semvec/pypi-packages huggingface dataset:
micral-classify-core 2.0
Classification tool between martensite, spheroidite and pearlite
License: MIT

Micral (MICROstructure anALysis)

Classification module - core module

-----

Description :

The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.
This tools doesn't plot anything, it just return raw data that could be get by an user, or use with the graphic module

-----

Installation :

pip install micral_classify_core

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_classify_core
print(micral_classify_core.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-name-core 2.0
Name tool between 7 available
License: MIT

Micral (MICROstructure anALysis)

Name module - core module

-----

Description :

The aim is to give a name between 7 available 
This tools output the name of the sample, and some others details as the accuracy.

-----

Installation :

pip install micral_name

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_name
print(micral_name.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-summary 2.0
User friendly outputing tool for Micral
License: MIT

Micral (MICROstructure anALysis)

Classification module - core module

-----

Description :

The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.
This tools doesn't plot anything, it just return raw data that could be get by an user, or use with the graphic module

-----

Installation :

pip install micral_classify_core

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_classify_core
print(micral_classify_core.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-grain 2.0
Separation tool between coarse and ultrafine grain
License: MIT

Micral (MICROstructure anALysis)

Grain separation module

-----

Description :

The aim is to distinguish between coarse and ultrafine graine microstructure.
This tools output the pourcentage of coarse grain.

-----

Installation :

pip install micral_grain

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_grain
print(micral_grain.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-name 2.0
Name tool between 7 available
License: MIT

Micral (MICROstructure anALysis)

Name module

-----

Description :

The aim is to give a name between 7 available 
This tools output the name of the sample, and some others details as the accuracy.

-----

Installation :

pip install micral_name

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_name
print(micral_name.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-harmonic 2.0
Measurement tools of the harmonicity of the microstructure
License: MIT

Micral (MICROstructure anALysis)

Harmonic measurement module

-----

Description :

The aim is to figure out in what extend a given microstrucure could be considered like an harmonic structure or not.

-----

Installation :

pip install micral_harmonic

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_harmonic
print(micral_harmonic.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-grain-core 2.0
Separation tool between coarse and ultrafine grain - core module
License: MIT

Micral (MICROstructure anALysis)

Grain separation module - core module

-----

Description :

The aim is to distinguish between coarse and ultrafine graine microstructure.
This tools output the pourcentage of coarse grain.
This tools doesn't plot anything, it just return raw data that could be get by an user, or use with the graphic module

-----

Installation :

pip install micral_grain_core

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_grain_core
print(micral_grain_core.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-core 2.0
Analyse tool for microstructure - core module
License: MIT

Micral (MICROstructure anALysis) - core module

-----

Description :

The aim is to gather information about microstructure.
It provide raw infomation.

-----

Installation :

pip install micral_core

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_core
print(micral_core.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral 2.0
Analyse tool for microstructure
License: MIT

Micral (MICROstructure anALysis)

-----

Description :

The aim of this tool is to gather information about microstructure.

-----

Installation :

pip install micral

-----

Usage :

In a python file or directly in the interpreter, type :

import micral

micral.analyse(<your image>)

-----

Input :

<your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]

Note that the image must be in the same directory than the python file (or in the current directory of the command)

-----

Output :

The output will be an Python dictionnary, and each entry is a dictionnary how gather the differents informations on the image.
micral-classify 2.0
Classification tool between martensite, spheroidite and pearlite
License: MIT

Micral (MICROstructure anALysis)

Classification module

-----

Description :

The aim is to distinguish between martensite, spheroidite and pearlite microstructure
This tools output the category, and some others details as the accuracy.

-----

Installation :

pip install micral_classify

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_classify
print(micral_classify.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)
micral-harmonic-core 2.0
Measurement tools of the harmonicity of the microstructure - core module
License: MIT

Micral (MICROstructure anALysis)

Harmonic measurement module - core module

-----

Description :

The aim is to figure out in what extend a given microstrucure could be considered like an harmonic structure or not.
This module don't plot any result, but only raw result to be use in others applications

-----

Installation :

pip install micral_harmonic_core

-----

Usage :

In a python file or directly in the interpreter, type :

import micral_harmonic_core
print(micral_harmonic_core.analyse(<your image>))

Where <your image> is the name of the image to analyse (with extension (ie jpg, png, bmp etc...))
The output is an unique value.

It's also possible to send multiple image at once : in that case, provide a list of name :
<your image> = [<image1>, <image2>, ...]
And the return will be also a list of all the results

Note that the image must be in the same directory than the python file (or in the current directory of the command)