture classes.

If it looks similar to QuickTime Player 10, that’s no mistake.

It’s QuickTime Player 10 actually uses an AVCaptureView in Yosemite.

Let’s take a quick look around the feature set.

It provides a nice HUD with standard UI for record and volume controls and an optional drop down menu for audio and video capture device picking.

Now, for a quick refresher on how AVFoundation’s capture classes work.

At the center of our capture universe is an AV capture session.

That’s the object that you tell to start and stop running.

It doesn’t do anything very interesting, however, until you provide it with some input.

We represent these as AVCaptureInputs.

Here I have an AVCapture camera and a microphone as devices and the data needs to flow somewhere.

And we represent these as AVCaptureOutputs.

Here I have a concrete AVCaptureMovieFileOutput, which is used for writing movie files.

The connections from inputs to outputs flow through the session and are referred to as AVCaptureConnections in our API set.

Now, let’s refer all this back to the new AVCaptureView.

How does that all work?

Well, in the default case, the simple case, you just instantiate an AVCaptureView and all of this is taken care of for you.

You either instantiate it or drop it into your NIB and it will manage that AVCapture session for you.

All you need to do is implement a single delegate method to make recording work and it looks like this.

There’s a single method, which gets called when someone clicks on the Record button.

In the simple case all you need do is pass the file output, the call to startRecordingToOutputFile and you’re done.

You have a fully functioning recording UI.

The second case is the custom or slightly more complicated mode of running.

In this mode of running you provide your own AVCaptureSession configured to your liking.

So, you could set it with a custom AVCaptureSession preset, a custom frame rate, anything that you’d like.

AVCaptureView will still manage the inputs for you and provide the UI.

That’s it for AVCaptureView.

Let’s move on to a great new feature called iOS Screen Recording on OS X.

Try to wrap your brain around that.

New in Yosemite you can plug your iPhone or iOS device into a Mac and it shows up as a selectable camera.

So, you can do stuff like this in QuickTime Player.

You have the standard recording UI.

[ Applause ]

And you can record what’s happening on your iOS screen and then you can publish for instance a how-to video, give something to your mom to show her how to do something or you can do an app preview, which we’ll talk about more in a minute.

There are some special considerations, however.

First of all, iOS devices are presented as CoreMedia IO DAL plug-ins the same way as any third party camera interface.

But, we consider them special, because they’re really a screen grabbing device not a live camera feed.

So, we don’t want to confuse shipping apps by having this weird camera show up suddenly and unexpectedly.

Therefore, you need to opt in to get this behavior to see iOS devices as AVCapture devices in your app and this is how you do it.

There’s a single property call that you make on the CoreMedia IO system object telling it to allow screen capture devices.

Once you’ve opted in, if you iterate through the list of AVCapture devices you’ll find your iOS devices there.

And as you plug them in or unplug them they’ll come and go.

There is in fact, a whole session devoted to this topic and its tomorrow at 3:15 in Russian Hill.

Also, if you’d like to learn more specifically about how the DAL part works, we invite you to come and visit us in the labs.

All right, that’s it for Yosemite.

Let’s move on to our iOS 8 Capture Enhancements.

First up, machine-readable codes aka barcodes.

Last year we introduced support for barcode detection during real-time video capture and we support a long list of bar codes as you can see here.

In iOS 8 we’re supporting three new symbology types, Data Matrix, Interleaved 2 of 5 and ITF14.

Next up, we’re continuing with our efforts to provide greater transparency to users.

If you’ll recall in iOS 7, for the first time, these dialogues showed up.

The first time your app tries to use the microphone you get the microphone dialogue and in some regions the first time your app attempts to use the camera, it would show the second dialogue.

We only showed that dialogue in regions where it was required by law.

In iOS 8, however, we’re requiring user consent to use the camera or mic in all regions.

A couple of reasons for that, it’s a good thing for you as developers, because you get a more consistent behavior across all regions.

It’s easier to debug your code.

And for us it’s great, because we have a more consistent platform experience and people feel like their privacy is being respected.

You can refer back to last year’s session 610 for coding examples of how to deal with these dialogues.

All right, I hope you enjoyed the appetizer, because it’s time for the main course.

We’ve done this for four years now, I think.

AVFoundation came out four years ago.

And so for four years you’ve been coming to the labs.

You’ve been talking to us, filing enhancement requests, filing bug reports and we read all of them.

There have been some really interesting enhancement requests.

In fact, there have been a lot of enhancement requests.

No, like a lot of enhancement requests and it turns out a lot of you want the same things and we listen.

We read all of those bug reports and so we know what the majority of you want.

Your top two feature requests were direct access to the H.264 video encoder during real-time capture and manual camera controls.

Good news, you’re getting both of them in iOS 8.

[ Applause ]

So, H.264 video encoder.

We’re introducing support for access to the hardware H.264 video encoder via the video toolbox APIs, which have already been available on Mac OS X, but now they’re available on iOS as well.

What this means for you as a capture client is if you’re using a video data output you get uncompressed buffers.

