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NeuTomPy-toolbox | Python package for tomographic data processing | Computer Vision library

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kandi X-RAY | NeuTomPy-toolbox Summary

NeuTomPy-toolbox is a Python library typically used in Artificial Intelligence, Computer Vision applications. NeuTomPy-toolbox has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. However NeuTomPy-toolbox has 1 bugs. You can install using 'pip install NeuTomPy-toolbox' or download it from GitHub, PyPI.
NeuTomPy toolbox is a Python package for tomographic data processing and reconstruction. Such toolbox includes pre-processing algorithms, artifacts removal and a wide range of iterative reconstruction methods as well as the Filtered Back Projection algorithm. The NeuTomPy toolbox was conceived primarily for Neutron Tomography and developed to support the need of users and researchers to compare state-of-the-art reconstruction methods and choose the optimal data-processing workflow for their data.

kandi-support Support

  • NeuTomPy-toolbox has a low active ecosystem.
  • It has 17 star(s) with 4 fork(s). There are 2 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 2 have been closed. On average issues are closed in 33 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of NeuTomPy-toolbox is current.

quality kandi Quality

  • NeuTomPy-toolbox has 1 bugs (0 blocker, 0 critical, 0 major, 1 minor) and 188 code smells.


  • NeuTomPy-toolbox has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • NeuTomPy-toolbox code analysis shows 0 unresolved vulnerabilities.
  • There are 2 security hotspots that need review.

license License

  • NeuTomPy-toolbox is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.


  • NeuTomPy-toolbox releases are not available. You will need to build from source code and install.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 2254 lines of code, 104 functions and 29 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA

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

  • Normalize projection .
  • Find the correlation axis .
  • Calculate COR axis correction .
  • Reconstruction of a stack
  • Reconstruct a binogram using a given method .
  • Reconstruct tomographic projection matrix .
  • Plot a line profile .
  • Read a FITS file .
  • Write data to a fits stack .
  • Write a tiff stack to a file .

NeuTomPy-toolbox Key Features

Readers and writers for TIFF and FITS files and stack of images

Data normalization with dose correction, correction of the rotation axis tilt, ring-filters, outlier removals, beam-hardening correction

A wide range of reconstruction algorithms powered by ASTRA toolbox: FBP, SIRT, SART, ART, CGLS, NN-FBP, MR-FBP

Image quality assessment with several metrics

NeuTomPy-toolbox Examples and Code Snippets

  • Installation
  • Update


conda create -n ntp_env python=3.6 
conda activate ntp_env

Community Discussions

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Trending Discussions on Computer Vision


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!)


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.


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


No vulnerabilities reported

Install NeuTomPy-toolbox

NeuTomPy toolbox supports Linux, Windows and Mac OS 64-bit operating systems.


Complete documentation can be found on Read the Docs: https://neutompy-toolbox.readthedocs.io. Tutorials and code examples of typical usage can be found in the folder examples. A sample dataset for testing purpose can be found here. This dataset includes neutron radiographs of a phantom sample acquired at the IMAT beamline, ISIS neutron spallation source, UK.

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