The biological ages of people oftentimes provide a significant information for many applications; for example, surveillance, product recommendation, etc. Existing commercial devices such as mobile phones, webcams are used to create visual data (images/videos) constantly. Given visual data with human faces, the facial age estimator model predicts the ages of the detected faces. With the predicted ages, the information can be applied to different algorithms such as "grouping" which provides the observations in statistics --- different groups of people for various activities.
In this Code Pattern we will use one of the models from the Model Asset Exchange (MAX), an exchange where developers can find and experiment with open source deep learning models. Specifically we will be using the Facial Age Estimator to create a web application that will first detect human faces and then output the ages with the bounding boxes of the associated detected faces. The web application provides an user-friendly interface backed by a lightweight python server. The server takes webcam as input via the UI and sends them to a REST end point for the model. The model's REST endpoint is set up using the docker image provided on MAX. The Web UI displays the estimated age with the associated bounding box for each person.
When the reader has completed this Code Pattern, they will understand how to:
- Build a Docker image of the Facial Age Estimator MAX Model
- Deploy a deep learning model with a REST endpoint
- Generate the estimated ages for an image using the MAX Model's REST API
- Run a web application that using the model's REST API
- Server sends the captured video frame-by-frame from webcam to Model API.
- Web UI requests the age and bounding box data for frame(s) from Server.
- Server receives data from Model API and updates the result to Web UI.
- IBM Model Asset Exchange: A place for developers to find and use free and open source deep learning models.
- Docker: Docker is a tool designed to make it easier to create, deploy, and run applications by using containers.
- Python: Python is a programming language that lets you work more quickly and integrate your systems more effectively.
- JQuery: jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML.
- Bootstrap 3: Bootstrap is a free and open-source front-end library for designing websites and web applications.
Ways to run the code pattern:
You can also deploy the model and web app on Kubernetes using the latest docker images on Docker Hub.
On your Kubernetes cluster, run the following commands:
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Facial-Age-Estimator/master/max-facial-age-estimator.yaml
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Facial-Age-Estimator-Web-App/master/max-facial-age-estimator-web-app.yaml
The web app will be available at port 7000 of your cluster.
The model will only be available internally, but can be accessed externally through the NodePort.
NOTE: The set of instructions in this section are a modified version of the one found on the Facial Age Estimator Project Page
To run the docker image, which automatically starts the model serving API, run:
docker run -it -p 5000:5000 codait/max-facial-age-estimator
This will pull a pre-built image from Docker Hub (or use an existing image if already cached locally) and run it. If you'd rather build the model locally you can follow the steps in the model README.
Note that currently this docker image is CPU only (we will add support for GPU images later).
The API server automatically generates an interactive Swagger documentation page.
Go to http://localhost:5000 to load it. From there you see the API with the test requests.
Use the model/predict endpoint to load a test file and get estimated ages and bounding boxes for the image from the API.
The model assets folder contains one image you can use to test out the API, or you can use your own.
You can also test it on the command line, for example:
curl -F "image=@path/to/tom_cruise.jpg" -X POST http://localhost:5000/model/predict
{
"status": "Okay",
"predictions": [
{
"age_estimation": [
48
],
"face_box": [
"303",
"174",
"379",
"515"
]
}
]
}Clone the Age Estimation Web App repository locally by running the following command:
git clone https://github.com/IBM/MAX-Facial-Age-Estimator-Web-App
Note: You may need to
cd ..out of the MAX-Age-Estimation directory first
Then change directory into the local repository
cd MAX-Facial-Age-Estimator-Web-App
Before running this web app you must install its dependencies:
pip install -r requirements.txt
You then start the web app by running:
python app.py
You can then access the web app at: http://localhost:7000
The Facial Age Estimator endpoint must be available at http://localhost:5000 for the web app to successfully start.
To run the web app with Docker the containers running the web server and the REST endpoint need to share the same network stack. This is done in the following steps:
Modify the command that runs the Facial Age Estimator REST endpoint to map an additional port in the container to a
port on the host machine. In the example below it is mapped to port 7000 on the host but other ports can also be used.
docker run -it -p 5000:5000 -p 7000:7000 --name=max-facial-age-estimator codait/max-facial-age-estimator
Build the web app image by running:
docker build -t max-age-estimator-web-app .
Run the web app container using:
docker run -it --net='container:max-facial-age-estimator' max-age-estimator-web-app
You can also deploy the web app with the latest docker image available on DockerHub by running:
docker run --net='container:max-facial-age-estimator' -it codait/max-facial-age-estimator-web-app
This will use the model docker container run above and can be run without cloning the web app repo locally.
- Model Asset eXchange (MAX)
- Center for Open-Source Data & AI Technologies (CODAIT)
- MAX Announcement Blog
- Artificial Intelligence Code Patterns: Enjoyed this Code Pattern? Check out our other Artificial Intelligence Code Patterns
- AI and Data Code Pattern Playlist: Bookmark our playlist with all of our Code Pattern videos
- Watson Studio: Master the art of data science with IBM's Watson Studio
- Deep Learning with Watson Studio: Design and deploy deep learning models using neural networks, easily scale to hundreds of training runs. Learn more at Deep Learning with Watson Studio.
This code pattern is licensed under the Apache Software License, Version 2. Separate third party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the Developer Certificate of Origin, Version 1.1 (DCO) and the Apache Software License, Version 2.