1121 lines
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<div class="subTitle">org.opencv.features2d</div>
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<h2 title="Class ORB" class="title">Class ORB</h2>
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<ul class="inheritance">
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<li>java.lang.Object</li>
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<li><a href="../../../org/opencv/core/Algorithm.html" title="class in org.opencv.core">org.opencv.core.Algorithm</a></li>
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<ul class="inheritance">
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<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>
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extends <a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">Feature2D</a></pre>
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<div class="block">Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
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described in CITE: RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects
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the strongest features using FAST or Harris response, finds their orientation using first-order
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moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or
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k-tuples) are rotated according to the measured orientation).</div>
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<caption><span>Fields</span><span class="tabEnd"> </span></caption>
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<th class="colFirst" scope="col">Modifier and Type</th>
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<th class="colLast" scope="col">Field and Description</th>
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#FAST_SCORE">FAST_SCORE</a></span></code> </td>
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</tr>
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<tr class="rowColor">
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<td class="colFirst"><code>static int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#HARRIS_SCORE">HARRIS_SCORE</a></span></code> </td>
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<h3>Method Summary</h3>
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<table class="memberSummary" border="0" cellpadding="3" cellspacing="0" summary="Method Summary table, listing methods, and an explanation">
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<caption><span id="t0" class="activeTableTab"><span>All Methods</span><span class="tabEnd"> </span></span><span id="t1" class="tableTab"><span><a href="javascript:show(1);">Static Methods</a></span><span class="tabEnd"> </span></span><span id="t2" class="tableTab"><span><a href="javascript:show(2);">Instance Methods</a></span><span class="tabEnd"> </span></span><span id="t4" class="tableTab"><span><a href="javascript:show(8);">Concrete Methods</a></span><span class="tabEnd"> </span></span></caption>
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<tr id="i0" class="altColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#Z:Z__fromPtr__-long-">__fromPtr__</a></span>(long addr)</code> </td>
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</tr>
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<tr id="i1" class="rowColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create--">create</a></span>()</code>
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<div class="block">The ORB constructor
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pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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will degrade feature matching scores dramatically.</div>
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</td>
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</tr>
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<tr id="i2" class="altColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-">create</a></span>(int nfeatures)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i3" class="rowColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-">create</a></span>(int nfeatures,
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float scaleFactor)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i4" class="altColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-">create</a></span>(int nfeatures,
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float scaleFactor,
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int nlevels)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i5" class="rowColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-">create</a></span>(int nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i6" class="altColor">
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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>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#create-int-float-int-int-int-">create</a></span>(int nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i7" class="rowColor">
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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>
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<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 nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel,
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int WTA_K)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i8" class="altColor">
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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>
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<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 nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel,
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int WTA_K,
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int scoreType)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i9" class="rowColor">
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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>
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<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 nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel,
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int WTA_K,
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int scoreType,
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int patchSize)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i10" class="altColor">
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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>
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<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 nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel,
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int WTA_K,
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int scoreType,
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int patchSize,
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int fastThreshold)</code>
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<div class="block">The ORB constructor</div>
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</td>
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</tr>
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<tr id="i11" class="rowColor">
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<td class="colFirst"><code>java.lang.String</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getDefaultName--">getDefaultName</a></span>()</code>
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<div class="block">Returns the algorithm string identifier.</div>
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</td>
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</tr>
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<tr id="i12" class="altColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getEdgeThreshold--">getEdgeThreshold</a></span>()</code> </td>
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</tr>
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<tr id="i13" class="rowColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getFastThreshold--">getFastThreshold</a></span>()</code> </td>
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</tr>
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<tr id="i14" class="altColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getFirstLevel--">getFirstLevel</a></span>()</code> </td>
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</tr>
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<tr id="i15" class="rowColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getMaxFeatures--">getMaxFeatures</a></span>()</code> </td>
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</tr>
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<tr id="i16" class="altColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getNLevels--">getNLevels</a></span>()</code> </td>
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</tr>
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<tr id="i17" class="rowColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getPatchSize--">getPatchSize</a></span>()</code> </td>
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</tr>
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<tr id="i18" class="altColor">
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<td class="colFirst"><code>double</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getScaleFactor--">getScaleFactor</a></span>()</code> </td>
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</tr>
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<tr id="i19" class="rowColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getScoreType--">getScoreType</a></span>()</code> </td>
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</tr>
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<tr id="i20" class="altColor">
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<td class="colFirst"><code>int</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#getWTA_K--">getWTA_K</a></span>()</code> </td>
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</tr>
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<tr id="i21" class="rowColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setEdgeThreshold-int-">setEdgeThreshold</a></span>(int edgeThreshold)</code> </td>
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</tr>
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<tr id="i22" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setFastThreshold-int-">setFastThreshold</a></span>(int fastThreshold)</code> </td>
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</tr>
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<tr id="i23" class="rowColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setFirstLevel-int-">setFirstLevel</a></span>(int firstLevel)</code> </td>
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</tr>
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<tr id="i24" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setMaxFeatures-int-">setMaxFeatures</a></span>(int maxFeatures)</code> </td>
