fix(edgeai): Update section headers per 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]>

Author: cshilwant

source/edgeai/configuration_file.rst

@@ -1,8 +1,8 @@
 .. _pub_edgeai_configuration:
 
-========================
+########################
 Configuring applications
-========================
+########################
 
 The demo config file uses YAML format to define input sources, models, outputs
 and finally the flows which defines how everything is connected. Config files
@@ -19,8 +19,9 @@ Config file is divided in 4 sections:
 #. Outputs
 #. Flows
 
+******
 Inputs
-======
+******
 
 The input section defines a list of supported inputs like camera, video files etc.
 Their properties like shown below.
@@ -55,7 +56,7 @@ Below are the details of most commonly used inputs.
 .. _pub_edgeai_camera_sources:
 
 Camera sources (v4l2)
----------------------
+=====================
 
 **v4l2src** GStreamer element is used to capture frames from camera sources
 which are exposed as v4l2 devices. In Linux, there are many devices which are
@@ -80,8 +81,8 @@ and prints the detail like below in the console:
 script can also be run manually later to get the camera details.
 
 From the above log we can determine that 1 USB camera is connected
-(:file:`/dev/video-usb-cam0`), and 1 CSI camera is connected (:file:`/dev/video-imx219-cam0`) which is IMX219 raw
-sensor and needs ISP.
+(:file:`/dev/video-usb-cam0`), and 1 CSI camera is connected (:file:`/dev/video-imx219-cam0`),
+which is IMX219 raw sensor and needs ISP.
 
 Using this method, you can configure correct device for camera capture in the
 input section of config file.
@@ -109,10 +110,10 @@ camera to allow GStreamer to negotiate the format. ``rggb`` for sensor
 that needs ISP.
 
 Video sources
--------------
+=============
 
 H.264 and H.265 encoded videos can be provided as input sources to the demos.
-Sample video files are provided under :file:`/opt/edgeai-test-data/videos/`
+The :file:`/opt/edgeai-test-data/videos/` directory contains sample video files.
 
 .. code-block:: yaml
 
@@ -135,12 +136,11 @@ By default the format is set to ``auto`` which will then use the GStreamer
 bin ``decodebin`` instead.
 
 Image sources
--------------
+=============
 
-JPEG compressed images can be provided as inputs to the demos. A sample set of
-images are provided under :file:`/opt/edgeai-test-data/images`. The names of the
-files are numbered sequentially and incrementally and the demo plays the files
-at the fps specified by the user.
+JPEG compressed images can be provided as inputs to the demos. The :file:`/opt/edgeai-test-data/images`
+directory contains sample images. The names of the files are numbered sequentially and incrementally
+and the demo plays the files at the fps specified by the user.
 
 .. code-block:: yaml
 
@@ -152,7 +152,7 @@ at the fps specified by the user.
       framerate: 1
 
 RTSP sources
-------------
+============
 
 H.264 encoded video streams either coming from a RTSP compliant IP camera or
 via RTSP server running on a remote PC can be provided as inputs to the demo.
@@ -165,8 +165,9 @@ via RTSP server running on a remote PC can be provided as inputs to the demo.
       height: 720
       framerate: 30
 
+******
 Models
-======
+******
 
 The model section defines a list of models that are used in the demo. Path to
 the model directory is a required argument for each model and rest are optional
@@ -200,9 +201,9 @@ Below are some of the use case specific properties:
 The content of the model directory and its structure is discussed in detail in
 :ref:`pub_edgeai_import_custom_models`
 
-
+*******
 Outputs
-=======
+*******
 
 The output section defines a list of supported outputs.
 
@@ -239,7 +240,7 @@ All supported outputs are listed in template config file.
 Below are the details of most commonly used outputs
 
 Display sink (kmssink)
-----------------------
+======================
 
 When you have only one display connected to the SK, kmssink will try to use
 it for displaying the output buffers. In case you have connected multiple
@@ -261,7 +262,7 @@ Following command finds out the connected displays available to use.
 Configure the required connector ID in the output section of the config file.
 
