😊 Face Detection Application with OpenCV and Tkinter 🎉

Welcome to the Face Detection Application! This application leverages OpenCV for real-time face, eye, smile, and object detection. It also includes advanced features such as voice commands, encryption, and decryption. Below, you'll find a detailed explanation of the functionalities and features implemented.

📦 Features

Real-Time Detection
- Detects faces, eyes, smiles, and objects using pre-trained Haar Cascades.
- Displays detection results on the video stream from the webcam.
- Calculates and displays the frames per second (FPS).
Snapshot Capture 📸
- Take snapshots of the current video frame and save them as JPEG images.
Voice Commands 🎤
- Control the application using voice commands to start/stop detection, take snapshots, open settings, encrypt/decrypt data, and exit the application.
Encryption/Decryption 🔒
- Encrypt and decrypt data using AES encryption with a generated key.
- Generate and verify HMAC for data integrity.
Settings Configuration ⚙️
- Configure Region of Interest (ROI) for detection via a settings window.
Log Viewing 📜
- View detection logs that record face detection events with timestamps and coordinates.
Detect from Photo and Video 📷🎥
- Detect faces, eyes, smiles, and objects from static images and video files.

Purpose and Goals 🎯

Purpose

The purpose of this project is to develop a robust face detection application using OpenCV and Tkinter, integrated with advanced features such as voice command recognition, data encryption, and photo/video processing.

Goals

Implement real-time face, eye, smile, and object detection using Haar cascades.
Provide an intuitive user interface with Tkinter for easy interaction.
Enable voice commands for starting/stopping detection, taking snapshots, and other actions.
Implement AES encryption for secure data storage and transmission.
Allow users to configure regions of interest (ROIs) for detection.
Provide options to reduce the size of photos and videos to save storage space.
Implement logging for tracking detection events.

🚀 Getting Started

Prerequisites

Python 3.x
OpenCV
Tkinter
PIL (Pillow)
SpeechRecognition
Pyttsx3
Cryptography

Install the required libraries using pip:

pip install opencv-python-headless tk pillow SpeechRecognition pyttsx3 cryptography

Running the Application 🏃‍♂️

Clone the Repository 📂

git clone https://github.com/your-repo/face-detection-app.git
cd face-detection-app

Run the Application ▶️

python face detection/app.py

🛠️ Detailed Explanation

Main Components 🔧

Haar Cascades 📸

Loaded using OpenCV to detect faces, eyes, smiles, and objects.

face_cascade = cv2.CascadeClassifier('../Cascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('../Cascades/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('../Cascades/haarcascade_smile.xml')
object_cascade = cv2.CascadeClassifier('../Cascades/haarcascade_car.xml')

Camera Setup 🎥

Initializes the webcam for real-time video capture.

kamera = cv2.VideoCapture(0)
if not kamera.isOpened():
    messagebox.showerror("Error", "Could not open camera.")
    exit()

Detection and Display 👀

Continuously captures frames from the webcam, performs detection, and displays the results.

def detect_and_display():
    while not stop_thread:
        ret, frame = kamera.read()
        ...
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5, minSize=(20, 30))
        for (x, y, w, h) in faces:
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
            ...
        frame_queue.put(frame)
    kamera.release()
    out.release()
    cv2.destroyAllWindows()

Voice Commands 🎤

Uses the SpeechRecognition library to capture and interpret voice commands.

def detect_and_display():def voice_command_listener():
    recognizer = sr.Recognizer()
    mic = sr.Microphone()
    ...
    try:
        command = recognizer.recognize_google(audio).lower()
        ...
        if "start detection" in command:
            start_detection()
        ...
    except sr.UnknownValueError:
        print("Sorry, I did not understand the command.")
    except sr.RequestError:
        print("Could not request results from Google Speech Recognition service")

Encryption/Decryption 🔐

Implements AES encryption and decryption using the Cryptography library.

class CustomEncryption:
    def __init__(self, key=None):
        if key:
            self.key = key
        else:
            self.key = self.generate_key()
    ...
    def encrypt(self, data):
        iv = os.urandom(16)
        encryptor = Cipher(algorithms.AES(self.key), modes.CFB(iv), backend=default_backend()).encryptor()
        cipher_text = encryptor.update(data) + encryptor.finalize()
        return iv + cipher_text
    ...
    def decrypt(self, encrypted_data):
        iv = encrypted_data[:16]
        decryptor = Cipher(algorithms.AES(self.key), modes.CFB(iv), backend=default_backend()).decryptor()
        decrypted_data = decryptor.update(encrypted_data[16:]) + decryptor.finalize()
        return decrypted_data

