🧑‍💻 Face Detection with OpenCV and DNN 📸

This is a Python application that detects faces in an image using OpenCV's DNN-based face detection model. The program uses TensorFlow's pre-trained model for better accuracy (75%+ confidence). A simple GUI built with Tkinter allows you to select an image, detect faces, and display the result.

🚀 Features

• 👁️ Accurate face detection using DNN (Deep Neural Networks).
• 🖼️ Simple GUI built with Tkinter to load and display images.
• 🔍 Face detection with confidence threshold of 75% or higher.
• 🔄 Resizes the image to fit the window while maintaining aspect ratio.

⚙️ Requirements

• Python 3.x
• OpenCV
• Pillow
• NumPy
• Tkinter (usually comes pre-installed with Python)

🛠️ Installation

1. Open CMD 🖥️

Operating System	Steps
Windows 💻	1. Press Windows + R to open the "Run" dialog box. 2. Type cmd and hit Enter. 3. The Command Prompt (CMD) will open. Alternatively, you can search for "Command Prompt" in the Start menu and click to open it. 🔍 4. To navigate to the Desktop, type cd %USERPROFILE%\Desktop and hit Enter. 📂
Linux 🐧	1. Press Ctrl + Alt + T to open the terminal. 2. Alternatively, search for "Terminal" in your applications menu. 💨 3. To navigate to the Desktop, type cd ~/Desktop and hit Enter. 📂

2. Clone the repository:

git clone https://github.com/LaithALhaware/Fast-Face-Detection-with-OpenCV.git
cd face-detection-opencv

3. Install the required dependencies :

pip install -r requirements.txt

🚀 Usage

1- Run the script:

python face_detection.py

2- Click the Open Image button to load an image.

3- The application will detect faces in the image and display them with rectangles.

4- You can adjust the confidence threshold in the code if needed (default is 75%).

📝 Code Explanation

• DNN Model: We use OpenCV's DNN module with a pre-trained TensorFlow face detection model for more accurate face detection.
• GUI: Tkinter is used to create a simple interface for opening and displaying images.
• Confidence Filtering: The program only detects faces with confidence greater than 75%.

📝 License

This project is licensed under the License. See the LICENSE.txt ⚖️ file for details.

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