transform.html 48 KB

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  62. <h1>Source code for ipfml.processing.transform</h1><div class="highlight"><pre>
  63. <span></span><span class="sd">&quot;&quot;&quot;</span>
  64. <span class="sd">Functions which can be used to extract information from image or reduce it</span>
  65. <span class="sd">&quot;&quot;&quot;</span>
  66. <span class="c1"># main imports</span>
  67. <span class="kn">import</span> <span class="nn">os</span>
  68. <span class="kn">import</span> <span class="nn">random</span>
  69. <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
  70. <span class="c1"># image processing imports</span>
  71. <span class="kn">from</span> <span class="nn">numpy.linalg</span> <span class="k">import</span> <span class="n">svd</span>
  72. <span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">misc</span>
  73. <span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
  74. <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>
  75. <span class="kn">import</span> <span class="nn">cv2</span>
  76. <span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
  77. <span class="c1"># ipfml imports</span>
  78. <span class="kn">from</span> <span class="nn">ipfml.processing</span> <span class="k">import</span> <span class="n">compression</span>
  79. <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>
  80. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into Lab</span>
  81. <span class="sd"> Args:</span>
  82. <span class="sd"> image: image to convert</span>
  83. <span class="sd"> Returns:</span>
  84. <span class="sd"> Lab information</span>
  85. <span class="sd"> Usage:</span>
  86. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  87. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  88. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  89. <span class="sd"> &gt;&gt;&gt; Lab = transform.get_LAB(img)</span>
  90. <span class="sd"> &gt;&gt;&gt; Lab.shape</span>
  91. <span class="sd"> (200, 200, 3)</span>
  92. <span class="sd"> &quot;&quot;&quot;</span>
  93. <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>
  94. <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>
  95. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into Lab and returns L</span>
  96. <span class="sd"> Args:</span>
  97. <span class="sd"> image: image to convert</span>
  98. <span class="sd"> Returns:</span>
  99. <span class="sd"> The L chanel from Lab information</span>
  100. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  101. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  102. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  103. <span class="sd"> &gt;&gt;&gt; L = transform.get_LAB_L(img)</span>
  104. <span class="sd"> &gt;&gt;&gt; L.shape</span>
  105. <span class="sd"> (200, 200)</span>
  106. <span class="sd"> &quot;&quot;&quot;</span>
  107. <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>
  108. <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>
  109. <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>
  110. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into LAB and returns a</span>
  111. <span class="sd"> Args:</span>
  112. <span class="sd"> image: image to convert</span>
  113. <span class="sd"> Returns:</span>
  114. <span class="sd"> The a chanel from Lab information</span>
  115. <span class="sd"> Usage:</span>
  116. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  117. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  118. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  119. <span class="sd"> &gt;&gt;&gt; a = transform.get_LAB_a(img)</span>
  120. <span class="sd"> &gt;&gt;&gt; a.shape</span>
  121. <span class="sd"> (200, 200)</span>
  122. <span class="sd"> &quot;&quot;&quot;</span>
  123. <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>
  124. <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>
  125. <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>
  126. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into LAB and returns b</span>
  127. <span class="sd"> Args:</span>
  128. <span class="sd"> image: image to convert</span>
  129. <span class="sd"> Returns:</span>
  130. <span class="sd"> The b chanel from Lab information</span>
  131. <span class="sd"> Usage :</span>
  132. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  133. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  134. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  135. <span class="sd"> &gt;&gt;&gt; b = transform.get_LAB_b(img)</span>
  136. <span class="sd"> &gt;&gt;&gt; b.shape</span>
  137. <span class="sd"> (200, 200)</span>
  138. <span class="sd"> &quot;&quot;&quot;</span>
  139. <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>
  140. <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>
  141. <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>
  142. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into XYZ</span>
  143. <span class="sd"> Args:</span>
  144. <span class="sd"> image: image to convert</span>
  145. <span class="sd"> Returns:</span>
  146. <span class="sd"> XYZ information obtained from transformation</span>
  147. <span class="sd"> Usage:</span>
  148. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  149. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  150. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  151. <span class="sd"> &gt;&gt;&gt; transform.get_XYZ(img).shape</span>
