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Traffic Management System
Traffic Management System python project source code – The number of vehicles has increased drastically in the last few decades making it difficult to monitor each and every vehicle for the traffic management system and law enforcement purposes. We proposed a computer vision-based solution using deep learning that automatically detects traffic violators.
The main objective is to detect vehicles that do not follow the rules of traffic, like overspeeding, overloading, not wearing a helmet, and running on the incorrect side of the road. We use Yolov3 for object detection and DeepSort for tracking vehicles and pedestrians. The system detects the sort of violation along with the vehicle information, maintains a log of violations, provides an in-depth dashboard, and provides alerts to the traffic police personnel. The logs also can be used for forensic purposes.
Datasets and Models
I have used the dataset available on the internet for helmet images and trained the model, the Model is required to run the project. Kindly download it.
Working
I have done the detection of traffic management system & violations in real time using our own trained haar cascades for vehicle and pedestrians detection, our own trained helmet detection model written in YOLO v3, and got satisfactory results.
Code Example
import cv2
import dlib
import time
import threading
import math
import helm
carCascade = cv2.CascadeClassifier('cars.xml')
bikeCascade = cv2.CascadeClassifier('motor-v4.xml')
video = cv2.VideoCapture('test.mp4')
LAG=7
WIDTH = 1280
HEIGHT = 720
OPTIMISE= 7
def estimateSpeed(location1, location2,fps):
d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
# ppm = location2[2] / carWidht
ppm = 8.8
d_meters = d_pixels / ppm
if fps == 0.0:
fps = 18
speed = d_meters * fps * 3.6
return speed
def trackMultipleObjects():
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
currentBikeID=0
fps = 0
carTracker = {}
bikeTracker = {}
bikeNumbers = {}
carNumbers = {}
bikeLocation1 = {}
carLocation1 = {}
bikeLocation2 = {}
carLocation2 = {}
speed = [None] * 1000
go =[False for i in range(1000)]
identity = [0 for i in range(1000)]
snaps = [False for i in range(1000)]
types = ["cars" for i in range(1000)]
Helmets = ["No Helmet Detected" for i in range(1000)]
out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (WIDTH,HEIGHT))
while True:
start_time = time.time()
rc, image = video.read()
if type(image) == type(None):
break
image = cv2.resize(image, (WIDTH, HEIGHT))
resultImage = image.copy()
frameCounter = frameCounter + 1
carIDtoDelete = []
for carID in carTracker.keys():
trackingQuality = carTracker[carID].update(image)
if trackingQuality < 7:
carIDtoDelete.append(carID)
for carID in carIDtoDelete:
print ('Removing carID ' + str(carID) + ' from list of trackers.')
print ('Removing carID ' + str(carID) + ' previous location.')
print ('Removing carID ' + str(carID) + ' current location.')
