1. Images In Java Paul Cockshott ALMA TADEMA RIVALES INCONSCIENTES 1893

2. Introduction The AWT and how to put a simple image onto the screen Layout managers The Jimage Class,

3. Summary of this section At the end of this lecture you should have an idea of how to display a JPEG image on the screen, and how to load it into the Jimage class to carry out further image processing.

4. Agenda AWT Images Image Producers and Consumers Jimage class Pixel Representations JPEG files

5. Overview AWT abstract windows toolkit, supported by JavaSoft Operating system independent layer for windowing in Java Fiendishly obscure Designed around requirements of images being streamed off the web

6. Connections Simple image display program to show how to display a JPEG file Pipeline model of image production Jimages act as image consumers Jimages allow arithmetic on image Jimages provide output to AWT images and JPEG

7. How to display a picture 1 import java.awt.*; import java.awt.image.*; import java.util.*; class JPEGshow extends Frame {... static public void main(String[] args) { if (args.length == 1) new JPEGshow(args[0]); else System.err.println("usage: java JPEGshow "); } This is a standard Java Program class with a public static void main method

8. Constructor for JPEGshow JPEGshow(String filename) { super("JPEG show Example"); add( new ImageCanvas(getToolkit().getImage(filename) ), BorderLayout.CENTER); setSize(700, 540); show(); } See slide on these Read in a JPEG file Size of a frame

9. The toolkit Each frame has associated with it a toolkit object the provides an interface to OS specific operations. CreateImage CreateMenu CreateLabel CreateMenuBar …. etc

10. Roll your own ImageCanvas class ImageCanvas extends Component { Image image; ImageCanvas(Image image) {this.image = image;} public void paint(Graphics g) { g.drawImage(image, 0, 0, this);} } Paint is called whenever a component must be shown, the Graphics object does the actual drawing, it has to be passed in because it is what knows about physically drawing on the screen Constructor just stores the image

11. Image Class Pipeline flow model of image processing Images are just tokens linking producers and consumers Image ImageProducer ImageConsumer

12. ImageProducer Methods addConsumer(ImageConsumer ic) This method is used to register an ImageConsumer with the ImageProducer for access to the image data during a later reconstruction of the Image. removeConsumer(ImageConsumer ic) This method removes the given ImageConsumer object from the list of consumers currently registered to receive image data. startProduction(ImageConsumer ic) This method starts an immediate reconstruction of the image data

13. ImageConsumer methods void setDimensions(int width, int height) The dimensions of the source image are reported using the setDimensions method call. Void setPixels(int x, int y, int w, int h, ColorModel model, byte[] pixels, int off, int scansize) The pixels of the image are delivered using one or more calls to the setPixels method.

14. Image Class continued Image ImageProducer ImageConsumer Images contain a pointer to their producer which holds the actual data for the image. This can be recovered using the getSource method. This allows a consumer to get at the pixel data of an image by adding itself to the producer and starting production Image.getSource

15. Summary AWT is operating system independent Streaming image model Images as tokens Producer - consumer pipeline See chapters 6 of textbook

16. Buffered Image Class Standard AWT images are just tokens for data streams. A BufferedImage actually contains the data. Colour model Raster BufferedImage

17. JPEGCodec class This class has factory methods to create JPEG encoders and decoders: createJPEGDecoder(InputStream s) createJPEGEncoder(OutputStream d)

18. Read a BufferedImage FileInputStream in = new FileInputStream(myfile.jpg); JPEGImageDecoder dec= JPEGCodec.createJPEGDecoder(in); BufferedImage im = decoder.decodeAsBufferedImage();

19. getRGB You can access the pixels of a buffered image using int getRGB(int x, int y) The default colour representation is: alphared green blue Bit 0Bit 31

20. Writing pixels This can be done with the setRGB method. This takes x, and y co-ordinates and a pixel encoded as a 32 bit integer im. setRGB(2, 5, 255); Would set pixel 2,5 to 255 = bright blue.

21. Creating sub images You can create a sub area within a buffered image using the public BufferedImage getSubimage( – int x, – int y, – int w, – int h); Method of BufferedImage

22. Jimage implements ImageConsumer Library of image processing classes developed in the department Available for student practicals Algebraic rather than stream oriented Interfaces to MMX hardware under windows

23. Algebraic orientation By this we mean the it is structured around algebraic expressions whose values are images Thus if and are images and is some operator then is also an image

24. Jimage operators Arithmetic J Universal plus(Universal) I-J Universal minus(Universal) I×J Universal times(Universal) I÷J Universal divide(Universal) I Universal abs() Filtering Jimage convolve(double[] k) convolve with symmetrical separable kernel. public abstract Jimage convolve(double[][] kernel) with non separable kernel

25. Scaling Jimage getScaledInstance(int nwidth, int nheight) This scales with bicubic interpolation. Jimage getScaledInstance(int nwidth, int nheight, int ndepth) This method allows the depth as well as area of an image to be altered if it is reduced the planes are aggregated if increased they are interpolated.

