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IMAGE PROCESSING USING MATLAB – LEARNING TOOL
LO SHING LOONG
UNIVERSITI TEKNOLOGI MALAYSIA
DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT Author’s full name :LO SHING LOONG Date of birth :8thFEBRUARY 1987 Title :IMAGE PROCESSING USING MATLAB – LEARNING TOOL Academic Session :
2009/2010
I declare that this thesis is classified as : CONFIDENTIAL (Contains confidential information under the Official Secret ActHAPS 1972)* “I declare that this thesis entitled “Interference from Ground Station to a Radiorelay station” is the result of my own research except as cited in the references. The RESTRICTED (Contains restricted information as specified by the organisation where was done)* thesis has not been accepted for any degree and is research not concurrently submitted in candidature of any degree”. √ OPEN ACCESS
I agree that my thesis to be published as online open access (full text)
Signature : …………………….. I acknowledged that Universiti Teknologi Malaysia reserves the right as follows : Name : CHAN MING YIAN 1. The thesis is the property of Universiti Teknologi Malaysia. Date : 20 MAY 2009 2. The Library of Universiti Teknologi Malaysia has the right to make copies for the purpose of research only. 3. The Library has the right to make copies of the thesis for academic exchange. Certified by :
____870208055131 (NEW IC NO. /PASSPORT NO.) Date :
NOTES
APRIL 2010
DR. YUSRI YUNUS_ NAME OF SUPERVISOR Date : APRIL 2010
: * If the thesis is CONFIDENTIAL or RESTRICTED, please attach with the letter from the organisation with period and reasons for confidentiality or restriction.
“I hereby declare that I have read this thesis and in my opinion this thesis is sufficient in terms of scope and quality for the award of the degree of Bachelor of Electrical Engineering ”
Signature
:
Name of Supervisor : DR. YUSRI YUNUS Date
: APRIL 2010
IMAGE PROCESSING USING MATLAB – LEARNING TOOL
LO SHING LOONG
A thesis submitted in fulfillment of the requirements for the award of the degree of Bachelor of Engineering (Telecommunication)
Faculty of Electrical Engineering Universiti Teknologi Malaysia
APRIL 2010
ii
DECLARATION
“I declare that this work as the product of my own effort with the exception of excerpts cited form other works of which the sources were duly noted”
Signature
:
Author’s Name
:Lo Shing Loong
Date
: April 2010
iii
DECLARATION
Dedicated,
in
thankful
appreciation
for
support,
understandings to my beloved mother, father, brothers and sisters.
encouragement
and
iv
ACKNOWLEDGEMENT
First and foremost, I would like to express my heartily gratitude to my supervisor, Dr. Yusri Yunus for the guidance and enthusiasm given throughout the progress of this project.
My appreciation also goes to my family who has been so tolerant and supports me all these years. Thanks for their encouragement, love and emotional supports that they had given to me.
.
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ABSTRACT
The purpose for this study is to investigate a software application that can show how the image is process in computer platform. The process will be done in converting the picture to matrix model by using MATLAB program. Picture will be shown when program is running successfully. Real image or picture will be resulted from the matrix using the function in the MATHLAB. User can use varies of functions such as filter or rotate depending on the user itself. In this study, the picture or real image used is from Internet, scanner and etc. Basic mathematical calculation does not apply in this project as it only used MATHLAB program. The significant of this project is to educated user to learn how the image can be changes after the function indicated by the program.
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ABSTRAK
Tujuan kajian ini adalah untuk mencari suatu aplikasi perisian yang dapat menunjukkan bagaimana imej di proses dalam platform komputer. Proses ini akan dilakukan dalam menukar imej-imej untuk model matriks dengan menggunakan program MATLAB. Imej akan dipaparkan ketika program ini beroperasi dengan jayanya. Imej tulen atau gambar akan dihasilkan dari matriks menggunakan fungsi di MATHLAB. Pengguna boleh menggunakan fungsi bervariasi seperti penapis atau lainlain, bergantung pada pengguna itu sendiri. Dalam kajian ini, imej atau bayangan nyata yang digunakan adalah dari Internet, scanner, dan lain-lain Dasar perhitungan matematik tidak berlaku dalam projek ini kerana hanya menggunakan program MATHLAB. Pengguna dididik untuk mempelajari bagaimana imej boleh berubah setelah melalui berbagai fungsi yang ditunjukkan oleh program.
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TABLE OF CONTENTS
CHAPTER TITLE
1
PAGE
TITLE OF RESEARCH
i
DECLARATION
ii
ACKNOWLEDGEMENT
iv
ABSTRACT
v
TABLE OF CONTENT
vii
LIST OF TABLE
x
LIST OF FIGURES
xi
INTRODUCTION
1.1
Introduction
1
1.2
Objective of The Project
3
1.3
Scope of Project
3
1.4
Outline of Thesis
3
1.5
Summary of Work
4
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2
THEORETICAL BACKGROUND FOR IMAGE PROCESSING 2.1
Introduction
8
2.2
Histogram
9
2.3
Blur/Deblurring Image Using Blind Deconvolution
9
Algorithm
3
2.4
Threshold/Edge Detection
9
2.5
Enhancing Multispectral Color Composite Images
10
2.6
Identify Round Object
10
2.7
Digital Filters
11
2.8
Low Pass Filter
11
2.9 High Pass Filter
14
METODOLOGY
3.1
Introduction
17
3.2
Blur/Deblurring Image Using Blind Deconvolution
19
Algorithm 3.3
20
Enhancing Multispectral Color Composite Images Threshold / Edge Detection
3.4
Threshold / Edge Detection
20
3.5
Identify the Round Object
21
3.6
Rotate
22
3.7
Open Image
23
3.8
Reset
24
3.9
Save Image
24
3.10 Show Original Picture
25
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4
5
RESULT AND DISCCUSION
4.1 Introduction
26
4.2 GUI (Graphical User Interface)
27
4.3 Image Processing Result
29
CONCLUSION AND RECOMMENDATION
5.1 Conclsion
35
5.2 Problems
36
5.3 Recommendation
37
REFERENCES
38
APPENDICES A
39
APPENDICES B
43
x
LIST OF TABLES
TABLE NO
TITLE
PAGE
2.1
Effect of increasing the size of the neighborhood region
14
xi
LIST OF FIGURES
FIGURE NO. TITLE
PAGE
1.1
Process block
4
1.2
Gantt Chart of the project schedule for semester 1
6
1.3
Gantt Chart of the project schedule for semester 2
7
2.1
Filter
12
2.2
Coefficient values of low pass filter
13
2.3
Effect of low pass filter (a) matrix with bad element (b) matrix
14
after low pass filter 2.4
High pass filter coefficient set for 3x3 matrix convolution mask
15
2.5
Mask, data and element
16
3.1
Processing block
18
4.1
Step planning
26
4.2
layout of image processing program
29
4.3
Image opened in program
30
4.4
Image after simulated with blur and nois
30
4.5
Image after deblurred
31
4.6
Image enhanced after blurred
31
4.7
Image enhance after deblurred
32
4.8
Image rotated
32
4.9
Original image after threshold
33
4.10
Enhanced image after threshold
33
4.11
Original and modified image shows together
34
CHAPTER I
INTRODUCTION
1.1
Introduction
Image processing is any form of signal processing for which the input is an image, such as photographs; the output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. Image processing – converting the image to another form by using direction in MATLAB/Toolboxes/Image Processing tables for example is image input/output, color operation, image enhancement/ analysis and another method.
