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<div class="section" id="module-arrayfire.vision">
<span id="arrayfire-vision-module"></span><h1>arrayfire.vision module<a class="headerlink" href="#module-arrayfire.vision" title="Permalink to this headline">¶</a></h1>
<p>Computer vision functions (FAST, ORB, etc)</p>
<dl class="py function">
<dt id="arrayfire.vision.dog">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">dog</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#dog"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.dog" title="Permalink to this definition">¶</a></dt>
<dd><p>Difference of gaussians.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array specifying an image.</p>
</dd>
<dt><strong>radius1</strong><span class="classifier">scalar.</span></dt><dd><p>The radius of first gaussian kernel.</p>
</dd>
<dt><strong>radius2</strong><span class="classifier">scalar.</span></dt><dd><p>The radius of second gaussian kernel.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">af.Array</span></dt><dd><p>A multi dimensional array containing the difference of gaussians.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.fast">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">fast</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arc_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#fast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.fast" title="Permalink to this definition">¶</a></dt>
<dd><p>FAST feature detector.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array representing an image.</p>
</dd>
<dt><strong>threshold</strong><span class="classifier">scalar. optional. default: 20.0.</span></dt><dd><p>FAST threshold for which a pixel of the circle around a central pixel is consdered.</p>
</dd>
<dt><strong>arc_length</strong><span class="classifier">scalar. optional. default: 9</span></dt><dd><p>The minimum length of arc length to be considered. Max length should be 16.</p>
</dd>
<dt><strong>non_max</strong><span class="classifier">Boolean. optional. default: True</span></dt><dd><p>A boolean flag specifying if non max suppression has to be performed.</p>
</dd>
<dt><strong>feature_ratio</strong><span class="classifier">scalar. optional. default: 0.05 (5%)</span></dt><dd><p>Specifies the maximum ratio of features to pixels in the image.</p>
</dd>
<dt><strong>edge</strong><span class="classifier">scalar. optional. default: 3.</span></dt><dd><p>Specifies the number of edge rows and columns to be ignored.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>features</strong><span class="classifier">af.Features()</span></dt><dd><p>Contains the location and score. Orientation and size are not computed.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.gloh">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">gloh</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contrast_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intensity_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.00390625</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#gloh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.gloh" title="Permalink to this definition">¶</a></dt>
<dd><p>GLOH feature detector and descriptor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array representing an image</p>
</dd>
<dt><strong>num_layers</strong><span class="classifier">optional: integer. Default: 3</span></dt><dd><p>Number of layers per octave. The number of octaves is calculated internally.</p>
</dd>
<dt><strong>contrast_threshold</strong><span class="classifier">optional: float. Default: 0.04</span></dt><dd><p>Threshold used to filter out features that have low contrast.</p>
</dd>
<dt><strong>edge_threshold</strong><span class="classifier">optional: float. Default: 10.0</span></dt><dd><p>Threshold used to filter out features that are too edge-like.</p>
</dd>
<dt><strong>initial_sigma</strong><span class="classifier">optional: float. Default: 1.6</span></dt><dd><p>The sigma value used to filter the input image at the first octave.</p>
</dd>
<dt><strong>double_input</strong><span class="classifier">optional: bool. Default: True</span></dt><dd><p>If True, the input image will be scaled to double the size for the first octave.</p>
</dd>
<dt><strong>intensity_scale</strong><span class="classifier">optional: float. Default: 1.0/255</span></dt><dd><p>The inverse of the difference between maximum and minimum intensity values.</p>
</dd>
<dt><strong>feature_ratio</strong><span class="classifier">optional: float. Default: 0.05</span></dt><dd><p>Specifies the maximum number of features to detect as a ratio of image pixels.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>(features, descriptor)</strong><span class="classifier">tuple of (af.Features(), af.Array)</span></dt><dd><ul class="simple">
<li><p>descriptor is an af.Array of size N x 272</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.hamming_matcher">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">hamming_matcher</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">database</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_nearest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#hamming_matcher"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.hamming_matcher" title="Permalink to this definition">¶</a></dt>
<dd><p>Hamming distance matcher.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>query</strong><span class="classifier">af.Array</span></dt><dd><p>A query feature descriptor</p>
</dd>
<dt><strong>database</strong><span class="classifier">af.Array</span></dt><dd><p>A multi dimensional array containing the feature descriptor database.</p>
</dd>
<dt><strong>dim</strong><span class="classifier">scalar. optional. default: 0.</span></dt><dd><p>Specifies the dimension along which feature descriptor lies.</p>
</dd>
<dt><strong>num_nearest: scalar. optional. default: 1.</strong></dt><dd><p>Specifies the number of nearest neighbors to find.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(location, distance): tuple of af.Array</dt><dd><p>location and distances of closest matches.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.harris">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">harris</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_response</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#harris"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.harris" title="Permalink to this definition">¶</a></dt>
