使用树莓派进行简易人脸识别

http://shumeipai.nxez.com/2017/03/16/raspberry-pi-face-recognition-system.html

使用树莓派2和OpenCV制作一个简易的人脸识别和追踪系统。

所需硬件

需要：树莓派2、Pi Camera
非必须（如果需要追踪人脸运动，需要一个有两个马达的小云台）：云台

安装OpenCV

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sudo apt-get update sudo apt-get upgrade sudo apt-get install python-opencv

安装PiCamera

由于我没有使用USB摄像头，而是用了特殊的Pi Camera，样子如下图， 所以需要安装PiCamera来控制摄像头。

安装PiCamera:

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sudo apt-get install python-pip sudo apt-get install python-dev sudo pip install picamera

至此人脸识别所需要的准备工作已经完成，可以使用下面的演示代码进行测试。

示例代码

Demo.1

第一个演示只使用单核，由于树莓派的性能有限，在只使用一个CPU核心的情况下视频的帧数非常之低，只有5帧左右，效果不太理想， 另外代码中通过Servo Blaster 控制云台的电机，来实现追踪人脸的功能，不过考虑到这个功能不是必须，所以不在此进行介绍。

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### Imports ###################################################################   from picamera.array import PiRGBArray from picamera import PiCamera import time import cv2 import os     ### Setup #####################################################################   # Center coordinates cx = 160 cy = 120   os.system( "echo 0=150 > /dev/servoblaster" ) os.system( "echo 1=150 > /dev/servoblaster" )   xdeg = 150 ydeg = 150   # Setup the camera camera = PiCamera() camera.resolution = ( 320, 240 ) camera.framerate = 60 rawCapture = PiRGBArray( camera, size=( 320, 240 ) )   # Load a cascade file for detecting faces face_cascade = cv2.CascadeClassifier( '/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml' )   t_start = time.time() fps = 0     ### Main ######################################################################   # Capture frames from the camera for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):       image = frame.array       # Use the cascade file we loaded to detect faces     gray = cv2.cvtColor( image, cv2.COLOR_BGR2GRAY )     faces = face_cascade.detectMultiScale( gray )       print "Found " + str( len( faces ) ) + " face(s)"       # Draw a rectangle around every face and move the motor towards the face     for ( x, y, w, h ) in faces:           cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )         cv2.putText( image, "Face No." + str( len( faces ) ), ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )           tx = x + w/2         ty = y + h/2           if   ( cx - tx >  10 and xdeg <= 190 ): xdeg += 3 os.system( "echo 0=" + str( xdeg ) + " > /dev/servoblaster" )         elif ( cx - tx < -10 and xdeg >= 110 ):             xdeg -= 3             os.system( "echo 0=" + str( xdeg ) + " > /dev/servoblaster" )           if   ( cy - ty >  10 and ydeg >= 110 ):             ydeg -= 3             os.system( "echo 1=" + str( ydeg ) + " > /dev/servoblaster" )         elif ( cy - ty < -10 and ydeg <= 190 ): ydeg += 3 os.system( "echo 1=" + str( ydeg ) + " > /dev/servoblaster" )       # Calculate and show the FPS     fps = fps + 1     sfps = fps / ( time.time() - t_start )     cv2.putText( image, "FPS : " + str( int( sfps ) ), ( 10, 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )          # Show the frame     cv2.imshow( "Frame", image )     cv2.waitKey( 1 )       # Clear the stream in preparation for the next frame     rawCapture.truncate( 0 )

 

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另外请注意由于我使用HaarCascade来进行人脸检测， 需要使用到识别人脸的XML，这些人脸识别的XML文件是随着OpenCV一起安装的，不需要额外的安装， 不过当你在自己树莓派上运行时，请注意调整XML文件的路径， 就是调整这一行：

 

1 2	# Load a cascade file for detecting faces face_cascade = cv2.CascadeClassifier( '你的XML文件路径' )

