Face and Eye Detection using Haar Cascades

This project demonstrates how to detect faces and eyes in an image using Haar cascades in OpenCV. Haar cascades are trained classifiers capable of identifying specific objects or features within an image.

Prerequisites

• Python 3.x
• OpenCV library
• Haar cascade files: haarcascade_frontalface_default.xml and haarcascade_eye.xml

Installation

1. Install Python by downloading it from the official Python website (https://www.python.org) and following the installation instructions for your operating system.

2. Install OpenCV library using pip:

   pip install opencv-python
   

Usage

1. Clone or download the project files from the repository.

2. Replace the image path in the code (C:/Users/91630/Downloads/gg.jfif) with the path to your own image.

3. Run the Python script, and the output will display the detected faces and eyes in separate images.

Code Explanation

1. Import the necessary libraries: cv2 for image processing, numpy for numerical computations, and matplotlib.pyplot for visualizations.

2. Read the input image and create copies.

3. Load the Haar cascade classifiers for face and eye detection.

4. Define a function adjusted_detect_face to detect faces in the image using the face cascade classifier. This function draws rectangles around the detected faces and returns the modified image.

5. Define a function detect_eyes to detect eyes in the image using the eye cascade classifier. This function draws rectangles around the detected eyes and returns the modified image.

6. Apply the adjusted_detect_face function to the image copy, and display the detected faces using plt.imshow().

7. Save the image with detected faces using cv2.imwrite().

8. Apply the detect_eyes function to another image copy, and display the detected eyes using plt.imshow().

9. Save the image with detected eyes using cv2.imwrite().

10. Create a weighted combination of the images with detected faces and eyes using cv2.addWeighted(). This produces an overlay of the two images.

11. Display the final image with the overlay using plt.imshow().

12. Save the final image using cv2.imwrite().

13. Show the image using cv2.imshow(), and wait for a key press to close the window.

14. Clean up and close all windows using cv2.destroyAllWindows().

Result

The script detects faces and eyes in the input image and saves three images: one with detected faces, one with detected eyes, and one with a combination of both. The images are also displayed during the execution of the script.# python-open-cv I have used an image of Israeli actress Gal Gadot for face and eye detection. Here is the original image:

The script detects faces and eyes in the image and produces the following results:

1.Detected faces: