Traffic light control using python

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Traffic light control using python

We are now going to look at controlling the outside world using your Raspberry Pi. There are lots of things a Raspberry Pi can control, such as lights, motors and buzzers.

For this we are going to use a Pibrella add on, as it has everything we need on a little board that fits snugly on top of the GPIO pins. Using the Python language we will control the LEDs to simulate a set of traffic lights. Wait for the Raspberry Pi to boot, and boot in to the desktop. To show how easy it is to turn on an LED with the Raspberry Pi, lets make the red light flash on and off.

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Now run the script again, and you should notice the red LED come on for 1 second and then go off again. Loops allow the same piece of code to be repeated, such as flashing the LED, without having to type the same code over and over again. This is a handy key to remember as it will let you cancel python scripts or loops, as well as terminate any running program in LXTerminal.

Now we have created a loop to light the LEDs in sequence, we shall now use a def inition block to keep our code in.

traffic light control using python

Using a def block allows the use of repetitive code so we can refer to it again easily. Now we have the script setup to easily run in a loop by running runloopwe can start the def block with an input.

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Luckily the Pibrella has an input ready to use, the big red button. So now lets use this button to start the loop. Now in this activity you have grasped the basics of turning an LED on and off, learnt how to use loops, read button inputs and create a definition block for repetitive code, you can use the knowledge you have learnt to create even more awesome projects.

If you want to try some more things based on this activity, see the Things to try section below. If you have noticed at pedestrian crossings in the UK, the amber light flashes a few times before changing to green, how about adding a loop that flashes 5 times after the red has gone off?

Instead of immediately starting the loop after pressing the button, try importing the random module:. Contents 1 Introduction 1. What do I need?One obvious and really fun project is a button operated traffic light. This post will build on an earlier project about connecting a button using a breadboard and the RPi.

GPIO libraryso if you have any questions about the initial setup of the electronics, check out that post. In this post, I want to take a little bit more time to write out a good Python program to handle our traffic light.

If you want to get the code, you can check out the GitHub repo here or run this command in a terminal:. Here is the wiring configuration I used for the electronic components of the Pi traffic light project. The GPIO naming convention is not physical pins, so be sure to keep those naming conventions in mind when setting up your circuits.

The Python code for this project will be using the same basic structure we looked at in my post on using the cleanup method of the RPi. GPIO library. Spaghetti code is what it suggests, a big, tangled ball of different code statements put together to make a meal. Each line stands on its own and is responsible for doing something while there is little attempt at structuring the code into reusable components.

For the most part, it worked the way that I wanted it to. However, if we look at the guts of the program that sets the GPIO output on the pins, it reeks of spaghetti code. For example, say I wanted to change from a one second delay between lights to two seconds, or half a second. One of the ways you can combat spaghetti code is to organize your code into functions, which are reusable code blocks that can make your programs more flexible.

This function is one small step toward optimizing the code and its reusability. First, I define and name the function and then specify four parameters that I pass into the function: led1, led2, led3, delay. Later, when we call that function, we pass in the values we want the function to use to replace our parameter placeholders.

traffic light control using python

Within the function you can use that parameter as a placeholder. For example, I pass a parameter called delay into the lightTraffic function. Inside of the function, anywhere I want to use that delay value, I simply put delay: time.

When I run the function and pass it actual values, in this case 1, the function replaces all instances of the delay parameter with the value of 1 that I passed in.

Finally, now that we have all of our traffic light code organized into a function, we need to call or execute that function when the button is pressed. Check out the finished script here:.

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Now, we are starting to reap the benefits of ravioli code. Our lightTraffic function is a nice self-contained piece of code, while our while loop that reacts to input is another ravioli. There are tons of other way I could have made this code more reusable, so if you have a cool idea or solid suggestion, leave a comment below.

I'm working on building the most comprehensive course available on building workflows with Google Apps Script. Seems like you would just need some different lights and more advanced wiring, possible soldered to a device. Your email address will not be published. Toggle navigation JE. Home About Me Work Contact.Step by step how to implement a traffic system. You won't need network access, and you won't need to download imaging software.

