MediaPipe Object Detection Demo

This guide shows how to implement MediaPipe graph using OVMS.

Example usage of graph that accepts Mediapipe::ImageFrame as a input:

Prerequisites

Model preparation: Python 3.9 or higher with pip

Model Server deployment: Installed Docker Engine or OVMS binary package according to the baremetal deployment guide

Prepare the repository

Clone the repository and enter mediapipe object_detection directory

git clone https://github.com/openvinotoolkit/model_server.git
cd model_server/demos/mediapipe/object_detection

Prepare models

pip install -r requirements.txt

python mediapipe_object_detection.py --download_models

Server Deployment

Deploying with Docker

docker run -d -v $PWD:/demo -p 9000:9000 openvino/model_server:latest --config_path /demo/config.json --port 9000

Deploying on Bare Metal

Assuming you have unpacked model server package, make sure to:

• On Windows: run setupvars script

• On Linux: set LD_LIBRARY_PATH and PATH environment variables

as mentioned in deployment guide, in every new shell that will start OpenVINO Model Server.

cd demos\mediapipe\object_detection
ovms --config_path config.json --port 9000

Run the client:

python mediapipe_object_detection.py --grpc_port 9000 --images ./input_images.txt
Start processing:
	Graph name: objectDetection
airliner.jpeg
Iteration 0; Processing time: 41.05 ms; speed 24.36 fps
golden_retriever.jpeg
Iteration 1; Processing time: 25.04 ms; speed 39.93 fps
pelican.jpeg
Iteration 2; Processing time: 29.88 ms; speed 33.46 fps
zebra.jpeg
Iteration 3; Processing time: 26.61 ms; speed 37.59 fps

Received images with bounding boxes will be located in ./results directory.

Real time stream analysis

For demo featuring real time stream application see real_time_stream_analysis