使用樹莓派2和OpenCV制作一個簡易的人臉識別和追蹤系統。
所需硬件
需要：
樹莓派2
Pi Camera
非必須(如果需要追蹤人臉運動，需要一個有兩個馬達的小云臺)：
云臺
安裝OpenCV
sudo apt-get update sudo apt-get upgrade sudo apt-get install python-opencv123123
安裝PiCamera
由于我沒有使用USB攝像頭，而是用了特殊的Pi Camera，樣子如下圖， 所以需要安裝PiCamera來控制攝像頭。
安裝PiCamera:
sudo apt-get install python-pip sudo apt-get install python-dev sudo pip install picamera123123
至此人臉識別所需要的準備工作已經完成，可以使用下面的演示代碼進行測試。
示例代碼
Demo.1
第一個演示只使用單核，由于樹莓派的性能有限，在只使用一個CPU核心的情況下視頻的幀數非常之低，只有5幀左右，效果不太理想， 另外代碼中通過Servo Blaster 控制云臺的電機，來實現追蹤人臉的功能，不過考慮到這個功能不是必須，所以不在此進行介紹。
### 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 )
另外請注意由于我使用HaarCascade來進行人臉檢測， 需要使用到識別人臉的XML，這些人臉識別的XML文件是隨著OpenCV一起安裝的，不需要額外的安裝， 不過當你在自己樹莓派上運行時，請注意調整XML文件的路徑， 就是調整這一行：
# Load a cascade file for detecting faces face_cascade = cv2.CascadeClassifier( '你的XML文件路徑' )
Demo.2
通過同時使用不同的XML文件，可以實現同時識別不同物體的功能，比如下面這段代碼可以同時識別人臉和黑色手機，識別手機所需要的XML文件是由Radamés Ajna和Thiago Hersan制作的， 來源在這里。 更進一步的，我們可以根據自己的需要訓練自己的Cascade文件，Naotoshi Seo在此處 給出了詳細的教程， 比較簡易的還有Thorsten Ball的香蕉識別教程。
### 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文件進行識別，幀數降低到了2~3幀。
Demo.3
為了解決幀數較低的問題，有一個比較簡單的方法就是跳幀，可以不對每一幀圖像都進行識別，而是隔幾幀識別一次(因為最初因為懶不想將程序寫成多線程，但是為了提高幀數，所以有了這個蛋疼的方法…)。
### 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 )
這樣子幀數會提高到10幀左右，已經不像原來那么卡頓，但是當你移動速度很快的時候，識別框會出現滯后。
Demo.4
畢竟跳幀只是權宜之計，這個版本使用了全部的CPU核心，幀數穩定在了15幀左右。
### 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 )幀數上升到了13左右，而且識別框沒有延遲。
Demo.5
搞定了低幀數問題，我又試了試多核加跳幀…幀數可到28幀左右。
### 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 )
跳幀加多核，強行30幀哈哈，不過還是建議最終使用Demo4。
09/23/2016 Update:
我使用的云臺是這個，馬達型號是sg90。
ServoBlaster的下載地址是：鏈接
本文原文來源于：使用樹莓派進行簡易人臉識別
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