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Getting Started

NOTE:

By default the application runs by pulling the pre-built images. If you want to build the images locally and then run the application, set the flag:

REGISTRY=false

usage: make <command> REGISTRY=false (applicable for all commands like benchmark, benchmark-stream-density..)
Example: make run-lp REGISTRY=false

(If this is the first time, it will take some time to download videos, models, docker images and build images)

Step by step instructions:

  1. Download the models using download_models/downloadModels.sh

    make download-models

  2. Update github submodules

    make update-submodules

  3. Download sample videos used by the performance tools

    make download-sample-videos

  4. Run the LP application

    make run-render-mode

    NOTE:- User can directly run single make command that internally called all above command and run the Loss Prevention application.

  5. Run Loss Prevention appliaction with single command. 

    make run-lp
    • Running Loss Prevention application with ENV variables: 
      CAMERA_STREAM=camera_to_workload_full.json WORKLOAD_DIST=workload_to_pipeline_cpu.json make run-lp
      CAMERA_STREAM=camera_to_workload_full.json: runs all 6 workloads.
      WORKLOAD_DIST=workload_to_pipeline_cpu.json: all workloads run on CPU.

  6. To build the images locally step by step:

    • Follow the following steps: 

      make download-models REGISTRY=false
make update-submodules REGISTRY=false
make download-sample-videos
make run-render-mode REGISTRY=false

    • The above series of commands can be executed using only one command:

    make run-lp REGISTRY=false

  7. 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

  8. Verify Docker containers

    docker ps --all
    Result: 
    NAMES                    STATUS                     IMAGE
src-pipeline-runner-1    Up 17 seconds (healthy)    pipeline-runner:lp
model-downloader         Exited(0) 17 seconds       model-downloader:lp

  9. 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_

    /GstPipeline:pipeline0/GstGvaWatermark:gvawatermark0/GstCapsFilter:capsfilter1: caps = video/x-raw(memory:VASurface), format=(string)RGBA
/GstPipeline:pipeline0/GstFPSDisplaySink:fpsdisplaysink0/GstXImageSink:ximagesink0: sync = true
Got context from element 'vaapipostproc1': gst.vaapi.Display=context, gst.vaapi.Display=(GstVaapiDisplay)"\(GstVaapiDisplayGLX\)\ vaapidisplayglx0", gst.vaapi.Display.GObject=(GstObject)"\(GstVaapiDisplayGLX\)\ vaapidisplayglx0";
Progress: (open) Opening Stream
Pipeline is PREROLLED ...
Prerolled, waiting for progress to finish...
Progress: (connect) Connecting to rtsp://localhost:8554/camera_0
Progress: (open) Retrieving server options
Progress: (open) Retrieving media info
Progress: (request) SETUP stream 0

    pipeline

    14.58
14.58
15.47
15.47
15.10
15.10
14.60
14.60
14.88
14.88

    r

    {
    "objects": [
        {
            "detection": {
                "bounding_box": {
                    "x_max": 0.7873924346958825,
                    "x_min": 0.6701603093745723,
                    "y_max": 0.7915918938548927,
                    "y_min": 0.14599881349270305
                },
                "confidence": 0.8677337765693665,
                "label": "apple",
                "label_id": 39
            },
            "h": 697,
            "region_id": 610,
            "roi_type": "apple",
            "w": 225,
            "x": 1287,
            "y": 158
        },
        {
            "detection": {
                "bounding_box": {
                    "x_max": 0.3221945836811382,
                    "x_min": 0.19950163649114616,
                    "y_max": 0.7924592239981934,
                    "y_min": 0.1336837231479251
                },
                "confidence": 0.8625879287719727,
                "label": "apple",
                "label_id": 39
            },
            "h": 711,
            "region_id": 611,
            "roi_type": "apple",
            "w": 236,
            "x": 383,
            "y": 144
        },
        {
            "detection": {
                "bounding_box": {
                    "x_max": 0.5730873789069046,
                    "x_min": 0.42000878963365595,
                    "y_max": 0.9749763191740435,
                    "y_min": 0.12431765065780453
                },
                "confidence": 0.854443371295929,
                "label": "apple",
                "label_id": 39
            },
            "h": 919,
            "region_id": 612,
            "roi_type": "apple",
            "w": 294,
            "x": 806,
            "y": 134
        }
    ],
    "resolution": {
        "height": 1080,
        "width": 1920
    },
    "timestamp": 755106652
}

  10. Stop the containers:

    When pre-built images are pulled-

    make down-lp

    When images are built locally-

    make down-lp REGISTRY=false

Proceed to Advanced Settings