Tkinter
DOWNLOAD
Download Tkinter PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Tkinter book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Python Tkinter 35 Mini Projects
DOWNLOAD
Author : VAISHALI B. BHAGAT
language : en
Publisher:
Release Date :
Python Tkinter 35 Mini Projects written by VAISHALI B. BHAGAT and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Dive into the world of Python GUI programming with Tkinter through 35 exciting mini projects! Perfect for beginners and those looking to enhance their skills, this book offers a hands-on approach to learning. From creating simple interfaces to building interactive applications, each project is designed to help you grasp Tkinter concepts effortlessly. With clear explanations and practical examples, you'll gain confidence in GUI development while unleashing your creativity. Start your journey today and discover the power of Python Tkinter!
Python Gui Programming With Tkinter
DOWNLOAD
Author : Alan D. Moore
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-28
Python Gui Programming With Tkinter written by Alan D. Moore and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-28 with Computers categories.
Transform your evolving user requirements into feature-rich Tkinter applications Key FeaturesExtensively revised with new content on RESTful networking, classes in Tkinter, and the Notebook widgetTake advantage of Tkinter's lightweight, portable, and easy-to-use featuresBuild better-organized code and learn to manage an evolving codebaseBook Description Tkinter is widely used to build GUIs in Python due to its simplicity. In this book, you'll discover Tkinter's strengths and overcome its challenges as you learn to develop fully featured GUI applications. Python GUI Programming with Tkinter, Second Edition, will not only provide you with a working knowledge of the Tkinter GUI library, but also a valuable set of skills that will enable you to plan, implement, and maintain larger applications. You'll build a full-blown data entry application from scratch, learning how to grow and improve your code in response to continually changing user and business needs. You'll develop a practical understanding of tools and techniques used to manage this evolving codebase and go beyond the default Tkinter widget capabilities. You'll implement version control and unit testing, separation of concerns through the MVC design pattern, and object-oriented programming to organize your code more cleanly. You'll also gain experience with technologies often used in workplace applications, such as SQL databases, network services, and data visualization libraries. Finally, you'll package your application for wider distribution and tackle the challenge of maintaining cross-platform compatibility. What you will learnProduce well-organized, functional, and responsive GUI applicationsExtend the functionality of existing widgets using classes and OOPPlan wisely for the expansion of your app using MVC and version controlMake sure your app works as intended through widget validation and unit testingUse tools and processes to analyze and respond to user requestsBecome familiar with technologies used in workplace applications, including SQL, HTTP, Matplotlib, threading, and CSVUse PostgreSQL authentication to ensure data security for your applicationWho this book is for This book is for programmers who understand the syntax of Python, but do not yet have the skills, techniques, and knowledge to design and implement a complete software application. A fair grasp of basic Python syntax is required.
Tkinter Programming Essentials
DOWNLOAD
Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-23
Tkinter Programming Essentials written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
"TKinter Programming Essentials" "TKinter Programming Essentials" offers a comprehensive and expertly structured exploration of Python’s venerable GUI toolkit, TKinter. This book demystifies the inner workings of TKinter through an architectural lens, beginning with its foundational relationship to Tk and Python, and continuing through best practices in modular application design, environment interoperability, and seamless integration with the broader Python ecosystem. Alongside deep-dives into initialization routines and mainloop management, readers will gain the background needed to develop robust, cross-platform interfaces for Windows, macOS, and Linux. The book then transitions into sophisticated topics crucial for modern GUI development: the advanced widget system, responsive layout patterns, geometry management, and the nuances of event-driven programming. Readers will benefit from detailed expositions on widget inheritance, styling and theming with ttk, and resource management principles for building efficient, maintainable applications. User-centric chapters examine the development of intuitive menus, interactive dialogs, and adaptive interfaces that remain accessible and internationalization-friendly, as well as high-performance canvas graphics and animation techniques. Beyond the essential mechanics, "TKinter Programming Essentials" is distinguished by its focus on professional-quality software architecture, testing methodologies, and real-world integration scenarios. It delivers guidance on persistence, application state, and supporting large-scale MVC patterns while ensuring security and performance through profiling, memory management, and continuous integration. The book concludes by addressing future directions in Python GUI development, including hybrid web integrations, native code interfacing, and cloud networking—making it the definitive reference for both new and experienced Python developers committed to mastering desktop application development with TKinter.