Image processing and computer vision practitioners tend concentrate on a particular area of specialization. People refer to their research interests as “texture”, “surface mapping”, “video tracking”, and the like. Nevertheless, there is a strong need to appreciate the spectrum and hierarchy of processing levels.
Image processing is the manipulation of the image by using a computer, with the objective to enhance or evaluate some aspect of an image which is not readily apparent in its original form. This is done through the development and implementation of processing means necessary to operate on the image. Processing image using a digital computer provides the greatest flexibility and power for general image processing
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application, since the programming of a computer can be changed easily which allows operation to be modified quickly.
Interest in image processing technique dates back to early 1920’s when digitized pictures of world news events were first transmitted by submarine cable between Newyork and London. However, application of digital image processing concepts did not become widespread until the middle 1960’s, when third-generation digital computers began to offer the speed and storage capabilities required for practical implementation of image processing algorithms. Since then, this area has experienced vigorous growth and has been subjected of study and research in such fields as engineering, computer science, statistics, information science, physics, chemistry and medicine.
The result of these efforts have established the value of image processing technique in of problem with application in diverse fields, including automated factory controlled, astronomy, meteorology, agriculture, medicine, art and military application. With the increasing availability of reasonably inexpensive hardware and some very importance application on the horizon, image technology is expected to continue its growth and to play an important role in the future.
From the MATLAB software we have the Toolbox for image processing and Professional MATLAB. MATLAB is the interactive environment, scientists and engineers are able to analyze and develop algorithms with exceptional improvements n productivity and creativity. As a result of new algorithms with application-specific uses. The MathWorks offers a series of application toolboxes that contain set of MATLAB ofr the Linear algebra, high-speed computational kernel, extensive mathematical functionality, data analysis, 2-D and 3-D graphic rapid algorithm development, matrix based programming environment. In MATLAB Toolboxes professional version but priced at a lower rate for academic use.
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1.2
Objective of The Project
The objective of this project is actually to educate newcomer to basic and fundamental technique in image processing through integrated image processing software. All fundamental algorithms of image processing will be exposed through this package. This package will also provided easy-to-learn mechanisms turn user-friendly by pop-up menu and graphic-orientation environment.
These operations include preprocessing, spatial filtering, image enhancement, feature detection, image compression and image restoration involves process which restores a degraded image to something close to the ideal. Generally, in computer vision, especially in MATLAB program (image understanding or scene analysis) involves technique from image processing, pattern recognition and artificial intelligent. Particularly, MATLAB program offers many features and are more multifaceted then any calculator. MATLAB toolbox is a tool for making mathematical calculations.
1.3
Scope of Project
Study and understand the image processing in varies method, mainly in MATLAB. Create a GUI (Graphical User Interface) MATLAB program with several functions.
1.4
Outline of Thesis
This thesis consists four chapters. In first chapter, it discuss about the objective and scope of this project as long as summary of works. While Chapter 2 will discuss
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more on theory and literature reviews that have been done. It well discuss about types of motor, various kind of speed measurement and controllers (thyristor, phase-lock loop and PWM technique) that can be used to control the speed of the motor.
In Chapter 3, the discussion will be on the methodology hardware and software implementation of this project. The result and discussion will be presented in Chapter 4. Last but not least, Chapter 5 discusses the conclusion of this project and future work that can be done.
1.5
Summary of Work
Every digital image processing system can be represented by a block diagram containing three main elements, as shown in figure 1.1. The 4 major elements of every image processing system include the following,
Image MATLAB Process Block
Processed Image
Figure 1.1 Process block
Image and MATLAB involves the conversion of scene into a digital representation that can be processed by a digital computer. This can be performed by a
5
sensor system specially designed to view a scene and provide a digital representation of the scene.
Process block. When the images are installed in MATLAB, flowers picture for example, the color of that image is first analyzed. In the process include several functions of image processing technique.
Processed Image is the image display after the process. Gantt charts as shown in Figure 1.2 and Figure 1.3 show the detail of the works of the project that had been implemented in the first and second semester.
6
Figure 1.2 Gantt Chart of the project schedule for semester 1
7
Figure 1.3 Gantt Chart of the project schedule for semester 2
CHAPTER II
THEORETICAL BACKGROUND FOR IMAGE PROCESSING
2.1
Introduction
Histogram is one of the technique most commonly used in contrast enhancement. In many cases, the subjective contrast of an image is improved if one equalizes the histogram of the histogram of gray-level of the picture elements argued that one should make the histogram of the subjective brightness of the picture element uniform, or equivalently making the gray-level histogram hyperbola is shape, it may hurt the further machine analysis of the image.
