FRAME FILTERING AND EDGES-DETECTION USING PYTHON AND TKINTER

The first project, leveraging libraries like OpenCV, Pillow, imageio, and Matplotlib, offers a streamlined interface for analyzing RGB histograms from video files. The main window is initialized using the AnalyzeHistogramFrame class, where users interact with buttons, labels, and canvases. Upon loading a video file via the "Open Video" button, the open_video() method utilizes imageio to display the first frame in the GUI canvas. Playback controls such as "Play/Pause" and "Stop" manage the video's playback state, with the show_frame() method continuously updating the displayed frame. Users can engage with the frame by zooming with the mouse wheel or defining a region of interest (ROI) through click-and-drag actions. Upon releasing the mouse button, the analyze_histogram method extracts the ROI, displaying it alongside its RGB histogram in a separate window, courtesy of Matplotlib. The histogram analysis process involves plotting individual RGB channel histograms, combined into a unified histogram. These plots are converted into Tkinter-compatible images for seamless integration into the GUI, empowering users with a comprehensive tool for visualizing and exploring video frame data. The second project is a Python application built with Tkinter, a GUI library, to enable users to analyze RGB histograms of the filtered of cropped image of a certain frame. It combines several libraries like PIL, imageio, OpenCV, NumPy, and Matplotlib to provide a comprehensive interface and analytical capabilities. The application's structure revolves around a class named Filter_CroppedFrame, responsible for managing the GUI and functionalities. Initially, the script imports necessary libraries and defines the Filter_CroppedFrame class. This class initializes the main window, sets up attributes, and creates GUI elements such as buttons, comboboxes, and canvas for video display. Users can load video files using a file dialog, which triggers the open_video() method to load the video via imageio. Playback controls for play, pause, and stop are provided, managed by methods like play_video(), toggle_play_pause(), and stop_video(). The show_frame() method updates the displayed frame based on the playback state and zoom level. Interactive analysis is facilitated through user interactions like zooming and drawing bounding boxes, handled by methods such as on_mousewheel(), on_press(), on_drag(), and on_release(). After drawing a bounding box and releasing the mouse button, the ...