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<div class="subTitle">org.opencv.features2d</div>
<h2 title="Class ORB" class="title">Class ORB</h2>
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<div class="contentContainer">
<ul class="inheritance">
<li>java.lang.Object</li>
<li>
<ul class="inheritance">
<li><a href="../../../org/opencv/core/Algorithm.html" title="class in org.opencv.core">org.opencv.core.Algorithm</a></li>
<li>
<ul class="inheritance">
<li><a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">org.opencv.features2d.Feature2D</a></li>
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<li>org.opencv.features2d.ORB</li>
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<pre>public class <span class="typeNameLabel">ORB</span>
extends <a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">Feature2D</a></pre>
<div class="block">Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
described in CITE: RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
the strongest features using FAST or Harris response, finds their orientation using first-order
moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
k-tuples) are rotated according to the measured orientation).</div>
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<caption><span>Fields</span><span class="tabEnd">&nbsp;</span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
<th class="colLast" scope="col">Field and Description</th>
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<tr class="altColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#FAST_SCORE">FAST_SCORE</a></span></code>&nbsp;</td>
</tr>
<tr class="rowColor">
<td class="colFirst"><code>static int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#HARRIS_SCORE">HARRIS_SCORE</a></span></code>&nbsp;</td>
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<caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd">&nbsp;</span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd">&nbsp;</span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd">&nbsp;</span></span></caption>
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<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long&nbsp;addr)</code>&nbsp;</td>
</tr>
<tr id="i1" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create--">create</a></span>()</code>
<div class="block">The ORB constructor
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically.</div>
</td>
</tr>
<tr id="i2" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-">create</a></span>(int&nbsp;nfeatures)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i3" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i4" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i5" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i6" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i7" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i8" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i9" class="rowColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-int-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType,
int&nbsp;patchSize)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i10" class="altColor">
<td class="colFirst"><code>static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a></code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-int-int-int-int-">create</a></span>(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType,
int&nbsp;patchSize,
int&nbsp;fastThreshold)</code>
<div class="block">The ORB constructor</div>
</td>
</tr>
<tr id="i11" class="rowColor">
<td class="colFirst"><code>java.lang.String</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getDefaultName--">getDefaultName</a></span>()</code>
<div class="block">Returns the algorithm string identifier.</div>
</td>
</tr>
<tr id="i12" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getEdgeThreshold--">getEdgeThreshold</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i13" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getFastThreshold--">getFastThreshold</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i14" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getFirstLevel--">getFirstLevel</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i15" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getMaxFeatures--">getMaxFeatures</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i16" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getNLevels--">getNLevels</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i17" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getPatchSize--">getPatchSize</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i18" class="altColor">
<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getScaleFactor--">getScaleFactor</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i19" class="rowColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getScoreType--">getScoreType</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i20" class="altColor">
<td class="colFirst"><code>int</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getWTA_K--">getWTA_K</a></span>()</code>&nbsp;</td>
</tr>
<tr id="i21" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setEdgeThreshold-int-">setEdgeThreshold</a></span>(int&nbsp;edgeThreshold)</code>&nbsp;</td>
</tr>
<tr id="i22" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setFastThreshold-int-">setFastThreshold</a></span>(int&nbsp;fastThreshold)</code>&nbsp;</td>
</tr>
<tr id="i23" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setFirstLevel-int-">setFirstLevel</a></span>(int&nbsp;firstLevel)</code>&nbsp;</td>
</tr>
<tr id="i24" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setMaxFeatures-int-">setMaxFeatures</a></span>(int&nbsp;maxFeatures)</code>&nbsp;</td>
</tr>
<tr id="i25" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setNLevels-int-">setNLevels</a></span>(int&nbsp;nlevels)</code>&nbsp;</td>
</tr>
<tr id="i26" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setPatchSize-int-">setPatchSize</a></span>(int&nbsp;patchSize)</code>&nbsp;</td>
</tr>
<tr id="i27" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setScaleFactor-double-">setScaleFactor</a></span>(double&nbsp;scaleFactor)</code>&nbsp;</td>
</tr>
<tr id="i28" class="altColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setScoreType-int-">setScoreType</a></span>(int&nbsp;scoreType)</code>&nbsp;</td>
</tr>
<tr id="i29" class="rowColor">
<td class="colFirst"><code>void</code></td>
<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setWTA_K-int-">setWTA_K</a></span>(int&nbsp;wta_k)</code>&nbsp;</td>
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<h3>Methods inherited from class&nbsp;org.opencv.features2d.<a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">Feature2D</a></h3>
