Author: Vivian SiahaanPublisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 47
Book Description
In this project, we explored a Python script designed for object recognition using Haar Cascades within a graphical user interface (GUI) built with the Tkinter library. The script was organized into multiple modules, each serving a distinct purpose. The core functionality was encapsulated within the Form_Haar_Cascades class, which defined a Tkinter window containing various widgets for specifying parameters and visualizing object detection results. The class utilized Haar Cascades for detecting facial features, such as eyes, noses, and mouths, in images. It also integrated noise generation features through the Noise_Utils class, enhancing the versatility of the object recognition application. A key aspect of the script was the integration of noise parameters, allowing users to introduce different types of noise (e.g., Gaussian, salt-and-pepper) to the input image before applying Haar Cascades. This feature was facilitated by the Noise_Utils class, which utilized NumPy and OpenCV for image manipulation. Additionally, the GUI offered flexibility by enabling users to adjust Haar Cascades parameters, such as the scale factor, minimum neighbors, and line width, through interactive widgets. The plotting capabilities of the application were extended using the Plot_Utils class, which created a separate window for visualizing the results of Haar Cascades object detection. This additional functionality enhanced the user experience by providing a dedicated space for exploring the outcomes of different object detection scenarios. The modular design of the script, with distinct classes for Haar Cascades, noise generation, and plotting, promoted code organization and maintainability. The main program, represented by the Main_Program class, orchestrated the integration of these components, configuring the layout of the main Tkinter window, handling event bindings, and managing the overall flow of the GUI application. Finally, the script was encapsulated in a conditional block that checked if the script was executed as the main program. If so, it instantiated the Tkinter root window, initialized the Main_Program class, and entered the Tkinter event loop to display the GUI. This ensured that the application could be run independently, launching the GUI for users to interact with and explore Haar Cascades object recognition with integrated noise features.
