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- <h1>Source code for ipfml.filters.noise</h1><div class="highlight"><pre>
- <span></span><span class="sd">"""</span>
- <span class="sd">Noise filters to apply on images</span>
- <span class="sd">"""</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
- <span class="kn">import</span> <span class="nn">random</span>
- <span class="kn">from</span> <span class="nn">ipfml</span> <span class="k">import</span> <span class="n">processing</span>
- <span class="k">def</span> <span class="nf">_normalise</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">_normalise</span><span class="p">,</span> <span class="n">x</span><span class="p">)))</span>
- <span class="k">if</span> <span class="n">x</span> <span class="o">></span> <span class="mi">255</span><span class="p">:</span>
- <span class="k">return</span> <span class="mi">255</span>
- <span class="k">if</span> <span class="n">x</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
- <span class="k">return</span> <span class="mi">0</span>
- <span class="k">return</span> <span class="n">x</span>
- <span class="k">def</span> <span class="nf">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
- <span class="sd">"""White noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> generator: lambda function used to generate random numpy array with specific distribution</span>
- <span class="sd"> updator: lambda function used to update pixel value</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with specified noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import _global_noise_filter as gf</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> generator = lambda h, w: np.random.uniform(-0.5, 0.5, (h, w))</span>
- <span class="sd"> >>> n = 10</span>
- <span class="sd"> >>> k = 0.2</span>
- <span class="sd"> >>> updator = lambda x, noise: x + n * k * noise</span>
- <span class="sd"> >>> noisy_image = gf(image, generator, updator)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">image_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="n">nb_chanel</span> <span class="o">=</span> <span class="mi">1</span>
- <span class="k">if</span> <span class="n">image_array</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
- <span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">nb_chanel</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
- <span class="k">if</span> <span class="n">nb_chanel</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">identical</span><span class="p">:</span>
- <span class="n">noise_filter</span> <span class="o">=</span> <span class="n">generator</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span>
- <span class="c1"># final output numpy array</span>
- <span class="n">output_array</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="c1"># check number of chanel</span>
- <span class="k">if</span> <span class="n">nb_chanel</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">noisy_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">updator</span><span class="p">,</span> <span class="n">image_array</span><span class="p">,</span> <span class="n">noise_filter</span><span class="p">)))</span>
- <span class="c1"># normalise values</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">_normalise</span><span class="p">,</span> <span class="n">noisy_image</span><span class="p">)),</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># final output numpy array</span>
- <span class="n">output_array</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">chanel</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">nb_chanel</span><span class="p">):</span>
- <span class="c1"># getting flatten information from image and noise</span>
- <span class="n">image_array_chanel</span> <span class="o">=</span> <span class="n">image_array</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">chanel</span><span class="p">]</span>
- <span class="c1"># redefine noise if necessary</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="n">identical</span><span class="p">:</span>
- <span class="n">noise_filter</span> <span class="o">=</span> <span class="n">generator</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">width</span><span class="p">)</span>
- <span class="c1"># compute new pixel value</span>
- <span class="c1"># x + n * k * white_noise_filter[i] as example</span>
- <span class="n">noisy_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
- <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">updator</span><span class="p">,</span> <span class="n">image_array_chanel</span><span class="p">,</span> <span class="n">noise_filter</span><span class="p">)))</span>
- <span class="c1"># normalise values</span>
- <span class="n">noisy_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">_normalise</span><span class="p">,</span> <span class="n">noisy_image</span><span class="p">)),</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="c1"># in order to concatenate output array</span>
- <span class="n">noisy_image</span> <span class="o">=</span> <span class="n">noisy_image</span><span class="p">[:,</span> <span class="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
- <span class="c1"># append new chanel</span>
- <span class="n">output_array</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">noisy_image</span><span class="p">)</span>
- <span class="c1"># concatenate RGB image</span>
- <span class="n">output_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">output_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">output_array</span>
- <div class="viewcode-block" id="white_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.white_noise">[docs]</a><span class="k">def</span> <span class="nf">white_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.2</span><span class="p">):</span>
- <span class="sd">"""White noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (-0.5, 0.5))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.2)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with white noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import white_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = white_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</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="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> <span class="n">noise</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="gaussian_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.gaussian_noise">[docs]</a><span class="k">def</span> <span class="nf">gaussian_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
- <span class="sd">"""Gaussian noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (0, 1))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.1)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with gaussian noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import gaussian_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = gaussian_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> <span class="n">noise</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="laplace_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.laplace_noise">[docs]</a><span class="k">def</span> <span class="nf">laplace_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