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2.2
Histogram
Histogram is graphical presentations of the frequency count of the occurrence each intensity (gray level) in the image. The horizontal axis of the graph represents the tonal variations, while the vertical axis represents the number of pixels in that particular tone. The left side of the horizontal axis represents the black and dark areas, the middle represents medium grey and the right hand side represents light and pure white areas. The vertical axis represents the size of the area that is captured in each one of these zones.
2.3
Blur/Deblurring Image Using Blind Deconvolution Algorithm
The Blind Deconvolution Algorithm can be used effectively when no information about the distortion (blurring and noise) is known. The algorithm restores the image and the point-spread function (PSF) simultaneously. What happens is that what should be seen as a sharp point gets smeared out, usually into a disc shape. In image terms this means that each pixel in the source image gets spread over and mixed into surrounding pixels. Another way to look at this is that each pixel in the destination image is made up out of a mixture of surrounding pixels from the source image.
2.4
Threshold / Edge Detection
An image processing method distinguishes an object from within an image area by determining a stable threshold value of light intensity values from within the image area. Received light is converted into image data denoting light intensity values of the received light at a plurality of points from within the image area. A bright portion average value denoting an average of the light intensity values which exceed a
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predetermined threshold and a dark portion average value denoting an average of the light intensity values which are less than the predetermined threshold are calculated. A new threshold is obtained by applying the bright portion average value and the dark portion average value to a predetermined dividing ratio. The object within the image area is distinguished using the new threshold. The method is also applicable to the use of multi value thresholds.
2.5
Enhancing Multispectral Color Composite Images
Enhance multispectral radiance or reflectance data to create an image that is suitable for visual interpretation. Another way to enhance the true color composite is to use a decorrelation stretch, which enhances color separation across highly correlated channels.
Use decorrstretch to perform the decorrelation stretch. Again, surface features have become much more clearly visible, but in a different way. The spectral differences across the scene have been exaggerated.
2.6
Identify the Round Object
Classify objects based on their roundness using bwboundaries, a boundary tracing routine. In this process, the MATLAB will convert the picture to black and white first before to remove the noise (pixels which do not belong to the objects of interest) inside the picture.
Estimate each object's area and perimeter. Use these results to form a simple metric indicating the roundness of an object:
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Metric = 4*pi*area/perimeter^2.
This metric is equal to one only for a circle and it is less than one for any other shape. The discrimination process can be controlled by setting an appropriate threshold. In this example use a threshold of 0.94 so that only the pills will be classified as round.
2.7
Digital Filters
The digital filter can be categorized as a low pass or high pass filter depending on which part of the frequency spectrum it affects. The effect on a composite electrical analog signal containing all frequency components. The output signal of the high pass filter will contain the high frequency components. The output signal of the low pass filter will contain the DC bias and low frequency components. The specific cutoff frequency cab be varied the selection of the value used in the filter, but that is beyond the scope of this course.
The low frequency components in an image are characterized by a slow change in the contrast or values of adjacent pixel in relation to high frequency components, due to the rapid change in contrast encountered at the edge of an object.
2.8
Low Pass Filter
The low pass filter will not affect low frequency components in the image data and will attenuate the high frequency components as illustrated in figure 2.1. Random speckles in an image can be considered as noise and are high frequency components since the pixel values of adjacent pixels change very rapidly. The effect of the speckles can be reduced by using a simple averaging filter. The coefficient matrix for an
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averaging filter for a 3x3 local area convolution would contain nine elements as shown below.
High Pass Filter
(b)
Low Pass Filter (a)
(c) Figure 2.1 Filter
Result of low pass (b) and high pass (c) digital filter on a composite signal (a) containing both low and high frequency as well as a DC bias.
The value for the pixel in the output matrix is calculated by convoluting the image data, p1,j with the coefficient matrix f1,j and placing the resultant value at the center pixel of the area in the new image matrix. The clearly for low pass filter, the coefficient values of a low pass filter can declared with matrix form as show below figure 2.2;
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,
,
,
,
,
,
,
,
,
1 1 1 ⎡ 9 9 9⎤ 1 1 1 ⎢ ⎥ = 9 9⎥ ⎢ 9 1 1 1 ⎣ 9 9 9⎦
Figure 2.2 coefficient values of low pass filter
The new value of q2,2 is given by
q2 , 2 =
,
x
,
+
,
x
,
+…+
,
x
,
The rules for a low pass filter are;
1. All the coefficient must be positive, and
2. The sum of all the coefficients must be equal 1.
If the sum of the coefficient must be equal 1, amplification will result. If the sum is less than 1, attenuation will result, and the magnitude of the pixel value over the entire image will be reduced. Hence, the image would appear darker.
The application of the 3x3 coefficient matrix to a data field where all the pixel have the same value will have no effect except where there is a gradient between adjacent pixels. The effect of the low pass digital filter in reducing noise can be illustrated by considering the case where there is one pixel in the matrix which is zero due to bad element in the sensor array used to acquire the data; all the adjacent pixels have a high value show is figure 2.3.
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7 7 7 7 0 7 7 7 7 (a)
7 7 7
7 7 7 7 7 7
(b)
Figure 2.3 effect of low pass filter (a) matrix with bad element (b) matrix after low pass filter
If the bad pixel value had been high instead of low as the result of a white speck, the application of the low filter would have had the effect of reducing the magnitude of the value of bad pixel.
The effect of increasing the size of the neighborhood region 5 x 5 or 9 x9 can be illustrated by comparing the result of applying a low pass filter to the case one of one bad pixel value, (0 in a region contain all ones) as is done in Table 2.1.
Table 2.1 effect of increasing the size of the neighborhood region Size of Matrix
2.9
Convoluted Value
Raw Value of Bad
Relative
Element
Computation Time
3x3
1/9
8/9
1
5 x5
1/25
24.25
25/9 = 2-7/9
9x9
1/81
80/81
81/9 = 9
High Pass Filter
The high pass digital filter has the inverse characteristic of low pass filter. The filter will not change the high frequency component of a signal and will attenuate the low frequency component as well as eliminate any bias in the signal. The effect of background light would result in a DC bias in the vision system data since the minimum value of all pixel would be above some given some given value. The effect of applying a
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high pass filter to the data for a 6 x 6 image is shown in the histogram of figure 2.4 below. The high pixel values may or may not change depending on the degree of saturation of the image.
