Apr 29, 2007

Cocoa Application with custom Core Image filter 6: Embedding a Quartz Composer composition directly in an app.

In the last episode, we built a Cocoa application to filter an image using Convolution. This application followed a more traditional path to developing a Cocoa application. We took an image from our view, handed it to the model to be processed and sent it back to the view for display. With the advent of Quartz Composer, this application can be implemented in an entirely different way that opens up all kinds of interesting possibilities for interactive graphics applications. I'm sure all of you have seen that Quartz Compositions can respond to external inputs. Quartz Composer can take input from keyboard and mouse, spotlight image searches, folders of images, and even RSS feeds. Apple's RSS screensaver is a prime example of a Quartz Composition that uses RSS input.

An other way of feeding input and getting output from a composition is by publishing input and output parameters from your compositions. In the most simple sense, these will allow Quartz Composer itself to present a simple UI to allow you to interact with your composition. For example, in my test.qtz composition, I publish the inputImage and all the coefficients so that QC can provide a simple user interface.

More interesting than this, however, is the use of a composition embedded within a QCView and accessing its parameters using Cocoa bindings technology and the QCPatchController. I've provided a link to this project so you can see how I did it: Convolver.zip

The first thing you need to do to use these in your projects is to add the Quartz Composer palette to Interface Builder. To do this:

Open Interface Builder Preferences:

IB preferences showing palettes tab

Click the Add... button:

selecting the QuartzComposer palette

You will get this new palette in Interface Builder.

the Quartz Composer palette

Next, we need to copy the test.qtz composition into the bundle for our application. It doesn't really matter where you put it. For our purposes, we just need to make sure that it is packaged with the app when it ships. After that we need to open the composition and make sure that we publish all the inputs that we care about so that the QCPatchController can access them. In order to do this, you have to right-click on whatever patches in you composition have external inputs or outputs and check off each one you wish to publish. As far as I've been able to tell, there are no shortcuts to this, you have to select each one individually.

Publishing inputs from the compostion

Whatever you name the inputs or outputs at this point is the key that will be used to access that parameter later.

Now that we have a properly set up composition, we have to modify the original project to use the QCView and QCPatchController instead of old-school target/action. First thing to do is to replace the outputImage NSImageView on the right of the main window of the app with a QCView. Then, drag a QCPatchController into the nib file. I set the erase color of the QCView to transparent to avoid the large black square on the right side of the application when it starts.

Next., we'll go ahead and set up the bindings for the project. The QCView's patch binding is bound to the patch key on the QCPatchController.


We set the value binding of the NSImageView to the patch of the QCPatchController with the src.value key path.


Finally, each cell of the NSMatrix gets bound to the correct value in the composition:


The final piece of this setup is to load the composition into the QCPatchController


Now, you can actually test the working application from within IB. If you choose File->Test Interface and drop an image on the left image view you will see the convolved output image displayed on the right. Of course, this is exploring the barest minimum of the possibly utility of Quartz Compositions and QCView.

This has greatly reduced the amount of Objective-C code we need to write to build this application. The entire Convolver class that converted the image to a CIImage, called the filter and reconverted the result back into an NSImage is not needed any more. I still want to be able to select File->Open... to drop down a sheet and open the image, so that's really the only thing I need the ConvolutionController to do at this point. There turns out to be one small catch that I'll point out when I get to it.

Here's the new header for the ConvolverController class:

/* ConvolverController */

#import <Cocoa/Cocoa.h>
#import <QuartzComposer/QuartzComposer.h>
#import <QuartzCore/QuartzCore.h>/span>


@interface ConvolverController : NSObject
{
IBOutlet QCView *resultImage;
IBOutlet NSImageView *sourceImage;
IBOutlet NSWindow *window;
}
- (IBAction)openImage:(id)sender;
@end
Some of you may look at this and wonder why I need a pointer to the resultImage any more since the bindings should take care of it. That's where the catch comes in, as you'll see below.