Once you have installed an operating system, your Pi will boot as normal. These pins are a physical interface between the Raspberry Pi and the outside world.

Traffic Light System Using Raspberry Pi 3 (Python)

To limit the current going through the LED, you should always use a register in series with it. Long leg an LED to the Pi 3.

It comes installed by default in Rasbian. Save the file as trffic. Write the following code in your new file. But that's not all you can do. Similarly checks the Buzzer and Button. Just import a Buzzer and Button for the header file. View All. Ravishankar Velladurai Updated date, Apr 06 LEDs are delicate little things. If you connect special purpose GPIO pin are connect to long leg, make sure to write the import code. We need a breadboard, three LEDs, a button, a buzzer, and the necessary jumper cables and registers.

Place the components on the breadboard and connect them to the Raspberry Pi GPIO pins, according to the following diagram. Open Python 3 from the main menu Create a new file just save with the project name.

Next Recommended Article. Getting Started With. NET 5.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to run this code in python and I want the code to stop looping when the user enters 30, here I have used break to stop the loop, is there any other way?

Any suggestion would be helpful. Hm I'm not quite sure what you actually want to achieve. Your version does not loop since the break statement will always be met after the first iteration. Additionally, you only ask for user input once before the loop actually starts. Here is what I suppose you want to do, so I moved the user input part inside the loop and added the break condition to the pythonic "switch" statement.

This will just ask the user for input for as long as he doesn't enter In this, I am utilizing a condition in the while loop. The condition is for the loop to iterate the user value until it becomes The reason for this is because of your condition in the Red light line.

I explained this further in the Problem 2 below. This portion doesn't properly handle negative numbers and numbers greater than So, you definitely have to think about what the purpose of this is.

traffic light control using python

Check out the following code that constantly loops until the value is greater than 30 :. The reason why I scripted to loop until the value is greater than 30 is because of the following lines in your code:.

If you wanted the loop to break only when the user inputs 30then those lines of code above works against that theory.

Traffic Light Control System Using Image Processing Technique

You should also look into this flaw of your logic. Learn more. Traffic light program using while loop in python Ask Question. Asked 2 years, 3 months ago. Active 2 years, 3 months ago. Viewed 5k times. Anupriya Krishnamoorthy Anupriya Krishnamoorthy 23 1 1 silver badge 5 5 bronze badges. Your code does not loop. If the user inputs a number, the code finds the appropriate condition, prints, breaks, and ends. If I input 28I get a print output of Red light and that's it.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to display a traffic light using the tkinter. I am unable to get anything to display except for the radio buttons.

I have tried different adjustments to get it to display but nothing is working. Example with function assigned to Radiobutton but you could assign function to self. Learn more. Asked 5 years, 9 months ago. Active 3 years, 9 months ago. Viewed 7k times. You define three times the same function displayOval - you can have only one - always last version ovewrites previous ones.

You use self. You compare self. Active Oldest Votes. Thank you. Your code was very useful. I added the rectangle to make it a stoplight. Works in Python3 if you change the first line to have a lowercase "T" in "Tkinter" Laurel 5, 10 10 gold badges 23 23 silver badges 42 42 bronze badges.

First: you could add this to your question. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.JavaScript seems to be disabled in your browser. You must have JavaScript enabled in your browser to utilize the functionality of this website. Traffic signals are essential to guarantee safe driving at road intersections. However, they disturb and reduce the traffic fluency due to the queue delay at each traffic flow.

This algorithm considers the real-time traffic characteristics of each traffic flow that intends to cross the road intersection of interest, whilst scheduling the time phases of each traffic light. The introduced algorithm aims at increasing the traffic fluency by decreasing the waiting time of traveling vehicles at the signalized road intersections.

Moreover, it aims to increase the number of vehicles crossing the road intersection per second. In modern life we have to face with many problems one of which is traffic congestion becoming more serious day after day.

Traffic flow determination can play a principle role in gathering information about them. This data is used to establish censorious flow time periods such as the effect of large vehicle, specific part on vehicular traffic flow and providing a factual record of traffic volume trends. This recorded information also useful for process the better traffic in terms of periodic time of traffic lights. There are many routes to count the number of vehicles passed in a particular time, and can give judgment of traffic flow.

