hungerzs
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ImageFeatrueExtraction


			
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  <h1>Source code for Image</h1><div class="highlight"><pre>
<span></span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">scipy.misc</span> <span class="k">as</span> <span class="nn">sm</span>
<span class="kn">from</span> <span class="nn">skimage.measure</span> <span class="k">import</span> <span class="n">label</span>

<span class="kn">from</span> <span class="nn">skimage.segmentation</span> <span class="k">import</span> <span class="n">clear_border</span>
<span class="kn">from</span> <span class="nn">skimage.color</span> <span class="k">import</span> <span class="n">label2rgb</span>
<span class="kn">from</span> <span class="nn">skimage.measure</span> <span class="k">import</span> <span class="n">label</span><span class="p">,</span> <span class="n">regionprops</span>
<span class="kn">from</span> <span class="nn">skimage</span> <span class="k">import</span> <span class="n">filters</span><span class="p">,</span> <span class="n">io</span>

<span class="kn">from</span> <span class="nn">image_features_extraction</span> <span class="k">import</span> <span class="n">Regions</span>
<span class="kn">from</span> <span class="nn">image_features_extraction</span> <span class="k">import</span> <span class="n">MyException</span>
<span class="kn">from</span> <span class="nn">image_features_extraction</span> <span class="k">import</span> <span class="n">Features</span>
<span class="kn">from</span> <span class="nn">image_features_extraction</span> <span class="k">import</span> <span class="n">Utils</span>

<span class="kn">import</span> <span class="nn">Voronoi_Features.Voronoi</span> <span class="k">as</span> <span class="nn">VF</span>


<div class="viewcode-block" id="Image"><a class="viewcode-back" href="../code.html#Image.Image">[docs]</a><span class="k">class</span> <span class="nc">Image</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This class instantiate an object Image through the :class:`Images` and refers to a specific file image</span>

<span class="sd">    :example:</span>
<span class="sd">    &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">    &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">    &gt;&gt;&gt; img = imgs.item(1)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_name</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__full_file_name</span> <span class="o">=</span> <span class="n">full_name</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__regions</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__regionsprops</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__image_intensity</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__image</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">__image_semented</span> <span class="o">=</span> <span class="kc">None</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="c1"># load  image and  segment</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__image</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__full_file_name</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__image_semented</span>  <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_regions</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__regionsprops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_regionsprop</span><span class="p">()</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__centroids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prop_values</span><span class="p">(</span><span class="s1">&#39;centroid&#39;</span><span class="p">)</span>
        <span class="k">except</span> <span class="n">MyException</span><span class="o">.</span><span class="n">MyException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">)</span>


<div class="viewcode-block" id="Image.Voronoi"><a class="viewcode-back" href="../code.html#Image.Image.Voronoi">[docs]</a>    <span class="k">def</span> <span class="nf">Voronoi</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Image Voronoi diagram (refer to documentaiton of my package Voronoi_Features in my github )</span>

<span class="sd">        :return: Voronoi object for the current image</span>
<span class="sd">        :rtype: Voronoi object</span>
<span class="sd">        &gt;&gt;&gt; import matplotlib.pyplot as plt</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">        &gt;&gt;&gt; img = imgs.item(1)</span>
<span class="sd">        &gt;&gt;&gt;</span>
<span class="sd">        &gt;&gt;&gt; voro = img.Voronoi()</span>
<span class="sd">        &gt;&gt;&gt; # show voronoi diagram</span>
<span class="sd">        &gt;&gt;&gt; fig = plt.figure(figsize=(20,20))</span>
<span class="sd">        &gt;&gt;&gt; plt.imshow(vor.get_voronoi_map(), cmap=plt.get_cmap(&#39;jet&#39;))</span>


<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="n">VF</span><span class="o">.</span><span class="n">Voronoi</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__centroids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></div>


<div class="viewcode-block" id="Image.width"><a class="viewcode-back" href="../code.html#Image.Image.width">[docs]</a>    <span class="k">def</span> <span class="nf">width</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span></div>


