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<!DOCTYPE html>
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<li class="toctree-l1 current"><a class="reference internal" href="tlseparation.html">tlseparation package</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="tlseparation.html#subpackages">Subpackages</a><ul class="current">
<li class="toctree-l3 current"><a class="current reference internal" href="#">tlseparation.classification package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.classes_reference">tlseparation.classification.classes_reference module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.classify_wood">tlseparation.classification.classify_wood module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.gmm">tlseparation.classification.gmm module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.path_detection">tlseparation.classification.path_detection module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.point_features">tlseparation.classification.point_features module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification.wlseparation">tlseparation.classification.wlseparation module</a></li>
<li class="toctree-l4"><a class="reference internal" href="#module-tlseparation.classification">Module contents</a></li>
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<div class="section" id="tlseparation-classification-package">
<h1>tlseparation.classification package<a class="headerlink" href="#tlseparation-classification-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-tlseparation.classification.classes_reference">
<span id="tlseparation-classification-classes-reference-module"></span><h2>tlseparation.classification.classes_reference module<a class="headerlink" href="#module-tlseparation.classification.classes_reference" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="tlseparation.classification.classes_reference.DefaultClass">
<em class="property">class </em><code class="descclassname">tlseparation.classification.classes_reference.</code><code class="descname">DefaultClass</code><a class="reference internal" href="_modules/tlseparation/classification/classes_reference.html#DefaultClass"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.classes_reference.DefaultClass" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines a default reference class to be used in classification of
tree point clouds.</p>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification.classify_wood">
<span id="tlseparation-classification-classify-wood-module"></span><h2>tlseparation.classification.classify_wood module<a class="headerlink" href="#module-tlseparation.classification.classify_wood" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="tlseparation.classification.classify_wood.reference_classification">
<code class="descclassname">tlseparation.classification.classify_wood.</code><code class="descname">reference_classification</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>knn_list</em>, <em>n_classes=4</em>, <em>prob_threshold=0.95</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/classify_wood.html#reference_classification"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.classify_wood.reference_classification" title="Permalink to this definition">¶</a></dt>
<dd><p>Classifies wood material points from a point cloud. This function
uses <em>wlseparate_ref_voting</em> to perform the basic classification and then
apply <em>class_filter</em> to filter out potentially misclassified wood points.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud: numpy.ndarray</strong></dt>
<dd><p class="first last">2D (n x 3) array containing n points in 3D space (x, y, z).</p>
</dd>
<dt><strong>knn_list: list</strong></dt>
<dd><p class="first last">List of knn values to be used iteratively in the voting separation.</p>
</dd>
<dt><strong>n_classes: int</strong></dt>
<dd><p class="first last">Number of intermediate classes. Minimum classes should be 3, but
default value is set to 4 in order to accommodate for noise/outliers
classes.</p>
</dd>
<dt><strong>prob_threshold: float</strong></dt>
<dd><p class="first last">Classification probability threshold to filter classes. This aims to
avoid selecting points that are not confidently enough assigned to
any given class. Default is 0.95.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>wood_points: numpy.ndarray</strong></dt>
<dd><p class="first last">2D (nw x 3) array containing n wood points in 3D space (x, y, z).</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.classify_wood.threshold_classification">
<code class="descclassname">tlseparation.classification.classify_wood.</code><code class="descname">threshold_classification</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>knn</em>, <em>n_classes=3</em>, <em>prob_threshold=0.95</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/classify_wood.html#threshold_classification"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.classify_wood.threshold_classification" title="Permalink to this definition">¶</a></dt>
<dd><p>Classifies wood material points from a point cloud. This function
uses <em>wlseparate_abs</em> to perform the basic classification and then
apply <em>class_filter</em> to filter out potentially misclassified wood points.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud</strong> <span class="classifier-delimiter">:</span> <span class="classifier">numpy.ndarray</span></dt>
