python人脸识别应用环境搭建

工具DLIb+face_rcognition+opencv

安装过程如下：

step1：准备安装包

1.Anaconda3-5.0.1-Windows-x86_64.exe

2.dlib-19.7.0-cp36-cp36m-win_amd64.whl

3.face_recognition-1.0.0-py2.py3-none-any.whl

4.opencv_python-3.3.0.10-cp36-cp36m-win_amd64.whl

step2 安装

1.直接运行Anaconda安装文件

2.命令行下pip install dlib-19.7.0-cp36-cp36m-win_amd64.whl

3.命令行下pip install face_recognition-1.0.0-py2.py3-none-any.whl

4.命令行下pip install opencv_python-3.3.0.10-cp36-cp36m-win_amd64.whl

step3 实时人脸识别

spyder打开face_recognition\examples\facerec_from_webcam_faster.py，稍加修改即可实现。

代码如下：

import face_recognition

import cv2

import time

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the

# other example, but it includes some basic performance tweaks to make things run a lot faster:

#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)

#   2. Only detect faces in every other frame of video.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.

# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this

# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)

video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.

obama_image = face_recognition.load_image_file("hdk.jpg")

#128 dimensions feature

obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Initialize some variables

face_locations = []

face_encodings = []

face_names = []

process_this_frame = True

while True:

    # Grab a single frame of video

    ret, frame = video_capture.read()

    

    #if(frame==None):  

        #continue

    # Resize frame of video to 1/4 size for faster face recognition processing

    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Only process every other frame of video to save time

    if process_this_frame:

        # Find all the faces and face encodings in the current frame of video

        starttime = time.clock()

        face_locations = face_recognition.face_locations(small_frame)

        detectTime = time.clock()

        face_encodings = face_recognition.face_encodings(small_frame, face_locations)

        featureExTime = time.clock()

        print("face detect time:%f s" %(detectTime-starttime))

        print("face  features extract time:%f s" %(featureExTime-detectTime))

        face_names = []

        for face_encoding in face_encodings:

            # See if the face is a match for the known face(s)

            starttime =  time.clock()

            match = face_recognition.compare_faces([obama_face_encoding], face_encoding)

            fvTime= ( time.clock()-starttime)

            print("face verification time:%f s" %(fvTime))

            name = "Unknown"

            if match[0]:

                name = "hudekun"

            face_names.append(name)

    process_this_frame = not process_this_frame

    # Display the results

    for (top, right, bottom, left), name in zip(face_locations, face_names):

        # Scale back up face locations since the frame we detected in was scaled to 1/4 size

        top *= 4

        right *= 4

        bottom *= 4

        left *= 4

        # Draw a box around the face

        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face

        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)

        font = cv2.FONT_HERSHEY_DUPLEX

        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image

    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

# Release handle to the webcam

video_capture.release()

cv2.destroyAllWindows()

step 4 效果如下：

总结，由于采用深度学习，人脸特征学习时间0.436052 s，需要做实时性改进。