In this tutorial, we shall go through the complete process of using DeepStack to build a Face Recognition system.
Install and Setup DeepStack Using the Install Guide. If you have a system with Nvidia GPU, follow instruction on Using DeepStack with NVIDIA GPU to install the GPU Version of DeepStack
Starting DeepStack on Docker
Below we start DeepStack with only the face APIs enabled.
sudo docker run -e VISION-FACE=True -v localstorage:/datastore -p 80:5000 \deepquestai/deepstack
sudo docker run --rm --runtime=nvidia -e VISION-FACE=True -v localstorage:/datastore \-p 80:5000 deepquestai/deepstack:gpu
Basic Parameters
-e VISION-FACE=True This enables the face recognition APIs, all apis are disabled by default.
-v localstorage:/datastore This specifies the local volume where deepstack will store all data.
-p 80:5000 This makes deepstack accessible via port 80 of the machine.
sudo deepstack start "VISION-FACE=True"
Start the DeepStack App, Click Start Server, Select the Face API and click Start Now
Face Recognition
Think of a software that can identify known people by their names. Face Recognition does exactly that. Register a picture of a number of people and the system will be able to recognize them again anytime. Face Recognition is a two step process: The first is to register a known face and second is to recognize these faces in new pictures.
REGISTERING A FACE
Here we are building an application that can tell the names of a number of popular celebrities. First we collect pictures of a number of celebrities and we register them with deepstack
Below we will register the faces with their names
const request = require("request")const fs = require("fs")run_prediction("cruise.jpg","Tom Cruise")run_prediction("elba.jpg","Idris Elba")run_prediction("perri.jpg","Christina Perri")run_prediction("adele.jpg","Adele")function run_prediction(image_path,userid){image_stream = fs.createReadStream(image_path)var form = {"image":image_stream,"userid":userid}request.post({url:"http://localhost:80/v1/vision/face/register", formData:form},function(err,res,body){response = JSON.parse(body)console.log(response)})}
{ success: true, message: 'face added' }{ success: true, message: 'face added' }{ success: true, message: 'face added' }
RECOGNITION
const request = require("request")const fs = require("fs")image_stream = fs.createReadStream("test-image.jpg")var form = {"image":image_stream}request.post({url:"http://localhost:80/v1/vision/face/recognize", formData:form},function(err,res,body){response = JSON.parse(body)predictions = response["predictions"]for(var i =0; i < predictions.length; i++){console.log(predictions[i]["userid"])}})
Adele
We have just created a face recognition system. You can try with different people and test on different pictures of them.
The next tutorial is dedicated to the full power of the face recognition api as well as best practices to make the best out of it.
Performance
DeepStack offers three modes allowing you to tradeoff speed for peformance. During startup, you can specify performance mode to be , “High” , “Medium” and “Low”
The default mode is “Medium”
You can specify a different mode during startup as seen below as seen below
sudo docker run -e MODE=High VISION-FACE=True -v localstorage:/datastore -p 80:5000 \deepquestai/deepstack
sudo docker run --rm --runtime=nvidia -e MODE=High -e VISION-FACE=True -v localstorage:/datastore \-p 80:5000 deepquestai/deepstack:gpu
Note the -e MODE=High above
On Windows, you can easily select the High mode in the UI
Note the High radio button selected above
Speed Modes are not available on the Raspberry PI Version