Accueil Explorer Aide
Connexion
Prise3D
/
ipfml
2
0
Fork 0
Fichiers Tickets 0 Pull Requests 0 Wiki
Aborescence: 76b254f539
Branches Tags
ipfml
/
docs
/
build
/
html
/
_modules
/
ipfml
/
processing.html

processing.html 46 KB
Historique Raw

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681 
  1. 

  2. 

  3. <!DOCTYPE html>
    4. 

  4. <!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
    5. 

  5. <!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
    6. 

  6. <head>
    7. 

  7.   <meta charset="utf-8">
    8. 

  8.   
    9. 

  9.   <meta name="viewport" content="width=device-width, initial-scale=1.0">
    10. 

  10.   
    11. 

  11.   <title>ipfml.processing &mdash; IPFML v0.3.7 documentation</title>
    12. 

  12.   
    13. 

  13. 

  14.   
    15. 

  15.   
    16. 

  16.   
    17. 

  17.   
    18. 

  18. 

  19.   
    20. 

  20. 

  21.   
    22. 

  22.   
    23. 

  23.     
    24. 

  24. 

  25.   
    26. 

  26. 

  27.   <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
    28. 

  28.   <link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
    29. 

  29.     <link rel="index" title="Index" href="../../genindex.html" />
    30. 

  30.     <link rel="search" title="Search" href="../../search.html" /> 
    31. 

  31. 

  32.   
    33. 

  33.   <script src="../../_static/js/modernizr.min.js"></script>
    34. 

  34. 

  35. </head>
    36. 

  36. 

  37. <body class="wy-body-for-nav">
    38. 

  38. 

  39.    
    40. 

  40.   <div class="wy-grid-for-nav">
    41. 

  41. 

  42.     
    43. 

  43.     <nav data-toggle="wy-nav-shift" class="wy-nav-side">
    44. 

  44.       <div class="wy-side-scroll">
    45. 

  45.         <div class="wy-side-nav-search">
    46. 

  46.           
    47. 

  47. 

  48.           
    49. 

  49.             <a href="../../index.html" class="icon icon-home"> IPFML
    50. 

  50.           
    51. 

  51. 

  52.           
    53. 

  53.           </a>
    54. 

  54. 

  55.           
    56. 

  56.             
    57. 

  57.             
    58. 

  58.               <div class="version">
    59. 

  59.                 0.3.7
    60. 

  60.               </div>
    61. 

  61.             
    62. 

  62.           
    63. 

  63. 

  64.           
    65. 

  65. <div role="search">
    66. 

  66.   <form id="rtd-search-form" class="wy-form" action="../../search.html" method="get">
    67. 

  67.     <input type="text" name="q" placeholder="Search docs" />
    68. 

  68.     <input type="hidden" name="check_keywords" value="yes" />
    69. 

  69.     <input type="hidden" name="area" value="default" />
    70. 

  70.   </form>
    71. 

  71. </div>
    72. 

  72. 

  73.           
    74. 

  74.         </div>
    75. 

  75. 

  76.         <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
    77. 

  77.           
    78. 

  78.             
    79. 

  79.             
    80. 

  80.               
    81. 

  81.             
    82. 

  82.             
    83. 

  83.               <p class="caption"><span class="caption-text">Contents:</span></p>
    84. 

  84. <ul>
    85. 

  85. <li class="toctree-l1"><a class="reference internal" href="../../description.html">Description</a></li>
    86. 

  86. <li class="toctree-l1"><a class="reference internal" href="../../ipfml.html">Documentation</a></li>
    87. 

  87. <li class="toctree-l1"><a class="reference internal" href="../../examples.html">Examples</a></li>
    88. 

  88. <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing</a></li>
    89. 

  89. </ul>
    90. 

  90. 

  91.             
    92. 

  92.           
    93. 

  93.         </div>
    94. 

  94.       </div>
    95. 

  95.     </nav>
    96. 

  96. 

  97.     <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
    98. 

  98. 

  99.       
    100. 

  100.       <nav class="wy-nav-top" aria-label="top navigation">
    101. 

  101.         
    102. 

  102.           <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
    103. 

  103.           <a href="../../index.html">IPFML</a>
    104. 

  104.         
    105. 

  105.       </nav>
    106. 

  106. 

  107. 

  108.       <div class="wy-nav-content">
    109. 

  109.         
    110. 

  110.         <div class="rst-content">
    111. 

  111.         
    112. 

  112.           
    113. 

  113. 

  114. 

  115. 

  116. 

  117. 

  118. 

  119. 

