Mqtt publishing message when face is detected

Question

I'm working on a university project combining MQTT and Face recognition, using a raspberry pi. First of all I want to say that face recognition works normally when it's being run on its own but I have something that bugs me when applying it with MQTT.

Inside the Try section, when a person is being recognized or unknown it publishes really fine every 3 secs. The strange thing is that if I put my hand in front of the camera or if I get out of range, it keeps on sending the ID of the last person it had recognized.

I want it either do nothing or publish none.

Any idea or suggestion to fix this? Thanks in advance

EDIT 1: Basically as I figured out, it has to do more with the face recognition part. When using just a face recognition code using opencv it runs smoothly. But the thing complicates when I put my hand in front of the camera or go away, because the capture freezes at the last thing it sees so it keeps on printing/publishing the same thing. Haven't figured out a way to avoid this yet. Any help would be appreciated

import cv2
import json
import time
import datetime as dt
import paho.mqtt.client as mqtt


detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
recognizer = cv2.face.LBPHFaceRecognizer_create()

broker = "*****"
port = *****
username = '*****'
password = '*****'


def on_connect(client, userdata, flags, rc):
    print("Connected with code: ", rc)
    client.subscribe('test/#')


def on_message(client, userdata, msg):
    print(str(msg.payload))


client = mqtt.Client("Python1")
client.on_connect = on_connect
client.on_message = on_message

client.username_pw_set(username, password)
client.connect(broker, port, 60)

client.loop_start()
time.sleep(1)


def facerecognizer():

    recognizer.read("trainerdir/training.yml")

    font = cv2.FONT_HERSHEY_SIMPLEX

    # Loading data

    with open('data.json', 'r') as f:
        names = json.load(f)

    # reverse the data
    # NOTE: for k, v !!
    # else it raises error !
    names = {v: k for k, v in names.items()}
    # print(names)
    print("[INFO] Face recognition is starting..")

    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 360)

    try:

        while True:

            ret, img = cap.read()

            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

            faces = detector.detectMultiScale(gray,
                                              scaleFactor=1.3,
                                              minNeighbors=5
                                              # minSize = (20,20)
                                              )

            for (x, y, w, h) in faces:
                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)

                roi_gray = gray[y:y + h, x:x + w]
                roi_color = img[y:y + h, x:x + w]

                ID, confidence = recognizer.predict(roi_gray)

                if (confidence < 100):

                    ID = names[ID]

                    confidence = "{}%".format(round(100 - confidence))

                    while True:
                        client.publish("Tutorial/", ID + " " + str(dt.datetime.now()))
                        print('ID sent')
                        time.sleep(3)

                    else:
                        client.publish("Tutorial/", 'None')
                        print('ID sent')
                        time.sleep(3)

                    client.loop_forever()

                else:
                    ID = "Unkown"
                    confidence = "{}%".format(round(100 - confidence))
                    print(ID, dt.datetime.now())
                    while True:
                        client.publish("Tutorial/", ID + " " + str(dt.datetime.now()))
                        print('ID sent')
                        time.sleep(3)
                    else:
                        client.publish("Tutorial/", 'None')
                        print('ID sent')
                        time.sleep(3)

                    client.loop_forever()

    # except UnboundLocalError:
    #     print("Error occured. Exitting..")

    except KeyboardInterrupt:
        pass
    except KeyError as K:
        print(K)
        print('[INFO] Name Value is a string and not an integer')

    print("[INFO] Exiting program..")
    cap.release()

Answer 1

From the documentation on mqtt clients, you seem to be misusing the client.loop_forever() method.

http://www.steves-internet-guide.com/loop-python-mqtt-client/

TLDR

• Use client.loop(timeout) in a loop to control processing of updates.
• Use client.loop_start() starts a new thread which handles updates for you.
• Use client.loop_forever() to block and process updates.

Since you have already called client.loop_start() , you don't need to call loop_forever() . Removing those calls from your code should resolve the issue.

Side note: your time.sleep(3) call could be moved to the end of the loop, since it should happen no matter which conditional path it takes.

Answer 2

By using if True: instead of while True: I fixed it. I also removed the subclient.loop_forever() and it works fine. Here is the sample code that is different from the code I posted to my question

   for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)

        roi_gray = gray[y:y + h, x:x + w]
        # roi_color = img[y:y + h, x:x + w]

        ID, confidence = recognizer.predict(roi_gray)

        # 0 is 100% accuracy , and 100 is 0% accuracy
        if (confidence < 100):

            ID = names[ID]
            if True:

                subclient.publish("project/IDReceiver", ID + " " + str(dt.datetime.now()))

                print('ID sent')
                time.sleep(3)

            # subclient.loop_forever()

        else:
            ID = "Unknown"
            if True:

                subclient.publish("project/IDReceiver", "Unknown " + str(dt.datetime.now()))

                print('ID sent')
                time.sleep(3)