Advanced Scratch Programming
DOWNLOAD
Download Advanced Scratch Programming PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Advanced Scratch Programming 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
Advanced Scratch Programming
DOWNLOAD
Author : Abhay B. Joshi
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-08-15
Advanced Scratch Programming written by Abhay B. Joshi and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-15 with Computer games categories.
"Abhay views computer programming not just as a useful skill to build careers, but also as a "powerful medium for learning" in which students actively engage in a creative, entertaining, and intellectually challenging pursuit. Abhay has been conducting Scratch classes for middle and high school students since 2007"--Page 4 of cover
Scratch Programming
DOWNLOAD
Author : Shalonda Sprinzl
language : en
Publisher:
Release Date : 2021-07-15
Scratch Programming written by Shalonda Sprinzl and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with categories.
Scratch is the computer programming language that makes it easy and fun to create interactive stories, games and animations This book will provide students Scratch and CS concepts, the disign process, profects management, and much more. The book would be a great resource for teachers who teach Scratch programming. They could use the projects to teach advanced tricks of programming and to show how complex programs are designed.
Learn Scratch By Reading And Analysing Projects
DOWNLOAD
Author : Claudia Yao
language : en
Publisher:
Release Date : 2020-09-21
Learn Scratch By Reading And Analysing Projects written by Claudia Yao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-21 with categories.
This book is written to support those who would like to touch the high ceiling of Scratch programming and to understand how to develop challenging projects on Scratch. I will concentrate on the use of List, Clone and Pen functionalities by analyzing a few sample projects in depth. Through combining those features artfully, we could realize some amazing functionalities.
Scratch By Example
DOWNLOAD
Author : Eduardo A. Vlieg
language : en
Publisher: Apress
Release Date : 2016-09-12
Scratch By Example written by Eduardo A. Vlieg and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-12 with Computers categories.
This is a book about learning the Scratch language so that you can use it in teaching and other instructional situations. The book explains the visual nature of the language, showing you how to write programs by dragging and dropping visual blocks representing common compute operations. Scratch is visual language that even young children can master. and makes computer programming as easy as dragging and dropping graphical blocks that represent programming commands, eliminating the traditional stumbling blocks of typing and syntax errors. With a drag-and-drop interface that runs in any web browser, and on devices from iPads to PCs to Macs to Microsoft Surface tablets, Scratch is an easily accessible way to enter the world of computer programming. This book teaches how to use Scratch in a fun and simple way that relies on examples and learning by doing. Progressing from simple three-block scripts that move a character across the screen to complex projects that involve motion, sound, and user input, this book: Imparts a thorough understanding of the Scratch interface. Shows how to create a range of Scratch projects, including simple games. Builds a solid foundation for future programming in other languages What You Will Learn Navigate the Scratch interface Create sprites and backdrops Learn programming skills good in all languages Program simple games and animations Share programs with friends worldwide Who This Book Is For Scratch for Absolute Beginners is intended for complete beginners to the world of computer programming and the Scratch language. Learning to program in Scratch is an easy and fun way for anybody seven years and older to learn about computer programming. Scratch's drag-and-drop interface in a web browsermakes the book easy and accessible to young children and adults alike.
Scratch Programming Playground
DOWNLOAD
Author : Al Sweigart
language : en
Publisher: No Starch Press
Release Date : 2016-09-16
Scratch Programming Playground written by Al Sweigart and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-16 with Juvenile Nonfiction categories.
Scratch, the colorful drag-and-drop programming language, is used by millions of first-time learners, and in Scratch Programming Playground, you’ll learn to program by making cool games. Get ready to destroy asteroids, shoot hoops, and slice and dice fruit! Each game includes easy-to-follow instructions, review questions, and creative coding challenges to make the game your own. Want to add more levels or a cheat code? No problem, just write some code. You’ll learn to make games like: –Maze Runner: escape the maze! –Snaaaaaake: gobble apples and avoid your own tail –Asteroid Breaker: smash space rocks –Fruit Slicer: a Fruit Ninja clone –Brick Breaker: a remake of Breakout, the brick-breaking classic –Platformer: a game inspired by Super Mario Bros. Learning how to program shouldn’t be dry and dreary. With Scratch Programming Playground, you’ll make a game of it! Uses Scratch 2
E Learning And Games For Training Education Health And Sports
DOWNLOAD
Author : Stefan Göbel
language : en
Publisher: Springer
Release Date : 2012-09-04
E Learning And Games For Training Education Health And Sports written by Stefan Göbel and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-04 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Conference on E-Learning and Games, Edutainment 2012, held in conjunction with the 3rd International Conference on Serious Games for Training, Education, Health and Sports, GameDays 2012, held in Darmstadt, Germany, in September 2012. The 21 full papers presented were carefully reviewed and selected for inclusion in this book. They are organized in topical sections named: game-based training; game-based teaching and learning; emerging learning and gaming technologies; authoring tools and mechanisms; and serious games for health.