  120. 

  121. 

  122. 

  123. 

  124. 

  125. 

  126. 

  127. 

  128. <div role="navigation" aria-label="breadcrumbs navigation">
    129. 

  129. 

  130.   <ul class="wy-breadcrumbs">
    131. 

  131.     
    132. 

  132.       <li><a href="../../index.html">Docs</a> &raquo;</li>
    133. 

  133.         
    134. 

  134.           <li><a href="../index.html">Module code</a> &raquo;</li>
    135. 

  135.         
    136. 

  136.       <li>ipfml.processing</li>
    137. 

  137.     
    138. 

  138.     
    139. 

  139.       <li class="wy-breadcrumbs-aside">
    140. 

  140.         
    141. 

  141.       </li>
    142. 

  142.     
    143. 

  143.   </ul>
    144. 

  144. 

  145.   
    146. 

  146.   <hr/>
    147. 

  147. </div>
    148. 

  148.           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
    149. 

  149.            <div itemprop="articleBody">
    150. 

  150.             
    151. 

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

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

  153. <span class="sd">Functions to quickly extract reduced information from image</span>
    154. 

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

  155. 

  156. <span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
    157. 

  157. <span class="kn">import</span> <span class="nn">random</span>
    158. 

  158. 

  159. <span class="kn">import</span> <span class="nn">cv2</span>
    160. 

  160. <span class="kn">from</span> <span class="nn">skimage</span> <span class="k">import</span> <span class="n">transform</span><span class="p">,</span> <span class="n">color</span>
    161. 

  161. <span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">signal</span>
    162. 

  162. 

  163. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
    164. 

  164. <span class="kn">import</span> <span class="nn">ipfml.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
    165. 

  165. <span class="kn">from</span> <span class="nn">ipfml</span> <span class="k">import</span> <span class="n">exceptions</span>
    166. 

  166. 

  167. <span class="kn">import</span> <span class="nn">os</span>
    168. 

  168. 

  169. 

  170. <div class="viewcode-block" id="get_LAB_L_SVD"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    171. 

  171.     <span class="sd">&quot;&quot;&quot;Returns Singular values from LAB L Image information</span>
    172. 

  172. 

  173. <span class="sd">    Args:</span>
    174. 

  174. <span class="sd">        image: PIL Image or Numpy array</span>
    175. 

  175. 

  176. <span class="sd">    Returns:</span>
    177. 

  177. <span class="sd">        U, s, V information obtained from SVD compression using Lab</span>
    178. 

  178. 

  179. <span class="sd">    Example:</span>
    180. 

  180. 

  181. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    182. 

  182. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    183. 

  183. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    184. 

  184. <span class="sd">    &gt;&gt;&gt; U, s, V = processing.get_LAB_L_SVD(img)</span>
    185. 

  185. <span class="sd">    &gt;&gt;&gt; U.shape</span>
    186. 

  186. <span class="sd">    (200, 200)</span>
    187. 

  187. <span class="sd">    &gt;&gt;&gt; len(s)</span>
    188. 

  188. <span class="sd">    200</span>
    189. 

  189. <span class="sd">    &gt;&gt;&gt; V.shape</span>
    190. 

  190. <span class="sd">    (200, 200)</span>
    191. 

  191. <span class="sd">    &quot;&quot;&quot;</span>
    192. 

  192.     <span class="n">L</span> <span class="o">=</span> <span class="n">metrics</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>
    193. 

  193.     <span class="k">return</span> <span class="n">metrics</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>
    194. 

  194. 

  195. 

  196. <div class="viewcode-block" id="get_LAB_L_SVD_s"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    197. 

  197.     <span class="sd">&quot;&quot;&quot;Returns s (Singular values) SVD from L of LAB Image information</span>
    198. 

  198. 

  199. <span class="sd">    Args:</span>
    200. 

  200. <span class="sd">        image: PIL Image or Numpy array</span>
    201. 

  201. 

  202. <span class="sd">    Returns:</span>
    203. 

  203. <span class="sd">        vector of singular values</span>
    204. 

  204. 

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

  206. 

  207. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    208. 

  208. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    209. 

  209. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    210. 

  210. <span class="sd">    &gt;&gt;&gt; s = processing.get_LAB_L_SVD_s(img)</span>
    211. 

  211. <span class="sd">    &gt;&gt;&gt; len(s)</span>
    212. 

  212. <span class="sd">    200</span>
    213. 

  213. <span class="sd">    &quot;&quot;&quot;</span>
    214. 