 Video sinks
------------
+===========
 The post-processed outputs can be encoded in H.264 format and stored on disk.
 Please specify the location of the video file in the configuration file.
 
@@ -273,7 +274,7 @@ Please specify the location of the video file in the configuration file.
       height: 1080
 
 Image sinks
------------
+===========
 The post-processed outputs can be stored as JPEG compressed images.
 Please specify the location of the image files in the configuration file.
 The images will be named sequentially and incrementally as shown.
@@ -286,7 +287,7 @@ The images will be named sequentially and incrementally as shown.
       height: 1080
 
 Remote sinks
-------------
+============
 Post-processed frames can be encoded as jpeg or h264 frames and send as udp packets
 to a port. Please specify the sink as remote in the configuration file. The udp port and
 host to send packets to can be defined. If not, default port is 8081 and host
@@ -310,9 +311,9 @@ on localhost (127.0.0.1) and can be used to view the frames remotely.
 
    /opt/edgeai-gst-apps# node scripts/remote_streaming/server.js
 
-
+*****
 Flows
-=====
+*****
 
 The flows section defines how inputs, models and outputs are connected.
 Multiple flows can be defined to achieve multi input, multi inference as shown
@@ -338,7 +339,6 @@ for optimization. Along with input, models and outputs it is required to define
 plane. This is needed because multiple inference outputs can be rendered to same
 output (Ex: Display).
 
-
 GStreamer plugins
 =================
 

source/edgeai/docker_environment.rst

@@ -1,8 +1,8 @@
 .. _pub_edgeai_docker_env:
 
-==================
+##################
 Docker Environment
-==================
+##################
 
 Docker is a set of "platform as a service" products that uses the OS-level
 virtualization to deliver software in packages called containers.
@@ -16,8 +16,9 @@ additional 3rd party applications and packages as required.
 
 .. _pub_edgeai_docker_build_ontarget:
 
+*********************
 Building Docker image
-======================
+*********************
 
 The `docker/Dockerfile` in the edgeai-gst-apps repo describes the recipe for
 creating the Docker container image. Feel free to review and update it to
@@ -40,8 +41,9 @@ Initiate the Docker image build as shown,
 
    /opt/edgeai-gst-apps/docker# ./docker_build.sh
 
+****************************
 Running the Docker container
-============================
+****************************
 
 Enter the Docker session as shown,
 
@@ -83,16 +85,17 @@ access camera, display and other hardware accelerators the SoC has to offer.
 
 .. _pub_edgeai_docker_additional_commands:
 
+**************************
 Additional Docker commands
-==========================
+**************************
 
 .. note::
 
    This section is provided only for additional reference and not required to
    run out-of-box demos
 
-**Commit Docker container**
-
+Commit Docker container
+=======================
 Generally, containers have a short life cycle. If the container has any local
 changes it is good to save the changes on top of the existing Docker image.
 When re-running the Docker image, the local changes can be restored.
@@ -111,7 +114,8 @@ the container.
 For more information refer:
 `Commit Docker image <https://docs.docker.com/engine/reference/commandline/commit/>`_
 
-**Save Docker Image**
+Save Docker Image
+=================
 
 Docker image can be saved as tar file by using the command below:
 
@@ -120,9 +124,10 @@ Docker image can be saved as tar file by using the command below:
    docker save --output <pre_built_docker_image.tar>
 
 For more information refer here.
-`Save Docker image <https://docs.docker.com/engine/reference/commandline/save/>`_
+`docker image save <https://docs.docker.com/engine/reference/commandline/save/>`_
 
-**Load Docker image**
+Load Docker image
+=================
 
 Load a previously saved Docker image using the command below:
 
@@ -131,9 +136,10 @@ Load a previously saved Docker image using the command below:
    docker load --input <pre_built_docker_image.tar>
 
 For more information refer here.
-`Load Docker image <https://docs.docker.com/engine/reference/commandline/load/>`_
+`docker image load <https://docs.docker.com/engine/reference/commandline/load/>`_
 
-**Remove Docker image**
+Remove Docker image
+===================
 
 Docker image can be removed by using the command below:
 
@@ -149,7 +155,8 @@ For more information refer
 `rmi reference <https://docs.docker.com/engine/reference/commandline/rmi/>`_ and
 `Image prune reference <https://docs.docker.com/engine/reference/commandline/image_prune/>`_
 
-**Remove Docker container**
+Remove Docker container
+=======================
 
 Docker container can be removed by using the command below:
 
@@ -220,7 +227,9 @@ current location is the desired location then exit this procedure.
 6. Anytime the SD card is updated with a new targetfs, steps (1), (3), and
    (4) need to be followed.
 
