Python Gui For Signal And Image Processing
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Python Gui For Signal And Image Processing
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-10-05
Python Gui For Signal And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-05 with Computers categories.
You will learn to create GUI applications using the Qt toolkit. The Qt toolkit, also popularly known as Qt, is a cross-platform application and UI framework developed by Trolltech, which is used to develop GUI applications. You will develop an existing GUI by adding several Line Edit widgets to read input, which are used to set the range and step of the graph (signal). Next, Now, you can use a widget for each graph. Add another Widget from Containers in gui_graphics.ui using Qt Designer. Then, Now, you can use two Widgets, each of which has two canvases. The two canvases has QVBoxLayout in each Widget. Finally, you will apply those Widgets to display the results of signal and image processing techniques.
Learn From Scratch Signal And Image Processing With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-14
Learn From Scratch Signal And Image Processing With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Technology & Engineering categories.
In this book, you will learn how to use OpenCV, NumPy library and other libraries to perform signal processing, image processing, object detection, and feature extraction with Python GUI (PyQt). You will learn how to filter signals, detect edges and segments, and denoise images with PyQt. You will also learn how to detect objects (face, eye, and mouth) using Haar Cascades and how to detect features on images using Harris Corner Detection, Shi-Tomasi Corner Detector, Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST). In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. In Chapter 4, you will learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, and Tutorial Steps To Implement Image Denoising. In Chapter 5, you will learn: Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, and Tutorial Steps To Extract Detected Objects. In Chapter 6, you will learn: Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). You can download the XML files from https://viviansiahaan.blogspot.com/2023/06/learn-from-scratch-signal-and-image.html.
Learn From Scratch Signal And Image Processing With Python Gui
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Author : RISMON HASIHOLAN. SIANIPAR
language : en
Publisher:
Release Date : 2021
Learn From Scratch Signal And Image Processing With Python Gui written by RISMON HASIHOLAN. SIANIPAR and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Postgresql For Java Gui Database And Image Processing
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-08-27
Postgresql For Java Gui Database And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-27 with Computers categories.
In this book, you will learn how to build from scratch a criminal records management database system using Java/PostgreSQL. All Java code for digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to MySQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done.In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done. In the second chapter, you will learn querying data from the postgresql using jdbc including establishing a database connection, creating a statement object, executing the query, processing the resultset object, querying data using a statement that returns multiple rows, querying data using a statement that has parameters, inserting data into a table using jdbc, updating data in postgresql database using jdbc, calling postgresql stored function using jdbc, deleting data from a postgresql table using jdbc, and postgresql jdbc transaction. In third chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the fourth chapter, you will be taught how to create Crime database and its tables. In the fifth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the sixth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In the seventh chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the eigthth chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.
Start From Scratch Digital Image Processing With Tkinter
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-10-21
Start From Scratch Digital Image Processing With Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-21 with Computers categories.
"Start from Scratch: Digital Image Processing with Tkinter" is a beginner-friendly guide that delves into the basics of digital image processing using Python and Tkinter, a popular GUI library. The project is divided into distinct modules, each focusing on a specific aspect of image manipulation. The journey begins with an exploration of Image Color Space. Here, readers encounter the Main Form, which serves as the entry point to the application. It provides a user-friendly interface for loading images, selecting color spaces, and visualizing various color channels. The Fundamental Utilities play a crucial role by providing core functionalities like loading images, converting color spaces, and manipulating pixel data. The project also includes forms dedicated to displaying individual color channels and offering insights into the current color space through histograms. The Plotting Utilities module facilitates the creation of visual representations such as plots and graphs, enhancing the user's understanding of color spaces. Moving on, the Image Transformation section introduces readers to techniques like the Fast Fourier Transform (FFT). The Fast Fourier Transform Utilities module enables the implementation of FFT algorithms for converting images from spatial to frequency domains. A corresponding form allows users to view images in the frequency domain, with additional adjustments made to the plotting utilities for effective visualization. In the context of Discrete Cosine Transform (DCT), readers gain insights into algorithms and functions for transforming images. The Form for Discrete Cosine Transform aids in visualizing images in the DCT domain, while the plotting utilities are modified to accommodate these transformed images. The Discrete Sine Transform (DST) section