carTracker.pop(carID, None)
carLocation1.pop(carID, None)
carLocation2.pop(carID, None)
if not (frameCounter % 10):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cars = carCascade.detectMultiScale(gray, 1.1, 13, 18, (24, 24))
bikes = bikeCascade.detectMultiScale(gray, 1.1 , 13, 18, (24,24))
for (_x, _y, _w, _h) in cars:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
roi = image[y:y+h,x:x+w]
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print ('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
currentCarID = currentCarID + 1
for (_x, _y, _w, _h) in bikes:
x = int(_x)
y = int(_y)
w = int(_w)
h = int(_h)
x_bar = x + 0.5 * w
y_bar = y + 0.5 * h
matchCarID = None
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
t_x_bar = t_x + 0.5 * t_w
t_y_bar = t_y + 0.5 * t_h
if ((t_x <= x_bar <= (t_x + t_w)) and (t_y <= y_bar <= (t_y + t_h)) and (x <= t_x_bar <= (x + w)) and (y <= t_y_bar <= (y + h))):
matchCarID = carID
if matchCarID is None:
print ('Creating new tracker ' + str(currentCarID))
tracker = dlib.correlation_tracker()
tracker.start_track(image, dlib.rectangle(x, y, x + w, y + h))
carTracker[currentCarID] = tracker
carLocation1[currentCarID] = [x, y, w, h]
types[currentCarID]= "bikes"
currentCarID = currentCarID + 1
for carID in carTracker.keys():
trackedPosition = carTracker[carID].get_position()
t_x = int(trackedPosition.left())
t_y = int(trackedPosition.top())
t_w = int(trackedPosition.width())
t_h = int(trackedPosition.height())
cv2.rectangle(resultImage, (t_x, t_y), (t_x + t_w, t_y + t_h), rectangleColor, 4)
carLocation2[carID] = [t_x, t_y, t_w, t_h]
end_time = time.time()
fps=0.0
for i in carLocation1.keys():
if frameCounter % 1 == 0:
[x1, y1, w1, h1] = carLocation1[i]
[x2, y2, w2, h2] = carLocation2[i]
carLocation1[i] = [x2, y2, w2, h2]
if [x1, y1, w1, h1] != [x2, y2, w2, h2]:
result = False
roi = resultImage[y1:y1+h1,x1:x1+w1]
if types[i]=="bikes" and Helmets[i] == "No Helmet Detected" and identity[i]< OPTIMISE:
result = helm.detect(roi)
if result==True:
Helmets[i]= "Helmet Detected"
if 7==7:
if not (end_time == start_time):
fps = 1.0/(end_time - start_time)
speed[i] = estimateSpeed([x1, y1, w1, h1], [x2, y2, w2, h2],fps)
if int(speed[i])>40:
speed[i]= speed[i]%40
if go[i] == True and int(speed[i])<10:
speed[i]=speed[i]+15
if int(speed[i])==0:
continue
if int(speed[i])>30:
go[i]=True
cv2.putText(resultImage, "OverSpeeding ALERT", (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
elif speed[i] != None and y1 >= 180 and speed[i]!=0:
ans= str(int(speed[i])) + " km/hr "
if types[i]=="bikes":
ans= ans+ Helmets[i]
cv2.putText(resultImage, ans, (int(x1 + w1/2), int(y1-5)),cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)
identity[i]+=1
cv2.imshow('result', resultImage)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
if __name__ == '__main__':
trackMultipleObjects()The main file to run the project…
from time import sleep
import cv2 as cv
import argparse
import sys
import numpy as np
import os.path
from glob import glob
#from PIL import image
frame_count = 0 # used in mainloop where we're extracting images., and then to drawPred( called by post process)
frame_count_out=0 # used in post process loop, to get the no of specified class value.
# Initialize the parameters
confThreshold = 0.5 #Confidence threshold
nmsThreshold = 0.4 #Non-maximum suppression threshold
inpWidth = 416 #Width of network's input image
inpHeight = 416 #Height of network's input image
# Load names of classes
classesFile = "obj.names";
classes = None
with open(classesFile, 'rt') as f:
classes = f.read().rstrip('\n').split('\n')
# Give the configuration and weight files for the model and load the network using them.
modelConfiguration = "yolov3-obj.cfg";
modelWeights = "yolov3-obj_2400.weights";
net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
# Get the names of the output layers
def getOutputsNames(net):
# Get the names of all the layers in the network
layersNames = net.getLayerNames()
# Get the names of the output layers, i.e. the layers with unconnected outputs
return [layersNames[i-1] for i in net.getUnconnectedOutLayers()]
# Draw the predicted bounding box
def drawPred(classId, conf, left, top, right, bottom, frame):
global frame_count
# Draw a bounding box.
#cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
label = '%.2f' % conf
# Get the label for the class name and its confidence
if classes:
assert(classId < len(classes))
label = '%s:%s' % (classes[classId], label)
#Display the label at the top of the bounding box
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
top = max(top, labelSize[1])
#print(label) #testing
#print(labelSize) #testing
#print(baseLine) #testing
label_name,label_conf = label.split(':') #spliting into class & confidance. will compare it with person.
if label_name == 'Helmet':
#will try to print of label have people.. or can put a counter to find the no of people occurance.