-**Additional references**
+*********************
+Additional references
+*********************
 
 | https://docs.docker.com/engine/reference/commandline/images/
 | https://docs.docker.com/engine/reference/commandline/ps/

source/edgeai/edgeai_dataflows.rst

@@ -1,8 +1,8 @@
 .. _pub_edgeai_dataflows:
 
-=================
+#################
 Edge AI dataflows
-=================
+#################
 
 The reference edgeai application at a high level can be split into 3 parts,
 
@@ -16,11 +16,14 @@ GStreamer launch strings that is generated. User can interact with the applicati
 
 .. _pub_edgeai_optiflow_data_flow:
 
+********
 OpTIFlow
-====================
+********
+
+.. _pub_edgeai_optiflow_image_classification:
 
 Image Classification
---------------------
+====================
 
 | **Input: USB Camera**
 | **DL Task: Classification**
@@ -59,7 +62,7 @@ GStreamer pipeline:
    OpTIFlow pipeline for image classification demo with USB camera and display
 
 Object Detection
---------------------
+================
 
 | **Input: IMX219 Camera**
 | **DL Task: Detection**
@@ -100,7 +103,7 @@ GStreamer pipeline:
    OpTIFlow pipeline for object detection demo with IMX219 camera and save to file
 
 Semantic Segmentation
----------------------
+=====================
 
 | **Input: H264 Video**
 | **DL Task: Segmentation**
@@ -140,7 +143,7 @@ GStreamer pipeline:
    OpTIFlow pipeline for semantic segmentation demo with file input and remote streaming
 
 Single Input Multi Inference
-----------------------------
+============================
 
 | **Input: H264 Video**
 | **DL Task: Detection, Detection, Classification, Segmentation**
@@ -187,7 +190,7 @@ GStreamer pipeline:
    OpTIFlow pipeline for single input multi inference
 
 Multi Input Multi Inference
-----------------------------
+===========================
 
 | **Input: USB Camera, H264 Video**
 | **DL Task: Detection, Detection, Classification, Segmentation**
@@ -235,11 +238,12 @@ GStreamer pipeline:
 
    OpTIFlow pipeline for multi input multi inference
 
+***************
 Python/C++ apps
-======================
+***************
 
 Image Classification
---------------------
+====================
 
 | **Input: USB Camera**
 | **DL Task: Classification**
@@ -282,7 +286,7 @@ GStreamer output pipeline:
    Python/C++ application data-flow for image classification demo with USB camera and display
 
 Object Detection
---------------------
+================
 
 | **Input: IMX219 Camera**
 | **DL Task: Detection**
@@ -326,7 +330,7 @@ GStreamer output pipeline:
    Python/C++ application data-flow for object detection demo with IMX219 camera and save to file
 
 Semantic Segmentation
----------------------
+=====================
 
 | **Input: H264 Video**
 | **DL Task: Segmentation**
@@ -369,7 +373,7 @@ GStreamer output pipeline:
    Python/C++ application data-flow for semantic segmentation demo with file input and remote streaming
 
 Single Input Multi Inference
-----------------------------
+============================
 
 | **Input: H264 Video**
 | **DL Task: Detection, Detection, Classification, Segmentation**
@@ -420,7 +424,7 @@ GStreamer output pipeline:
    Python/C++ application data-flow for single input multi inference
 
 Multi Input Multi Inference
-----------------------------
+===========================
 
 | **Input: USB Camera, H264 Video**
 | **DL Task: Detection, Detection, Classification, Segmentation**