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<h1>Source code for arrayfire.vision</h1><div class="highlight"><pre>
<span></span><span class="c1">#######################################################</span>
<span class="c1"># Copyright (c) 2015, ArrayFire</span>
<span class="c1"># All rights reserved.</span>
<span class="c1">#</span>
<span class="c1"># This file is distributed under 3-clause BSD license.</span>
<span class="c1"># The complete license agreement can be obtained at:</span>
<span class="c1"># http://arrayfire.com/licenses/BSD-3-Clause</span>
<span class="c1">########################################################</span>
<span class="sd">"""</span>
<span class="sd">Computer vision functions (FAST, ORB, etc)</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">.library</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.array</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.features</span> <span class="k">import</span> <span class="o">*</span>
<div class="viewcode-block" id="fast"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.fast">[docs]</a><span class="k">def</span> <span class="nf">fast</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">20.0</span><span class="p">,</span> <span class="n">arc_length</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">non_max</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">feature_ratio</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">edge</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> FAST feature detector.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array representing an image.</span>
<span class="sd"> threshold : scalar. optional. default: 20.0.</span>
<span class="sd"> FAST threshold for which a pixel of the circle around a central pixel is consdered.</span>
<span class="sd"> arc_length : scalar. optional. default: 9</span>
<span class="sd"> The minimum length of arc length to be considered. Max length should be 16.</span>
<span class="sd"> non_max : Boolean. optional. default: True</span>
<span class="sd"> A boolean flag specifying if non max suppression has to be performed.</span>
<span class="sd"> feature_ratio : scalar. optional. default: 0.05 (5%)</span>
<span class="sd"> Specifies the maximum ratio of features to pixels in the image.</span>
<span class="sd"> edge : scalar. optional. default: 3.</span>
<span class="sd"> Specifies the number of edge rows and columns to be ignored.</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> features : af.Features()</span>
<span class="sd"> Contains the location and score. Orientation and size are not computed.</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_fast</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">feat</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_float_t</span><span class="p">(</span><span class="n">threshold</span><span class="p">),</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">arc_length</span><span class="p">),</span> <span class="n">non_max</span><span class="p">,</span>
<span class="n">c_float_t</span><span class="p">(</span><span class="n">feature_ratio</span><span class="p">),</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">edge</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="harris"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.harris">[docs]</a><span class="k">def</span> <span class="nf">harris</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">max_corners</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">min_response</span><span class="o">=</span><span class="mi">1</span><span class="n">E5</span><span class="p">,</span> <span class="n">sigma</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">block_size</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">k_thr</span><span class="o">=</span><span class="mf">0.04</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Harris corner detector.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array specifying an image.</span>
<span class="sd"> max_corners : scalar. optional. default: 500.</span>
<span class="sd"> Specifies the maximum number of corners to be calculated.</span>
<span class="sd"> min_response : scalar. optional. default: 1E5</span>
<span class="sd"> Specifies the cutoff score for a corner to be considered</span>
<span class="sd"> sigma : scalar. optional. default: 1.0</span>
<span class="sd"> - Specifies the standard deviation of a circular window.</span>
<span class="sd"> - Only used when block_size == 0. Must be >= 0.5 and <= 5.0.</span>
<span class="sd"> block_size : scalar. optional. default: 0</span>
<span class="sd"> Specifies the window size.</span>
<span class="sd"> k_thr : scalar. optional. default: 0.04</span>
<span class="sd"> Harris constant. must be >= 0.01</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> features : af.Features()</span>
<span class="sd"> Contains the location and score. Orientation and size are not computed.</span>
<span class="sd"> Note</span>
<span class="sd"> ------</span>
<span class="sd"> The covariation matrix will be square when `block_size` is used and circular when `sigma` is used.</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_harris</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">feat</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">max_corners</span><span class="p">),</span> <span class="n">c_float_t</span><span class="p">(</span><span class="n">min_response</span><span class="p">),</span>
