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<!DOCTYPE html>
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<div class="section" id="module-arrayfire.image">
<span id="arrayfire-image-module"></span><h1>arrayfire.image module<a class="headerlink" href="#module-arrayfire.image" title="Permalink to this headline">¶</a></h1>
<p>Image processing functions.</p>
<dl class="py function">
<dt id="arrayfire.image.anisotropic_diffusion">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">anisotropic_diffusion</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">time_step</span></em>, <em class="sig-param"><span class="pre">conductance</span></em>, <em class="sig-param"><span class="pre">iterations</span></em>, <em class="sig-param"><span class="pre">flux_function_type=<FLUX.QUADRATIC:</span> <span class="pre">1></span></em>, <em class="sig-param"><span class="pre">diffusion_kind=<DIFFUSION.GRAD:</span> <span class="pre">1></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#anisotropic_diffusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.anisotropic_diffusion" title="Permalink to this definition">¶</a></dt>
<dd><p>Anisotropic smoothing filter.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image: af.Array</strong></dt><dd><p>The input image.</p>
</dd>
<dt><strong>time_step: scalar.</strong></dt><dd><p>The time step used in solving the diffusion equation.</p>
</dd>
<dt><strong>conductance:</strong></dt><dd><p>Controls conductance sensitivity in diffusion equation.</p>
</dd>
<dt><strong>iterations:</strong></dt><dd><p>Number of times the diffusion step is performed.</p>
</dd>
<dt><strong>flux_function_type:</strong></dt><dd><dl class="simple">
<dt>Type of flux function to be used. Available flux functions:</dt><dd><ul class="simple">
<li><p>Quadratic (af.FLUX.QUADRATIC)</p></li>
<li><p>Exponential (af.FLUX.EXPONENTIAL)</p></li>
</ul>
</dd>
</dl>
</dd>
<dt><strong>diffusion_kind:</strong></dt><dd><dl class="simple">
<dt>Type of diffusion equatoin to be used. Available diffusion equations:</dt><dd><ul class="simple">
<li><p>Gradient diffusion equation (af.DIFFUSION.GRAD)</p></li>
<li><p>Modified curvature diffusion equation (af.DIFFUSION.MCDE)</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>Anisotropically-smoothed output image.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.bilateral">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">bilateral</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">s_sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_color</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/image.html#bilateral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.bilateral" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply bilateral filter to the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>s_sigma</strong><span class="classifier">scalar.</span></dt><dd><ul class="simple">
<li><p>Sigma value for the co-ordinate space.</p></li>
</ul>
</dd>
<dt><strong>c_sigma</strong><span class="classifier">scalar.</span></dt><dd><ul class="simple">
<li><p>Sigma value for the color space.</p></li>
</ul>
</dd>
<dt><strong>is_color</strong><span class="classifier">optional: bool. default: False.</span></dt><dd><ul class="simple">
<li><p>Specifies if the third dimension is 3rd channel (if True) or a batch (if False).</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The image after the application of the bilateral filter.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.canny">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">canny</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">low_threshold</span></em>, <em class="sig-param"><span class="pre">high_threshold=None</span></em>, <em class="sig-param"><span class="pre">threshold_type=<CANNY_THRESHOLD.MANUAL:</span> <span class="pre">0></span></em>, <em class="sig-param"><span class="pre">sobel_window=3</span></em>, <em class="sig-param"><span class="pre">is_fast=False</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#canny"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.canny" title="Permalink to this definition">¶</a></dt>
<dd><p>Canny edge 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image</p></li>
</ul>
</dd>
<dt><strong>threshold_type</strong><span class="classifier">optional: af.CANNY_THRESHOLD. default: af.CANNY_THRESHOLD.MANUAL.</span></dt><dd><p>Can be one of:
- af.CANNY_THRESHOLD.MANUAL
- af.CANNY_THRESHOLD.AUTO_OTSU</p>
</dd>
<dt><strong>low_threshold</strong><span class="classifier">required: float.</span></dt><dd><p>Specifies the % of maximum in gradient image if threshold_type is MANUAL.