GUI with Tkinter 🎨

The application interface is built using Tkinter, with buttons for different functionalities and a video display area.

root = Tk()
root.title("Face Detection with OpenCV and Tkinter")
root.geometry("900x700")
root.configure(bg='lightblue')
...
lbl_video = Label(root, bg='black')
lbl_video.pack(fill=BOTH, expand=YES, padx=10, pady=10)
...
frame_controls = ttk.Frame(root)
...
btn_start = ttk.Button(frame_controls, text="Start Detection", command=start_detection)
...
btn_exit = ttk.Button(frame_controls, text="Exit", command=root.quit)
...
lbl_info = ttk.Label(root, text="Press 'ESC' to exit the video display.")
lbl_info.pack(pady=5)

Advanced Functionalities 🌟

Region of Interest (ROI) Configuration 🔍

Allows the user to specify a region of interest for detection.

def open_settings():
    ...
    lbl_scale_factor = Label(settings_window, text="ROI x1")
    ...
    def save_settings():
        global roi
        x1 = int(scale_factor_entry.get())
        y1 = int(min_neighbors_entry.get())
        x2 = int(min_size_entry.get())
        y2 = int(min_size_entry2.get())
        roi = (x1, y1, x2, y2)
        settings_window.destroy()
        speak("Settings saved")

Snapshot Capture 📸

Saves the current video frame as a JPEG image.

def take_snapshot():
    if not stop_thread:
        ret, frame = kamera.read()
        ...
        cv2.imwrite(filename, frame)
        messagebox.showinfo("Snapshot", "Snapshot saved successfully!")
        speak("Snapshot saved successfully")

Log Viewing 📜

Displays a log of detection events.

def view_log():
    log_window = Toplevel(root)
    log_window.title("Detection Log")
    log_window.geometry("600x400")
    with open('detection_log.txt', 'r') as file:
        log_content = file.read()
    log_text = Text(log_window)
    log_text.insert(1.0, log_content)
    log_text.pack(expand=True, fill=BOTH)
    log_window.mainloop()

Additional Features to Add to the Project 🚀

Cybersecurity Features 🔒

Data Encryption: Encrypt data using AES encryption with the Cryptography library and store encrypted data.
HMAC Verification: Use HMAC (Hash-based Message Authentication Code) for data integrity verification.

Photo and Video Processing Features 📷🎥

Photo Size Reduction: Option to reduce the size of captured photos.
Video Size Reduction: Option to reduce the size of recorded videos.

Other Features 🎙️

Logging: Write detection events to a log file.
Voice Command Recognition: Use the SpeechRecognition library to detect and process voice commands.

Frequently Asked Questions (FAQ) ❓

Question: How can I start the application?
Answer: To start the application, run the command python app.py in your terminal.

Question: How can I process photos and videos?
Answer: You can process photos and videos by selecting the Photo/Video option to detect them.

Question: How can I use voice commands in the application?
Answer: Use your microphone to detect and process commands. For example, start the detection by saying "Start Detection".

Question: How can I configure the region of interest (ROI) for detection?
Answer: Open the settings window and enter the desired coordinates for x1, y1, x2, and y2.

Question: Can I view the log of detection events?
Answer: Yes, you can view the log of detection events by selecting the View Log option in the menu.

Question: How does the application handle encryption and decryption?
Answer: The application uses AES encryption for secure data storage and transmission. You can encrypt and decrypt data using the Encrypt and Decrypt options.

Question: What languages does the application support for voice commands?
Answer: The application currently supports English for voice commands. Support for other languages may be added in future updates.

Collaboration and Contact Information 🤝📧

Collaborate

Contributions: Contributions are welcome! Feel free to fork the repository and submit pull requests.
Issues: Found a bug or have a feature request? Open an issue on GitHub.

Contact

Email: You can reach us at piinartp@gmail.com for any inquiries.
LinkedIn: Connect with us on LinkedIn for updates and discussions.

🌟 Conclusion

This Face Detection Application is a robust and feature-rich tool for real-time face and object detection, enhanced with voice command functionality and data encryption capabilities. Feel free to explore and modify the code to suit your needs. Enjoy using the application! 🎉

For any issues or contributions, please open an issue or submit a pull request on our GitHub repository.

ThecoderPinar/AdvancedFaceEyeSmileRecognitionAndCryptographicSystem