<span class="n">c_float_t</span><span class="p">(</span><span class="n">sigma</span><span class="p">),</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">block_size</span><span class="p">),</span> <span class="n">c_float_t</span><span class="p">(</span><span class="n">k_thr</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="orb"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.orb">[docs]</a><span class="k">def</span> <span class="nf">orb</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">20.0</span><span class="p">,</span> <span class="n">max_features</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span> <span class="n">scale</span> <span class="o">=</span> <span class="mf">1.5</span><span class="p">,</span> <span class="n">num_levels</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">blur_image</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> ORB Feature descriptor.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array representing an image.</span>
<span class="sd"> threshold : scalar. optional. default: 20.0.</span>
<span class="sd"> FAST threshold for which a pixel of the circle around a central pixel is consdered.</span>
<span class="sd"> max_features : scalar. optional. default: 400.</span>
<span class="sd"> Specifies the maximum number of features to be considered.</span>
<span class="sd"> scale : scalar. optional. default: 1.5.</span>
<span class="sd"> Specifies the factor by which images are down scaled at each level.</span>
<span class="sd"> num_levles : scalar. optional. default: 4.</span>
<span class="sd"> Specifies the number of levels used in the image pyramid.</span>
<span class="sd"> blur_image : Boolean. optional. default: False.</span>
<span class="sd"> Flag specifying if the input has to be blurred before computing descriptors.</span>
<span class="sd"> A gaussian filter with sigma = 2 is applied if True.</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> (features, descriptor) : tuple of (af.Features(), af.Array)</span>
<span class="sd"> - descriptor is an af.Array of size N x 8</span>
<span class="sd"> """</span>
<span class="n">feat</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">desc</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_orb</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">feat</span><span class="o">.</span><span class="n">feat</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">desc</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">c_float_t</span><span class="p">(</span><span class="n">threshold</span><span class="p">),</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">max_features</span><span class="p">),</span>
<span class="n">c_float_t</span><span class="p">(</span><span class="n">scale</span><span class="p">),</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">num_levels</span><span class="p">),</span> <span class="n">blur_image</span><span class="p">))</span>
<span class="k">return</span> <span class="n">feat</span><span class="p">,</span> <span class="n">desc</span></div>
<div class="viewcode-block" id="hamming_matcher"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.hamming_matcher">[docs]</a><span class="k">def</span> <span class="nf">hamming_matcher</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_nearest</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Hamming distance matcher.</span>
<span class="sd"> Parameters</span>
<span class="sd"> -----------</span>
<span class="sd"> query : af.Array</span>
<span class="sd"> A query feature descriptor</span>
<span class="sd"> database : af.Array</span>
<span class="sd"> A multi dimensional array containing the feature descriptor database.</span>
<span class="sd"> dim : scalar. optional. default: 0.</span>
<span class="sd"> Specifies the dimension along which feature descriptor lies.</span>
<span class="sd"> num_nearest: scalar. optional. default: 1.</span>
<span class="sd"> Specifies the number of nearest neighbors to find.</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> (location, distance): tuple of af.Array</span>
<span class="sd"> location and distances of closest matches.</span>
<span class="sd"> """</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_hamming_matcher</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">query</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">database</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span>
<span class="n">c_dim_t</span><span class="p">(</span><span class="n">dim</span><span class="p">),</span> <span class="n">c_dim_t</span><span class="p">(</span><span class="n">num_nearest</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">index</span><span class="p">,</span> <span class="n">dist</span></div>
<div class="viewcode-block" id="nearest_neighbour"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.nearest_neighbour">[docs]</a><span class="k">def</span> <span class="nf">nearest_neighbour</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">num_nearest</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">match_type</span><span class="o">=</span><span class="n">MATCH</span><span class="o">.</span><span class="n">SSD</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Nearest Neighbour matcher.</span>
<span class="sd"> Parameters</span>
