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  <div class="section" id="ipfml-filters">
<h1>ipfml.filters<a class="headerlink" href="#ipfml-filters" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-ipfml.filters.noise">
<span id="ipfml-filters-noise"></span><h2>ipfml.filters.noise<a class="headerlink" href="#module-ipfml.filters.noise" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="ipfml.filters.noise.cauchy_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">cauchy_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(0</em>, <em>1)</em>, <em>k=0.0002</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.cauchy_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>Cauchy noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (0, 1))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.0002)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpy array with Cauchy noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">cauchy_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">cauchy_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="ipfml.filters.noise.gaussian_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">gaussian_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(0</em>, <em>1)</em>, <em>k=0.1</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.gaussian_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>Gaussian noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (0, 1))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpy array with gaussian noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">gaussian_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">gaussian_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="ipfml.filters.noise.laplace_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">laplace_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(0</em>, <em>1)</em>, <em>k=0.1</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.laplace_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>Laplace noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (0, 1))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.1)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpay array with Laplace noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">laplace_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">laplace_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="ipfml.filters.noise.log_normal_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">log_normal_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(0</em>, <em>1)</em>, <em>k=0.05</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.log_normal_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>Log-normal noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (0, 1))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.05)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpy array with Log-normal noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">log_normal_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">log_normal_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="ipfml.filters.noise.mut_white_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">mut_white_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(-0.5</em>, <em>0.5)</em>, <em>k=0.2</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.mut_white_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>Multiplied White noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (-0.5, 0.5))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpy array with multiplied white noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">mut_white_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">mut_white_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="ipfml.filters.noise.white_noise">
<code class="descclassname">ipfml.filters.noise.</code><code class="descname">white_noise</code><span class="sig-paren">(</span><em>image</em>, <em>n</em>, <em>identical=False</em>, <em>distribution_interval=(-0.5</em>, <em>0.5)</em>, <em>k=0.2</em><span class="sig-paren">)</span><a class="headerlink" href="#ipfml.filters.noise.white_noise" title="Permalink to this definition">¶</a></dt>
<dd><p>White noise filter to apply on image</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>image</strong> – image used as input (2D or 3D image representation)</li>
<li><strong>n</strong> – used to set importance of noise [1, 999]</li>
<li><strong>identical</strong> – keep or not identical noise distribution for each canal if RGB Image (default False)</li>
<li><strong>distribution_interval</strong> – set the distribution interval of normal law distribution (default (-0.5, 0.5))</li>
<li><strong>k</strong> – variable that specifies the amount of noise to be taken into account in the output image (default 0.2)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">2D Numpy array with white noise applied</p>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">ipfml.filters.noise</span> <span class="k">import</span> <span class="n">white_noise</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span> <span class="o">=</span> <span class="n">white_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">noisy_image</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(100, 100)</span>
</pre></div>
</div>
</dd></dl>

</div>
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