LBPHFaceRecognizer slows down processes

asked 2020-06-18 04:05:06 -0500

Gian.Gian gravatar image

updated 2020-06-23 12:06:19 -0500

supra56 gravatar image

Hi to all, i'm using RaspberryPI4+Python 3.7+ openCV to move servo motors. I have seen that using LBPHFaceRecognizer the program is much slower. Do you think it's normal or something in the code that slows it down? Using only Haar cascade_Frontalface servo motors are very fast, but if at the same time i want to use LBPHFaceRecognizer they are very slow..:

#!/usr/bin/python
import cv2
import os
import numpy as np
import time
import math

from adafruit_servokit import ServoKit

capture = cv2.VideoCapture(0)
capture.set(3,320)
capture.set(4,240)

#start position

kit = ServoKit(channels=8)
ang=90

kit.servo[0].angle = ang
time.sleep(1)

angy=90
kit.servo[1].angle = angy
time.sleep(1)

#functions
def faceDetection(img):   
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    
    haar_face=cv2.CascadeClassifier('/home/pi/CV/haarcascade_frontalface_default.xml')    
    face=haar_face.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=8)

    return face,gray

def training_data(directory):   
    faces=[]   
    facesID=[]

    for path,subdir,filename in os.walk(directory):        
        for filename in filename:           
            if filename.startswith("."):               
                print("skipping system file")            
                continue
            id=os.path.basename(path)

            image_path=os.path.join(path,filename)            
            img_test=cv2.imread(image_path)

            if img_test is None:                
                print ("error opening image")               
                continue

            face,gray=faceDetection(img_test)           
            if len(face)>0:                
                continue

            (x,y,w,h)=face[0]           
            region=gray[y:y+w, x:x+h]           
            faces.append(region)            
            facesID.append(int(id))

    return faces, facesID

def train_classifier(faces,facesID):    
    face_recognizer=cv2.face.LBPHFaceRecognizer_create()    
    face_recognizer.train(faces,np.array(facesID))

    return face_recognizer

def put_name(test_img,text,x,y,w,h):    
    cv2.putText(test_img,text,(x,y),cv2.QT_FONT_NORMAL,0.5,(0,0,255),1)

#code

faceRecognizer=cv2.face.LBPHFaceRecognizer_create()
faceRecognizer.read('/home/pi/CV/trainedData.yml')

name={0:"Gian", 1:"Gian"}

while True:
    ret,test_img=capture.read()    
    faces_detected,gray=faceDetection(test_img)   
    for face in faces_detected:       
        (x,y,w,h)=face        
        region=gray[y:y+w, x:x+h]

        label, confidence=faceRecognizer.predict(region)       
        predictedName=name[label]  
        put_name(test_img, predictedName,x,y,w,h) 

    resized = cv2.resize(test_img,None,fx=0.5,fy=0.5)    
    cv2.imshow("face",test_img)
    cv2.waitKey(1)

    if (x <= 109 and len(faces_detected)>0):       
        if x<=39:           
            ang+=3            
        elif x<=89 and x>39:            
            ang+=2           
        else:         
            ang+=1        
        kit.servo[0].angle = ang        
        time.sleep(0.001)        
    elif (x >= 151 and len(faces_detected)>0):        
        if x>=220:            
            ang-=3            
        elif x>=171 and x<220:            
            ang-=2            
        else:            
            ang-=1

        kit.servo[0].angle = ang        
        time.sleep(0.001)

    if (y <= 60 and len(faces_detected)>0):    
        if x<=20:            
            angy+=2        
        else:           
            angy+=1    
        kit.servo[1].angle = angy        
        time.sleep(0.001)   
    elif (y >= 90 and len(faces_detected)>0):        
        if y>=130:           
            angy-=2           
        else:            
            angy-=1

        kit.servo[1].angle = angy        
        time.sleep(0.001)
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Comments

  • extract the face reco code (put it into a seperate file) and measure that

  • how do you measure time here ? don't use a "wall clock" like time.time(), but a CPU one, like cv2.getTickCount()

  • check the face region size, eventually resize it to like 100x100 (faster feature extraction)

also, just saying, nobody here will be able to reproduce or try this "wall of code" containing servo access and such

berak gravatar imageberak ( 2020-06-18 04:24:58 -0500 )edit

You can't used duplicately kit.servo[0] and kit.servo[1] inside if/else condition block. You can this outside of if/else condition block kit.servo[0] and kit.servo[1] You are limited 2. Nothing more.

supra56 gravatar imagesupra56 ( 2020-06-23 12:00:55 -0500 )edit

The timer is very slowly. You should set to time.sleep(0.2)

supra56 gravatar imagesupra56 ( 2020-06-23 12:22:43 -0500 )edit