Specifies the % of auto dervied high value if threshold_type is AUTO_OTSU.</p>
</dd>
<dt><strong>high_threshold</strong><span class="classifier">optional: float. default: None</span></dt><dd><p>Specifies the % of maximum in gradient image if threshold_type is MANUAL.
Ignored if threshold_type is AUTO_OTSU</p>
</dd>
<dt><strong>sobel_window</strong><span class="classifier">optional: int. default: 3</span></dt><dd><p>Specifies the size of sobel kernel when computing the gradient image.</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><ul class="simple">
<li><p>A binary image containing the edges</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.color_space">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">color_space</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">to_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">from_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#color_space"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.color_space" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert an image from one color space to another.</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><ul class="simple">
<li><p>A multi dimensional array representing batch of images in <cite>from_type</cite> color space.</p></li>
</ul>
</dd>
<dt><strong>to_type</strong><span class="classifier">af.CSPACE</span></dt><dd><ul class="simple">
<li><p>An enum for the destination color space.</p></li>
</ul>
</dd>
<dt><strong>from_type</strong><span class="classifier">af.CSPACE</span></dt><dd><ul class="simple">
<li><p>An enum for the source color space.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>An image in the <cite>to_type</cite> color space.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.confidenceCC">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">confidenceCC</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">seedx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seedy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiplier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segmented_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#confidenceCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.confidenceCC" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the confidence connected components in the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image.
Expects non-integral type</p></li>
</ul>
</dd>
<dt><strong>seedx</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>An array with x-coordinates of seed points</p></li>
</ul>
</dd>
<dt><strong>seedy</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>An array with y-coordinates of seed points</p></li>
</ul>
</dd>
<dt><strong>radius</strong><span class="classifier">scalar</span></dt><dd><ul class="simple">
<li><p>The neighborhood region to be considered around
each seed point</p></li>
</ul>
</dd>
<dt><strong>multiplier</strong><span class="classifier">scalar</span></dt><dd><ul class="simple">
<li><p>Controls the threshold range computed from
the mean and variance of seed point neighborhoods</p></li>
</ul>
</dd>
<dt><strong>iters</strong><span class="classifier">scalar</span></dt><dd><ul class="simple">
<li><p>is number of iterations</p></li>
</ul>
</dd>
<dt><strong>segmented_value</strong><span class="classifier">scalar</span></dt><dd><ul class="simple">
<li><p>the value to which output array valid
pixels are set to.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>Output array with resulting connected components</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.dilate">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">dilate</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">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#dilate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.dilate" title="Permalink to this definition">¶</a></dt>
<dd><p>Run image dilate on the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>mask</strong><span class="classifier">optional: af.Array. default: None.</span></dt><dd><ul class="simple">
<li><p>Specifies the neighborhood of a pixel.</p></li>
<li><p>When None, a [3, 3] array of all ones is used.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The dilated image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.dilate3">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">dilate3</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">volume</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#dilate3"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.dilate3" title="Permalink to this definition">¶</a></dt>
<dd><p>Run volume dilate on a volume.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>volume</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>A 3 D arrayfire array representing a volume, or</p></li>
<li><p>A multi dimensional array representing batch of volumes.</p></li>
</ul>
</dd>
<dt><strong>mask</strong><span class="classifier">optional: af.Array. default: None.</span></dt><dd><ul class="simple">
<li><p>Specifies the neighborhood of a pixel.</p></li>
<li><p>When None, a [3, 3, 3] array of all ones is used.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The dilated volume.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.erode">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">erode</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">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#erode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.erode" title="Permalink to this definition">¶</a></dt>
<dd><p>Run image erode on the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>mask</strong><span class="classifier">optional: af.Array. default: None.</span></dt><dd><ul class="simple">
<li><p>Specifies the neighborhood of a pixel.</p></li>
