Matlab: Saving And Publishing
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This document discusses saving and publishing in Matlab. It describes how to save variables and images using commands like save and imsave. It also explains how to publish animations by converting MATLAB movie files to AVI format using the movie2avi function. This involves creating an AVI file object with avifile, then capturing a sequence of graphs using addframe and putting them into the AVI file.
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3. Have the whole middle be a JLabel in which you will display Images stored in a directory.
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Solution
Please refer this code for your application :
--------------------------------------------------------------------------------------------------------------------
--------------
import java.awt.Image;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
import javax.swing.ImageIcon;
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initComponents();
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/**
*Initialize components
*/
private void initComponents() {
setTitle(\"Image Viewer\");
setDefaultCloseOperation(EXIT_ON_CLOSE);
setLayout(new java.awt.BorderLayout());// The layout is BorderLayout
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Matlab provides the GUIDE tool for building graphical user interfaces (GUIs) which provide a rich, hassle-free environment for viewing and manipulating programs. GUIDE allows selecting a prototype and provides a GUI editor for adding elements like buttons and axes. Elements can be aligned using tools and the GUI is saved and run to test functionality like opening an image when a button is pressed, demonstrating a simple example of a GUI created with GUIDE.
Matlab: Linear Methods, Quantiles
This document discusses quantiles and quantile functions in Matlab. It defines quantiles as points taken at regular intervals from a cumulative distribution function that divide ordered data into essentially equal subsets. It lists some common quantile names like median, tertiles, quartiles, quintiles, deciles, and percentiles. It provides an example quantile function in Matlab that returns quantiles of input data values at specified cumulative probability values.
Matlab Data And Statistics
Statistics is the science of making effective use of numerical data relating to groups of individuals or experiments. It deals with collecting, analyzing, and interpreting data through surveys, experiments, and statistical models described by probability distributions. Samples drawn from populations are used to infer properties, and histograms created with functions like hist and rose show the distribution of data values across a range.
Matlab Image Enhancement Techniques
The document discusses various image enhancement techniques in Matlab, including filtering, predefined filters, image enhancement tools, image restoration, dilation/erosion functions, and dithering. Filtering can be used to modify images through operations like smoothing, sharpening, and edge enhancement. Predefined filters like 'gaussian' and 'laplacian' can be applied to images with functions like fspecial and imfilter. Additional tools for operations such as histogram equalization, noise addition, and filtering are also covered.
Matlab Image Restoration Techniques
Linear filters like averaging and Gaussian filters can remove grain noise by averaging pixel values in a neighborhood. Median filters are better at removing outliers without reducing sharpness by setting a pixel to the median value in its neighborhood. The document demonstrates applying averaging and median filters in Matlab to remove noise, and using morphological opening to estimate and subtract a background illumination to rectify it.
Matlab Importing Data
This document provides steps for importing text files into MATLAB: save the data in a text file, open the import data window in MATLAB and select the file, select values in the import wizard, finalize the import, and then view the imported data in the MATLAB workspace. It also lists several methods for importing data into MATLAB such as entering it at the command prompt, loading it from an ASCII flat file, or using MATLAB I/O functions. The document concludes by recommending visiting additional self-help tutorials on the website www.dataminingtools.net.
Matlab Organizing Data
This document summarizes different ways to organize data in Matlab, including cell arrays, function handles, and structures.
Cell arrays allow different data types to be stored in containers called cells. Function handles pass functions as inputs to other functions. Structures store selected data together in fields, with each field containing an array of a Matlab data type.
Matlab Text Files
To write to a file in Matlab, use the fopen function to open a file with write permission and the fprintf function to write text to the file. To read from a file, use the fopen function to open the file with read permission and the fscanf function to read data from the file. Matlab has functions like circshift to circularly shift matrix elements, sort to sort arrays in ascending or descending order, and issorted to check if elements are already sorted.
Matlab Visualizing Data
The document discusses visualizing data in Matlab. It describes how the plot function can be used to create graphs with different parameters. It also explains how to create animations by saving multiple frames from figures using getframe and playing them back with movie. An example is provided to generate an animation by plotting the FFT of an identity matrix over increasing sizes and saving each frame.
Matlab Working With Images
The document discusses different types of images in Matlab including binary, grayscale, indexed, and RGB images. It also summarizes commands to convert between image types such as converting grayscale to indexed or truecolor to binary. Finally, it provides examples of how to view images, measure pixel values and distances, and crop images using the imtool command.
Matlab: Non Linear Methods
This document discusses nonlinear curve fitting in Matlab. It describes how the Curve Fitting Toolbox uses nonlinear least squares to fit nonlinear models to data, such as Gaussians, ratios of polynomials, and power functions. The process of fitting a surface to data involves opening the fitting tool, selecting and importing data, refining the fit, removing outliers, selecting validation data, and exploring and customizing plots. Various steps for fitting, viewing residuals and fits, and analyzing the fit are outlined. The document recommends visiting additional self-help tutorials on the website.
Matlab: Procedures And Functions
Script files, also called M-files, make MATLAB programming more efficient than entering individual commands. M-files are text files that contain MATLAB commands to perform specific tasks. They can be created and edited in any plain text editor or the built-in MATLAB editor. User defined functions are a special type of M-file that take inputs and produce outputs, with the function name and arguments specified in the first line. Strings in MATLAB are matrices of character elements that can be manipulated using various functions like converting between data types and extracting substrings.
Matlab: Programming Environment
This document provides an overview of the MATLAB programming environment and basic functions. It describes the main components of the MATLAB interface including the toolbar, file manager, command window, workspace, and command history. It also explains how to access help documentation, use the function browser to search for commands, define basic functions and vectors, and clear variables from the workspace.
Matlab: Regression
Regression analysis models the relationship between variables, including dependent and independent variables. Linear regression models take forms like straight lines, polynomials, trigonometric, and interaction terms. Multiple linear regression is useful for understanding variable effects, predicting values, and dealing with multicollinearity using methods like ridge regression, partial least squares, and stepwise regression. Nonlinear and generalized linear models also describe nonlinear relationships. Multivariate regression involves multiple response variables.
Matlab: Spectral Analysis, Fourier Analysis, Filterbank Analysis
This document discusses spectral analysis in Matlab, including power spectral density, mean-square spectrum, and pseudospectrum estimators. It also covers using the fdatool tool for spectral analysis and designing filters. Finally, it briefly introduces Fourier analysis and decomposing signals into constituent frequencies, and provides a link to additional self-help tutorials on data mining tools.
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5th Power Grid Model Meet-up
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Matlab: Saving And Publishing
- 2. Saving variables save stores all variables from the current MATLAB workspace in a MATLAB formatted file (MAT-file) named matlab.mat that resides in the current working directory. save filename stores all variables in the current workspace in the file filename. save filename content stores only those variables specified by content in file filename.
- 3. Saving variables Command format: save myfile.mat firstnamelastname Function format: save('myfile.mat', 'firstname', 'lastname')
- 4. Saving Images imsave creates a Save Image tool in a separate figure that is associated with the image in the current figure, called the target image. The Save Image tool displays an interactive file chooser dialog box (shown below) in which you can specify a path and filename.
- 6. Publishing animations For converting an MATLAB animation movie file into Audio Video Interleaved format (AVI), the ‘movie2avi’ function can be used.
- 9. Capture the sequence of graphs and put them into the AVI file, using the addframe() function.
- 10. for k=1:25
- 11. h = plot(fft(eye(k+16)));
- 12. axis equal; frame = getframe(gca);