What is the UML Class diagram for accident detection using CNN.

i have make the class diagram which is not sufficient to my guide.

Class diagram:.

SYSTEM ARCHITECTURE:

Code for the accident detection:-

from tkinter import messagebox

from tkinter import *

from tkinter import simpledialog

import tkinter

from tkinter import filedialog

from tkinter.filedialog import askopenfilename

import time

import cv2

import tensorflow as tf

from collections import namedtuple

import numpy as np

import winsound

main = tkinter.Tk()

main.title("Accident Detection")

main.geometry("1300x1200")

net = cv2.dnn.readNetFromCaffe("model/MobileNetSSD_deploy.prototxt.txt","model/MobileNetSSD_deploy.caffemodel")

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",

"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",

"dog", "horse", "motorbike", "person", "pottedplant", "sheep",

"sofa", "train", "tvmonitor"]

COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

global filename

global detectionGraph

global msg

def loadModel():

global detectionGraph

detectionGraph = tf.Graph()

with detectionGraph.as_default():

od_graphDef = tf.compat.v1.GraphDef()

with tf.compat.v2.io.gfile.GFile('model/frozen_inference_graph.pb', 'rb') as file:

serializedGraph = file.read()

od_graphDef.ParseFromString(serializedGraph)

tf.import_graph_def(od_graphDef, name='')

messagebox.showinfo("Training model loaded","Training model loaded")

def beep():

frequency = 2500 # Set Frequency To 2500 Hertz

duration = 1000 # Set Duration To 1000 ms == 1 second

winsound.Beep(frequency, duration)

def uploadVideo():

global filename

filename = filedialog.askopenfilename(initialdir="videos")

pathlabel.config(text=filename)

text.delete('1.0', END)

text.insert(END,filename+" loaded ");

def calculateCollision(boxes,classes,scores,image_np):

global msg

#cv2.putText(image_np, "NORMAL!", (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 2, cv2.LINE_AA)

for i, b in enumerate(boxes[0]):

if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:

if scores[0][i] > 0.5:

for j, c in enumerate(boxes[0]):

if (i != j) and (classes[0][j] == 3 or classes[0][j] == 6 or classes[0][j] == 8) and scores[0][j]> 0.5:

Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')

ra = Rectangle(boxes[0][i][3], boxes[0][i][2], boxes[0][i][1], boxes[0][i][3])

rb = Rectangle(boxes[0][j][3], boxes[0][j][2], boxes[0][j][1], boxes[0][j][3])

ar = rectArea(boxes[0][i][3], boxes[0][i][1],boxes[0][i][2],boxes[0][i][3])

col_threshold = 0.6*np.sqrt(ar)

area(ra, rb)

if (area(ra,rb)

print('accident')

msg = 'ACCIDENT!'

beep()

return True

else:

return False

def rectArea(xmax, ymax, xmin, ymin):

x = np.abs(xmax-xmin)

y = np.abs(ymax-ymin)

return x*y

def load_image_into_numpy_array(image):

(im_width, im_height) = image.size

return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)

def area(a, b): # returns None if rectangles don't intersect

dx = min(a.xmax, b.xmax) - max(a.xmin, b.xmin)

dy = min(a.ymax, b.ymax) - max(a.ymin, b.ymin)

return dx*dy

def detector():

global msg

msg = ''

cap = cv2.VideoCapture(filename)

with detectionGraph.as_default():

with tf.compat.v1.Session(graph=detectionGraph) as sess:

while True:

ret, image_np = cap.read()

(h, w) = image_np.shape[:2]

blob = cv2.dnn.blobFromImage(cv2.resize(image_np, (300, 300)),0.007843, (300, 300), 127.5)

net.setInput(blob)

detections = net.forward()

for i in np.arange(0, detections.shape[2]):

confidence = detections[0, 0, i, 2]

if confidence > 0.2:

idx = int(detections[0, 0, i, 1])

box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])

(startX, startY, endX, endY) = box.astype("int")

if (confidence * 100) > 50:

label = "{}: {:.2f}%".format(CLASSES[idx],confidence * 100)

cv2.rectangle(image_np, (startX, startY), (endX, endY),COLORS[idx], 2)

y = startY - 15 if startY - 15 > 15 else startY + 15

image_np_expanded = np.expand_dims(image_np, axis=0)

image_tensor = detectionGraph.get_tensor_by_name('image_tensor:0')

boxes = detectionGraph.get_tensor_by_name('detection_boxes:0')

scores = detectionGraph.get_tensor_by_name('detection_scores:0')

classes = detectionGraph.get_tensor_by_name('detection_classes:0')

num_detections = detectionGraph.get_tensor_by_name('num_detections:0')

if image_np_expanded[0] is not None:

(boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections], feed_dict={image_tensor: image_np_expanded})

calculateCollision(boxes, classes, scores, image_np)

cv2.putText(image_np, msg, (230, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 2, cv2.LINE_AA)

cv2.imshow('Accident Detection', image_np)

if cv2.waitKey(5) & 0xFF == ord('q'):

cv2.destroyAllWindows()

break

def exit():

main.destroy()

font = ('times', 16, 'bold')

title = Label(main, text='Accident Detection')

title.config(bg='light cyan', fg='pale violet red')

title.config(font=font)

title.config(height=3, width=120)

title.place(x=0,y=5)

font1 = ('times', 13, 'bold')

uploadButton = Button(main, text="Load & Generate CNN Model", command=loadModel)

uploadButton.place(x=50,y=100)

uploadButton.config(font=font1)

pathlabel = Label(main)

pathlabel.config(bg='light cyan', fg='pale violet red')

pathlabel.config(font=font1)

pathlabel.place(x=460,y=100)

webcamButton = Button(main, text="Browse System Videos", command=uploadVideo)

webcamButton.place(x=50,y=150)

webcamButton.config(font=font1)

webcamButton = Button(main, text="Start Accident Detector", command=detector)

webcamButton.place(x=50,y=200)

webcamButton.config(font=font1)

exitButton = Button(main, text="Exit", command=exit)

exitButton.place(x=330,y=250)

exitButton.config(font=font1)

font1 = ('times', 12, 'bold')

text=Text(main,height=20,width=150)

scroll=Scrollbar(text)

text.configure(yscrollcommand=scroll.set)

text.place(x=10,y=250)

text.config(font=font1)

main.config(bg='snow3')

main.mainloop()

an 4.1 SYSTEM ARCHITECTTRE: 4.2 DATA FLOW DIAGRAM: 1. The DFD is also called as bubble chart. It is a simple graphical formalism that can be used to represent a system in terms of input data to the system, various processing carried out on this data, and the output data is