<span class="sd"> -----------</span>
<span class="sd"> query : af.Array</span>
<span class="sd"> A query feature descriptor</span>
<span class="sd"> database : af.Array</span>
<span class="sd"> A multi dimensional array containing the feature descriptor database.</span>
<span class="sd"> dim : scalar. optional. default: 0.</span>
<span class="sd"> Specifies the dimension along which feature descriptor lies.</span>
<span class="sd"> num_nearest: scalar. optional. default: 1.</span>
<span class="sd"> Specifies the number of nearest neighbors to find.</span>
<span class="sd"> match_type: optional: af.MATCH. default: af.MATCH.SSD</span>
<span class="sd"> Specifies the match function metric.</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> (location, distance): tuple of af.Array</span>
<span class="sd"> location and distances of closest matches.</span>
<span class="sd"> """</span>
<span class="n">index</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">dist</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_nearest_neighbour</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">query</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">database</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span>
<span class="n">c_dim_t</span><span class="p">(</span><span class="n">dim</span><span class="p">),</span> <span class="n">c_dim_t</span><span class="p">(</span><span class="n">num_nearest</span><span class="p">),</span>
<span class="n">match_type</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="n">index</span><span class="p">,</span> <span class="n">dist</span></div>
<div class="viewcode-block" id="match_template"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.match_template">[docs]</a><span class="k">def</span> <span class="nf">match_template</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">template</span><span class="p">,</span> <span class="n">match_type</span> <span class="o">=</span> <span class="n">MATCH</span><span class="o">.</span><span class="n">SAD</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Find the closest match of a template in an image.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A multi dimensional array specifying an image or batch of images.</span>
<span class="sd"> template : af.Array</span>
<span class="sd"> A multi dimensional array specifying a template or batch of templates.</span>
<span class="sd"> match_type: optional: af.MATCH. default: af.MATCH.SAD</span>
<span class="sd"> Specifies the match function metric.</span>
<span class="sd"> Returns</span>
<span class="sd"> --------</span>
<span class="sd"> out : af.Array</span>
<span class="sd"> An array containing the score of the match at each pixel.</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_match_template</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">template</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span>
<span class="n">match_type</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="susan"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.susan">[docs]</a><span class="k">def</span> <span class="nf">susan</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">diff_thr</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">geom_thr</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">feature_ratio</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">edge</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> SUSAN corner detector.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array specifying an image.</span>
<span class="sd"> radius : scalar. optional. default: 500.</span>
<span class="sd"> Specifies the radius of each pixel neighborhood.</span>
<span class="sd"> diff_thr : scalar. optional. default: 1E5</span>
<span class="sd"> Specifies the intensity difference threshold.</span>
<span class="sd"> geom_thr : scalar. optional. default: 1.0</span>
<span class="sd"> Specifies the geometric threshold.</span>
<span class="sd"> feature_ratio : scalar. optional. default: 0.05 (5%)</span>
<span class="sd"> Specifies the ratio of corners found to number of pixels.</span>
<span class="sd"> edge : scalar. optional. default: 3</span>
<span class="sd"> Specifies the number of edge rows and columns that are ignored.</span>
<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> features : af.Features()</span>
<span class="sd"> Contains the location and score. Orientation and size are not computed.</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_susan</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">feat</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_uint_t</span><span class="p">(</span><span class="n">radius</span><span class="p">),</span> <span class="n">c_float_t</span><span class="p">(</span><span class="n">diff_thr</span><span class="p">),</span>
<span class="n">c_float_t</span><span class="p">(</span><span class="n">geom_thr</span><span class="p">),</span> <span class="n">c_float_t</span><span class="p">(</span><span class="n">feature_ratio</span><span class="p">),</span>
<span class="n">c_uint_t</span><span class="p">(</span><span class="n">edge</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="dog"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.dog">[docs]</a><span class="k">def</span> <span class="nf">dog</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">radius1</span><span class="p">,</span> <span class="n">radius2</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Difference of gaussians.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array specifying an image.</span>
