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MultiProcessing or Multithreading with python and Opencv to detect a face

Recently i working on raspberry pi 3(OS raspbian) with OPENCV + PYTHON to detect face on live camera. And I see detected face on raspberry pi3 use opencv very slow, about 4 - 5 FPS/s. So I wonder can I use MultiThread or MultiProcessing to speed up the FPS and if yes, how can i do that?

Please help me, any idea will appreciate. Thanks

2 Answers
2

Yes, you can multithread the process.
Use the threading library in python 2, or thread in python 3.

Here is a simple example.

Besides your main thread,

Have one thread only operating the camera and constantly updating the latest frame, which is global in this case.

def camera_thread():
cam = cv2.VideoCapture(1)
_ret, self.image = cam.read()
cv2.imshow('camera', self.image)


A second thread can run the inference from the model using the latest frame.

An optional third could perhaps draw a bounding box over the face, or perform other operations etc.

You might need mutex locks in between 2nd and 3rd threads in this case as you can only begin drawing boxes after you obtain an output from the model. This allows your 2nd thread to begin inference of the next frame without waiting for other threads.

The above example will result in a smooth video output with the inference lagging behind slightly. If you're not sure how multithreading works, I suggest reading up on the basics first.

The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.

update. I use separate thread to capture image from camera and I don't see any speed up of FPS compare to serial... Please see my code.

import threading
import time
import cv2
import numpy as np
class myThread (threading.Thread):
def __init__(self, src):
print("thread -------------init-------------")
threading.Thread.__init__(self)
self.cap = cv2.VideoCapture(src)
self.stop = False
def run(self):
while(self.stop == False):
self.ret, self.frame = self.cap.read()

def Stop(self):
self.cap.release()
self.stop = True

def read(self):
return self.ret, self.frame

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);

thread = myThread(0)
thread.start()
time.sleep(1)

start = time.time()
frames = 0
font = cv2.FONT_HERSHEY_SIMPLEX
cap = cv2.VideoCapture(0)
while(True):
ret, frame = thread.read()
frame = cv2.resize(frame, (640, 480))
frames += 1
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray, 1.2, 5)
for(x,y,w,h) in faces:
# Create rectangle around the face
cv2.rectangle(frame, (x-20,y-20), (x+w+20,y+h+20), (0,255,0), 4)
Id, con = recognizer.predict(gray[y:y+h,x:x+w])
print(Id, con)

# Check the ID if exist
if(con < 60):
if(Id == 1):
Id = "HUY"
if(Id == 2):
Id = "HOA"
#If not exist, then it is Unknown
else:
#print(Id)
Id = "Unknow"

# Put text describe who is in the picture
cv2.rectangle(frame, (x-22,y-90), (x+w+22, y-22), (0,255,0), -1)
cv2.putText(frame, str(Id), (x,y-40), font, 2, (255,255,255), 3)

if cv2.waitKey(10) & 0xFF == ord('q'):
thread.Stop()
break
cv2.imshow("frame", frame)
end = time.time()
second = end - start
print("second:", + second)
print(frames/second)
cv2.destroyAllWindows()


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