Teaching And Learning In A Digital World
DOWNLOAD
Author : Michael E. Auer
language : en
Publisher: Springer
Release Date : 2017-12-26
Teaching And Learning In A Digital World written by Michael E. Auer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-26 with Technology & Engineering categories.
This book gathers the Proceedings of the 20th International Conference on Interactive Collaborative Learning (ICL2017), held in Budapest, Hungary on 27–29 September 2017. The authors are currently witnessing a significant transformation in the development of education. The impact of globalisation on all areas of human life, the exponential acceleration of technological developments and global markets, and the need for flexibility and agility are essential and challenging elements of this process that have to be tackled in general, but especially in engineering education. To face these current real-world challenges, higher education has to find innovative ways to quickly respond to them. Since its inception in 1998, this conference has been devoted to new approaches in learning with a focus on collaborative learning. Today the ICL conferences offer a forum for exchange concerning relevant trends and research results, and for sharing practical experience gained while developing and testing elements of new technologies and pedagogies in the learning context.
Advanced Video Processing Projects With Python And Tkinter
DOWNLOAD
Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2024-05-27
Advanced Video Processing Projects With 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 2024-05-27 with Computers categories.
The book focuses on developing Python-based GUI applications for video processing and analysis, catering to various needs such as object tracking, motion detection, and frame analysis. These applications utilize libraries like Tkinter for GUI development and OpenCV for video processing, offering user-friendly interfaces with interactive controls. They provide functionalities like video playback, frame navigation, ROI selection, filtering, and histogram analysis, empowering users to perform detailed analysis and manipulation of video content. Each project tackles specific aspects of video analysis, from simplifying video processing tasks through a graphical interface to implementing advanced algorithms like Lucas-Kanade, Kalman filter, and Gaussian pyramid optical flow for optical flow computation and object tracking. Moreover, they integrate features like MD5 hashing for video integrity verification and filtering techniques such as bilateral filtering, anisotropic diffusion, and denoising for enhancing video quality and analysis accuracy. Overall, these projects demonstrate the versatility and effectiveness of Python in developing comprehensive tools for video analysis, catering to diverse user needs in fields like computer vision, multimedia processing, forensic analysis, and content verification. The first project aims to simplify video processing tasks through a user-friendly graphical interface, allowing users to execute various operations like filtering, edge detection, hashing, motion analysis, and object tracking effortlessly. The process involves setting up the GUI framework using tkinter, adding descriptive titles and containers for buttons, defining button actions to execute Python scripts, and dynamically generating buttons for organized presentation. Functionalities cover a wide range of video processing tasks, including frame operations, motion analysis, and object tracking. Users interact by launching the application, selecting an operation, and viewing results. Advantages include ease of use, organized access to functionalities, and extensibility for adding new tasks. Overall, this project bridges Python scripting with a user-friendly interface, democratizing advanced video processing for a broader audience. The second project aims to develop a video player application with advanced frame analysis functionalities, allowing users to open video files, navigate frames, and analyze them extensively. The application, built using tkinter, features a canvas for video display with zoom and drag capabilities, playback controls, and frame extraction options. Users can jump to specific times, extract frames for analysis, and visualize RGB histograms while calculating MD5 hash values for integrity verification. Additionally, users can open multiple instances of the player for parallel analysis. Overall, this tool caters to professionals in forensic analysis, video editing, and educational fields, facilitating comprehensive frame-by-frame examination and evaluation. The third project is a robust Python tool tailored for video frame analysis and filtering, employing Tkinter for the GUI. Users can effortlessly load, play, and dissect video files frame by frame, with options to extract frames, implement diverse filtering techniques, and visualize color channel histograms. Additionally, it computes and exhibits hash values for extracted frames, facilitating frame comparison and verification. With an array of functionalities, including OpenCV integration for image processing and filtering, alongside features like wavelet transform and denoising algorithms, this application is a comprehensive solution for users requiring intricate video frame scrutiny and manipulation. The fourth project is a robust application designed for edge detection on video frames, featuring a