  152. <span class="sd"> (200, 200, 3)</span>
  153. <span class="sd"> &quot;&quot;&quot;</span>
  154. <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>
  155. <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>
  156. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into XYZ and returns X</span>
  157. <span class="sd"> Args:</span>
  158. <span class="sd"> image: image to convert</span>
  159. <span class="sd"> Returns:</span>
  160. <span class="sd"> The X chanel from XYZ information</span>
  161. <span class="sd"> Usage:</span>
  162. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  163. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  164. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  165. <span class="sd"> &gt;&gt;&gt; x = transform.get_XYZ_X(img)</span>
  166. <span class="sd"> &gt;&gt;&gt; x.shape</span>
  167. <span class="sd"> (200, 200)</span>
  168. <span class="sd"> &quot;&quot;&quot;</span>
  169. <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>
  170. <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>
  171. <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>
  172. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into XYZ and returns Y</span>
  173. <span class="sd"> Args:</span>
  174. <span class="sd"> image: image to convert</span>
  175. <span class="sd"> Returns:</span>
  176. <span class="sd"> The Y chanel from XYZ information</span>
  177. <span class="sd"> Usage:</span>
  178. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  179. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  180. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  181. <span class="sd"> &gt;&gt;&gt; y = transform.get_XYZ_Y(img)</span>
  182. <span class="sd"> &gt;&gt;&gt; y.shape</span>
  183. <span class="sd"> (200, 200)</span>
  184. <span class="sd"> &quot;&quot;&quot;</span>
  185. <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>
  186. <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>
  187. <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>
  188. <span class="sd">&quot;&quot;&quot;Transforms RGB Image into XYZ and returns Z</span>
  189. <span class="sd"> Args:</span>
  190. <span class="sd"> image: image to convert</span>
  191. <span class="sd"> Returns:</span>
  192. <span class="sd"> The Z chanel from XYZ information</span>
  193. <span class="sd"> Raises:</span>
  194. <span class="sd"> ValueError: If `nb_bits` has unexpected value. `nb_bits` needs to be in interval [1, 8].</span>
  195. <span class="sd"> Usage:</span>
  196. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  197. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  198. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  199. <span class="sd"> &gt;&gt;&gt; z = transform.get_XYZ_Z(img)</span>
  200. <span class="sd"> &gt;&gt;&gt; z.shape</span>
  201. <span class="sd"> (200, 200)</span>
  202. <span class="sd"> &quot;&quot;&quot;</span>
  203. <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>
  204. <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>
  205. <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>
  206. <span class="sd">&quot;&quot;&quot;Returns Image or Numpy array with data information reduced using only low bits</span>
  207. <span class="sd"> Args:</span>
  208. <span class="sd"> image: image to convert</span>
  209. <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep</span>
  210. <span class="sd"> Returns:</span>
  211. <span class="sd"> Numpy array with reduced values</span>
  212. <span class="sd"> Usage:</span>
  213. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  214. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  215. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  216. <span class="sd"> &gt;&gt;&gt; low_bits_img = transform.get_low_bits_img(img, 5)</span>
  217. <span class="sd"> &gt;&gt;&gt; low_bits_img.shape</span>
  218. <span class="sd"> (200, 200, 3)</span>
  219. <span class="sd"> &quot;&quot;&quot;</span>
  220. <span class="k">if</span> <span class="n">nb_bits</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
  221. <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
  222. <span class="s2">&quot;unexpected value of number of bits to keep. @nb_bits needs to be positive and greater than 0.&quot;</span>
  223. <span class="p">)</span>
  224. <span class="k">if</span> <span class="n">nb_bits</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">:</span>
  225. <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
  226. <span class="s2">&quot;Unexpected value of number of bits to keep. @nb_bits needs to be in interval [1, 8].&quot;</span>
  227. <span class="p">)</span>
  228. <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>
  229. <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>
  230. <span class="k">return</span> <span class="n">img_arr</span> <span class="o">&amp;</span> <span class="n">bits_values</span></div>
  231. <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>
  232. <span class="sd">&quot;&quot;&quot;Returns only bits specified into the interval</span>
  233. <span class="sd"> Args:</span>
  234. <span class="sd"> image: image to convert using this interval of bits value to keep</span>
