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Object Matching In Digital Video Using Descriptors With Python And Tkinter
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Book Synopsis OBJECT MATCHING IN DIGITAL VIDEO USING DESCRIPTORS WITH PYTHON AND TKINTER by : Vivian Siahaan
Download or read book OBJECT MATCHING IN DIGITAL VIDEO USING DESCRIPTORS WITH PYTHON AND TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2024-06-14 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first project is a sophisticated tool for comparing and matching visual features between images using the Scale-Invariant Feature Transform (SIFT) algorithm. Built with Tkinter, it features an intuitive GUI enabling users to load images, adjust SIFT parameters (e.g., number of features, thresholds), and customize BFMatcher settings. The tool detects keypoints invariant to scale, rotation, and illumination, computes descriptors, and uses BFMatcher for matching. It includes a ratio test for match reliability and visualizes matches with customizable lines. Designed for accessibility and efficiency, SIFTMacher_NEW.py integrates advanced computer vision techniques to support diverse applications in image processing, research, and industry. The second project is a Python-based GUI application designed for image matching using the ORB (Oriented FAST and Rotated BRIEF) algorithm, leveraging OpenCV for image processing, Tkinter for GUI development, and PIL for image format handling. Users can load and match two images, adjusting parameters such as number of features, scale factor, and edge threshold directly through sliders and options provided in the interface. The application computes keypoints and descriptors using ORB, matches them using a BFMatcher based on Hamming distance, and visualizes the top matches by drawing lines between corresponding keypoints on a combined image. ORBMacher.py offers a user-friendly platform for experimenting with ORB's capabilities in feature detection and image matching, suitable for educational and practical applications in computer vision and image processing. The third project is a Python application designed for visualizing keypoint matches between images using the FAST (Features from Accelerated Segment Test) detector and SIFT (Scale-Invariant Feature Transform) descriptor. Built with Tkinter for the GUI, it allows users to load two images, adjust detector parameters like threshold and non-maximum suppression, and visualize matches in real-time. The interface includes controls for image loading, parameter adjustment, and features a scrollable canvas for exploring matched results. The core functionality employs OpenCV for image processing tasks such as keypoint detection, descriptor computation, and matching using a Brute Force Matcher with L2 norm. This tool is aimed at enhancing user interaction and analysis in computer vision applications. The fourth project creates a GUI for matching keypoints between images using the AGAST (Adaptive and Generic Accelerated Segment Test) algorithm with BRIEF descriptors. Utilizing OpenCV for image processing and Tkinter for the interface, it initializes a window titled "AGAST Image Matcher" with a control_frame for buttons and sliders. Users can load two images using load_button1 and load_button2, which trigger file dialogs and display images on a scrollable canvas via load_image1(), load_image2(), and show_image(). Adjustable parameters include AGAST threshold and BRIEF descriptor bytes. Clicking match_button invokes match_images(), checking image loading, detecting keypoints with AGAST, computing BRIEF descriptors, and using BFMatcher for matching and visualization. The matched image, enhanced with color-coded lines, replaces previous images on the canvas, ensuring clear, interactive results presentation. The fifth project is a Python-based application that utilizes the AKAZE feature detection algorithm from OpenCV for matching keypoints between images. Implemented with Tkinter for the GUI, it features a "AKAZE Image Matcher" window with buttons for loading images and adjusting AKAZE parameters like detection threshold, octaves, and octave layers. Upon loading images via file dialog, the app reads and displays them on a scrollable canvas, ensuring smooth navigation for large images. The match_images method manages keypoint detection using AKAZE and descriptor matching via BFMatcher with Hamming distance, sorting matches for visualization with color-coded lines. It updates the canvas with the matched image, clearing previous content for clarity and enhancing user interaction in image analysis tasks. The sixth project is a Tkinter-based Python application designed to facilitate the matching and visualization of keypoint descriptors between two images using the BRISK feature detection and description algorithm. Upon initialization, it creates a window titled "BRISK Image Matcher" with a canvas (control_frame) for hosting buttons ("Load Image 1", "Load Image 2", "Match Images") and sliders to adjust BRISK parameters like Threshold, Octaves, and Pattern Scale. Loaded images are displayed on canvas_frame with scrollbars for navigation, utilizing methods like load_image1() and load_image2() to handle image loading and show_image() to convert and display images in RGB format compatible with Tkinter. The match_images() method manages keypoint detection, descriptor calculation using BRISK, descriptor matching with the Brute-Force Matcher, and visualization of matched keypoints with colored lines on canvas_frame. This comprehensive interface empowers users to explore and analyze image similarities based on distinct keypoints effectively. The seventh project utilizes Tkinter to create a GUI application tailored for processing and analyzing video frames. It integrates various libraries such as Pillow, imageio, OpenCV, numpy, matplotlib, pywt, and os to support functionalities ranging from video handling to image processing and feature analysis. At its core is the Filter_CroppedFrame class, which manages the GUI layout and functionality. The application features control buttons for video playback, comboboxes for selecting zoom levels, filters, and matchers, and a canvas for displaying video frames with support for interactive navigation and frame processing. Event handlers facilitate tasks like video file loading, playback control, and frame navigation, while offering options for applying filters and feature matching algorithms to enhance video analysis capabilities.
