Getting Started
Step by step instructions:
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Download the models using download_models/downloadModels.sh
make download-models -
Update github submodules
make update-submodules -
Download sample videos used by the performance tools
make download-sample-videos -
Run the LP application
make run-render-modeNOTE:- User can directly run single make command that internally called all above command and run the Loss Prevention application.
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Run Loss Prevention appliaction with single command.
make run-lp- Running Loss Prevention application with ENV variables:
REGISTRY=true CAMERA_STREAM=camera_to_workload_full.json WORKLOAD_DIST=workload_to_pipeline_cpu.json make run-lpREGISTRY=true: pre-built images are pulled.
CAMERA_STREAM=camera_to_workload_full.json: runs all 6 workloads.
WORKLOAD_DIST=workload_to_pipeline_cpu.json: all workloads run on CPU.
- Running Loss Prevention application with ENV variables:
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View the Dynamically Generated GStreamer Pipeline. >Since the GStreamer pipeline is generated dynamically based on the provided configuration(camera_to_workload and workload_to_pipeline json), the pipeline.sh file gets updated every time the user runs make run-lp or make benchmark. This ensures that the pipeline reflects the latest changes.
src/pipelines/pipeline.sh -
Verify Docker containers
Result:docker ps --allNAMES STATUS IMAGE src-pipeline-runner-1 Up 17 seconds (healthy) pipeline-runner:lp model-downloader Exited(0) 17 seconds model-downloader:lp -
Verify Results
After starting Loss Prevention you will begin to see result files being written into the results/ directory. Here are example outputs from the 3 log files.
gst-launch_
pipeline
r
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Stop the demo using docker compose down
make down-lp