#will try if it satisfy the condition otherwise, we won't print the boxes or leave it.
#cv.rectangle(frame, (left, top - round(1.5*labelSize[1])), (left + round(1.5*labelSize[0]), top + baseLine), (255, 255, 255), cv.FILLED)
#cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0,0,0), 1)
frame_count+=1
#print(frame_count)
if(frame_count> 0):
return frame_count
# Remove the bounding boxes with low confidence using non-maxima suppression
def postprocess(frame, outs):
frameHeight = frame.shape[0]
frameWidth = frame.shape[1]
frame_count_out=0
classIds = []
confidences = []
boxes = []
# Scan through all the bounding boxes output from the network and keep only the
# ones with high confidence scores. Assign the box's class label as the class with the highest score.
classIds = [] #have to fins which class have hieghest confidence........=====>>><<<<=======
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
classId = np.argmax(scores)
confidence = scores[classId]
if confidence > confThreshold:
center_x = int(detection[0] * frameWidth)
center_y = int(detection[1] * frameHeight)
width = int(detection[2] * frameWidth)
height = int(detection[3] * frameHeight)
left = int(center_x - width / 2)
top = int(center_y - height / 2)
classIds.append(classId)
#print(classIds)
confidences.append(float(confidence))
boxes.append([left, top, width, height])
# Perform non maximum suppression to eliminate redundant overlapping boxes with
# lower confidences.
indices = cv.dnn.NMSBoxes(boxes, confidences, confThreshold, nmsThreshold)
count_person=0 # for counting the classes in this loop.
for i in indices:
i = i[0]
box = boxes[i]
left = box[0]
top = box[1]
width = box[2]
height = box[3]
#this function in loop is calling drawPred so, try pushing one test counter in parameter , so it can calculate it.
frame_count_out = drawPred(classIds[i], confidences[i], left, top, left + width, top + height, frame)
#increase test counter till the loop end then print...
#checking class, if it is a person or not
my_class='Helmet' #======================================== mycode .....
unknown_class = classes[classId]
if my_class == unknown_class:
count_person += 1
#if(frame_count_out > 0):
#print(frame_count_out)
if count_person >= 1:
path = 'test_out/'
# frame_name=os.path.basename(fn) # trimm the path and give file name.
#cv.imwrite(str(path)+frame_name, frame) # writing to folder.
#print(type(frame))
#cv.imshow('img',frame)
#cv.waitKey(800)
return 1
else:
return 0
#cv.imwrite(frame_name, frame)
#======================================mycode.........
# Process inputs
winName = 'Deep learning object detection in OpenCV'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
def detect(frame):
#frame = cv.imread(fn)
frame_count =0
# Create a 4D blob from a frame.
blob = cv.dnn.blobFromImage(frame, 1/255, (inpWidth, inpHeight), [0,0,0], 1, crop=False)
# Sets the input to the network
net.setInput(blob)
# Runs the forward pass to get output of the output layers
outs = net.forward(getOutputsNames(net))
# Remove the bounding boxes with low confidence
# Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
t, _ = net.getPerfProfile()
#print(t)
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
#print(label)
#cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
#print(label)
k=postprocess(frame, outs)
if k:
return 1
else:
return 0Trained haar cascades
- cars.xml
- motor-v4.xml
- myhaar.xml
- obj.names
Traffic Management System – Github And Source Code
Get Started
- Clone the Repository – Github
- Download the model and copy it into the same directory
- Make sure all files are in the same directory.
- Go to line 9 of main.py and rename the video.
- Run python main.py
Output
Traffic Management System For more click here…
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