original_app/backend.py

import cv2
import numpy as np
import socket
import pickle
import struct

# Load YOLO model
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]

resolved_label = ''

# Set up socket
HOST = ''
PORT = 8089
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
print('Socket created')
s.bind((HOST, PORT))
print('Socket bind complete')
s.listen(10)
print('Socket now listening')

# Accept connections
conn, addr = s.accept()

# Receive and process frames
data = b''
payload_size = struct.calcsize("L")
while True:
    # Retrieve message size
    while len(data) < payload_size:
        data += conn.recv(4096)
    packed_msg_size = data[:payload_size]
    data = data[payload_size:]
    msg_size = struct.unpack("L", packed_msg_size)[0]

    # Retrieve all data based on message size
    while len(data) < msg_size:
        data += conn.recv(4096)
    frame_data = data[:msg_size]
    data = data[msg_size:]

    # Extract frame
    frame = pickle.loads(frame_data)

    # Run YOLO on frame
    blob = cv2.dnn.blobFromImage(frame, 1/255.0, (416, 416), swapRB=True, crop=False)
    net.setInput(blob)
    outputs = net.forward(net.getUnconnectedOutLayersNames())
    boxes = []
    confidences = []
    class_ids = []
    for output in outputs:
        for detection in output:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                center_x = int(detection[0] * frame.shape[1])
                center_y = int(detection[1] * frame.shape[0])
                w = int(detection[2] * frame.shape[1])
                h = int(detection[3] * frame.shape[0])
                x = int(center_x - w/2)
                y = int(center_y - h/2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)
    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    if len(indexes) > 0:
        for i in indexes.flatten():
            resolved_label = classes[class_ids[i]]
            print(resolved_label)

    # Display frame
    cv2.imshow('frame', frame)
    cv2.waitKey(1)

    # Send response to client
    try:
        if len(indexes) > 0:
            response = "[Scarecrow]: " + resolved_label
        else:
            response = "[Scarecrow]: NONE"
    except IndexError:
        response = "[Scarecrow]: ERROR"
    conn.sendall(response.encode())