introduces readers to DST algorithms and their role in image transformation. A dedicated form for visualizing images in the DST domain is provided, and the plotting utilities are further extended to handle these transformations effectively. Moving Average Smoothing is another critical aspect covered in the project. The Filter2D Utilities facilitate the application of moving average smoothing techniques. Additionally, metrics utilities enable the assessment of the smoothing process, with forms available for displaying both metrics and the smoothed images. Next, the project addresses Exponential Moving Average techniques, modifying the existing utilities to accommodate this specific approach. Similarly, forms for visualizing results and metrics are provided. Readers are then introduced to techniques like Median Filtering, Savitzky-Golay Filtering, and Wiener Filtering. The Filter2D Utilities are adapted to facilitate these filtering methods, and metrics utilities are employed to assess the effectiveness of each technique. Forms dedicated to each filtering method provide a platform for visualizing the results. The final section of the project explores techniques such as Total Variation Denoising, Non-Local Means Denoising, and PCA Denoising. The Filter2D Utilities are once again modified to support these denoising techniques. Metrics utilities are employed to evaluate the denoising process, and dedicated forms offer visualization capabilities. By breaking down the project into these modules, readers can systematically grasp the fundamentals of digital image processing, gradually building their skills from one concept to the next. Each section provides hands-on experience and practical knowledge, making it an ideal starting point for beginners in image processing.
Java Gui With Mysql Database And Image Processing
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-08-26
Java Gui With Mysql Database And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-26 with Computers categories.
In this book, you will learn how to build from scratch a criminal records management database system using Java / MySQL. All Java code for digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to MySQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will be shown the number of devices needed to be downloaded and installed. You need to know how to add external libraries to the NetBeans environment. These tools are needed so that you can run the Java scripts. In the second chapter, you will be taught how to create Crime database and its tables. In third chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the fourth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the fifth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In the sixth chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the seventh chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.
Start From Scratch Digital Signal Processing With Tkinter
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-10-13
Start From Scratch Digital Signal Processing With Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Computers categories.
In this project, you will create a multi-form GUI to implement digital signal processing. Creating a GUI involves designing an interface where users can input parameters and visualize the results of various signal processing techniques. Each form corresponds to a specific technique and is implemented using the tkinter library. The "Simple Sinusoidal Form" allows users to generate and visualize a basic sinusoidal signal. It includes input fields for parameters like frequency, amplitude, and time period. The utilities associated with this form provide functions to generate and plot the simple sinusoidal signal. The "Two Sinusoidals Form" extends the previous form, enabling users to generate and visualize two combined sinusoidal signals. It provides input fields for frequencies, amplitudes, and time periods of both signals. The utilities handle the generation and plotting of the combined sinusoidal signals. The "More Two Sinusoidals Form" further extends the previous form to generate and visualize additional combined sinusoidal signals. It includes input fields for frequencies, amplitudes, and time periods of three sinusoidal signals. The utilities handle the generation and plotting of these combined signals. Forms for various modulation techniques (AM, FM, PM, ASK, FSK, PSK) are available. These allow users to generate and visualize modulated signals by providing input fields for modulation indices, carrier frequencies, and time periods. The utilities in each form handle the signal generation and modulation process, as well as the plotting of the modulated signals. Forms for different filter designs (FIR, Butterworth, Chebyshev Type 1) cover lowpass, highpass, bandpass, and bandstop filters. They include input fields for filter order, cutoff frequencies, and other relevant parameters. The utilities in each form implement the filter design and frequency response plotting. Wavelet transformation forms focus on wavelet-based techniques, including scaling, decomposition, and denoising. They provide input fields for wavelet type, thresholding methods, and other wavelet-specific parameters. The utilities handle the wavelet transformations, denoising, and visualizing the results. Forms for various denoising techniques (MA, EMA, Median, SGF, Wiener, TV, NLM, PCA) cover different smoothing and denoising methods. They offer input fields for relevant denoising parameters. The utilities for each form implement the denoising process and display the denoised signals. Each form's utility methods interact with the GUI elements, taking user inputs and performing the corresponding signal processing tasks. These utilities encapsulate the underlying algorithms and ensure a seamless interaction between the user interface and the backend computations. In summary, this session involves creating a comprehensive GUI for a wide range of signal processing techniques, including signal generation, modulation, filtering, wavelet transformations, and various denoising methods. Each form and its associated utilities handle specific tasks, ensuring an intuitive and effective user experience.
