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使用树莓派进行简易人脸识别

昨天的晚会让人脸识别又火了,转载一篇来自 CSDN JireRen 的精彩博文,借花献佛给大家一起尝试DIY树莓派上的人脸识别。

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

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:

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。

### 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 )

GIF

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:

# 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

### 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 )

GIF

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…)。

### 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。

### 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。

### 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 )

GIF

跳帧加多核,强行30帧哈哈,不过还是建议最终使用Demo4。

这篇博客节选翻译自我自己的课程报告, 同样的内容也出现于我自己的英文博客, 最后出镜的是我的搭档Andre Heil。

via 转自 JireRen 的博客。

本文来自:树莓派实验室
链接地址:http://shumeipai.nxez.com/2017/03/16/raspberry-pi-face-recognition-system.html

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