Now, you can compress them.

You can do I-frame insertion, bitrate adjustment, choose what kind of GOPs you want, a whole lot of features at your disposal.

There is in fact, too much to talk about at one session so we’re going to do it again tomorrow, Thursday 11:30 where we’ll just talk about the specifics of H.264 encoding.

Don’t miss it.

Now onto the meat of the matter, which is manual camera controls.

Our aim here is nothing short of making iOS the premier platform for computational and pro photography.

Now, when I say manual controls what does that evoke?

What sort of picture comes to your mind?

Is it something like this, lots of dangerous looking knobs and buttons?

Well, it’s true.

Manual camera controls aren’t for everyone.

It’s the age-old problem, automatic versus manual.

Well, our automatic controls work great for most apps and manual controls offer a greater degree of creative control and more freedom to experiment.

But, with great power comes great responsibility.

So, while we’re providing the power you must provide the responsibility and the common sense.

The manual shifter sure makes for a fun drive, but it’s not going to prevent you from going from second gear into fifth or forgetting to push the clutch in and grinding your gears.

You see where I’m going with that.

All right, we’re going to talk about four things in particular, focus, exposure, exposure compensation and white balance.

Again, referring to our diagram of AVCapture objects all of the manual control APIs I’m going to talk about are implemented in a single class, the AVCaptureDevice.

First up is manual focus.

Focus refers to the sharpness of objects in the frame and we have a great autofocus mechanism.

Its job is to try to keep the most important things sharp.

But, with manual focus you’ve got some more creative control.

For instance, you could allow a pro photographer to soften up the image such as right here or do a dramatic focus pull.

You as a developer might want to develop your own focus stacking algorithm, so you can pull different objects in and out of focus.

If you’re a scientist or a writer of medical applications, you might want to programmatically move the lens position around for experiments.

Here’s how it works.

You’ve got a subject, here it’s the candle on the left and you’ve got a sensor on the right.

In the middle there’s a lens and the job of the lens is to focus light rays onto the sensor.

Focus is altered by moving the lens nearer or farther from the sensor.

And the farther the lens is from the sensor, distant objects look sharper.

So, here we’ve got a problem.

The candle image is blurry because our lens is focusing light rays from the candle in front of the sensor.

But, as we move the lens closer to the sensor we see that the candle image becomes sharp, because the light rays converge in the right place.

Now, let’s talk a few focus terms.

First is depth of field.

This is the distance between the nearest and farthest objects that can be judged to be in focus.

At the near end we have what’s called macro and that’s the closest distance at which the lens can focus.

At the far end is infinity.

Somewhere in between there is a sweet spot called hyperfocal distance and that’s the distance that maximizes your depth of field.

Because if you find this position and focus there, everything from infinity to half of the hyperfocal distance is going to be sharp.

The last is lens position and that’s what makes all this magic happen.

When you move the lens position you are moving the distance of the lens from the sensor and therefore altering focus.

Now, quickly let’s talk about what you can do already in iOS 7 and earlier.

We provide great automatic controls in a AVCaptureDevice.

Three modes: locked, one shot autofocus, which sweeps the lens position through all ranges until it finds sharp focus and then parks it there.

And continuous autofocus, which gives the camera freedom to refocus anytime it thinks the scene has become sufficiently blurry.

We also provide, there we go, a focus point of interest, which is settable.

It lets you tap to focus on a particular area so that it becomes sharp.

And you can also know when the lens is moving by key value observing the isAdjustingFocus property.

Last year we offered some specialty modifiers for autofocus.

The first is range restriction and that hints the AF algorithm to limit its search to a particular range.

The near range is good if you have something that only wants to search up close, for instance, a barcode scanning app.

We also have the far range, which is good for distant objects such as oh, barcodes painted on the sides of buildings.

And then finally we have smooth autofocus, which is a modifier that slows down the AF algorithm and steps it in smaller increments so that it avoids the throbbing artifacts that you don’t want to see when you’re recording a video.

Now, new in iOS 8 we allow full manual control of the lens position when you’re locking focus and we allow you to key value observe the exact lens position at any time, in any focus mode.

But, I think a demo is worth a thousand words so let me call up Aparna from the Camera Software Team to give you a demo.

Take it away, Aparna.

[ Applause ]

Thanks Brad.

Hi everyone.

I’m Aparna.

I’m an engineer in the Camera Software Team.

I’m very excited to be here and show the demo for manual focus control.

So, I have the app on here and there are two modes, auto unlock.

The auto is same as today, but I have the lens position slider here whose values are updated based on the key values for the lens position property during the autofocus.

So, as you can see I’m trying to focus on this flower here.

The lens position is changing.

Now, if I want to focus at Brad here, I have to move my phone and bring Brad in focus.

Now, let me switch to the new Locked mode.

Here I have full control on the lens position so I can move my slider and bring my target of interest in focus.

So, I am going to move the slider here and frame a scene with Brad in focus and I can take a shot and I can change the slider position, I mean the lens position and bring the flower in focus.

So, that’s manual focus control in iOS 8, thank you.