Tkinter Gui Programming By Example
DOWNLOAD
Author : David Love
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-04-25
Tkinter Gui Programming By Example written by David Love and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-25 with Computers categories.
Leverage the power of Python and its de facto GUI framework to build highly interactive interfaces Key Features The fundamentals of Python and GUI programming with Tkinter. Create multiple cross-platform projects by integrating a host of third-party libraries and tools. Build beautiful and highly-interactive user interfaces that target multiple devices. Book Description Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs. What you will learn Create a scrollable frame via theCanvas widget Use the pack geometry manager andFrame widget to control layout Learn to choose a data structurefor a game Group Tkinter widgets, such asbuttons, canvases, and labels Create a highly customizablePython editor Design and lay out a chat window Who this book is for This book is for beginners to GUI programming who haven’t used Tkinter yet and are eager to start building great-looking and user-friendly GUIs. Prior knowledge of Python programming is expected.
Tkinter Gui Application Development Cookbook
DOWNLOAD
Author : Alejandro Rodas de Paz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Tkinter Gui Application Development Cookbook written by Alejandro Rodas de Paz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Computers categories.
As one of the more versatile programming languages, Python is well-known for its batteries-included philosophy, which includes a rich set of modules in its standard library; Tkinter is the library included for building desktop applications. Due to this, Tkinter is a common choice for rapid GUI development, and more complex applications can ...
Tkinter Gui Application Development Blueprints
DOWNLOAD
Author : Bhaskar Chaudhary
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-11-30
Tkinter Gui Application Development Blueprints written by Bhaskar Chaudhary and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-30 with Computers categories.
Master GUI programming in Tkinter as you design, implement, and deliver ten real-world applications from start to finish About This Book Conceptualize and build state-of-art GUI applications with Tkinter Tackle the complexity of just about any size GUI application with a structured and scalable approach A project-based, practical guide to get hands-on into Tkinter GUI development Who This Book Is For Software developers, scientists, researchers, engineers, students, or programming hobbyists with basic familiarity in Python will find this book interesting and informative. People familiar with basic programming constructs in other programming language can also catch up with some brief reading on Python. No GUI programming experience is expected. What You Will Learn Get to know the basic concepts of GUI programming, such as Tkinter top-level widgets, geometry management, event handling, using callbacks, custom styling, and dialogs Create apps that can be scaled in size or complexity without breaking down the core Write your own GUI framework for maximum code reuse Build apps using both procedural and OOP styles, understanding the strengths and limitations of both styles Learn to structure and build large GUI applications based on Model-View-Controller (MVC) architecture Build multithreaded and database-driven apps Create apps that leverage resources from the network Learn basics of 2D and 3D animation in GUI applications Develop apps that can persist application data with object serialization and tools such as configparser In Detail Tkinter is the built-in GUI package that comes with standard Python distributions. It is a cross-platform package, which means you build once and deploy everywhere. It is simple to use and intuitive in nature, making it suitable for programmers and non-programmers alike. This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, media player, drawing application, chat application, screen saver, port scanner, and many more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database driven programs and more. You will also get to know the modern best practices involved in writing GUI apps. With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice. Style and approach An easy-to-follow guide, full of hands-on examples of real-world GUI programs. The first chapter is a must read as it explains most of the things you need to get started with writing GUI programs with Tkinter. Each subsequent chapter is a stand-alone project that discusses some aspects of GUI programming in detail. These chapters can be read sequentially or randomly depending upon the readers experience with Python.
