Face Detection with OpenCV Haar Classifier

This function uses the Haar feature classifier in the OpenCV library for face detection, reads the image, converts it to grayscale, then detects faces in the image, and draws rectangles to mark the face positions on the original image.

Technology Stack : OpenCV, NumPy

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
def face_detection(image_path):
    import cv2
    import numpy as np

    # Load the face detection model
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    # Read the image
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

    # Draw rectangles around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)

    # Return the image with rectangles drawn around the faces
    return image