[ Applause ]

Great, thank you Aparna.

That magic was all provided for by this magical lensPosition property, which was being key-value observed and that’s how you saw that slider flying all over the place every time the lens was moving.

It’s a key-value observable property and its range is from zero to one.

Zero means macro or the closest that the lens can focus.

One means completely stretched out or the farthest that it can park, which may be infinity or it may be beyond infinity and we’ll talk about that later.

Instead of a simple setter property AVCaptureDevice provides a compound setter, setFocusModeLocked WithLensPosition completionHandler.

That’s a mouthful, but it does three important things.

It locks focus at an explicit lens position and it calls you back when the command has completed and it does so with a timestamp.

And that sync time is the presentation time of the first video frame, which reflects your change.

This is far superior to the adjusting focus key-value observation because you know exactly which frame you can synchronize this with.

Now, the sync time is on the timeline or clock of the AVCaptureDevice.

That’s important, because if you’re going to synchronize it with things coming out of an output you will need to sync the time to a different clock.

AVCaptureVideoDataOutput buffers are going to be on the AVCapture session’s master clocks timeline.

The way you would do it is by using CMSync services.

Here I’ve taken the time provided and the master clock from the session and I call CMSyncConvertTime to go from the device clock to the master clock and now I have my synchronization point for output buffers.

Next, there’s a special lens position parameter that you can pass to that setter called current.

And what that does is say I want you to set the lens position to exactly where it is right now and then tell me when you’re done.

So, it will lock it in the position where it is, but it does so without avoiding race conditions.

Imagine if you tried to get the lens position and then set it.

Well, if the lens is moving at the time you may have actually set it to the wrong point, because it will now jump back to an undesired position.

So, the following two are equivalent.

If you set it with the new flavor using the current property and no completionHandler it’s the same as calling the old focus mode locked.

Now, why did we choose to go with unit-less values, scalar values from zero one rather than a distance in meters, for instance?

The reason lies in our implementation on focus on iOS devices.

The lens is physically moved with a spring and a magnet.

That means that there is some hysteresis involved when it moves, or bounce, and that bounce prevents precise, repeatable positioning at a particular distance.

And also, that means that gravity will affect spring stretch.

So, a lens position of .5 will be different if you’re pointing it straight out versus up versus down.

Also, the lens position distance may vary from device to device or over time as the spring stretches out more.

For all these reasons, we chose to go with the scalar value instead of an absolute distance.

And I’d like to caution you to not try to correlate lens position with a particular distance, because as I said, it changes depending on gravity and other factors.

Next up is how to help users achieve sharp focus.

One inherent problem with having a small device is is it has a small screen and AVCaptureVideoPreviewLayer is not at the resolution of the buffers that you’re getting from the camera usually.

It’s at screen resolution meaning that it’s scaled down.

So, how can you help people know if they’re in good focus?

A couple possible techniques, you could zoom the AV cap- using AVCaptureDevice you could zoom the video preview layer up so that people can see larger pixels and make a better decision or if you’re using a video data output you’re getting the buffer so you could compute your own focus score and then highlight the areas that are sharp.

That’s sometimes called focus peaking.

That’s it for our first third of manual controls, now onto manual exposure.

Exposure refers to the brightness of an image, which means how much light hits the sensor and for how long.

Our auto exposure tries really hard to keep the scene well lit.

That’s its job.

With manual exposure again you’ve got some more freedom.

You can go for a more stylized look like here.

You could go for instance, from something unrealistically bright to something kind of ghostly dark or somewhere in between.

You could get some motion blur.

You could provide some grain.

Manual exposure also allows you to devise your own alternate auto exposure algorithm to Apple’s if you prefer.

Now, when talking about exposure you have to draw a triangle.

That’s the way we do things.

There are three components to exposure and they are shutter speed, ISO and lens aperture.

First up, shutter speed.

Shutter speed is the length of the time that shutter- that the shutter is open to let light in.

In a conventional camera there is a physical shutter, which opens and for all the time that it’s open, it’s letting light into that dark room and then when it closes that’s all the light you get.

So, looking at the picture on the left you see a short exposure.

It lets in less light, because it’s open for less time, but the image is crisp.

The motion is crisp compared to the image on the left, which lets in more light so it has the- it can be brighter.

But, it also lets in more motion blur, because it’s open for longer.

Long exposures are great for photographing stationary scenes in low light, but they’re not so good when shooting things that move around a lot like, say, my kids.

Shutter speed is measured in seconds.

The second one is ISO and that’s a borrowed term from film photography if you’ve ever done that.

It refers to the sensitivity of the chemicals in the film to light.

So, a higher ISO film is more sensitive to light than a low ISO film.

And as you can see from the higher ISO image a higher ISO number means it’s going to be brighter.

It’s more sensitive.

But, it does that at the cost of introducing some noise or grain into the photo.

So, as you can see in the cloth flower on the right there is definitely more grain to the image, more noise compared to the one on the left.

The third component of- in the exposure triangle is lens aperture.