The high pass filter coefficient set for set for a 3 x 3 matrix convolution mask is
−1 −1 −1 −1 −8 −1 −1 −1 −1 Figure 2.4 high pass filter coefficient set for 3x3 matrix convolution mask
1.
The coefficient can be positive or negative.
2.
The sum of the coefficient is 0.
Since the sum of all the coefficients is equal to zero, the dc component will be completely suppressed. If the sum were equal to +1, the DC value would have been retained at the same value as the original signal. The filter is applied by overlaying the 3x3 coefficient matrix filter mask on the image data, multiplying the coefficient and data in each of the nine elements, and adding the nine resultant products to obtain the value of the new element located at the center of 3 x 3 matrixes. The mask is then shifted by one unit and the process repeated to obtain the value of the next new element.
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+1 +1 +1
+1 −8 +1
+1 +1 +1
mask
4 4 4
8 8 8 8 8 8 data
− − −
− − [] − − −
element
Figure 2.5 mask, data and element
(2,2)
=
+(1x4)+(1x8)+(1x8)+(1x4)-(8x8)-(8x8)+(1x8)+(1x4)+(1x8)+(1x8)
=
+4+8+8+4-64+8+4+8+8
=`
+52-62
=
-12
CHAPTER III
METHODOLOGY
3.1
Introduction
Basically, MATLAB software has many functions/commands to apply in image processing. How to manipulate the program depending to us but must be practically know what item is MATLAB program will be used. Creativity in MATLAB can make the interesting result. Even, the complex data can be solved in MATLAB. Especially when the data involved is very complex. Here, we can create some image from converting data by using the some program in MATLAB, which just applied all procedure in the MATLAB program. MATLAB toolbox is a tool for making mathematical calculations. Image processing toolbox is user friendly programming language with feature more advanced. In the program also used the GUI (Graphical User Interface) to create develop the program.
Every digital image processing system can be represented by a block diagram containing three main elements, as shown in figure 3.1. The 4 major elements of every image processing system include the following,
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Image MATLAB Process Block
Processed Image
Figure 3.1 Processing block
Image and MATLAB involves the conversion of scene into a digital representation that can be processed by a digital computer. This can be performed by a sensor system specially designed to view a scene and provide a digital representation of the scene.
When the images are installed in MATLAB, flowers picture for example, the color of that image is first analyzed. In the process include several functions of image processing technique. Processed Image is the image display after the process.
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3.2
Blur/Deblurring Image Using Blind Deconvolution Algorithm
In this process, by referring to the M-file
a) LEN = 21, linear motion across 31 pixels
b) THETA = 11, angle of 11 degrees
c) PSF =
fspecial('motion',
LEN,
THETA), point-spread function,
PSF,
corresponding to the linear motion across 31 pixels (LEN=31), at an angle of 11 degrees (THETA=11).
d) blurred = imfilter(IMG, PSF, 'conv', 'circular'), simulate the blur, convolve the filter with the image using imfilter.
e) noise_mean = 0; noise_var = 0.001; blurred_noisy = imnoise(blurred, 'gaussian', ...noise_mean, noise_var), adding the noise to the picture.
f) deconvwnr(blurred_noisy, PSF, 0.01) restore the blurred picture.
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3.3
Enhancing Multispectral Color Composite Images Threshold / Edge Detection
In this process, by referring to the M-file
a) truecolor = IMG, set the truecolor equal to the IMG
b) decorrstretched_truecolor
=
decorrstretch(truecolor,
'Tol',
0.01),
Use
decorrstretch to perform the decorrelation stretch (followed by a linear contrast stretch, as specified by the optional parameter-value pair 'Tol' and 0.1).
c) IMG=decorrstretched_truecolor, set the IMG equal to decorrstretched_truecolor
d) imshow(IMG), display the image IMG.
3.4
Threshold / Edge Detection
a) level = graythresh(IMG), bw = im2bw(IMG,level), level = graythresh(I) computes a global threshold (level) that can be used to convert an intensity image to a binary image with im2bw. level is a normalized intensity value that lies in the range [0, 1]. The graythresh function uses Otsu's method, which chooses the threshold to minimize the intraclass variance of the black and white pixels. Multidimensional arrays are converted automatically to 2-D arrays using reshape. The graythresh function ignores any nonzero imaginary part of I.
b) bw = bwareaopen(bw, 50), Morphologically open binary image (remove small objects)
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c) IMG=bw, set the IMG equal to bw
d) imshow(IMG), display the IMG to screen
3.5
Identify the Round Object
a) I = rgb2gray(RGB);threshold = graythresh(I);bw = im2bw(I,threshold), computes a global threshold (level) that can be used to convert an intensity image to a binary image with im2bw. level is a normalized intensity value that lies in the range [0, 1]. The graythresh function uses Otsu's method, which chooses the threshold to minimize the intraclass variance of the black and white pixels. Multidimensional arrays are converted automatically to 2-D arrays using reshape. The graythresh function ignores any nonzero imaginary part of I.
