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\n"); 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)<col_threshold) : 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, .

1. 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",

2. "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():

3. global filename
filename = filedialog.askopenfilename(initialdir="videos")
pathlabel.config(text=filename)
text.delete('1.0', END)
text.insert(END,filename+" loadedn");
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)<col_threshold) :
print('accident')
msg = 'ACCIDENT!'
beep()

4. 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]

5. 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)

6. 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)

7. 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