It’s the size of the lens opening, meaning how much can it physically open to let more light in.

On iOS cameras, the lens aperture is fixed on all products that we have shipped.

So, that means practically on iOS the only two things we have to play with are shutter speed and ISO.

Now, what have we- what do we support currently in iOS 7 and earlier?

We have two exposure modes that we support, the locked mode, or the continuous mode.

And continuous, as you might expect, just continuously adjusts the scene to get it well lit.

There is also an exposurePointOfInterest settable property that you can use to tap to expose if you’ve got a complicated scene with various objects that are light and dark you can tell it which thing you want to expose on.

And finally, you can know when exposure is being adjusted through this key-value observable property.

Now, new in iOS 8 we’re introducing support for fully manual exposure or what we call in the API, custom exposure.

In custom mode you can get, set and key-value observe ISO and shutter speed.

Now, we refer to shutter speed as exposure duration in the API set since our cameras don’t have a physical shutter.

I’ll use those two terms interchangeably from here on out.

I feel a demo coming on.

Let’s have Matt Calhoun come up and show us how manual exposure works in AVCam, Matt.

[ Applause ]

Thank you Brad.

My name is Matthew Calhoun.

I’m also an engineer on the Camera Software Team and I’m very excited to show you our manual exposure API in action.

So, let me switch over to the exposure mode of our demo application and we’ve got this nice romantic scene prepared.

And you’ll notice that-let me go back into focus mode and put us in auto.

Now, we’re in focus.

You’ll notice that there are a few more controls in the exposure view.

But, I’d first like to draw your attention to the middle two sliders.

There’s Duration and there’s ISO.

And Duration, as Brad said, you can think of as shutter speed.

And ISO, if this were a film camera it would be film speed, but in this case it’s the gain applied to the sensor coming off the signal or the signal coming off the sensor.

And you’ll see since we’re in Auto Mode right now as I move the device around you will see duration and ISO changing.

That is the auto exposure algorithm in action trying to achieve a perfect exposure by changing duration and ISO as the scene changes.

And as it does that I’d like to draw your attention to the slider above Duration, which is Offset.

So, that’s sort of our exposure meter.

That’s the- that represents the difference between our target exposure and our actual exposure and since we’re in Auto Mode that should be hovering around zero all the time, unless we’re in some extreme lighting situation that we can’t meet the target exposure.

So, I’m going to lock the tripod on this scene now and you’ll see that we’re pretty well exposed on the flowers now.

But, what if I want to enhance the drama in this scene?

Well, that’s exactly what manual controls are for.

So, I’m going to switch over to Custom Mode and you may have noticed that the sliders, the Duration and ISO sliders became enabled now.

So, that means that I can let’s see, let’s get as little noise in this scene as possible by lowering the ISO.

And then since this scene is static and I’m on a tripod I can crank up the Duration or lower and sort of get what wouldn’t be considered a perfect exposure, but may be perhaps a more artistic exposure and that is manual exposure, thanks.

Thanks Matt.

[ Applause ]

Great demo.

So, let’s talk about how continuous auto exposure works.

I want to do that, because to understand manual exposure you really need to visualize how our auto exposure internals work.

Here’s a peek under the hood.

First, there is an auto exposure or AE block and its job is calculating ideal exposure and it’s fed with metering stats continuously.

So, it knows how far off of that exposure target it is.

Then its job is to calculate the right mixture of ISO and duration to get the scene well lit.

That’s the AE loop, constant feedback through the metering stats, constant adjustment through ISO and duration.

In the locked exposure mode, which we’ve had now for several releases, that AE block is still there.

It’s still active.

So, it’s still able to set ISO and Duration, but the difference is the metering stats engine is disconnected.

So, ISO and Duration are never going to change, because the metering isn’t changing.

Now, the new custom exposure mode, in iOS 8 we allow you to manually control ISO and Duration with a new mode called Custom.

You set ISO and Duration together in that one setter, which by now should look pretty familiar and there’s a completionHandler that’s fired on the first frame where the change is reflected.

There are two special parameters.

The first one means don’t touch the Duration.

Keep it where it is.

I only want to mess around with ISO and the second one is keep the ISO where it is.

So, you could have a slider UI where you’re only adjusting ISO or only adjusting Duration.

Now, we have supported ranges: min and maxes for ISO and exposure duration.

Those vary according to the device format so take note.

They’re implemented in the AVCaptureDeviceFormat not in the AVCaptureDevice.

Min and max ISO and min and max ExposureDuration tell you the limits that you can use to set these two properties and do use them otherwise we’ll throw an exception if you try to set an out of range value.

Also, as you might expect, we have three observable properties for the three elements in the exposure triangle: ISO, exposureDuration and lensAperture.

LensAperture I’ll remind you, you can key-value observe it all day long.

It’s never going to change.

Now, how does custom exposure mode work?

Well again, we have that auto exposure block.

It’s still there.

The metering stats are connected so it’s being fed real-time stats about how far off it is from ideal exposure.

So, it can still compute a target offset, which you can use as a meter.