<div class="viewcode-block" id="Image.height"><a class="viewcode-back" href="../code.html#Image.Image.height">[docs]</a>    <span class="k">def</span> <span class="nf">height</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>


<div class="viewcode-block" id="Image.file_name"><a class="viewcode-back" href="../code.html#Image.Image.file_name">[docs]</a>    <span class="k">def</span> <span class="nf">file_name</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        full file name of the image</span>

<span class="sd">        :returns: file name</span>
<span class="sd">        :rtype: string</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__full_file_name</span></div>


<div class="viewcode-block" id="Image.set_image_intensity"><a class="viewcode-back" href="../code.html#Image.Image.set_image_intensity">[docs]</a>    <span class="k">def</span> <span class="nf">set_image_intensity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">image_intensity</span><span class="p">):</span>
            <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">            Sets the image to measurs  image&#39;s region intensity properties (e.g, mean_intensity)</span>

<span class="sd">            : param image_intensity:  Image object for intensity measurement</span>
<span class="sd">            : type image_intensity: Object Image</span>
<span class="sd">            :returns:  the set Image object for intensity measurement</span>
<span class="sd">            :rtype:  Object Image</span>
<span class="sd">            &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">            &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">            &gt;&gt;&gt; img = imgs.item(1) # this is the binary image used for segmentation</span>
<span class="sd">            &gt;&gt;&gt; img_intensity = imgs.item(0) # this is the original image on which to measure intensities</span>
<span class="sd">            &gt;&gt;&gt; img.set_image_intensity(img_intensity)</span>
<span class="sd">            &gt;&gt;&gt; features = IMG.features([&#39;label&#39;, &#39;area&#39;,&#39;perimeter&#39;, &#39;centroid&#39;,&#39;major_axis_length&#39;, &#39;moments&#39;,&#39;mean_intensity&#39;], class_value=5)</span>

<span class="sd">            &quot;&quot;&quot;</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__image_intensity</span> <span class="o">=</span> <span class="n">image_intensity</span><span class="o">.</span><span class="n">__image</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">__regionsprops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_regionsprop</span><span class="p">()</span></div>

<div class="viewcode-block" id="Image.regions"><a class="viewcode-back" href="../code.html#Image.Image.regions">[docs]</a>    <span class="k">def</span> <span class="nf">regions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        regions(...) returns the Object Regions</span>

<span class="sd">        :returns:  :class:`Regions`</span>
<span class="sd">        :rtype: string</span>
<span class="sd">        &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">        &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">        &gt;&gt;&gt; img = imgs.item(1)</span>
<span class="sd">        &gt;&gt;&gt; regs = img.Regions()</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">return</span> <span class="n">Regions</span><span class="o">.</span><span class="n">Regions</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__image_semented</span><span class="p">)</span> <span class="c1"># (self.__get_regions())</span>
        <span class="k">except</span> <span class="n">MyException</span><span class="o">.</span><span class="n">MyException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">)</span>
            <span class="k">return</span> <span class="kc">None</span></div>


    <span class="k">def</span> <span class="nf">__get_regions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="c1"># apply thresholding</span>
        <span class="n">val</span> <span class="o">=</span> <span class="n">filters</span><span class="o">.</span><span class="n">threshold_otsu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="p">)</span>
        <span class="c1"># segmentation</span>
        <span class="n">image_thresh</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__image</span> <span class="o">&gt;</span> <span class="n">val</span>
        <span class="c1"># returns the single segmented elements of the image</span>
        <span class="n">image_segment</span> <span class="o">=</span> <span class="n">label</span><span class="p">(</span><span class="n">image_thresh</span><span class="p">)</span>
        <span class="c1"># removes the image elements at the borde</span>
        <span class="k">return</span>  <span class="n">clear_border</span><span class="p">(</span><span class="n">image_segment</span><span class="p">)</span>


    <span class="c1">#def __get_mask(self, redo=False):</span>
    <span class="c1">#    if redo == False:</span>
    <span class="c1">#        return self.__mask</span>
    <span class="c1">#    # ithresholding to build the map</span>
    <span class="c1">#    val = filters.threshold_otsu(self.__image)</span>
    <span class="c1">#    # cretes the mask</span>
    <span class="c1">#    return self.__image &gt; val</span>