This project focuses on a firmware-based novel technique for vehicle detection. This approach detects the vehicles in the source image, and applies an existing identifier for each of the vehicle. Later it classifies each vehicle on its vehicle-type group and counts them all by individually.

The developed approach was implemented in a firmware platform which results is better accuracy, high reliability and less errors. Traffic lights play a very significant role in traffic control and regulation on a daily basis. Using MATLAB the density of the roads is determined and the microcontroller changes the duration of green light given for each road as per the output after image processing. The traffic lights used in India are basically pre-timed wherein the time of each lane to have a green signal is fixed.

In a four lane traffic signal one lane is given a green signal at a time. Thus, the traffic light allows the vehicles of all lanes to pass in a sequence. So, the traffic can advance in either straight direction or turn by 90 degrees as shown in Fig.

Interactive Traffic Lights with Python

So even if the traffic density in a particular lane is the least, it has to wait unnecessarily for a long time and when it gets the green signal it unnecessarily makes other lanes wait for even longer durations. In this system we are going to implement crowd based traffic light signal,lane will be get open on the basis of crowd at the desired lane. It is identified by the capturing the vehicle crowd images in the lane and identifying the number of vehicles in that desired lane.

Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called "background image", or "background model".

Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation. In this modern era as the population is increased rapidly the usage of vehicles has also increased tremendously. The cause of it is heavy traffic. In order to avoid this problem it is better that we flow new communication methods such as image processing based intelligent traffic controlling and monitoring system using ARDUINO.

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By using this method we can get the details about information about vehicles in particular junctions through internet access. This is more beneficial for the emergency travelling. Shalini, Prof. Rs 8, INR The goal of the challenge was to recognize the traffic light state in images taken by drivers using the Nexar app. In any given image, the classifier needed to output whether there was a traffic light in the scene, and whether it was red or green.

More specifically, it should only identify traffic lights in the driving direction. The images above are examples of the three possible classes I needed to predict: no traffic light leftred traffic light center and green traffic light right. The challenge required the solution to be based on Convolutional Neural Networksa very popular method used in image recognition with deep neural networks.

Smaller models got higher scores. Nexar provided 18, labeled images as training data. I used Caffe to train the models.

Traffic Lights with Python

The main reason I chose Caffe was because of the large variety of pre-trained models. Not cheap. The code and files I used to train and run the model are on GitHub. The final classifier achieved an accuracy of The process of getting higher accuracy involved a LOT of trial and error.

The final classifier details are described right after. I started off with trying to fine-tune a model which was pre-trained on ImageNet with the GoogLeNet architecture. Not exactly sure what I did wrong there, I might look into it. Since the competition rewards solutions that use small models, early on I decided to look for a compact network with as few parameters as possible that can still produce good results.

Most of the recently published networks are very deep and have a lot of parameters. For more details, I recommend reading this blog post by Lab41 or the original paper. Very cool! Most of the images were horizontal like the one above, but about 2.

See below. Unfortunately, there was no EXIF data in the jpeg images specifying the orientation. At first I considered doing some heuristic to identify the sky and flip the image accordingly, but that did not seem straightforward.

Instead, I tried to make the model invariant to rotations. But when averaging the predictions of 4 rotations for each image, there was improvement! This type of data augmentation makes the network generalize better. Similarly, when generating predictions, I took several crops of the input image and averaged the results. I used 5 crops: 4 corners and a center crop.

The implementation was free by using existing caffe code for this. All models were starting to overfit after a certain point. I noticed this by watching the validation-set loss start to rise at some point. I stopped the training at that point because the model was probably not generalizing any more.

I tried resuming the training process at the point where the model started overfitting with a learning rate 10 times lower than the original one.

This usually improved the accuracy by When analyzing the mistakes the classifier had on the validation set, I noticed that some of the mistakes have very high confidence.

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Notice that in the plot above, the right-most bar is pretty high. When examining these cases up close I saw these were usually mistakes in the ground-truth of the training set rather than in the trained model. I decided to fix these errors in the training set. The reasoning was that these mistakes confuse the model, making it harder for it to generalize.


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