Added SVM

Author: SaketKulkarni121

documentation/html/build/doctrees/environment.pickle

@@ -40,6 +40,12 @@ object detection, lane detection, and cone detection. These algorithms are used
 understanding of the environment around it. To understand the specifics of these algorithms, please refer to the
 documentation.
 
+This documentation is a guide to the perception algorithms which follows the same structure as the Perceptions Library Github repository.
+The documentation is divided into two main sections: the perception algorithms and the scripts that are used to run the algorithms. 
+The perception algorithms section provides a detailed explanation of the algorithms used in the perception stack. The scripts section
+provides a detailed explanation of the scripts that are used to run the perception algorithms. For further details about the perception
+algorithms, please refer to the github, which has detailed inline comments for each function.
+
 Getting Started
 ===============
 

documentation/html/build/doctrees/index.doctree

@@ -27,4 +27,7 @@ what the decision boundary is between the 2 colors of cones, we then downsample
 all of the cones in the mesh frame as a better center line for the car.
 
 Additionally, we also provide 2 base cones behind the cars original location to provide the SVM with an understanding of color and distance.
-After we get the decision boundaries from the SVM, we then use the decision boundaries to improve coloring for our LiDAR algorithm.
+After we get the decision boundaries from the SVM, we then use the decision boundaries to improve coloring for our LiDAR algorithm.
+
+.. image :: ../../../../utils/svm.png
+    :align: center

documentation/html/build/doctrees/perc22a/svm/SVM.doctree

@@ -101,6 +101,11 @@ <h2>What is Perceptions?<a class="headerlink" href="#what-is-perceptions" title=
 object detection, lane detection, and cone detection. These algorithms are used to provide the car with a complete
 understanding of the environment around it. To understand the specifics of these algorithms, please refer to the
 documentation.</p>
+<p>This documentation is a guide to the perception algorithms which follows the same structure as the Perceptions Library Github repository.
+The documentation is divided into two main sections: the perception algorithms and the scripts that are used to run the algorithms.
+The perception algorithms section provides a detailed explanation of the algorithms used in the perception stack. The scripts section
+provides a detailed explanation of the scripts that are used to run the perception algorithms. For further details about the perception
+algorithms, please refer to the github, which has detailed inline comments for each function.</p>
 </section>
 <section id="getting-started">
 <h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading"></a></h2>

documentation/html/build/html/_images/svm.png

@@ -109,6 +109,7 @@ <h2>How does it work?<a class="headerlink" href="#how-does-it-work" title="Perma
 all of the cones in the mesh frame as a better center line for the car.</p>
 <p>Additionally, we also provide 2 base cones behind the cars original location to provide the SVM with an understanding of color and distance.
 After we get the decision boundaries from the SVM, we then use the decision boundaries to improve coloring for our LiDAR algorithm.</p>
+<img alt="../../_images/svm.png" class="align-center" src="../../_images/svm.png" />
 </section>
 </section>
 

documentation/html/build/html/_sources/index.rst.txt

@@ -27,4 +27,7 @@ what the decision boundary is between the 2 colors of cones, we then downsample
 all of the cones in the mesh frame as a better center line for the car.
 
 Additionally, we also provide 2 base cones behind the cars original location to provide the SVM with an understanding of color and distance.
-After we get the decision boundaries from the SVM, we then use the decision boundaries to improve coloring for our LiDAR algorithm.
+After we get the decision boundaries from the SVM, we then use the decision boundaries to improve coloring for our LiDAR algorithm.
+
+.. image :: ../../../../utils/svm.png
+    :align: center