Building Modern Guis With Tkinter And Python
DOWNLOAD
Author : Saurabh Chandrakar
language : en
Publisher: BPB Publications
Release Date : 2023-06-28
Building Modern Guis With Tkinter And Python written by Saurabh Chandrakar and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-28 with Computers categories.
Learn how to create stunning user interfaces using the tkinter Python library KEY FEATURES ● Explore the art of presenting information effectively using display widgets like labels, text boxes, images, and buttons. ● Delve into advanced topics like working with images, canvas drawing, database interactions, and handling multiple windows. ● Develop the skills to build professional and user-friendly GUI applications, regardless of your level of experience. DESCRIPTION Are you looking to create stunning graphical user interfaces (GUIs) using Python? Look no further. This comprehensive guide will take you on a journey through the powerful capabilities of tkinter, Python's standard GUI library. This comprehensive guide explores the power of Python's tkinter library. This book covers various classes of GUI widgets, including buttons, input fields, displays, containers, and item widgets. It teaches you how to create interactive and visually appealing user interfaces, handle file selection, gather widget information, and trace changes. Additionally, it includes a hands-on project on creating a user login system using tkinter and sqlite3 database. Whether you're a beginner or an experienced developer, this book will empower you to build professional and intuitive GUI applications effortlessly. By the end of the book, you will have gained knowledge and skills in creating modern user interfaces using the tkinter Python library. WHAT YOU WILL LEARN ● Gain a solid understanding of the various classes for GUI widgets in tkinter. ● Learn how to create dynamic and interactive buttons that respond to user input and perform actions. ● Explore different layout management options in tkinter. ● Discover how to create dialogs and message boxes using the tkinter library. ● Learn how to use trace mechanisms to monitor and respond to changes in your GUI applications. WHO THIS BOOK IS FOR This book is suitable for a wide range of individuals, including engineering and science students at the diploma, undergraduate, and postgraduate levels. It also caters to programming and software professionals, as well as students in grades 8 to 12 studying under CBSE or state boards. Additionally, GUI and .Net engineers will find value in the book's content. TABLE OF CONTENTS 1. tkinter Introduction 2. Inbuilt Variable Classes for Python tkinter GUI Widgets 3. Getting Insights of Button Widgets in tkinter 4. Getting Insights of Input Widgets in tkinter 5. Getting Insights of Display Widgets in tkinter 6. Getting Insights of Container Widgets in tkinter 7. Getting Insights of Item Widgets in tkinter 8. Getting Insights of tkinter User Interactive Widgets 9. Handling File Selection in tkinter 10. Getting Widget Information and Trace in tkinter 11. UserLogin Project in tkinter GUI Library with sqlite3 Database
Tkinter Gui Application Development Blueprints Second Edition
DOWNLOAD
Author : Bhaskar Chaudhary
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-20
Tkinter Gui Application Development Blueprints Second Edition written by Bhaskar Chaudhary and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-20 with Computers categories.
Geometry Management, Event Handling, and more Key Features A Practical, guide to learn the application of Python and GUI programming with tkinter Create multiple cross-platform real-world projects by integrating host of third party libraries and tools Learn to build beautiful and highly interactive user interfaces, targeting multiple devices. Book Description Tkinter is the built-in GUI package that comes with standard Python distributions. It is a cross-platform package, which means you build once and deploy everywhere. It is simple to use and intuitive in nature, making it suitable for programmers and non-programmers alike. This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, audio player, drawing application, piano tutor, chat application, screen saver, port scanner, and much more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database-driven programs, asyncio based programming and more. You will also get to know the modern best practices involved in writing GUI apps. With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice. What you will learn -A Practical, guide to help you learn the application of Python and GUI programming with Tkinter - Create multiple, cross-platform, real-world projects by integrating a host of third-party libraries and tools - Learn to build beautiful and highly interactive user interfaces, targeting multiple devices. Who this book is for This book is for a beginner to intermediate-level Pythonists who want to build modern, cross-platform GUI applications with the amazingly powerful Tkinter. Prior knowledge of Tkinter is required.
