https://github.com/utsanjan/traffic-prediction-model

Traffic prediction model built with python using Machine learning and Image processing
https://github.com/utsanjan/traffic-prediction-model

anaconda image-processing ipynb jupyter-notebook machine-learning matplotlib numpy pip python python3 tensorflow traffic-prediction

Last synced: 2 months ago
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Traffic prediction model built with python using Machine learning and Image processing

• Host: GitHub
• URL: https://github.com/utsanjan/traffic-prediction-model
• Owner: utsanjan
• License: apache-2.0
• Created: 2022-02-10T13:06:30.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-27T09:48:56.000Z (about 2 years ago)
• Last Synced: 2024-05-01T19:45:04.156Z (9 months ago)
• Topics: anaconda, image-processing, ipynb, jupyter-notebook, machine-learning, matplotlib, numpy, pip, python, python3, tensorflow, traffic-prediction
• Language: Jupyter Notebook
• Homepage: https://utsanjan.bio.link/
• Size: 81.9 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

        
# 🚗 Traffic Prediction With ML

[![Buy Me A Coffee](https://img.shields.io/open-vsx/stars/redhat/java?color=D8B024&label=buy%20me%20a%20coffee&style=plastic)](https://www.buymeacoffee.com/utsanjan)‎ ‎

[![](https://img.shields.io/github/license/utsanjan/Traffic-Prediction-Model?logoColor=red&style=plastic)](https://github.com/utsanjan/Traffic-Prediction-Model/blob/main/LICENSE)‎ ‎

[![](https://img.shields.io/github/languages/count/utsanjan/Traffic-Prediction-Model?style=plastic)](https://github.com/utsanjan/Traffic-Prediction-Model/search?l=shell)‎ ‎

[![](https://img.shields.io/github/languages/top/utsanjan/Traffic-Prediction-Model?color=light%20green&style=plastic)](https://github.com/utsanjan/Traffic-Prediction-Model)‎ ‎ 
 

The traffic prediction model is built with python using Machine learning and Image processing. Jupyter Notebook is used as the base IDE for making this project. It detects the number of vehicle passing a red imaginary line also known as the ROI (Region of interest) and predicts the traffic density in that specific area. It also save details like the car's color serially in a csv file and at the same time also saves individual images of each detected vehicles inside a folder named **detected_vehicles** in png format.


 

> ### Points to be noted:

> Feel free to fork this project from my repo,

> 
and do report me the bugs if you find one.

> 
Incase anyone wants to use the source code

> 
you can clone it from the Repository itself.

## 📲 Working Demo



## 🛠️ Usage Guide

This is a Jupyter notebook project. Hence, simply just open the project folder


with [Jupyter Notebook](https://jupyter.org) and run all the cells of **vehicle_detection_main.ipynb**


one by one. And yes also make sure to install all the requirements by using:


```

pip install -r requirements.txt

```

## 📞 Contacts 

For Queries: [My Instagram Profile](https://www.instagram.com/utsanjan/)  


[Check Out My YouTube Channel](https://www.youtube.com/DopeSatan)