kandi background
Explore Kits

scifio | SCientific Image Format Input & Output | Computer Vision library

 by   scifio Java Version: Current License: BSD-2-Clause

 by   scifio Java Version: Current License: BSD-2-Clause

Download this library from

kandi X-RAY | scifio Summary

scifio is a Java library typically used in Artificial Intelligence, Computer Vision applications. scifio has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
SCientific Image Format Input & Output: a flexible, extensible framework for image I/O. *EXPERIMENTAL* All API is subject to change, so depend at your own risk! See also @openmicroscopy/bioformats.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • scifio has a low active ecosystem.
  • It has 73 star(s) with 35 fork(s). There are 23 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 195 open issues and 182 have been closed. On average issues are closed in 894 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of scifio is current.
scifio Support
Best in #Computer Vision
Average in #Computer Vision
scifio Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • scifio has 0 bugs and 0 code smells.
scifio Quality
Best in #Computer Vision
Average in #Computer Vision
scifio Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • scifio has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • scifio code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
scifio Security
Best in #Computer Vision
Average in #Computer Vision
scifio Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • scifio is licensed under the BSD-2-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
scifio License
Best in #Computer Vision
Average in #Computer Vision
scifio License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • scifio releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • It has 47027 lines of code, 3619 functions and 338 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
scifio Reuse
Best in #Computer Vision
Average in #Computer Vision
scifio Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

kandi has reviewed scifio and discovered the below as its top functions. This is intended to give you an instant insight into scifio implemented functionality, and help decide if they suit your requirements.

  • Add the attribute group .
    • Convert the given pixels to a 24 - bit integer array .
      • Extract value from Tiff entry .
        • Writes the footer .
          • Try to guess the dimension order of the given axes .
            • Write an image if possible .
              • Reads a plane .
                • Reads planes from a reader .
                  • Populate the image metadata
                    • Build location patterns .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      scifio Key Features

                      SCientific Image Format Input & Output: a flexible, extensible framework for image I/O. *EXPERIMENTAL* All API is subject to change, so depend at your own risk! See also @openmicroscopy/bioformats.

                      default

                      copy iconCopydownload iconDownload
                      SCIFIO's primary purpose is to provide a clear convention for supporting image
                      input and output. By lowering the barrier for adding new image formats, all
                      SCIFIO-backed software will grow more versatile and powerful.
                      
                      
                      Supported formats
                      -----------------
                      
                      The SCIFIO core includes support for:
                      * APNG
                      * AVI
                      * BMP
                      * DICOM
                      * EPS
                      * FITS
                      * GIF
                      * ICS
                      * JPEG
                      * JPEG2000
                      * MNG
                      * Micro-Manager
                      * NRRD
                      * OBF
                      * PCX
                      * PGM
                      * QuickTime
                      * TIFF
                      * Zipped images
                      
                      Additionally,
                      [Bio-Formats](https://www.openmicroscopy.org/site/products/bio-formats) is
                      [available as a SCIFIO plugin](https://github.com/scifio/scifio-bf-compat) for
                      supporting more than a hundred additional proprietary formats.
                      
                      
                      For users
                      ---------
                      
                      [ImageJ2](https://github.com/imagej/imagej) and
                      [Fiji](https://github.com/fiji/fiji) use SCIFIO for image I/O.
                      
                      
                      For developers
                      --------------
                      
                      Several software libraries use SCIFIO for image I/O:
                      * SCIFIO has built-in support for opening and saving
                        [ImgLib2](https://github.com/imagej/imglib) data structures
                        (see the [io.scif.img](src/main/java/io/scif/img) package).
                      * We have [updated Bio-Formats](https://github.com/scifio/bioformats) to
                        also support SCIFIO plugins, backwards compatibly with existing code.
                      * [ITK](https://github.com/Kitware/ITK) has an
                        [ImageIO module](https://github.com/scifio/scifio-imageio)
                        for reading and writing images using SCIFIO.
                      