  214.     <span class="n">L</span> <span class="o">=</span> <span class="n">metrics</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>
    215. 

  215.     <span class="k">return</span> <span class="n">metrics</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>
    216. 

  216. 

  217. 

  218. <div class="viewcode-block" id="get_LAB_L_SVD_U"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    219. 

  219.     <span class="sd">&quot;&quot;&quot;Returns U SVD from L of LAB Image information</span>
    220. 

  220. 

  221. <span class="sd">    Args:</span>
    222. 

  222. <span class="sd">        image: PIL Image or Numpy array</span>
    223. 

  223. 

  224. <span class="sd">    Returns:</span>
    225. 

  225. <span class="sd">        U matrix of SVD compression</span>
    226. 

  226. 

  227. <span class="sd">    Example:</span>
    228. 

  228. 

  229. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    230. 

  230. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    231. 

  231. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    232. 

  232. <span class="sd">    &gt;&gt;&gt; U = processing.get_LAB_L_SVD_U(img)</span>
    233. 

  233. <span class="sd">    &gt;&gt;&gt; U.shape</span>
    234. 

  234. <span class="sd">    (200, 200)</span>
    235. 

  235. <span class="sd">    &quot;&quot;&quot;</span>
    236. 

  236.     <span class="n">L</span> <span class="o">=</span> <span class="n">metrics</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>
    237. 

  237.     <span class="k">return</span> <span class="n">metrics</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>
    238. 

  238. 

  239. 

  240. <div class="viewcode-block" id="get_LAB_L_SVD_V"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    241. 

  241.     <span class="sd">&quot;&quot;&quot;Returns V SVD from L of LAB Image information</span>
    242. 

  242. 

  243. <span class="sd">    Args:</span>
    244. 

  244. <span class="sd">        image: PIL Image or Numpy array</span>
    245. 

  245. 

  246. <span class="sd">    Returns:</span>
    247. 

  247. <span class="sd">        V matrix of SVD compression</span>
    248. 

  248. 

  249. <span class="sd">    Example:</span>
    250. 

  250. 

  251. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    252. 

  252. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    253. 

  253. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    254. 

  254. <span class="sd">    &gt;&gt;&gt; V = processing.get_LAB_L_SVD_V(img)</span>
    255. 

  255. <span class="sd">    &gt;&gt;&gt; V.shape</span>
    256. 

  256. <span class="sd">    (200, 200)</span>
    257. 

  257. <span class="sd">    &quot;&quot;&quot;</span>
    258. 

  258. 

  259.     <span class="n">L</span> <span class="o">=</span> <span class="n">metrics</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>
    260. 

  260.     <span class="k">return</span> <span class="n">metrics</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>
    261. 

  261. 

  262. 

  263. <div class="viewcode-block" id="rgb_to_mscn"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    264. 

  264.     <span class="sd">&quot;&quot;&quot;Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)</span>
    265. 

  265. 

  266. <span class="sd">    Args:</span>
    267. 

  267. <span class="sd">        image: 3D RGB image Numpy array or PIL RGB image</span>
    268. 

  268. 

  269. <span class="sd">    Returns:</span>
    270. 

  270. <span class="sd">        2D Numpy array with MSCN information</span>
    271. 

  271. 

  272. <span class="sd">    Example:</span>
    273. 

  273. 

  274. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    275. 

  275. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    276. 

  276. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    277. 

  277. <span class="sd">    &gt;&gt;&gt; img_mscn = processing.rgb_to_mscn(img)</span>
    278. 

  278. <span class="sd">    &gt;&gt;&gt; img_mscn.shape</span>
    279. 

  279. <span class="sd">    (200, 200)</span>
    280. 

  280. <span class="sd">    &quot;&quot;&quot;</span>
    281. 

  281. 

  282.     <span class="c1"># check if PIL image or not</span>
    283. 

  283.     <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>
    284. 

  284. 

  285.     <span class="c1"># convert rgb image to gray</span>
    286. 

  286.     <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">&#39;uint8&#39;</span><span class="p">)</span>
    287. 

  287. 

  288.     <span class="k">return</span> <span class="n">metrics</span><span class="o">.</span><span class="n">gray_to_mscn</span><span class="p">(</span><span class="n">im</span><span class="p">)</span></div>
    289. 

  289. 

  290. 

  291. <div class="viewcode-block" id="rgb_to_grey_low_bits"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    292. 

  292.     <span class="sd">&quot;&quot;&quot;Convert RGB Image into grey image using only 4 low bits values</span>
    293. 

  293. 

  294. <span class="sd">    Args:</span>
    295. 