This is the source code for the class:

#import “ConvolverController.h“


@implementation ConvolverController

- (IBAction)openImage:(id)sender
{
NSOpenPanel *panel = [NSOpenPanel openPanel];
[panel beginSheetForDirectory: nil
file:nil
types:
[NSImage imageFileTypes]

modalForWindow: window
modalDelegate:self
didEndSelector:
@selector(openPanelDidEnd:returnCode:contextInfo:)

contextInfo:nil];
}

- (void)openPanelDidEnd:(NSOpenPanel *)panel
returnCode:(int)returnCode
contextInfo:(void *)contextInfo{

NSArray *files = [panel filenames];
NSString *filename = [files objectAtIndex:0];
NSImage *image = [[[NSImage alloc]
initByReferencingFile:filename]
autorelease];

[sourceImage setImage: image];
[resultImage setValue: image forInputKey: @“src“];
}
@end

This is normal sheet handling code, just like in the previous project, but a little simpler. The catch comes in when I have to call [resultImage setValue: image forInputKey: @“src“] even though you might think that the bindings for NSImageView should automatically be updated, they are not. Apparently it's an already filed bug and this one line of code provides a simple workaround.

So, that ends the journey of building a Cocoa app with a custom Core Image filter. Hope it was useful to you. This was a very simple example, there's so much more that can be done with these amazing technologies. Until next time, happy coding!

Apr 10, 2007

Cocoa application with custom Core Image filter 5: calling the filter from a Cocoa app.

Last time in the village we packaged our convolution filter as an image unit. Since the filter was executable, we had to develop an Objective-C class that inherited from CIFilter and implemented a few methods. This time we will see how we can call our image unit from a Cocoa application. This technique will generalize to any other Image Unit in the system, built in or otherwise.

For the purposes of testing, I've created a simple Cocoa application called Convolver. As you can see, Convolver has one main window with two NSImageViews and a separate pallette with a 3x3 NSMatrix of coefficients. Dropping an image on the left view causes the right view to update with a processed version of the left image. Changing the coefficients immediately causes the filter to run and change the rightmost image.

The structure of this application is pretty simple. There is an application controller called ConvolverController and a very simple model (Convolver) which only takes an unprocessed NSImage with an array of coeficients and returns a processed one. Taking a look at the nib file there are the user interface objects, the controller and the model all instantiated in the nib.

The inspector for the ConvolverController object shows the outlets for the controller.

The sourceImage, convolutionMatrix and the File->Open menu all have their targets set to the convolve: method of the controller, so that opening an image or changing the coefficients of the matrix will cause the image to be processed again.

The Convolution controller class is very simple. Here's the interface for that class

/* ConvolverController */

#import <Cocoa/Cocoa.h>
#import “Convolver.h“

@interface ConvolverController : NSObject
{
IBOutlet NSMatrix *convolutionMatrix;
IBOutlet Convolver *convolver;
IBOutlet NSImageView *resultImage;
IBOutlet NSImageView *sourceImage;
IBOutlet NSWindow *window;
}
- (IBAction)convolve:(id)sender;
- (IBAction)openImage:(id)sender;
@end

and here's the implementation:

#import “ConvolverController.h“

@implementation ConvolverController

- (IBAction)convolve:(id)sender
{
NSImage *source = [sourceImage image];
NSImage *dest = [convolver processImage: source
withCoefficients: [convolutionMatrix cells]];
[resultImage setImage: dest];
}

- (IBAction)openImage:(id)sender
{
NSOpenPanel *panel = [NSOpenPanel openPanel];
[panel beginSheetForDirectory: nil
file:nil
types: [NSImage imageFileTypes]
modalForWindow: window
modalDelegate:self
didEndSelector:
@selector(openPanelDidEnd:returnCode:contextInfo:)

contextInfo:nil];
}

- (void)openPanelDidEnd:(NSOpenPanel *)panel
returnCode:(int)returnCode
contextInfo:(void *)contextInfo{

NSArray *files = [panel filenames];
NSString *filename = [files objectAtIndex:0];
NSImage *image =
[[[NSImage alloc]
initByReferencingFile:filename] autorelease];

