A simple face detection project using Python and OpenCV, a popular computer vision library:

import cv2

# Load the pre-trained face detection model

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Load the image

image = cv2.imread('test_image.jpg')

# Convert the image to grayscale

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the grayscale image

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around the detected faces

for (x, y, w, h) in faces:

    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the image with detected faces

cv2.imshow('Face Detection', image)

cv2.waitKey(0)

cv2.destroyAllWindows()

1. We import the cv2 module from OpenCV, which provides functions for image processing and computer vision tasks.
2. We load the pre-trained face detection model using cv2.CascadeClassifier. This model is trained to detect frontal faces in images.
3. We load an image using cv2.imread.
4. We convert the image to grayscale using cv2.cvtColor.
5. We use the detectMultiScale method of the face cascade classifier to detect faces in the grayscale image. This method returns a list of rectangles representing the bounding boxes of the detected faces.
6. We iterate over the detected faces and draw rectangles around them using cv2.rectangle.
7. Finally, we display the image with detected faces using cv2.imshow. Press any key to close the window.