Tkinter-based GUI for user interaction. It facilitates video loading, frame navigation, and application of various edge detection algorithms, alongside offering analyses like histograms and hash values. With functionalities for frame extraction, edge detection selection, and interactive zooming, the project provides a comprehensive solution for users in fields requiring detailed video frame analysis and processing, such as computer vision and multimedia processing. The fifth project presents a sophisticated graphical application tailored for video frame processing and MD5 hashing. It offers users a streamlined interface to load videos, inspect individual frames, and compute hash values, crucial for tasks like video forensics and integrity verification. Utilizing Python libraries such as Tkinter, PIL, and moviepy, the project ensures efficient video handling, metadata extraction, and histogram visualization, providing a robust solution for diverse video analysis needs. With its focus on frame-level hashing and extensible architecture, the project stands as a versatile tool adaptable to various applications in video analysis and content verification. The sixth project presents a robust graphical tool designed for video analysis and frame extraction. By leveraging Python and key libraries like Tkinter, PIL, and imageio, users can effortlessly open videos, visualize frames, and extract specific frames for analysis. Notably, the application computes hash values using eight different algorithms, including MD5, SHA-1, and SHA-256, enhancing its utility for tasks such as video forensics and integrity verification. With features like frame zooming, navigation controls, and support for multiple instances, this project offers a versatile platform for comprehensive video analysis, catering to diverse user needs in fields like content authentication and forensic investigation. The seventh project offers a graphical user interface (GUI) for computing hash values of video files, ensuring their integrity and authenticity through multiple hashing algorithms. Key features include video playback controls, hash computation using algorithms like MD5, SHA-1, and SHA-256, and displaying and saving hash values for reference. Users can open multiple instances to handle different videos simultaneously. The tool is particularly useful in digital forensics, data verification, and content security, providing a user-friendly interface and robust functionalities for reliable video content verification. The eighth project aims to develop a GUI application that lets users interact with video files through various controls, including play, pause, stop, frame navigation, and time-specific jumps. It also offers features like zooming, noise reduction via a mean filter, and the ability to open multiple instances. Users can load videos, adjust playback, apply filters, and handle video frames dynamically, enhancing video viewing and manipulation. The ninth project aims to develop a GUI application for filtering video frames using anisotropic diffusion, allowing users to load videos, apply the filter, and interact with the frames. The core component, AnisotropicDiffusion, handles video processing and GUI interactions. Users can control playback, zoom, and navigate frames, with the ability to apply the filter dynamically. The GUI features panels for video display, control buttons, and supports multiple instances. Event handlers enable smooth interaction, and real-time updates reflect changes in playback and filtering. The application is designed for efficient memory use, intuitive controls, and a responsive user experience. The tenth project involves creating a GUI application that allows users to filter video frames using a bilateral filter. Users can load video files, apply the filter, and interact with the filtered frames. The BilateralFilter class handles video processing and GUI interactions, initializing attributes like the video source and GUI elements. The GUI includes panels for displaying video frames and control buttons for opening files, playback, zoom, and navigation. Users can control playback, zoom, pan, and apply the filter dynamically. The application supports multiple instances, efficient rendering, and real-time updates, ensuring a responsive and user-friendly experience. The twelfth project involves creating a GUI application for filtering video frames using the Non-Local Means Denoising technique. The NonLocalMeansDenoising class manages video processing and GUI interactions, initializing attributes like video source, frame index, and GUI elements. Users can load video files, apply the denoising filter, and interact with frames through controls for playback, zoom, and navigation. The GUI supports multiple instances, allowing users to compare videos. Efficient rendering ensures smooth playback, while adjustable parameters fine-tune the filter's performance. The application maintains aspect ratios, handles errors, and provides feedback, prioritizing a seamless user experience. The thirteenth performs Canny edge detection on video frames. It allows users to load video files, view original frames, and see Canny edge-detected results side by side. The VideoCanny class handles video processing and GUI interactions, initializing necessary attributes. The interface includes panels for video display and control buttons for loading videos, adjusting zoom, jumping to specific times, and controlling playback. Users