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112e30a
First Commit
shubham0108 Jul 13, 2017
a2b0932
First Commit
shubham0108 Jul 13, 2017
a5d76ca
Second Commit
shubham0108 Jul 27, 2017
ac4881d
Second Commit with images
shubham0108 Jul 27, 2017
4ce5fd6
Set up CI with Azure Pipelines
shubham0108 Apr 11, 2020
743740a
Create ccpp.yml
shubham0108 May 1, 2020
68cddb2
Update ccpp.yml
shubham0108 May 1, 2020
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18 changes: 18 additions & 0 deletions .github/workflows/ccpp.yml
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name: C/C++ CI

on:
push:
branches: [ master ]
pull_request:
branches: [ master ]

jobs:
build:

runs-on: ubuntu-latest

steps:
- uses: actions/checkout@v2

- name: make distcheck
run: make distcheck
19 changes: 19 additions & 0 deletions azure-pipelines.yml
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# Starter pipeline
# Start with a minimal pipeline that you can customize to build and deploy your code.
# Add steps that build, run tests, deploy, and more:
# https://aka.ms/yaml

trigger:
- master

pool:
vmImage: 'ubuntu-latest'

steps:
- script: echo Hello, world!
displayName: 'Run a one-line script'

- script: |
echo Add other tasks to build, test, and deploy your project.
echo See https://aka.ms/yaml
displayName: 'Run a multi-line script'
77 changes: 77 additions & 0 deletions help/en_US/adaptHistEq.xml
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<?xml version="1.0" encoding="UTF-8"?>

<!--
*
* This help file was generated from adaptHistEq.sci using help_from_sci().
*
-->

<refentry version="5.0-subset Scilab" xml:id="adaptHistEq" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">

<refnamediv>
<refname>adaptHistEq</refname>
<refpurpose>Performs Contrast Limited Adaptive Histogram Equalisation</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
outputImage = adaptHistEq(inputImage)
outputImage = adaptHistEq(inputImage, clip_limit)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>inputImage :</term>
<listitem><para> The input image on which histogram equalisation is to be implemented</para></listitem></varlistentry>
<varlistentry><term>clip_limit :</term>
<listitem><para> The target hgram bins.</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
As an alternative to using histeq, you can perform contrast-limited adaptive histogram equalization (CLAHE) using the adapthisteq function. While histeq works on the entire image, adapthisteq operates on small regions in the image, called tiles. Each tile's contrast is enhanced, so that the histogram of the output region approximately matches a specified histogram. After performing the equalization, adapthisteq combines neighboring tiles using bilinear interpolation to eliminate artificially induced boundaries.
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a=imread("lena.jpeg");
k=adaptHistEq(a);

]]></programlisting>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a=imread("photo.jpg");
k=adaptHistEq(a,64);

]]></programlisting>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Shubham Lohakare, NITK Surathkal</member>
<member>Yash Balghat</member>
</simplelist>
</refsection>
</refentry>
86 changes: 86 additions & 0 deletions help/en_US/boxfilter.xml
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<?xml version="1.0" encoding="UTF-8"?>

<!--
*
* This help file was generated from boxfilter.sci using help_from_sci().
*
-->

<refentry version="5.0-subset Scilab" xml:id="boxfilter" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">

<refnamediv>
<refname>boxfilter</refname>
<refpurpose>Blurs an image using the box filter.</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
outputImage = boxfilter(InputArraysrc, intddepth,size1x,size1y,pointx,pointy, bool_normalize)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>InputArraysrc :</term>
<listitem><para> Input image to be blurred</para></listitem></varlistentry>
<varlistentry><term>intddepth :</term>
<listitem><para> The output image depth (-1 to use src.depth())</para></listitem></varlistentry>
<varlistentry><term>size1x :</term>
<listitem><para> Width for the blurring kernel size</para></listitem></varlistentry>
<varlistentry><term>size1y :</term>
<listitem><para> Height for the blurring kernel size</para></listitem></varlistentry>
<varlistentry><term>pointx :</term>
<listitem><para> x-coordinate for anchor point (Default value -1)</para></listitem></varlistentry>
<varlistentry><term>pointy :</term>
<listitem><para> y-coordinate for anchor point (Default value -1)</para></listitem></varlistentry>
<varlistentry><term>bool_normalize :</term>
<listitem><para> flag, specifying whether the kernel is normalized by its area or not</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on).
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("lena.jpeg");
k=boxfilter(a,-1,40,80,-1,-1,"False");

]]></programlisting>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("photo.jpg");
k=boxfilter(a,-1,8,8,-1,-1,"True");