Postgresql For Java Gui Database Cryptography And Image Processing
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-09-01
Postgresql For Java Gui Database Cryptography And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-01 with Computers categories.
In this book, you will learn how to build from scratch a criminal records management database system using Java/PostgreSQL. All Java code for cryptography and digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector / J to facilitate Java to PostgreSQL connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will learn: How to install NetBeans, JDK 11, and the PostgreSQL connector; How to integrate external libraries into projects; How the basic PostgreSQL commands are used; How to query statements to create databases, create tables, fill tables, and manipulate table contents is done. In the second chapter, you will learn querying data from the postgresql using jdbc including establishing a database connection, creating a statement object, executing the query, processing the resultset object, querying data using a statement that returns multiple rows, querying data using a statement that has parameters, inserting data into a table using jdbc, updating data in postgresql database using jdbc, calling postgresql stored function using jdbc, deleting data from a postgresql table using jdbc, and postgresql jdbc transaction. In the second chapter, you will learn the basics of cryptography using Java. Here, you will learn how to write a Java program to count Hash, MAC (Message Authentication Code), store keys in a KeyStore, generate PrivateKey and PublicKey, encrypt / decrypt data, and generate and verify digital prints. In the third chapter, you will learn how to create and store salt passwords and verify them. You will create a Login table. In this case, you will see how to create a Java GUI using NetBeans to implement it. In addition to the Login table, in this chapter you will also create a Client table. In the case of the Client table, you will learn how to generate and save public and private keys into a database. You will also learn how to encrypt / decrypt data and save the results into a database. In the fourth chapter, you will create an Account table. This account table has the following ten fields: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In this case, you will learn how to implement generating and verifying digital prints and storing the results into a database. In the fifth chapter, you create a table with the name of the Account, which has ten columns: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In the sixth chapter, you will create a Client_Data table, which has the following seven fields: client_data_id (primary key), account_id (primary_key), birth_date, address, mother_name, telephone, and photo_path. In the seventh chapter, you will be taught how to create Crime database and its tables. In eighth chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the nineth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the tenth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. In the eleventh chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the twelfth chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.
Mariadb With Java Gui For Cryptography And Image Processing
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Author : Vivian Siahaan
language : en
Publisher: SPARTA PUBLISHING
Release Date : 2019-09-02
Mariadb With Java Gui For Cryptography And Image Processing written by Vivian Siahaan and has been published by SPARTA PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-02 with Computers categories.
This book is Java/MariaDB version of our previous books which used Java/MySQL and Java/PostgreSQL. In this book, you will learn how to build from scratch a criminal records management database system and simple bank database system using Java/MariaDB. All Java code for digital image processing in this book is Native Java. Intentionally not to rely on external libraries, so that readers know in detail the process of extracting digital images from scratch in Java. There are only three external libraries used in this book: Connector/J to facilitate Java to MariaDB connections, JCalendar to display calendar controls, and JFreeChart to display graphics. Digital image techniques to extract image features used in this book are grascaling, sharpening, invertering, blurring, dilation, erosion, closing, opening, vertical prewitt, horizontal prewitt, Laplacian, horizontal sobel, and vertical sobel. For readers, you can develop it to store other advanced image features based on descriptors such as SIFT and others for developing descriptor based matching. In the first chapter, you will learn the basics of cryptography using Java. Here, you will learn how to write a Java program to count Hash, MAC (Message Authentication Code), store keys in a KeyStore, generate PrivateKey and PublicKey, encrypt / decrypt data, and generate and verify digital prints. In the second chapter, you will learn how to create and store salt passwords and verify them. You will create a Login table. In this case, you will see how to create a Java GUI using NetBeans to implement it. In addition to the Login table, in this chapter you will also create a Client table. In the case of the Client table, you will learn how to generate and save public and private keys into a database. You will also learn how to encrypt / decrypt data and save the results into a database. In the third chapter, you will create an Account table. This account table has the following ten fields: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In this case, you will learn how to implement generating and verifying digital prints and storing the results into a database. In the fourth chapter, You create a table with the name