!["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)](https://image.slidesharecdn.com/ihavetounderstandthiscodeandihavetoexplaintheeachlineof-230314165706-b497d5bc/85/I-have-to-understand-this-code-and-i-have-to-explain-the-each-line-of-pdf-2-320.jpg)
![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()
return True
else:
return False](https://image.slidesharecdn.com/ihavetounderstandthiscodeandihavetoexplaintheeachlineof-230314165706-b497d5bc/85/I-have-to-understand-this-code-and-i-have-to-explain-the-each-line-of-pdf-3-320.jpg)
![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()](https://image.slidesharecdn.com/ihavetounderstandthiscodeandihavetoexplaintheeachlineof-230314165706-b497d5bc/85/I-have-to-understand-this-code-and-i-have-to-explain-the-each-line-of-pdf-4-320.jpg)
![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'):](https://image.slidesharecdn.com/ihavetounderstandthiscodeandihavetoexplaintheeachlineof-230314165706-b497d5bc/85/I-have-to-understand-this-code-and-i-have-to-explain-the-each-line-of-pdf-5-320.jpg)
I have to understand this code and i have to explain the each line of.pdf
- 1. I have to understand this code and i have to explain the each line of code to my Project guide Can you plz add comment to each line of code which would be helpful me to understand the code Code for Accident detection using CNN: 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",
- 2. "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)
- 3. 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() return True else: return False
- 4. 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()
- 5. 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'):
- 6. 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)
- 7. 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()