Frame Analysis And Processing In Digital Video Using Python And Tkinter
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2024-03-27
Frame Analysis And Processing In Digital Video 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 2024-03-27 with Computers categories.
The first project in chapter one which is Canny Edge Detector presented here is a graphical user interface (GUI) application built using Tkinter in Python. This application allows users to open video files (of formats like mp4, avi, or mkv) and view them along with their corresponding Canny edge detection frames. The application provides functionalities such as playing, pausing, stopping, navigating through frames, and jumping to specific times within the video. Upon opening the application, users are greeted with a clean interface comprising two main sections: the video display panel and the control panel. The video display panel consists of two canvas widgets, one for displaying the original video and another for displaying the Canny edge detection result. These canvases allow users to visualize the video and its corresponding edge detection in real-time. The control panel houses various buttons and widgets for controlling the video playback and interaction. Users can open video files using the "Open Video" button, select a zoom scale for viewing convenience, jump to specific times within the video, play/pause the video, stop the video, navigate through frames, and even open another instance of the application for simultaneous use. The core functionality lies in the methods responsible for displaying frames and performing Canny edge detection. The show_frame() method retrieves frames from the video, resizes them based on the selected zoom scale, and displays them on the original video canvas. Similarly, the show_canny_frame() method applies the Canny edge detection algorithm to the frames, enhances the edges using dilation, and displays the resulting edge detection frames on the corresponding canvas. The application also supports mouse interactions such as dragging to pan the video frames within the canvas and scrolling to navigate through frames. These interactions are facilitated by event handling methods like on_press(), on_drag(), and on_scroll(), ensuring smooth user experience and intuitive control over video playback and exploration. Overall, this project provides a user-friendly platform for visualizing video content and exploring Canny edge detection results, making it valuable for educational purposes, research, or practical applications involving image processing and computer vision. This second project in chapter one implements a graphical user interface (GUI) application for performing edge detection using the Prewitt operator on videos. The purpose of the code is to provide users with a tool to visualize videos, apply the Prewitt edge detection algorithm, and interactively control playback and visualization parameters. The third project in chapter one which is "Sobel Edge Detector" is implemented in Python using Tkinter and OpenCV serves as a graphical user interface (GUI) for viewing and analyzing videos with real-time Sobel edge detection capabilities. The "Frei-Chen Edge Detection" project as fourth project in chapter one is a graphical user interface (GUI) application built using Python and the Tkinter library. The application is designed to process and visualize video files by detecting edges using the Frei-Chen edge detection algorithm. The core functionality of the application lies in the implementation of the Frei-Chen edge detection algorithm. This algorithm involves convolving the video frames with predefined kernels to compute the gradient magnitude, which represents the strength of edges in the image. The resulting edge-detected frames are thresholded to convert grayscale values to binary values, enhancing the visibility of edges. The application also includes features for user interaction, such as mouse wheel scrolling to zoom in and out, click-and-drag functionality to pan across the video frames, and input fields for jumping to specific times within the video. Additionally, users have the option to open multiple instances of the application simultaneously to analyze different videos concurrently, providing flexibility and convenience in video processing tasks. Overall, the "Frei-Chen Edge Detection" project offers a user-friendly interface for edge detection in videos, empowering users to explore and analyze visual data effectively. The "KIRSCH EDGE DETECTOR" project as the fifth project in chapter one is a Python application built using Tkinter, OpenCV, and NumPy libraries for performing edge detection on video files. It handles the visualization of the edge-detected frames in real-time. It retrieves the current frame from the video, applies Gaussian blur for noise reduction, performs Kirsch edge detection, and applies thresholding to obtain the binary edge image. The processed frame is then displayed on the canvas alongside the original video. This "SCHARR EDGE DETECTOR" as the sixth project in chapter one is creating a graphical user interface (GUI) to visualize edge detection in videos using the Scharr algorithm. It allows users to open video files, play/pause video