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- <h1>Source code for ipfml.processing</h1><div class="highlight"><pre>
- <span></span><span class="sd">"""</span>
- <span class="sd">Functions to quickly extract reduced information from image</span>
- <span class="sd">"""</span>
- <span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
- <span class="kn">import</span> <span class="nn">random</span>
- <span class="kn">import</span> <span class="nn">cv2</span>
- <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>
- <span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">signal</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
- <span class="kn">import</span> <span class="nn">ipfml.metrics</span> <span class="k">as</span> <span class="nn">metrics</span>
- <span class="kn">from</span> <span class="nn">ipfml</span> <span class="k">import</span> <span class="n">exceptions</span>
- <span class="kn">import</span> <span class="nn">os</span>
- <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>
- <span class="sd">"""Returns Singular values from LAB L Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> U, s, V information obtained from SVD compression using Lab</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U, s, V = processing.get_LAB_L_SVD(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">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="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>
- <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>
- <span class="sd">"""Returns s (Singular values) SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> vector of singular values</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> s = processing.get_LAB_L_SVD_s(img)</span>
- <span class="sd"> >>> len(s)</span>
- <span class="sd"> 200</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">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="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>
- <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>
- <span class="sd">"""Returns U SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> U matrix of SVD compression</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> U = processing.get_LAB_L_SVD_U(img)</span>
- <span class="sd"> >>> U.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">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="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>
- <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>
- <span class="sd">"""Returns V SVD from L of LAB Image information</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> V matrix of SVD compression</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> V = processing.get_LAB_L_SVD_V(img)</span>
- <span class="sd"> >>> V.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L</span> <span class="o">=</span> <span class="n">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="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>
- <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>
- <span class="sd">"""Convert RGB Image into Mean Subtracted Contrast Normalized (MSCN)</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with MSCN information</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> img_mscn = processing.rgb_to_mscn(img)</span>
- <span class="sd"> >>> img_mscn.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="c1"># check if PIL image or not</span>
- <span class="n">img_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="c1"># convert rgb image to gray</span>
- <span class="n">im</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">color</span><span class="o">.</span><span class="n">rgb2gray</span><span class="p">(</span><span class="n">img_arr</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span><span class="p">,</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">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>
- <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>
- <span class="sd">"""Convert RGB Image into grey image using only 4 low bits values</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with low bits information kept</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_grey_img = processing.rgb_to_grey_low_bits(img, 5)</span>
- <span class="sd"> >>> low_bits_grey_img.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">img_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="n">grey_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">color</span><span class="o">.</span><span class="n">rgb2gray</span><span class="p">(</span><span class="n">img_arr</span><span class="p">)</span> <span class="o">*</span> <span class="mi">255</span><span class="p">,</span> <span class="s1">'uint8'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">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>
- <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>
- <span class="sd">"""Convert RGB Image into Lab L channel image using only 4 low bits values</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: 3D RGB image Numpy array or PIL RGB image</span>
- <span class="sd"> nb_bits: optional parameter which indicates the number of bits to keep (default 4)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with low bits information kept</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> low_bits_Lab_l_img = processing.rgb_to_LAB_L_low_bits(img, 5)</span>
- <span class="sd"> >>> low_bits_Lab_l_img.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">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">'uint8'</span><span class="p">)</span>
- <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>
- <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>
- <span class="sd">"""Returns only bits from LAB L canal specified into the interval</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: image to convert using this interval of bits value to keep</span>
- <span class="sd"> interval: (begin, end) of bits values</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> 2D Numpy array with reduced values</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> img = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> bits_Lab_l_img = processing.rgb_to_LAB_L_bits(img, (2, 6))</span>
- <span class="sd"> >>> bits_Lab_l_img.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="n">L_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">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">'uint8'</span><span class="p">)</span>