of the Account, which has ten columns: account_id (primary key), client_id (primarykey), account_number, account_date, account_type, plain_balance, cipher_balance, decipher_balance, digital_signature, and signature_verification. In the fifth chapter, you will create a Client_Data table, which has the following seven fields: client_data_id (primary key), account_id (primary_key), birth_date, address, mother_name, telephone, and photo_path. In the sixth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Suspect table data. This table has eleven columns: suspect_id (primary key), suspect_name, birth_date, case_date, report_date, suspect_ status, arrest_date, mother_name, address, telephone, and photo. In the seventh chapter, you will be taught how to create Crime database and its tables. In nineth chapter, you will be taught how to extract image features, utilizing BufferedImage class, in Java GUI. In the eighth chapter, you will be taught to create Java GUI to view, edit, insert, and delete Feature_Extraction table data. This table has eight columns: feature_id (primary key), suspect_id (foreign key), feature1, feature2, feature3, feature4, feature5, and feature6. All six fields (except keys) will have a BLOB data type, so that the image of the feature will be directly saved into this table. In the nineth chapter, you will add two tables: Police_Station and Investigator. These two tables will later be joined to Suspect table through another table, File_Case, which will be built in the seventh chapter. The Police_Station has six columns: police_station_id (primary key), location, city, province, telephone, and photo. The Investigator has eight columns: investigator_id (primary key), investigator_name, rank, birth_date, gender, address, telephone, and photo. Here, you will design a Java GUI to display, edit, fill, and delete data in both tables. In the eleventh chapter, you will add two tables: Victim and File_Case. The File_Case table will connect four other tables: Suspect, Police_Station, Investigator and Victim. The Victim table has nine columns: victim_id (primary key), victim_name, crime_type, birth_date, crime_date, gender, address, telephone, and photo. The File_Case has seven columns: file_case_id (primary key), suspect_id (foreign key), police_station_id (foreign key), investigator_id (foreign key), victim_id (foreign key), status, and description. Here, you will also design a Java GUI to display, edit, fill, and delete data in both tables. Finally, this book is hopefully useful for you.
Digital Video Processing Projects Using Python And Tkinter
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2024-03-23
Digital Video Processing Projects Using Python And Tkinter written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-23 with Computers categories.
The first project is a video player application with an additional feature to compute and display the MD5 hash of each frame in a video. The user interface is built using Tkinter, a Python GUI toolkit, providing buttons for opening a video file, playing, pausing, and stopping the video playback. Upon opening a video file, the application displays metadata such as filename, duration, resolution, FPS, and codec information in a table. The video can be navigated using a slider to seek to a specific time point. When the video is played, the application iterates through each frame, extracts it from the video clip, calculates its MD5 hash, and displays the frame along with its histogram and MD5 hash. The histogram represents the pixel intensity distribution of each color channel (red, green, blue) in the frame. The computed MD5 hash for each frame is displayed in a label below the video frame. Additionally, the frame hash along with its index is saved to a text file for further analysis or verification purposes. The class encapsulates the functionality of the application, providing methods for opening a video file, playing and controlling video playback, updating metadata, computing frame histogram, plotting histogram, calculating MD5 hash for each frame, and saving frame hashes to a file. The main function initializes the Tkinter root window, instantiates the class, and starts the Tkinter event loop to handle user interactions and update the GUI accordingly. The second project is a video player application with additional features for frame extraction and visualization of RGB histograms for each frame. Developed using Tkinter, a Python GUI toolkit, the application provides functionalities such as opening a video file, playing, pausing, and stopping video playback. The user interface includes buttons for controlling video playback, a combobox for selecting zoom scale, an entry for specifying a time point to jump to, and buttons for frame extraction and opening another instance of the application. Upon opening a video file, the application loads it using the imageio library and displays the frames in a canvas. Users can play, pause, and stop the video using dedicated buttons. The zoom scale can be adjusted, and the video can be navigated using scrollbar or time entry. Additionally, users can extract a specific frame by entering its frame number, which opens a new window displaying the extracted frame along with its RGB histograms and MD5 hash value. The class encapsulates the application's functionalities, including methods for opening a video file, playing/pausing/stopping video, updating zoom scale, displaying frames, handling mouse events for dragging and scrolling, jumping to a specified time, and extracting frames. The main function initializes the Tkinter root window and starts the application's event loop to handle user interactions and update the GUI accordingly. Users can also open multiple instances of the application simultaneously to work with different video files concurrently. The third