<li><p>When None, a [3, 3] array of all ones is used.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The eroded image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.erode3">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">erode3</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">volume</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#erode3"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.erode3" title="Permalink to this definition">¶</a></dt>
<dd><p>Run volume erode on the volume.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>volume</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>A 3 D arrayfire array representing an volume, or</p></li>
<li><p>A multi dimensional array representing batch of volumes.</p></li>
</ul>
</dd>
<dt><strong>mask</strong><span class="classifier">optional: af.Array. default: None.</span></dt><dd><ul class="simple">
<li><p>Specifies the neighborhood of a pixel.</p></li>
<li><p>When None, a [3, 3, 3] array of all ones is used.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The eroded volume.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.gaussian_kernel">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">gaussian_kernel</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_r</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#gaussian_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.gaussian_kernel" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a gaussian kernel with the given parameters.</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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>rows</strong><span class="classifier">int</span></dt><dd><ul class="simple">
<li><p>The number of rows in the gaussian kernel.</p></li>
</ul>
</dd>
<dt><strong>cols</strong><span class="classifier">int</span></dt><dd><ul class="simple">
<li><p>The number of columns in the gaussian kernel.</p></li>
</ul>
</dd>
<dt><strong>sigma_r</strong><span class="classifier">optional: number. default: None.</span></dt><dd><ul class="simple">
<li><p>The sigma value along rows</p></li>
<li><p>If None, calculated as (0.25 * rows + 0.75)</p></li>
</ul>
</dd>
<dt><strong>sigma_c</strong><span class="classifier">optional: number. default: None.</span></dt><dd><ul class="simple">
<li><p>The sigma value along columns</p></li>
<li><p>If None, calculated as (0.25 * cols + 0.75)</p></li>
</ul>
</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><ul class="simple">
<li><p>A gaussian kernel of size (rows, cols)</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.gradient">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">gradient</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#gradient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.gradient" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the horizontal and vertical gradients.</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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>(dx, dy)</strong><span class="classifier">Tuple of af.Array.</span></dt><dd><ul class="simple">
<li><p><cite>dx</cite> containing the horizontal gradients of <cite>image</cite>.</p></li>
<li><p><cite>dy</cite> containing the vertical gradients of <cite>image</cite>.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.gray2rgb">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">gray2rgb</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">r_factor</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">g_factor</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">b_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#gray2rgb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.gray2rgb" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert Grayscale image to an RGB 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>r_factor</strong><span class="classifier">optional: scalar. default: 1.0.</span></dt><dd><ul class="simple">
<li><p>Scale factor for the red channel.</p></li>
</ul>
</dd>
<dt><strong>g_factor</strong><span class="classifier">optional: scalar. default: 1.0.</span></dt><dd><ul class="simple">
<li><p>Scale factor for the green channel.</p></li>
</ul>
</dd>
<dt><strong>b_factor</strong><span class="classifier">optional: scalar. default: 1.0</span></dt><dd><ul class="simple">
<li><p>Scale factor for the blue channel.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>An RGB image.</p></li>
<li><p>The channels are not coalesced, i.e. they appear along the third dimension.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.hist_equal">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">hist_equal</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">hist</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#hist_equal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.hist_equal" title="Permalink to this definition">¶</a></dt>
<dd><p>Equalize an image based on a histogram.</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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>hist</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>Containing the histogram of an image.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The equalized image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.histogram">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">histogram</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">nbins</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.histogram" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the histogram of 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>nbins</strong><span class="classifier">int.</span></dt><dd><ul class="simple">
<li><p>Number of bins in the histogram.</p></li>
</ul>
</dd>
<dt><strong>min_val</strong><span class="classifier">optional: scalar. default: None.</span></dt><dd><ul class="simple">