Demo.2

通过同时使用不同的XML文件，可以实现同时识别不同物体的功能，比如下面这段代码可以同时识别人脸和黑色手机，识别手机所需要的XML文件是由Radamés Ajna和Thiago Hersan制作的， 来源在这里。 更进一步的，我们可以根据自己的需要训练自己的Cascade文件，Naotoshi Seo在此处 给出了详细的教程， 比较简易的还有Thorsten Ball的香蕉识别教程。

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### Imports ###################################################################   from picamera.array import PiRGBArray from picamera import PiCamera   import time import cv2 import os import pygame     ### Setup #####################################################################   os.putenv('SDL_FBDEV', '/dev/fb1')   # Setup the camera camera = PiCamera() camera.resolution = ( 320, 240 ) camera.framerate = 40 rawCapture = PiRGBArray( camera, size=( 320, 240 ) )   # Load the cascade files for detecting faces and phones face_cascade = cv2.CascadeClassifier( '/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml' ) phone_cascade = cv2.CascadeClassifier( 'cascade.xml' )   t_start = time.time() fps = 0     ### Main ######################################################################   # Capture frames from the camera for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):       image = frame.array       # Look for faces and phones in the image using the loaded cascade file     gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)     faces  = face_cascade.detectMultiScale(gray)     phones = phone_cascade.detectMultiScale(gray)       # Draw a rectangle around every face     for (x,y,w,h) in faces:         cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 255, 255, 0 ), 2 )         cv2.putText( image, "Face No." + str( len( faces ) ), ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       # Draw a rectangle around every phone     for (x,y,w,h) in phones:         cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 255, 0, 0 ), 2 )         cv2.putText( image, "iPhone", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 255, 255 ), 2 )       # Calculate and show the FPS     fps = fps + 1     sfps = fps / ( time.time() - t_start )     cv2.putText( image, "FPS : " + str( int( sfps ) ), ( 10, 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       cv2.imshow( "Frame", image )     cv2.waitKey( 1 )       # Clear the stream in preparation for the next frame     rawCapture.truncate( 0 )

 

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由于使用了更多的XML文件进行识别，帧数降低到了2~3帧。

 

Demo.3

为了解决帧数较低的问题，有一个比较简单的方法就是跳帧，可以不对每一帧图像都进行识别，而是隔几帧识别一次（因为最初因为懒不想将程序写成多线程，但是为了提高帧数，所以有了这个蛋疼的方法…）。

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### Imports ###################################################################   from picamera.array import PiRGBArray from picamera import PiCamera   import time import cv2 import os import pygame     ### Setup #####################################################################   os.putenv( 'SDL_FBDEV', '/dev/fb1' )   # Setup the camera camera = PiCamera() camera.resolution = ( 320, 240 ) camera.framerate = 30 rawCapture = PiRGBArray( camera, size=( 320, 240 ) )   fcounter = 0 facefind = 0   # Load a cascade file for detecting faces face_cascade = cv2.CascadeClassifier( '/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml' )   t_start = time.time() fps = 0   ### Main ######################################################################   # Capture frames from the camera for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):       image = frame.array       # Run the face detection algorithm every four frames     if fcounter == 3:           fcounter = 0           # Look for faces in the image using the loaded cascade file         gray = cv2.cvtColor( image, cv2.COLOR_BGR2GRAY )         faces = face_cascade.detectMultiScale( gray )           print "Found " + str( len( faces ) ) + " face(s)"           if str( len( faces ) ) != 0:             facefind = 1             facess = faces         else:             facefind = 0           # Draw a rectangle around every face         for ( x, y, w, h ) in faces:             cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 200, 255, 0 ), 2 )             cv2.putText( image, "Face No." + str( len( facess ) ), ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )             facess = faces       else:         if facefind == 1 and str( len( facess ) ) != 0:               # Continue to draw the rectangle around every face             for ( x, y, w, h ) in facess:                 cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 200, 255, 0 ), 2 )                 cv2.putText( image, "Face No." + str( len( facess ) ), ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       fcounter += 1         # Calculate and show the FPS     fps = fps + 1     sfps = fps / ( time.time() - t_start )     cv2.putText( image, "FPS : " + str( int( sfps ) ), ( 10, 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       cv2.imshow( "Frame", image )     cv2.waitKey( 1 )       # Clear the stream in preparation for the next frame     rawCapture.truncate( 0 )