<dd><p>Harris corner detector.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array specifying an image.</p>
</dd>
<dt><strong>max_corners</strong><span class="classifier">scalar. optional. default: 500.</span></dt><dd><p>Specifies the maximum number of corners to be calculated.</p>
</dd>
<dt><strong>min_response</strong><span class="classifier">scalar. optional. default: 1E5</span></dt><dd><p>Specifies the cutoff score for a corner to be considered</p>
</dd>
<dt><strong>sigma</strong><span class="classifier">scalar. optional. default: 1.0</span></dt><dd><ul class="simple">
<li><p>Specifies the standard deviation of a circular window.</p></li>
<li><p>Only used when block_size == 0. Must be >= 0.5 and <= 5.0.</p></li>
</ul>
</dd>
<dt><strong>block_size</strong><span class="classifier">scalar. optional. default: 0</span></dt><dd><p>Specifies the window size.</p>
</dd>
<dt><strong>k_thr</strong><span class="classifier">scalar. optional. default: 0.04</span></dt><dd><p>Harris constant. must be >= 0.01</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>features</strong><span class="classifier">af.Features()</span></dt><dd><p>Contains the location and score. Orientation and size are not computed.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.homography">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">homography</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">x_src</span></em>, <em class="sig-param"><span class="pre">y_src</span></em>, <em class="sig-param"><span class="pre">x_dst</span></em>, <em class="sig-param"><span class="pre">y_dst</span></em>, <em class="sig-param"><span class="pre">htype=<HOMOGRAPHY.RANSAC:</span> <span class="pre">0></span></em>, <em class="sig-param"><span class="pre">ransac_threshold=3.0</span></em>, <em class="sig-param"><span class="pre">iters=1000</span></em>, <em class="sig-param"><span class="pre">out_type=<Dtype.f32:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#homography"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.homography" title="Permalink to this definition">¶</a></dt>
<dd><p>Homography estimation</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>x_src</strong><span class="classifier">af.Array</span></dt><dd><p>A list of x co-ordinates of the source points.</p>
</dd>
<dt><strong>y_src</strong><span class="classifier">af.Array</span></dt><dd><p>A list of y co-ordinates of the source points.</p>
</dd>
<dt><strong>x_dst</strong><span class="classifier">af.Array</span></dt><dd><p>A list of x co-ordinates of the destination points.</p>
</dd>
<dt><strong>y_dst</strong><span class="classifier">af.Array</span></dt><dd><p>A list of y co-ordinates of the destination points.</p>
</dd>
<dt><strong>htype</strong><span class="classifier">optional: af.HOMOGRAPHY. Default: HOMOGRAPHY.RANSAC</span></dt><dd><dl class="simple">
<dt>htype can be one of</dt><dd><ul class="simple">
<li><p>HOMOGRAPHY.RANSAC: RANdom SAmple Consensus will be used to evaluate quality.</p></li>
<li><p>HOMOGRAPHY.LMEDS : Least MEDian of Squares is used to evaluate quality.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt><strong>ransac_threshold</strong><span class="classifier">optional: scalar. Default: 3.0</span></dt><dd><p>If <cite>htype</cite> is HOMOGRAPHY.RANSAC, it specifies the L2-distance threshold for inliers.</p>
</dd>
<dt><strong>out_type</strong><span class="classifier">optional. af.Dtype. Default: Dtype.f32.</span></dt><dd><p>Specifies the output data type.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>(H, inliers)</strong><span class="classifier">A tuple of (af.Array, integer)</span></dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.match_template">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">match_template</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">template</span></em>, <em class="sig-param"><span class="pre">match_type=<MATCH.SAD:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#match_template"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.match_template" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the closest match of a template in an image.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A multi dimensional array specifying an image or batch of images.</p>
</dd>
<dt><strong>template</strong><span class="classifier">af.Array</span></dt><dd><p>A multi dimensional array specifying a template or batch of templates.</p>
</dd>
<dt><strong>match_type: optional: af.MATCH. default: af.MATCH.SAD</strong></dt><dd><p>Specifies the match function metric.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">af.Array</span></dt><dd><p>An array containing the score of the match at each pixel.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.nearest_neighbour">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">nearest_neighbour</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">query</span></em>, <em class="sig-param"><span class="pre">database</span></em>, <em class="sig-param"><span class="pre">dim=0</span></em>, <em class="sig-param"><span class="pre">num_nearest=1</span></em>, <em class="sig-param"><span class="pre">match_type=<MATCH.SSD:</span> <span class="pre">3></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#nearest_neighbour"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.nearest_neighbour" title="Permalink to this definition">¶</a></dt>
<dd><p>Nearest Neighbour matcher.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>query</strong><span class="classifier">af.Array</span></dt><dd><p>A query feature descriptor</p>
</dd>
<dt><strong>database</strong><span class="classifier">af.Array</span></dt><dd><p>A multi dimensional array containing the feature descriptor database.</p>
</dd>
<dt><strong>dim</strong><span class="classifier">scalar. optional. default: 0.</span></dt><dd><p>Specifies the dimension along which feature descriptor lies.</p>
</dd>
