SihabSahariar/PyQt5-MVC-FaceDetection

This Python application allows you to detect faces in real-time from a webcam using the PyQt5 library for the graphical user interface and OpenCV for face detection. It features a simple graphical user interface with a "Start Webcam" button to toggle the webcam feed and display live face detection.

Face Detection from Webcam using PyQt5 and OpenCV (MVC Architecture)

Overview

This Python application allows you to detect faces in real-time from a webcam using the PyQt5 library for the graphical user interface and OpenCV for face detection. It features a simple graphical user interface with a "Start Webcam" button to toggle the webcam feed and display live face detection.

Table of Contents

• Components
• Usage
• Requirements
• Important Notes
• Credits

Components

1. Model:

   • Responsibility: The Model component represents the application's data and core logic. In this case, it's responsible for webcam access, video capture, and face detection.
   • Implementation:
     • The Model class encapsulates webcam-related functionality.
     • start_webcam() initializes and starts the webcam feed using OpenCV's video capture capabilities.
     • stop_webcam() stops the webcam feed and releases the video capture when the user decides to stop capturing video.
     • detect_faces() is responsible for capturing frames from the webcam, detecting faces in each frame, and processing the results.

2. View:

   • Responsibility: The View component is responsible for the user interface and presenting data to the user.
   • Implementation:
     • The View class is responsible for creating the GUI using PyQt5.
     • It defines the layout and contains a label for displaying the captured frames.
     • The display_frame(frame) method updates the label with the current frame that contains detected faces.
     • The toggle_webcam() method is called when the "Start Webcam" button is clicked. It toggles the webcam feed and updates the button text accordingly.

3. Controller:

   • Responsibility: The Controller component acts as an intermediary between the Model and View. It handles user input and controls the flow of data.
   • Implementation:
     • In the provided code, the Controller is not explicitly defined as a separate class. Instead, the Controller's role is shared between the Model and View classes.
     • The main functionality of starting and stopping the webcam feed is controlled by the toggle_webcam() method in the View class. This method interacts with the Model to initiate and terminate the webcam feed.

In this implementation:

• The View class takes user input (e.g., clicking the "Start Webcam" button) and communicates with the Model to start and stop the webcam feed.
• The Model handles the low-level webcam access, captures frames, performs face detection, and provides the processed data (frames with detected faces) to the View.
• The View class updates the graphical user interface with the data received from the Model.

Usage

1. Run the application.
2. Click the "Start Webcam" button to start the webcam feed and real-time face detection.
3. Click the button again to stop the webcam feed.

Requirements

• Python 3.x
• PyQt5: You can install it using pip: pip install PyQt5
• OpenCV (cv2): Install it with pip: pip install opencv-python

Important Notes

• The toggle_webcam method in the View class is responsible for starting and stopping the webcam feed. Adjust the timer interval in the Model class if needed to control the frame capture rate.
• Ensure that you have a working webcam and OpenCV correctly installed on your system for real-time face detection.

Credits

This code is based on the original code provided by the user, modified to enable real-time face detection from a webcam. The original code was adapted to this specific use case.

For a more detailed explanation and code implementation, please see the Python code in this repository.