  235. <span class="sd"> interval: (begin, end) of bits values</span>
  236. <span class="sd"> Returns:</span>
  237. <span class="sd"> Numpy array with reduced values</span>
  238. <span class="sd"> Raises:</span>
  239. <span class="sd"> ValueError: If min value from interval is not &gt;= 1.</span>
  240. <span class="sd"> ValueError: If max value from interval is not &lt;= 8.</span>
  241. <span class="sd"> ValueError: If min value from interval &gt;= max value.</span>
  242. <span class="sd"> Usage:</span>
  243. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  244. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  245. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  246. <span class="sd"> &gt;&gt;&gt; bits_img = transform.get_bits_img(img, (2, 5))</span>
  247. <span class="sd"> &gt;&gt;&gt; bits_img.shape</span>
  248. <span class="sd"> (200, 200, 3)</span>
  249. <span class="sd"> &quot;&quot;&quot;</span>
  250. <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>
  251. <span class="n">begin</span><span class="p">,</span> <span class="n">end</span> <span class="o">=</span> <span class="n">interval</span>
  252. <span class="k">if</span> <span class="n">begin</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
  253. <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
  254. <span class="s2">&quot;Unexpected value of interval. Interval min value needs to be &gt;= 1.&quot;</span>
  255. <span class="p">)</span>
  256. <span class="k">if</span> <span class="n">end</span> <span class="o">&gt;</span> <span class="mi">8</span><span class="p">:</span>
  257. <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
  258. <span class="s2">&quot;Unexpected value of interval. Interval min value needs to be &lt;= 8.&quot;</span>
  259. <span class="p">)</span>
  260. <span class="k">if</span> <span class="n">begin</span> <span class="o">&gt;=</span> <span class="n">end</span><span class="p">:</span>
  261. <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unexpected interval values order.&quot;</span><span class="p">)</span>
  262. <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>
  263. <span class="k">return</span> <span class="n">img_arr</span> <span class="o">&amp;</span> <span class="n">bits_values</span></div>
  264. <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>
  265. <span class="sd">&quot;&quot;&quot;Convert Grayscale Image into Mean Subtracted Contrast Normalized (MSCN)</span>
  266. <span class="sd"> Args:</span>
  267. <span class="sd"> image: grayscale image</span>
  268. <span class="sd"> Returns:</span>
  269. <span class="sd"> MSCN matrix obtained from transformation</span>
  270. <span class="sd"> Usage:</span>
  271. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  272. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  273. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  274. <span class="sd"> &gt;&gt;&gt; img = transform.get_LAB_L(img)</span>
  275. <span class="sd"> &gt;&gt;&gt; img_mscn = transform.gray_to_mscn(img)</span>
  276. <span class="sd"> &gt;&gt;&gt; img_mscn.shape</span>
  277. <span class="sd"> (200, 200)</span>
  278. <span class="sd"> &quot;&quot;&quot;</span>
  279. <span class="n">s</span> <span class="o">=</span> <span class="mi">7</span> <span class="o">/</span> <span class="mi">6</span>
  280. <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>
  281. <span class="n">s</span><span class="p">)</span> <span class="c1"># apply gaussian blur to the image</span>
  282. <span class="n">blurred_sq</span> <span class="o">=</span> <span class="n">blurred</span> <span class="o">*</span> <span class="n">blurred</span>
  283. <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>
  284. <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>
  285. <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>
  286. <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>
  287. <span class="k">return</span> <span class="n">mscn</span></div>
  288. <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>
  289. <span class="sd">&quot;&quot;&quot;Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)</span>
  290. <span class="sd"> Args:</span>
  291. <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
  292. <span class="sd"> Returns:</span>
  293. <span class="sd"> 2D Numpy array with MSCN information</span>
  294. <span class="sd"> Example:</span>
  295. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  296. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  297. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  298. <span class="sd"> &gt;&gt;&gt; img_mscn = transform.rgb_to_mscn(img)</span>
  299. <span class="sd"> &gt;&gt;&gt; img_mscn.shape</span>
  300. <span class="sd"> (200, 200)</span>
  301. <span class="sd"> &quot;&quot;&quot;</span>
  302. <span class="c1"># check if PIL image or not</span>
  303. <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>
  304. <span class="c1"># convert rgb image to gray</span>
  305. <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>
  306. <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>
  307. <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>
  308. <span class="sd">&quot;&quot;&quot;Compute the Mean Substracted Constrast Normalized coefficients of an image</span>
  309. <span class="sd"> Args:</span>