    <span class="k">def</span> <span class="nf">__get_regionsprop</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">regionprops</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_regions</span><span class="p">(),</span> <span class="n">intensity_image</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">__image_intensity</span><span class="p">)</span>


<div class="viewcode-block" id="Image.get_image_segmentation"><a class="viewcode-back" href="../code.html#Image.Image.get_image_segmentation">[docs]</a>    <span class="k">def</span> <span class="nf">get_image_segmentation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Builds the image with mask overlay to show the segmentation</span>

<span class="sd">        :returns: The image in RGB format, in a 3-D array of shape (.., .., 3).</span>
<span class="sd">        :rtype: ndarray</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">return</span>  <span class="n">label2rgb</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_regions</span><span class="p">(),</span> <span class="n">image</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">__image</span><span class="p">)</span>
        <span class="k">except</span> <span class="n">MyException</span><span class="o">.</span><span class="n">MyException</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">)</span>
            <span class="k">return</span> <span class="kc">None</span></div>

<div class="viewcode-block" id="Image.features"><a class="viewcode-back" href="../code.html#Image.Image.features">[docs]</a>    <span class="k">def</span> <span class="nf">features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">features_list</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">suffix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns a table with all  values for the property names given in input, and supplies an</span>
<span class="sd">        additional parameter for feature classification</span>

<span class="sd">        :param features_list: list of property/measure names (e.g, &#39;area&#39;, &#39;centroid&#39;, etc )</span>
<span class="sd">        :type features_list: List</span>
<span class="sd">        :param prefix: prefix for features name</span>
<span class="sd">        :type prefix:  string</span>
<span class="sd">        :param suffix: prefix for features name</span>
<span class="sd">        :type suffix: string</span>
<span class="sd">        :returns: Features Object</span>
<span class="sd">        :rtype: Features Object</span>
<span class="sd">        :example:</span>
<span class="sd">        &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">        &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">        &gt;&gt;&gt; img = imgs.item(1)</span>
<span class="sd">        &gt;&gt;&gt; feature = img.get_features([&#39;label&#39;, &#39;area&#39;,&#39;perimeter&#39;, &#39;centroid&#39;])</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="c1">#self.__regionsprops = self.__get_regionsprop()</span>
            <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__regionsprops</span><span class="p">)</span>
            <span class="n">df</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">n</span><span class="p">)</span>
            <span class="k">for</span> <span class="n">feature_name</span> <span class="ow">in</span> <span class="n">features_list</span><span class="p">:</span>
                <span class="n">values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prop_values</span><span class="p">(</span><span class="n">feature_name</span><span class="p">)</span>
                <span class="n">Utils</span><span class="o">.</span><span class="n">insert_values</span><span class="p">(</span><span class="n">prefix</span> <span class="o">+</span> <span class="n">feature_name</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span>

            <span class="k">return</span> <span class="n">Features</span><span class="o">.</span><span class="n">Features</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;one or more input labels might be wrong:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">e</span><span class="p">))</span>
            <span class="k">return</span> <span class="kc">None</span></div>


<div class="viewcode-block" id="Image.prop_values"><a class="viewcode-back" href="../code.html#Image.Image.prop_values">[docs]</a>    <span class="k">def</span> <span class="nf">prop_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prop_name</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Measure the values of the specified  property/measure name (e.g., &#39;area&#39;) for all</span>
<span class="sd">        elements contained in the object Regions.</span>

<span class="sd">        :param prop_name: name of the property to measure (e.g, &#39;area&#39;)</span>
<span class="sd">        :type prop_name: string</span>
<span class="sd">        :returns:  property name  values</span>
<span class="sd">        :rtype: List</span>

<span class="sd">        :example:</span>
<span class="sd">        &gt;&gt;&gt; import image_features_extraction as fe</span>
<span class="sd">        &gt;&gt;&gt; imgs = fe.Images(folder_name)</span>
<span class="sd">        &gt;&gt;&gt; img = imgs.item(1)</span>
<span class="sd">        &gt;&gt;&gt; regs = img.Regions()</span>
<span class="sd">        &gt;&gt;&gt; areas = regs.prop_values(&#39;area&#39;)</span>

