====== ContextInsights ======

This page describes how to use the "ContextInsights" extension for Orbit 3DM Feature Extraction Pro. \\
Specific system requirements and installation guidelines, see [[224:technology:platforms:install_contextinsights|]].

{{orbit_desktop:how_to_find.png?25&nolink  |}} [[224:desktop:workspace:main_toolbar|Main Toolbar]] > Extensions > Context Insights

===== Concepts =====

==== Context Insights ====

ContextInsights extension enables automatic detection on mapping and point cloud resources. \\
It relies on detectors to identify objects or regions of interest.

Running Context Insights detectors requires Python, Context Insights Engine, and original images from any mapping resource. The results can be directly imported in the mapping resources via the Annotations procedure. 

==== Jobs ==== 
 
A ContextInsights job will leverage machine learning to execute different types of detection in your reality data. \\
There are various types of jobs available:
  * Image 2D Objects: Will detect elements as regular 2D-bounding boxes in images. Regular 2D boxes around assets.
  * Image 2D Segmentation: Will detect regions in images and deliver segmented raster. Regions to segmented raster.
  * Point Cloud 3D Segmentation: Attribute a class to each point of laserscan data. Classify each element of a pointcloud.

Each job type requires a suited detector to be executed on reality data. E.g: a 2D segmentation detector will only be usable for image based segmentation jobs

==== Detectors ====

A detector is a frozen model that is pre-trained by Bentley Systems. Running on your reality data through a ContextInsights job, it will automatically recognize elements of interest. List of pre-trained detectors is available here: \\
https://communities.bentley.com/products/3d_imaging_and_point_cloud_software/w/wiki/54656/context-insights-detectors-download-page

A detector is specific to a certain job type. The quality of the detection will depend on the similarity between your dataset and the training dataset’s description.

===== Launch Context Insights Engine =====

Launch ContextInsights Engine \\
{{:224:desktop_ext:cinsights_engine.jpg?200|}}

===== Context Insights Sidebar =====

==== Preferences ====

Configure the directories to Python, the virtual environment, and the Context Insights detectors.
The detectors and specs are listed underneath. 

==== Action ====

Define whether you want to create a new job or open existing one
  * Create a new job and enter the target location of the results. 
  * Open an existing job and browse to the location of the results. 

==== Detectors ====

Depending on the added detectors, the following annotation types will be available.

<note important>Image based jobs can only run on JPG images</note>

==== Analyze ====

Delegate the process to the task manager or start now. Running the Context Capture Engine is required before starting the process. 

==== Import ====

=== Open Procedure ===

Open the Annotations procedure where the result xml file is already pre-filled as source object annotation file. \\
Import 2D Objects or 2D Segmentations, depending on the chosen Annotation Type. 

=== Open File Location ===

Open the target directory to verify the results. 

=== Image 2D Objects ===

Import the scene xml file as 2D Object annotations to overlay the results on original or optimized images

=== Image 2D Segmentation ===

Apply Segmentation at optimize imagery to display the result on optimized images