- <span class="sd">"""Laplace noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (0, 1))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.1)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpay array with Laplace noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import laplace_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = laplace_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">laplace</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> <span class="n">noise</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="cauchy_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.cauchy_noise">[docs]</a><span class="k">def</span> <span class="nf">cauchy_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.0002</span><span class="p">):</span>
- <span class="sd">"""Cauchy noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (0, 1))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.0002)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with Cauchy noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import cauchy_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = cauchy_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">standard_cauchy</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> <span class="n">noise</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="log_normal_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.log_normal_noise">[docs]</a><span class="k">def</span> <span class="nf">log_normal_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.05</span><span class="p">):</span>
- <span class="sd">"""Log-normal noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (0, 1))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.05)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with Log-normal noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import log_normal_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = log_normal_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">lognormal</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span> <span class="o">*</span> <span class="n">noise</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="mut_white_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.mut_white_noise">[docs]</a><span class="k">def</span> <span class="nf">mut_white_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span>
- <span class="n">n</span><span class="p">,</span>
- <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">distribution_interval</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
- <span class="n">k</span><span class="o">=</span><span class="mf">0.002</span><span class="p">):</span>
- <span class="sd">"""Multiplied White noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> distribution_interval: set the distribution interval of normal law distribution (default (0, 1))</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.002)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with multiplied white noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import mut_white_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = mut_white_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="n">min_value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">k</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
- <span class="n">max_value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
- <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">distribution_interval</span>
- <span class="n">generator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">:</span> <span class="n">min_value</span> <span class="o">+</span> <span class="p">(</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="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_value</span> <span class="o">-</span> <span class="n">min_value</span><span class="p">))</span>
- <span class="n">updator</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="nb">pow</span><span class="p">(</span><span class="n">noise</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">generator</span><span class="p">,</span> <span class="n">updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
- <div class="viewcode-block" id="salt_pepper_noise"><a class="viewcode-back" href="../../../ipfml/ipfml.filters.noise.html#ipfml.filters.noise.salt_pepper_noise">[docs]</a><span class="k">def</span> <span class="nf">salt_pepper_noise</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">identical</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
- <span class="sd">"""Pepper salt noise filter to apply on image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image used as input (2D or 3D image representation)</span>
- <span class="sd"> n: used to set importance of noise [1, 999]</span>
- <span class="sd"> identical: keep or not identical noise distribution for each canal if RGB Image (default False)</span>
- <span class="sd"> p: probability to increase pixel value otherwise decrease it</span>
- <span class="sd"> k: variable that specifies the amount of noise to be taken into account in the output image (default 0.5)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with salt and pepper noise applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from ipfml.filters.noise import salt_pepper_noise</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))</span>
- <span class="sd"> >>> noisy_image = salt_pepper_noise(image, 10)</span>
- <span class="sd"> >>> noisy_image.shape</span>
- <span class="sd"> (100, 100)</span>
- <span class="sd"> """</span>
- <span class="k">def</span> <span class="nf">_generator</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span>
- <span class="n">x</span> <span class="o">=</span> <span class="n">w</span> <span class="o">*</span> <span class="n">h</span>
- <span class="n">nb_elem</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span>
- <span class="n">elements</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="n">elements</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">nb_elem</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
- <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">elements</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">elements</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
- <span class="c1"># here noise variable is boolean to update or not pixel value</span>
- <span class="k">def</span> <span class="nf">_updator</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">):</span>
- <span class="c1"># apply specific changes to each value of 1D array</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
- <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">_updator</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">noise</span><span class="p">)))</span>
- <span class="c1"># probabilty to increase or decrease pixel value</span>
- <span class="n">rand</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">1</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">noise</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">rand</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">x</span> <span class="o">-</span> <span class="n">n</span> <span class="o">*</span> <span class="n">k</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">x</span>
- <span class="k">return</span> <span class="n">_global_noise_filter</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">_generator</span><span class="p">,</span> <span class="n">_updator</span><span class="p">,</span> <span class="n">identical</span><span class="p">)</span></div>
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