[sourceImage setImage: image];
[self convolve: self];
}
@end
The convolve: method is called whenever the source image or coefficient matrix is changed. Notice that I pass the matrix cells unaltered to the model. At first I thought I would pull the information out of these cells and pass an array of NSNumber, but then I decided that I would just make sure that the model would take the floatValue: of whatever input it got to make sure I got the correct input type. Without strong typing, it seemed that I would have to do this in the model anyway, so I just do it there. The openImage: method opens a sheet to allow the user to select an image, and the openPanelDidEnd:returnCode:contextInfo: method sets the image file to the source image and calls convolve:

The implementation of the Convolver class and the processImage:withCoefficients: method are the most important part of this exercise. Here's the header:

/* Convolver */
#import <Cocoa/Cocoa.h>
#import <QuartzCore/QuartzCore.h>

@interface Convolver : NSObject
{
CIFilter *convolution;
NSDictionary *filterAttributes;
CIContext *context;
}
-(NSImage *)processImage:(NSImage *)image
withCoefficients:(NSArray *)coefficients;

@end

and here's the implementation:


#import “Convolver.h“

@implementation Convolver

-(id)init
{
if( self = [super init] ){
[CIPlugIn loadAllPlugIns];
convolution = [CIFilter filterWithName:
@“Convolution3by3“];

[convolution retain];
filterAttributes = [[convolution attributes]
retain];

}
return self;
}
The init method loads all the Core Image plugins in the system, and gets the Convolution3x3 filter from the system. We are not going to use the attributes but you can access them and use them if you wish by using the attributes message on the filter you load.

The only other method in this class is processImage:withCoefficients and I'll break it down for you a step at a time.

-(NSImage*)processImage:(NSImage*)image
withCoefficients:(NSArray*)coefficients

{
if( context == nil ){
context = [CIContext contextWithCGContext:
[[NSGraphicsContext currentContext]
graphicsPort]
options:nil];
[context retain];
}

The first thing we need to do is to get a Core Image graphics context which we get from our current graphics context.

Now, since we are using an NSImageView we need to convert the NSImage within into a CIImage. The way I am doing this is by using the initWithFocusedViewRect: method to get a bitmap representation of the NSImage and then using that bitmap to initialize a CIImage object with that bitmap. Personally I'm not sure why there are multiple image types in Cocoa (I'm sure someone at Apple would have the reason) but it's just something we have to deal with.

NSSize size = [image size];
[image lockFocus];

NSRect imageRect =
NSMakeRect(0, 0, size.width, size.height);


NSBitmapImageRep* rep = [[NSBitmapImageRep alloc]
initWithFocusedViewRect:imageRect];

[rep autorelease];
CIImage *bitmap = [[CIImage alloc]
initWithBitmapImageRep: rep];

[bitmap autorelease];
[image unlockFocus];

The next step is to set the parameters for the filter. We first call setDefaults to get the parameters in a known good state in case we don't want to set all of them. Core Image uses Key-Value coding to set all its parameters. Apple uses this technology so often, and it has turned out to be so useful for all kinds of applications that I don't know what we did before it. One minor annoyance is that we have to make NSNumber objects for each of the float parameters since Cocoa doesn't have any sort of automatic coercion like there is in Java 1.5.

[convolution setDefaults];
[convolution setValue:bitmap
forKey:@“inputImage“];


NSArray *keys = [NSArray arrayWithObjects:
@“r00“, @“r01“, @“r02“,
@“r10“, @“r11“, @“r12“,
@“r20“, @“r21“, @“r22“, nil];

NSEnumerator *en = [keys objectEnumerator];
int i = 0;
NSString *key;
while( key = [en nextObject] ){
NSNumber *param =
[NSNumber numberWithFloat:
[[coefficients objectAtIndex:i++] floatValue]];

NSLog(@“key %@ index %d value %@“, key, i-1, param);
[convolution setValue: param forKey: key];
}

Finally, we get the value for the “outputImage” key which calls the outputImage method in the filter class and actually produce the result.

