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Binary file added data/argset3.dat
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51,620 changes: 51,620 additions & 0 deletions data/emclassi3.yml

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31,570 changes: 31,570 additions & 0 deletions data/knnclassi3.yml

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21 changes: 21 additions & 0 deletions help/builder_help.sce
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// Copyright (C) 2015 - IIT Bombay - FOSSEE
//
// Author: Shamika Mohanan
// Organization: FOSSEE, IIT Bombay
// Email: [email protected]
// This file must be used under the terms of the BSD.
// This source file is licensed as described in the file LICENSE, which
// you should have received as part of this distribution. The terms
// are also available at
// https://opensource.org/licenses/BSD-3-Clause

mode(-1)
lines(0)

toolbox_title = "FOSSEE_Image_Processing_Toolbox"

help_dir = get_absolute_file_path('builder_help.sce');

tbx_builder_help_lang("en_US", help_dir);

clear toolbox_title;
21 changes: 21 additions & 0 deletions help/builder_help.sce~
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// Copyright (C) 2015 - IIT Bombay - FOSSEE
//
// Author: Harpreet Singh
// Organization: FOSSEE, IIT Bombay
// Email: [email protected]
// This file must be used under the terms of the BSD.
// This source file is licensed as described in the file LICENSE, which
// you should have received as part of this distribution. The terms
// are also available at
// https://opensource.org/licenses/BSD-3-Clause

mode(-1)
lines(0)

toolbox_title = "FOSSEE_Optimization_Toolbox"

help_dir = get_absolute_file_path('builder_help.sce');

tbx_builder_help_lang("en_US", help_dir);

clear toolbox_title;
94 changes: 94 additions & 0 deletions help/en_US/blobAnalysis.xml
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<?xml version="1.0" encoding="UTF-8"?>

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

<refentry version="5.0-subset Scilab" xml:id="blobAnalysis" 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>blobAnalysis</refname>
<refpurpose>Detects blob in the source image</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
[blob] = blobAnalysis(srcImg)
[blob] = blobAnalysis(srcImg, Name, Value)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>srcImg:</term>
<listitem><para> The input image Matrix.</para></listitem></varlistentry>
<varlistentry><term>ROI:</term>
<listitem><para> This defines a particular in the image in of which you want the features.</para></listitem></varlistentry>
<varlistentry><term>filterByThreshold:</term>
<listitem><para> This filter compares the intensity of a binary image at the center of a</para></listitem></varlistentry>
<varlistentry><term>filterByArea:</term>
<listitem><para> Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).</para></listitem></varlistentry>
<varlistentry><term>filterByCircularity:</term>
<listitem><para> Extracted blobs have circularity ( 4∗π∗Areaperimeter∗perimeter) between</para></listitem></varlistentry>
<varlistentry><term>filterByConvexity:</term>
<listitem><para> Extracted blobs have convexity (area / area of blob convex hull) between minConvexity</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
The function uses SimpleBlobDetector function to detect the blobs then it checks for different Name
Value pair arguments and accordingly returns the parameters of the blob such as 2D coordinates of the
blob, size of the blob.
</para>
<para>
The Name-Value pair may be any of following types<itemizedlist><listitem><para>bool filterByArea, vector [minArea maxArea]</para></listitem><listitem><para>bool filterByCircularity, vector [minCircularity maxCircularity]</para></listitem><listitem><para>bool filterByConvexity, vector [minConvexity maxConvexity]</para></listitem><listitem><para>double ROI, vector </para></listitem><listitem><para>bool filterByThreshold, vector [minThreshold maxThreshold]</para></listitem></itemizedlist>
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
img_2 = imread("/images/blob.jpg", 0);

lis1 = blobAnalysis(img_2, "filterByThreshold", [200, 255]);
lis2 = blobAnalysis(img_2, "filterByCircularity", [0.1, 0.9]);
lis3 = blobAnalysis(img_2, "filterByArea", [1500, 1600]);

dimage1 = drawKeypoints(img_2, lis1.Points, "color", [0, 255, 0]);
dimage2 = drawKeypoints(img_2, lis2.Points, "color", [0, 255, 0]);
dimage3 = drawKeypoints(img_2, lis3.Points, "color", [0, 255, 0]);

]]></programlisting>
</refsection>

<refsection>
<title>See also</title>
<simplelist type="inline">
<member><link linkend="imread,">drawKeypoints</link></member>
</simplelist>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Deepshikha</member>
</simplelist>
</refsection>
</refentry>
17 changes: 17 additions & 0 deletions help/en_US/build_help.sce
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// Copyright (C) 2015 - IIT Bombay - FOSSEE
//
// Author: Shamika Mohanan
// Organization: FOSSEE, IIT Bombay
// Email: [email protected]
// This file must be used under the terms of the BSD.
// This source file is licensed as described in the file LICENSE, which
// you should have received as part of this distribution. The terms
// are also available at
// https://opensource.org/licenses/BSD-3-Clause

help_lang_dir = get_absolute_file_path('build_help.sce');

tbx_build_help(TOOLBOX_TITLE, help_lang_dir);

clear help_lang_dir;

