A few days ago, I obtained access to the Face container. It provides access to the Face API via a container you can run where you want: on your pc, at the network edge or in your datacenter. You should allocate 6 GB or RAM and 2 cores for the container to run well. Note that you still need to create a Face API resource in the Azure Portal. The container needs to be associated with the Azure Face API via the endpoint and access key:
I used the Standard tier, which charges 0.84 euros per 1000 calls. As noted, the container will not function without associating it with an Azure Face API resource.
When you gain access to the container registry, you can pull the container:
docker pull containerpreview.azurecr.io/microsoft/cognitive-services-face:latest
After that, you can run the container as follows (for API billing endpoint in West Europe):
docker run --rm -it -p 5000:5000 --memory 6g --cpus 2 containerpreview.azurecr.io/microsoft/cognitive-services-face Eula=accept Billing=https://westeurope.api.cognitive.microsoft.com/face/v1.0 ApiKey=YOUR_API_KEY
The container will start. You will see the output (–it):
And here’s the spec:
Before showing how to use the detection feature, note that the container needs Internet access for billing purposes. You will not be able to run the container in fully offline scenarios.
Over at https://github.com/gbaeke/msface-go, you can find a simple example in Go that uses the container. The Face API can take a byte stream of an image or a URL to an image. The example takes the first approach and loads an image from disk as specified by the -image parameter. The resulting io.Reader is passed to the getFace function which does the actual call to the API (uri = http://localhost:5000/face/v1.0/detect):
request, err := http.NewRequest("POST", uri+"?returnFaceAttributes="+params, m)
request.Header.Add("Content-Type", "application/octet-stream")
// Send the request to the local web service
resp, err := client.Do(request)
if err != nil {
return "", err
}
The response contains a Body attribute and that attribute is unmarshalled to a variable of type interface. That one is marshalled with indentation to a byte slice (b) which is returned by the function as a string:
var response interface{}
err = json.Unmarshal(respBody, &response)
if err != nil {
return "", err
}
b, err := json.MarshalIndent(response, "", "\t")
Now you can use a picture like the one below:
Here are some parts of the input, following the command
detectface -image smiling.jpg
Emotion is clearly happiness with additional features such as age, gender, hair color, etc…
[
{
"faceAttributes": {
"accessories": [],
"age": 33,
"blur": {
"blurLevel": "high",
"value": 1
},
"emotion": {
"anger": 0,
"contempt": 0,
"disgust": 0,
"fear": 0,
"happiness": 1,
"neutral": 0,
"sadness": 0,
"surprise": 0
},
"exposure": {
"exposureLevel": "goodExposure",
"value": 0.71
},
"facialHair": {
"beard": 0.6,
"moustache": 0.6,
"sideburns": 0.6
},
"gender": "male",
"glasses": "NoGlasses",
"hair": {
"bald": 0.26,
"hairColor": [
{
"color": "black",
"confidence": 1
}],
"faceId": "b6d924c1-13ef-4d19-8bc9-34b0bb21f0ce",
"faceRectangle": {
"height": 1183,
"left": 944,
"top": 167,
"width": 1183
}
}
]
That’s it! Give the Face API container a go with the tool. You can get it here: https://github.com/gbaeke/msface-go/releases/tag/v0.0.1 (Windows)
