Face Detection with OpenCV Tutorials

This page provides tutorials on face detection using OpenCV, a popular computer vision library. You can find step-by-step guides, code examples, and tips to help you get started with face detection.

What is Face Detection?

Face detection is the process of identifying and locating faces in digital images or videos. It's widely used in applications like surveillance, facial recognition, and augmented reality.

Tutorials

• Basic Face Detection: Learn how to perform basic face detection using OpenCV's built-in functions.
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• Advanced Face Detection: Explore more advanced techniques, including multi-scale face detection and face landmark detection.
  • 教程链接
• Real-time Face Detection: Discover how to implement real-time face detection using OpenCV and a web camera.
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Sample Code

Here's a simple example of face detection using OpenCV:

import cv2

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

# Read the image
image = cv2.imread('example.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

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

# Display the image
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Images

Here's an example of a detected face:

For more examples and tutorials, visit our OpenCV tutorials.