17 changes: 17 additions & 0 deletions help/en_US/build_help.sce~
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@@ -0,0 +1,17 @@
// Copyright (C) 2015 - IIT Bombay - FOSSEE
//
// Author: Shamika Mohanan
// Organization: FOSSEE, IIT Bombay
// Email: [email protected]
// This file must be used under the terms of the BSD.
// This source file is licensed as described in the file LICENSE, which
// you should have received as part of this distribution. The terms
// are also available at
// https://opensource.org/licenses/BSD-3-Clause

help_lang_dir = get_absolute_file_path('build_help.sce');

tbx_build_help(TOOLBOX_TITLE, help_lang_dir);

clear help_lang_dir;

74 changes: 74 additions & 0 deletions help/en_US/decorrstretch.xml
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<?xml version="1.0" encoding="UTF-8"?>

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

<refentry version="5.0-subset Scilab" xml:id="decorrstretch" 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>decorrstretch</refname>
<refpurpose>Applies decorrelation stretch to an image.</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
outputImage = decorrstretch(image);

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>image:</term>
<listitem><para> Input image.</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
This function applies decorrelation stretch to an input image which causes the image to enhance
by way of amplifying image difference.
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
i = imread("images/lena.jpg");
outputImage = decorrstretch(i);
imshow(i);

]]></programlisting>
</refsection>

<refsection>
<title>See also</title>
<simplelist type="inline">
<member><link linkend="imread">imread</link></member>
<member><link linkend="imshow">imshow</link></member>
</simplelist>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Dhruti Shah</member>
</simplelist>
</refsection>
</refentry>
106 changes: 106 additions & 0 deletions help/en_US/detectAndComputeORB.xml
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<?xml version="1.0" encoding="UTF-8"?>

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

<refentry version="5.0-subset Scilab" xml:id="detectAndComputeORB" 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>detectAndComputeORB</refname>
<refpurpose>Oriented FAST and rotated BRIEF (ORB) is used to detect and compute the corners in an image.</refpurpose>
</refnamediv>


<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
[orb] = detectAndComputeORB(srcImg)
[orb] = detectAndComputeORB(srcImg, Name(same as the ones given under description), Value, ...)

</synopsis>
</refsynopsisdiv>

<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>srcImg:</term>
<listitem><para> The input image Matrix</para></listitem></varlistentry>
<varlistentry><term>ROI:</term>
<listitem><para> This defines a particular in the image in of which you want the features.</para></listitem></varlistentry>
<varlistentry><term>maxFeatures:</term>
<listitem><para> The maximum number of features to retain.</para></listitem></varlistentry>
<varlistentry><term>scaleFactor:</term>
<listitem><para> Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid,</para></listitem></varlistentry>
<varlistentry><term>nLevels:</term>
<listitem><para> The number of pyramid levels. The smallest level will have linear size equal to</para></listitem></varlistentry>
<varlistentry><term>edgeThreshold:</term>
<listitem><para> This is size of the border where the features are not detected. It should roughly match</para></listitem></varlistentry>
<varlistentry><term>firstLevel:</term>
<listitem><para> It should be 0 in the current implementation.</para></listitem></varlistentry>
<varlistentry><term>scoreType:</term>
<listitem><para> The default HARRIS_SCORE (flag value = 0) means that Harris algorithm is used to rank features</para></listitem></varlistentry>
<varlistentry><term>patchSize:</term>
<listitem><para> Size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers</para></listitem></varlistentry>
<varlistentry><term>fastThreshold:</term>
<listitem><para> The threshold value used for feature detection.</para></listitem></varlistentry>
</variablelist>
</refsection>

<refsection>
<title>Description</title>
<para>
Oriented FAST and rotated BRIEF (ORB) is a fast robust local feature detector, first presented
by Ethan Rublee in 2011,that can be used in computer vision tasks like object recognition
or 3D reconstruction.
It is based on the FAST keypoint detector and the visual descriptor BRIEF (Binary Robust
Independent Elementary Features).
Its aim is to provide a fast and efficient alternative to SIFT.
</para>
<para>
The Name-Value pair may be any of following types<itemizedlist><listitem><para> edgeThreshold </para></listitem><listitem><para> fastThreshold </para></listitem><listitem><para> firstLevel </para></listitem><listitem><para> maxFeatures </para></listitem><listitem><para> nLevels </para></listitem><listitem><para> patchSize </para></listitem><listitem><para> scaleFactor </para></listitem><listitem><para> scoreType </para></listitem></itemizedlist>
</para>
<para>
</para>
</refsection>

<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
img_1 = imread("images/checkerBoard.jpg", 0);
img_2 = imread("images/chess.jpg", 0);
lis1 = detectAndComputeORB(img_1);
lis2 = detectAndComputeORB(img_2);
dimage = drawKeypoints(img_2, lis2.Points);
[matches, distance] = matchFeatures(lis1.Features, lis1.Features, "Method", "Exhaustive", "Metric", "Hamming");
matchedImage = drawMatch(img_1, img_2, lis1.Points, lis2.Points, matches, distance);

]]></programlisting>
</refsection>

<refsection>
<title>See also</title>
<simplelist type="inline">
<member><link linkend="imread">imread</link></member>
<member><link linkend="drawMatch">drawMatch</link></member>
<member><link linkend="drawKeypoints">drawKeypoints</link></member>
<member><link linkend="matchFeatures">matchFeatures</link></member>
</simplelist>
</refsection>

<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Siddhant Narang</member>
</simplelist>
</refsection>
</refentry>
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