<dd><p class="first last">2D (n x 3) array containing n points in 3D space (x, y, z).</p>
</dd>
<dt><strong>knn</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of neighbors to select around each point. Used to describe
local point arrangement.</p>
</dd>
<dt><strong>n_classes: int</strong></dt>
<dd><p class="first last">Number of intermediate classes. Default is 3.</p>
</dd>
<dt><strong>prob_threshold: float</strong></dt>
<dd><p class="first last">Classification probability threshold to filter classes. This aims to
avoid selecting points that are not confidently enough assigned to
any given class. Default is 0.95.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>wood_points: numpy.ndarray</strong></dt>
<dd><p class="first last">2D (nw x 3) array containing n wood points in 3D space (x, y, z).</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification.gmm">
<span id="tlseparation-classification-gmm-module"></span><h2>tlseparation.classification.gmm module<a class="headerlink" href="#module-tlseparation.classification.gmm" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="tlseparation.classification.gmm.class_select_abs">
<code class="descclassname">tlseparation.classification.gmm.</code><code class="descname">class_select_abs</code><span class="sig-paren">(</span><em>classes</em>, <em>cm</em>, <em>nbrs_idx</em>, <em>feature=5</em>, <em>threshold=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/gmm.html#class_select_abs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.gmm.class_select_abs" title="Permalink to this definition">¶</a></dt>
<dd><p>Select from GMM classification results which classes are wood and which
are leaf based on a absolute value threshold from a single feature in
the parameter space.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list or array</span></dt>
<dd><p class="first last">Classes labels for each observation from the input variables.</p>
</dd>
<dt><strong>cm</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (c x n) of each class (c) parameter space mean
valuess (n).</p>
</dd>
<dt><strong>nbrs_idx</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Nearest Neighbors indices relative to every point of the array that
originated the classes labels.</p>
</dd>
<dt><strong>feature</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Column index of the feature to use as constraint.</p>
</dd>
<dt><strong>threshold</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Threshold value to mask classes. All classes with means >= threshold
are masked as true.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>mask</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list</span></dt>
<dd><p class="first last">List of booleans where True represents wood points and False
represents leaf points.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.gmm.class_select_ref">
<code class="descclassname">tlseparation.classification.gmm.</code><code class="descname">class_select_ref</code><span class="sig-paren">(</span><em>classes</em>, <em>cm</em>, <em>classes_ref</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/gmm.html#class_select_ref"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.gmm.class_select_ref" title="Permalink to this definition">¶</a></dt>
<dd><p>Selects from the classification results which classes are wood and which
are leaf.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list</span></dt>
<dd><p class="first last">List of classes labels for each observation from the input variables.</p>
</dd>
<dt><strong>cm</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (c x n) of each class (c) parameter space mean
valuess (n).</p>
</dd>
<dt><strong>classes_ref</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Reference classes values.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>mask</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">List of booleans where True represents wood points and False
represents leaf points.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.gmm.classify">
<code class="descclassname">tlseparation.classification.gmm.</code><code class="descname">classify</code><span class="sig-paren">(</span><em>variables</em>, <em>n_classes</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/gmm.html#classify"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.gmm.classify" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to perform the classification of a dataset using sklearn’s
Gaussian Mixture Models with Expectation Maximization.</p>
<table class="docutils field-list" frame="void" rules="none">
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<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>variables</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (m x n) containing a set of parameters (n)
over a set of observations (m).</p>
</dd>
<dt><strong>n_classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of classes to assign the input variables.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list</span></dt>
<dd><p class="first last">List of classes labels for each observation from the input variables.</p>
</dd>
<dt><strong>means</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (c x n) of each class (c) parameter space means
(n).</p>
</dd>
<dt><strong>probability</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Probability of samples belonging to every class in the classification.