- <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>
- <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>
- <span class="sd">'''Divide image into equal size blocks</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> block: tuple (width, height) representing the size of each dimension of the block</span>
- <span class="sd"> pil: block type returned as PIL Image (default True)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> list containing all 2D Numpy blocks (in RGB or not)</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> ValueError: If `image_width` or `image_height` are not compatible to produce correct block sizes</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> from ipfml import metrics</span>
- <span class="sd"> >>> image_values = np.random.randint(255, size=(800, 800, 3))</span>
- <span class="sd"> >>> blocks = divide_in_blocks(image_values, (20, 20))</span>
- <span class="sd"> >>> len(blocks)</span>
- <span class="sd"> 1600</span>
- <span class="sd"> >>> blocks[0].width</span>
- <span class="sd"> 20</span>
- <span class="sd"> >>> blocks[0].height</span>
- <span class="sd"> 20</span>
- <span class="sd"> >>> img_l = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> L = metrics.get_LAB_L(img_l)</span>
- <span class="sd"> >>> blocks_L = divide_in_blocks(L, (100, 100))</span>
- <span class="sd"> >>> len(blocks_L)</span>
- <span class="sd"> 4</span>
- <span class="sd"> >>> blocks_L[0].width</span>
- <span class="sd"> 100</span>
- <span class="sd"> '''</span>
- <span class="n">blocks</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'RGB'</span>
- <span class="c1"># convert in Numpy array</span>
- <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>
- <span class="c1"># check dimension of input image</span>
- <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>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'L'</span>
- <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>
- <span class="k">else</span><span class="p">:</span>
- <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>
- <span class="c1"># check size compatibility</span>
- <span class="n">width</span><span class="p">,</span> <span class="n">height</span> <span class="o">=</span> <span class="n">block_size</span>
- <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>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Width size issue, block size not compatible"</span><span class="p">)</span>
- <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>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Height size issue, block size not compatible"</span><span class="p">)</span>
- <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>
- <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>
- <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>
- <span class="n">begin_x</span> <span class="o">=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">width</span>
- <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>
- <span class="n">begin_y</span> <span class="o">=</span> <span class="n">j</span> <span class="o">*</span> <span class="n">height</span>
- <span class="c1"># getting sub block information</span>
- <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>
- <span class="n">begin_y</span> <span class="o">+</span> <span class="n">height</span><span class="p">)]</span>
- <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
- <span class="n">blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
- <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">'uint8'</span><span class="p">),</span> <span class="n">mode</span><span class="p">))</span>
- <span class="k">else</span><span class="p">:</span>
- <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>
- <span class="k">return</span> <span class="n">blocks</span></div>
- <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>
- <span class="sd">'''Fusion array of images into single image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> images: array of images (PIL Image or Numpy array)</span>
- <span class="sd"> pil: block type returned as PIL Image (default True)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> merged image from array of images</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> ValueError: if `images` is not an array or is empty</span>
- <span class="sd"> NumpyShapeComparisonException: if `images` array contains images with different shapes</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> image_values_1 = np.random.randint(255, size=(800, 800, 3))</span>
- <span class="sd"> >>> image_values_2 = np.random.randint(255, size=(800, 800, 3))</span>
- <span class="sd"> >>> merged_image = processing.fusion_images([image_values_1, image_values_2], pil=False)</span>
- <span class="sd"> >>> merged_image.shape</span>
- <span class="sd"> (800, 800, 3)</span>
- <span class="sd"> '''</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'RGB'</span>
- <span class="n">dim</span> <span class="o">=</span> <span class="mi">1</span>
- <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>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Empty array of images provided...'</span><span class="p">)</span>
- <span class="c1"># convert image in numpy array (perhaps not necessary)</span>
- <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>
- <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>
- <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>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'L'</span>
- <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>
- <span class="k">else</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="o">=</span> <span class="n">image_array</span><span class="o">.</span><span class="n">shape</span>
- <span class="c1"># raise exception if all images do not have same shape</span>
- <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>
- <span class="k">raise</span> <span class="n">NumpyShapeComparisonException</span><span class="p">()</span>
- <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <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="k">else</span><span class="p">:</span>
- <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>
- <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>