The difference is ISO and Duration are cut off and that’s where you come in.

You get to set them directly using these APIs, but you can still use the target offset to know how you’re doing.

All right, now let’s talk about a close cousin of manual exposure, which is exposure compensation.

Exposure compensation is a modifier to our auto exposure block.

It lets you bias the AE algorithm towards something slightly darker or slightly brighter.

It’s kind of like a gentler, kinder version of manual controls.

So, if you don’t want to get your hands really dirty, but you still want to affect our AE algorithm somewhat, you can use this instead.

So, it biases the decision of the AE algorithm as I said, towards something brighter or darker and this can be used in the Continuous AE mode or in the locked mode.

The way it’s expressed is in f-stops.

If you’ve used a DSLR you’ll be familiar with this.

An f-stop or exposure value is double the brightness if you’re going in the positive direction and it’s half the brightness if you’re going in the negative direction.

I know you all are going to have a conniption fit if you don’t get another demo right now so take it away Matt.

[ Applause ]

Okay, so let’s go back into Auto mode and we’re going to do something similar to what we just did with Manual controls, except this this time I don’t really want to think about a specific shutter speed or a specific ISO.

I just want the AE algorithm to handle it.

But, I do want to make the scene lighter or darker.

So, that’s exactly what this last slider is for.

I simply move it to the left to lower the exposure target or move it to the right to increase it.

And that exposure target is locked in at a higher value now.

So, you’ll see as I move the device around, let me lower it a little bit.

You should see Exposure and Duration still updating in response to scene changes.

But, they are being updated to meet a lower exposure target.

So, I also would like to show you how this works in Locked mode.

It’s very similar except that in Locked mode we only respond to changes in the bias.

So, Duration and ISO will get updated if I change the exposure target.

But, once I stop changing that target if I move the device around and point at different scenes, Duration and ISO will not change.

That is exposure compensation.



Thank you Matt.

[ Applause ]

Exposure compensation is supported in all exposure modes.

And by now you should be very familiar with this pattern.

We have a compound setter, setExposureTargetBias.

We don’t call it exposure compensation in the API.

We call it target bias.

And you’re provided a completionHandler on the first frame where your change has been reflected.

The supported range, min and max ExposureTargetBias are a constant right now on all devices.

The min is minus eight and the max is plus eight, but that may change in the future so we do encourage you to use these min and max TargetBiasValues before a setting.

We will throw an exception if you use an out of range value.

There are some key-value observable getters.

ExposureTargetBias is again what we’re calling exposure compensation.

That will never change unless you change it.

So, exposureTargetBias is zero unless you set it to something else.

The exposureTargetOffset, as Matt just showed you, is the thing that hovers around zero usually if you’re in a well-mannered environment.

It’s able to a good enough job to get the scene well-lit, and when it’s well-lit, targetOffset is at zero.

When you’re at some extreme condition the targetOffset might move around.

You can use it as a meter.

Now, let’s talk about how exposure compensation works with our familiar diagram now.

In Continuous Auto Exposure mode, we have again the metering stats providing real-time information to the block and its setting ISO and Duration.

Now, you can bias that decision of the AE block by setting a new target bias.

The AE block uses those metered stats plus your bias to set ISO and Duration to a new Target Offset.

Now, that offset is influenced by your bias.

So, if you set a bias of plus one then the Target Offset will hit zero when the plus one target has been reached.

Now, let’s look at the Locked Exposure mode.

And this is pretty cool that even though it’s locked you can still kind of influence the exposure.

The metering stats are disconnected so normally ISO and Duration will not change at all.

It’s locked.

But, you can still set bias and when you do that it will now need to fluctuate ISO and Duration to meet that bias and it will report all of that in the Target Offset.

If you’d like to see a great use of exposure compensation, look no further than Apple’s own camera app in iOS 8.

You might have noticed by now that if you tap, when you tap to expose, you’re presented with a new UI.

It’s sort of a brightness UI with a little sun in it and that shows you that you can swipe up or down to bias exposure up or down.

So, it’s influencing the AE algorithm to pick something a little bit brighter or a little bit darker.

Try it out.

It’s really pretty cool.

All right, two down, one to go.

Let’s talk about Manual White Balance.

White balance is all about making the color in your photos look realistic.

Sounds simple enough right, but it’s not.

Different light sources have different color temperatures.

For instance, daylight casts kind of a bluish tint whereas an incandescent light would give you something warmer like a yellowish tint.

Now, our brain is really good at adjusting to these color tints, but cameras can’t do that.

So, under a blue light source, your camera needs to compensate for that by boosting up the opposite colors, to boost up the red to compensate.

All right, this is the nerdiest slide you’ll see here today.

This is a CIE 1931 chromaticity diagram.

You don’t need to know that.

But, it shows us all the colors visible to a human being from pure blue, to bright green on top, to bright red.

And any point on the diagram can be plotted with a little X and Y value, as shown here by these axes.

Take into account here that this is 2D color we’re talking about, so brightness is orthogonal.

We’re just talking about color.