  310. <span class="sd"> image: PIL Image, Numpy array or path of image</span>
  311. <span class="sd"> Returns:</span>
  312. <span class="sd"> MSCN coefficients</span>
  313. <span class="sd"> Raises:</span>
  314. <span class="sd"> FileNotFoundError: If `image` is set as str path and image was not found</span>
  315. <span class="sd"> ValueError: If `image` numpy shape are not correct</span>
  316. <span class="sd"> Example:</span>
  317. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  318. <span class="sd"> &gt;&gt;&gt; import numpy as np</span>
  319. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  320. <span class="sd"> &gt;&gt;&gt; image_values = Image.open(&#39;./images/test_img.png&#39;)</span>
  321. <span class="sd"> &gt;&gt;&gt; mscn_coefficients = transform.get_mscn_coefficients(image_values)</span>
  322. <span class="sd"> &gt;&gt;&gt; mscn_coefficients.shape</span>
  323. <span class="sd"> (200, 200)</span>
  324. <span class="sd"> &quot;&quot;&quot;</span>
  325. <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>
  326. <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>
  327. <span class="c1"># open image directly as grey level image</span>
  328. <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>
  329. <span class="k">else</span><span class="p">:</span>
  330. <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>
  331. <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>
  332. <span class="c1"># convert if necessary to grey level numpy array</span>
  333. <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>
  334. <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
  335. <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>
  336. <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>
  337. <span class="k">else</span><span class="p">:</span>
  338. <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>
  339. <span class="k">else</span><span class="p">:</span>
  340. <span class="c1"># if PIL Image</span>
  341. <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>
  342. <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>
  343. <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
  344. <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>
  345. <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>
  346. <span class="k">else</span><span class="p">:</span>
  347. <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>
  348. <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>
  349. <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span> <span class="o">/</span> <span class="mf">255.0</span>
  350. <span class="c1"># calculating MSCN coefficients</span>
  351. <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>
  352. <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>
  353. <span class="n">mu_sq</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">mu</span>
  354. <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>
  355. <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>
  356. <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>
  357. <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>
  358. <span class="k">return</span> <span class="n">structdis</span></div>
  359. <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>
  360. <span class="sd">&quot;&quot;&quot;Returns Singular values from LAB L Image information</span>
  361. <span class="sd"> Args:</span>
  362. <span class="sd"> image: PIL Image or Numpy array</span>
  363. <span class="sd"> Returns:</span>
  364. <span class="sd"> U, s, V information obtained from SVD compression using Lab</span>
  365. <span class="sd"> Example:</span>
  366. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  367. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  368. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  369. <span class="sd"> &gt;&gt;&gt; U, s, V = transform.get_LAB_L_SVD(img)</span>
  370. <span class="sd"> &gt;&gt;&gt; U.shape</span>
  371. <span class="sd"> (200, 200)</span>
  372. <span class="sd"> &gt;&gt;&gt; len(s)</span>
  373. <span class="sd"> 200</span>
  374. <span class="sd"> &gt;&gt;&gt; V.shape</span>
  375. <span class="sd"> (200, 200)</span>
  376. <span class="sd"> &quot;&quot;&quot;</span>
  377. <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>
  378. <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>
  379. <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>
  380. <span class="sd">&quot;&quot;&quot;Returns s (Singular values) SVD from L of LAB Image information</span>
  381. <span class="sd"> Args:</span>
  382. <span class="sd"> image: PIL Image or Numpy array</span>
  383. <span class="sd"> Returns:</span>
  384. <span class="sd"> vector of singular values</span>
  385. <span class="sd"> Example:</span>
  386. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  387. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  388. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  389. <span class="sd"> &gt;&gt;&gt; s = transform.get_LAB_L_SVD_s(img)</span>
  390. <span class="sd"> &gt;&gt;&gt; len(s)</span>
  391. <span class="sd"> 200</span>
  392. <span class="sd"> &quot;&quot;&quot;</span>
  393. <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>
  394. <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>