<span class="sd"> radius1 : scalar.</span>
<span class="sd"> The radius of first gaussian kernel.</span>
<span class="sd"> radius2 : scalar.</span>
<span class="sd"> The radius of second gaussian kernel.</span>
<span class="sd"> Returns</span>
<span class="sd"> --------</span>
<span class="sd"> out : af.Array</span>
<span class="sd"> A multi dimensional array containing the difference of gaussians.</span>
<span class="sd"> Note</span>
<span class="sd"> ------</span>
<span class="sd"> The sigma values are calculated to be 0.25 * radius.</span>
<span class="sd"> """</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_dog</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">radius1</span><span class="p">,</span> <span class="n">radius2</span><span class="p">))</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="sift"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.sift">[docs]</a><span class="k">def</span> <span class="nf">sift</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">contrast_threshold</span><span class="o">=</span><span class="mf">0.04</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">initial_sigma</span> <span class="o">=</span> <span class="mf">1.6</span><span class="p">,</span>
<span class="n">double_input</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">intensity_scale</span> <span class="o">=</span> <span class="mf">0.00390625</span><span class="p">,</span> <span class="n">feature_ratio</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> SIFT feature detector and descriptor.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array representing an image</span>
<span class="sd"> num_layers : optional: integer. Default: 3</span>
<span class="sd"> Number of layers per octave. The number of octaves is calculated internally.</span>
<span class="sd"> contrast_threshold : optional: float. Default: 0.04</span>
<span class="sd"> Threshold used to filter out features that have low contrast.</span>
<span class="sd"> edge_threshold : optional: float. Default: 10.0</span>
<span class="sd"> Threshold used to filter out features that are too edge-like.</span>
<span class="sd"> initial_sigma : optional: float. Default: 1.6</span>
<span class="sd"> The sigma value used to filter the input image at the first octave.</span>
<span class="sd"> double_input : optional: bool. Default: True</span>
<span class="sd"> If True, the input image will be scaled to double the size for the first octave.</span>
<span class="sd"> intensity_scale : optional: float. Default: 1.0/255</span>
<span class="sd"> The inverse of the difference between maximum and minimum intensity values.</span>
<span class="sd"> feature_ratio : optional: float. Default: 0.05</span>
<span class="sd"> Specifies the maximum number of features to detect as a ratio of image pixels.</span>
<span class="sd"> Returns</span>
<span class="sd"> --------</span>
<span class="sd"> (features, descriptor) : tuple of (af.Features(), af.Array)</span>
<span class="sd"> - descriptor is an af.Array of size N x 128</span>
<span class="sd"> """</span>
<span class="n">feat</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">desc</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">af_sift</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">feat</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">desc</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">contrast_threshold</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="p">,</span>
<span class="n">initial_sigma</span><span class="p">,</span> <span class="n">double_input</span><span class="p">,</span> <span class="n">intensity_scale</span><span class="p">,</span> <span class="n">feature_ratio</span><span class="p">))</span>
<span class="k">return</span> <span class="p">(</span><span class="n">feat</span><span class="p">,</span> <span class="n">desc</span><span class="p">)</span></div>
<div class="viewcode-block" id="gloh"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.gloh">[docs]</a><span class="k">def</span> <span class="nf">gloh</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">num_layers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">contrast_threshold</span><span class="o">=</span><span class="mf">0.04</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="o">=</span><span class="mf">10.0</span><span class="p">,</span> <span class="n">initial_sigma</span> <span class="o">=</span> <span class="mf">1.6</span><span class="p">,</span>
<span class="n">double_input</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">intensity_scale</span> <span class="o">=</span> <span class="mf">0.00390625</span><span class="p">,</span> <span class="n">feature_ratio</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> GLOH feature detector and descriptor.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> image : af.Array</span>
<span class="sd"> A 2D array representing an image</span>
<span class="sd"> num_layers : optional: integer. Default: 3</span>
<span class="sd"> Number of layers per octave. The number of octaves is calculated internally.</span>
<span class="sd"> contrast_threshold : optional: float. Default: 0.04</span>