  295. <span class="sd">        image: 3D RGB image Numpy array or PIL RGB image</span>
    296. 

  296. <span class="sd">        nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
    297. 

  297. 

  298. <span class="sd">    Returns:</span>
    299. 

  299. <span class="sd">        2D Numpy array with low bits information kept</span>
    300. 

  300. 

  301. <span class="sd">    Example:</span>
    302. 

  302. 

  303. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    304. 

  304. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    305. 

  305. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    306. 

  306. <span class="sd">    &gt;&gt;&gt; low_bits_grey_img = processing.rgb_to_grey_low_bits(img, 5)</span>
    307. 

  307. <span class="sd">    &gt;&gt;&gt; low_bits_grey_img.shape</span>
    308. 

  308. <span class="sd">    (200, 200)</span>
    309. 

  309. <span class="sd">    &quot;&quot;&quot;</span>
    310. 

  310. 

  311.     <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>
    312. 

  312.     <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">&#39;uint8&#39;</span><span class="p">)</span>
    313. 

  313. 

  314.     <span class="k">return</span> <span class="n">metrics</span><span class="o">.</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>
    315. 

  315. 

  316. 

  317. <div class="viewcode-block" id="rgb_to_LAB_L_low_bits"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    318. 

  318.     <span class="sd">&quot;&quot;&quot;Convert RGB Image into Lab L channel image using only 4 low bits values</span>
    319. 

  319. 

  320. <span class="sd">    Args:</span>
    321. 

  321. <span class="sd">        image: 3D RGB image Numpy array or PIL RGB image</span>
    322. 

  322. <span class="sd">        nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
    323. 

  323. 

  324. <span class="sd">    Returns:</span>
    325. 

  325. <span class="sd">        2D Numpy array with low bits information kept</span>
    326. 

  326. 

  327. <span class="sd">    Example:</span>
    328. 

  328. 

  329. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    330. 

  330. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    331. 

  331. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    332. 

  332. <span class="sd">    &gt;&gt;&gt; low_bits_Lab_l_img = processing.rgb_to_LAB_L_low_bits(img, 5)</span>
    333. 

  333. <span class="sd">    &gt;&gt;&gt; low_bits_Lab_l_img.shape</span>
    334. 

  334. <span class="sd">    (200, 200)</span>
    335. 

  335. <span class="sd">    &quot;&quot;&quot;</span>
    336. 

  336. 

  337.     <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">metrics</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="s1">&#39;uint8&#39;</span><span class="p">)</span>
    338. 

  338. 

  339.     <span class="k">return</span> <span class="n">metrics</span><span class="o">.</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>
    340. 

  340. 

  341. 

  342. <div class="viewcode-block" id="rgb_to_LAB_L_bits"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    343. 

  343.     <span class="sd">&quot;&quot;&quot;Returns only bits from LAB L canal specified into the interval</span>
    344. 

  344. 

  345. <span class="sd">    Args:</span>
    346. 

  346. <span class="sd">        image: image to convert using this interval of bits value to keep</span>
    347. 

  347. <span class="sd">        interval: (begin, end) of bits values</span>
    348. 

  348. 

  349. <span class="sd">    Returns:</span>
    350. 

  350. <span class="sd">        2D Numpy array with reduced values</span>
    351. 

  351. 

  352. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    353. 

  353. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    354. 

  354. <span class="sd">    &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
    355. 

  355. <span class="sd">    &gt;&gt;&gt; bits_Lab_l_img = processing.rgb_to_LAB_L_bits(img, (2, 6))</span>
    356. 

  356. <span class="sd">    &gt;&gt;&gt; bits_Lab_l_img.shape</span>
    357. 

  357. <span class="sd">    (200, 200)</span>
    358. 

  358. <span class="sd">    &quot;&quot;&quot;</span>
    359. 

  359. 

  360.     <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">metrics</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="s1">&#39;uint8&#39;</span><span class="p">)</span>
    361. 

  361. 

  362.     <span class="k">return</span> <span class="n">metrics</span><span class="o">.</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>
    363. 

  363. 

  364. 

  365. <div class="viewcode-block" id="divide_in_blocks"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.divide_in_blocks">[docs]</a><span class="k">def</span> <span class="nf">divide_in_blocks</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">pil</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    366. 

  366.     <span class="sd">&#39;&#39;&#39;Divide image into equal size blocks</span>
    367. 

  367. 

  368. <span class="sd">    Args:</span>
    369. 

  369. <span class="sd">        image: PIL Image or Numpy array</span>
    370. 

