Extraction API

With the Extraction API component, you can flexibly configure the format of API responses to extract various face data, including the bounding box coordinates, normalized face, gender, age, and emotions, as well as the face feature vector (facen). Implementing this feature to your system can remarkably broaden the scope of analytic tasks it is capable of fulfilling.

Note

Being a findface-facenapi counterpart when it comes to data extraction via API, Extraction API is more resource-demanding. The component cannot fully substitute findface-facenapi as it doesn’t allow adding faces and working with the database.

Note

You can also use Extraction API as a facen extractor, i. e. as an alternative to findface-nnapi.

Tip

Normalized images received from Extraction API in base64 are qualified for posting to findface-facenapi.

Important

To use such Extraction API functions as face quality estimation and auto-rotation of original images, activate the Face Quality module. Please contact support for details by info@ntechlab.com.

In this section:

Install Extraction API

To install and configure the Extraction API component, do the following:

Note

Extraction API requires the packages with models <findface-data>.deb. Make sure they have been installed.

  1. Install the component.

    sudo apt-get install findface-extraction-api
    
  2. Open the findface-extraction-api.ini configuration file.

    sudo vi /etc/findface-extraction-api.ini
    
  3. If NTLS is remote, specify its IP address.

    license_ntls_server: 192.168.113.2:3133
    
  4. Configure other parameters if needed. For example, enable or disable fetching Internet images.

    fetch:
      enabled: true
      size_limit: 10485760
    
  5. The min_face_size and max_face_size parameters do not work as filters. They rather indicate the guaranteed detection interval. Pick up their values carefully as these parameters affect performance.

    nnd:
      min_face_size: 30
      max_face_size: .inf
    
  6. The model_instances parameter indicates how many instances of each enabled face detector (nnd, legacy or prenormalized) and each enabled model (facen, gender, age, emotions) run concurrently. The default value (0) means that this number is equal to the number of CPU cores. If it severely affects RAM consumption (for example, extraction-api fails), adjust the parameter value.

    model_instances: 2
    
  7. To estimate the face quality, enable the quality_estimator. In this case, extraction-api will return the quality score in the detection_score parameter.

    Tip

    Interpret the quality score further in analytics. Upright faces in frontal position are considered the best quality. They result in values around 0, mostly negative (such as -0.00067401276, for example). Inverted faces and large face angles are estimated with negative values some -5 and less.

    quality_estimator: true
    

    Important

    This function is available only if you have activated the Face Quality module. Please contact support for details by info@ntechlab.com.

  8. Enable the Extraction API service autostart and launch the service.

    sudo systemctl enable findface-extraction-api && sudo systemctl start findface-extraction-api
    

API Requests

The Extraction API component accepts POST requests to http://127.0.0.1:18666/.

There are 2 ways to format the request body:

  • application/json: the request body contains only JSON.
  • multipart/form-data: the request body contains a JSON part with the request itself, other body parts are used for image transfer.

The JSON part of the request body contains a set of requests:

{
    "requests": [request1, request2, .., requestN]
}

Each request in the set applies to a specific image or region in the image and accepts the following parameters:

  • "image": an uploaded image (use multipart:part to refer to a relevant request body part), or a publicly accessible image URL (http:, https:).

  • "roi": a region of interest in the image. If the region is not specified, the entire image is processed.

  • "detector": a face detector to apply to the image (legacy, nnd or prenormalized). The prenormalized mode accepts normalized face images and omits detecting faces. Use nnd if you need to estimate the face quality ("quality_estimator": true).

  • "need_facen": if true, the request returns a facen in the response.

  • "need_gender": returns gender.

  • "need_emotions": returns emotions.

  • "need_age": returns age.

  • "need_normalized": returns a normalized face image encoded in base64. The normalized image can then be posted again to the Extraction API component as “prenormalized”.

  • "auto_rotate": if true, auto-rotates an original image to 4 different orientations and returns faces detected in each orientation. Works only if "detector": "nnd" and "quality_estimator": true.

    Important

    This function is available only if you have activated the Face Quality module. Please contact support for details by info@ntechlab.com.