 

GIF

这样子帧数会提高到10帧左右，已经不像原来那么卡顿，但是当你移动速度很快的时候，识别框会出现滞后。

 

Demo.4

毕竟跳帧只是权宜之计，这个版本使用了全部的CPU核心，帧数稳定在了15帧左右。

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### Imports ###################################################################   from picamera.array import PiRGBArray from picamera import PiCamera from functools import partial   import multiprocessing as mp import cv2 import os import time     ### Setup #####################################################################   os.putenv( 'SDL_FBDEV', '/dev/fb0' )   resX = 320 resY = 240   cx = resX / 2 cy = resY / 2   os.system( "echo 0=150 > /dev/servoblaster" ) os.system( "echo 1=150 > /dev/servoblaster" )   xdeg = 150 ydeg = 150     # Setup the camera camera = PiCamera() camera.resolution = ( resX, resY ) camera.framerate = 60   # Use this as our output rawCapture = PiRGBArray( camera, size=( resX, resY ) )   # The face cascade file to be used face_cascade = cv2.CascadeClassifier('/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml')   t_start = time.time() fps = 0     ### Helper Functions ##########################################################   def get_faces( img ):       gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )     faces = face_cascade.detectMultiScale( gray )       return faces, img   def draw_frame( img, faces ):       global xdeg     global ydeg     global fps     global time_t       # Draw a rectangle around every face     for ( x, y, w, h ) in faces:           cv2.rectangle( img, ( x, y ),( x + w, y + h ), ( 200, 255, 0 ), 2 )         cv2.putText(img, "Face No." + str( len( faces ) ), ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )           tx = x + w/2         ty = y + h/2           if   ( cx - tx > 15 and xdeg <= 190 ): xdeg += 1 os.system( "echo 0=" + str( xdeg ) + " > /dev/servoblaster" )         elif ( cx - tx < -15 and xdeg >= 110 ):             xdeg -= 1             os.system( "echo 0=" + str( xdeg ) + " > /dev/servoblaster" )           if   ( cy - ty > 15 and ydeg >= 110 ):             ydeg -= 1             os.system( "echo 1=" + str( ydeg ) + " > /dev/servoblaster" )         elif ( cy - ty < -15 and ydeg <= 190 ): ydeg += 1 os.system( "echo 1=" + str( ydeg ) + " > /dev/servoblaster" )       # Calculate and show the FPS     fps = fps + 1     sfps = fps / (time.time() - t_start)     cv2.putText(img, "FPS : " + str( int( sfps ) ), ( 10, 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       cv2.imshow( "Frame", img )     cv2.waitKey( 1 )     ### Main ######################################################################   if __name__ == '__main__':       pool = mp.Pool( processes=4 )     fcount = 0       camera.capture( rawCapture, format="bgr" )        r1 = pool.apply_async( get_faces, [ rawCapture.array ] )        r2 = pool.apply_async( get_faces, [ rawCapture.array ] )        r3 = pool.apply_async( get_faces, [ rawCapture.array ] )        r4 = pool.apply_async( get_faces, [ rawCapture.array ] )          f1, i1 = r1.get()     f2, i2 = r2.get()     f3, i3 = r3.get()     f4, i4 = r4.get()       rawCapture.truncate( 0 )          for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):         image = frame.array           if   fcount == 1:             r1 = pool.apply_async( get_faces, [ image ] )             f2, i2 = r2.get()             draw_frame( i2, f2 )           elif fcount == 2:             r2 = pool.apply_async( get_faces, [ image ] )             f3, i3 = r3.get()             draw_frame( i3, f3 )           elif fcount == 3:             r3 = pool.apply_async( get_faces, [ image ] )             f4, i4 = r4.get()             draw_frame( i4, f4 )           elif fcount == 4:             r4 = pool.apply_async( get_faces, [ image ] )             f1, i1 = r1.get()             draw_frame( i1, f1 )               fcount = 0           fcount += 1           rawCapture.truncate( 0 )