<li><p>The lower bound for the bin values.</p></li>
<li><p>If None, <cite>af.min(image)</cite> is used.</p></li>
</ul>
</dd>
<dt><strong>max_val</strong><span class="classifier">optional: scalar. default: None.</span></dt><dd><ul class="simple">
<li><p>The upper bound for the bin values.</p></li>
<li><p>If None, <cite>af.max(image)</cite> is used.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>hist</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>Containing the histogram of the image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.hsv2rgb">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">hsv2rgb</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#hsv2rgb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.hsv2rgb" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert HSV image to RGB.</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><ul class="simple">
<li><p>A 3 D arrayfire array representing an 3 channel image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>A HSV image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.inverseDeconv">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">inverseDeconv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">psf</span></em>, <em class="sig-param"><span class="pre">gamma</span></em>, <em class="sig-param"><span class="pre">algo=<ITERATIVE_DECONV.DEFAULT:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#inverseDeconv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.inverseDeconv" title="Permalink to this definition">¶</a></dt>
<dd><p>Inverse deconvolution algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image: af.Array</strong></dt><dd><p>The blurred input image.</p>
</dd>
<dt><strong>psf: af.Array</strong></dt><dd><p>The kernel(point spread function) known to have caused
the blur in the system.</p>
</dd>
<dt><strong>gamma: scalar.</strong></dt><dd><p>is a user defined regularization constant</p>
</dd>
<dt><strong>algo:</strong></dt><dd><p>takes enum value of type af.INVERSE_DECONV
indicating the inverse deconvolution algorithm to be used</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>sharp image estimate generated from the blurred input</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.is_image_io_available">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">is_image_io_available</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#is_image_io_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.is_image_io_available" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to check if the arrayfire library was built with Image IO support.</p>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.iterativeDeconv">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">iterativeDeconv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">psf</span></em>, <em class="sig-param"><span class="pre">iterations</span></em>, <em class="sig-param"><span class="pre">relax_factor</span></em>, <em class="sig-param"><span class="pre">algo=<ITERATIVE_DECONV.DEFAULT:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#iterativeDeconv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.iterativeDeconv" title="Permalink to this definition">¶</a></dt>
<dd><p>Iterative deconvolution algorithm.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>image: af.Array</strong></dt><dd><p>The blurred input image.</p>
</dd>
<dt><strong>psf: af.Array</strong></dt><dd><p>The kernel(point spread function) known to have caused
the blur in the system.</p>
</dd>
<dt><strong>iterations:</strong></dt><dd><p>Number of times the algorithm will run.</p>
</dd>
<dt><strong>relax_factor: scalar.</strong></dt><dd><p>is the relaxation factor multiplied with distance
of estimate from observed image.</p>
</dd>
<dt><strong>algo:</strong></dt><dd><p>takes enum value of type af.ITERATIVE_DECONV
indicating the iterative deconvolution algorithm to be used</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>sharp image estimate generated from the blurred input</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.load_image">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">load_image</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_color</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/image.html#load_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.load_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Load an image on the disk as an array.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>file_name: str</strong></dt><dd><ul class="simple">
<li><p>Full path of the file name on disk.</p></li>
</ul>
</dd>
<dt><strong>is_color</strong><span class="classifier">optional: bool. default: False.</span></dt><dd><ul class="simple">
<li><p>Specifies if the image is loaded as 1 channel (if False) or 3 channel image (if True).</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>image - af.Array</dt><dd><p>A 2 dimensional (1 channel) or 3 dimensional (3 channel) array containing the image.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.load_image_native">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">load_image_native</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#load_image_native"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.load_image_native" title="Permalink to this definition">¶</a></dt>
<dd><p>Load an image on the disk as an array in native format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>file_name: str</strong></dt><dd><ul class="simple">