Book Synopsis Object Matching in Digital Video Using Descriptors with Python and Tkinter by : Rismon Hasiholan Sianipar
Download or read book Object Matching in Digital Video Using Descriptors with Python and Tkinter written by Rismon Hasiholan Sianipar and published by Independently Published. This book was released on 2024-06-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first project is a sophisticated tool for comparing and matching visual features between images using the Scale-Invariant Feature Transform (SIFT) algorithm. Built with Tkinter, it features an intuitive GUI enabling users to load images, adjust SIFT parameters (e.g., number of features, thresholds), and customize BFMatcher settings. The tool detects keypoints invariant to scale, rotation, and illumination, computes descriptors, and uses BFMatcher for matching. It includes a ratio test for match reliability and visualizes matches with customizable lines. Designed for accessibility and efficiency, SIFTMacher_NEW.py integrates advanced computer vision techniques to support diverse applications in image processing, research, and industry. The second project is a Python-based GUI application designed for image matching using the ORB (Oriented FAST and Rotated BRIEF) algorithm, leveraging OpenCV for image processing, Tkinter for GUI development, and PIL for image format handling. Users can load and match two images, adjusting parameters such as number of features, scale factor, and edge threshold directly through sliders and options provided in the interface. The application computes keypoints and descriptors using ORB, matches them using a BFMatcher based on Hamming distance, and visualizes the top matches by drawing lines between corresponding keypoints on a combined image. ORBMacher.py offers a user-friendly platform for experimenting with ORB's capabilities in feature detection and image matching, suitable for educational and practical applications in computer vision and image processing. The third project is a Python application designed for visualizing keypoint matches between images using the FAST (Features from Accelerated Segment Test) detector and SIFT (Scale-Invariant Feature Transform) descriptor. Built with Tkinter for the GUI, it allows users to load two images, adjust detector parameters like threshold and non-maximum suppression, and visualize matches in real-time. The interface includes controls for image loading, parameter adjustment, and features a scrollable canvas for exploring matched results. The core functionality employs OpenCV for image processing tasks such as keypoint detection, descriptor computation, and matching using a Brute Force Matcher with L2 norm. This tool is aimed at enhancing user interaction and analysis in computer vision applications. The fourth project creates a GUI for matching keypoints between images using the AGAST (Adaptive and Generic Accelerated Segment Test) algorithm with BRIEF descriptors. Utilizing OpenCV for image processing and Tkinter for the interface, it initializes a window titled "AGAST Image Matcher" with a control_frame for buttons and sliders. Users can load two images using load_button1 and load_button2, which trigger file dialogs and display images on a scrollable canvas via load_image1(), load_image2(), and show_image(). Adjustable parameters include AGAST threshold and BRIEF descriptor bytes. Clicking match_button invokes match_images(), checking image loading, detecting keypoints with AGAST, computing BRIEF descriptors, and using BFMatcher for matching and visualization. The matched image, enhanced with color-coded lines, replaces previous images on the canvas, ensuring clear, interactive results presentation. The fifth project is a Python-based application that utilizes the AKAZE feature detection algorithm from OpenCV for matching keypoints between images. Implemented with Tkinter for the GUI, it features a "AKAZE Image Matcher" window with buttons for loading images and adjusting AKAZE parameters like detection threshold, octaves, and octave layers. Upon loading images via file dialog, the app reads and displays them ...