<span class="sd">        The following properties can be accessed as attributes or keys:</span>

<span class="sd">        **area** : int</span>
<span class="sd">            Number of pixels of region.</span>
<span class="sd">        **bbox** : tuple</span>
<span class="sd">            Bounding box ``(min_row, min_col, max_row, max_col)``.</span>
<span class="sd">            Pixels belonging to the bounding box are in the half-open interval</span>
<span class="sd">            ``[min_row; max_row)`` and ``[min_col; max_col)``.</span>
<span class="sd">        **bbox_area** : int</span>
<span class="sd">            Number of pixels of bounding box.</span>
<span class="sd">        **centroid** : array</span>
<span class="sd">            Centroid coordinate tuple ``(row, col)``.</span>
<span class="sd">        **convex_area** : int</span>
<span class="sd">            Number of pixels of convex hull image.</span>
<span class="sd">        **convex_image** : (H, J) ndarray</span>
<span class="sd">            Binary convex hull image which has the same size as bounding box.</span>
<span class="sd">        **coords** : (N, 2) ndarray</span>
<span class="sd">            Coordinate list ``(row, col)`` of the region.</span>
<span class="sd">        **eccentricity** : float</span>
<span class="sd">            Eccentricity of the ellipse that has the same second-moments as the</span>
<span class="sd">            region. The eccentricity is the ratio of the focal distance</span>
<span class="sd">            (distance between focal points) over the major axis length.</span>
<span class="sd">            The value is in the interval [0, 1).</span>
<span class="sd">            When it is 0, the ellipse becomes a circle.</span>
<span class="sd">        **equivalent_diameter** : float</span>
<span class="sd">            The diameter of a circle with the same area as the region.</span>
<span class="sd">        **euler_number** : int</span>
<span class="sd">            Euler characteristic of region. Computed as number of objects (= 1)</span>
<span class="sd">            subtracted by number of holes (8-connectivity).</span>
<span class="sd">        **extent** : float</span>
<span class="sd">            Ratio of pixels in the region to pixels in the total bounding box.</span>
<span class="sd">            Computed as ``area / (rows * cols)``</span>
<span class="sd">        **filled_area** : int</span>
<span class="sd">            Number of pixels of filled region.</span>
<span class="sd">        **filled_image** : (H, J) ndarray</span>
<span class="sd">            Binary region image with filled holes which has the same size as</span>
<span class="sd">            bounding box.</span>
<span class="sd">        **image** : (H, J) ndarray</span>
<span class="sd">            Sliced binary region image which has the same size as bounding box.</span>
<span class="sd">        **inertia_tensor** : (2, 2) ndarray</span>
<span class="sd">            Inertia tensor of the region for the rotation around its mass.</span>
<span class="sd">        **inertia_tensor_eigvals** : tuple</span>
<span class="sd">            The two eigen values of the inertia tensor in decreasing order.</span>
<span class="sd">        **intensity_image** : ndarray</span>
<span class="sd">            Image inside region bounding box.</span>
<span class="sd">        **label** : int</span>
<span class="sd">            The label in the labeled input image.</span>
<span class="sd">        **local_centroid** : array</span>
<span class="sd">            Centroid coordinate tuple ``(row, col)``, relative to region bounding</span>
<span class="sd">            box.</span>
<span class="sd">        **major_axis_length** : float</span>
<span class="sd">            The length of the major axis of the ellipse that has the same</span>
<span class="sd">            normalized second central moments as the region.</span>
<span class="sd">        **max_intensity** : float</span>
<span class="sd">            Value with the greatest intensity in the region.</span>
<span class="sd">        **mean_intensity** : float</span>
<span class="sd">            Value with the mean intensity in the region.</span>
<span class="sd">        **min_intensity** : float</span>
<span class="sd">            Value with the least intensity in the region.</span>
<span class="sd">        **minor_axis_length** : float</span>
<span class="sd">            The length of the minor axis of the ellipse that has the same</span>
<span class="sd">            normalized second central moments as the region.</span>
<span class="sd">        **moments** : (3, 3) ndarray</span>