can also open multiple instances for comparing videos. The application ensures smooth playback and real-time edge detection with efficient rendering and robust error handling. The fourteenth project is a GUI application built with Tkinter and OpenCV for real-time edge detection in video streams using the Kirsch algorithm. The main class, VideoKirsch, initializes the GUI components, providing features like video loading, frame display, zoom control, playback control, and Kirsch edge detection. The interface displays original and edge-detected frames side by side, with control buttons for loading videos, adjusting zoom, jumping to specific times, and controlling playback. Users can play, pause, stop, and navigate through video frames, with real-time edge detection and dynamic frame updates. The application supports multiple instances for comparing videos, employs efficient rendering for smooth playback, and includes robust error handling. Overall, it offers a user-friendly tool for real-time edge detection in videos. The fifteenth project is a Python-based GUI application for computing and visualizing optical flow in video streams using the Lucas-Kanade method. Utilizing tkinter, PIL, imageio, OpenCV, and numpy, it features panels for original and optical flow-processed frames, control buttons, and adjustable parameters. The VideoOpticalFlow class handles video loading, playback, optical flow computation, and error handling. The GUI allows smooth video playback, zooming, time jumping, and panning. Optical flow is visualized in real-time, showing motion vectors. Users can open multiple instances to analyze various videos simultaneously, making this tool valuable for computer vision and video analysis tasks. The sixteenth project is a Python application designed to analyze optical flow in video streams using the Kalman filter method. It utilizes libraries such as tkinter, PIL, imageio, OpenCV, and numpy to create a GUI, process video frames, and implement the Kalman filter algorithm. The VideoKalmanOpticalFlow class manages video loading, playback control, optical flow computation, canvas interactions, and Kalman filter implementation. The GUI layout features panels for original and optical flow-processed frames, along with control buttons and widgets for adjusting parameters. Users can open video files, control playback, and visualize optical flow in real-time, with the Kalman filter improving accuracy by incorporating temporal dynamics and reducing noise. Error handling ensures a robust experience, and multiple instances can be opened for simultaneous video analysis, making this tool valuable for computer vision and video analysis tasks. The seventeenth project is a Python application designed to analyze optical flow in video streams using the Gaussian pyramid method. It utilizes libraries such as tkinter, PIL, imageio, OpenCV, and numpy to create a GUI, process video frames, and implement optical flow computation. The VideoGaussianPyramidOpticalFlow class manages video loading, playback control, optical flow computation, canvas interactions, and GUI creation. The GUI layout features panels for original and optical flow-processed frames, along with control buttons and widgets for adjusting parameters. Users can open video files, control playback, and visualize optical flow in real-time, providing insights into motion patterns within the video stream. Error handling ensures a robust user experience, and multiple instances can be opened for simultaneous video analysis. The eighteenth project is a Python application developed for tracking objects in video streams using the Lucas-Kanade optical flow algorithm. It utilizes libraries like tkinter, PIL, imageio, OpenCV, and numpy to create a GUI, process video frames, and implement tracking functionalities. The ObjectTrackingLucasKanade class manages video loading, playback control, object tracking, GUI creation, and event handling. The GUI layout includes a video display panel with a canvas widget for showing video frames and a list box for displaying tracked object coordinates. Users interact with the video by defining bounding boxes around objects for tracking. The application provides buttons for opening video files, adjusting zoom, controlling playback, and clearing object tracking data. Error handling ensures a smooth user experience, making it suitable for various computer vision and video analysis tasks. The nineteenth project is a Python application utilizing Tkinter to create a GUI for analyzing RGB histograms of video frames. It features the Filter_CroppedFrame class, initializing GUI elements like buttons and canvas for video display. Users can open videos, control playback, and navigate frames. Zooming is enabled, and users can draw bounding boxes for RGB histogram analysis. Filters like Gaussian, Mean, and Bilateral Filtering can be applied, with histograms displayed for the filtered image. Multiple instances of the GUI can be opened simultaneously. The project offers a user-friendly interface for image analysis and enhancement. The twentieth project creates a graphical user interface (GUI) for motion analysis using the Block-based Gradient Descent Search (BGDS) optical flow algorithm. It initializes the VideoBGDSOpticalFlow class, setting up attributes and methods for video display, control buttons, and parameter input fields. Users can open videos, control playback, specify parameters, and analyze optical flow