Advanced Settings

Configuration Options

Local Image Building

By default, the application uses pre-built Docker images for faster setup. If you need to build images locally (for customization or development):

# Build and run locally instead of using pre-built images
make run-lp RENDER_MODE=1 REGISTRY=false

# Apply to any command
make <command> REGISTRY=false

# Examples:
make benchmark REGISTRY=false
make benchmark-stream-density REGISTRY=false

When to use local building: - Modifying source code or configurations - Development and testing changes - Air-gapped environments without internet access - Custom hardware optimizations

Note: Local building takes significantly longer (15-30 minutes) compared to pre-built images (2-5 minutes).

Step-by-Step Local Build Process

• 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 RENDER_MODE=1
  

  User Defined Workloads

The application is highly configurable via JSON files in the configs/ directory

To try a new camera or workload:

1. Create new camera to workload mapping in `configs/camera_to_workload_custom.json` to add your camera and assign workloads.
- **camera_to_workload_custom.json**: Maps each camera to one or more workloads. 
    - To add or remove a camera, edit the `lane_config.cameras` array in the file.
    - Each camera entry can specify its video source, region of interest, and assigned workloads.
    Example:
    ```json
        {
            "lane_config": {
            "cameras": [
                {
                    "camera_id": "cam1",
                    "streamUri": "rtsp://rtsp-streamer:8554/video-stream-name",
                    "workloads": ["items_in_basket", "multi_product_identification"],
                    "region_of_interest": {"x": 100, "y": 100, "x2": 800, "y2": 600}
                }
                ]
                }
         }
    ```
If adding new videos, place your video files in the directory **performance-tools/sample-media/** and update the `streamUri` path.

1. Connecting External RTSP Cameras:
To use real RTSP cameras instead of the built-in server:

```json
    {
      "camera_id": "external_cam1",
      "streamUri": "rtsp://192.168.1.100:554/stream1",
      "workloads": ["items_in_basket"]
   }
 ```

2. Create new `configs/workload_to_pipeline_custom.json` to define pipeline for your workload.
- **workload_to_pipeline_custom.json**: Maps each workload name to a pipeline definition (sequence of GStreamer elements and models). 
    Example:

  ```json
  {
    "workload_pipeline_map": {
      "custom_workload_1": [
        {"type": "gvadetect", "model": "yolo11n", "precision": "INT8", "device": "CPU"},
        {"type": "gvaclassify", "model": "efficientnet-v2-b0", "precision": "INT8", "device": "CPU"}
      ],
      "custom_workload_2": [
        {"type": "gvadetect", "model": "yolo11n", "precision": "INT16", "device": "NPU"},
        {"type": "gvaclassify", "model": "efficientnet-v2-b0", "precision": "INT16", "device": "NPU"}
      ],
      "custom_workload_3": [
        {"type": "gvadetect", "model": "yolo11n", "precision": "INT8", "device": "GPU"},
        {"type": "gvaclassify", "model": "efficientnet-v2-b0", "precision": "INT8", "device": "GPU"}
      ]
    }
  }
  ```
1. Run validate configs command, to verify configuration files

sh make validate-all-configs 2. Re-run the pipeline as described above.

[!NOTE] 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

Complete Workload Configuration Matrix

The preconfigured workloads support multiple hardware configurations out of the box. Use the CAMERA_STREAM and WORKLOAD_DIST variables to customize which cameras and hardware (CPU, GPU, NPU) are used by your pipeline.

Usage Pattern:

CAMERA_STREAM=<camera_stream> WORKLOAD_DIST=<workload_dist> make run-lp
# Or for benchmarking:
CAMERA_STREAM=<camera_stream> WORKLOAD_DIST=<workload_dist> make benchmark

Loss Prevention Configurations

Description	CAMERA_STREAM	WORKLOAD_DIST
CPU (Default)	camera_to_workload.json	workload_to_pipeline.json
GPU	camera_to_workload.json	workload_to_pipeline_gpu.json
NPU + GPU	camera_to_workload.json	workload_to_pipeline_gpu-npu.json
Heterogeneous	camera_to_workload.json	workload_to_pipeline_hetero.json
VLM	camera_to_workload_vlm.json	workload_to_pipeline_vlm.json

[!NOTE] Included Sub-Workloads: The following detection types are automatically enabled in all Loss Prevention configurations:

• items_in_basket - Monitors items placed in shopping baskets
• hidden_items - Detects concealed or hidden products
• fake_scan_detection - Identifies scanning without actual item registration
• multi_product_identification - Tracks multiple products simultaneously
• product_switching - Detects when customers switch high-value items for lower-value ones
• sweet_heartening - Monitors for collusion between customers and cashiers

Automated Self-Checkout Configurations

Description	CAMERA_STREAM	WORKLOAD_DIST
Object Detection (CPU)	camera_to_workload_asc_object_detection.json	workload_to_pipeline_asc_object_detection_cpu.json
Object Detection (GPU)	camera_to_workload_asc_object_detection.json	workload_to_pipeline_asc_object_detection_gpu.json
Object Detection (NPU)	camera_to_workload_asc_object_detection.json	workload_to_pipeline_asc_object_detection_npu.json
Object Detection & Classification (CPU)	camera_to_workload_asc_object_detection_classification.json	workload_to_pipeline_asc_object_detection_classification_cpu.json
Object Detection & Classification (GPU)	camera_to_workload_asc_object_detection_classification.json	workload_to_pipeline_asc_object_detection_classification_gpu.json
Object Detection & Classification (NPU)	camera_to_workload_asc_object_detection_classification.json	workload_to_pipeline_asc_object_detection_classification_npu.json
Age Prediction & Face Detection (CPU)	camera_to_workload_asc_age_verification.json	workload_to_pipeline_asc_age_verification_cpu.json
Age Prediction & Face Detection (GPU)	camera_to_workload_asc_age_verification.json	workload_to_pipeline_asc_age_verification_gpu.json
Age Prediction & Face Detection (NPU)	camera_to_workload_asc_age_verification.json	workload_to_pipeline_asc_age_verification_npu.json
Age Verification Heterogeneous	camera_to_workload_asc_age_verification.json	workload_to_pipeline_asc_age_verification_hetero.json
Object Detection Heterogeneous	camera_to_workload_asc_object_detection_classification.json	workload_to_pipeline_asc_object_detection_classification_hetero.json