  370. <span class="sd">        block: tuple (width, height) representing the size of each dimension of the block</span>
    371. 

  371. <span class="sd">        pil: block type returned as PIL Image (default True)</span>
    372. 

  372. 

  373. <span class="sd">    Returns:</span>
    374. 

  374. <span class="sd">        list containing all 2D Numpy blocks (in RGB or not)</span>
    375. 

  375. 

  376. <span class="sd">    Raises:</span>
    377. 

  377. <span class="sd">        ValueError: If `image_width` or `image_height` are not compatible to produce correct block sizes</span>
    378. 

  378. 

  379. <span class="sd">    Example:</span>
    380. 

  380. 

  381. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    382. 

  382. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    383. 

  383. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    384. 

  384. <span class="sd">    &gt;&gt;&gt; from ipfml import metrics</span>
    385. 

  385. <span class="sd">    &gt;&gt;&gt; image_values = np.random.randint(255, size=(800, 800, 3))</span>
    386. 

  386. <span class="sd">    &gt;&gt;&gt; blocks = divide_in_blocks(image_values, (20, 20))</span>
    387. 

  387. <span class="sd">    &gt;&gt;&gt; len(blocks)</span>
    388. 

  388. <span class="sd">    1600</span>
    389. 

  389. <span class="sd">    &gt;&gt;&gt; blocks[0].width</span>
    390. 

  390. <span class="sd">    20</span>
    391. 

  391. <span class="sd">    &gt;&gt;&gt; blocks[0].height</span>
    392. 

  392. <span class="sd">    20</span>
    393. 

  393. <span class="sd">    &gt;&gt;&gt; img_l = Image.open(&#39;./images/test_img.png&#39;)</span>
    394. 

  394. <span class="sd">    &gt;&gt;&gt; L = metrics.get_LAB_L(img_l)</span>
    395. 

  395. <span class="sd">    &gt;&gt;&gt; blocks_L = divide_in_blocks(L, (100, 100))</span>
    396. 

  396. <span class="sd">    &gt;&gt;&gt; len(blocks_L)</span>
    397. 

  397. <span class="sd">    4</span>
    398. 

  398. <span class="sd">    &gt;&gt;&gt; blocks_L[0].width</span>
    399. 

  399. <span class="sd">    100</span>
    400. 

  400. <span class="sd">    &#39;&#39;&#39;</span>
    401. 

  401. 

  402.     <span class="n">blocks</span> <span class="o">=</span> <span class="p">[]</span>
    403. 

  403.     <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;RGB&#39;</span>
    404. 

  404. 

  405.     <span class="c1"># convert in Numpy array</span>
    406. 

  406.     <span class="n">image_array</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>
    407. 

  407. 

  408.     <span class="c1"># check dimension of input image</span>
    409. 

  409.     <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>
    410. 

  410.         <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;L&#39;</span>
    411. 

  411.         <span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
    412. 

  412.     <span class="k">else</span><span class="p">:</span>
    413. 

  413.         <span class="n">image_width</span><span class="p">,</span> <span class="n">image_height</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
    414. 

  414. 

  415.     <span class="c1"># check size compatibility</span>
    416. 

  416.     <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">block_size</span>
    417. 

  417. 

  418.     <span class="k">if</span> <span class="p">(</span><span class="n">image_width</span> <span class="o">%</span> <span class="n">width</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
    419. 

  419.         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Width size issue, block size not compatible&quot;</span><span class="p">)</span>
    420. 

  420. 

  421.     <span class="k">if</span> <span class="p">(</span><span class="n">image_height</span> <span class="o">%</span> <span class="n">height</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
    422. 

  422.         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Height size issue, block size not compatible&quot;</span><span class="p">)</span>
    423. 

  423. 

  424.     <span class="n">nb_block_width</span> <span class="o">=</span> <span class="n">image_width</span> <span class="o">/</span> <span class="n">width</span>
    425. 

  425.     <span class="n">nb_block_height</span> <span class="o">=</span> <span class="n">image_height</span> <span class="o">/</span> <span class="n">height</span>
    426. 

  426. 

  427.     <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="nb">int</span><span class="p">(</span><span class="n">nb_block_width</span><span class="p">)):</span>
    428. 

  428. 

  429.         <span class="n">begin_x</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">width</span>
    430. 

  430. 

  431.         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">nb_block_height</span><span class="p">)):</span>
    432. 

  432. 

  433.             <span class="n">begin_y</span> <span class="o">=</span> <span class="n">j</span> <span class="o">*</span> <span class="n">height</span>
    434. 

  434. 

