Book Description

The first project is a video player application with an additional feature to compute and display the MD5 hash of each frame in a video. The user interface is built using Tkinter, a Python GUI toolkit, providing buttons for opening a video file, playing, pausing, and stopping the video playback. Upon opening a video file, the application displays metadata such as filename, duration, resolution, FPS, and codec information in a table. The video can be navigated using a slider to seek to a specific time point. When the video is played, the application iterates through each frame, extracts it from the video clip, calculates its MD5 hash, and displays the frame along with its histogram and MD5 hash. The histogram represents the pixel intensity distribution of each color channel (red, green, blue) in the frame. The computed MD5 hash for each frame is displayed in a label below the video frame. Additionally, the frame hash along with its index is saved to a text file for further analysis or verification purposes. The class encapsulates the functionality of the application, providing methods for opening a video file, playing and controlling video playback, updating metadata, computing frame histogram, plotting histogram, calculating MD5 hash for each frame, and saving frame hashes to a file. The main function initializes the Tkinter root window, instantiates the class, and starts the Tkinter event loop to handle user interactions and update the GUI accordingly. The second project is a video player application with additional features for frame extraction and visualization of RGB histograms for each frame. Developed using Tkinter, a Python GUI toolkit, the application provides functionalities such as opening a video file, playing, pausing, and stopping video playback. The user interface includes buttons for controlling video playback, a combobox for selecting zoom scale, an entry for specifying a time point to jump to, and buttons for frame extraction and opening another instance of the application. Upon opening a video file, the application loads it using the imageio library and displays the frames in a canvas. Users can play, pause, and stop the video using dedicated buttons. The zoom scale can be adjusted, and the video can be navigated using scrollbar or time entry. Additionally, users can extract a specific frame by entering its frame number, which opens a new window displaying the extracted frame along with its RGB histograms and MD5 hash value. The class encapsulates the application's functionalities, including methods for opening a video file, playing/pausing/stopping video, updating zoom scale, displaying frames, handling mouse events for dragging and scrolling, jumping to a specified time, and extracting frames. The main function initializes the Tkinter root window and starts the application's event loop to handle user interactions and update the GUI accordingly. Users can also open multiple instances of the application simultaneously to work with different video files concurrently. The third project is a GUI application built with Tkinter for calculating hash values of video frames and displaying them in a listbox. The interface consists of different frames for video display and hash values, along with buttons for controlling video playback, calculating hashes, saving hash values to a file, and opening a new instance of the application. Users can open a video file using the "Open Video" button, after which they can play, pause, or stop the video using corresponding buttons. Upon opening a video file, the application reads frames from the video capture and displays them in the designated frame. Users can interact with the video using playback buttons to control the video's flow. Hash values for each frame are calculated using various hashing algorithms such as MD5, SHA-1, SHA-256, and others. These hash values are then displayed in the listbox, allowing users to view the hash values corresponding to each algorithm. Additionally, users can save the calculated hash values to a text file by clicking the "Save Hashes" button, providing a convenient way to store and analyze the hash data. Lastly, users can open multiple instances of the application simultaneously by clicking the "Open New Instance" button, facilitating concurrent processing of different video files. The fourth project is a GUI application developed using Tkinter for analyzing video frames through frame hashing and histogram visualization. The interface presents a canvas for displaying the video frames along with control buttons for video playback, frame extraction, and zoom control. Users can open a video file using the "Open Video" button, and the application provides functionality to play, pause, and stop the video playback. Additionally, users can jump to specific time points within the video using the time entry field and "Jump to Time" button. Upon extracting a frame, the application opens a new window displaying the selected frame along with its histogram and multiple hash values calculated using various algorithms such as MD5, SHA-1, SHA-256, and others. The histogram visualization presents the distribution of pixel values across the RGB channels, aiding in the analysis of color composition within the frame. The hash values are displayed in a listbox within the frame extraction window, providing users with comprehensive information about the frame's content and characteristics. Furthermore, users can open multiple instances of the application simultaneously, enabling concurrent analysis of different video files. The fifth project implements a video player application with edge detection capabilities using various algorithms. The application is designed using the Tkinter library for the graphical user interface (GUI). Upon execution, the user is presented with a window containing control buttons and panels for displaying the video and extracted frames. The main functionalities of the application include opening a video file, playing, pausing, and stopping the video playback. Additionally, users can jump to a specific time in the video, extract frames, and open another instance of the video player application. The video playback is displayed on a canvas, allowing for zooming in and out using a combobox to adjust the scale. One of the key features of this application is the ability to perform edge detection on frames extracted from the video. When a frame is extracted, the application displays the original frame alongside its edge detection result using various algorithms such as Canny, Sobel, Prewitt, Laplacian, Scharr, Roberts, FreiChen, Kirsch, Robinson, Gaussian, or no edge detection. Histogram plots for each RGB channel of the frame are also displayed, along with hash values computed using different hashing algorithms for integrity verification. The edge detection result and histogram plots are updated dynamically based on the selected edge detection algorithm. Overall, this application provides a convenient platform for visualizing video content and performing edge detection analysis on individual frames, making it useful for tasks such as video processing, computer vision, and image analysis. The sixth project is a Python application built using the Tkinter library for creating a graphical user interface (GUI) to play videos and apply various filtering techniques to individual frames. The application allows users to open video files in common formats such as MP4, AVI, and MKV. Once a video is opened, users can play, pause, stop, and jump to specific times within the video. The GUI consists of two main panels: one for displaying the video and another for control buttons. The video panel contains a canvas where the frames of the video are displayed. Users can zoom in or out on the video frames using a combobox, and they can also scroll horizontally through the video using a scrollbar. Control buttons such as play/pause, stop, extract frame, and open another video player are provided in the control panel. When a frame is extracted, the application opens a new window displaying the extracted frame along with options to apply various filtering methods. These methods include Gaussian blur, mean blur, median blur, bilateral filtering, non-local means denoising, anisotropic diffusion, total variation denoising, Wiener filter, adaptive thresholding, and wavelet transform. Users can select a filtering method from a dropdown menu, and the filtered result along with the histogram and hash values of the frame are displayed in real-time. The application also provides functionality to open another instance of the video player, allowing users to work with multiple videos simultaneously. Overall, this project provides a user-friendly interface for playing videos and applying filtering techniques to individual frames, making it useful for tasks such as video processing, analysis, and editing.