<code><a href="../../../org/opencv/features2d/Feature2D.html#compute-java.util.List-java.util.List-java.util.List-">compute</a>, <a href="../../../org/opencv/features2d/Feature2D.html#compute-org.opencv.core.Mat-org.opencv.core.MatOfKeyPoint-org.opencv.core.Mat-">compute</a>, <a href="../../../org/opencv/features2d/Feature2D.html#defaultNorm--">defaultNorm</a>, <a href="../../../org/opencv/features2d/Feature2D.html#descriptorSize--">descriptorSize</a>, <a href="../../../org/opencv/features2d/Feature2D.html#descriptorType--">descriptorType</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detect-java.util.List-java.util.List-">detect</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detect-java.util.List-java.util.List-java.util.List-">detect</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detect-org.opencv.core.Mat-org.opencv.core.MatOfKeyPoint-">detect</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detect-org.opencv.core.Mat-org.opencv.core.MatOfKeyPoint-org.opencv.core.Mat-">detect</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detectAndCompute-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.MatOfKeyPoint-org.opencv.core.Mat-">detectAndCompute</a>, <a href="../../../org/opencv/features2d/Feature2D.html#detectAndCompute-org.opencv.core.Mat-org.opencv.core.Mat-org.opencv.core.MatOfKeyPoint-org.opencv.core.Mat-boolean-">detectAndCompute</a>, <a href="../../../org/opencv/features2d/Feature2D.html#empty--">empty</a>, <a href="../../../org/opencv/features2d/Feature2D.html#read-java.lang.String-">read</a>, <a href="../../../org/opencv/features2d/Feature2D.html#write-java.lang.String-">write</a></code></li>
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<h3>Methods inherited from class&nbsp;org.opencv.core.<a href="../../../org/opencv/core/Algorithm.html" title="class in org.opencv.core">Algorithm</a></h3>
<code><a href="../../../org/opencv/core/Algorithm.html#clear--">clear</a>, <a href="../../../org/opencv/core/Algorithm.html#getNativeObjAddr--">getNativeObjAddr</a>, <a href="../../../org/opencv/core/Algorithm.html#save-java.lang.String-">save</a></code></li>
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<h3>Methods inherited from class&nbsp;java.lang.Object</h3>
<code>equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
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<h4>FAST_SCORE</h4>
<pre>public static final&nbsp;int FAST_SCORE</pre>
<dl>
<dt><span class="seeLabel">See Also:</span></dt>
<dd><a href="../../../constant-values.html#org.opencv.features2d.ORB.FAST_SCORE">Constant Field Values</a></dd>
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<pre>public static final&nbsp;int HARRIS_SCORE</pre>
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<dd><a href="../../../constant-values.html#org.opencv.features2d.ORB.HARRIS_SCORE">Constant Field Values</a></dd>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;__fromPtr__(long&nbsp;addr)</pre>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create()</pre>
<div class="block">The ORB constructor
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
roughly match the patchSize parameter.
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</div>
<dl>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
roughly match the patchSize parameter.
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
roughly match the patchSize parameter.
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
roughly match the patchSize parameter.
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.</dd>
<dd><code>firstLevel</code> - The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.</dd>
<dd><code>firstLevel</code> - The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.</dd>
<dd><code>WTA_K</code> - The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.</dd>
<dd><code>firstLevel</code> - The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.</dd>
<dd><code>WTA_K</code> - The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).</dd>
<dd><code>scoreType</code> - The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType,
int&nbsp;patchSize)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.</dd>
<dd><code>firstLevel</code> - The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.</dd>
<dd><code>WTA_K</code> - The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).</dd>
<dd><code>scoreType</code> - The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.</dd>
<dd><code>patchSize</code> - size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<pre>public static&nbsp;<a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a>&nbsp;create(int&nbsp;nfeatures,
float&nbsp;scaleFactor,
int&nbsp;nlevels,
int&nbsp;edgeThreshold,
int&nbsp;firstLevel,
int&nbsp;WTA_K,
int&nbsp;scoreType,
int&nbsp;patchSize,
int&nbsp;fastThreshold)</pre>
<div class="block">The ORB constructor</div>
<dl>
<dt><span class="paramLabel">Parameters:</span></dt>
<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.</dd>
<dd><code>nlevels</code> - The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.</dd>
<dd><code>firstLevel</code> - The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.</dd>
<dd><code>WTA_K</code> - The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).</dd>
<dd><code>scoreType</code> - The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.</dd>
<dd><code>patchSize</code> - size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.</dd>
<dd><code>fastThreshold</code> - the fast threshold</dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
</ul>
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<pre>public&nbsp;java.lang.String&nbsp;getDefaultName()</pre>
<div class="block"><span class="descfrmTypeLabel">Description copied from class:&nbsp;<code><a href="../../../org/opencv/core/Algorithm.html#getDefaultName--">Algorithm</a></code></span></div>
<div class="block">Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.</div>
<dl>
<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
<dd><code><a href="../../../org/opencv/features2d/Feature2D.html#getDefaultName--">getDefaultName</a></code>&nbsp;in class&nbsp;<code><a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">Feature2D</a></code></dd>
<dt><span class="returnLabel">Returns:</span></dt>
<dd>automatically generated</dd>
</dl>
</li>
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<pre>public&nbsp;int&nbsp;getEdgeThreshold()</pre>
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<pre>public&nbsp;int&nbsp;getFastThreshold()</pre>
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<h4>getFirstLevel</h4>
<pre>public&nbsp;int&nbsp;getFirstLevel()</pre>
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<h4>getMaxFeatures</h4>
<pre>public&nbsp;int&nbsp;getMaxFeatures()</pre>
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