b) bw = bwareaopen(bw,30), Using morphology functions, remove pixels which do not belong to the objects of interest. remove all object containing fewer than 30 pixels
c) se = strel('disk',2);bw = imclose(bw,se), fill a gap in the pen's cap
d) bw=imfill(bw,'holes');imshow(bw);[B,L]=bwboundaries(bw,'noholes'), fill any holes, so that regionprops can be used to estimate the area enclosed by each of the boundaries
e) IMG=label2rgb(L, @jet, [.5 .5 .5]);imshow(IMG)hold on, Display the label matrix and draw each boundary
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f) For k=1:length(B);boundary = B{k};plot(boundary(:,2),boundary(:,1),
'w',
'LineWidth', 2) end stats = regionprops(L,'Area','Centroid');threshold = 0.94, loop over the boundaries
g) for k = 1:length(B);boundary = B{k}, obtain (X,Y) boundary coordinates corresponding to label 'k'
h) delta_sq=diff(boundary).^2;perimeter=sum(sqrt(sum(delta_sq,2))), compute a simple estimate of the object's perimeter
i)
area = stats(k).Area, obtain the area calculation corresponding to label 'k'
j) metric = 4*pi*area/perimeter^2, display the results
k) if metric>threshold; centroid = stats(k); Centroid; plot(centroid(1),centroid(2),'ko'); end mark objects above the threshold with a black circle
3.6
Rotate
In this process, by referring to M-file a) image=double(IMG);s=size(image);result=zeros(s(2),s(1),s(3)), Allocating memory for fast execution
b) i = 1:s(1)
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j=1:s(2); k=s(2)-j+1; result(k,i,:)=image(i,j,:),Computing the new matrix
c) res=uint8(result); IMG=res; imshow(IMG), Displaying the rotated image
3.7
Open Image
In this process, by referring to M-file
Loading the Image [filename, pathname, filterindex]=uigetfile( ... {'*.jpg','JPEG File (*.jpg)'; ... '*.*','Any Image file (*.*)'}, ... 'Pick an image file'); var=strcat(pathname,filename); ORI_IMG=imread(var); IMG=ORI_IMG; % Showing the origional image imshow(IMG);title('original image')
3.8
Reset
In this process, by referring to M-file
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a) global IMG ORI_IMG;IMG=ORI_IMG;, reset the image after process
b) imshow(IMG);title('original image'), show the image
3.9
Save image
In this process, by referring to M-file
a) image=uint8(IMG), define a 8-bit unsigned integer.
b) [filename,pathname] = uiputfile( ... {'*.jpg', 'JPEG Image File'; ... '*.*', 'All Files (*.*)'}, ... 'Save current image as'); Open standard dialog box for saving files, displays a dialog box titled 'Save current image as’ with the filter specifier set to .jpg file, JPEG Image file and all files.
c) var=strcat(pathname,filename,'.jpg'), horizontally concatenates corresponding rows of the character arrays pathname, filename and ‘.jpg’.
d) imwrite(image,var), write the image to pc in the format specified by var
3.10
Show original picture
In this process, by referring to M-file
a) (get(handles.checkbox3,'Value') == 0),
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subplot(1.2,0.99,1.05); Deleting all subplots present imshow(IMG);
b) subplot(1.3,1.8,1), imshow(ORI_IMG),title('original image'); Displaying origional image
c) subplot(1.3,1.8,2), imshow(IMG); Displaying the rotated image
CHAPTER IV
RESULTS AND DISCUSSION
4.1
Introduction
Before processing the image, we must know what process should be planning. The planning as shown in figure 4.1 is step to process the image by applying MATLAB toolbox program.
-Modified Image -Show Image
1. JPG 2. Any image Get image Image memory
Operator 1. 2. 3. 4. 5. 6.
RESULT
Figure 4.1 step planning
Enhance Threshold Rotate Blur Deblur Identify Round Object
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Regarding to figure 4.1 we can know the step to identify which the images are .JPG and any image file. To develop the program, we should know how to create GUI (Graphical User Interface) function program.
4.2
GUI (Graphical User Interface)
A graphical user interface (GUI) is a graphical display in one or more windows containing controls, called components that enable a user to perform interactive tasks. The user of the GUI does not have to create a script or type commands at the command line to accomplish the tasks. Unlike coding programs to accomplish tasks, the user of a GUI need not understand the details of how the tasks are performed.
GUI components can include menus, toolbars, push buttons, radio buttons, list boxes, and sliders—just to name a few. GUIs created using MATLAB tools can also perform any type of computation, read and write data files, communicate with other GUIs, and display data as tables or as plots
Most GUIs wait for their user to manipulate a control, and then respond to each action in turn. Each control, and the GUI itself, has one or more user-written routines (executable MATLAB code) known as callbacks, named for the fact that they "call back" to MATLAB to ask it to do things. The execution of each callback is triggered by a particular user action such as pressing a screen button, clicking a mouse button, selecting a menu item, typing a string or a numeric value, or passing the cursor over a component. The GUI then responds to these events. You, as the creator of the GUI, provide callbacks which define what the components do to handle events.
This kind of programming is often referred to as event-driven programming. In the example, a button click is one such event. In event-driven programming, callback
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execution is asynchronous, that is, it is triggered by events external to the software. In the case of MATLAB GUIs, most events are user interactions with the GUI, but the GUI can respond to other kinds of events as well, for example, the creation of a file or connecting a device to the computer.
You can code callbacks in two distinct ways:
As MATLAB functions, written in M and stored in M-files
As strings containing MATLAB expressions or commands (such as 'c = sqrt(a*a + b*b);'or 'print')
Using functions stored in M-files as callbacks is preferable to using strings, as functions have access to arguments and are more powerful and flexible. MATLAB scripts (sequences of statements stored in M-files that do not define functions) cannot be used as callbacks.
Although you can provide a callback with certain data and make it do anything you want, you cannot control when callbacks will execute. That is, when your GUI is being used, you have no control over the sequence of events that trigger particular callbacks or what other callbacks might still be running at those times. This distinguishes event-driven programming from other types of control flow, for example, processing sequential data files.
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4.3
Image Processing Result
The image will be modified after the process in the program system. The GUI program I create will be show below and include all the results (modified images) for each function.
Figure 4.2 layout of image processing program
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Figure 4.3 image opened in program
Figure 4.4 Image after simulated with blur and noise
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Figure 4.5 image after deblurred
Figure 4.6 image enhanced after blurred
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Figure 4.7 image enhance after deblurred
Figure 4.8 image rotated
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Figure 4.9 original image after threshold
Figure 4.10 Enhanced image after threshold
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Figure 4.11 original and modified image shows together
CHAPTER IV
CONCLUSION AND RECOMMENDATION
5.1
Conclusion
Image can be solved with ant software programming such as C language, Matrix – X (Mainframe or workstation, based on MATLAB), Visual Basic, Java program and MATLAB program. Image applications frequently need to produce multiple degrees of manipulation interactively. Image applications frequently need to interactively manipulate an image by continuously changing a single parameter and multi-parameter. The best software program is the MATLAB toolbox for image processing. Interpolation and extrapolation of image can be a unifying approach, providing a simple function that supports many common images processing operation.
