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    151. 

  151.   <h1>Source code for ipfml.processing.reconstruction</h1><div class="highlight"><pre>
    152. 

  152. <span></span><span class="sd">&quot;&quot;&quot;</span>
    153. 

  153. <span class="sd">Functions for reconstruction process of image using reduction/compression methods</span>
    154. 

  154. <span class="sd">&quot;&quot;&quot;</span>
    155. 

  155. 

  156. <span class="c1"># main imports</span>
    157. 

  157. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
    158. 

  158. 

  159. <span class="c1"># image processing imports</span>
    160. 

  160. <span class="kn">from</span> <span class="nn">numpy.linalg</span> <span class="k">import</span> <span class="n">svd</span> <span class="k">as</span> <span class="n">np_svd</span>
    161. 

  161. <span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">FastICA</span><span class="p">,</span> <span class="n">IncrementalPCA</span>
    162. 

  162. 

  163. <span class="c1"># ipfml imports</span>
    164. 

  164. <span class="kn">from</span> <span class="nn">ipfml.processing</span> <span class="k">import</span> <span class="n">transform</span>
    165. 

  165. 

  166. 

  167. <div class="viewcode-block" id="svd"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.reconstruction.html#ipfml.processing.reconstruction.svd">[docs]</a><span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
    168. 

  168.     <span class="sd">&quot;&quot;&quot;Reconstruct an image from SVD compression using specific interval of Singular Values</span>
    169. 

  169. 

  170. <span class="sd">    Args:</span>
    171. 

  171. <span class="sd">        image: PIL Image, Numpy array or path of 3D image</span>
    172. 

  172. <span class="sd">        interval: Interval used for reconstruction</span>
    173. 

  173. 

  174. <span class="sd">    Returns:</span>
    175. 

  175. <span class="sd">        Reconstructed image</span>
    176. 

  176. 

  177. <span class="sd">    Example:</span>
    178. 

  178. 

  179. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    180. 

  180. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    181. 

  181. <span class="sd">    &gt;&gt;&gt; from ipfml.processing import reconstruction</span>
    182. 

  182. <span class="sd">    &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
    183. 

  183. <span class="sd">    &gt;&gt;&gt; reconstructed_image = reconstruction.svd(image_values, (100, 200))</span>
    184. 

  184. <span class="sd">    &gt;&gt;&gt; reconstructed_image.shape</span>
    185. 

  185. <span class="sd">    (200, 200)</span>
    186. 

  186. <span class="sd">    &quot;&quot;&quot;</span>
    187. 

  187. 

  188.     <span class="n">begin</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="n">interval</span>
    189. 

  189.     <span class="n">lab_img</span> <span class="o">=</span> <span class="n">transform</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>
    190. 

  190.     <span class="n">lab_img</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">lab_img</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
    191. 

  191. 

  192.     <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">np_svd</span><span class="p">(</span><span class="n">lab_img</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    193. 

  193. 

  194.     <span class="c1"># reconstruction using specific interval</span>
    195. 

  195.     <span class="n">smat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">end</span> <span class="o">-</span> <span class="n">begin</span><span class="p">,</span> <span class="n">end</span> <span class="o">-</span> <span class="n">begin</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
    196. 

  196.     <span class="n">smat</span><span class="p">[:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">begin</span><span class="p">:</span><span class="n">end</span><span class="p">])</span>
    197. 

  197.     <span class="n">output_img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">U</span><span class="p">[:,</span> <span class="n">begin</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">smat</span><span class="p">,</span> <span class="n">V</span><span class="p">[</span><span class="n">begin</span><span class="p">:</span><span class="n">end</span><span class="p">,</span> <span class="p">:]))</span>
    198. 

  198. 

  199.     <span class="k">return</span> <span class="n">output_img</span></div>
    200. 

  200. 

  201. 

  202. <div class="viewcode-block" id="fast_ica"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.reconstruction.html#ipfml.processing.reconstruction.fast_ica">[docs]</a><span class="k">def</span> <span class="nf">fast_ica</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">components</span><span class="p">):</span>
    203. 

  203.     <span class="sd">&quot;&quot;&quot;Reconstruct an image from Fast ICA compression using specific number of components to use</span>
    204. 

  204. 

  205. <span class="sd">    Args:</span>
    206. 

  206. <span class="sd">        image: PIL Image, Numpy array or path of 3D image</span>
    207. 

  207. <span class="sd">        components: Number of components used for reconstruction</span>
    208. 