Sum of sample-wise probability should be 1.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification.path_detection">
<span id="tlseparation-classification-path-detection-module"></span><h2>tlseparation.classification.path_detection module<a class="headerlink" href="#module-tlseparation.classification.path_detection" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="tlseparation.classification.path_detection.detect_main_pathways">
<code class="descclassname">tlseparation.classification.path_detection.</code><code class="descname">detect_main_pathways</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>k_retrace</em>, <em>knn</em>, <em>nbrs_threshold</em>, <em>verbose=False</em>, <em>max_iter=100</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/path_detection.html#detect_main_pathways"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.path_detection.detect_main_pathways" title="Permalink to this definition">¶</a></dt>
<dd><p>Detects the main pathways of an unordered 3D point cloud. Set as true
all points detected as part of all detected pathways that down to the
base of the graph.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional point cloud of a single tree to perform the
wood-leaf separation. This should be a n-dimensional array (m x n)
containing a set of coordinates (n) over a set of points (m).</p>
</dd>
<dt><strong>k_retrace</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of steps in the graph to retrace back to graph’s base. Every
node in graph will be moved k_retrace steps from the extremities
towards to base.</p>
</dd>
<dt><strong>knn</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of neighbors to fill gaps in detected paths. The larger the
better. A large knn will increase memory usage. Recommended value
between 50 and 150.</p>
</dd>
<dt><strong>nbrs_threshold</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Maximum distance to valid neighboring points used to fill gaps in
detected paths.</p>
</dd>
<dt><strong>verbose: bool</strong></dt>
<dd><p class="first last">Option to set verbose on/off.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first docutils">
<dt><strong>path_mask</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Boolean mask where ‘True’ represents points detected as part of the
main pathways and ‘False’ represents points not part of the pathways.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>AssertionError:</strong></dt>
<dd><p class="first last">point_cloud has the wrong shape or number of dimensions.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.path_detection.get_base">
<code class="descclassname">tlseparation.classification.path_detection.</code><code class="descname">get_base</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>base_height</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/path_detection.html#get_base"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.path_detection.get_base" title="Permalink to this definition">¶</a></dt>
<dd><p>Get the base of a point cloud based on a certain height from the bottom.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional point cloud of a single tree to perform the
wood-leaf separation. This should be a n-dimensional array (m x n)
containing a set of coordinates (n) over a set of points (m).</p>
</dd>
<dt><strong>base_height</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Height of the base slice to mask.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>mask</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Base slice masked as True.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.path_detection.path_detect_frequency">
<code class="descclassname">tlseparation.classification.path_detection.</code><code class="descname">path_detect_frequency</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>downsample_size</em>, <em>frequency_threshold</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/path_detection.html#path_detect_frequency"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.path_detection.path_detect_frequency" title="Permalink to this definition">¶</a></dt>
<dd><p>Detects points from major paths in a graph generated from a point cloud.
The detection is performed by comparing the frequency of all paths that
each node is present. Nodes with frequency larger than threshold are
selected as detected. In order to fill pathways regions with low nodes
density, neighboring points within downsampling_size * 1.5 distance are
also set as detected.</p>
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud</strong> <span class="classifier-delimiter">:</span> <span class="classifier">numpy.ndarray</span></dt>
<dd><p class="first last">2D (n x 3) array containing n points in 3D space (x, y, z).</p>
</dd>
<dt><strong>downsample_size</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Distance threshold used to group (downsample) the input point cloud.
Simplificaton of the cloud by downsampling, improves the results and
processing times.</p>
</dd>
<dt><strong>frequency_threshold</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Minimum path frequency for a node to be selected as part of major
pathways.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>path_points: numpy.ndarray</strong></dt>
<dd><p class="first last">2D (np x 3) array containing n points in 3D space (x, y, z) that
belongs to major pathways in the point cloud.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.path_detection.voxel_path_detection">
<code class="descclassname">tlseparation.classification.path_detection.</code><code class="descname">voxel_path_detection</code><span class="sig-paren">(</span><em>point_cloud</em>, <em>voxel_size</em>, <em>k_retrace</em>, <em>knn</em>, <em>nbrs_threshold</em>, <em>verbose=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/path_detection.html#voxel_path_detection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.path_detection.voxel_path_detection" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies detect_main_pathways but with a voxelization option to speed up
processing.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>point_cloud</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional point cloud of a single tree to perform the
wood-leaf separation. This should be a n-dimensional array (m x n)
containing a set of coordinates (n) over a set of points (m).</p>
</dd>
<dt><strong>voxel_size: float</strong></dt>
<dd><p class="first last">Voxel dimensions’ size.</p>
</dd>
<dt><strong>k_retrace</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of steps in the graph to retrace back to graph’s base. Every