<li><p>Full path of the file name on disk.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>image - af.Array</dt><dd><p>A 2 dimensional (1 channel) or 3 dimensional (3 or 4 channel) array containing the image.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.maxfilt">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">maxfilt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">w_len=3</span></em>, <em class="sig-param"><span class="pre">w_wid=3</span></em>, <em class="sig-param"><span class="pre">edge_pad=<PAD.ZERO:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#maxfilt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.maxfilt" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply max filter for the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>w0</strong><span class="classifier">optional: int. default: 3.</span></dt><dd><ul class="simple">
<li><p>The length of the filter along the first dimension.</p></li>
</ul>
</dd>
<dt><strong>w1</strong><span class="classifier">optional: int. default: 3.</span></dt><dd><ul class="simple">
<li><p>The length of the filter along the second dimension.</p></li>
</ul>
</dd>
<dt><strong>edge_pad</strong><span class="classifier">optional: af.PAD. default: af.PAD.ZERO</span></dt><dd><ul class="simple">
<li><p>Flag specifying how the max at the edge should be treated.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The image after max filter is applied.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.mean_shift">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">mean_shift</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">s_sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c_sigma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_color</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/image.html#mean_shift"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.mean_shift" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply mean shift to the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>s_sigma</strong><span class="classifier">scalar.</span></dt><dd><ul class="simple">
<li><p>Sigma value for the co-ordinate space.</p></li>
</ul>
</dd>
<dt><strong>c_sigma</strong><span class="classifier">scalar.</span></dt><dd><ul class="simple">
<li><p>Sigma value for the color space.</p></li>
</ul>
</dd>
<dt><strong>n_iter</strong><span class="classifier">int.</span></dt><dd><ul class="simple">
<li><p>Number of mean shift iterations.</p></li>
</ul>
</dd>
<dt><strong>is_color</strong><span class="classifier">optional: bool. default: False.</span></dt><dd><ul class="simple">
<li><p>Specifies if the third dimension is 3rd channel (if True) or a batch (if False).</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The image after the application of the meanshift.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.minfilt">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">minfilt</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">w_len=3</span></em>, <em class="sig-param"><span class="pre">w_wid=3</span></em>, <em class="sig-param"><span class="pre">edge_pad=<PAD.ZERO:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#minfilt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.minfilt" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply min filter for the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>w0</strong><span class="classifier">optional: int. default: 3.</span></dt><dd><ul class="simple">
<li><p>The length of the filter along the first dimension.</p></li>
</ul>
</dd>
<dt><strong>w1</strong><span class="classifier">optional: int. default: 3.</span></dt><dd><ul class="simple">
<li><p>The length of the filter along the second dimension.</p></li>
</ul>
</dd>
<dt><strong>edge_pad</strong><span class="classifier">optional: af.PAD. default: af.PAD.ZERO</span></dt><dd><ul class="simple">
<li><p>Flag specifying how the min at the edge should be treated.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>The image after min filter is applied.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.moments">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">moments</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">moment=<MOMENT.FIRST_ORDER:</span> <span class="pre">15></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#moments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.moments" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate image moments.</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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>moment</strong><span class="classifier">optional: af.MOMENT. default: af.MOMENT.FIRST_ORDER.</span></dt><dd><p>Moment(s) to calculate. Can be one of:
- af.MOMENT.M00
- af.MOMENT.M01
- af.MOMENT.M10
- af.MOMENT.M11
- af.MOMENT.FIRST_ORDER</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><ul class="simple">
<li><p>array containing requested moment(s) of each image</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.regions">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">regions</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">conn=<CONNECTIVITY.FOUR:</span> <span class="pre">4></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/image.html#regions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.regions" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the connected components in the 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image.</p></li>
</ul>
</dd>
<dt><strong>conn</strong><span class="classifier">optional: af.CONNECTIVITY. default: af.CONNECTIVITY.FOUR.</span></dt><dd><ul class="simple">
<li><p>Specifies the connectivity of the pixels.</p></li>
</ul>
</dd>
<dt><strong>out_type</strong><span class="classifier">optional: af.Dtype. default: af.Dtype.f32.</span></dt><dd><ul class="simple">
