Computer Vision Example in Python: Face Recognition
Python is a high-level programming language, easy to learn and understand, and suitable for beginners and professional developers. Python is widely used in the field of artificial intelligence, and computer vision is one of the very important applications. Face recognition is one of the most important applications in the field of computer vision. This article will introduce how to use Python to implement face recognition.
1. Introduction to face recognition
Face recognition technology refers to the technology that automatically locates, tracks, identifies and verifies faces in digital images. Face recognition can obtain images through various methods such as optical, infrared and artificial enhancement. The feature information of the face image is obtained through computer vision algorithms and compared with the existing face feature database to perform identity verification or identity recognition operations.
Face recognition technology has been widely used and plays an increasingly important role in finance, security, smart home and other fields. There are a wealth of computer vision libraries in Python that can support the implementation of face recognition technology. The implementation method of face recognition technology will be introduced below.
2. Face recognition library in Python
Python provides a variety of face recognition libraries, including OpenCV, dlib, etc. These libraries support computer vision technologies such as face detection, face recognition, face tracking, and face labeling.
OpenCV is one of the most popular computer vision libraries in Python. It provides a variety of algorithm libraries for implementing face recognition technology. Image processing, face detection, feature extraction, classifier training, etc. all have very complete support. dlib is an excellent face recognition library that uses deep learning technology and has good support for face detection.
3. Implementing face recognition in Python
Below we will use a simple example to implement face recognition in Python.
First, we need to install OpenCV and dlib libraries. We can install these libraries using pip as follows:
pip install opencv-python pip install dlib
Next, we need to prepare a set of face images as templates, which can be used for comparison and recognition. We can save these image files in local file system.
In Python, we need to use the computer vision library to process the image, as shown below:
import cv2
import dlib
import numpy as np
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
face_path = './faces'
def get_face_list(face_path):
return os.listdir(face_path)
faces_list = get_face_list(face_path)
face_descriptors = []
for face in faces_list:
img = cv2.imread(os.path.join(face_path, face))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for rect in faces:
shape = predictor(gray, rect)
face_descriptor = np.array(face_rec_model.compute_face_descriptor(img, shape))
face_descriptors.append(face_descriptor)The above code implements the function of detecting faces in the image. We used the face detector in the dlib library to detect faces in the image and obtain facial features, and record these features.
After obtaining the facial features, we need to use these features for face recognition, as shown below:
def recognize_face():
img = cv2.imread('test.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for rect in faces:
shape = predictor(gray, rect)
face_descriptor = np.array(face_rec_model.compute_face_descriptor(img, shape))
for i, face in enumerate(face_descriptors):
dist = np.linalg.norm(face - face_descriptor)
if dist < 0.6:
return "This is " + faces_list[i][:-4]
return "Unknown face"The above code realizes the matching of the face in the image with the existing Face database comparison function. We used numpy to calculate the Euclidean distance between the face feature values in the image and the feature values in the template library. If the distance is less than a certain threshold (usually 0.6), it is considered to be the same face. Finally, we will output the comparison results.
4. Summary
This article introduces how to use Python to implement face recognition technology. We used the OpenCV and dlib libraries to implement face detection and feature extraction functions, and used the numpy library to calculate the Euclidean distance between face feature values, thus realizing the face recognition technology. Python is widely used in the field of computer vision and also plays a very important role in actual development.
The above is the detailed content of Computer Vision Example in Python: Face Recognition. For more information, please follow other related articles on the PHP Chinese website!
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