CIImage *result =
[convolution valueForKey:@“outputImage“];


Now we have to convert back to a NSImage. Unfortunately from what I can tell, there's no way to just get a bitmap representation out of a CIImage object. If anyone knows of a better way to do this, please leave a comment! So, we draw the CIImage into our NSImage object and return it.

NSImage *outputImage =
[[[NSImage alloc] init] autorelease];

[outputImage setSize: size];

[outputImage lockFocus];
[result drawInRect: imageRect
fromRect: imageRect
operation: NSCompositeSourceOver
fraction:1.0];
[outputImage unlockFocus];

return outputImage;
}
@end

That's the end of this tale. There is another angle on this same problem, however. Instead of converting images and calling filters, we can embed the Quartz Composer composition we developed as a test directly into our application by using a QCView and controlling our composition using the QCPatchController. Next time we'll reimplement this app using those techniques.

Mar 29, 2007

Cocoa Application with custom Core Image filter 4: packaging the filter as an Image Unit

Greetings to all from my remote village. This month is turning out to be incredibly busy, so I'm glad to finally be posting this entry. Last time in the village, we introduced QuartzComposer to test the convolution filter. Now, it's time to package it into an Image Unit. In Core Image, an Image Unit is a way to provide an image processing function as a plug in so that any application in the system that uses Core Image can find and use its function.

Xcode provides an easy way to get started by providing a project template for an “Image Unit Plug in for Objective-C” under “StandardApplePlugins” in the XCode project window.

When this project is created, it already contains some stub Objective-C code, which we will never touch, An example kernel which will be deleted, and some .plist and .strings files for localization. There are two types of filters, executable and non-executable filters. Non-executable filters run entirely on the GPU and therefore there is no need for any objective-C code in the filter. A nonexecutable filter is determined if all “sample” calls in the filter are of the form color = sample(someSrc, samplerCoord(someSrc)); Since the sample instructions in our filter are of the form: sample(src, (samplerCoord(src) + loc)) which does not match the nonexecutable form, we have an executable filter.

For an executable filter, only the version number, filter class and filter name are read from the Description.plist file. The other parameters need to be defined in an Objective-C subclass of CIFilter. Here's the interface to that class:


#import <QuartzCore/QuartzCore.h>
#import <Cocoa/Cocoa.h>
@interface Convolution3by3: CIFilter
{
CIImage *inputImage;
NSNumber *r00;
NSNumber *r01;
NSNumber *r02;
NSNumber *r10;
NSNumber *r11;
NSNumber *r12;
NSNumber *r20;
NSNumber *r21;
NSNumber *r22;
}
- (NSArray *)inputKeys;
- (NSDictionary *)customAttributes;
- (CIImage *)outputImage;
@end


So, we declare our input parameters and override 4 methods: init, inputKeys, customAttributes, and outputImage. The names inputImage and outputImage are defined by convention and are used with key-value coding to call your filter. Also, filter processing is done in the outputImage method so that the filter is processed lazily - results are only generated as requested. Here's the implementation of init:


#import “Convolution3by3.h“
@implementation Convolution3by3

static CIKernel *convolutionKernel = nil;

- (id)init
{
if(convolutionKernel == nil){
NSBundle *bundle =
[NSBundle bundleForClass:[self class]];


NSString *code = [NSString stringWithContentsOfFile:
[bundle pathForResource:@“Convolution3by3“
ofType:@“cikernel“]];

NSArray *kernels = [CIKernel kernelsWithString:code];

convolutionKernel = [[kernels objectAtIndex:0] retain];
}
return [super init];
}


The init method loads the cikernel code out of the bundle and loads it into the static convolutionKernel variable. It is possible to define multiple kernels in a single cikernel file if you wanted to, but I would probably keep them separate in most cases.