Book Synopsis FEATURES-BASED MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER by : Vivian Siahaan
Download or read book FEATURES-BASED MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first project develops a tkinter-based graphical user interface (GUI) to facilitate the identification and tracking of keypoints in video files using the BRISK algorithm, commonly used in computer vision tasks like object detection and motion tracking. The GUI allows users to load, play, and navigate through video frames (supporting formats like .mp4 and .avi) and employs a canvas for enhanced visualization of keypoints at various scales. Users can interactively draw bounding boxes to define regions of interest, significantly improving the accuracy and relevance of the keypoints detected. Additionally, the project incorporates functionalities for dynamic updating of detected keypoints and their positions, and allows for customization of BRISK parameters such as threshold and pattern scale to optimize performance. Robust error handling ensures a smooth user experience by managing and reporting any issues that occur during video processing. Overall, this project not only simplifies the process of keypoint identification and analysis but also offers a tool that is accessible to both experts and novices in the field of computer vision. This second project develops a user-friendly graphical user interface (GUI) application that utilizes the FAST (Features from Accelerated Segment Test) algorithm to identify and analyze keypoints in video frames. By integrating FAST, known for its quick corner detection capabilities, the application provides real-time visualization of keypoints overlaid directly on video frames displayed through a panel. Key functionalities include video playback controls, frame navigation, and zoom adjustments for detailed viewing. Users can observe the dynamic distribution and characteristics of keypoints across frames, with detailed spatial information displayed in list boxes. This GUI also allows parameter adjustments like detection thresholds to enhance keypoint visibility, making it a practical tool for computer vision researchers, developers, and enthusiasts eager to delve into keypoint analysis and related applications. The third project, features_box_akaze.py, is a sophisticated Python application that leverages the Tkinter GUI library to analyze video content for keypoint detection using the AKAZE (Accelerated-KAZE) algorithm. This application introduces a class named KeyPoints_AKAZE, initializing with a master window for video loading and manipulation, structured to support interactive user engagement through video playback, zoom functionality, and bounding box selection on displayed frames. It features a dual-panel layout comprising a video display canvas and a control panel for adjusting AKAZE's parameters like threshold and descriptor size, which are crucial for fine-tuning the keypoint detection process. As videos are played, keypoints detected within user-defined regions of interest are dynamically illustrated and listed, providing immediate feedback and detailed analysis opportunities. This robust platform not only serves educational and research purposes by demonstrating AKAZE's capabilities but also offers a modular design for future expansion to incorporate additional functionalities for more advanced video analysis applications. The fourth project, features_box_agast.py, is a sophisticated GUI application crafted to demonstrate and analyze video content for keypoint detection using the AGAST (Adaptive and Generic Accelerated Segment Test) algorithm, utilizing Python and the Tkinter framework. Upon launch, users encounter a well-organized interface featuring video display, control panels, and list boxes that illustrate detected keypoints and their specific positions. Users can interactively select regions of interest on the video via canvas bindings that allow for bounding box drawing, focusing analysis on particular areas. The application supports dynamic adjustment of detection parameters like thresholds through entry widgets, enhancing real-time analysis while the zoom functionality aids in examining finer video details. Detected keypoints are both visualized on the video and enumerated in the interface, facilitating a detailed assessment of detection efficiency. This makes the application not only a robust tool for showcasing the AGAST algorithm but also an interactive platform for educational and research applications in computer vision. The fifth project, features_box_orb.py, is designed to create a user-friendly, tkinter-based GUI application that leverages the ORB (Oriented FAST and Rotated BRIEF) algorithm for efficient keypoint detection in video frames. Aimed at facilitating both educational and practical applications in video analysis, the application enables users to load videos, control playback frame-by-frame, and dynamically visualize keypoints detected by ORB, known for its efficiency and low resource consumption compared to methods like SIFT or SURF. The interface includes intuitive video playback controls, zoom functionalities, and interactive bounding box selection, allowing users to focus keypoint detection on specific video regions. Keypoints and their coordinates are prominently displayed in list boxes, providing detailed, real-time feedback and making the application accessible even to those with minimal background in computer vision or software development. This combination of advanced computer vision technology and interactive features makes the application a versatile tool for detailed video analysis and learning in various settings. The sixth project, utilizing the tkinter library for its GUI, OpenCV for image processing, and imageio for video operations, crafts an application for object tracking in videos through the BRISK algorithm. Upon launching, the ObjectTracking_BRISK class initializes, setting up a user interface with video playback controls, a canvas for display, and a listbox for logging coordinates of tracked objects. Users can select videos via an open dialog, navigate frames, and adjust the zoom for closer inspection. Tracking commences when a user defines a region of interest (ROI) by drawing a bounding box around the desired object. This ROI facilitates the BRISK-based tracking of the object across frames, continuously updating the object’s location and logging its path in real time. Enhanced functionalities such as zoom adjustments, error handling, and manual navigation controls enrich the application’s utility, making it robust for detailed object tracking analysis. The seventh establishes a GUI application for tracking objects in video files using the FAST (Features from Accelerated Segment Test) algorithm, known for its rapid feature detection capabilities suitable for real-time applications. Utilizing libraries like Tkinter for the GUI, OpenCV for image