playback, navigate frame by frame, and apply Scharr edge detection in real-time. The GUI consists of multiple components organized into panels. The main panel displays the original video on the left side and the edge-detected video using the Scharr algorithm on the right side. Both panels utilize Tkinter Canvas widgets for efficient rendering and manipulation of video frames. Users can interact with the application using control buttons located in the control panel. These buttons include options to open a video file, adjust the zoom scale, jump to a specific time in the video, play/pause video playback, stop the video, navigate to the previous or next frame, and open another instance of the application for parallel video analysis. The core functionality of the application lies in the VideoScharr class, which encapsulates methods for video loading, playback control, frame processing, and edge detection using the Scharr algorithm. The apply_scharr method implements the Scharr edge detection algorithm, applying a pair of 3x3 convolution kernels to compute horizontal and vertical derivatives of the image and then combining them to calculate the edge magnitude. Overall, the "SCHARR EDGE DETECTOR" project provides users with an intuitive interface to explore edge detection techniques in videos using the Scharr algorithm. It combines the power of image processing libraries like OpenCV and the flexibility of Tkinter for creating interactive and responsive GUI applications in Python. The first project in chapter two is designed to provide a user-friendly interface for processing video frames using Gaussian filtering techniques. It encompasses various components and functionalities tailored towards efficient video analysis and processing. The GaussianFilter Class serves as the backbone of the application, managing GUI initialization and video processing functionalities. The GUI layout is constructed with Tkinter widgets, comprising two main panels for video display and control buttons. Key functionalities include opening video files, controlling playback, adjusting zoom levels, navigating frames, and interacting with video frames via mouse events. Additionally, users can process frames using OpenCV for Gaussian filtering to enhance video quality and reduce noise. Time navigation functionality allows users to jump to specific time points in the video. Moreover, the application supports multiple instances for simultaneous video analysis in independent windows. Overall, this project offers a comprehensive toolset for video analysis and processing, empowering users with an intuitive interface and diverse functionalities. The second project in chapter two presents a Tkinter application tailored for video frame filtering utilizing a mean filter. It offers comprehensive functionalities including opening, playing/pausing, and stopping video playback, alongside options to navigate to previous and next frames, jump to specified times, and adjust zoom scale. Displayed on separate canvases, the original and filtered video frames are showcased distinctly. Upon video file opening, the application utilizes imageio.get_reader() for video reading, while play_video() and play_filtered_video() methods handle frame display. Individual frame rendering is managed by show_frame() and show_mean_frame(), incorporating noise addition through the add_noise() method. Mouse wheel scrolling, canvas dragging, and scrollbar scrolling are facilitated through event handlers, enhancing user interaction. Supplementary functionalities include time navigation, frame navigation, and the ability to open multiple instances using open_another_player(). The main() function initializes the Tkinter application and executes the event loop for GUI display. The third project in chapter two aims to develop a user-friendly graphical interface application for filtering video frames with a median filter. Supporting various video formats like MP4, AVI, and MKV, users can seamlessly open, play, pause, stop, and navigate through video frames. The key feature lies in real-time application of the median filter to enhance frame quality by noise reduction. Upon video file opening, the original frames are displayed alongside filtered frames, with users empowered to control zoom levels and frame navigation. Leveraging libraries such as tkinter, imageio, PIL, and OpenCV, the application facilitates efficient video analysis and processing, catering to diverse domains like surveillance, medical imaging, and scientific research. The fourth project in chapter two exemplifies the utilization of a bilateral filter within a Tkinter-based graphical user interface (GUI) for real-time video frame filtering. The script showcases the application of bilateral filtering, renowned for its ability to smooth images while preserving edges, to enhance video frames. The GUI integrates two main components: canvas panels for displaying original and filtered frames, facilitating interactive viewing and manipulation. Upon video file opening, original frames are displayed on the left panel, while bilateral-filtered frames appear on the right. Adjustable parameters within the bilateral filter method enable fine-tuning for noise reduction and edge preservation based on