The inputKeys method simply returns an array of parameters for the filter:


-(NSArray *)inputKeys
{
return [NSArray arrayWithObjects:@“inputImage“,
@“r00“, @“r01“, @“r02“,
@“r10“, @“r11“, @“r12“,
@“r20“, @“r21“, @“r22“, nil];
}


By far the longest method of the Image Unit is the customAttributes method where we return a NSDictionary containing a separate NSDictionary for each of the filter parameters. Here's what it looks like:


- (NSDictionary *)customAttributes
{
NSNumber *minValue = [NSNumber numberWithFloat:-10.0];
NSNumber *maxValue = [NSNumber numberWithFloat:10.0];
NSNumber *zero = [NSNumber numberWithFloat:0.0];
NSNumber *one = [NSNumber numberWithFloat:1.0];

return [NSDictionary dictionaryWithObjectsAndKeys:
[NSDictionary dictionaryWithObjectsAndKeys:
@“NSNumber“, kCIAttributeClass,
kCIAttributeTypeScalar, kCIAttributeType,
minValue, kCIAttributeSliderMin,
maxValue, kCIAttributeSliderMax,
zero, kCIAttributeDefault, nil],
@“r00“,
...


This repeats for all 9 parameters, the only one that is different is r11, which is the center parameter. The value of this parameter defaults to 1, so that by default the filter returns its input unaltered.


...
[NSDictionary dictionaryWithObjectsAndKeys:
@“NSNumber“, kCIAttributeClass,
kCIAttributeTypeScalar, kCIAttributeType,
minValue, kCIAttributeSliderMin,
maxValue, kCIAttributeSliderMax,
zero, kCIAttributeDefault, nil],
@“r22“, nil];
}


As you can see, the attributes of the filter include minimum and maximum slider values in case you wanted to build a dynamic user interface for the filter. These constants are all defined in CIFilter.h.

The final method is the outputImage method. It is very simple:


- (CIImage *)outputImage
{
CISampler *src=[CISampler samplerWithImage:inputImage];
return [self apply: convolutionKernel, src,
r00, r01, r02,
r10, r11, r12,
r20, r21, r22, nil];

}


All we do here is get a sampler for the input image and call apply: with the kernel and all the parameters. This call returns our resulting image.

Once you've compiled this filter you can make it available to the system by placing it in the /Library/Graphics/Image Units folder, or for just your user in ~/Library/Graphics/Image Units. Next time we will see how we call this image unit from a Cocoa program.

Mar 18, 2007

All the Cocoa programming that could be crammed into 5 days

I just spent the last week attending the Cocoa bootcamp from Big Nerd Ranch. I have one thing I'd like to clear up before I get started. The hat is just a prop. That's right, when you go to WWDC and you see Aaron Hillegass walking around with his big texas style cowboy hat, he's doing it for marketing purposes. I guess it works, because when I decided to get some Cocoa training, I remembered Aaron and the hat walking around the Moscone center and on the back of his book: Cocoa(R) Programming for Mac(R) OS X (2nd Edition)

Now, I'm not normally a fan of technical training. Give me a book and a web browser and a project and I'm good. Of course this leads to interesting situations like the time I reverse engineered the Windows virtual to physical address mapping structures while not knowing how to refresh an explorer window (It's F5, right?).

I've been poking around in Cocoa for a while now, and though I feel like I made some decent progress, I'm really interested in this technology and wanted to get a comprehensive overview of it from an expert. Sometimes you just want to do things “right.” I figured that in 5 days I'd get a quick but comprehensive overview of most of the technologies in Cocoa from a well known expert in the technology.

The course did not disappoint. We dove right in to code, with Aaron leading us through an example application immediately, before even lecturing. All the lectures were relatively short, and most of the time in the class was spent actually coding. We mostly followed along with the exercises in the book, but we were encouraged to explore and change things as we progressed. In four and one half days we covered nearly the entire book, as well as several additional chapters that were part of the course materials.

In the evenings, most people chose to come back to the classroom and either continue to work on the exercises or to work on a personal project. Aaron typically stayed around until about 10PM answering questions and being generally helpful.