processing, and imageio for video handling, the application initializes with a main window and various controls including video playback buttons and a canvas for displaying video frames. Users can open video files, navigate through frames, and interactively define bounding boxes around areas of interest directly on the canvas. These regions are then tracked using FAST, with the track_object() method updating the bounding box position as objects move across frames. The application supports zoom functionality for detailed viewing, logs tracking data in a listbox, and provides intuitive controls like video play/pause and frame navigation, creating a comprehensive tool for detailed analysis and monitoring of object movements in various applications such as surveillance or sports analytics. The eighth project, ObjectTracking_AKAZE.py, develops a user-friendly application for tracking objects in video streams using the AKAZE (Accelerated-KAZE) algorithm, aimed at users in fields such as video surveillance, activity monitoring, and academic research. Built with the Tkinter GUI for ease of use and OpenCV for robust image processing, this tool allows users to load videos in various formats, play, pause, and meticulously navigate through frames to adjust tracking parameters dynamically. The application employs AKAZE to detect key features across frames, updating the position of a bounding box that visualizes the tracked object's location on screen. Users initiate tracking by selecting a region of interest, adjusting the bounding box manually as needed, which adds flexibility in handling unpredictable object movements. As the video progresses, the application visualizes real-time tracking updates and logs bounding box coordinates for detailed motion analysis, further supported by features for clearing sessions, zoom adjustments, and straightforward navigation controls. This comprehensive setup combines advanced tracking capabilities with intuitive controls, making it an invaluable tool for diverse applications requiring precise object tracking. The ninth project ObjectTracking_AGAST.py, leverages the AGAST (Adaptive and Generic Accelerated Segment Test) feature detection algorithm to create a user-friendly GUI application for tracking objects in video sequences, ideal for applications in surveillance, sports analysis, and robotics where real-time, efficient tracking is crucial. Built with the Tkinter library, the application allows users to load videos, navigate through frames, and select regions of interest for precise tracking. Upon selecting an object by drawing a bounding box, the AGAST algorithm— an optimized variant of FAST—detects keypoints within this area, tracking these across frames to update the bounding box's position based on calculated motion vectors. The system efficiently maintains tracking even with rapid movements or changes in orientation by comparing keypoints frame-to-frame and employing a brute force matcher for continuity and accuracy. Additional features such as zoom control and navigation tools enhance the user experience by allowing detailed examination and adjustment, while a logging function records the tracked object’s center coordinates for further analysis. With robust error handling and options to reset tracking or clear logs, this application provides a powerful yet accessible tool for diverse tracking needs, combining advanced computer vision technology with practical usability. The tenth project, ObjectTracking_GLOH.py, is a sophisticated application designed for object tracking in video sequences using the Gradient Location-Orientation Histogram (GLOH) algorithm, an advanced version of SIFT that excels in dealing with scale, noise, and illumination variations. Developed with tkinter, the application provides a user-friendly GUI that facilitates real-time video processing, integrating features like video loading, interactive bounding box creation for object tracking, and comprehensive frame navigation controls. Users can directly interact with the video to select objects for tracking by drawing bounding boxes, which initializes the tracking process where GLOH vectors compute and match features frame-by-frame, ensuring precise object following. Additional functionalities include zoom capabilities for detailed observation, real-time logging of bounding box coordinates for further analysis, and robust error handling to maintain stability and responsiveness. Designed with extensibility in mind, this tool not only brings advanced computer vision capabilities to practical applications but also allows for future enhancements like integrating object recognition, making it highly valuable for surveillance, research, and various industry-specific applications. The eleventh project, ObjectTracking_ORB.py, is a sophisticated application designed to enable object tracking in video streams using the ORB (Oriented FAST and Rotated BRIEF) algorithm, integrating advanced computer vision techniques into a user-friendly graphical user interface (GUI). Developed with Python and utilizing libraries like Tkinter for the GUI, OpenCV for image processing, and imageio for video handling, this tool supports various applications including surveillance and sports analytics. Users can load videos in multiple formats, interactively select objects by drawing bounding boxes, and control playback through an intuitive interface. ORB's implementation allows for efficient real-time feature detection and matching, tracking the movement of objects across frames and logging the trajectory data for analysis. The application's modular design not only facilitates robust tracking but also provides a flexible framework for future enhancements or integration of different tracking algorithms, making it a valuable tool for both practical and advanced image processing tasks.
Book Synopsis GRADIENT-BASED BLOCK MATCHING MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER by : Vivian Siahaan