node in graph will be moved k_retrace steps from the extremities
towards to base.</p>
</dd>
<dt><strong>knn</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of neighbors to fill gaps in detected paths. The larger the
better. A large knn will increase memory usage. Recommended value
between 50 and 150.</p>
</dd>
<dt><strong>nbrs_threshold</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Maximum distance to valid neighboring points used to fill gaps in
detected paths.</p>
</dd>
<dt><strong>verbose: bool</strong></dt>
<dd><p class="first last">Option to set verbose on/off.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first docutils">
<dt><strong>path_mask</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Boolean mask where ‘True’ represents points detected as part of the
main pathways and ‘False’ represents points not part of the pathways.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Raises:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>AssertionError:</strong></dt>
<dd><p class="first last">point_cloud has the wrong shape or number of dimensions.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification.point_features">
<span id="tlseparation-classification-point-features-module"></span><h2>tlseparation.classification.point_features module<a class="headerlink" href="#module-tlseparation.classification.point_features" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="tlseparation.classification.point_features.calc_features">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">calc_features</code><span class="sig-paren">(</span><em>e</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#calc_features"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.calc_features" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates the geometric features using a set of eigenvalues, based on Ma
et al. <a class="footnote-reference" href="#id3" id="id1">[1]</a> and Wang et al. <a class="footnote-reference" href="#id4" id="id2">[2]</a>.</p>
<table class="docutils field-list" frame="void" rules="none">
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<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>e</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (m x 3) containing sets of 3 eigenvalues per
row (m).</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>features</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (m x 6) containing the calculated geometric
features from ‘e’.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<table class="docutils footnote" frame="void" id="id3" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id1">[1]</a></td><td>Ma et al., 2015. Improved Salient Feature-Based Approach for
Automatically Separating Photosynthetic and Nonphotosynthetic
Components Within Terrestrial Lidar Point Cloud Data of Forest
Canopies.</td></tr>
</tbody>
</table>
<table class="docutils footnote" frame="void" id="id4" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id2">[2]</a></td><td>Wang et al., 2015. A Multiscale and Hierarchical Feature Extraction
Method for Terrestrial Laser Scanning Point Cloud Classification.</td></tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.point_features.curvature">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">curvature</code><span class="sig-paren">(</span><em>arr</em>, <em>nbrs_idx</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#curvature"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.curvature" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates pointwise curvature of a point cloud.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional (m x n) array of a point cloud, where the
coordinates are represented in the columns (n) and the points are
represented in the rows (m).</p>
</dd>
<dt><strong>nbr_idx</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array of indices from a nearest neighbors search of the
point cloud in ‘arr’, where the rows (m) represents the points in
‘arr’ and the columns represents the indices of the nearest neighbors
from ‘arr’.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>c</strong> <span class="classifier-delimiter">:</span> <span class="classifier">numpy.ndarray</span></dt>
<dd><p class="first last">1D (m x 1) array containing the curvature of each point in ‘arr’.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.point_features.knn_evals">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">knn_evals</code><span class="sig-paren">(</span><em>arr_stack</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#knn_evals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.knn_evals" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates eigenvalues of a stack of arrays.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr_stack</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (l x m x n) containing a stack of data, where the
rows (m) represents the points coordinates, the columns (n) represents
the axis coordinates and the layer (l) represents the stacks of points.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>evals</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (l x n) of eigenvalues calculated from
‘arr_stack’. The rows (l) represents the stack layers of points in
‘arr_stack’ and the columns (n) represent the parameters in
‘arr_stack’.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.point_features.knn_features">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">knn_features</code><span class="sig-paren">(</span><em>arr</em>, <em>nbr_idx</em>, <em>block_size=200000</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#knn_features"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.knn_features" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates geometric descriptors: salient features and tensor features
from an array and an indexing with fixed numbers of neighbors.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional (m x n) array of a point cloud, where the
coordinates are represented in the columns (n) and the points are
represented in the rows (m).</p>
</dd>
<dt><strong>nbr_idx</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array of indices from a nearest neighbors search of the
point cloud in ‘arr’, where the rows (m) represents the points in
‘arr’ and the columns represents the indices of the nearest neighbors
from ‘arr’.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>features</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (m x 6) of the calculated geometric descriptors.