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</tr>
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<tr id="i25" class="rowColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setNLevels-int-">setNLevels</a></span>(int nlevels)</code> </td>
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</tr>
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<tr id="i26" class="altColor">
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setPatchSize-int-">setPatchSize</a></span>(int patchSize)</code> </td>
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setScaleFactor-double-">setScaleFactor</a></span>(double scaleFactor)</code> </td>
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setScoreType-int-">setScoreType</a></span>(int scoreType)</code> </td>
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<td class="colFirst"><code>void</code></td>
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<td class="colLast"><code><span class="memberNameLink"><a href="../../../org/opencv/features2d/ORB.html#setWTA_K-int-">setWTA_K</a></span>(int wta_k)</code> </td>
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<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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<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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<code>equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait</code></li>
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<pre>public static final int FAST_SCORE</pre>
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<dt><span class="seeLabel">See Also:</span></dt>
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<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 int HARRIS_SCORE</pre>
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<dt><span class="seeLabel">See Also:</span></dt>
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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 <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> __fromPtr__(long addr)</pre>
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create()</pre>
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<div class="block">The ORB constructor
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pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
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will mean that to cover certain scale range you will need more pyramid levels and so the speed
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will suffer.
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input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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roughly match the patchSize parameter.
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with upscaled source image.
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default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
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so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
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random points (of course, those point coordinates are random, but they are generated from the
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pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
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rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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(the score is written to KeyPoint::score and is used to retain best nfeatures features);
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FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
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but it is a little faster to compute.
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pyramid layers the perceived image area covered by a feature will be larger.</div>
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<dl>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>nfeatures</code> - The maximum number of features to retain.
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pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
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will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
|
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will mean that to cover certain scale range you will need more pyramid levels and so the speed
|
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will suffer.
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input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
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roughly match the patchSize parameter.
|
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with upscaled source image.
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default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
|
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so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
|
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random points (of course, those point coordinates are random, but they are generated from the
|
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pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
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rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
|
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output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
|
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denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
|
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bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
|
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(the score is written to KeyPoint::score and is used to retain best nfeatures features);
|
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FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
|
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but it is a little faster to compute.
|
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pyramid layers the perceived image area covered by a feature will be larger.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
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float scaleFactor)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
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<dd><code>scaleFactor</code> - Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
|
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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.
|
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input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
|
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roughly match the patchSize parameter.
|
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with upscaled source image.
|
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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,
|
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but it is a little faster to compute.
|
|
pyramid layers the perceived image area covered by a feature will be larger.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
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float scaleFactor,
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int nlevels)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
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<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>
|
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<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).
|
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roughly match the patchSize parameter.
|
|
with upscaled source image.
|
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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>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
|
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<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
|
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<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>
|
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<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>
|
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<dd><code>edgeThreshold</code> - This is size of the border where the features are not detected. It should
|
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roughly match the patchSize parameter.
|
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with upscaled source image.
|
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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>
|
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</dl>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
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float scaleFactor,
|
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int nlevels,
|
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int edgeThreshold,
|
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int firstLevel)</pre>
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<div class="block">The ORB constructor</div>
|
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<dl>
|
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<dt><span class="paramLabel">Parameters:</span></dt>
|
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<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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</li>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
|
|
float scaleFactor,
|
|
int nlevels,
|
|
int edgeThreshold,
|
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int firstLevel,
|
|
int WTA_K)</pre>
|
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<div class="block">The ORB constructor</div>
|
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<dl>
|
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<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
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input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).</dd>
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<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
|
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random points (of course, those point coordinates are random, but they are generated from the
|
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pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
|
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rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
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output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
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denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
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bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
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(the score is written to KeyPoint::score and is used to retain best nfeatures features);
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FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
|
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but it is a little faster to compute.