motion vectors between consecutive frames. The GUI provides an intuitive interface for efficient motion analysis tasks, enhancing user interaction with video playback controls and optical flow visualization tools. The twenty first project is a Python project that constructs a graphical user interface (GUI) for optical flow analysis using the Diamond Search Algorithm (DSA). It initializes a VideoFSBM_DSAOpticalFlow class, setting up attributes for video display, control buttons, and parameter input fields. Users can open videos, control playback, specify algorithm parameters, and visualize optical flow motion vectors efficiently. The GUI layout includes canvas widgets for displaying the original video and optical flow result, with interactive functionalities such as zooming and navigating between frames. The script provides an intuitive interface for optical flow analysis tasks, enhancing user interaction and visualization capabilities. The twenty second project "Object Tracking with Block-based Gradient Descent Search (BGDS)" demonstrates object tracking in videos using a block-based gradient descent search algorithm. It utilizes tkinter for GUI development, PIL for image processing, imageio for video file handling, and OpenCV for computer vision tasks. The main class, ObjectTracking_BGDS, initializes the GUI window and implements functionalities such as video playback control, frame navigation, and object tracking using the BGDS algorithm. Users can interactively select a bounding box around the object of interest for tracking, and the application provides parameter inputs for algorithm adjustment. Overall, it offers a user-friendly interface for motion analysis tasks, showcasing the application of computer vision techniques in object tracking. The tenty third project "Object Tracking with AGAST (Adaptive and Generic Accelerated Segment Test)" is a Python application tailored for object tracking in videos via the AGAST algorithm. It harnesses libraries like tkinter, PIL, imageio, and OpenCV for GUI, image processing, video handling, and computer vision tasks respectively. The main class, ObjectTracking_AGAST, orchestrates the GUI setup, featuring buttons for video control, a combobox for zoom selection, and a canvas for displaying frames. The pivotal agast_vectors method employs OpenCV's AGAST feature detector to compute motion vectors between frames. The track_object method utilizes AGAST for object tracking within specified bounding boxes. Users can interactively select objects for tracking, making it a user-friendly tool for motion analysis tasks. The twenty fourth project "Object Tracking with AKAZE (Accelerated-KAZE)" offers a user-friendly Python application for real-time object tracking within videos, leveraging the efficient AKAZE algorithm. Its tkinter-based graphical interface features a Video Display Panel for live frame viewing, Control Buttons Panel for playback management, and Zoom Scale Combobox for precise zoom adjustment. With the ObjectTracking_AKAZE class at its core, the app facilitates seamless video playback, AKAZE-based object tracking, and interactive bounding box selection. Users benefit from comprehensive tracking insights provided by the Center Coordinates Listbox, ensuring accurate and efficient object monitoring. Overall, it presents a robust solution for dynamic object tracking, integrating advanced computer vision techniques with user-centric design. The twenty fifth project "Object Tracking with BRISK (Binary Robust Invariant Scalable Keypoints)" delivers a sophisticated Python application tailored for real-time object tracking in videos. Featuring a tkinter-based GUI, it offers intuitive controls and visualizations to enhance user experience. Key elements include a Video Display Panel for live frame viewing, a Control Buttons Panel for playback management, and a Center Coordinates Listbox for tracking insights. Powered by the ObjectTracking_BRISK class, the application employs the BRISK algorithm for precise tracking, leveraging features like zoom adjustment and interactive bounding box selection. With robust functionalities like frame navigation and playback control, coupled with a clear interface design, it provides users with a versatile tool for analyzing object movements in videos effectively. The twenty sixth project "Object Tracking with GLOH" is a Python application designed for video object tracking using the Gradient Location-Orientation Histogram (GLOH) method. Featuring a Tkinter-based GUI, users can load videos, navigate frames, and visualize tracking outcomes seamlessly. Key functionalities include video playback control, bounding box initialization via mouse events, and dynamic zoom scaling. With OpenCV handling computer vision tasks, the project offers precise object tracking and real-time visualization, demonstrating the effective integration of advanced techniques with an intuitive user interface for enhanced usability and analysis. The twenty seventh project "boosting_tracker.py" is a Python-based application utilizing Tkinter for its GUI, designed for object tracking in videos via the Boosting Tracker algorithm. Its interface, titled "Object Tracking with Boosting Tracker," allows users to load videos, navigate frames, define tracking regions, apply filters, and visualize histograms. The core class, "BoostingTracker," manages video operations, object tracking, and filtering. The GUI features controls like play/pause