Out on our daily walk (image courtesy of Paul Warren)

The people who took the class came from all over the country and the UK and had varied backgrounds and skill levels, but overall, they were extremely competent and interesting folk. It was enjoyable to meet each of them. I was surprised at how many people were paying their own way to take this class, which was not an insignificant investment in personal time and money. I estimate that about half of the people were there on their own ticket.

The price of the course covers ground transportation to and from the airport, lodging and all meals as well as a copy of Aaron's book and course materials. If you don't want to bring your own computer, a modern iMac will be provided for you to use.

View from my suite

The place we stayed, Serenbe Southern Country Inn, was probably one of the nicest places I've ever stayed. My personal suite had a screened in porch, a kitchen, a large living room with fireplace, a king size bed, whirlpool tub and huge glass shower. They fed us three excellent meals each day, along with snacks and drinks as we wanted them through the day.

I really benefited from this class. Some topics seem complex when you try to explore them on your own, but Aaron was able to make them very accessible. Cocoa Bindings had always seemed somewhat inscrutable to me, but now things are much clearer. The Undo Manager in Cocoa is amazing; it really showcases how a dynamic language can be leveraged to make something that can be hard in other frameworks really easy. And I now feel like I understand how to build custom Views - something that always seemed difficult to me but is actually quite straightforward now.

The idea of a total immersion environment, with all details taken care of and few distractions, along with a very experienced and patient instructor, leads to a learning environment that encourages success. I highly recommend this class to anyone who would like to learn Cocoa.

Mar 10, 2007

Cocoa Application with custom Core Image filter 3: Testing the kernel with QuartzComposer

In the last post I wrote a convolution kernel for Core Image. But these kind of things need to be tested. Now, we could package the filter inside an Image Unit (which I'll do in a future post) but that would mean we would have to recompile and install the filter each time we wanted to make a change. It would be much better if there was an interactive environment that could be used to test any changes and to allow us to debug the filter code.

Luckily, Apple has provided just such an environment in QuartzComposer. If you are not familiar, Quartz Composer is an amazing application that provides a visual programming environment that allows the creation of all kinds of visualizations. If you've seen the RSS screensaver. then you've seen a QuartzComposer composition. If you've seen a preview of Time Machine, the swirling galaxy in the background is a QuartzComposer composition. I'm not going to explore this amazing piece of software in depth here, I suggest you look at some of these websites for more information: Quartz Composer Journal or www.quartzcompositions.com or boinx or Quartonian.

As a basic introduction, a composition is defined by dragging patches onto the workspace and connecting them together graphically. Patches contain ports which represent parameters which are passed between the patches. The results are displayed in real time in the viewer window, so you get immediate feedback as you change things. As we'll see later, Quartz composer compositions can be embedded in a QCView in your Cocoa apps, and controlled via Cocoa Bindings.

I've provided a link to my test.qtz composition which consists of four patches. The convolution patch is built by dragging a core image kernel patch onto the editor and then copying the text of the kernel into the patch. You can use the inspector to change the input parameters to adjust the coefficients and test out the composition.


This is the editor window, showing the composition.


Here's the inspector for the Convolution patch showing where you put the kernel code.


This is the viewer, showing the input image filtered by the edge detection filter defined in the Input Parameters to the Convolution filter.


If you have an iSight camera or other video source, replace the Image Importer with a video input, and you will see the convolution filter applied to your video stream in real time. That's just extremely cool.

Here's the editor window with a Video Input instead or the Image Importer.
And here's a view of my living room with edge detection. Notice the frame rate of nearly 60 frames per second on my Mac Book Pro.

Using this basic composition, with some modifications, you should be able to test out any core image kernel you can come up with. Next time, I'll build this filter into an image unit that can be used from any application that uses Core Image filters.

Mar 9, 2007

Off to Cocoa Bootcamp

Next week I'm headed off to Cocoa Bootcamp from Big Nerd Ranch. I've been poking around in Cocoa for a while and I know parts of it pretty well, but I've decided to get a solid foundation moving forward. From what I have heard from friends, their classes are excellent.

