Traffic Signs Recognition Using CNN & Keras In Python With Source Code

Traffic Signs Recognition Using CNN & Keras In Python With Source Code

The Traffic Signs Recognition Using CNN & Keras In Python was developed using Python Programming with CNN & Keras, There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to.

A Traffic Signs Recognition Python project example, we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

This Python Project Using CNN and Keras also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way, if you are new to python programming and you don’t know what would be the Python IDE to use, I have here a list of the Best Python IDE for Windows, Linux, Mac OS that will suit you. I also have here How to Download and Install the Latest Version of Python on Windows.

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To start executing Traffic Signs Recognition Using CNN & Keras In Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.

Traffic Signs Recognition Using CNN & Keras In Python With Source Code : Steps on how to run the project

Time needed: 5 minutes.

These are the steps on how to run Traffic Signs Recognition Using CNN & Keras In Python With Source Code

  1. Step 1: Download the given source code below.

    First, download the given source code below and unzip the source code.
    Traffic Signs Recognition download source code

  2. Step 2: Import the project to your PyCharm IDE.

    Next, import the source code you’ve download to your PyCharm IDE.
    Traffic Signs Recognition open project

  3. Step 3: Run the project.

    last, run the project with the command “py main.py”
    Traffic Signs Recognition run project

Installed Libraries

import tkinter as tk
from tkinter import filedialog
from tkinter import *
from PIL import ImageTk, Image

import numpy
#load the trained model to classify sign
from keras.models import load_model

Complete Source Code

import tkinter as tk
from tkinter import filedialog
from tkinter import *
from PIL import ImageTk, Image

import numpy
#load the trained model to classify sign
from keras.models import load_model
model = load_model('traffic_classifier.h5')

#dictionary to label all traffic signs class.
classes = { 1:'Speed limit (20km/h)',
            2:'Speed limit (30km/h)',      
            3:'Speed limit (50km/h)',       
            4:'Speed limit (60km/h)',      
            5:'Speed limit (70km/h)',    
            6:'Speed limit (80km/h)',      
            7:'End of speed limit (80km/h)',     
            8:'Speed limit (100km/h)',    
            9:'Speed limit (120km/h)',     
           10:'No passing',   
           11:'No passing veh over 3.5 tons',     
           12:'Right-of-way at intersection',     
           13:'Priority road',    
           14:'Yield',     
           15:'Stop',       
           16:'No vehicles',       
           17:'Veh > 3.5 tons prohibited',       
           18:'No entry',       
           19:'General caution',     
           20:'Dangerous curve left',      
           21:'Dangerous curve right',   
           22:'Double curve',      
           23:'Bumpy road',     
           24:'Slippery road',       
           25:'Road narrows on the right',  
           26:'Road work',    
           27:'Traffic signals',      
           28:'Pedestrians',     
           29:'Children crossing',     
           30:'Bicycles crossing',       
           31:'Beware of ice/snow',
           32:'Wild animals crossing',      
           33:'End speed + passing limits',      
           34:'Turn right ahead',     
           35:'Turn left ahead',       
           36:'Ahead only',      
           37:'Go straight or right',      
           38:'Go straight or left',      
           39:'Keep right',     
           40:'Keep left',      
           41:'Roundabout mandatory',     
           42:'End of no passing',      
           43:'End no passing veh > 3.5 tons' }
                 
#initialise GUI
top=tk.Tk()
top.geometry('800x600')
top.title('Traffic sign classification')
top.configure(background='#CDCDCD')

label=Label(top,background='#CDCDCD', font=('arial',15,'bold'))
sign_image = Label(top)

def classify(file_path):
    global label_packed
    image = Image.open(file_path)
    image = image.resize((30,30))
    image = numpy.expand_dims(image, axis=0)
    image = numpy.array(image)
    print(image.shape)
    pred = model.predict_classes([image])[0]
    sign = classes[pred+1]
    print(sign)
    label.configure(foreground='#011638', text=sign) 
   

def show_classify_button(file_path):
    classify_b=Button(top,text="Classify Image",command=lambda: classify(file_path),padx=10,pady=5)
    classify_b.configure(background='#364156', foreground='white',font=('arial',10,'bold'))
    classify_b.place(relx=0.79,rely=0.46)

def upload_image():
    try:
        file_path=filedialog.askopenfilename()
        uploaded=Image.open(file_path)
        uploaded.thumbnail(((top.winfo_width()/2.25),(top.winfo_height()/2.25)))
        im=ImageTk.PhotoImage(uploaded)
        
        sign_image.configure(image=im)
        sign_image.image=im
        label.configure(text='')
        show_classify_button(file_path)
    except:
        pass

upload=Button(top,text="Upload an image",command=upload_image,padx=10,pady=5)
upload.configure(background='#364156', foreground='white',font=('arial',10,'bold'))

upload.pack(side=BOTTOM,pady=50)
sign_image.pack(side=BOTTOM,expand=True)
label.pack(side=BOTTOM,expand=True)
heading = Label(top, text="Know Your Traffic Sign",pady=20, font=('arial',20,'bold'))
heading.configure(background='#CDCDCD',foreground='#364156')
heading.pack()
top.mainloop()

Output

Download Source Code below

Summary

In this Python project with source code, we have successfully classified the traffic signs classifier with 95% accuracy and also visualized how our accuracy and loss changes with time, which is pretty good from a simple CNN model.

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