]]></programlisting>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Shubham Lohakare, NITK Surathkal</member>
<member>Priyanka Hiranandani, NIT Surat</member>
</simplelist>
</refsection>
</refentry>
91 changes: 91 additions & 0 deletions help/en_US/denoiseColor.xml
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<?xml version="1.0" encoding="UTF-8"?>

<!--
*
* This help file was generated from denoiseColor.sci using help_from_sci().
*
-->

<refentry version="5.0-subset Scilab" xml:id="denoiseColor" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">

<refnamediv>
<refname>denoiseColor</refname>
<refpurpose>Removes colored noise from the image</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
outputImage = denoiseColor(src, strengthFilter,strengthFilterColor, tempWindowSize, searchWindowSize)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>src :</term>
<listitem><para> The input image with colored noise.</para></listitem></varlistentry>
<varlistentry><term>strengthFilter :</term>
<listitem><para> The noise filter for white gaussian noise.</para></listitem></varlistentry>
<varlistentry><term>strengthFilterColor :</term>
<listitem><para> The noise filter for colored noise.</para></listitem></varlistentry>
<varlistentry><term>tempWindowSize :</term>
<listitem><para> Size in pixels of the template patch that is used to compute weights. Should be odd.</para></listitem></varlistentry>
<varlistentry><term>searchWindowSize :</term>
<listitem><para> Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd.</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
The function is used to remove colored noise from an image. It is the modification of fasNlmeansDenoising function
The output is a denoised image
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("taj.jpg");
strengthFilter = 200;
strengthFilterColor = 40;
tempWindowSize = 7;
searchWindowSize =21;
p = denoiseColor(a,strengthFilter,strengthFilterColor,tempWindowSize,searchWindowSize);

]]></programlisting>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("cnoise1.jpg");
strengthFilter = 20;
strengthFilterColor = 100;
tempWindowSize = 7;
searchWindowSize =21;
p = denoiseColor(a,strengthFilter,strengthFilterColor,tempWindowSize,searchWindowSize);

]]></programlisting>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Shubham Lohakare, NITK Surathkal</member>
<member>Ashish Mantosh Barik, NIT Rourkela</member>
</simplelist>
</refsection>
</refentry>
90 changes: 90 additions & 0 deletions help/en_US/fastNlMeansDenoising.xml
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<?xml version="1.0" encoding="UTF-8"?>

<!--
*
* This help file was generated from fastNlMeansDenoising.sci using help_from_sci().
*
-->

<refentry version="5.0-subset Scilab" xml:id="fastNlMeansDenoising" xml:lang="en"
xmlns="http://docbook.org/ns/docbook"
xmlns:xlink="http://www.w3.org/1999/xlink"
xmlns:svg="http://www.w3.org/2000/svg"
xmlns:ns3="http://www.w3.org/1999/xhtml"
xmlns:mml="http://www.w3.org/1998/Math/MathML"
xmlns:scilab="http://www.scilab.org"
xmlns:db="http://docbook.org/ns/docbook">

<refnamediv>
<refname>fastNlMeansDenoising</refname>
<refpurpose>Removes gaussian white noise from images</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
outputImage=fastNlMeansDenoising(src, strengthFilter, tempWindowSize, searchWindowSize, choice)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>src :</term>
<listitem><para> Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.</para></listitem></varlistentry>
<varlistentry><term>strengthFilter :</term>
<listitem><para> Parameter regulating filter strength.</para></listitem></varlistentry>
<varlistentry><term>templateWindowSize :</term>
<listitem><para> Size in pixels of the template patch that is used to compute weights. Should be odd.</para></listitem></varlistentry>
<varlistentry><term>searchWindowSize :</term>
<listitem><para> Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd.</para></listitem></varlistentry>
<varlistentry><term>choice :</term>
<listitem><para> Chooses the method</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
Perform image denoising using Non-local Means Denoising algorithm with several computational optimizations. Noise expected to be a gaussian white noise.
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("man.jpg");
strengthFilter = 30;
tempWindowSize = 7;
searchWindowSize = 40;
choice=2;
k = fastNlMeansDenoising(a, strengthFilter, tempWindowSize, searchWindowSize,choice);

]]></programlisting>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
a = imread("noise.jpg");
strengthFilter = 100;
tempWindowSize = 7;
searchWindowSize = 50;
choice = 3;
k = fastNlMeansDenoising(a, strengthFilter, tempWindowSize, searchWindowSize,choice);

]]></programlisting>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Shubham Lohakare, NITK Surathkal</member>
<member>Ashish Mantosh, NIT Rourkela</member>
</simplelist>
</refsection>
</refentry>
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