So, on the X axis and on the Y axis from zero to one you can plot any point on that color diagram.

And as you can see there are going to be some crazy values that are outside the range of human visibility and, therefore, they can’t be faithfully represented or reproduced by a camera.

Also note that there is a narrower range there that’s represented by a tight little curve and that curve there is called the Planckian locus.

You also don’t need to know that.

But, every time you say Planckian locus an angel get its wings.

This little curve here expresses the color temperatures in degrees Kelvin with higher numbers on the blue side, that’s hotter and lower numbers on the red side in degrees Kelvin again.

And these are useful for typical lighting situations.

In typical lighting situations, the light will go right along this nice curve.

Sometimes though you have mixed lighting conditions and it’s not quite as cut and dry as just moving along that curve.

Sometimes you need to shift a little bit to deviate from the nice curve, because there is a little bit of a green shift or a magenta shift in that’s what those little tick marks are up there.

So, the Planckian locus is talking about temperature and the little hash marks off of it are tint.

That’s adjusting for red or magenta or green shift.

Now, what possible uses might you have for manual white balance?

Well, our auto white balance does a pretty good job.

It’s trying to guess what your lighting source is and compensate for casts that might be coming off of that lighting source.

But, it’s not perfect.

It might make mistakes.

If you have manual white balance you can do a manual temperature and tint UI.

You could come up with some presets for standard lighting conditions and give the power back to the user.

You could use a gray card to assist in doing a neutral white balance or you just might want to do some crazy color cast and have little green men in your pictures.

IOS devices compensate for color casts by boosting the opposite color gain.

So for instance, if a scene has too much blue it’s over on the left side, then the red gain must be boosted up a lot and the green a little to compensate.

These gain values are calibrated for our devices so they are said to be device-dependent gain values rather than device-independent values such as are represented on this diagram.

Okay, what do we already support?

Similar to exposure, we have a locked mode and a continuous whiteBalanceMode, which continuously tries to adjust for the lighting source conditions.

And we let you know when the white balance is being adjusted.

New in iOS 8 we give you full manual control of the device RGB gains.

This isn’t, you know, just a baby sort of temp and tint UI.

This is full control.

You can key-value observe those device RGB gains.

And we also have support for white balance using a gray card.

We’ll get into that more later and we provide conversion routines to get you from chromaticity values, X and Y values to our gains or from temperature tint along that Planckian locus.

Another angel just got its wings.

All right, I think it’s time for a demo of this.

Let’s bring up both Matt and Aparna to give us a demo of manual white balance.

Take it away guys.

[ Applause ]

Okay, let’s put exposure back into Auto mode now and switch over to the white balance view.

And I’d like to show you just a few of things that are possible with the manual white balance API.

So, we got a common theme here, we’ve got a Mode switch at the top and then a couple of sliders and then a button that I’ll talk about in just a moment.

The sliders are for temperature and tint and, as Brad explained, one goes between yellow cast and blue cast and one goes between green and purple or magenta.

And since we’re in Auto mode and we have somewhat of a mixed lighting situation here in Moscone, you can see the auto white balance algorithm subtly changing these values as I move the device around.

But, what’s more fun is to go into the Locked mode so that we can change them ourselves and let me point at the flowers here.

So, in manual mode, you can really see that we can go between a yellowish cast, a bluish cast and, with tint, we can go between a magenta cast and a greenish cast.

So, now let’s go back to Auto mode and let’s talk about another use case that we’ve enabled, which is the use of a gray card to lock white balance at an appropriate value for a neutral gray.

So, you’ll see down here, we’ve got a yellow gray button.

How’s that for cognitive dissonance?

So, normally the auto white balance algorithm does not assume that you are pointing your camera at a gray card.

In fact, it really has no way of knowing that.

So, that’s what this gray button is for.

When we tap this button we are going to tell it-we are telling the API essentially, “Okay, we are looking at a gray card.

Give us the correct values and then we’re going to lock at those values.”

So, you should see a subtle, in this light, change.

Let me go back to Auto and again a subtle change in the color of the scene when I tap the gray button.

And notice that we’ve gone automatically from Auto to Locked mode.

That is manual white balance.

Thanks, Brad.

[ Applause ]

[ Silence ]

Awesome demos.

Alright, manual white balance is my favorite of the three new camera control APIs and that’s because we got to introduce some new C structs, always a good day when you can introduce a C struct.

We don’t want you to set red, green and blue individually.

They need to be set as a team so therefore we need a struct for red gain, green gain and blue gain.

You set them all at once.

Of course, there is a max that you can set.

On all our devices it’s currently four.

That’s the max white balance gain you can set.

And so the legal range is from one to four.

That may change in the future, so do use maxWhiteBalanceGain.

Also as you saw from the UI that Matt and Aparna just showed you, the temp and tint sliders were moving around in Auto mode.

That’s because they were observing the deviceWhiteBalanceGains and then converting them into temperature and tint.

So, these deviceWhiteBalanceGains are key-value observable and update constantly in your-in the Auto mode.