buttons, zoom scale selection, and filter options. Object tracking begins with user-defined bounding boxes, and the application supports various filters for enhancing video regions. Histogram analysis provides insights into pixel value distributions. Error handling ensures smooth functionality, and advanced filters like Haar Wavelet Transform are available. Overall, "boosting_tracker.py" integrates computer vision and GUI components effectively, offering a versatile tool for video analysis with user-friendly interaction and comprehensive functionalities. The twenty eighth project "csrt_tracker.py" offers a comprehensive GUI for object tracking using the CSRT algorithm. Leveraging tkinter, imageio, OpenCV (cv2), and PIL, it facilitates video handling, tracking, and image processing. The CSRTTracker class manages tracking functionalities, while create_widgets sets up GUI components like video display, control buttons, and filters. Methods like open_video, play_video, and stop_video handle video playback, while initialize_tracker and track_object manage CSRT tracking. User interaction, including mouse event handlers for zooming and ROI selection, is supported. Filtering options like Wiener filter and adaptive thresholding enhance image processing. Overall, the script provides a versatile and interactive tool for object tracking and analysis, showcasing effective integration of various libraries for enhanced functionality and user experience. The twenty ninth project, KCFTracker, is a robust object tracking application with a Tkinter-based GUI. The KCFTracker class orchestrates video handling, user interaction, and tracking functionalities. It sets up GUI elements like video display and control buttons, enabling tasks such as video playback, bounding box definition, and filter application. Methods like open_video and play_video handle video loading and playback, while toggle_play_pause manages playback control. User interaction for defining bounding boxes is facilitated through mouse event handlers. The analyze_histogram method processes selected regions for histogram analysis. Various filters, including Gaussian and Median filtering, enhance image processing. Overall, the project offers a comprehensive tool for real-time object tracking and video analysis. The thirtieth project, MedianFlow Tracker, is a Python application built with Tkinter for the GUI and OpenCV for object tracking. It provides users with interactive video manipulation tools, including playback controls and object tracking functionalities. The main class, MedianFlowTracker, initializes the interface and handles video loading, playback, and object tracking using OpenCV's MedianFlow tracker. Users can define bounding boxes for object tracking directly on the canvas, with real-time updates of the tracked object's center coordinates. Additionally, the project offers various image processing filters, parameter controls for fine-tuning tracking, and histogram analysis of the tracked object's region. Overall, it demonstrates a comprehensive approach to video analysis and object tracking, leveraging Python's capabilities in multimedia applications. The thirty first project, MILTracker, is a Python application that implements object tracking using the Multiple Instance Learning (MIL) algorithm. Built with Tkinter for the GUI and OpenCV for video processing, it offers a range of features for video analysis and tracking. Users can open video files, select regions of interest (ROI) for tracking, and apply various filters to enhance tracking performance. The GUI includes controls for video playback, navigation, and zoom, while mouse interactions allow for interactive ROI selection. Advanced features include histogram analysis of the ROI and error handling for smooth operation. Overall, MILTracker provides a comprehensive tool for video tracking and analysis, demonstrating the integration of multiple technologies for efficient object tracking. The thirty second project, MOSSE Tracker, implemented in the mosse_tracker.py script, offers advanced object tracking capabilities within video files. Utilizing Tkinter for the GUI and OpenCV for video processing, it provides a user-friendly interface for video playback, object tracking, and image analysis. The application allows users to open videos, control playback, select regions of interest for tracking, and apply various filters. It supports zooming, mouse interactions for ROI selection, and histogram analysis of the selected areas. With methods for navigating frames, clearing data, and updating visuals, the MOSSE Tracker project stands as a robust tool for video analysis and object tracking tasks. The thirty third project, TLDTracker, offers a versatile and powerful tool for object tracking using the TLD algorithm. Built with Tkinter, it provides an intuitive interface for video playback, frame navigation, and object selection. Key features include zoom functionality, interactive ROI selection, and real-time tracking with OpenCV's TLD implementation. Users can apply various filters, analyze histograms, and utilize advanced techniques like wavelet transforms. The tool ensures efficient processing, robust error handling, and extensibility for future enhancements. Overall, TLDTracker stands as a valuable asset for both research and practical video analysis tasks, offering a seamless user experience and advanced image processing capabilities. The thirty fourth project, motion detection