specific video characteristics. Control functionalities for playback, frame navigation, zoom scaling, and time jumping enhance user interaction, providing flexibility in exploring diverse video filtering techniques. Overall, the script offers a practical demonstration of bilateral filtering in real-time video processing within a Tkinter GUI, enabling efficient exploration of filtering methodologies. The fifth project in chapter two integrates a video player application with non-local means denoising functionality, utilizing tkinter for GUI design, PIL for image processing, imageio for video file reading, and OpenCV for denoising. The GUI, set up by the NonLocalMeansDenoising class, includes controls for playback, zoom, time navigation, and frame browsing, alongside features like mouse wheel scrolling and dragging for user interaction. Video loading and display are managed through methods like open_video and play_video(), which iterate through frames, resize them, and add noise for display on the canvas. Non-local means denoising is applied using the apply_non_local_denoising() method, enhancing frames before display on the filter canvas via show_non_local_frame(). The GUI fosters user interaction, offering controls for playback, zoom, time navigation, and frame browsing, while also ensuring error handling for seamless operation during video loading, processing, and denoising. The sixth project in chapter two provides a platform for filtering video frames using anisotropic diffusion. Users can load various video formats and control playback (play, pause, stop) while adjusting zoom levels and jumping to specific timestamps. Original video frames are displayed alongside filtered versions achieved through anisotropic diffusion, aiming to denoise images while preserving critical edges and structures. Leveraging OpenCV and imageio for image processing and PIL for manipulation tasks, the application offers a user-friendly interface with intuitive control buttons and multi-video instance support, facilitating efficient analysis and enhancement of video content through anisotropic diffusion-based filtering. The seventh project in chapter two is built with Tkinter and OpenCV for filtering video frames using the Wiener filter. It offers a user-friendly interface for opening video files, controlling playback, adjusting zoom levels, and applying the Wiener filter for noise reduction. With separate panels for displaying original and filtered video frames, users can interact with the frames via zooming, scrolling, and dragging functionalities. The application handles video processing internally by adding random noise to frames and applying the Wiener filter, ensuring enhanced visual quality. Overall, it provides a convenient tool for visualizing and analyzing videos while showcasing the effectiveness of the Wiener filter in image processing tasks. The first project in chapter three showcases optical flow observation using the Lucas-Kanade method. Users can open video files, play, pause, and stop them, adjust zoom levels, and jump to specific frames. The interface comprises two panels for original video display and optical flow results. With functionalities like frame navigation, zoom adjustment, and time-based jumping, users can efficiently analyze optical flow patterns. The Lucas-Kanade algorithm computes optical flow between consecutive frames, visualized as arrows and points, allowing users to observe directional changes and flow strength. Mouse wheel scrolling facilitates zoom adjustments for detailed inspection or broader perspective viewing. Overall, the application provides intuitive navigation and robust optical flow analysis tools for effective video observation. The second project in chapter three is designed to visualize optical flow with Kalman filtering. It features controls for video file manipulation, frame navigation, zoom adjustment, and parameter specification. The application provides side-by-side canvases for displaying original video frames and optical flow results, allowing users to interact with the frames and explore flow patterns. Internally, it employs OpenCV and NumPy for optical flow computation using the Farneback method, enhancing stability and accuracy with Kalman filtering. Overall, it offers a user-friendly interface for analyzing video data, benefiting fields like computer vision and motion tracking. The third project in chapter three is for optical flow analysis in videos using Gaussian pyramid techniques. Users can open video files and visualize optical flow between consecutive frames. The interface presents two panels: one for original video frames and the other for computed optical flow. Users can adjust zoom levels and specify optical flow parameters. Control buttons enable common video playback actions, and multiple instances can be opened for simultaneous analysis. Internally, OpenCV, Tkinter, and imageio libraries are used for video processing, GUI development, and image manipulation, respectively. Optical flow computation relies on the Farneback method, with resulting vectors visualized on the frames to reveal motion patterns.
Digital Video Processing Projects Using Python And Tkinter
DOWNLOAD
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.