So, here is what the API set looks like for manual white balance.

You set the white balance mode to “locked” with explicit RGB gain values.

Again, these are device-dependent gain values not device-independent chromaticity values.

There is also a current special parameter that you can set if you just want to lock where it is right now.

Now, about those conversion routines.

Of course, we need structs for them too.

When we’re talking about chromaticity values or the X and Y on that chromaticity diagram they go from a min of zero to a max of one.

Remember, not all of them will fall within human perceivable color and then there’s also temperature and tint as a struct, which can be set together.

Temperature is a floating point value in Kelvin and tint is an offset for red, or magenta, from zero to 150.

Positive values go in the green direction, negative in the magenta direction.

Now, to convert them unfortunately you need to be very verbose so I need to use three lines per function call, but bear with me.

The first one is, when you have RGB gains from the device and you want to turn those into X, Y chromaticity values, you call this.

There is also, to go in the other direction, you can provide X and Y values and we’ll convert them into our device-dependent RGZB gain values.

Also, you can take our gains and turn them into temperature and tint and vice versa.

Now, note that our conversion routines are more accurate the closer you are to that curve, to the Planckian locus.

The farther away you get the crazier results might get.

As I said, some X and Y temperature and tint combinations will yield out of range RGB gain values.

But, we’re not going to hide that from you.

If you call a conversion method with a crazy X and Y value, we are going to convert it to the corresponding device RGB gain value without clamping it.

But, that might not be a legal value that you can use when setting the white balance gains on the AVCaptureDevice.

So, when using these conversion utilities you must do your own range checking.

You must check for out of range values otherwise an exception will be thrown.

Now, let’s talk about the gray card support or what we call Gray world.

What is Gray world?

It is not the land of perpetual depression.

It is-think of it as an alternative to our auto white balance algorithm.

AWB is very complicated and preferenced.

That’s because it needs to make guesses all the time about what the lighting sources are, but it can be tricked.

If for instance, you have predominantly red in your scene like a scene of autumn leaves it might think that that’s your lighting source when in fact it’s just predominantly the color in the scene.

It needs to make those assumptions and sometimes it guesses wrong.

Well, you can remove the guesswork by using a gray card.

And so the gray world is-think of it as a parallel universe of AWB values that are computed all the time as if you had a gray card in front of the camera.

So, you can get the regular device RGB gains or you can get the alternate gray world gains at any time.

And what it does is try to make white look neutral white.

So, if you have a gray card and you have a UI for this, you can really take the guesswork out.

It does assume that that neutral subject or gray card fills at least 50 percent of the center of the frame.

Now, how might you do this in a UI?

Well, you could prompt the user to put a gray card in front of the camera then you could wait for the gains to settle down for a minute and then sample the gray world device white balance gains property and then lock white balance mode with those gray world gains.

Now, you know you have a neutral white and you’ve taken all of the guesswork out of AWB.

That’s it for the manual controls.

Let’s talk about where all three of these are supported.

Manual focus, it’s supported everywhere, everywhere where you can focus our camera, you can use the manual controls all the way back to an iPhone 4s.

Manual exposure and manual white balance: no restrictions.

You can use them on all iOS devices supported in iOS 8.

Also, the talk has generally been geared towards digital photography, still photography, but you can use these manual controls with any AVCaptureSession preset or any active format.

So, they’re equally applicable to video recording, barcode scanning.

You can set manual controls for any use case.

Whew, are you stuffed yet?

Is there any room for dessert at all?

I hope so, because you’re going to want to hear about bracketed capture.

Think of this as a twist on the manual controls that we’ve spent the bulk of the talk on.

All of the AVCaptureDevice manual controls happen in real time.

You set an exposure, it executes your command.

That’s great, but sometimes you need to capture a moment in time a variety of different ways.

You need to capture one picture but you want to set different settings.

So, wouldn’t it be great if you could preprogram the camera to give you several images in a row of the same scene, but with different exposure values for each one.

And then issue that command and have it execute that command as a group and then give you those three or four images back.

That’s exactly what bracketed capture is.

It’s a burst of still images taken with varied settings from picture to picture.

Some common examples would be an exposure bracket, two different flavors.

The first is an auto exposure bracket where you are differing the bias from image to image, minus two, zero, plus two.

Some reasons for doing that might be if you want to do some highlight recovery such as an HDR fusion algorithm.

You could take an underexposed image, an overexposed image, fuse them together.

The other case is manual where you have full control over shutter speed and ISO and you set those independently for each image in the bracket.

Why might you want to do that?

Well, creative exposure effects, different combinations of long and short exposure duration, for instance.

And the simplest of all brackets is the bracket where you don’t vary anything.

It’s just a simple burst bracket and that might be good for a finish line.

So, without further ado let’s have a demo of bracketed capture with John.

Come on up.

[ Applause ]

Thanks, Brad.

My name is John Papandriopoulos.

I’m great, it’s great to be here.

I’m an engineer on the Camera Software Team.

So, this is a two-part demo.

