Practical demonstration: use Python to write face recognition test code, so that your application has intelligent perception

Face recognition is an important application in the field of computer vision. It uses computer algorithms to identify and verify facial features, and is often used in security authentication, video surveillance, face comparison, etc. In recent years, with the development of deep learning technology, the performance of face recognition has been greatly improved, and it has become an important part of intelligent perception applications. In this article, we will use Python to write face recognition test code, so that your application can also have IntelliSense.

1. Environmental preparation

Before we start, we need to prepare the Python environment and related dependent libraries. The following are the main libraries we need to use:

OpenCV: a computer vision library that provides various image and video processing functions;
NumPy: a numerical computing library for efficiently processing arrays and matrices;
face_recognition: a face recognition library based on deep learning that provides trained face Detect and identify models.
The installation method can be completed using the pip command, the specific command is as follows:

pip install opencv-python numpy face_recognition

2. Face detection

Before performing face recognition, we first need to detect the faces in the image. In this article, we are using the face detection algorithm in the face_recognition library. Based on deep learning, the algorithm can quickly and accurately detect human faces in images.

Here is a simple code example demonstrating how to use the face_recognition library for face detection:

import face_recognition
import cv2

# 加载图像
image = cv2.imread('test.jpg')

# 将图像从BGR格式转换为RGB格式
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 检测图像中的所有人脸
face_locations = face_recognition.face_locations(rgb_image)

# 在图像中绘制人脸框
for (top, right, bottom, left) in face_locations:
    cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)

# 显示结果图像
cv2.imshow('Image', image)
cv2.waitKey(0)

After running the code, the original image will be displayed in the window, and the detected face frame will be marked in the image.

3. Face recognition

Face recognition is the process of comparing a detected face with known faces to determine its identity. In this article, we are using the face recognition algorithm in the face_recognition library. The algorithm uses deep learning technology to efficiently recognize faces and compare identities.

Here is a simple code example demonstrating how to use the face_recognition library for face recognition:

import face_recognition
import cv2

# 加载已知人脸图像和对应的身份信息
known_faces = [
    face_recognition.load_image_file("person1.jpg"),
    face_recognition.load_image_file("person2.jpg"),
    face_recognition.load_image_file("person3.jpg"),
]
known_names = ["Person 1", "Person 2", "Person 3"]

# 加载测试图像
image = cv2.imread('test.jpg')

# 将图像从BGR格式转换为RGB格式
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# 检测图像中的所有人脸
face_locations = face_recognition.face_locations(rgb_image)
face_encodings = face_recognition.face_encodings(rgb_image, face_locations)

# 遍历所有检测到的人脸，进行比对
for face_encoding in face_encodings:
    # 与已知人脸进行比对
    matches = face_recognition.compare_faces(known_faces, face_encoding)
    name = "Unknown"
    # 找到最佳匹配的人脸
    if True in matches:
        match_index = matches.index(True)
        name = known_names[match_index]
    # 在图像中绘制人脸框和身份信息
    top, right, bottom, left = face_locations[0]
    cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
    cv2.putText(image, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 1)

# 显示结果图像
cv2.imshow('Image', image)
cv2.waitKey(0)

After running the code, the original image will be displayed in the window, and the detected face frame and corresponding identity information will be marked in the image.

Four. Summary

In this article, we use Python to write test codes for face detection and face recognition, and demonstrate how to use the face_recognition library to implement these functions. Face recognition technology has been widely used in modern intelligent perception applications, such as face recognition access control, face comparison, face recognition payment, etc. Using Python to write face recognition code can help us quickly develop applications with intelligent perception.