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- <h1>Source code for ipfml.metrics</h1><div class="highlight"><pre>
- <span></span><span class="sd">"""</span>
- <span class="sd">Functions which can be used to extract information from image</span>
- <span class="sd">"""</span>
- <span class="kn">from</span> <span class="nn">numpy.linalg</span> <span class="k">import</span> <span class="n">svd</span>
- <span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">misc</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
- <span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
- <span class="kn">from</span> <span class="nn">skimage</span> <span class="k">import</span> <span class="n">io</span><span class="p">,</span> <span class="n">color</span>
- <span class="kn">import</span> <span class="nn">cv2</span>
- <div class="viewcode-block" id="get_SVD"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_SVD">[docs]</a><span class="k">def</span> <span class="nf">get_SVD</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms Image using SVD compression</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert into SVD compression</span>
- <span class="sd"> Return:</span>
- <span class="sd"> U, s, V obtained from SVD compression</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U, s, V = metrics.get_SVD(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 3, 3)</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">svd</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_SVD_s"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_SVD_s">[docs]</a><span class="k">def</span> <span class="nf">get_SVD_s</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms Image into SVD and returns only 's' part</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> vector of singular values obtained from SVD compression</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> s = metrics.get_SVD_s(img)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> """</span>
- <span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">svd</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">s</span></div>
- <div class="viewcode-block" id="get_SVD_U"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_SVD_U">[docs]</a><span class="k">def</span> <span class="nf">get_SVD_U</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms Image into SVD and returns only 'U' part</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> U matrix from SVD compression</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U = metrics.get_SVD_U(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">svd</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">U</span></div>
- <div class="viewcode-block" id="get_SVD_V"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_SVD_V">[docs]</a><span class="k">def</span> <span class="nf">get_SVD_V</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms Image into SVD and returns only 'V' part</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> V matrix obtained from SVD compression</span>
- <span class="sd"> Usage :</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> V = metrics.get_SVD_V(img)</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 3, 3)</span>
- <span class="sd"> """</span>
- <span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">svd</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">V</span></div>
- <div class="viewcode-block" id="get_LAB"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_LAB">[docs]</a><span class="k">def</span> <span class="nf">get_LAB</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into Lab</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Lab information</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> Lab = metrics.get_LAB(img)</span>
- <span class="sd"> >>> Lab.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">color</span><span class="o">.</span><span class="n">rgb2lab</span><span class="p">(</span><span class="n">image</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_LAB_L"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_LAB_L">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into Lab and returns L</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The L chanel from Lab information</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> L = metrics.get_LAB_L(img)</span>
- <span class="sd"> >>> L.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">lab</span> <span class="o">=</span> <span class="n">get_LAB</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">lab</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_LAB_a"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_LAB_a">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_a</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into LAB and returns a</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The a chanel from Lab information</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> a = metrics.get_LAB_a(img)</span>
- <span class="sd"> >>> a.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">lab</span> <span class="o">=</span> <span class="n">get_LAB</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">lab</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_LAB_b"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_LAB_b">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_b</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into LAB and returns b</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The b chanel from Lab information</span>
- <span class="sd"> Usage :</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> b = metrics.get_LAB_b(img)</span>
- <span class="sd"> >>> b.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">lab</span> <span class="o">=</span> <span class="n">get_LAB</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">lab</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_XYZ"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_XYZ">[docs]</a><span class="k">def</span> <span class="nf">get_XYZ</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into XYZ</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> XYZ information obtained from transformation</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> metrics.get_XYZ(img).shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="k">return</span> <span class="n">color</span><span class="o">.</span><span class="n">rgb2xyz</span><span class="p">(</span><span class="n">image</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_XYZ_X"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_XYZ_X">[docs]</a><span class="k">def</span> <span class="nf">get_XYZ_X</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into XYZ and returns X</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The X chanel from XYZ information</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> x = metrics.get_XYZ_X(img)</span>
- <span class="sd"> >>> x.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">xyz</span> <span class="o">=</span> <span class="n">color</span><span class="o">.</span><span class="n">rgb2xyz</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">xyz</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_XYZ_Y"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_XYZ_Y">[docs]</a><span class="k">def</span> <span class="nf">get_XYZ_Y</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into XYZ and returns Y</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The Y chanel from XYZ information</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> y = metrics.get_XYZ_Y(img)</span>
