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Nowadays, face recognition and detection is an indispensable part of life, withlinux face recognition technology being used to facilitate security and access control in banks and airports. In this article, we will discuss how to use Linux to implement face recognition and detection.
發(fā)展壯大離不開(kāi)廣大客戶(hù)長(zhǎng)期以來(lái)的信賴(lài)與支持，我們將始終秉承“誠(chéng)信為本、服務(wù)至上”的服務(wù)理念，堅(jiān)持“二合一”的優(yōu)良服務(wù)模式，真誠(chéng)服務(wù)每家企業(yè)，認(rèn)真做好每個(gè)細(xì)節(jié)，不斷完善自我，成就企業(yè)，實(shí)現(xiàn)共贏。行業(yè)涉及地磅秤等，在重慶網(wǎng)站建設(shè)公司、營(yíng)銷(xiāo)型網(wǎng)站、WAP手機(jī)網(wǎng)站、VI設(shè)計(jì)、軟件開(kāi)發(fā)等項(xiàng)目上具有豐富的設(shè)計(jì)經(jīng)驗(yàn)。
First, we will need to install the necessary packages on a Linux system. Packages such as opencv, face_recognition, imutils, and numpy are needed to perform Linux face recognition and detection. Installing the packages is simple, and the commands can be found in the opencv documentation.
Once the packages are installed, the next step is to create a motion detection script in Linux. Python and bash are both great scripting languages to use for Linux face recognition and detection. Python is particularly well-suited to this task, as it is easy to use and provides powerful tools for image manipulation and analysis. Here is a basic example of a motion detection script written in python:
import cv2, imutils, face_recognition
cam = cv2.VideoCapture(0)
while True:
ret, frame = cam.read()
# Resize and find the face in the frame
frame = imutils.resize(frame, width=500)
face_locations = face_recognition.face_locations(frame)
# Iterate over each located face
for (top, right, bottom, left) in face_locations:
# Draw a blue rectangle around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
# Show the frame
cv2.imshow(‘Detection’, frame)
# Quit program if ‘q’ is pressed
if cv2.waitKey(1) & 0xFF == ord(‘q’):
break
cam.release()
cv2.destroyAllWindows()
The next step is to create a recognition script. This script will be used to identify the faces in the frame and output the results. The recognition script will use the face recognition package to compare the face in the frame to the known faces in a database. This is the basic structure of the recognition script:
# Load known faces from the database
known_faces = face_recognition.load_image_file(‘known_faces.jpg’)
encode_known_faces = face_recognition.face_encodings(known_faces)[0]
# Iterate over each face in the frame
for face in face_locations:
# Encode the face
face_encoding = face_recognition.face_encodings(face)[0]
# Compare it to the known faces
match = face_recognition.compare_faces([encode_known_faces], face_encoding)[0]
# Output the results
if match:
print(‘MATCHED!’)
else:
print(‘NO MATCH!’)
With the motion detection and recognition scripts in place, we now have a fully functional Linux face recognition and detection system. This system can be used in many applications including security, access control, and identity verification. The scripts can be easily modified and added to in order to make the system more sophisticated and accurate. Linux face recognition and detection is an important technology in today’s society, and it is only getting better.
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本文標(biāo)題：Linux實(shí)現(xiàn)的人臉識(shí)別與檢測(cè)（linux人臉檢測(cè)）
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