                      Developer documentation:
                      * See the [SCIFIO tutorials](https://github.com/scifio/scifio-tutorials) for a
                        step-by-step introduction to the SCIFIO API.
                      * See also the
                        [SCIFIO Javadocs](https://javadoc.scijava.org/SCIFIO/).
                      
                      
                      More information
                      ----------------
                      
                      For more information, see the [SCIFIO FAQ](https://github.com/scifio/scifio/wiki/FAQ)
                      and [SCIFIO web site](https://scif.io/).
                      
                      
                      Getting help
                      ------------
                      
                      SCIFIO uses the [Image.sc Forum](https://forum.image.sc/) for support. Start
                      a new topic [tagged with `scifio`](https://forum.image.sc/tag/scifio).
                      
                      Contributing to SCIFIO
                      ----------------------
                      
                      SCIFIO is an open project and anyone is very welcome to submit pull requests
                      to the [SCIFIO repository](https://github.com/scifio/scifio).
                      
                      With SCIFIO's focus on extensibility, you typically will not need to make
                      upstream changes to get your formats into users' hands. However, if you are
                      interested in submitting a pull request, that's great!
                      All we ask is that you check:
                      
                          mvn clean test
                      
                      from the top level.
                      
                      If you're adding a new feature, it would be fantastic if you
                      could write a unit test for it! Simply base it on JUnit
                      to have it run by the SCIFIO test suite.

                      Community Discussions

                      Trending Discussions on Computer Vision
                      • Image similarity in swift
                      • When using pandas_profiling: "ModuleNotFoundError: No module named 'visions.application'"
                      • Classify handwritten text using Google Cloud Vision
                      • cv2 findChessboardCorners does not detect corners
                      • Fastest way to get the RGB average inside of a non-rectangular contour in the CMSampleBuffer
                      • UIViewController can't override method from it's superclass
                      • X and Y-axis swapped in Vision Framework Swift
                      • Swift's Vision framework not recognizing Japanese characters
                      • Boxing large objects in image containing both large and small objects of similar color and in high density from a picture
                      • Create a LabVIEW IMAQ image from a binary buffer/file with and without NI Vision
                      Trending Discussions on Computer Vision

                      QUESTION

                      Image similarity in swift

                      Asked 2022-Mar-25 at 11:42

                      The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

                      What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

                      import UIKit
                      import Vision
                      func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
                      let requestHandler = VNImageRequestHandler(url: url, options: [:])
                      let request = VNGenerateImageFeaturePrintRequest()
                      do {
                        try requestHandler.perform([request])
                        return request.results?.first as? VNFeaturePrintObservation
                      } catch {
                        print("Vision error: \(error)")
                        return nil
                      }
                        }
                       let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
                      let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
                      let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
                      var distance = Float(0)
                      try apple1!.computeDistance(&distance, to: apple2!)
                      var distance2 = Float(0)
                      try apple1!.computeDistance(&distance2, to: pear!)
                      

                      ANSWER

                      Answered 2022-Mar-25 at 10:26

                      It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.

                      1-(distance/Float.greatestFiniteMagnitude)*100
                      

                      A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

                      1-(min(distance, 10)/10)*100
                      

                      Here the artificial ceiling would be 10, but it can be any arbitrary number.

                      Source https://stackoverflow.com/questions/71615277

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

                      Vulnerabilities

                      No vulnerabilities reported

                      Install scifio

                      You can download it from GitHub.
                      You can use scifio like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the scifio component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                      Support

                      For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

                      DOWNLOAD this Library from

                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      Share this Page

                      share link
                      Consider Popular Computer Vision Libraries
                      Try Top Libraries by scifio
                      Compare Computer Vision Libraries with Highest Support
                      Compare Computer Vision Libraries with Highest Quality
                      Compare Computer Vision Libraries with Highest Security
                      Compare Computer Vision Libraries with Permissive License
                      Compare Computer Vision Libraries with Highest Reuse
                      Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                      over 430 million Knowledge Items
                      Find more libraries
                      Reuse Solution Kits and Libraries Curated by Popular Use Cases
                      Explore Kits

                      Save this library and start creating your kit

                      • © 2022 Open Weaver Inc.