Where the rows (m) represent the points from ‘arr’ and the columns
represents the features.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.point_features.svd_evals">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">svd_evals</code><span class="sig-paren">(</span><em>arr</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#svd_evals"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.svd_evals" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculates eigenvalues of an array using SVD.</p>
<table class="docutils field-list" frame="void" rules="none">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">nxm numpy.ndarray where n is the number of samples and m is the number
of dimensions.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>evals</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">1xm numpy.ndarray containing the calculated eigenvalues in decrescent
order.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.point_features.vectorized_app">
<code class="descclassname">tlseparation.classification.point_features.</code><code class="descname">vectorized_app</code><span class="sig-paren">(</span><em>arr_stack</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/point_features.html#vectorized_app"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.point_features.vectorized_app" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to calculate the covariance of a stack of arrays. This function
uses einstein summation to make the covariance calculation more efficient.
Based on a reply from the user Divakar <a class="footnote-reference" href="#id6" id="id5">[3]</a> at stackoverflow.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr_stack</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (l x m x n) containing a stack of data, where the
rows (m) represents the points coordinates, the columns (n) represents
the axis coordinates and the layer (l) represents the stacks of
points.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>cov</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">N-dimensional array (l x n x n) of covariance values calculated from
‘arr_stack’. Each layer (l) contains a (n x n) covariance matrix
calculated from the layers (l) in ‘arr_stack’.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
<p class="rubric">References</p>
<table class="docutils footnote" frame="void" id="id6" rules="none">
<colgroup><col class="label" /><col /></colgroup>
<tbody valign="top">
<tr><td class="label"><a class="fn-backref" href="#id5">[3]</a></td><td>Divakar, 2016. <a class="reference external" href="http://stackoverflow.com/questions/35756952/quickly-compute-eigenvectors-for-each-element-of-an-array-in-python">http://stackoverflow.com/questions/35756952/quickly-compute-eigenvectors-for-each-element-of-an-array-in-python</a>.</td></tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification.wlseparation">
<span id="tlseparation-classification-wlseparation-module"></span><h2>tlseparation.classification.wlseparation module<a class="headerlink" href="#module-tlseparation.classification.wlseparation" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="tlseparation.classification.wlseparation.fill_class">
<code class="descclassname">tlseparation.classification.wlseparation.</code><code class="descname">fill_class</code><span class="sig-paren">(</span><em>arr1</em>, <em>arr2</em>, <em>noclass</em>, <em>k</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/wlseparation.html#fill_class"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.wlseparation.fill_class" title="Permalink to this definition">¶</a></dt>
<dd><p>Assigns noclass entries to either arr1 or arr2, depending on
neighborhood majority analisys.</p>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr1</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Point coordinates for entries of the first class.</p>
</dd>
<dt><strong>arr2</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Point coordinates for entries of the second class.</p>
</dd>
<dt><strong>noclass</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Point coordinates for noclass entries.</p>
</dd>
<dt><strong>k</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of neighbors to use in the neighborhood majority analysis.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>arr1</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Point coordinates for entries of the first class.</p>
</dd>
<dt><strong>arr2</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Point coordinates for entries of the second class.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.wlseparation.wlseparate_abs">
<code class="descclassname">tlseparation.classification.wlseparation.</code><code class="descname">wlseparate_abs</code><span class="sig-paren">(</span><em>arr</em>, <em>knn</em>, <em>knn_downsample=1</em>, <em>n_classes=3</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/wlseparation.html#wlseparate_abs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.wlseparation.wlseparate_abs" title="Permalink to this definition">¶</a></dt>
<dd><p>Classifies a point cloud (arr) into three main classes, wood, leaf and
noclass.</p>
<p>The final class selection is based on the absolute value of the last
geometric feature (see point_features module).
