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-opencv123123
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 picamera123123
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 PiRGBArrayfrom picamera import PiCameraimport timeimport cv2import os### Setup ###################################################################### Center coordinatescx = 160cy = 120os.system( "echo 0=150 > /dev/servoblaster" ) os.system( "echo 1=150 > /dev/servoblaster" ) xdeg = 150ydeg = 150# Setup the cameracamera = PiCamera() camera.resolution = ( 320, 240 ) camera.framerate = 60rawCapture = PiRGBArray( camera, size=( 320, 240 ) )# Load a cascade file for detecting facesface_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 camerafor 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 )
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 PiRGBArrayfrom picamera import PiCameraimport timeimport cv2import osimport pygame### Setup #####################################################################os.putenv('SDL_FBDEV', '/dev/fb1')# Setup the cameracamera = PiCamera()
camera.resolution = ( 320, 240 )
camera.framerate = 40rawCapture = PiRGBArray( camera, size=( 320, 240 ) )# Load the cascade files for detecting faces and phonesface_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 camerafor 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 )
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 PiRGBArrayfrom picamera import PiCameraimport timeimport cv2import osimport pygame### Setup #####################################################################os.putenv( 'SDL_FBDEV', '/dev/fb1' )# Setup the cameracamera = PiCamera() camera.resolution = ( 320, 240 ) camera.framerate = 30rawCapture = PiRGBArray( camera, size=( 320, 240 ) ) fcounter = 0facefind = 0# Load a cascade file for detecting facesface_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 camerafor 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 )
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 PiRGBArrayfrom picamera import PiCamerafrom functools import partialimport multiprocessing as mpimport cv2import osimport time### Setup #####################################################################os.putenv( 'SDL_FBDEV', '/dev/fb0' )
resX = 320resY = 240cx = resX / 2cy = resY / 2os.system( "echo 0=150 > /dev/servoblaster" )
os.system( "echo 1=150 > /dev/servoblaster" )
xdeg = 150ydeg = 150# Setup the cameracamera = PiCamera()
camera.resolution = ( resX, resY )
camera.framerate = 60# Use this as our outputrawCapture = PiRGBArray( camera, size=( resX, resY ) )# The face cascade file to be usedface_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, imgdef 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。
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 PiRGBArrayfrom picamera import PiCamerafrom functools import partialimport multiprocessing as mpimport cv2import os### Setup #####################################################################os.putenv( 'SDL_FBDEV', '/dev/fb0' ) resX = 320resY = 240# Setup the cameracamera = PiCamera() camera.resolution = ( resX, resY ) camera.framerate = 90t_start = time.time() fps = 0# Use this as our outputrawCapture = PiRGBArray( camera, size=( resX, resY ) )# The face cascade file to be usedface_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 ), imgdef 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 )
跳帧加多核，强行30帧哈哈，不过还是建议最终使用Demo4。
09/23/2016 Update:
我使用的云台是这个，马达型号是sg90。
ServoBlaster的下载地址是：链接
本文原文来源于：使用树莓派进行简易人脸识别
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