  435.             <span class="c1"># getting sub block information</span>
    436. 

  436.             <span class="n">current_block</span> <span class="o">=</span> <span class="n">image_array</span><span class="p">[</span><span class="n">begin_x</span><span class="p">:(</span><span class="n">begin_x</span> <span class="o">+</span> <span class="n">width</span><span class="p">),</span> <span class="n">begin_y</span><span class="p">:(</span>
    437. 

  437.                 <span class="n">begin_y</span> <span class="o">+</span> <span class="n">height</span><span class="p">)]</span>
    438. 

  438. 

  439.             <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
    440. 

  440.                 <span class="n">blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
    441. 

  441.                     <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">current_block</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;uint8&#39;</span><span class="p">),</span> <span class="n">mode</span><span class="p">))</span>
    442. 

  442.             <span class="k">else</span><span class="p">:</span>
    443. 

  443.                 <span class="n">blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">current_block</span><span class="p">)</span>
    444. 

  444. 

  445.     <span class="k">return</span> <span class="n">blocks</span></div>
    446. 

  446. 

  447. 

  448. <div class="viewcode-block" id="fusion_images"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.fusion_images">[docs]</a><span class="k">def</span> <span class="nf">fusion_images</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">pil</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    449. 

  449.     <span class="sd">&#39;&#39;&#39;Fusion array of images into single image</span>
    450. 

  450. 

  451. <span class="sd">    Args:</span>
    452. 

  452. <span class="sd">        images: array of images (PIL Image or Numpy array)</span>
    453. 

  453. <span class="sd">        pil: block type returned as PIL Image (default True)</span>
    454. 

  454. 

  455. <span class="sd">    Returns:</span>
    456. 

  456. <span class="sd">        merged image from array of images</span>
    457. 

  457. 

  458. <span class="sd">    Raises:</span>
    459. 

  459. <span class="sd">        ValueError: if `images` is not an array or is empty</span>
    460. 

  460. <span class="sd">        NumpyShapeComparisonException: if `images` array contains images with different shapes</span>
    461. 

  461. 

  462. <span class="sd">    Example:</span>
    463. 

  463. 

  464. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    465. 

  465. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    466. 

  466. <span class="sd">    &gt;&gt;&gt; image_values_1 = np.random.randint(255, size=(800, 800, 3))</span>
    467. 

  467. <span class="sd">    &gt;&gt;&gt; image_values_2 = np.random.randint(255, size=(800, 800, 3))</span>
    468. 

  468. <span class="sd">    &gt;&gt;&gt; merged_image = processing.fusion_images([image_values_1, image_values_2], pil=False)</span>
    469. 

  469. <span class="sd">    &gt;&gt;&gt; merged_image.shape</span>
    470. 

  470. <span class="sd">    (800, 800, 3)</span>
    471. 

  471. <span class="sd">    &#39;&#39;&#39;</span>
    472. 

  472. 

  473.     <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;RGB&#39;</span>
    474. 

  474.     <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span>
    475. 

  475. 

  476.     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
    477. 

  477.         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Empty array of images provided...&#39;</span><span class="p">)</span>
    478. 

  478. 

  479.     <span class="c1"># convert image in numpy array (perhaps not necessary)</span>
    480. 

  480.     <span class="n">images</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">]</span>
    481. 

  481.     <span class="n">image_array</span> <span class="o">=</span> <span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
    482. 

  482. 

  483.     <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>
    484. 

  484.         <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;L&#39;</span>
    485. 

  485.         <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
    486. 

  486.     <span class="k">else</span><span class="p">:</span>
    487. 

  487.         <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">dim</span> <span class="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
    488. 

  488. 

  489.     <span class="c1"># raise exception if all images do not have same shape</span>
    490. 

  490.     <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">image_array</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">a</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">images</span><span class="p">])</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
    491. 

  491.         <span class="k">raise</span> <span class="n">NumpyShapeComparisonException</span><span class="p">()</span>
    492. 

  492. 

  493.     <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    494. 

  494.         <span class="n">image_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">])</span>
    495. 

  495.     <span class="k">else</span><span class="p">:</span>
    496. 

  496.         <span class="n">image_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">dim</span><span class="p">])</span>
    497. 

  497. 

  498.     <span class="n">nb_images</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
    499. 

  499. 

  500.     <span class="c1"># construction of mean image from rotation</span>
    501. 

  501.     <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">width</span><span class="p">):</span>
    502. 

  502.         <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">height</span><span class="p">):</span>
    503. 

  503. 

  504.             <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
    505. 