project is a GUI application built with Tkinter for calculating hash values of video frames and displaying them in a listbox. The interface consists of different frames for video display and hash values, along with buttons for controlling video playback, calculating hashes, saving hash values to a file, and opening a new instance of the application. Users can open a video file using the "Open Video" button, after which they can play, pause, or stop the video using corresponding buttons. Upon opening a video file, the application reads frames from the video capture and displays them in the designated frame. Users can interact with the video using playback buttons to control the video's flow. Hash values for each frame are calculated using various hashing algorithms such as MD5, SHA-1, SHA-256, and others. These hash values are then displayed in the listbox, allowing users to view the hash values corresponding to each algorithm. Additionally, users can save the calculated hash values to a text file by clicking the "Save Hashes" button, providing a convenient way to store and analyze the hash data. Lastly, users can open multiple instances of the application simultaneously by clicking the "Open New Instance" button, facilitating concurrent processing of different video files. The fourth project is a GUI application developed using Tkinter for analyzing video frames through frame hashing and histogram visualization. The interface presents a canvas for displaying the video frames along with control buttons for video playback, frame extraction, and zoom control. Users can open a video file using the "Open Video" button, and the application provides functionality to play, pause, and stop the video playback. Additionally, users can jump to specific time points within the video using the time entry field and "Jump to Time" button. Upon extracting a frame, the application opens a new window displaying the selected frame along with its histogram and multiple hash values calculated using various algorithms such as MD5, SHA-1, SHA-256, and others. The histogram visualization presents the distribution of pixel values across the RGB channels, aiding in the analysis of color composition within the frame. The hash values are displayed in a listbox within the frame extraction window, providing users with comprehensive information about the frame's content and characteristics. Furthermore, users can open multiple instances of the application simultaneously, enabling concurrent analysis of different video files. The fifth project implements a video player application with edge detection capabilities using various algorithms. The application is designed using the Tkinter library for the graphical user interface (GUI). Upon execution, the user is presented with a window containing control buttons and panels for displaying the video and extracted frames. The main functionalities of the application include opening a video file, playing, pausing, and stopping the video playback. Additionally, users can jump to a specific time in the video, extract frames, and open another instance of the video player application. The video playback is displayed on a canvas, allowing for zooming in and out using a combobox to adjust the scale. One of the key features of this application is the ability to perform edge detection on frames extracted from the video. When a frame is extracted, the application displays the original frame alongside its edge detection result using various algorithms such as Canny, Sobel, Prewitt, Laplacian, Scharr, Roberts, FreiChen, Kirsch, Robinson, Gaussian, or no edge detection. Histogram plots for each RGB channel of the frame are also displayed, along with hash values computed using different hashing algorithms for integrity verification. The edge detection result and histogram plots are updated dynamically based on the selected edge detection algorithm. Overall, this application provides a convenient platform for visualizing video content and performing edge detection analysis on individual frames, making it useful for tasks such as video processing, computer vision, and image analysis. The sixth project is a Python application built using the Tkinter library for creating a graphical user interface (GUI) to play videos and apply various filtering techniques to individual frames. The application allows users to open video files in common formats such as MP4, AVI, and MKV. Once a video is opened, users can play, pause, stop, and jump to specific times within the video. The GUI consists of two main panels: one for displaying the video and another for control buttons. The video panel contains a canvas where the frames of the video are displayed. Users can zoom in or out on the video frames using a combobox, and they can also scroll horizontally through the video using a scrollbar. Control buttons such as play/pause, stop, extract frame, and open another video player are provided in the control panel. When a frame is extracted, the application opens a new window displaying the extracted frame along with options to apply various filtering methods. These methods include Gaussian blur, mean blur, median blur, bilateral filtering, non-local means denoising, anisotropic diffusion, total variation denoising, Wiener filter, adaptive thresholding, and wavelet transform. Users can select a filtering method from a dropdown menu, and the filtered result along with the histogram and hash values of the frame are displayed in real-time. The application also provides functionality to open another instance of the video player, allowing users to work with multiple videos simultaneously. Overall, this project provides a user-friendly interface for playing videos and applying filtering techniques to individual frames, making it useful for tasks such as video processing, analysis, and editing.