<dt><strong>num_nearest: scalar. optional. default: 1.</strong></dt><dd><p>Specifies the number of nearest neighbors to find.</p>
</dd>
<dt><strong>match_type: optional: af.MATCH. default: af.MATCH.SSD</strong></dt><dd><p>Specifies the match function metric.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(location, distance): tuple of af.Array</dt><dd><p>location and distances of closest matches.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.orb">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">orb</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">400</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_levels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blur_image</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#orb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.orb" title="Permalink to this definition">¶</a></dt>
<dd><p>ORB Feature descriptor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array representing an image.</p>
</dd>
<dt><strong>threshold</strong><span class="classifier">scalar. optional. default: 20.0.</span></dt><dd><p>FAST threshold for which a pixel of the circle around a central pixel is consdered.</p>
</dd>
<dt><strong>max_features</strong><span class="classifier">scalar. optional. default: 400.</span></dt><dd><p>Specifies the maximum number of features to be considered.</p>
</dd>
<dt><strong>scale</strong><span class="classifier">scalar. optional. default: 1.5.</span></dt><dd><p>Specifies the factor by which images are down scaled at each level.</p>
</dd>
<dt><strong>num_levles</strong><span class="classifier">scalar. optional. default: 4.</span></dt><dd><p>Specifies the number of levels used in the image pyramid.</p>
</dd>
<dt><strong>blur_image</strong><span class="classifier">Boolean. optional. default: False.</span></dt><dd><p>Flag specifying if the input has to be blurred before computing descriptors.
A gaussian filter with sigma = 2 is applied if True.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>(features, descriptor)</strong><span class="classifier">tuple of (af.Features(), af.Array)</span></dt><dd><ul class="simple">
<li><p>descriptor is an af.Array of size N x 8</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.sift">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">sift</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">contrast_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.04</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">intensity_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.00390625</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#sift"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.sift" title="Permalink to this definition">¶</a></dt>
<dd><p>SIFT feature detector and descriptor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array representing an image</p>
</dd>
<dt><strong>num_layers</strong><span class="classifier">optional: integer. Default: 3</span></dt><dd><p>Number of layers per octave. The number of octaves is calculated internally.</p>
</dd>
<dt><strong>contrast_threshold</strong><span class="classifier">optional: float. Default: 0.04</span></dt><dd><p>Threshold used to filter out features that have low contrast.</p>
</dd>
<dt><strong>edge_threshold</strong><span class="classifier">optional: float. Default: 10.0</span></dt><dd><p>Threshold used to filter out features that are too edge-like.</p>
</dd>
<dt><strong>initial_sigma</strong><span class="classifier">optional: float. Default: 1.6</span></dt><dd><p>The sigma value used to filter the input image at the first octave.</p>
</dd>
<dt><strong>double_input</strong><span class="classifier">optional: bool. Default: True</span></dt><dd><p>If True, the input image will be scaled to double the size for the first octave.</p>
</dd>
<dt><strong>intensity_scale</strong><span class="classifier">optional: float. Default: 1.0/255</span></dt><dd><p>The inverse of the difference between maximum and minimum intensity values.</p>
</dd>
<dt><strong>feature_ratio</strong><span class="classifier">optional: float. Default: 0.05</span></dt><dd><p>Specifies the maximum number of features to detect as a ratio of image pixels.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>(features, descriptor)</strong><span class="classifier">tuple of (af.Features(), af.Array)</span></dt><dd><ul class="simple">
<li><p>descriptor is an af.Array of size N x 128</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.vision.susan">
<code class="sig-prename descclassname"><span class="pre">arrayfire.vision.</span></code><code class="sig-name descname"><span class="pre">susan</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diff_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_thr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/vision.html#susan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.vision.susan" title="Permalink to this definition">¶</a></dt>
<dd><p>SUSAN corner detector.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image</strong><span class="classifier">af.Array</span></dt><dd><p>A 2D array specifying an image.</p>
</dd>
<dt><strong>radius</strong><span class="classifier">scalar. optional. default: 500.</span></dt><dd><p>Specifies the radius of each pixel neighborhood.</p>
</dd>
<dt><strong>diff_thr</strong><span class="classifier">scalar. optional. default: 1E5</span></dt><dd><p>Specifies the intensity difference threshold.</p>
</dd>
<dt><strong>geom_thr</strong><span class="classifier">scalar. optional. default: 1.0</span></dt><dd><p>Specifies the geometric threshold.</p>
</dd>
<dt><strong>feature_ratio</strong><span class="classifier">scalar. optional. default: 0.05 (5%)</span></dt><dd><p>Specifies the ratio of corners found to number of pixels.</p>
</dd>
<dt><strong>edge</strong><span class="classifier">scalar. optional. default: 3</span></dt><dd><p>Specifies the number of edge rows and columns that are ignored.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>features</strong><span class="classifier">af.Features()</span></dt><dd><p>Contains the location and score. Orientation and size are not computed.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</div>
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