BoofCV | Fast computer vision library for SFM calibration | Computer Vision library

 by   lessthanoptimal Java Version: v0.43.1 License: No License

kandi X-RAY | BoofCV Summary

kandi X-RAY | BoofCV Summary

BoofCV is a Java library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. BoofCV has no bugs, it has no vulnerabilities, it has build file available and it has medium support. You can download it from GitHub, Maven.

BoofCV is an open source real-time computer vision library written entirely in Java and released under the Apache License 2.0. Functionality includes low-level image processing, camera calibration, feature detection/tracking, structure-from-motion, classification, and recognition.
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              BoofCV has a medium active ecosystem.
              It has 969 star(s) with 248 fork(s). There are 69 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 39 open issues and 169 have been closed. On average issues are closed in 340 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of BoofCV is v0.43.1

            kandi-Quality Quality

              BoofCV has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

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

            kandi-Reuse Reuse

              BoofCV releases are available to install and integrate.
              Deployable package is available in Maven.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              BoofCV saves you 191833 person hours of effort in developing the same functionality from scratch.
              It has 408085 lines of code, 29601 functions and 4332 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed BoofCV and discovered the below as its top functions. This is intended to give you an instant insight into BoofCV implemented functionality, and help decide if they suit your requirements.
            • Fills a Gaussian distribution .
            • Convert a bit matrix to a matrix .
            • Returns the value of the function at the specified index .
            • Prints the results of the coverage data .
            • Converts a class file into a series of macros .
            • helper function .
            • Process the output directory .
            • Initialize video .
            • Normalizes the kernel .
            • 2 - D matrix .
            Get all kandi verified functions for this library.

            BoofCV Key Features

            No Key Features are available at this moment for BoofCV.

            BoofCV Examples and Code Snippets

            No Code Snippets are available at this moment for BoofCV.

            Community Discussions

            QUESTION

            BoofCV: Unresolved Compilation Problems
            Asked 2022-Feb-03 at 23:32

            I'm using a Raspberry Pi (4) and hoped I'd be able to implement QR detection through a Rasp Cam. I found BoofCV that could do the job - so I copied the code from the ExampleDetectQrCode repo but its throwing a few errors...

            ...

            ANSWER

            Answered 2022-Feb-03 at 23:32

            You need to import the "boofcv-swing" artifact to have the built in Swing components.

            Source https://stackoverflow.com/questions/70962678

            QUESTION

            How to generate fiducial markers for BoofCV?
            Asked 2020-Jan-31 at 05:23

            I'm wanting to use BoofCV with Processing, but its recognition of any size of ArUco markers generated by this tool is unstable. Is there a resource or an app where I can get or generate a collection of markers that will be consistently recognized by BoofCV in Processing?

            ...

            ANSWER

            Answered 2020-Jan-31 at 05:23

            BoofCV doesn't work with Aruco Markers, yet at least. It's been on the wish list for a while now since everything needed to process Aruco Markers is there but it doesn't support their dictionaries.

            https://boofcv.org/index.php?title=Tutorial_Fiducials

            Instructions for creating BoofCV markers is there. Basically you can use a command line tool for creating PDFs. There might even be a GUI, I can't remember, but it's not difficult.

            Source https://stackoverflow.com/questions/59987555

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

            Vulnerabilities

            No vulnerabilities reported

            Install BoofCV

            To run BoofCV you need Java 11 or newer and any free version will work, e.g. Zulu. To build BoofCV, just let the Gradle script handle everything. It will automatically download what it needs and ignore your local JDK if it's not compatible. All the code for examples and demonstrations is in boofcv/examples and boofcv/demonstrations. Example code is intended easy to understand, so look there first. Click HERE for instruction on building and running Applications. There you can calibrate cameras, create QR codes, batch scan for QR codes, batch downsample images, ... etc.

            Support

            For questions or comments about BoofCV please use the message board. Only post a bug report after doing some due diligence to make sure it is really a bug and that it has not already been reported.
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