circuit_recognizer | final project that propose a way to convert hand

 by   mahmut-aksakalli Python Version: Current License: No License

kandi X-RAY | circuit_recognizer Summary

kandi X-RAY | circuit_recognizer Summary

circuit_recognizer is a Python library typically used in Manufacturing, Utilities, Energy, Utilities applications. circuit_recognizer has no bugs, it has no vulnerabilities and it has low support. However circuit_recognizer build file is not available. You can download it from GitHub.

Circuit simulation programs are very efficient and fast way of observing behaviors of electrical circuits. They are fundamental tools for engineers to analyze characteristics of circuits and get simulation results. To use the benefits of simulation programs, engineers need circuit schematic which contains components and interconnections between them. The simulation programs provide a graphical interface to create circuit schematics. However, it’s popular to create initial concepts of electrical circuits using pencil and paper for engineers. Because of that, engineers spend additional time to transform hand-drawn circuit to required schematic file of simulation program. This project explains a method to create schematic file from hand-drawn circuit sketch. There are some challenges in circuit sketch recognition such as segmentation of components, recognition of components and getting interconnections between them. Some earlier researches are available to solve these problems [1][2][3]. A system uses block adjacency graph (BAG) to segment components, then it identifies components using a contour based classification [1]. Another system extracts potential connection lines and potential components using some assumptions about lines and components [2]. A project describes a system that makes an analysis to obtain branches and end points of components, then it uses Fourier descriptor of component region to make SVM classification [3]. This project proposes a way to segment components and connection wires using combination of end point analysis and line detection, then it describes component region using HOG features. It recognizes components using SVM classification. An overview of our project shown Figure 1. Figure 1: Block diagram of project.
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            kandi-support Support

              circuit_recognizer has a low active ecosystem.
              It has 34 star(s) with 11 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 3 open issues and 0 have been closed. On average issues are closed in 224 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of circuit_recognizer is current.

            kandi-Quality Quality

              circuit_recognizer has 0 bugs and 0 code smells.

            kandi-Security Security

              circuit_recognizer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              circuit_recognizer code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              circuit_recognizer does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              circuit_recognizer releases are not available. You will need to build from source code and install.
              circuit_recognizer has no build file. You will be need to create the build yourself to build the component from source.
              It has 983 lines of code, 25 functions and 6 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed circuit_recognizer and discovered the below as its top functions. This is intended to give you an instant insight into circuit_recognizer implemented functionality, and help decide if they suit your requirements.
            • Filter skeleton endpoints
            • Calculate thinning
            • Thinning iteration
            Get all kandi verified functions for this library.

            circuit_recognizer Key Features

            No Key Features are available at this moment for circuit_recognizer.

            circuit_recognizer Examples and Code Snippets

            No Code Snippets are available at this moment for circuit_recognizer.

            Community Discussions

            No Community Discussions are available at this moment for circuit_recognizer.Refer to stack overflow page for discussions.

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install circuit_recognizer

            You can download it from GitHub.
            You can use circuit_recognizer like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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          • HTTPS

            https://github.com/mahmut-aksakalli/circuit_recognizer.git

          • CLI

            gh repo clone mahmut-aksakalli/circuit_recognizer

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            git@github.com:mahmut-aksakalli/circuit_recognizer.git

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