帧数上升到了13左右，而且识别框没有延迟。

GIF

Demo.5

搞定了低帧数问题，我又试了试多核加跳帧…帧数可到28帧左右。

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### Imports ###################################################################   from picamera.array import PiRGBArray from picamera import PiCamera from functools import partial   import multiprocessing as mp import cv2 import os     ### Setup #####################################################################   os.putenv( 'SDL_FBDEV', '/dev/fb0' )   resX = 320 resY = 240   # Setup the camera camera = PiCamera() camera.resolution = ( resX, resY ) camera.framerate = 90   t_start = time.time() fps = 0   # Use this as our output rawCapture = PiRGBArray( camera, size=( resX, resY ) )   # The face cascade file to be used face_cascade = cv2.CascadeClassifier( '/home/pi/opencv-2.4.9/data/lbpcascades/lbpcascade_frontalface.xml' )     ### Helper Functions ##########################################################   def get_faces( img ):       gray = cv2.cvtColor( img, cv2.COLOR_BGR2GRAY )     return face_cascade.detectMultiScale( gray ), img   def draw_frame( img, faces ):       global fps     global time_t       # Draw a rectangle around every face     for ( x, y, w, h ) in faces:         cv2.rectangle( img, ( x, y ),( x + w, y + h ), ( 200, 255, 0 ), 2 )       # Calculate and show the FPS     fps = fps + 1     sfps = fps / (time.time() - t_start)     cv2.putText(img, "FPS : " + str( int( sfps ) ), ( 10, 10 ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )       cv2.imshow( "Frame", img )     cv2.waitKey( 1 )     ### Main ######################################################################   if __name__ == '__main__':       pool = mp.Pool( processes=4 )       i = 0     rList = [None] * 17     fList = [None] * 17     iList = [None] * 17       camera.capture( rawCapture, format="bgr" )        for x in range ( 17 ):         rList[x] = pool.apply_async( get_faces, [ rawCapture.array ] )         fList[x], iList[x] = rList[x].get()         fList[x] = []       rawCapture.truncate( 0 )          for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):         image = frame.array           if   i == 1:             rList[1] = pool.apply_async( get_faces, [ image ] )             draw_frame( iList[2], fList[1] )           elif i == 2:             iList[2] = image             draw_frame( iList[3], fList[1] )           elif i == 3:             iList[3] = image             draw_frame( iList[4], fList[1] )           elif i == 4:             iList[4] = image             fList[5], iList[5] = rList[5].get()             draw_frame( iList[5], fList[5] )           elif i == 5:             rList[5] = pool.apply_async( get_faces, [ image ] )             draw_frame( iList[6], fList[5] )           elif i == 6:             iList[6] = image             draw_frame( iList[7], fList[5] )           elif i == 7:             iList[7] = image             draw_frame( iList[8], fList[5] )           elif i == 8:             iList[8] = image             fList[9], iList[9] = rList[9].get()             draw_frame( iList[9], fList[9] )           elif i == 9:             rList[9] = pool.apply_async( get_faces, [ image ] )             draw_frame( iList[10], fList[9] )           elif i == 10:             iList[10] = image             draw_frame( iList[11], fList[9] )           elif i == 11:             iList[11] = image             draw_frame( iList[12], fList[9] )           elif i == 12:             iList[12] = image             fList[13], iList[13] = rList[13].get()             draw_frame( iList[13], fList[13] )           elif i == 13:             rList[13] = pool.apply_async( get_faces, [ image ] )             draw_frame( iList[14], fList[13] )           elif i == 14:             iList[14] = image             draw_frame( iList[15], fList[13] )           elif i == 15:             iList[15] = image             draw_frame( iList[16], fList[13] )           elif i == 16:             iList[16] = image             fList[1], iList[1] = rList[1].get()             draw_frame( iList[1], fList[1] )               i = 0           i += 1           rawCapture.truncate( 0 )

 

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