<span class="sd"> Threshold used to filter out features that have low contrast.</span>
<span class="sd"> edge_threshold : optional: float. Default: 10.0</span>
<span class="sd"> Threshold used to filter out features that are too edge-like.</span>
<span class="sd"> initial_sigma : optional: float. Default: 1.6</span>
<span class="sd"> The sigma value used to filter the input image at the first octave.</span>
<span class="sd"> double_input : optional: bool. Default: True</span>
<span class="sd"> If True, the input image will be scaled to double the size for the first octave.</span>
<span class="sd"> intensity_scale : optional: float. Default: 1.0/255</span>
<span class="sd"> The inverse of the difference between maximum and minimum intensity values.</span>
<span class="sd"> feature_ratio : optional: float. Default: 0.05</span>
<span class="sd"> Specifies the maximum number of features to detect as a ratio of image pixels.</span>
<span class="sd"> Returns</span>
<span class="sd"> --------</span>
<span class="sd"> (features, descriptor) : tuple of (af.Features(), af.Array)</span>
<span class="sd"> - descriptor is an af.Array of size N x 272</span>
<span class="sd"> """</span>
<span class="n">feat</span> <span class="o">=</span> <span class="n">Features</span><span class="p">()</span>
<span class="n">desc</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">af_gloh</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">feat</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">desc</span><span class="p">),</span>
<span class="n">image</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">contrast_threshold</span><span class="p">,</span> <span class="n">edge_threshold</span><span class="p">,</span>
<span class="n">initial_sigma</span><span class="p">,</span> <span class="n">double_input</span><span class="p">,</span> <span class="n">intensity_scale</span><span class="p">,</span> <span class="n">feature_ratio</span><span class="p">))</span>
<span class="k">return</span> <span class="p">(</span><span class="n">feat</span><span class="p">,</span> <span class="n">desc</span><span class="p">)</span></div>
<div class="viewcode-block" id="homography"><a class="viewcode-back" href="../../arrayfire.vision.html#arrayfire.vision.homography">[docs]</a><span class="k">def</span> <span class="nf">homography</span><span class="p">(</span><span class="n">x_src</span><span class="p">,</span> <span class="n">y_src</span><span class="p">,</span> <span class="n">x_dst</span><span class="p">,</span> <span class="n">y_dst</span><span class="p">,</span> <span class="n">htype</span> <span class="o">=</span> <span class="n">HOMOGRAPHY</span><span class="o">.</span><span class="n">RANSAC</span><span class="p">,</span>
<span class="n">ransac_threshold</span> <span class="o">=</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">iters</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="n">out_type</span> <span class="o">=</span> <span class="n">Dtype</span><span class="o">.</span><span class="n">f32</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Homography estimation</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x_src : af.Array</span>
<span class="sd"> A list of x co-ordinates of the source points.</span>
<span class="sd"> y_src : af.Array</span>
<span class="sd"> A list of y co-ordinates of the source points.</span>
<span class="sd"> x_dst : af.Array</span>
<span class="sd"> A list of x co-ordinates of the destination points.</span>
<span class="sd"> y_dst : af.Array</span>
<span class="sd"> A list of y co-ordinates of the destination points.</span>
<span class="sd"> htype : optional: af.HOMOGRAPHY. Default: HOMOGRAPHY.RANSAC</span>
<span class="sd"> htype can be one of</span>
<span class="sd"> - HOMOGRAPHY.RANSAC: RANdom SAmple Consensus will be used to evaluate quality.</span>
<span class="sd"> - HOMOGRAPHY.LMEDS : Least MEDian of Squares is used to evaluate quality.</span>
<span class="sd"> ransac_threshold : optional: scalar. Default: 3.0</span>
<span class="sd"> If `htype` is HOMOGRAPHY.RANSAC, it specifies the L2-distance threshold for inliers.</span>
<span class="sd"> out_type : optional. af.Dtype. Default: Dtype.f32.</span>
<span class="sd"> Specifies the output data type.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> (H, inliers) : A tuple of (af.Array, integer)</span>
<span class="sd"> """</span>
<span class="n">H</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">inliers</span> <span class="o">=</span> <span class="n">c_int_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_homography</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">H</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">inliers</span><span class="p">),</span>
<span class="n">x_src</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">y_src</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">x_dst</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">y_dst</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span>
<span class="n">htype</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">ransac_threshold</span><span class="p">,</span> <span class="n">iters</span><span class="p">,</span> <span class="n">out_type</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">inliers</span><span class="p">)</span></div>
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