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fix(edgeai): Standardize RST section headers to follow sphinx guidelines
Update all rst files under source/edgeai to conform to sphinx guidelines for section headers [0] [0] - https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#sections Signed-off-by: Chirag Shilwant <[email protected]>
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‎source/edgeai/configuration_file.rst‎

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.. _pub_edgeai_configuration:
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########################
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Configuring applications
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========================
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########################
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The demo config file uses YAML format to define input sources, models, outputs
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and finally the flows which defines how everything is connected. Config files
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#. Outputs
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#. Flows
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******
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Inputs
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======
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******
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The input section defines a list of supported inputs like camera, video files etc.
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Their properties like shown below.
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.. _pub_edgeai_camera_sources:
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Camera sources (v4l2)
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=====================
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**v4l2src** GStreamer element is used to capture frames from camera sources
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which are exposed as v4l2 devices. In Linux, there are many devices which are
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that needs ISP.
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Video sources
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=============
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H.264 and H.265 encoded videos can be provided as input sources to the demos.
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Sample video files are provided under :file:`/opt/edgeai-test-data/videos/`
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bin ``decodebin`` instead.
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Image sources
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=============
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JPEG compressed images can be provided as inputs to the demos. A sample set of
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images are provided under :file:`/opt/edgeai-test-data/images`. The names of the
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framerate: 1
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RTSP sources
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============
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H.264 encoded video streams either coming from a RTSP compliant IP camera or
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via RTSP server running on a remote PC can be provided as inputs to the demo.
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height: 720
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framerate: 30
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******
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Models
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The model section defines a list of models that are used in the demo. Path to
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the model directory is a required argument for each model and rest are optional
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The content of the model directory and its structure is discussed in detail in
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:ref:`pub_edgeai_import_custom_models`
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*******
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Outputs
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=======
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*******
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The output section defines a list of supported outputs.
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Below are the details of most commonly used outputs
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Display sink (kmssink)
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======================
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When you have only one display connected to the SK, kmssink will try to use
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it for displaying the output buffers. In case you have connected multiple
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Configure the required connector ID in the output section of the config file.
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Video sinks
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===========
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The post-processed outputs can be encoded in H.264 format and stored on disk.
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Please specify the location of the video file in the configuration file.
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height: 1080
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Image sinks
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The post-processed outputs can be stored as JPEG compressed images.
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Please specify the location of the image files in the configuration file.
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The images will be named sequentially and incrementally as shown.
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height: 1080
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Remote sinks
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Post-processed frames can be encoded as jpeg or h264 frames and send as udp packets
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to a port. Please specify the sink as remote in the configuration file. The udp port and
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host to send packets to can be defined. If not, default port is 8081 and host
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/opt/edgeai-gst-apps# node scripts/remote_streaming/server.js
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Flows
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The flows section defines how inputs, models and outputs are connected.
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Multiple flows can be defined to achieve multi input, multi inference as shown
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plane. This is needed because multiple inference outputs can be rendered to same
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output (Ex: Display).
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GStreamer plugins
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=================
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‎source/edgeai/docker_environment.rst‎

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.. _pub_edgeai_docker_env:
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Docker Environment
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##################
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Docker is a set of "platform as a service" products that uses the OS-level
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virtualization to deliver software in packages called containers.
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Building Docker image
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The `docker/Dockerfile` in the edgeai-gst-apps repo describes the recipe for
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/opt/edgeai-gst-apps/docker# ./docker_build.sh
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Running the Docker container
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Enter the Docker session as shown,
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Additional Docker commands
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**Commit Docker container**
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Commit Docker container
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Generally, containers have a short life cycle. If the container has any local
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For more information refer:
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**Save Docker Image**
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Save Docker Image
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Docker image can be saved as tar file by using the command below:
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**Additional references**
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‎source/edgeai/edgeai_dataflows.rst‎

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.. _pub_edgeai_dataflows:
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Edge AI dataflows
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The reference edgeai application at a high level can be split into 3 parts,
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OpTIFlow
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Image Classification
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Python/C++ apps
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