This page documents the following Segmentation examples:
- 360 Degree Video Dewarping
- Perspective Video Dewarping
- Industrial Segmentation and Viewing
- Semantic Segmentation and Viewing
video_dewarper_360.py- cpp example is still to be done
# This example shows the use of a Video Dewarper to dewarp a 360d camera stream
# - recorded from a 360d camera and provided by NVIDIA as a sample stream.
#
# The Dewarper component is created with the following parameters
# - a config "file config_dwarper_txt" which tailors this 360d camera
# multi-surface use-case.
# - and a camera-id which refers to the first column of the CSV files
# (i.e. csv_files/nvaisle_2M.csv & csv_files/nvspot_2M.csv).
# The dewarping parameters for the given camera are read from CSV
# files and used to generate dewarp surfaces (i.e. multiple aisle
# and spot surface) from 360d input video stream.
# All files are located under:
# /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-dewarper-test/video_dewarper_perspective.py- cpp example is still to be done
#
# This example shows the use of a Video Dewarper to dewarp a perspective view.
#
# The Dewarper component is created with the following parameters:
# - a config "config_dewarper_perspective.txt" which defines all dewarping
# parameters - i.e. the csv files are not used for this example.
# - and a camera-id which is NOT USED! Perspecitve dewarping requires that all
# parameters be defined in the config file.
# All files are located under:
# /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-dewarper-test/
#segmentation_industrial.py- cpp example is still to be done
#
# The simple example demonstrates how to create a set of Pipeline components,
# specifically:
# - URI Source to read a jpeg image file
# - Primary GST Inference Engine (PGIE)
# - Segmentation Visualizer
# - Window Sink
# ...and how to add them to a new Pipeline and play
#
# The example registers handler callback functions with the Pipeline for:
# - key-release events
# - delete-window events
#
# NOTE: The Primary GST Inference engine is configured for Industrial Segmentation.
# The NVIDIA provided PGIE configuration file can be found at
# /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-segmentation-test/
#
# The URI Source will push a single frame followed by an End of File (EOF) event.
# segmentation_semantic.py- cpp example is still to be done
#
# The simple example demonstrates how to create a set of Pipeline components,
# specifically:
# - URI Source to read a jpeg image file
# - Primary GST Inference Engine (PGIE)
# - Segmentation Visualizer
# - Window Sink
# ...and how to add them to a new Pipeline and play
#
# The example registers handler callback functions with the Pipeline for:
# - key-release events
# - delete-window events
#
# NOTE: The Primary GST Inference engine is configured for Semantic Segmentation.
# The NVIDIA provided PGIE configuration file can be found at
# /opt/nvidia/deepstream/deepstream/sources/apps/sample_apps/deepstream-segmentation-test/
#
# The URI Source will push a single frame followed by an End of File (EOF) event.
#