Awesome

<div align="center"> <a href="https://github.com/aaanthonyyy"> <img src="CircuitNet-Logo-01.svg" alt="Logo" width="200" height="200"> </a> <h3 align="center">CircuitNet</h3> <p align="center"> Schematic Sketch to Circuit Diagram Using Deep Learning <br/> <a href="https://colab.research.google.com/drive/1ox4m12Oa3GjwP47UdRArwCaUXbgUSLBf?authuser=2"><strong>Interactive Colab Demo »</strong></a> </p> </div>

Project Overview

A deep learning algorithm is proposed to automatically convert schematic sketches into circuit diagrams. The algorithm is promising, achieving a detection accuracy of 90% and a classification accuracy of 96.5%.

Component Segmentation

<div align="center"> <img src="https://user-images.githubusercontent.com/43044255/170148823-b921898d-1d6a-457b-8dfa-ffa2ad8d0ea7.png" alt="Logo" width="600"/> <br/> <br/> </div> There are a variety of feature detection algorithms possible, but we opted for traditional image processing techniques due to the inavailability of labeled data. <br/> <br/>

Classification Architecture

Software Dependancies

This project was built using the following open-source libraries:

Numpy is an array manipulation library, used for linear algebra, Fourier transform, and random number capabilities.
CV2 is a library for computer vision tasks.
Skimage is a library which supports image processing applications on python.
Matplotlib is a library which generates figures and provides graphical user interface toolkit.
Tensorflow is an end-to-end open source machine learning platform
SVG Schematic is a library to build a schematic using Python to instantiate and place the symbols and wires
Cairo SVG is a library for processing SVG in python