  395. <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>
  396. <span class="sd">&quot;&quot;&quot;Returns U SVD from L of LAB Image information</span>
  397. <span class="sd"> Args:</span>
  398. <span class="sd"> image: PIL Image or Numpy array</span>
  399. <span class="sd"> Returns:</span>
  400. <span class="sd"> U matrix of SVD compression</span>
  401. <span class="sd"> Example:</span>
  402. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  403. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  404. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  405. <span class="sd"> &gt;&gt;&gt; U = transform.get_LAB_L_SVD_U(img)</span>
  406. <span class="sd"> &gt;&gt;&gt; U.shape</span>
  407. <span class="sd"> (200, 200)</span>
  408. <span class="sd"> &quot;&quot;&quot;</span>
  409. <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>
  410. <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>
  411. <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>
  412. <span class="sd">&quot;&quot;&quot;Returns V SVD from L of LAB Image information</span>
  413. <span class="sd"> Args:</span>
  414. <span class="sd"> image: PIL Image or Numpy array</span>
  415. <span class="sd"> Returns:</span>
  416. <span class="sd"> V matrix of SVD compression</span>
  417. <span class="sd"> Example:</span>
  418. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  419. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  420. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  421. <span class="sd"> &gt;&gt;&gt; V = transform.get_LAB_L_SVD_V(img)</span>
  422. <span class="sd"> &gt;&gt;&gt; V.shape</span>
  423. <span class="sd"> (200, 200)</span>
  424. <span class="sd"> &quot;&quot;&quot;</span>
  425. <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>
  426. <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>
  427. <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>
  428. <span class="sd">&quot;&quot;&quot;Convert RGB Image into grey image using only 4 low bits values</span>
  429. <span class="sd"> Args:</span>
  430. <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
  431. <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
  432. <span class="sd"> Returns:</span>
  433. <span class="sd"> 2D Numpy array with low bits information kept</span>
  434. <span class="sd"> Example:</span>
  435. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  436. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  437. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  438. <span class="sd"> &gt;&gt;&gt; low_bits_grey_img = transform.rgb_to_grey_low_bits(img, 5)</span>
  439. <span class="sd"> &gt;&gt;&gt; low_bits_grey_img.shape</span>
  440. <span class="sd"> (200, 200)</span>
  441. <span class="sd"> &quot;&quot;&quot;</span>
  442. <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>
  443. <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>
  444. <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>
  445. <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>
  446. <span class="sd">&quot;&quot;&quot;Convert RGB Image into Lab L channel image using only 4 low bits values</span>
  447. <span class="sd"> Args:</span>
  448. <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
  449. <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
  450. <span class="sd"> Returns:</span>
  451. <span class="sd"> 2D Numpy array with low bits information kept</span>
  452. <span class="sd"> Example:</span>
  453. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  454. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  455. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  456. <span class="sd"> &gt;&gt;&gt; low_bits_Lab_l_img = transform.rgb_to_LAB_L_low_bits(img, 5)</span>
  457. <span class="sd"> &gt;&gt;&gt; low_bits_Lab_l_img.shape</span>
  458. <span class="sd"> (200, 200)</span>
  459. <span class="sd"> &quot;&quot;&quot;</span>
  460. <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">&#39;uint8&#39;</span><span class="p">)</span>
  461. <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>
  462. <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>
  463. <span class="sd">&quot;&quot;&quot;Returns only bits from LAB L canal specified into the interval</span>
  464. <span class="sd"> Args:</span>
  465. <span class="sd"> image: image to convert using this interval of bits value to keep</span>
  466. <span class="sd"> interval: (begin, end) of bits values</span>
  467. <span class="sd"> Returns:</span>
  468. <span class="sd"> 2D Numpy array with reduced values</span>
  469. <span class="sd"> &gt;&gt;&gt; from PIL import Image</span>
  470. <span class="sd"> &gt;&gt;&gt; from ipfml.processing import transform</span>
  471. <span class="sd"> &gt;&gt;&gt; img = Image.open(&#39;./images/test_img.png&#39;)</span>
  472. <span class="sd"> &gt;&gt;&gt; bits_Lab_l_img = transform.rgb_to_LAB_L_bits(img, (2, 6))</span>
  473. <span class="sd"> &gt;&gt;&gt; bits_Lab_l_img.shape</span>
  474. <span class="sd"> (200, 200)</span>
  475. <span class="sd"> &quot;&quot;&quot;</span>
  476. <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">&#39;uint8&#39;</span><span class="p">)</span>
  477. <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>
  478. </pre></div>
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