corneal | Ruby gem that is a Sinatra app generator | Application Framework library

by thebrianemory Ruby Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(6)Vulnerabilities Install Support

kandi X-RAY | corneal Summary

corneal is a Ruby library typically used in Server, Application Framework, Ruby On Rails applications. corneal has no vulnerabilities, it has a Permissive License and it has low support. However corneal has 5 bugs. You can download it from GitHub.

To generate your app:.

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Quality

Security

License

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Support

corneal has a low active ecosystem.

It has 255 star(s) with 86 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 7 have been closed. On average issues are closed in 68 days. There are 11 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of corneal is current.

Quality

corneal has 5 bugs (0 blocker, 0 critical, 0 major, 5 minor) and 2 code smells.

Security

corneal has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

corneal code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

corneal is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

corneal releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

corneal saves you 232 person hours of effort in developing the same functionality from scratch.

It has 567 lines of code, 38 functions and 27 files.

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

Top functions reviewed by kandi - BETA

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

Returns the current version of the application .

Get all kandi verified functions for this library.

corneal Key Features

No Key Features are available at this moment for corneal.

corneal Examples and Code Snippets

No Code Snippets are available at this moment for corneal.

Community Discussions

Trending Discussions on corneal

Using array to count word and phrase matches in text

Distance between points in contours opencv

How can I get the noun clause that is the object of a certain verb?

watershed segmentation always return black image

How to find correlation coefficients for non-numeric variables

calculate average with angularjs in nested json

QUESTION

Using array to count word and phrase matches in text

Asked 2020-Apr-12 at 07:12

I am new to VBA and trying to create a project for work. We hire transcriptionists based on a sample test file they listen to and type up. I've been trying to code a macro that will search the tests for specific keywords (some of which are used multiple times in the test), then give a message box with the results. This way we can gauge competency at a glance.

I've cadged together a prototype but there's one major problem: the counter doesn't seem to be counting right. I test it on a sample file that has all of the required keywords but it will only register about half of them. I'm not sure where the problem is and would appreciate any insight.

My code is below and a screenshot of the resulting message box is attached.

...

ANSWER

Answered 2018-Oct-14 at 04:56

The code seems working perfectly for words only. It will fail for counting phrases. Since I also consider the code as an impressively clean chunk of code as commented by Marcucciboy2, I tried to retain the main approach in the code already written. You may try to add the few lines

Source https://stackoverflow.com/questions/52788138

QUESTION

Distance between points in contours opencv

Asked 2019-Jan-14 at 20:33

I'm trying to develop a solution to segment corneal tomographies, and check for the distance between the cornea and a contact lens. For such, i thresholded the image and extracted the biggest 3 contours, being one of the cornea and the other 2 the interior and exterior edges of the lens.

I need to check for the distance between the green contour and the red one all along the x axis in the image.

I tried to check for contour distance in OpenCV but did not find anything.

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ANSWER

Answered 2019-Jan-14 at 20:33

This answer explains how to find the radial distance between the red line and the green line. I have manually edited the image so that only the relevant parts of the image are considered.

The first step is to find a function that describes the curve of the lens (the red line). Scipy can find functions from data. Scipy.interp1d() found the function (in cyan) from the red line's contours:

Next, I could find the distance from the green line (the contours of the cornea), to the nearest point on the curve:

Source https://stackoverflow.com/questions/54186399

QUESTION

How can I get the noun clause that is the object of a certain verb?

Asked 2018-Apr-07 at 04:41

I am working with data from pharmaceutical labels. The text is always structured using the verb phrase 'indicated for'.

For example:

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ANSWER

Answered 2018-Mar-28 at 19:46

You don't need SpaCy. You can do regular expression or just split:

Source https://stackoverflow.com/questions/49542787

QUESTION

watershed segmentation always return black image

Asked 2017-Dec-02 at 04:16

I've been recently working at a segmentation process for corneal endothelial cells, and I've found a pretty decent paper that describes ways to perform it with nice results. I have been trying to follow that paper and implement it all using scikit-image and openCV, but I've gotten stucked at the watershed segmentation.

I will briefly describe how is the process supposed to be:

First of all, you have the original endothelial cells image original image

Then, they instruct you to perform a morphological grayscale reconstruction, in order to level a little bit the grayscale of the image (however, they do not explain how to get the markers for the grayscale, so I've been fooling around and tried to get some on my own way)

This is what the reconstructed image was supposed to look like: desired reconstruction

This is what my reconstructed image (lets label it as r) looks like: my reconstruction

The purpose is to use the reconstructed image to get the markers for the watershed segmentation, how do we do that?! We get the original image (lets label it as f), and perform a threshold in (f - r) to extract the h-domes of the cell, i.e., our markers.

This is what the hdomes image was supposed to look like: desired hdomes

This is what my hdomes image looks like: my hdomes

I believe that the hdomes I've got are as good as theirs, so, the final step is to finally perform the watershed segmentation on the original image, using the hdomes we've been working so hard to get!

As input image, we will use the inverted original image, and as markers, our markers.

This is the derised output:

desired output

However, I am only getting a black image, EVERY PIXEL IS BLACK and I have no idea of what's happening... I've also tried using their markers and inverted image, however, also getting black image. The paper I've been using is Luc M. Vincent, Barry R. Masters, "Morphological image processing and network analysis of cornea endothelial cell images", Proc. SPIE 1769

I apologize for the long text, however I really wanted to explain everything in detail of what is my understanding so far, btw, I've tried watershed segmentation from both scikit-image and opencv, both gave me the black image.

Here is the following code that I have been using

...

ANSWER

Answered 2017-Dec-02 at 04:16

Here's a rough example for the watershed segmentation of your image with scikit-image.

What is missing in your script is calculating the Euclidean distance (see here and here) and extracting the local maxima from it.

Note that the watershed algorithm outputs a piece-wise constant image where pixels in the same regions are assigned the same value. What is shown in your 'desired output' panel (e) are the edges between the regions instead.

Source https://stackoverflow.com/questions/47538589

QUESTION

How to find correlation coefficients for non-numeric variables

Asked 2017-Sep-24 at 13:21

Species     Gender     Weight     Corneal Diameter Avg
Great Grey    M         971            19.5
Great Grey    F         1209           19.0
Great Grey    M         952            20.5
Great Grey    F         1793           20.5
Snowy         M         1658           22.0
Snowy         F         1899           22.75
Snowy         F         1975           24.50
Snowy         M         1646           23.00

...

ANSWER

Answered 2017-Sep-24 at 13:21

Use Spearman rank correlation. If x and y are the two vectors which may or may not be numeric.

Source https://stackoverflow.com/questions/46384546

QUESTION

calculate average with angularjs in nested json

Asked 2017-Apr-05 at 14:23

I receive a JSON response like this:

...

ANSWER

Answered 2017-Apr-05 at 13:57

You can use a function:

Source https://stackoverflow.com/questions/43233217

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

Vulnerabilities

No vulnerabilities reported

Install corneal

You can download it from GitHub.
On a UNIX-like operating system, using your system’s package manager is easiest. However, the packaged Ruby version may not be the newest one. There is also an installer for Windows. Managers help you to switch between multiple Ruby versions on your system. Installers can be used to install a specific or multiple Ruby versions. Please refer ruby-lang.org for more information.

Support

Bug reports and pull requests are welcome on GitHub at https://github.com/thebrianemory/corneal This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

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