26. More operations Data access int rgbpixel(int x,int y) Converts the plane information into a pixel in the direct color model of java. public abstract int upixel(int x, int y, int plane) - returns unsigned integer pixel public abstract float fpixel(int x, int y, int plane) Returns the pixel in the range -1 to +1.

27. Data Access public abstract void setPixel(int x, int y, int plane, double pix) –Pixel information in range -1 to +1 public void setSubImage(int x, int y, int z, Jimage im) –Update an area of an image with another one. The other one must not run off the edge of the one being written to. The source of the copying is the 0th plane of the source jimage.

28. Jimage input output public void putJPEGImage( java.lang.String fileName, int quality) throws java.io.IOException –Outputs the image to a jpeg file public boolean getImage(java.lang.String fileName) –Initialise the Jimage from the specified file. The file must be jpeg or gif.

29. Jimage to AWT Image conversion public java.awt.Image getAWTImage() public java.awt.image.ImageProducer getProducer()

30. Jimage implementations

31. An example program class Jimageshow extends Frame { Jimageshow(String filename) { super("Jimage show Example"); Jimage raw=new ByteImage(100,200,3); if (raw.getImage(filename)){ Jimage cooked = (Jimage)raw.times(0.3); add(new ImageCanvas(cooked.getAWTImage()), BorderLayout.CENTER); setSize(700, 540); show(); } Create Jimage with byte pixels Multiply by 0.3 Convert to AWT for display

32. Pixel Representations When dealing with displays it is conventional to assume that pixels are bytes holding numbers in the range 0 to 255. 0 Is assumed to be black 1 Is assumed to be white or maximum brightness of any given colour. For multicolour displays with 3 colour components, the convention is to have 3 fields of range 0..255 to hold the colour information.

33. Pixel Representations AWT For multicolour displays with 3 colour components, the convention is to have 3 fields of range 0..255 to hold the colour information. The AWT does this with the class Color. public Color(int rgb) –Creates a color with the specified RGB value, where the red component is in bits 16-23 of the argument, the green component is in bits 8-15 of the argument, and the blue component is in bits 0-7. The value zero indicates no contribution from the primary color component. –A Jimage returns this format with int rgbpixel().

34. Pixel Representations: Bytes The byte data type in Java does not take on the values 0..255. Instead it takes on the values -128 to 127. There are no unsigned bytes in Java. This creates a problem for the representation of pixels in Jimages. The solution adopted is to adopt the following representation -128 = black 0 = mid grey 127 = white

35. Pixel Representations: Floats If byte pixels are signed then so must other representations be. The solution adopted is to adopt the following representation for floats -1 = black 0 = mid grey 1 = white

36. Conversions between representations unsignedbytes shortsfloat min value0 -128 -2048 -1 maxval255 127 2047 1 medianval 127.5-0.5 -0.5 0 range 255 255 4095 2 As shown in table a pixel in representation is converted to a pixel in representation by the operation:

37. Signed Pixels : advantages Signed pixels seem at first to be counter-intuitive but they have numerous advantages. A value of 0 or mid grey can be viewed as the most likely value that a pixel takes on in the absence of other information. If you do arithmetic on images, in particular subtract one image from another, then negative values of pixels naturally arise. Signed pixels allow straightforward implementation of contrast adjustments. For instance multiplying an image by 0.5 halves the contrast in the image.

38. Signed Pixels : contrast adjustment Signed pixels allow straightforward implementation of contrast adjustments. For instance multiplying an image by 0.5 halves the contrast in the image. 0.5 1 0.5 Initial contrast range Finalcontrast range 0.5 -0.5 0.25 -0.25

39. Image Multiplication

40. Image Addition

41. Image subtraction

42. What Is Convolution Convolution takes a kernel of coefficients and multiplies each pixel in a neighbourhood by the corresponding coefficient, and then sums the result x y p[I+x, j+y]*k[x,y] Will give the convolved pixel at position i, j

43. 1 D convolution A 1 D convolution takes a one dimensional array as a kernel and applies it first in the X and then in Y dimension. This can often be performed faster than a 2d convolution

44. Image Convolution: smoothing double[] k= {0.1,0.1,0.2,0.2,0.2,0.1,0.1}; marble.convolve(k)= marble = Note sum of coefficients =1

45. Image Convolution: sharpening double[] k= {-0.3,1.6,-0.3} marble.convolve(k)= marble = Note sum of coefficients =1 number terms is odd

46. Convolution in Java2D Java 2D provides a standard library for convolution of buffered images This uses the class Kernel and ConvolveOp

47. Kernels in JAVA 2D float[] blur={ 0.0f, 0.1f, 0.0f, 0.1f, 0.6f, 0.1f, 0.0f, 0.1f, 0.1f}; Kernel k= new Kernel(3,3, blur); im = new ConvolveOp(K).filter(im,null); This will blur the image im by applying the 3 by 3 kernel blur to it.