Download or read book GRADIENT-BASED BLOCK MATCHING MOTION ESTIMATION AND OBJECT TRACKING WITH PYTHON AND TKINTER written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2024-04-17 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first project, gui_motion_analysis_gbbm.py, is designed to streamline motion analysis in videos using the Gradient-Based Block Matching Algorithm (GBBM) alongside a user-friendly Graphical User Interface (GUI). It encompasses various objectives, including intuitive GUI design with Tkinter, enabling video playback control, performing optical flow analysis, and allowing parameter configuration for tailored motion analysis. The GUI also facilitates interactive zooming, frame-wise analysis, and offers visual feedback through motion vector overlays. Robust error handling and multi-instance support enhance stability and usability, while dynamic title updates provide context within the interface. Overall, the project empowers users with a versatile tool for comprehensive motion analysis in videos. By integrating the GBBM algorithm with an intuitive GUI, gui_motion_analysis_gbbm.py simplifies motion analysis in videos. Its objectives range from GUI design to parameter configuration, enabling users to control video playback, perform optical flow analysis, and visualize motion patterns effectively. With features like interactive zooming, frame-wise analysis, and visual feedback, users can delve into motion dynamics seamlessly. Robust error handling ensures stability, while multi-instance support allows for concurrent analysis. Dynamic title updates enhance user awareness, culminating in a versatile tool for in-depth motion analysis. The second project, gui_motion_analysis_gbbm_pyramid.py, is dedicated to offering an accessible interface for video motion analysis, employing the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach. Its objectives encompass several crucial aspects. Primarily, the project responds to the demand for motion analysis in video processing across diverse domains like computer vision and robotics. By integrating the GBBM algorithm into a GUI, it democratizes motion analysis, catering to users without specialized programming or computer vision skills. Leveraging the GBBM algorithm's effectiveness, particularly with the Pyramid Approach, enhances performance and robustness, enabling accurate motion estimation across various scales. The GUI offers extensive control options and visualization features, empowering users to customize analysis parameters and inspect motion dynamics comprehensively. Overall, this project endeavors to advance video processing and analysis by providing an intuitive interface backed by cutting-edge algorithms, fostering accessibility and efficiency in motion analysis tasks. The third project, gui_motion_analysis_gbbm_adaptive.py, introduces a GUI application for video motion estimation, employing the Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size. Users can interact with video files, control playback, navigate frames, and visualize optical flow between consecutive frames, facilitated by features like zooming and panning. Developed with Tkinter in Python, the GUI provides intuitive controls for adjusting motion estimation parameters and playback options upon launch. At its core, the application dynamically adjusts block sizes based on local gradient magnitude, enhancing motion estimation accuracy, especially in areas with varying complexity. Utilizing PIL and OpenCV libraries, it handles image processing tasks and video file operations, enabling users to interact with the video display canvas for enhanced analysis. Overall, gui_motion_analysis_gbbm_adaptive.py offers a versatile solution for motion analysis in videos, empowering users with visualization tools and parameter customization for diverse applications like video compression and object tracking. The fourth project, gui_motion_analysis_gbbm_lucas_kanade.py, introduces a GUI for motion estimation in videos, incorporating both the Gradient-Based Block Matching Algorithm (GBBM) and Lucas-Kanade Optical Flow. It begins by importing necessary libraries such as tkinter for GUI development, PIL for image processing, imageio for video file handling, cv2 for computer vision operations, and numpy for numerical computation. The VideoGBBM_LK_OpticalFlow class serves as the application container, initializing attributes and defining methods for video loading, playback control, parameter setting, frame display, and optical flow visualization. With features like zooming, panning, and event handling for user interactions, the script offers a comprehensive tool for visualizing and analyzing motion dynamics in videos using two distinct optical flow estimation techniques. The fifth project, gui_motion_analysis_gbbm_sift.py, introduces a GUI application for optical flow analysis in videos, employing both the Gradient-Based Block Matching Algorithm (GBBM) and Scale-Invariant Feature Transform (SIFT). It begins by importing essential libraries such as tkinter for GUI development, PIL for image processing, imageio for video handling, and OpenCV for computer vision tasks like optical flow computation. The VideoGBBM_SIFT_OpticalFlow class orchestrates the application, initializing GUI elements and defining methods for video loading, playback control, frame display, and optical flow computation using both GBBM and SIFT algorithms. With features for parameter adjustment, frame navigation, zooming, and event handling for user interactions, the script offers a user-friendly interface for in-depth optical flow analysis, enabling insights into motion patterns and dynamics within videos. The sixth project, gui_motion_analysis_gbbm_orb.py script, offers a user-friendly interface for motion estimation in videos, utilizing both the Gradient-Based Block Matching Algorithm (GBBM) and ORB (Oriented FAST and Rotated BRIEF) optical flow techniques. Its primary goal is to enable users to analyze and visualize motion dynamics within video files effortlessly. The GUI application provides functionalities for opening video files, navigating frames, adjusting parameters like zoom scale and step size, and controlling playback with buttons for play, pause, stop, next frame, and previous frame. Key to the application's functionality is its ability to compute and visualize optical flow using both GBBM and ORB algorithms. Optical flow, depicting object motion in videos, is represented with vectors overlaid on video frames, aiding users in understanding motion patterns and dynamics. Interactive features such as mouse wheel zooming and dragging enhance user exploration of video frames and optical flow visualizations, allowing dynamic adjustment of viewing perspective to focus on specific regions or analyze motion at different scales. Overall, this project provides a comprehensive tool for video motion analysis, merging user-friendly interface elements with advanced motion estimation techniques to empower users in tasks ranging from surveillance to computer vision research. The seventh project showcases object tracking using the Gradient-Based Block Matching Algorithm (GBBM), vital in various computer vision applications like surveillance and robotics. By continuously locating and tracking objects of interest in video streams, it highlights GBBM's practical application for real-time tracking. The GUI interface simplifies interaction with video files, allowing easy opening and visualization of frames. Users control playback, navigate frames, and adjust zoom scale, while the heart of the project lies in GBBM's implementation for tracking objects. GBBM estimates object motion by comparing pixel blocks between consecutive frames, generating motion vectors that describe the object's movement. Users can select regions of interest for tracking, adjust algorithm parameters, and receive visual feedback