Points will be only classified as wood or leaf if their classification
probability is higher than prob_threshold. Otherwise, points are
assigned to noclass.</p>
<p>Class selection will mask points with feature value larger than a given
threshold as wood and the remaining points as leaf.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional point cloud of a single tree to perform the
wood-leaf separation. This should be a n-dimensional array (m x n)
containing a set of coordinates (n) over a set of points (m).</p>
</dd>
<dt><strong>knn</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of nearest neighbors to search to constitue the local subset of
points around each point in ‘arr’.</p>
</dd>
<dt><strong>knn_downsample</strong> <span class="classifier-delimiter">:</span> <span class="classifier">float</span></dt>
<dd><p class="first last">Downsample factor (0, 1) for the knn parameter. If less than 1, a
sample of size (knn * knn_downsample) will be selected from the
nearest neighbors indices. This option aims to maintain the spatial
representation of the local subsets of points, but reducing overhead
in memory and processing time.</p>
</dd>
<dt><strong>n_classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of classes to use in the Gaussian Mixture Classification.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>class_indices</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict</span></dt>
<dd><p class="first last">Dictionary containing indices for wood and leaf classes.</p>
</dd>
<dt><strong>class_probability</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict</span></dt>
<dd><p class="first last">Dictionary containing probabilities for wood and leaf classes.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="tlseparation.classification.wlseparation.wlseparate_ref_voting">
<code class="descclassname">tlseparation.classification.wlseparation.</code><code class="descname">wlseparate_ref_voting</code><span class="sig-paren">(</span><em>arr</em>, <em>knn_lst</em>, <em>class_file</em>, <em>n_classes=3</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/tlseparation/classification/wlseparation.html#wlseparate_ref_voting"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#tlseparation.classification.wlseparation.wlseparate_ref_voting" title="Permalink to this definition">¶</a></dt>
<dd><p>Classifies a point cloud (arr) into two main classes, wood and leaf.
Altough this function does not output a noclass category, it still
filters out results based on classification confidence interval in the
voting process (if lower than prob_threshold, then voting is not used
for current point and knn value).</p>
<p>The final class selection is based a voting scheme applied to a similar
approach of wlseparate_ref. In this case, the function iterates over a
series of knn values and apply the reference distance criteria to select
wood and leaf classes.</p>
<p>Each knn class result is accumulated in a list and in the end a voting
is applied. For each point, if the number of times it was classified as
wood is larger than threhsold, the final class is set to wood. Otherwise
it is set as leaf.</p>
<p>Class selection will mask points according to their class mean distance
to reference classes. The closes reference class gets assignes to each
intermediate class.</p>
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<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><dl class="first docutils">
<dt><strong>arr</strong> <span class="classifier-delimiter">:</span> <span class="classifier">array</span></dt>
<dd><p class="first last">Three-dimensional point cloud of a single tree to perform the
wood-leaf separation. This should be a n-dimensional array (m x n)
containing a set of coordinates (n) over a set of points (m).</p>
</dd>
<dt><strong>knn_lst</strong> <span class="classifier-delimiter">:</span> <span class="classifier">list</span></dt>
<dd><p class="first last">List of knn values to use in the search to constitue local subsets of
points around each point in ‘arr’. It can be a single knn value, as
long as it has list data type.</p>
</dd>
<dt><strong>class_file</strong> <span class="classifier-delimiter">:</span> <span class="classifier">pandas dataframe or str</span></dt>
<dd><p class="first last">Dataframe or path to reference classes file.</p>
</dd>
<dt><strong>n_classes</strong> <span class="classifier-delimiter">:</span> <span class="classifier">int</span></dt>
<dd><p class="first last">Number of classes to use in the Gaussian Mixture Classification.</p>
</dd>
</dl>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><dl class="first last docutils">
<dt><strong>class_dict</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict</span></dt>
<dd><p class="first last">Dictionary containing indices for all classes in class_ref. Classes
are labeled according to classes names in class_file.</p>
</dd>
<dt><strong>count_dict</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict</span></dt>
<dd><p class="first last">Dictionary containin votes count for all classes in class_ref. Classes
are labeled according to classes names in class_file.</p>
</dd>
<dt><strong>prob_dict</strong> <span class="classifier-delimiter">:</span> <span class="classifier">dict</span></dt>
<dd><p class="first last">Dictionary containing probabilities for all classes in class_ref.
Classes are labeled according to classes names in class_file.</p>
</dd>
</dl>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="module-tlseparation.classification">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-tlseparation.classification" title="Permalink to this headline">¶</a></h2>
</div>
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