48. Importance of speed Image may contain a million pixels, Arithmetic may be required on each one Important to optimise operations or they are very time consuming May need to use assembler kernels May need to use special purpose instructions

49. Multimedia Extensions MMX Intel and other CPU manufacturers have been adding to the instruction sets of their computers new extensions that handle multi-media data. The aim is to allow operations to proceed on multiple pixels each clock cycle

50. MMX 2 Standard Intel register set eax ebx ecx edx esp ebp esi edi 8 General Registers 8 floating point registers 32 bit 64 bit fp0 fp1 fp2 fp3 fp4 fp5 fp6 fp7

51. MMX 3 Standard Intel register set operating in MMX mode eax ebx ecx edx esp ebp esi edi 8 General Registers 8 multimedia registers 32 bit 64 bit mm0 mm1 mm2 mm3 mm4 mm5 mm6 mm7

52. MMX 4 motivation Existing operating systems must still work unchanged Applications not using MMX run unchanged No new state added to the CPU Hence, shared use of the FP registers, since these are already supported by exising OSs

53. MMX data formats One 64bit integer QUADWORD Two 32 bit integer DOUBLEWORDS Four 16 bit WORDS Eight 8 bit BYTES

54. Problem of overflows A problem with limited precision arithmetic is that overflows frequently occur. This can give rise to meaningless results: consider 200+175 = 375 but in 8 bit binary 11001000 +10101111 =101110111 Leading 1 is discarded This leaves an answer of 119 decimal – clearly wrong

55. Using saturation You can fix this by using conditionals unsigned char p1,p2,p3; int I3= (int)p1 + (int)p2; p3=(I3>255?255:(unsigned char)I3);

56. Expansion of the code 1 12: j=(int)(*p1++)+(int)(*p2++); 00401043 mov ecx,dword ptr [ebp-4] 00401046 xor edx,edx 00401048 mov dl,byte ptr [ecx] 0040104A mov eax,dword ptr [ebp-8] 0040104D xor ecx,ecx 0040104F mov cl,byte ptr [eax] 00401051 add edx,ecx 00401053 mov dword ptr [ebp-14h],edx 00401056 mov edx,dword ptr [ebp-8] 00401059 add edx,1 0040105C mov dword ptr [ebp-8],edx 0040105F mov eax,dword ptr [ebp-4] 00401062 add eax,1 00401065 mov dword ptr [ebp-4],eax

57. Expansion 2 13: 14: *p3 = (unsignedchar)(j>255?255:j); 00401068 cmp dword ptr [ebp-14h],0FFh 0040106F jle main+6Ah (0040107a) 00401071 mov dword ptr [ebp-18h],0FFh 00401078 jmp main+70h (00401080) 0040107A mov ecx,dword ptr [ebp-14h] 0040107D mov dword ptr [ebp-18h],ecx 00401080 mov edx,dword ptr [ebp-0Ch] 00401083 mov al,byte ptr [ebp-18h] 00401086 mov byte ptr [edx],al 15: p3++; 00401088 mov ecx,dword ptr [ebp-0Ch] 0040108B add ecx,1 0040108E mov dword ptr [ebp-0Ch],ecx Total of 26 instructions in the kernel

58. Alternative using mmx Iu8vec8 *v1,*v2,*v3; int i,j,k; for(i=0;i<31;i++){ *v3=(*v1++)+(*v2++); v3++; } _mm_empty(); Type represents 8 by 8bit integers Arithmetic on 8 bytes at a time Indicates MMX regs are now free

59. Optimised Assembler Loop mov ecx,32 ; load counter with 32 l1: movq mm0,[esi] ; load 8 bytes add esi,8 ; inc src pntr paddusb mm0,[edx] ; packed unsigned add bytes add edx,8 ; inc src pntr movq [edi],mm0 ; store 8 byte result add edi,8 ; inc dest pntr loop nz,l1 ; dec counter, ; repeat non zero Go round only 32 times not 256 Total of 6 instructions in kernel

60. Speed Gain On image of 256x256 pixels Old C code executes 26*256*256 instructions = 1,703,936 instructions Optimised mmx code executes 6*256*32 instructions = 49,152 Note that no compiler currently will give the optimised code. It has to be hand assembled.

61. Image Processing Library Intel Provide an image porcessing library that can be downloaded from their web site. It provides efficient access to the MMX hardware. It provides frequently used Image Processing Operations. It requires a set of DLLs in your path to run

62. Image Processing Library 2 At the core of IPL is the ability to write to a single API and get the best possible results for any Intel processor. The libraries have as many as six processor- specific branches for each function and six sets of carefully written assembly code, but only one entry point to each function.

63. Image Processing Library 3

64. Image Processing Library 4 Use of Intel IPL complex and requires C I have provided 2 java classes that call the IPL. IntelBImage and IntelFImage. These are documented in the Jimage web pages. They inherit from ByteImage and FloatImage To use them the Intel IPL must have been installed on your machine and be on the path. If you are forced to use Unix machines the libraries will not be available to you.

65. Where to get more information http://www.javasoft.com/products/jdk/1.2/d ocs/api/java/awt/package-summary.html http://developer.intel.com/vtune/perflibst/ip l/index.htm http://developer.intel.com/vtune/perflibst/ip l/ipapi.htm

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