Detecting objects and transferring images over MQTT

Section 1 — Publish

import paho.mqtt.client as mqtt
import paho.mqtt.publish as publish
import cv2
import numpy as np
import time
broker = ""
port = 1883
def save_image():
#cv2.VideoCapture(0) this can be 0, 1, 2 depending on your device id
videoCaptureObject = cv2.VideoCapture(0)
ret, frame =
cv2.imwrite(image_name, frame)
def process_image():
boxes = []
confs = []
class_ids = []

#loading the YoloV3 weights and configuration file using the open-cv dnn module
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")

#storing all the trained object names from the coco.names file in the list names[]
names = []
with open("coco.names", "r") as n:
names = [line.strip() for line in n.readlines()]

#running a foward pass by passing the names of layers of the output to be computed by net.getUnconnectedOutLayersNames()
output_layers = [layer_name for layer_name in net.getUnconnectedOutLayersNames()]
colors = np.random.uniform(0, 255, size=(len(names), 3))

#reading the image from the image_name variable (Same image which was saved by the save_image function)
image = cv2.imread(image_name)
height, width, channels = image.shape

#using blobFromImage function to preprocess the data
blob = cv2.dnn.blobFromImage(image, scalefactor=0.00392, size=(160, 160), mean=(0, 0, 0))

#getting X/Y cordinates of the object detected, scores for all the classes of objects in coco.names where the predicted object is class with the highest score, height/width of bounding box
outputs = net.forward(output_layers)
for output in outputs:
for check in output:
#this list scores stores confidence for each corresponding object
scores = check[5:]

#np.argmax() gets the class index with highest score which will help us get the name of the class for the index from the names list
class_id = np.argmax(scores)
conf = scores[class_id]
#predicting with a confidence value of more than 40%
if conf > 0.4:
center_x = int(check[0] * width)
center_y = int(check[1] * height)
w = int(check[2] * width)
h = int(check[3] * height)
x = int(center_x - w / 2)
y = int(center_y - h / 2)
boxes.append([x, y, w, h])

#drawing bounding boxes and adding labels while removing duplicate detection for same object using non-maxima suppression
indexes = cv2.dnn.NMSBoxes(boxes, confs, 0.5, 0.5)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(names[class_ids[i]])
color = colors[i]
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
cv2.putText(image, label, (x, y - 5), font, 1, color, 1)

#resizing and saving the the image
width = int(image.shape[1] * 220 / 100)
height = int(image.shape[0] * 220 / 100)
dim = (width, height)
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
cv2.imwrite('processed.jpg', resized)

#reading the image and converting it to bytearray
f = open("processed.jpg", "rb")
fileContent =
byteArr = bytes(fileContent)

#topic to publish for our MQTT
client = mqtt.Client()

#connecting to the MQTT broker
client.connect(broker, port, timelive)

#publishing the message with bytearr as the payload and IMAGE as topic
publish.single(TOPIC, byteArr, hostname=broker)
while True:

Section 2 — Subscribe

import paho.mqtt.client as mqtt
broker = ""
port = 1883
timelive = 60
def on_connect(client, userdata, flags, rc):
print("Connected with result code " + str(rc))
#subscribe to the topic IMAGE, this is the same topic which was used to published the image on the previous device
def on_message(client, userdata, msg):
#create/open jpg file [detected_objects.jpg] to write the received payload
f = open('detected_objects.jpg', "wb")
def mqtt_sub():
client = mqtt.Client()
client.connect(broker, port, timelive)
client.on_connect = on_connect
client.on_message = on_message



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