through dynamically adjusting bounding boxes around tracked objects, making it an educational tool for experimenting with object tracking techniques within an accessible interface. The eight project endeavors to create an application for object tracking using the Gradient-Based Block Matching Algorithm (GBBM) with a Pyramid Approach, catering to various computer vision applications like surveillance and autonomous vehicles. Built with Tkinter in Python, the user-friendly interface presents controls for video display, object tracking, and parameter adjustment upon launch. Users can load video files, play, pause, navigate frames, and adjust zoom levels effortlessly. Central to the application is the GBBM algorithm with a pyramid approach for robust object tracking. By refining search spaces at multiple resolutions, it efficiently estimates motion vectors, accommodating scale variations and occlusions. The application visualizes tracked objects with bounding boxes on the video canvas and updates object coordinates dynamically, providing users with insights into object movement. Advanced features, including dynamic parameter adjustment, enhance the algorithm's adaptability, enabling users to fine-tune tracking based on video characteristics and requirements. Overall, this project offers a practical implementation of object tracking within an accessible interface, catering to users across expertise levels in computer vision. The ninth project, "Object Tracking with Gradient-Based Block Matching Algorithm (GBBM) with Adaptive Block Size", focuses on developing a graphical user interface (GUI) application for object tracking in video files using computer vision techniques. Leveraging the GBBM algorithm, a prominent method for motion estimation, the project aims to enable efficient object tracking across video frames, enhancing user interaction and real-time monitoring capabilities. The GUI interface facilitates seamless video file loading, playback control, frame navigation, and real-time object tracking, empowering users to interact with video frames, adjust zoom levels, and monitor tracked object coordinates throughout the video sequence. Central to the project's functionality is the adaptive block size variant of the GBBM algorithm, dynamically adjusting block sizes based on gradient magnitudes to improve tracking accuracy and robustness across various scenarios. By simplifying object tracking processes through intuitive GUI interactions, the project caters to users with limited programming expertise, fostering learning opportunities in computer vision and video processing. Additionally, the project serves as a platform for collaboration and experimentation, promoting knowledge sharing and innovation within the computer vision community while showcasing the practical applications of computer vision algorithms in surveillance, video analysis, and human-computer interaction domains. The tenth project, "Object Tracking with SIFT Algorithm", introduces a GUI application developed with Python's tkinter library for tracking objects in videos using the Scale-Invariant Feature Transform (SIFT) algorithm. Upon launching, users access a window featuring video display, center coordinates of tracked objects, and control buttons. Supported video formats include mp4, avi, mkv, and wmv, with the "Open Video" button enabling file selection for display within the canvas widget. Playback control buttons like "Play/Pause," "Stop," "Previous Frame," and "Next Frame" facilitate seamless navigation and video playback adjustments. A zoom combobox enhances user experience by allowing flexible zoom scaling. The SIFT algorithm facilitates object tracking by detecting and matching keypoints between frames, estimating motion vectors used to update the bounding box coordinates of the tracked object in real-time. Users can manually define object bounding boxes by clicking and dragging on the video canvas, offering both automated and manual tracking options for enhanced user control. The eleventh project, "Object Tracking with ORB (Oriented FAST and Rotated BRIEF)", aims to develop a user-friendly GUI application for object tracking in videos using the ORB algorithm. Utilizing Python's Tkinter library, the project provides an interface where users can open video files of various formats and interact with playback and tracking functionalities. Users can control video playback, adjust zoom levels for detailed examination, and utilize the ORB algorithm for object detection and tracking. The application integrates ORB for computing keypoints and descriptors across video frames, facilitating the estimation of motion vectors for object tracking. Real-time visualization of tracking progress through overlaid bounding boxes enhances user understanding, while interactive features like selecting regions of interest and monitoring bounding box coordinates provide further control and feedback. Overall, the "Object Tracking with ORB" project offers a comprehensive solution for video analysis tasks, combining intuitive controls, real-time visualization, and efficient tracking capabilities with the ORB algorithm.
Book Synopsis Artificial Intelligence, Blockchain, Computing and Security Volume 2 by : Arvind Dagur
Download or read book Artificial Intelligence, Blockchain, Computing and Security Volume 2 written by Arvind Dagur and published by CRC Press. This book was released on 2023-12-01 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the conference proceedings of ICABCS 2023, a non-profit conference with the objective to provide a platform that allows academicians, researchers, scholars and students from various institutions, universities and industries in India and abroad to exchange their research and innovative ideas in the field of Artificial Intelligence, Blockchain, Computing and Security. It explores the recent advancement in field of Artificial Intelligence, Blockchain, Communication and Security in this digital era for novice to profound knowledge about cutting edges in artificial intelligence, financial, secure transaction, monitoring, real time assistance and security for advanced stage learners/ researchers/ academicians. The key features of this book are: Broad knowledge and research trends in artificial intelligence and blockchain with security and their role in smart living assistance Depiction of system model and architecture for clear picture of AI in real life Discussion on the role of Artificial Intelligence and Blockchain in various real-life problems across sectors including banking, healthcare, navigation, communication, security Explanation of the challenges and opportunities in AI and Blockchain based healthcare, education, banking, and related industries This book will be of great interest to researchers, academicians, undergraduate students, postgraduate students, research scholars, industry professionals, technologists, and entrepreneurs.
