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- <h1>Source code for ipfml.processing.transform</h1><div class="highlight"><pre>
- <span></span><span class="sd">"""</span>
- <span class="sd">Functions which can be used to extract information from image or reduce it</span>
- <span class="sd">"""</span>
- <span class="c1"># main imports</span>
- <span class="kn">import</span> <span class="nn">os</span>
- <span class="kn">import</span> <span class="nn">random</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
- <span class="c1"># image processing imports</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">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>
- <span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
- <span class="c1"># ipfml imports</span>
- <span class="kn">from</span> <span class="nn">ipfml.processing</span> <span class="k">import</span> <span class="n">compression</span>
- <div class="viewcode-block" id="get_LAB"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> Lab = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> L = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> a = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> b = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> x = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> y = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> z = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_img = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> bits_img = transform.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.processing.transform.html#ipfml.processing.transform.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.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> img = transform.get_LAB_L(img)</span>
- <span class="sd"> >>> img_mscn = transform.gray_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>
- <div class="viewcode-block" id="rgb_to_mscn"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.rgb_to_mscn">[docs]</a><span class="k">def</span> <span class="nf">rgb_to_mscn</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with MSCN information</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> img_mscn = transform.rgb_to_mscn(img)</span>
- <span class="sd"> >>> img_mscn.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="c1"># check if PIL image or not</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="c1"># convert rgb image to gray</span>
- <span class="n">im</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">color</span><span class="o">.</span><span class="n">rgb2gray</span><span class="p">(</span><span class="n">img_arr</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span><span class="p">,</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">gray_to_mscn</span><span class="p">(</span><span class="n">im</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_mscn_coefficients"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.get_mscn_coefficients">[docs]</a><span class="k">def</span> <span class="nf">get_mscn_coefficients</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Compute the Mean Substracted Constrast Normalized coefficients of an image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image, Numpy array or path of image</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> MSCN coefficients</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> FileNotFoundError: If `image` is set as str path and image was not found</span>
- <span class="sd"> ValueError: If `image` numpy shape are not correct</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> image_values = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> mscn_coefficients = transform.get_mscn_coefficients(image_values)</span>
- <span class="sd"> >>> mscn_coefficients.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="c1"># open image directly as grey level image</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">'Image not found in your system'</span><span class="p">)</span>
- <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">image</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="c1"># convert if necessary to grey level numpy array</span>
- <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
- <span class="k">if</span> <span class="n">image</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">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Incorrect image shape'</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># if PIL Image</span>
- <span class="n">image</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="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
- <span class="k">if</span> <span class="n">image</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">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Incorrect image shape'</span><span class="p">)</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span> <span class="o">/</span> <span class="mf">255.0</span>
- <span class="c1"># calculating MSCN coefficients</span>
- <span class="n">mu</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">imdist</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="mi">7</span> <span class="o">/</span> <span class="mi">6</span><span class="p">,</span> <span class="n">borderType</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">BORDER_CONSTANT</span><span class="p">)</span>
- <span class="n">mu_sq</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">mu</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">imdist</span> <span class="o">*</span> <span class="n">imdist</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="mi">7</span> <span class="o">/</span> <span class="mi">6</span><span class="p">,</span> <span class="n">borderType</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">BORDER_CONSTANT</span><span class="p">)</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">abs</span><span class="p">((</span><span class="n">sigma</span> <span class="o">-</span> <span class="n">mu_sq</span><span class="p">)))</span>
- <span class="n">structdis</span> <span class="o">=</span> <span class="p">(</span><span class="n">imdist</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sigma</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">structdis</span></div>
- <div class="viewcode-block" id="get_LAB_L_SVD"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.get_LAB_L_SVD">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_L_SVD</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Returns Singular values from LAB L Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> U, s, V information obtained from SVD compression using Lab</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U, s, V = transform.get_LAB_L_SVD(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">compression</span><span class="o">.</span><span class="n">get_SVD</span><span class="p">(</span><span class="n">L</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_LAB_L_SVD_s"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.get_LAB_L_SVD_s">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_L_SVD_s</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Returns s (Singular values) SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> vector of singular values</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> s = transform.get_LAB_L_SVD_s(img)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">compression</span><span class="o">.</span><span class="n">get_SVD_s</span><span class="p">(</span><span class="n">L</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_LAB_L_SVD_U"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.get_LAB_L_SVD_U">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_L_SVD_U</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Returns U SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> U matrix of SVD compression</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U = transform.get_LAB_L_SVD_U(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">compression</span><span class="o">.</span><span class="n">get_SVD_U</span><span class="p">(</span><span class="n">L</span><span class="p">)</span></div>
- <div class="viewcode-block" id="get_LAB_L_SVD_V"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.get_LAB_L_SVD_V">[docs]</a><span class="k">def</span> <span class="nf">get_LAB_L_SVD_V</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="sd">"""Returns V SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> V matrix of SVD compression</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> V = transform.get_LAB_L_SVD_V(img)</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">compression</span><span class="o">.</span><span class="n">get_SVD_V</span><span class="p">(</span><span class="n">L</span><span class="p">)</span></div>
- <div class="viewcode-block" id="rgb_to_grey_low_bits"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.rgb_to_grey_low_bits">[docs]</a><span class="k">def</span> <span class="nf">rgb_to_grey_low_bits</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">"""Convert RGB Image into grey image using only 4 low bits values</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with low bits information kept</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_grey_img = transform.rgb_to_grey_low_bits(img, 5)</span>
- <span class="sd"> >>> low_bits_grey_img.shape</span>
- <span class="sd"> (200, 200)</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">grey_block</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">color</span><span class="o">.</span><span class="n">rgb2gray</span><span class="p">(</span><span class="n">img_arr</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span><span class="p">,</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">get_low_bits_img</span><span class="p">(</span><span class="n">grey_block</span><span class="p">,</span> <span class="n">nb_bits</span><span class="p">)</span></div>
- <div class="viewcode-block" id="rgb_to_LAB_L_low_bits"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.rgb_to_LAB_L_low_bits">[docs]</a><span class="k">def</span> <span class="nf">rgb_to_LAB_L_low_bits</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">"""Convert RGB Image into Lab L channel image using only 4 low bits values</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with low bits information kept</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_Lab_l_img = transform.rgb_to_LAB_L_low_bits(img, 5)</span>
- <span class="sd"> >>> low_bits_Lab_l_img.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L_block</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">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">get_low_bits_img</span><span class="p">(</span><span class="n">L_block</span><span class="p">,</span> <span class="n">nb_bits</span><span class="p">)</span></div>
- <div class="viewcode-block" id="rgb_to_LAB_L_bits"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.transform.html#ipfml.processing.transform.rgb_to_LAB_L_bits">[docs]</a><span class="k">def</span> <span class="nf">rgb_to_LAB_L_bits</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 from LAB L canal 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"> 2D Numpy array with reduced values</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml.processing import transform</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> bits_Lab_l_img = transform.rgb_to_LAB_L_bits(img, (2, 6))</span>
- <span class="sd"> >>> bits_Lab_l_img.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L_block</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">get_LAB_L</span><span class="p">(</span><span class="n">image</span><span class="p">),</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">get_bits_img</span><span class="p">(</span><span class="n">L_block</span><span class="p">,</span> <span class="n">interval</span><span class="p">)</span></div>
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