|
|
pyramid layers the perceived image area covered by a feature will be larger.</dd>
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<dt><span class="returnLabel">Returns:</span></dt>
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<dd>automatically generated</dd>
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</dl>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
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float scaleFactor,
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int nlevels,
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int edgeThreshold,
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int firstLevel,
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int WTA_K,
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int scoreType)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
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<dt><span class="paramLabel">Parameters:</span></dt>
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<dd><code>nfeatures</code> - The maximum number of features to retain.</dd>
|
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<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>
|
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<dt><span class="returnLabel">Returns:</span></dt>
|
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<dd>automatically generated</dd>
|
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</dl>
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</li>
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
|
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float scaleFactor,
|
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int nlevels,
|
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int edgeThreshold,
|
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int firstLevel,
|
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int WTA_K,
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int scoreType,
|
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int patchSize)</pre>
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<div class="block">The ORB constructor</div>
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<dl>
|
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<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>
|
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</li>
|
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</ul>
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<a name="create-int-float-int-int-int-int-int-int-int-">
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<h4>create</h4>
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<pre>public static <a href="../../../org/opencv/features2d/ORB.html" title="class in org.opencv.features2d">ORB</a> create(int nfeatures,
|
|
float scaleFactor,
|
|
int nlevels,
|
|
int edgeThreshold,
|
|
int firstLevel,
|
|
int WTA_K,
|
|
int scoreType,
|
|
int patchSize,
|
|
int 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>
|
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</li>
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</ul>
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<a name="getDefaultName--">
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<h4>getDefaultName</h4>
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<pre>public java.lang.String getDefaultName()</pre>
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<div class="block"><span class="descfrmTypeLabel">Description copied from class: <code><a href="../../../org/opencv/core/Algorithm.html#getDefaultName--">Algorithm</a></code></span></div>
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<div class="block">Returns the algorithm string identifier.
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This string is used as top level xml/yml node tag when the object is saved to a file or string.</div>
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<dl>
|
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<dt><span class="overrideSpecifyLabel">Overrides:</span></dt>
|
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<dd><code><a href="../../../org/opencv/features2d/Feature2D.html#getDefaultName--">getDefaultName</a></code> in class <code><a href="../../../org/opencv/features2d/Feature2D.html" title="class in org.opencv.features2d">Feature2D</a></code></dd>
|
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<dt><span class="returnLabel">Returns:</span></dt>
|
|
<dd>automatically generated</dd>
|
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</dl>
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</li>
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</ul>
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<a name="getEdgeThreshold--">
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<h4>getEdgeThreshold</h4>
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<pre>public int getEdgeThreshold()</pre>
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<a name="getFastThreshold--">
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<h4>getFastThreshold</h4>
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<pre>public int getFastThreshold()</pre>
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<h4>getFirstLevel</h4>
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<pre>public int getFirstLevel()</pre>
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<pre>public int getMaxFeatures()</pre>
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<pre>public int getNLevels()</pre>
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<h4>getPatchSize</h4>
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<pre>public int getPatchSize()</pre>
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<a name="getScaleFactor--">
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<pre>public double getScaleFactor()</pre>
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<pre>public int getScoreType()</pre>
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<pre>public int getWTA_K()</pre>
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<a name="setEdgeThreshold-int-">
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<pre>public void setEdgeThreshold(int edgeThreshold)</pre>
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<pre>public void setFastThreshold(int fastThreshold)</pre>
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<pre>public void setFirstLevel(int firstLevel)</pre>
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<pre>public void setMaxFeatures(int maxFeatures)</pre>
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<pre>public void setNLevels(int nlevels)</pre>
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<pre>public void setPatchSize(int patchSize)</pre>
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<pre>public void setScaleFactor(double scaleFactor)</pre>
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<pre>public void setScoreType(int scoreType)</pre>
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<pre>public void setWTA_K(int wta_k)</pre>
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</ul>
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<li><a href="#method.detail">Method</a></li>
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<p class="legalCopy"><small>Generated on 2021-12-25 08:13:27 / OpenCV 4.5.5</small></p>
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