Since a degenerate image is sometimes easier to calculate, extrapolation may offer a more efficient method to achieve effects such as sharpening or saturation. Blending is a linear operation, and so it must be performed in linear, not gamma-warped space. Component range must be also be monitored, since clamping, especially of the degenerate image, causes inaccuracy.
By using the GUI (Graphical User Interface) program, the application of image processing can successfully created. The function of the image processing program
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include the enhance, threshold, rotate, blur (noise), deblur, identify round object and show the original and modified image together.
5.3
Problems
Although the image processing program function as we aspect, but there are still many way to improve this program. In the image processing program, the info for the function are not state clearly enough and make people understand. In the GUI program, the info should function as pop-up window after user press any function button.
As the project title is Image Processing using MATLAB – Learning Tool, the information is not good enough and clear to understand by people. The main problem is the effectiveness of people to understand it. MATLAB also can be used in industry are in the areas of bar coding, deck-top publication, copy preparation for printing and factory automation.
However, due to the information and studies this state of program of image processing that I only can create. More advance and more functional program can be creating by using MATLAB. Thereby, to write the program became problem and the this project not perfectly complete.
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5.3
Recommendations
The result of the program is slightly sluggish. For future works, some recommendations have been listed based on the problems in order to improve the performance.
a) Create a 3-D for the image processing and animation. Then the image can be more sophisticated if the 3-D program instead. Can spin and move the image with applying that command, so that we can create a great animation for the image.
b) Add more functional button of more function to the program. It is less filter to filter image, such as Robert Gradient, Sobel Edge Detector Operator, Gaussion filter and other filters. c) More information and detail about the function should be add in the program to educate people well.
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REFERENCES
1. FIONN MURTAGH. Image processing data analysis. The Multi-scale approach. University of Ulster
2. LOUIS J.GALBIATI, JR. Machine Vision and Digital Image Processing Fundamentals. Prentice-Hall International Editions.
3. ABDUL HALIM BIN BABA. Image processing Learning Tool-Edge detection. Bachelor Degree. University of Technology Malaysia 1996.
4. CHIN CHIOON KHENG. Window based image processing-learning tool. Bachelor Degree. University of Technology Malaysia 1997.
5. Fundamentals of Image Processing,
[email protected] (http://www.cs.dartmouth.edu/~farid)
6. JEAN-LUC STARCK, Centre d’Etudes de Saclay, Fionn Murtagh. Image processing and data analysis the multiscale approach. University of Ulster.
7. C. ANDERSON. "An alternative to the Burt pyramid algorithm", memo in preparation.
8. S LEVIALDI, “Digital Image Analysis”, University of Rome
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APPENDIX A
The contents of Image Processing Toolbox
Image input/output bmpread -Read a BMP (Microsoft Windows Bitmap) file from disk. bmpwrite -Write a BMP (Microsoft Windows Bitmap) file from disk. gifread -Read a GIF (Graphical Interchange Format) file from disk. gifread -Write a GIF (Graphical Interchange Format) file from disk. hdfpeek -Read data from HDF file. hdfwrite -Write data from HDF file. pcxread -Read a PCX (ZSoft Paint Format) file form disk. pcxwrite -Write a PCX (ZSoft Paint Format) file form disk. tiffread -Read a TIFF (Tagged Image File Format) file from disk. tiffwrite -Write a TIFF (Tagged Image File Format) file from disk. xwdread -Read a XWD (X window dump) file from disk. xwdwrite -Write a XWD (X window dump) file from disk.
Utility routines getimage -Get image data form axes isbw -True for black and white images. isgray -True for intensity images. isind -True for indexed images. Color operations brighten -Brighten or darken a colormap. cmunique -Find unique colormap colors and corresponding image. cmpermute -Permute colormap positions. cmgamma -Gamma correct colormap. cmgamdef -Default gamma correction table. dither -Floyd-Steinberg image dithering. hsv2rgb -Convert HSV values to RGB colorspace. imadjust -adjust and stretch image by an image with fewer colors. imapprox -Approximate indexed image by an image with fewer colors. ntsc2rgb -Convert NTSC values to RGB colorspace. rgb2gray -Convert RGB values to gray. rgb2hsv -Convert RGB values to HSV colorspace. rgb2ntsc -Convert RGB values to NTSC colorspace. rgbplot -Plot RGB colormap components.
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Geometric operations imcrop -Crop image imresize -Resize image imrotate -Rotate image truesize -Resize figure so that image is actual size imzoom -Zoom in and out an image or 2-D plot
Image enhancement/analysis brighten -Brigthen or darken a colormap. grayslice -Density (intensity) slicing. histeq -Histogram equalization. imadjust -Adjust and stretch image intensity imapprox -Approximate indexed image by an image with fewer colors. imhist -Image histogram impixel -color of a pixel improfile -intensity profile. interp2 -2-D data interpolation.
Image Statistics. mean2 -Mean of a matrix corr2 -2-D correlation coefficient. std2 -2-D standard deviation.
Morphological operations. bwarea -Area of object in binary image. dilate -Dilate (thicken) a binary image. erode -Erode (thin) a binary image. edge -Euler number. bwmorph -Morphological operators. bwperim -Perimeter of objects in a binary image.
FIR Filter design. fsamp2 -2-D FIR filter design via frequency sampling. fspecial -Special 2-D filter. ftrans2 -2-D FIR filter design via frequency transformation. fwind1 -2-D FIR filter design using 1-D windows. fwind2 -2-D FIR filter design using 2-D windows. imnoise -Image noise
Frequency response. freqspace -Frequency spacing for 2-D frequency responses. freqz2 -Two dimensional frequency response.