application based on the K-Nearest Neighbors (KNN) background subtraction method, offers a user-friendly interface for video processing and analysis. Utilizing Tkinter, it provides controls for video playback, frame navigation, and object detection. The MixtureofGaussiansWithFilter class orchestrates video handling, applying filters like Gaussian blur and background subtraction for motion detection. Users can interactively draw bounding boxes to select regions of interest (ROIs), triggering histogram analysis and various image filters. The application excels in its modular design, facilitating easy extension for custom research or application needs, and empowers users to explore video data effectively. The thirty fifth project, "Mixture of Gaussians with Filtering", is a Python script tailored for motion detection in videos using the MOG algorithm alongside diverse filtering methods. Leveraging tkinter for GUI and OpenCV for image processing, it facilitates interactive video playback, frame navigation, and object tracking. With features like adjustable motion detection thresholds and a wide range of filtering options including Gaussian blur, mean blur, and more, users can fine-tune analysis parameters. Object detection, highlighted by bounding boxes and centroid display, coupled with histogram analysis of selected regions, enhances the tool's utility for in-depth video examination. The thirty sixth project, "running_gaussian_average_with_filtering.py", implements motion detection using the Running Gaussian Average algorithm and offers a range of filtering techniques. It employs Tkinter for GUI creation and integrates OpenCV, PIL, imageio, matplotlib, pywt, and numpy modules. The core component, the RunningGaussianAverage class, orchestrates GUI setup, video processing, frame differencing, contour detection, and filtering. The GUI features a canvas for video display, a listbox for object center display, and control buttons for playback, navigation, and threshold adjustment. Mouse events handle zooming and object selection, while histogram analysis and filtering options enrich the analysis capabilities. Overall, it offers a comprehensive tool for motion detection and object tracking with user-friendly interaction and versatile filtering methods. The thirty seventh project, "kernel_density_estimation_with_filtering.py", implements motion detection using Kernel Density Estimation (KDE) alongside diverse filtering techniques, all wrapped in a Tkinter-based GUI for video file interaction and motion visualization. The main class, KDEWithFilter, orchestrates GUI setup, video frame processing, and interaction functionalities. Leveraging libraries like OpenCV, imageio, Matplotlib, PyWavelets, and NumPy, it handles tasks such as video I/O, background subtraction, contour detection, and filtering. Users can open, play/pause/stop videos, navigate frames, adjust thresholds, and apply filters. Mouse-driven ROI selection enables histogram analysis and filter application, while interactive parameter adjustments enhance flexibility. Overall, the script offers a comprehensive tool for motion detection and image filtering, catering to diverse computer vision needs.
I M An Advanced Scratch Coder
DOWNLOAD
Author : Max Wainewright
language : en
Publisher: Wayland
Release Date : 2018-05-15
I M An Advanced Scratch Coder written by Max Wainewright and has been published by Wayland this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-15 with categories.
I'm an Advanced Scratch Coder is perfect for those who have mastered the basics of Scratch, and want to try building some more tricky programs before progressing to real-world coding languages. Try writing thirteen different programs in Scratch, and get to grips with key coding concepts like loops, variables and functions. The Generation Code series is a hands-on guide to computer coding, designed to train you in the coding languages used by real-world computer programmers. You'll discover how to code exciting programs, web pages, apps and games, and learn how the tools and functions you're using can be applied to other situations. Other books in the Generation Code series: I'm a Python Programmer I'm an HTML Web Page Builder I'm an App Developer I'm a JavaScript Games Maker: The Basics I'm a JavaScript Games Maker: Advanced Coding
I M An Advanced Scratch Coder
DOWNLOAD
Author : Max Wainewright
language : en
Publisher: Wayland
Release Date : 2017-05-25
I M An Advanced Scratch Coder written by Max Wainewright and has been published by Wayland this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-25 with categories.
I'm an Advanced Scratch Coder is perfect for those who have mastered the basics of Scratch, and want to try building some more tricky programs before progressing to real-world coding languages. Try writing thirteen different programs in Scratch, and get to grips with key coding concepts like loops, variables and functions. The Generation Code series is a hands-on guide to computer coding, designed to train you in the coding languages used by real-world computer programmers. You'll discover how to code exciting programs, web pages, apps and games, and learn how the tools and functions you're using can be applied to other situations. Other books in the Generation Code series: I'm a Python Programmer I'm an HTML Web Page Builder I'm an App Developer I'm a JavaScript Games Maker: The Basics I'm a JavaScript Games Maker: Advanced Coding