Mar 5, 2007

Cocoa Application with custom Core Image Filter 2: Implementing the convolution kernel

I this post I'm going to implement our convolution filter as a Core Image kernel. Writing a Core Image kernel is relatively straightforward, as I think you'll see. Core Image kernels are written in a subset of the Open GL Shading language, which is basically just C with some added data types a few keywords and function calls. Apple's Core Image Kernel language reference describes the subset that you can use and also the parts of OpenGL shading language that are not implemented. Of note, are the lack of arrays and structures and severe restrictions on looping and conditionals. The vector types like vec3 and vec4 which provide vector types (very convenient to hold the R,G,B,alpha of a pixel) and the sampler which allows you to sample the image


A Core Image kernel has but a single pixel as its output, and is therefore applied once for each pixel in the output. So, you can sample any input pixels from as many input images as you want to generate your output pixel. The filter has to be expressed as a mapping from any set of input pixels to each single output pixel. In our case, this is not a problem. 3x3 Convolution is a pretty natural fit for Core Image, since we only have to sample the 9 pixels immediately surrounding any output pixel. So the first step in our code is to declare the header of the kernel:


kernel vec4 Convolution3by3(
sampler src,
float r00, float r01, float r02,
float r10, float r11, float r12,
float r20, float r21, float r22)
{
vec2 loc;
vec4 result = vec4(0,0,0,1);
//0,0 in my mind is left and up.

I declare a kernel called Convolution3by3. The kernel takes a src argument that represents the source image and 9 floats, which represent the 9 coefficients of the convolution. You can see that lack of support for arrays would make a 5x5 or 7x7 convolution quite cumbersome with this system. We also declare a loc variable to hold our current location and a vec4 for the result.

To perform the convolution, we need to sample the pixels, multiply them by the correct coefficient, and add that value to the result. We've made the conscious decision to maintain the alpha (transparency) value of the result pixel as the alpha value of the center input pixel. Here's the code for the first operation:


loc = vec2(-1.0,1.0);
vec4 p00 = unpremultiply(
sample(src,(samplerCoord(src) + loc) ));
result.rgb = p00.rgb * r00;


What's going on here? First, we call samplerCoord() to get the location that the current output pixel represents. Adding the loc to it allows us to grab the correct pixel in the matrix for this coeficient. Next, we call sample()to get the actual value of the pixel at that location. Core Image gives us pixel information with premultiplied alpha, which means that any transparency has been already multiplied through any RGB values for the pixel. This is a useful optimization since it makes compositing simpler. Since we are going to be using the alpha of the center pixel as the output of the result, we need to reverse this to correctly calculate the convolution, thus the call to unpremultiply(). Finally. we multiply the RGB values of the pixel by the coefficient for that pixel and add it to the result. This process is repeated for each sampled location.



loc = vec2(0.0,1.0);
vec4 p01 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p01.rgb * r01;

loc = vec2(1.0,1.0);
vec4 p02 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p02.rgb * r02;

loc = vec2(-1.0,0.0);
vec4 p10 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p10.rgb * r10;

vec4 p11 = unpremultiply(
sample(src, (samplerCoord(src)) ));
result.rgb += p11.rgb * r11;
result.a = p11.a;


Notice here that I copy the alpha from the input pixel to the result.



loc = vec2(1.0,0.0);
vec4 p12 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p12.rgb * r12;

loc = vec2(-1.0,-1.0);
vec4 p20 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p20.rgb * r20;

loc = vec2(0.0,-1.0);
vec4 p21 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p21.rgb * r21;

loc = vec2(1.0,-1.0);
vec4 p22 = unpremultiply(
sample(src, (samplerCoord(src) + loc) ));
result.rgb += p22.rgb * r22;

result = premultiply( result );
return result;
}

Finally, I premultiply() the result with the alpha value and return the result. As you can see, this is a pretty straightforward procedure: grab the values for each input pixel in the matrix, multiply them by their respective coefficients, accumulate the results and return.

If you want to download a copy of this kernel, it's available on my website: Convolution3by3.cikernel In my next post I'll describe how to test the kernel using the QuartzComposer application and also show how to apply this filter to live video as well as static images.