Book Synopsis Introduction to Image Processing and Analysis by : John C. Russ
Download or read book Introduction to Image Processing and Analysis written by John C. Russ and published by CRC Press. This book was released on 2017-12-19 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image processing comprises a broad variety of methods that operate on images to produce another image. A unique textbook, Introduction to Image Processing and Analysis establishes the programming involved in image processing and analysis by utilizing skills in C compiler and both Windows and MacOS programming environments. The provided mathematical background illustrates the workings of algorithms and emphasizes the practical reasons for using certain methods, their effects on images, and their appropriate applications. The text concentrates on image processing and measurement and details the implementation of many of the most widely used and most important image processing and analysis algorithms. Homework problems are included in every chapter with solutions available for download from the CRC Press website The chapters work together to combine image processing with image analysis. The book begins with an explanation of familiar pixel array and goes on to describe the use of frequency space. Chapters 1 and 2 deal with the algorithms used in processing steps that are usually accomplished by a combination of measurement and processing operations, as described in chapters 3 and 4. The authors present each concept using a mixture of three mutually supportive tools: a description of the procedure with example images, the relevant mathematical equations behind each concept, and the simple source code (in C), which illustrates basic operations. In particularly, the source code provides a starting point to develop further modifications. Written by John Russ, author of esteemed Image Processing Handbook now in its fifth edition, this book demonstrates functions to improve an image's of features and detail visibility, improve images for printing or transmission, and facilitate subsequent analysis.
Book Synopsis Python and Tkinter Programming by : John Grayson
Download or read book Python and Tkinter Programming written by John Grayson and published by Manning Publications. This book was released on 1999-03-01 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes full documentation for Tkinter, and also offers extensive examples for many real-world Python/Tkinter applications that will give programmers a quick start on their own projects.
Download or read book Learning Python written by Mark Lutz and published by "O'Reilly Media, Inc.". This book was released on 2013-06-12 with total page 1740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code. Explore Python’s major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python’s general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python’s object-oriented programming tool for structuring code Write large programs with Python’s exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing
Book Synopsis Python for the Lab by : Aquiles Carattino
Download or read book Python for the Lab written by Aquiles Carattino and published by . This book was released on 2020-10-11 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python for the Lab is the first book covering how to develop instrumentation software. It is ideal for researchers willing to automatize their setups and bring their experiments to the next level. The book is the product of countless workshops at different universities, and a carefully design pedagogical strategy. With an easy to follow and task-oriented design, the book uncovers all the best practices in the field. It also shows how to design code for long-term maintainability, opening the doors of fruitful collaboration among researchers from different labs.
Download or read book Python Projects written by Laura Cassell and published by John Wiley & Sons. This book was released on 2014-12-04 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to completing Python projects for those ready to take their skills to the next level Python Projects is the ultimate resource for the Python programmer with basic skills who is ready to move beyond tutorials and start building projects. The preeminent guide to bridge the gap between learning and doing, this book walks readers through the "where" and "how" of real-world Python programming with practical, actionable instruction. With a focus on real-world functionality, Python Projects details the ways that Python can be used to complete daily tasks and bring efficiency to businesses and individuals alike. Python Projects is written specifically for those who know the Python syntax and lay of the land, but may still be intimidated by larger, more complex projects. The book provides a walk-through of the basic set-up for an application and the building and packaging for a library, and explains in detail the functionalities related to the projects. Topics include: *How to maximize the power of the standard library modules *Where to get third party libraries, and the best practices for utilization *Creating, packaging, and reusing libraries within and across projects *Building multi-layered functionality including networks, data, and user interfaces *Setting up development environments and using virtualenv, pip, and more Written by veteran Python trainers, the book is structured for easy navigation and logical progression that makes it ideal for individual, classroom, or corporate training. For Python developers looking to apply their skills to real-world challenges, Python Projects is a goldmine of information and expert insight.