- <span class="sd"> >>> y.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">xyz</span> <span class="o">=</span> <span class="n">color</span><span class="o">.</span><span class="n">rgb2xyz</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">xyz</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_XYZ_Z"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_XYZ_Z">[docs]</a><span class="k">def</span> <span class="nf">get_XYZ_Z</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Transforms RGB Image into XYZ and returns Z</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> The Z chanel from XYZ information</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> ValueError: If `nb_bits` has unexpected value. `nb_bits` needs to be in interval [1, 8].</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> z = metrics.get_XYZ_Z(img)</span>
- <span class="sd"> >>> z.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">xyz</span> <span class="o">=</span> <span class="n">color</span><span class="o">.</span><span class="n">rgb2xyz</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">xyz</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span></div>
- <div class="viewcode-block" id="get_low_bits_img"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_low_bits_img">[docs]</a><span class="k">def</span> <span class="nf">get_low_bits_img</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">nb_bits</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
- <span class="sd">"""Returns Image or Numpy array with data information reduced using only low bits</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert</span>
- <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Numpy array with reduced values</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_img = metrics.get_low_bits_img(img, 5)</span>
- <span class="sd"> >>> low_bits_img.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="k">if</span> <span class="n">nb_bits</span> <span class="o"><=</span> <span class="mi">0</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
- <span class="s2">"unexpected value of number of bits to keep. @nb_bits needs to be positive and greater than 0."</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">nb_bits</span> <span class="o">></span> <span class="mi">8</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
- <span class="s2">"Unexpected value of number of bits to keep. @nb_bits needs to be in interval [1, 8]."</span>
- <span class="p">)</span>
- <span class="n">img_arr</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="n">image</span><span class="p">)</span>
- <span class="n">bits_values</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">nb_bits</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
- <span class="k">return</span> <span class="n">img_arr</span> <span class="o">&</span> <span class="n">bits_values</span></div>
- <div class="viewcode-block" id="get_bits_img"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.get_bits_img">[docs]</a><span class="k">def</span> <span class="nf">get_bits_img</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
- <span class="sd">"""Returns only bits specified into the interval</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert using this interval of bits value to keep</span>
- <span class="sd"> interval: (begin, end) of bits values</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Numpy array with reduced values</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> ValueError: If min value from interval is not >= 1.</span>
- <span class="sd"> ValueError: If max value from interval is not <= 8.</span>
- <span class="sd"> ValueError: If min value from interval >= max value.</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> bits_img = metrics.get_bits_img(img, (2, 5))</span>
- <span class="sd"> >>> bits_img.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="n">img_arr</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="n">image</span><span class="p">)</span>
- <span class="n">begin</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="n">interval</span>
- <span class="k">if</span> <span class="n">begin</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
- <span class="s2">"Unexpected value of interval. Interval min value needs to be >= 1."</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">end</span> <span class="o">></span> <span class="mi">8</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
- <span class="s2">"Unexpected value of interval. Interval min value needs to be <= 8."</span>
- <span class="p">)</span>
- <span class="k">if</span> <span class="n">begin</span> <span class="o">>=</span> <span class="n">end</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unexpected interval values order."</span><span class="p">)</span>
- <span class="n">bits_values</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">begin</span><span class="p">,</span> <span class="n">end</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
- <span class="k">return</span> <span class="n">img_arr</span> <span class="o">&</span> <span class="n">bits_values</span></div>
- <div class="viewcode-block" id="gray_to_mscn"><a class="viewcode-back" href="../../ipfml/ipfml.metrics.html#ipfml.metrics.gray_to_mscn">[docs]</a><span class="k">def</span> <span class="nf">gray_to_mscn</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: grayscale image</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> MSCN matrix obtained from transformation</span>
- <span class="sd"> Usage:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> img_mscn = processing.rgb_to_mscn(img)</span>
- <span class="sd"> >>> img_mscn.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">s</span> <span class="o">=</span> <span class="mi">7</span> <span class="o">/</span> <span class="mi">6</span>
- <span class="n">blurred</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">GaussianBlur</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span>
- <span class="n">s</span><span class="p">)</span> <span class="c1"># apply gaussian blur to the image</span>
- <span class="n">blurred_sq</span> <span class="o">=</span> <span class="n">blurred</span> <span class="o">*</span> <span class="n">blurred</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">GaussianBlur</span><span class="p">(</span><span class="n">image</span> <span class="o">*</span> <span class="n">image</span><span class="p">,</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">s</span><span class="p">)</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">sigma</span> <span class="o">-</span> <span class="n">blurred_sq</span><span class="p">)</span><span class="o">**</span><span class="mf">0.5</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma</span> <span class="o">+</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="mi">255</span> <span class="c1"># avoid DivideByZero Exception</span>
- <span class="n">mscn</span> <span class="o">=</span> <span class="p">(</span><span class="n">image</span> <span class="o">-</span> <span class="n">blurred</span><span class="p">)</span> <span class="o">/</span> <span class="n">sigma</span> <span class="c1"># MSCN(i, j) image</span>
- <span class="k">return</span> <span class="n">mscn</span></div>
- </pre></div>
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