Add module documentation

src/fswAlgorithms/imageProcessing/centerOfBrightness/centerOfBrightness.rst

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+Executive Summary
+-----------------
+
+Module reads in a message containing a pointer to an image and writes out the weighted center of brightness of the
+image for any pixel above a parametrized threshold.
+
+
+Message Connection Descriptions
+-------------------------------
+The following table lists all the module input and output messages.  The module msg connection is set by the
+user from python.  The msg type contains a link to the message structure definition, while the description
+provides information on what this message is used for.
+
+
+.. list-table:: Module I/O Messages
+    :widths: 25 25 50
+    :header-rows: 1
+
+    * - Msg Variable Name
+      - Msg Type
+      - Description
+    * - opnavCirclesOutMsg
+      - :ref:`OpNavCOBMsgPayload`
+      - output center of brightness message containing number of detected pixels, and center-of-brightness in pixel space
+    * - imageInMsg
+      - :ref:`CameraImageMsgPayload`
+      - camera image input message containing the pointer to the image
+
+Detailed Module Description
+---------------------------
+
+Image Processing
+^^^^^^^^^^^^^^^^^^^^^
+
+The module performs a small set of operations to prepare the image for the center of brightness detection.
+Firstly, it applies a Gaussian blur on the image, which helps soften the edges of bright objects and improves
+the detection performance.
+Secondly it thresholds the image in order to remove any dim pixels that may not be of interest to the detection
+algorith.
+Both of these are implemented using OpenCV methods.
+
+Afterwards the weighted center-of-brightness is implemented by finding all the non-zero pixels (all pixels that were
+not thresholded down), and finding their intensity (which will be their weight).
+Assuming the image is made up of pixels :math:`p` with and x and y component, that :math:`I` is the intensity, and
+:math:`I_{\mathrm{min}` is the threshold value, the center of brightness is then defined as:
+
+.. math::
+
+    \mathcal{P} &= \{p \in \mathrm{image} \hspace{5cm} |  \hspace{5cm} I(p) > I_{\mathrm{min}\} \\
+    I_\mathrm{tot} &= \sum_{p \in \mathcal{P}} I(p) \\
+    p_{\mathrm{cob}} &= \frac{1}{I_\mathrm{tot}}\sum_{p \in \mathcal{P}} I(p) * p }
+
+where the center of brightess :math:`p_{\mathrm{cob}}` has an x and y component which are then written to the message.
+
+If the incomping image is not valid, or there were no pixels above the threshold, the image is tagged as invalid.
+Downstream algorithms can therefore know when to skip a measurement.
+
+User Guide
+----------
+This section is to outline the steps needed to setup a Center of Brightness in Python.
+
+#. Import the centerOfBrightness class::
+
+    from Basilisk.fswAlgorithms import centerOfBrightness
+
+#. Create an instantiation of a Spinning body::
+
+    cobAlgorithm = centerOfBrightness.CenterOfBrightness()
+
+#. Define all physical parameters for a Spinning Body. For example::
+
+    cobAlgorithm.blurSize = 7
+    cobAlgorithm.threshold = 10
+
+#. Subscribe to the image message output by the camera model or visualization interface::
+
+    cobAlgorithm.imageInMsg.subscribeTo(imgInMsg)
+
+#. The angular states of the body are created using an output message ``opnavCOBOutMsg``.
+
+    sim.AddModelToTask(taskName, cobAlgorithm)