Run Object Detection Pipeline with New Model

OpenVINO Model Server has many ways to run inferencing pipeline: TensorFlow Serving gRPC API, KServe gRPC API, TensorFlow Serving REST API, KServe REST API and OVMS C API through OpenVINO model server (OVMS). For running object detection pipeline, it is based on KServe gRPC API method, default model used is ssd_mobilenet_v1_coco. You can use different model to run object detection. Here are the steps:

Add new section to config file for model server
Download new model
Update environment variables of detection pipeline for new model
Build and Run

Add New Section to Config File for Model Server

Here is the config file location: configs/opencv-ovms/models/2022/config_template.json, edit the file and append the following configuration section template

,
{
      "config": {
        "name": "ssd_mobilenet_v1_coco",
        "base_path": "/models/ssd_mobilenet_v1_coco/FP32",
        "nireq": 1,
        "batch_size": "1",
        "plugin_config": {
          "PERFORMANCE_HINT": "LATENCY"
        },
        "target_device": "{target_device}"
      },
      "latest": {
        "num_versions": 1
      }
    }

Note

Please leave target_device value as it is, as the value {target_device} will be recognized and replaced by script run.

You can find the parameter description in the ovms docs.

Download New Model

The pipeline run script automatically download the model files if it is part of open model zoo supported list; otherwise, please add your model files manually to configs/opencv-ovms/models/2022/. When you add your model manually, make sure to follow the model file structure as //1/modelfiles, for example:

ssd_mobilenet_v1_coco
├── FP32
   └── 1
       ├── ssd_mobilenet_v1_coco.bin
       └── ssd_mobilenet_v1_coco.xml

Update Environment Variables

You can update the object detection environment variables in file: configs/opencv-ovms/envs/object_detection.env, here is default value and explanation for each environment variable:

EV Name	Default Value	Description
DETECTION_MODEL_NAME	ssd_mobilenet_v1_coco	model name for object detection
DETECTION_LABEL_FILE	coco_91cl_bkgr.txt	label file name to use on object detection for model
DETECTION_ARCHITECTURE_TYPE	ssd	architecture type for object detection model
DETECTION_OUTPUT_RESOLUTION	1920x1080	output resolution for object detection result
DETECTION_THRESHOLD	0.50	threshold for object detection in floating point that needs to be between 0.0 to 1.0
MQTT		enable MQTT notification of result, value: empty
RENDER_MODE	1	display the input source video stream with the inferencing results, value: 0

Build and Run Pipeline

Build the python app and profile-launcher:
```
 make build-python-apps
```

Download sample video files:

cd benchmark-scripts/ && ./download_sample_videos.sh && cd ..

Start simulator camera if not started:
```
make run-camera-simulator
```

(Optional) Run MQTT broker:

docker run --network host --rm -d -it -p 1883:1883 -p 9001:9001 eclipse-mosquitto

Start the object detection pipeline:

PIPELINE_PROFILE="object_detection" RENDER_MODE=1 MQTT=127.0.0.1:1883 sudo -E ./run.sh --platform core --inputsrc rtsp://127.0.0.1:8554/camera_0

Note

Remove the MQTT environment variable if not using it.

If using MQTT, use the same container name as the MQTT topic to subscribe to the inference metadata. Use the command docker ps to know the container name.
Use one of the following commands for stopping and cleaning up:
1. To stop and clean up the client side containers, run:
```
make clean-profile-launcher
```
2. To stop and clean up everything, run:
```
make clean-all
```