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
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)
