retina-unet | Retina blood vessel segmentation with a convolutional | Machine Learning library

by orobix Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | retina-unet Summary

retina-unet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. retina-unet has no bugs, it has no vulnerabilities and it has medium support. However retina-unet build file is not available. You can download it from GitHub.

Retina blood vessel segmentation with a convolutional neural network

Support

Quality

Security

License

Reuse

Support

retina-unet has a medium active ecosystem.

It has 1183 star(s) with 467 fork(s). There are 82 watchers for this library.

It had no major release in the last 6 months.

There are 54 open issues and 28 have been closed. On average issues are closed in 38 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of retina-unet is current.

Quality

retina-unet has 0 bugs and 139 code smells.

Security

retina-unet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

retina-unet code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

retina-unet does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

retina-unet releases are not available. You will need to build from source code and install.

retina-unet has no build file. You will be need to create the build yourself to build the component from source.

retina-unet saves you 343 person hours of effort in developing the same functionality from scratch.

It has 822 lines of code, 31 functions and 8 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Create a GNet .
Extracts ground truth masks from images .
Get a unet .
Get training images .
Get the data for the image .
This function extracts a random image from an image .
Compute the overlap between two images .
Calculate the overlap between two images .
Paint the border of the image .
recompone the image

Get all kandi verified functions for this library.

retina-unet Key Features

No Key Features are available at this moment for retina-unet.

retina-unet Examples and Code Snippets

retina-api-java-sdk - A Java Client for the Cortical.io Retina API,Usage,FullClient

Java

Lines of Code : 87

License : Permissive (BSD-2-Clause)

Copy

// Create FullClient instance
FullClient fullClient = new FullClient(your_api_key);

Term term = ExpressionFactory.term("brain");

ExpressionModel andExpression = ExpressionFactory.and(ExpressionFactory.term("brain"), ExpressionFactory.term("cortex")

retina-sdk.py - A Python Client for the Cortical.io Retina API,Usage,FullClient Module

Python

Lines of Code : 79

License : Permissive (BSD-2-Clause)

Copy

import retinasdk
fullClient = retinasdk.FullClient("your_api_key", apiServer="http://api.cortical.io/rest", retinaName="en_associative")

>>> fullClient.getTerms(term="python")
[Term(df=0.00025051038056906765, term='python', score=0.0, pos_t

The ,The Retina model

Python

Lines of Code : 34

License : Strong Copyleft (GPL-3.0)

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import convis
retina = convis.retina.Retina()
print(retina)

import numpy as np
import matplotlib.pylab as plt
inp = np.ones((100,20,20))
output = retina(inp)
    
inp = np.ones((2000,20,20))
output = retina.run(inp,dt=100)

convis.plot_5d_time(outpu

Community Discussions

Trending Discussions on retina-unet

keras ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None

QUESTION

keras ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None

Asked 2019-Apr-03 at 14:32

I have Unet model from Retina Unet, However I have augmented the images as well as the masks. Now? it gives me this error ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None I want to train on augmented (images and masks) and validate on augmented images and masks.

batch generating function:

...

ANSWER

Answered 2017-Apr-30 at 16:35

In case someone run to the same issue later.

The problem is generator issue. fixed below

def batch_generator(X_gen,Y_gen): while true: yield(X_gen.next(),Y_gen.next())

Source https://stackoverflow.com/questions/43526362

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

Vulnerabilities

No vulnerabilities reported

Install retina-unet

You can download it from GitHub.
You can use retina-unet 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.

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 .

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