<li><p>Specifies the type for the output.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>An array where each pixel is labeled with its component number.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.resize">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">resize</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">scale=None</span></em>, <em class="sig-param"><span class="pre">odim0=None</span></em>, <em class="sig-param"><span class="pre">odim1=None</span></em>, <em class="sig-param"><span class="pre">method=<INTERP.NEAREST:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#resize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.resize" title="Permalink to this definition">¶</a></dt>
<dd><p>Resize 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>scale</strong><span class="classifier">optional: scalar. default: None.</span></dt><dd><ul class="simple">
<li><p>Scale factor for the image resizing.</p></li>
</ul>
</dd>
<dt><strong>odim0</strong><span class="classifier">optional: int. default: None.</span></dt><dd><ul class="simple">
<li><p>Size of the first dimension of the output.</p></li>
</ul>
</dd>
<dt><strong>odim1</strong><span class="classifier">optional: int. default: None.</span></dt><dd><ul class="simple">
<li><p>Size of the second dimension of the output.</p></li>
</ul>
</dd>
<dt><strong>method</strong><span class="classifier">optional: af.INTERP. default: af.INTERP.NEAREST.</span></dt><dd><ul class="simple">
<li><p>Interpolation method used for resizing.</p></li>
</ul>
</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><ul class="simple">
<li><p>Output image after resizing.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.rgb2gray">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">rgb2gray</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">r_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2126</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">g_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.7152</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0722</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#rgb2gray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.rgb2gray" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert RGB image to Grayscale.</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><ul class="simple">
<li><p>A 3 D arrayfire array representing an 3 channel image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>r_factor</strong><span class="classifier">optional: scalar. default: 0.2126.</span></dt><dd><ul class="simple">
<li><p>Weight for the red channel.</p></li>
</ul>
</dd>
<dt><strong>g_factor</strong><span class="classifier">optional: scalar. default: 0.7152.</span></dt><dd><ul class="simple">
<li><p>Weight for the green channel.</p></li>
</ul>
</dd>
<dt><strong>b_factor</strong><span class="classifier">optional: scalar. default: 0.0722.</span></dt><dd><ul class="simple">
<li><p>Weight for the blue channel.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>A grayscale image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.rgb2hsv">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">rgb2hsv</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">image</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#rgb2hsv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.rgb2hsv" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert RGB image to HSV.</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><ul class="simple">
<li><p>A 3 D arrayfire array representing an 3 channel image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>output</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>A RGB image.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.rgb2ycbcr">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">rgb2ycbcr</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">standard=<YCC_STD.BT_601:</span> <span class="pre">601></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#rgb2ycbcr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.rgb2ycbcr" title="Permalink to this definition">¶</a></dt>
<dd><p>RGB to YCbCr colorspace conversion.</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 containing an image or batch of images in RGB format.</p>
</dd>
<dt><strong>standard: YCC_STD. optional. default: YCC_STD.BT_601</strong></dt><dd><ul class="simple">
<li><p>Specifies the YCbCr format.</p></li>
<li><p>Can be one of YCC_STD.BT_601, YCC_STD.BT_709, and YCC_STD.BT_2020.</p></li>
</ul>
</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 an image or batch of images in YCbCr format</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.image.rotate">
<code class="sig-prename descclassname"><span class="pre">arrayfire.image.</span></code><code class="sig-name descname"><span class="pre">rotate</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">image</span></em>, <em class="sig-param"><span class="pre">theta</span></em>, <em class="sig-param"><span class="pre">is_crop=True</span></em>, <em class="sig-param"><span class="pre">method=<INTERP.NEAREST:</span> <span class="pre">0></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/image.html#rotate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.image.rotate" title="Permalink to this definition">¶</a></dt>
<dd><p>Rotate 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><ul class="simple">
<li><p>A 2 D arrayfire array representing an image, or</p></li>
<li><p>A multi dimensional array representing batch of images.</p></li>
</ul>
</dd>
<dt><strong>theta</strong><span class="classifier">scalar</span></dt><dd><ul class="simple">
<li><p>The angle to rotate in radians.</p></li>
</ul>
</dd>
<dt><strong>is_crop</strong><span class="classifier">optional: bool. default: True.</span></dt><dd><ul class="simple">
<li><p>Specifies if the output should be cropped to the input size.</p></li>
</ul>
</dd>
<dt><strong>method</strong><span class="classifier">optional: af.INTERP. default: af.INTERP.NEAREST.</span></dt><dd><ul class="simple">
<li><p>Interpolation method used for rotating.</p></li>
</ul>
</dd>