Single Face Analysis Pipeline Demo#

This document presents a models ensemble as an example of DAG Scheduler implementation. It describes how to combine several models to perform multiple inference operations with a single prediction call. When you need to execute several predictions on the same data, you can create a pipeline, which combines the results from several models.


Prepare workspace to run the demo#

In this example the following models are used:



Clone the repository and enter single_face_analysis_pipeline directory

git clone
cd model_server/demos/single_face_analysis_pipeline/python

You can prepare the workspace that contains all the above by just running


Final directory structure#

Once the make procedure is finished, you should have workspace directory ready with the following content.

├── age-gender-recognition-retail-0013
│   └── 1       ├── age-gender-recognition-retail-0013.bin
│       └── age-gender-recognition-retail-0013.xml
├── config.json
└── emotions-recognition-retail-0003
    └── 1
        ├── emotions-recognition-retail-0003.bin
        └── emotions-recognition-retail-0003.xml

Deploying OVMS#

Deploy OVMS with single face analysis pipeline using the following command:

docker run -p 9000:9000 -d -v ${PWD}/workspace:/workspace openvino/model_server --config_path /workspace/config.json --port 9000

Requesting the Service#

Exemplary client can be used to request pipeline deployed in previous step.

pip3 install -r requirements.txt

Now you can create directory for text images and run the client:

python --image_path ../../common/static/images/faces/face1.jpg --grpc_port 9000
Age results: [[[21.099792]]]
Gender results: Female: 0.9483401 ; Male: 0.051659837
Emotion results: Natural: 0.02335789 ; Happy: 0.9449672 ; Sad: 0.001236845 ; Surprise: 0.028111042 ; Angry: 0.0023269346

Next step#

For more advanced use case with extracting and analysing multiple faces on the same image see multi_faces_analysis_pipeline demo.