<span class="sd">            Spatial moments up to 3rd order::</span>
<span class="sd">                m_ji = sum{ array(x, y) * x^j * y^i }</span>
<span class="sd">            where the sum is over the `x`, `y` coordinates of the region.</span>
<span class="sd">        **moments_central** : (3, 3) ndarray</span>
<span class="sd">            Central moments (translation invariant) up to 3rd order::</span>
<span class="sd">                mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }</span>
<span class="sd">            where the sum is over the `x`, `y` coordinates of the region,</span>
<span class="sd">            and `x_c` and `y_c` are the coordinates of the region&#39;s centroid.</span>
<span class="sd">        **moments_hu** : tuple</span>
<span class="sd">            Hu moments (translation, scale and rotation invariant).</span>
<span class="sd">        **moments_normalized** : (3, 3) ndarray</span>
<span class="sd">            Normalized moments (translation and scale invariant) up to 3rd order::</span>
<span class="sd">                nu_ji = mu_ji / m_00^[(i+j)/2 + 1]</span>
<span class="sd">            where `m_00` is the zeroth spatial moment.</span>
<span class="sd">        **orientation** : float</span>
<span class="sd">            Angle between the X-axis and the major axis of the ellipse that has</span>
<span class="sd">            the same second-moments as the region. Ranging from `-pi/2` to</span>
<span class="sd">            `pi/2` in counter-clockwise direction.</span>
<span class="sd">        **perimeter** : float</span>
<span class="sd">            Perimeter of object which approximates the contour as a line</span>
<span class="sd">            through the centers of border pixels using a 4-connectivity.</span>
<span class="sd">        **solidity** : float</span>
<span class="sd">            Ratio of pixels in the region to pixels of the convex hull image.</span>
<span class="sd">        **weighted_centroid** : array</span>
<span class="sd">            Centroid coordinate tuple ``(row, col)`` weighted with intensity</span>
<span class="sd">            image.</span>
<span class="sd">        **weighted_local_centroid** : array</span>
<span class="sd">            Centroid coordinate tuple ``(row, col)``, relative to region bounding</span>
<span class="sd">            box, weighted with intensity image.</span>
<span class="sd">        **weighted_moments** : (3, 3) ndarray</span>
<span class="sd">            Spatial moments of intensity image up to 3rd order::</span>
<span class="sd">                wm_ji = sum{ array(x, y) * x^j * y^i }</span>
<span class="sd">            where the sum is over the `x`, `y` coordinates of the region.</span>
<span class="sd">        **weighted_moments_central** : (3, 3) ndarray</span>
<span class="sd">            Central moments (translation invariant) of intensity image up to</span>
<span class="sd">            3rd order::</span>
<span class="sd">                wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }</span>
<span class="sd">            where the sum is over the `x`, `y` coordinates of the region,</span>
<span class="sd">            and `x_c` and `y_c` are the coordinates of the region&#39;s weighted</span>
<span class="sd">            centroid.</span>
<span class="sd">        **weighted_moments_hu** : tuple</span>
<span class="sd">            Hu moments (translation, scale and rotation invariant) of intensity</span>
<span class="sd">            image.</span>
<span class="sd">        **weighted_moments_normalized** : (3, 3) ndarray</span>
<span class="sd">            Normalized moments (translation and scale invariant) of intensity</span>
<span class="sd">            image up to 3rd order::</span>
<span class="sd">                wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1]</span>
<span class="sd">            where ``wm_00`` is the zeroth spatial moment (intensity-weighted area).</span>


<span class="sd">        .. [1] http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">vals</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">__regionsprops</span><span class="p">:</span>
                <span class="n">vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">prop_name</span><span class="p">))</span>
            <span class="k">return</span> <span class="n">vals</span>
        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">args</span><span class="p">)</span>
            <span class="k">return</span> <span class="kc">None</span></div></div>
</pre></div>

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