  505.                 <span class="n">grey_value</span> <span class="o">=</span> <span class="mi">0</span>
    506. 

  506. 

  507.                 <span class="c1"># for each image we merge pixel values</span>
    508. 

  508.                 <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span>
    509. 

  509.                     <span class="n">grey_value</span> <span class="o">+=</span> <span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span>
    510. 

  510. 

  511.                 <span class="n">image_mean</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">grey_value</span> <span class="o">/</span> <span class="n">nb_images</span>
    512. 

  512. 

  513.             <span class="k">else</span><span class="p">:</span>
    514. 

  514.                 <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dim</span><span class="p">):</span>
    515. 

  515.                     <span class="n">canal_value</span> <span class="o">=</span> <span class="mi">0</span>
    516. 

  516. 

  517.                     <span class="c1"># for each image we merge pixel values</span>
    518. 

  518.                     <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">images</span><span class="p">:</span>
    519. 

  519.                         <span class="n">canal_value</span> <span class="o">+=</span> <span class="n">img</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">][</span><span class="n">k</span><span class="p">]</span>
    520. 

  520. 

  521.                     <span class="n">image_mean</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">][</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">canal_value</span> <span class="o">/</span> <span class="n">nb_images</span>
    522. 

  522. 

  523.     <span class="n">image_mean</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_mean</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
    524. 

  524. 

  525.     <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
    526. 

  526.         <span class="k">return</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">image_mean</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
    527. 

  527.     <span class="k">else</span><span class="p">:</span>
    528. 

  528.         <span class="k">return</span> <span class="n">image_mean</span></div>
    529. 

  529. 

  530. 

  531. <div class="viewcode-block" id="rotate_image"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.rotate_image">[docs]</a><span class="k">def</span> <span class="nf">rotate_image</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">angle</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">pil</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    532. 

  532.     <span class="sd">&quot;&quot;&quot;Rotate image using specific angle</span>
    533. 

  533. 

  534. <span class="sd">    Args:</span>
    535. 

  535. <span class="sd">        image: PIL Image or Numpy array</span>
    536. 

  536. <span class="sd">        angle: Angle value of the rotation</span>
    537. 

  537. <span class="sd">        pil: block type returned as PIL Image (default True)</span>
    538. 

  538. 

  539. <span class="sd">    Returns:</span>
    540. 

  540. <span class="sd">        Image with rotation applied</span>
    541. 

  541. 

  542. <span class="sd">    Example:</span>
    543. 

  543. 

  544. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    545. 

  545. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    546. 

  546. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    547. 

  547. <span class="sd">    &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
    548. 

  548. <span class="sd">    &gt;&gt;&gt; rotated_image = processing.rotate_image(image_values, 90, pil=False)</span>
    549. 

  549. <span class="sd">    &gt;&gt;&gt; rotated_image.shape</span>
    550. 

  550. <span class="sd">    (200, 200, 3)</span>
    551. 

  551. <span class="sd">    &quot;&quot;&quot;</span>
    552. 

  552. 

  553.     <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;RGB&#39;</span>
    554. 

  554.     <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>
    555. 

  555. 

  556.     <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>
    557. 

  557.         <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;L&#39;</span>
    558. 

  558. 

  559.     <span class="n">rotated_image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
    560. 

  560.         <span class="n">transform</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">image_array</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span><span class="p">,</span> <span class="s1">&#39;uint8&#39;</span><span class="p">)</span>
    561. 

  561. 

  562.     <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
    563. 

  563.         <span class="k">return</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">rotated_image</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
    564. 

  564.     <span class="k">else</span><span class="p">:</span>
    565. 

  565.         <span class="k">return</span> <span class="n">rotated_image</span></div>
    566. 

  566. 

  567. 

  568. <div class="viewcode-block" id="get_mscn_coefficients"><a class="viewcode-back" href="../../ipfml/ipfml.processing.html#ipfml.processing.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>
    569. 

  569.     <span class="sd">&quot;&quot;&quot;Compute the Mean Substracted Constrast Normalized coefficients of an image</span>
    570. 

  570. 

  571. <span class="sd">    Args:</span>
    572. 

  572. <span class="sd">        image: PIL Image, Numpy array or path of image</span>
    573. 

  573. 

  574. <span class="sd">    Returns:</span>
    575. 

  575. <span class="sd">        MSCN coefficients</span>
    576. 

  576. 

  577. <span class="sd">    Raises:</span>
    578. 

  578. <span class="sd">        FileNotFoundError: If `image` is set as str path and image was not found</span>
    579. 

