im-dpaul / Face-Detection-in-Image-using-Haar-Cascades

Project: Face and Eye Detection using Haar Cascade Classifier

Introduction:

This project demonstrates the use of Haar Cascade classifiers for real-time face and eye detection in images. It leverages OpenCV, a powerful library for image processing and computer vision tasks.

Key Concepts:

• Haar Cascade Classifiers: A type of machine learning algorithm used for object detection. It employs a cascade of simple features to efficiently detect objects like faces and eyes.
• OpenCV (Open Source Computer Vision Library): A comprehensive library with a wide range of functions for real-time computer vision.

Import Necessary Libraries:

• numpy: For numerical computations and array operations.
• cv2: For image processing and computer vision tasks.
• cv2_imshow (from Google Colab): To display images within a notebook environment.

Load Haar Cascade Classifiers:

• Load the pre-trained Haar cascade XML files for face and eye detection.

Read an Image:

• Load an image ("group_img.jpg" in this example) using cv2.imread().

Convert to Grayscale:

• Convert the image to grayscale using cv2.cvtColor().

Detect Faces:

• Apply the face cascade classifier to detect faces in the grayscale image.
• Iterate through the detected faces:
  • Draw rectangles around each face using cv2.rectangle().
  • Extract the region of interest (ROI) for each face for eye detection.

Detect Eyes:

• Apply the eye cascade classifier to detect eyes within each face ROI.
• Iterate through the detected eyes:
  • Draw rectangles around each eye using cv2.rectangle().

Display the Image:

• Show the final image with detected faces and eyes using cv2_imshow().

Running the Project:

1. Install required libraries: numpy and opencv-python.
2. Ensure you have the Haar cascade XML files (download from https://github.com/opencv/opencv/tree/master/data/haarcascades) in the same directory as your script.
3. Run the Python script to detect faces and eyes in the specified image.