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Filtering. colfilt conv2 filter2 medfilt2 mfilter2 nlfilter wiener2
-Local non-linear filtering as columns. -Two-dimensional convolution. -Two-dimensional filtering. -Two-dimensional median filtering. -Masked filter. -Local non-linear filtering. -Adaptive 2-D Wiener filtering
Block processing bestblk -Best block size for block processing. blkproc -Process an image in blocks. col2im -Rearrange distinct or sliding column blocks to form image, colfilt -Local non-linear filtering as columns. im2col -Rearrange distinct or sliding blocks into columns.
Region of interest (ROI) mfilter2 -Masked filter roipoly -Define polygonal region of interest (ROI). roicolor -Define region of interest (ROI) by color.
Transforms. dct2 fft2 fftshift idct2 ifft2 radon
-2-D discrete cosine transform. -2-D Fast Fourier Transform. -Move zeroth lag (DC component) to center. -2-D inverse discrete cosine transform. -2-D inverse FFt -Radon transform.
Conversions. dither gray2ind hsv2rgb im2bw imslice ind2rgb mat2gray ntsc2rgb rgb2gray rgb2hsv rgb2ind rgb2ntsc
-Floyed-Steinberg image dithering. -Convert gray scale intensity image to indexed image. -Covert HSV values to RGB colorspace. -convert image to black and white by thresholding. -Get image slices into an image deck -Convert indexed image to an RGB image. - Convert matrix to (gray) intensity image. - Convert NTSC values to an RGB image. - Convert an RGB image r values to gray. - Convert RGB values to HSV colorspace.. - Convert RGB image to indexed image. - Convert RGB values to NTSC colorspace.\
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Image diplay colorbar colormap gray hsv,hot,jet image imagesc imcoutour immovie imshow montage warp
-Display color bar (color scale) -Set or get the color look-up table. -Linear gray-scale color map -Colormaps. Types HELP COLOR for others -Display indexed image. -Scale data and display as image -Image coutour. -Make a movie of an image deck. -Display all type of image data. -Display an image deck as a rectangular montage. -Warp an image on
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APPENDIX B
function varargout = LearningTool(varargin) % LEARNINGTOOL M-file for LearningTool.fig %
LEARNINGTOOL, by itself, creates a new LEARNINGTOOL
%
or raises the existing singleton*.
% %
H = LEARNINGTOOL returns the handle to a new LEARNINGTOOL
%
or the handle to the existing singleton*.
% %
LEARNINGTOOL('CALLBACK',hObject,eventData,handles,...) calls the local
%
function named CALLBACK in LEARNINGTOOL.M
%
with the given input arguments.
% %
LEARNINGTOOL('Property','Value',...) creates a new
%
LEARNINGTOOL or raises the existing singleton*. \
%
Starting from the left, property value pairs are
%
applied to the GUI before LearningTool_OpeningFcn gets called. An
%
unrecognized property name or invalid value makes property application
%
stop. All inputs are passed to LearningTool_OpeningFcn via varargin.
% %
*See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
%
instance to run (singleton)".
% % See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help LearningTool
% Last Modified by GUIDE v2.5 12-Mar-2010 02:38:49
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% Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name',
mfilename, ...
'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @LearningTool_OpeningFcn, ... 'gui_OutputFcn', @LearningTool_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end
if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT
% --- Executes just before LearningTool is made visible. function LearningTool_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject
handle to figure
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% varargin command line arguments to LearningTool (see VARARGIN)
% Choose default command line output for LearningTool handles.output = hObject;
% Update handles structure
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guidata(hObject, handles);
% UIWAIT makes LearningTool wait for user response (see UIRESUME) % uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line. function varargout = LearningTool_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject
handle to figure
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure varargout{1} = handles.output;
% --- Executes on button press in OpenFile. function OpenFile_Callback(hObject, eventdata, handles) % hObject
handle to OpenFile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG ORI_IMG % Loading the Image [filename, pathname, filterindex]=uigetfile( ... {'*.jpg','JPEG File (*.jpg)'; ... '*.*','Any Image file (*.*)'}, ... 'Pick an image file'); var=strcat(pathname,filename); ORI_IMG=imread(var); IMG=ORI_IMG; % Showing the origional image imshow(IMG);title('original image');
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% --- Executes on button press in Reset. function Reset_Callback(hObject, eventdata, handles) % hObject
handle to Reset (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% reset the image after process global IMG ORI_IMG; IMG=ORI_IMG; %show the image imshow(IMG);title('original image');
% --- Executes on button press in Blur. function Blur_Callback(hObject, eventdata, handles) % hObject
handle to Blur (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; LEN = 21; THETA = 11; PSF = fspecial('motion', LEN, THETA); blurred = imfilter(IMG, PSF, 'conv', 'circular'); noise_mean = 0; noise_var = 0.001; blurred_noisy = imnoise(blurred, 'gaussian', ... noise_mean, noise_var); IMG=blurred_noisy; imshow(IMG) title('Simulate Blur and Noise')
% --- Executes on button press in Deblured.
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function Deblured_Callback(hObject, eventdata, handles) % hObject
handle to Deblured (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; LEN = 21; THETA = 11; PSF = fspecial('motion', LEN, THETA); blurred = imfilter(IMG, PSF, 'conv', 'circular'); noise_mean = 0; noise_var = 0.001; blurred_noisy = imnoise(blurred, 'gaussian', ... noise_mean, noise_var); A = deconvwnr(blurred_noisy, PSF, 0.001); IMG=A; imshow(IMG) title('Restoration of Blurred, Noisy Image Using NSR = 0.001')
% --- Executes on button press in Enhance. function Enhance_Callback(hObject, eventdata, handles) % hObject
handle to Enhance (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; truecolor = IMG; decorrstretched_truecolor = decorrstretch(truecolor, 'Tol', 0.01); IMG=decorrstretched_truecolor; imshow(IMG); title('Image Composite after Decorrelation Stretch'); % --- Executes on button press in Rotate.