  579. <span class="sd">        ValueError: If `image` numpy shape are not correct</span>
    580. 

  580. 

  581. <span class="sd">    Example:</span>
    582. 

  582. 

  583. <span class="sd">    &gt;&gt;&gt; from PIL import Image</span>
    584. 

  584. <span class="sd">    &gt;&gt;&gt; import numpy as np</span>
    585. 

  585. <span class="sd">    &gt;&gt;&gt; from ipfml import processing</span>
    586. 

  586. <span class="sd">    &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
    587. 

  587. <span class="sd">    &gt;&gt;&gt; mscn_coefficients = processing.get_mscn_coefficients(image_values)</span>
    588. 

  588. <span class="sd">    &gt;&gt;&gt; mscn_coefficients.shape</span>
    589. 

  589. <span class="sd">    (200, 200)</span>
    590. 

  590. <span class="sd">    &quot;&quot;&quot;</span>
    591. 

  591. 

  592.     <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>
    593. 

  593.         <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>
    594. 

  594.             <span class="c1"># open image directly as grey level image</span>
    595. 

  595.             <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>
    596. 

  596.         <span class="k">else</span><span class="p">:</span>
    597. 

  597.             <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">&#39;Image not found in your system&#39;</span><span class="p">)</span>
    598. 

  598. 

  599.     <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>
    600. 

  600.         <span class="c1"># convert if necessary to grey level numpy array</span>
    601. 

  601.         <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>
    602. 

  602.             <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
    603. 

  603.         <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>
    604. 

  604.             <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>
    605. 

  605.         <span class="k">else</span><span class="p">:</span>
    606. 

  606.             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incorrect image shape&#39;</span><span class="p">)</span>
    607. 

  607.     <span class="k">else</span><span class="p">:</span>
    608. 

  608.         <span class="c1"># if PIL Image</span>
    609. 

  609.         <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>
    610. 

  610. 

  611.         <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>
    612. 

  612.             <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
    613. 

  613.         <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>
    614. 

  614.             <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>
    615. 

  615.         <span class="k">else</span><span class="p">:</span>
    616. 

  616.             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Incorrect image shape&#39;</span><span class="p">)</span>
    617. 

  617. 

  618.     <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>
    619. 

  619.     <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span> <span class="o">/</span> <span class="mf">255.0</span>
    620. 

  620. 

  621.     <span class="c1"># calculating MSCN coefficients</span>
    622. 

  622.     <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>
    623. 

  623.         <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>
    624. 

  624.     <span class="n">mu_sq</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">mu</span>
    625. 

  625.     <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>
    626. 

  626.         <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>
    627. 

  627.     <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>
    628. 

  628.     <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>
    629. 

  629.     <span class="k">return</span> <span class="n">structdis</span></div>
    630. 

  630. </pre></div>
    631. 

  631. 

  632.            </div>
    633. 

  633.            
    634. 

  634.           </div>
    635. 

  635.           <footer>
    636. 

  636.   
    637. 

  637. 

  638.   <hr/>
    639. 

  639. 

  640.   <div role="contentinfo">
    641. 

  641.     <p>
    642. 

  642.         &copy; Copyright 2019, Jérôme BUISINE
    643. 

  643. 

  644.     </p>
    645. 

  645.   </div>
    646. 

  646.   Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. 
    647. 

  647. 

  648. </footer>
    649. 

  649. 

  650.         </div>
    651. 

  651.       </div>
    652. 

  652. 

  653.     </section>
    654. 

  654. 

  655.   </div>
    656. 

  656.   
    657. 

  657. 

  658. 

  659.   
    660. 

  660. 

  661.     
    662. 

  662.     
    663. 

  663.       <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
    664. 

  664.         <script type="text/javascript" src="../../_static/jquery.js"></script>
    665. 

  665.         <script type="text/javascript" src="../../_static/underscore.js"></script>
    666. 

  666.         <script type="text/javascript" src="../../_static/doctools.js"></script>
    667. 

  667.         <script type="text/javascript" src="../../_static/language_data.js"></script>
    668. 

  668.     
    669. 

  669. 

  670.   
    671. 

  671. 

  672.   <script type="text/javascript" src="../../_static/js/theme.js"></script>
    673. 

  673. 

  674.   <script type="text/javascript">
    675. 

  675.       jQuery(function () {
    676. 

  676.           SphinxRtdTheme.Navigation.enable(true);
    677. 

  677.       });
    678. 

  678.   </script> 
    679. 

  679. 

  680. </body>
    681. 

  681. </html>
    682.