Download or read book Fluent Python written by Luciano Ramalho and published by "O'Reilly Media, Inc.". This book was released on 2015-07-30 with total page 755 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python’s simplicity lets you become productive quickly, but this often means you aren’t using everything it has to offer. With this hands-on guide, you’ll learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—features. Author Luciano Ramalho takes you through Python’s core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time. Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age Functions as objects: view Python functions as first-class objects, and understand how this affects popular design patterns Object-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritance Control flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packages Metaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work
Book Synopsis Python Pocket Reference by : Mark Lutz
Download or read book Python Pocket Reference written by Mark Lutz and published by "O'Reilly Media, Inc.". This book was released on 2014-01-22 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated for both Python 3.4 and 2.7, this convenient pocket guide is the perfect on-the-job quick reference. Youâ??ll find concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python tools. The handy index lets you pinpoint exactly what you need. Written by Mark Lutzâ??widely recognized as the worldâ??s leading Python trainerâ??Python Pocket Reference is an ideal companion to Oâ??Reillyâ??s classic Python tutorials, Learning Python and Programming Python, also written by Mark. This fifth edition covers: Built-in object types, including numbers, lists, dictionaries, and more Statements and syntax for creating and processing objects Functions and modules for structuring and reusing code Pythonâ??s object-oriented programming tools Built-in functions, exceptions, and attributes Special operator overloading methods Widely used standard library modules and extensions Command-line options and development tools Python idioms and hints The Python SQL Database API
Download or read book Learning Python written by Mark Lutz and published by "O'Reilly Media, Inc.". This book was released on 2007-10-22 with total page 749 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portable, powerful, and a breeze to use, Python is ideal for both standalone programs and scripting applications. With this hands-on book, you can master the fundamentals of the core Python language quickly and efficiently, whether you're new to programming or just new to Python. Once you finish, you will know enough about the language to use it in any application domain you choose. Learning Python is based on material from author Mark Lutz's popular training courses, which he's taught over the past decade. Each chapter is a self-contained lesson that helps you thoroughly understand a key component of Python before you continue. Along with plenty of annotated examples, illustrations, and chapter summaries, every chapter also contains Brain Builder, a unique section with practical exercises and review quizzes that let you practice new skills and test your understanding as you go. This book covers: Types and Operations -- Python's major built-in object types in depth: numbers, lists, dictionaries, and more Statements and Syntax -- the code you type to create and process objects in Python, along with Python's general syntax model Functions -- Python's basic procedural tool for structuring and reusing code Modules -- packages of statements, functions, and other tools organized into larger components Classes and OOP -- Python's optional object-oriented programming tool for structuring code for customization and reuse Exceptions and Tools -- exception handling model and statements, plus a look at development tools for writing larger programs Learning Python gives you a deep and complete understanding of the language that will help you comprehend any application-level examples of Python that you later encounter. If you're ready to discover what Google and YouTube see in Python, this book is the best way to get started.
Book Synopsis Handbook of Open Source Tools by : Sandeep Koranne
Download or read book Handbook of Open Source Tools written by Sandeep Koranne and published by Springer Science & Business Media. This book was released on 2010-10-17 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Open Source Tools introduces a comprehensive collection of advanced open source tools useful in developing software applications. The book contains information on more than 200 open-source tools which include software construction utilities for compilers, virtual-machines, database, graphics, high-performance computing, OpenGL, geometry, algebra, graph theory , GUIs and more. Special highlights for software construction utilities and application libraries are included. Each tool is covered in the context of a real like application development setting. This unique handbook presents a comprehensive discussion of advanced tools, a valuable asset used by most application developers and programmers; includes a special focus on Mathematical Open Source Software not available in most Open Source Software books, and introduces several tools (eg ACL2, CLIPS, CUDA, and COIN) which are not known outside of select groups, but are very powerful. Handbook of Open Source Tools is designed for application developers and programmers working with Open Source Tools. Advanced-level students concentrating on Engineering, Mathematics and Computer Science will find this reference a valuable asset as well.
Book Synopsis Advanced Guide to Python 3 Programming by : John Hunt
Download or read book Advanced Guide to Python 3 Programming written by John Hunt and published by Springer Nature. This book was released on 2023-11-02 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level. This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules.
Book Synopsis Text Processing in Python by : David Mertz
Download or read book Text Processing in Python written by David Mertz and published by Addison-Wesley Professional. This book was released on 2003 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: bull; Demonstrates how Python is the perfect language for text-processing functions. bull; Provides practical pointers and tips that emphasize efficient, flexible, and maintainable approaches to text-processing challenges. bull; Helps programmers develop solutions for dealing with the increasing amounts of data with which we are all inundated.
Book Synopsis Using Asyncio in Python by : Caleb Hattingh
Download or read book Using Asyncio in Python written by Caleb Hattingh and published by O'Reilly Media. This book was released on 2020-01-30 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re among the Python developers put off by asyncio’s complexity, it’s time to take another look. Asyncio is complicated because it aims to solve problems in concurrent network programming for both framework and end-user developers. The features you need to consider are a small subset of the whole asyncio API, but picking out the right features is the tricky part. That’s where this practical book comes in. Veteran Python developer Caleb Hattingh helps you gain a basic understanding of asyncio’s building blocks—enough to get started writing simple event-based programs. You’ll learn why asyncio offers a safer alternative to preemptive multitasking (threading) and how this API provides a simpleway to support thousands of simultaneous socket connections. Get a critical comparison of asyncio and threading for concurrent network programming Take an asyncio walk-through, including a quickstart guidefor hitting the ground looping with event-based programming Learn the difference between asyncio features for end-user developers and those for framework developers Understand asyncio’s new async/await language syntax, including coroutines and task and future APIs Get detailed case studies (with code) of some popular asyncio-compatible third-party libraries