  208. 

  209. <span class="sd">    Returns:</span>
    210. 

  210. <span class="sd">        Reconstructed image</span>
    211. 

  211. 

  212. <span class="sd">    Example:</span>
    213. 

  213. 

  214. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    215. 

  215. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    216. 

  216. <span class="sd">    &gt;&gt;&gt; from ipfml.processing import reconstruction</span>
    217. 

  217. <span class="sd">    &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
    218. 

  218. <span class="sd">    &gt;&gt;&gt; reconstructed_image = reconstruction.fast_ica(image_values, 25)</span>
    219. 

  219. <span class="sd">    &gt;&gt;&gt; reconstructed_image.shape</span>
    220. 

  220. <span class="sd">    (200, 200)</span>
    221. 

  221. <span class="sd">    &quot;&quot;&quot;</span>
    222. 

  222. 

  223.     <span class="n">lab_img</span> <span class="o">=</span> <span class="n">transform</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>
    224. 

  224.     <span class="n">lab_img</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">lab_img</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
    225. 

  225. 

  226.     <span class="n">ica</span> <span class="o">=</span> <span class="n">FastICA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
    227. 

  227.     <span class="c1"># run ICA on image</span>
    228. 

  228.     <span class="n">ica</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">lab_img</span><span class="p">)</span>
    229. 

  229.     <span class="c1"># reconstruct image with independent components</span>
    230. 

  230.     <span class="n">image_ica</span> <span class="o">=</span> <span class="n">ica</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">lab_img</span><span class="p">)</span>
    231. 

  231.     <span class="n">restored_image</span> <span class="o">=</span> <span class="n">ica</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">image_ica</span><span class="p">)</span>
    232. 

  232. 

  233.     <span class="k">return</span> <span class="n">restored_image</span></div>
    234. 

  234. 

  235. 

  236. <div class="viewcode-block" id="ipca"><a class="viewcode-back" href="../../../ipfml/ipfml.processing.reconstruction.html#ipfml.processing.reconstruction.ipca">[docs]</a><span class="k">def</span> <span class="nf">ipca</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">components</span><span class="p">,</span> <span class="n">_batch_size</span><span class="o">=</span><span class="mi">25</span><span class="p">):</span>
    237. 

  237.     <span class="sd">&quot;&quot;&quot;Reconstruct an image from IPCA compression using specific number of components to use and batch size</span>
    238. 

  238. 

  239. <span class="sd">    Args:</span>
    240. 

  240. <span class="sd">        image: PIL Image, Numpy array or path of 3D image</span>
    241. 

  241. <span class="sd">        components: Number of components used for reconstruction</span>
    242. 

  242. <span class="sd">        batch_size: Batch size used for learn (default 25)</span>
    243. 

  243. 

  244. <span class="sd">    Returns:</span>
    245. 

  245. <span class="sd">        Reconstructed image</span>
    246. 

  246. 

  247. <span class="sd">    Example:</span>
    248. 

  248. 

  249. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    250. 

  250. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    251. 

  251. <span class="sd">    &gt;&gt;&gt; from ipfml.processing import reconstruction</span>
    252. 

  252. <span class="sd">    &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
    253. 

  253. <span class="sd">    &gt;&gt;&gt; reconstructed_image = reconstruction.ipca(image_values, 20)</span>
    254. 

  254. <span class="sd">    &gt;&gt;&gt; reconstructed_image.shape</span>
    255. 

  255. <span class="sd">    (200, 200)</span>
    256. 

  256. <span class="sd">    &quot;&quot;&quot;</span>
    257. 

  257.     <span class="n">lab_img</span> <span class="o">=</span> <span class="n">transform</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>
    258. 

  258.     <span class="n">lab_img</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">lab_img</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
    259. 

  259. 

  260.     <span class="n">transformer</span> <span class="o">=</span> <span class="n">IncrementalPCA</span><span class="p">(</span>
    261. 

  261.         <span class="n">n_components</span><span class="o">=</span><span class="n">components</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">_batch_size</span><span class="p">)</span>
    262. 

  262. 

  263.     <span class="n">transformed_image</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">lab_img</span><span class="p">)</span>
    264. 

  264.     <span class="n">restored_image</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">transformed_image</span><span class="p">)</span>
    265. 

  265. 

  266.     <span class="k">return</span> <span class="n">restored_image</span></div>
    267. 

  267. </pre></div>
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  318. </html>
    319.