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function Rotate_Callback(hObject, eventdata, handles) % hObject
handle to Rotate (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; image=double(IMG); s=size(image); result=zeros(s(2),s(1),s(3)); % Allocating memory for fast execution for i = 1:s(1) j=1:s(2); k=s(2)-j+1; result(k,i,:)=image(i,j,:); % Computing the new matrix end res=uint8(result); IMG=res; imshow(IMG); % Displaying the rotated image title('Rotate Image');
% --- Executes on button press in Threshold. function Threshold_Callback(hObject, eventdata, handles) % hObject
handle to Threshold (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; level = graythresh(IMG); bw = im2bw(IMG,level); IMG = bwareaopen(bw, 50); imshow(IMG); title('Gray Image');
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% --- Executes on button press in IdentifyingRoundObjects. function IdentifyingRoundObjects_Callback(hObject, eventdata, handles) % hObject
handle to IdentifyingRoundObjects (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
global IMG; RGB = IMG; I = rgb2gray(RGB); threshold = graythresh(I); bw = im2bw(I,threshold); % Using morphology functions, remove pixels which do not belong to the % objects of interest. % remove all object containing fewer than 30 pixels bw = bwareaopen(bw,30); % fill a gap in the pen's cap se = strel('disk',2); bw = imclose(bw,se); % fill any holes, so that regionprops can be used to estimate % the area enclosed by each of the boundaries bw = imfill(bw,'holes'); imshow(bw) [B,L] = bwboundaries(bw,'noholes'); % Display the label matrix and draw each boundary IMG=label2rgb(L, @jet, [.5 .5 .5]); imshow(IMG) hold on for k = 1:length(B) boundary = B{k}; plot(boundary(:,2), boundary(:,1), 'w', 'LineWidth', 2) end stats = regionprops(L,'Area','Centroid');
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threshold = 0.94;
% loop over the boundaries for k = 1:length(B)
% obtain (X,Y) boundary coordinates corresponding to label 'k' boundary = B{k};
% compute a simple estimate of the object's perimeter delta_sq = diff(boundary).^2; perimeter = sum(sqrt(sum(delta_sq,2)));
% obtain the area calculation corresponding to label 'k' area = stats(k).Area;
% compute the roundness metric metric = 4*pi*area/perimeter^2;
% display the results metric_string = sprintf('%2.2f',metric);
% mark objects above the threshold with a black circle if metric > threshold centroid = stats(k).Centroid; plot(centroid(1),centroid(2),'ko'); end
text(boundary(1,2)-35,boundary(1,1)+13,metric_string,'Color','y',... 'FontSize',14,'FontWeight','bold');
end
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% --- If Enable == 'on', executes on mouse press in 5 pixel border. % --- Otherwise, executes on mouse press in 5 pixel border or over OpenFile. function OpenFile_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to OpenFile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function OpenFile_CreateFcn(hObject, eventdata, handles) % hObject
handle to OpenFile (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- If Enable == 'on', executes on mouse press in 5 pixel border. % --- Otherwise, executes on mouse press in 5 pixel border or over Reset. function Reset_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to Reset (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function Reset_CreateFcn(hObject, eventdata, handles) % hObject
handle to Reset (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- Executes on mouse press over axes background.
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function axes1_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to axes1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function axes1_CreateFcn(hObject, eventdata, handles) % hObject
handle to axes1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% Hint: place code in OpeningFcn to populate axes1
% --- If Enable == 'on', executes on mouse press in 5 pixel border. % --- Otherwise, executes on mouse press in 5 pixel border or over Rotate. function Rotate_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to Rotate (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function Rotate_CreateFcn(hObject, eventdata, handles) % hObject
handle to Rotate (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- If Enable == 'on', executes on mouse press in 5 pixel border.
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% --- Otherwise, executes on mouse press in 5 pixel border or over Blur. function Blur_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to Blur (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function Blur_CreateFcn(hObject, eventdata, handles) % hObject
handle to Blur (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- Executes during object creation, after setting all properties. function pushbutton2_CreateFcn(hObject, eventdata, handles) % hObject
handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- Executes on button press in Close. function Close_Callback(hObject, eventdata, handles) % hObject
handle to Close (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
clear all; close;clear
% --- Executes on button press in Save. function Save_Callback(hObject, eventdata, handles) % hObject
handle to Save (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
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% handles
structure with handles and user data (see GUIDATA)
global IMG image=uint8(IMG); [filename,pathname] = uiputfile( ... {'*.jpg', 'JPEG Image File'; ... '*.*', 'All Files (*.*)'}, ... 'Save current image as');
var=strcat(pathname,filename,'.jpg'); imwrite(image,var);
% --- Executes on selection change in listbox1. function listbox1_Callback(hObject, eventdata, handles) % hObject
handle to listbox1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% Hints: contents = get(hObject,'String') returns listbox1 contents as cell array %
contents{get(hObject,'Value')} returns selected item from listbox1
% --- Executes during object creation, after setting all properties. function listbox1_CreateFcn(hObject, eventdata, handles) % hObject
handle to listbox1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% Hint: listbox controls usually have a white background on Windows. %
See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
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set(hObject,'BackgroundColor','white'); end
% --- If Enable == 'on', executes on mouse press in 5 pixel border. % --- Otherwise, executes on mouse press in 5 pixel border or over text2. function text2_ButtonDownFcn(hObject, eventdata, handles) % hObject
handle to text2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% --- Executes during object creation, after setting all properties. function text2_CreateFcn(hObject, eventdata, handles) % hObject
handle to text2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
empty - handles not created until after all CreateFcns called
% --- Executes on button press in checkbox3. function checkbox3_Callback(hObject, eventdata, handles) % hObject
handle to checkbox3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB % handles
structure with handles and user data (see GUIDATA)
% Hint: get(hObject,'Value') returns toggle state of checkbox3 global ORI_IMG IMG if (get(handles.checkbox3,'Value') == 0), subplot(1.2,0.99,1.05); % Deleting all subplots present imshow(IMG); else
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subplot(1.3,1.8,1), imshow(ORI_IMG),title('original image'); % Displaying origional image subplot(1.3,1.8,2), imshow(IMG); % Displaying the rotated image
end