The first part we’re going to be looking at exposure compensation and performing a bracketed capture of three frames where we have an underexposed frame or image, a well-lit image and an overexposed image using EV values of minus two, zero and plus two.

So, let’s go ahead and take a capture and what we’ve done is, as these frames have been captured, we process them in real-time and stripe them.

So, we actually take a strip from the first image and then put that into an output buffer that you see on the screen here.

The second one beneath that comes from the second captured frame, in this case a well-lit frame and then an underexposed frame following that.

So, now I’m going to go into the fully manual bracketed capture mode that we support where we have full control over ISO and duration.

What we’re going to do in this case is we’re going to perform a three frame bracketed capture where we have control over the duration or shutter speed and we’re going to be setting that for a short shutter speed, a medium shutter speed and then a fast shutter speed.

So Brad, if you were to walk across there and we capture that what we can see here is that we have a blurry image at the top of Brad’s head.

That’s where we had a very slow shutter.

We have a little bit more crisp for his chin there and then you can see his torso is quite crisp.

And if we look really closely there you might see that there’s a lot of noise there.

What we’ve had to do is adjust the ISO and increase the gain to compensate for the small amount of light that was going in at that fast shutter speed.

Thanks very much, can’t wait to see what you do with it.

[ Applause ]

Thanks John for slicing and dicing me.

Bracketed capture is all implemented in a single class and that’s the AVCaptureStillImageOutput.

That’s the object that you use to take pictures in our API.

So, how does it work?

Well, if you’ve used AVCaptureStillImageOutput before you’ll be familiar with its single image capture interface, which is this.

You call captureStillImage AsynchronouslyFrom Connection.

That takes a picture and at some point when it’s done it calls you back with that single image buffer.

For bracketed capture it looks largely the same.

The bracketed capture interface is captureStillImageBracket AsynchronouslyFromConnection.

The only difference is that it has a second parameter, an additional parameter, which is the settings array and that’s where you’re giving it the array of things that you want to vary from picture to picture.

We have two new objects, two new classes to represent the settings for a single image in the bracket.

The first is for the exposure compensation or auto exposure bracket.

So, one AVCaptureAutoExposure BracketedStillImageSettings object equals one of those pictures in the bracket.

And here you get to set an exposure target bias, minus one, plus one, etc. For manual exposure brackets you use an object that lets you set both ISO and duration for that one picture.

Now, there are some dos and don’ts.

Let’s cover the don’ts first.

In bracketed capture we don’t allow you to mix bracketed settings classes.

That means you can’t have a half manual, half auto exposure bracket.

You have to have an all auto or all manual exposure bracket.

Also, there is a limit to the number of images you can take in a bracket and you must not request more than maxBracketedCapture StillImageCount.

That will vary from platform to platform and also depends on resolution and the output format that you are asking for.

Now, the dos, do prepare for the worst case and the way that you do that is by calling prepare.

You’re telling the AVCapture still image output at some point in the future I am going to take a bracket.

And you tell it what kind of bracket you’re going to take by passing the exact settings that you’re going to use.

And by telling it beforehand to prepare itself for that bracket, it can do all of the buffer allocations that it needs to up front so that when you ask for the bracket there’ll be no shutter lag.

It will happen very quickly.

You should always assume that the sample buffers are coming from a shared memory pool.

In other words, don’t ask for one bracketed capture and then hold onto those buffers and then ask for a second one, because the second one is going to fail.

You must CFrelease the buffers given from the first bracket before asking for a second bracket.

If you want to reclaim the memory that was prepared from a previous prepare call the way to do it is just call prepare again with an array of one object and that will reclaim the memory.

Now, some fine details about bracketed capture.

What happens-what’s the interaction between the AVCaptureDevice manual controls and bracketed capture?

Well, the bracketed capture wins.

So, when you’re doing a bracketed capture all the manual controls you set on the AVCaptureDevice are temporarily overridden and then they go back to what they were after the bracketed capture.

Also, flash and still image stabilization are ignored during a bracket.

And all images in a single bracket must have the same output format, be it jpeg, 420f, VGRA.

Also note that because you might be doing long durations, it might need to expose for a long time, that it is typical for you to see video preview drop frames while an exposure bracket is being taken.

And finally, bracketed capture is supported on all iOS devices in iOS 8.

Whew, that concludes our lovely lunch, time for the check.

So, what do we got here?

We’ve got AVCaptureView on Yosemite, a standard UI for doing recording, iOS screen recording for app previews-for all you people that make apps on the App Store, you’ll want to check that out, access to the hardware video encoder in real-time, special session about that tomorrow, powerful new camera controls.

We talked about focus, exposure, exposure compensation and white balance, and finally, still image bracketing.

The two apps that we used today for the demos, AVCamManual and BracketStripes are available now.

So, if you go and look at the session info you can go download those, see what we did there, try them out.

For more information here’s our evangelism contact.

There are a number of related sessions to this one, both for AVFoundation and for the photos framework.

Some have already happened.

If so, go check them out in the videos and there are some still to come.

Thank you and have a great show.

[ Applause ]

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