- <span class="c1"># construction of mean image from rotation</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">width</span><span class="p">):</span>
- <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>
- <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">grey_value</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="c1"># for each image we merge pixel values</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>
- <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>
- <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>
- <span class="k">else</span><span class="p">:</span>
- <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>
- <span class="n">canal_value</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="c1"># for each image we merge pixel values</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>
- <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>
- <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>
- <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">'uint8'</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
- <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>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">image_mean</span></div>
- <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>
- <span class="sd">"""Rotate image using specific angle</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image or Numpy array</span>
- <span class="sd"> angle: Angle value of the rotation</span>
- <span class="sd"> pil: block type returned as PIL Image (default True)</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> Image with rotation applied</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> image_values = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> rotated_image = processing.rotate_image(image_values, 90, pil=False)</span>
- <span class="sd"> >>> rotated_image.shape</span>
- <span class="sd"> (200, 200, 3)</span>
- <span class="sd"> """</span>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'RGB'</span>
- <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>
- <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>
- <span class="n">mode</span> <span class="o">=</span> <span class="s1">'L'</span>
- <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>
- <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">'uint8'</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">pil</span><span class="p">:</span>
- <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>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">rotated_image</span></div>
- <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>
- <span class="sd">"""Compute the Mean Substracted Constrast Normalized coefficients of an image</span>
- <span class="sd"> Args:</span>
- <span class="sd"> image: PIL Image, Numpy array or path of image</span>
- <span class="sd"> Returns:</span>
- <span class="sd"> MSCN coefficients</span>
- <span class="sd"> Raises:</span>
- <span class="sd"> FileNotFoundError: If `image` is set as str path and image was not found</span>
- <span class="sd"> ValueError: If `image` numpy shape are not correct</span>
- <span class="sd"> Example:</span>
- <span class="sd"> >>> from PIL import Image</span>
- <span class="sd"> >>> import numpy as np</span>
- <span class="sd"> >>> from ipfml import processing</span>
- <span class="sd"> >>> image_values = Image.open('./images/test_img.png')</span>
- <span class="sd"> >>> mscn_coefficients = processing.get_mscn_coefficients(image_values)</span>
- <span class="sd"> >>> mscn_coefficients.shape</span>
- <span class="sd"> (200, 200)</span>
- <span class="sd"> """</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">image</span><span class="p">):</span>
- <span class="c1"># open image directly as grey level image</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">'Image not found in your system'</span><span class="p">)</span>
- <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
- <span class="c1"># convert if necessary to grey level numpy array</span>
- <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
- <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Incorrect image shape'</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="c1"># if PIL Image</span>
- <span class="n">image</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">image</span>
- <span class="k">if</span> <span class="n">image</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'Incorrect image shape'</span><span class="p">)</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
- <span class="n">imdist</span> <span class="o">=</span> <span class="n">imdist</span> <span class="o">/</span> <span class="mf">255.0</span>
- <span class="c1"># calculating MSCN coefficients</span>
- <span class="n">mu</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">GaussianBlur</span><span class="p">(</span>
- <span class="n">imdist</span><span class="p">,</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="mi">7</span> <span class="o">/</span> <span class="mi">6</span><span class="p">,</span> <span class="n">borderType</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">BORDER_CONSTANT</span><span class="p">)</span>
- <span class="n">mu_sq</span> <span class="o">=</span> <span class="n">mu</span> <span class="o">*</span> <span class="n">mu</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">GaussianBlur</span><span class="p">(</span>
- <span class="n">imdist</span> <span class="o">*</span> <span class="n">imdist</span><span class="p">,</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="mi">7</span> <span class="o">/</span> <span class="mi">6</span><span class="p">,</span> <span class="n">borderType</span><span class="o">=</span><span class="n">cv2</span><span class="o">.</span><span class="n">BORDER_CONSTANT</span><span class="p">)</span>
- <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">abs</span><span class="p">((</span><span class="n">sigma</span> <span class="o">-</span> <span class="n">mu_sq</span><span class="p">)))</span>
- <span class="n">structdis</span> <span class="o">=</span> <span class="p">(</span><span class="n">imdist</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">sigma</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">structdis</span></div>
- </pre></div>
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