{
    "image": "http://static.findface.pro/sample.jpg",
    "roi": {"left": 0, "right": 1000, "top": 0, "bottom": 1000},
    "detector": "nnd",
    "need_facen": true,
    "need_gender": true,
    "need_emotions": true,
    "need_age": true,
    "need_normalized": true,
    "auto_rotate": true
}

API Response Format

A typical response from the Extraction API component contains a set of responses to the requests wrapped into the main API request:

{
    "response": [response1, response2, .., responseN]
}

Each response in the set contains the following JSON data:

  • "faces": a set of faces detected in the provided image or region of interest.
  • "error": an error occurred during processing (if any). The error body includes the error code which can be interpreted automatically ("code") and a human-readable description ("desc").
{
    "faces": [face1, face2, .., faceN],
    "error": {
        "code": "IMAGE_DECODING_FAILED",
        "desc": "Failed to decode: reason"
    }
}

Each face in the set is provided with the following data:

  • "bbox": coordinates of a bounding box with the face.
  • "detection_score": either the face detection accuracy, or the face quality score (depending on whether quality_estimator is false or true at /etc/findface-extraction-api.ini). Upright faces in frontal position are considered the best quality. They result in values around 0, mostly negative (such as -0.00067401276, for example). Inverted faces and large face angles are estimated with negative values some -5 and less.
  • "facen": the face feature vector.
  • "gender": gender information (MALE or FEMALE) with recognition accuracy if requested.
  • "age": age estimate if requested.
  • "emotions": all available emotions in descending order of probability if requested.
  • "normalized": a normalized face image encoded in base64 if requested.
{
    "bbox": { "left": 1, "right": 2, "top": 3, "bottom": 4},
    "detection_score": -0.0004299,
    "facen": "...",
    "gender": {
        "gender": "MALE",
        "score": "1.123"
    },
    "age": 23.59,
    "emotions": [
        { "emotion": "neutral", "score": 0.95 },
        { "emotion": "angry", "score": 0.55 },
        ...
    ],
    "normalized": "...",
}

Examples

Request #1

curl -X POST -F sample=@sample.jpg -F 'request={"requests":[{"image":"multipart:sample","detector":"nnd", "need_gender":true, "need_normalized": true, "need_facen": true}]}' http://127.0.0.1:18666/| jq

Response

{
  "responses": [
    {
      "faces": [
        {
          "bbox": {
            "left": 595,
            "top": 127,
            "right": 812,
            "bottom": 344
          },
          "detection_score": -0.0012599,
          "facen": "qErDPTE...vd4oMr0=",
          "gender": {
            "gender": "FEMALE",
            "score": -2.6415858
          },
          "normalized": "iVBORw0KGgoAAAANSUhE...79CIbv"
        }
      ]
    }
  ]
}

Request #2

curl -X POST  -F 'request={"requests": [{"need_age": true, "need_gender": true, "detector": "nnd", "roi": {"left": -2975, "top": -635, "right": 4060, "bottom": 1720}, "image": "https://static.findface.pro/sample.jpg", "need_emotions": true}]}' http://127.0.0.1:18666/ |jq

Response

{
  "responses": [
    {
      "faces": [
        {
          "bbox": {
            "left": 595,
            "top": 127,
            "right": 812,
            "bottom": 344
          },
          "detection_score": 0.9999999,
          "gender": {
            "gender": "FEMALE",
            "score": -2.6415858
          },
          "age": 26.048346,
          "emotions": [
            {
              "emotion": "neutral",
              "score": 0.90854686
            },
            {
              "emotion": "sad",
              "score": 0.051211596
            },
            {
              "emotion": "happy",
              "score": 0.045291856
            },
            {
              "emotion": "surprise",
              "score": -0.024765536
            },
            {
              "emotion": "fear",
              "score": -0.11788454
            },
            {
              "emotion": "angry",
              "score": -0.1723868
            },
            {
              "emotion": "disgust",
              "score": -0.35445923
            }
          ]
        }
      ]
    }
  ]
}

Request #3. Auto-rotation

curl -s -F '[email protected]/path/to/your/photo.png' -F 'request={"requests":[{"image":"multipart:sample","detector":"nnd", "auto_rotate": true, "need_normalized": true }]}' http://192.168.113.79:18666/

Response

{
 "responses": [
   {
     "faces": [
       {
         "bbox": {
           "left": 96,
           "top": 99,
           "right": 196,
           "bottom": 198
         },
         "detection_score": -0.00019264,
         "normalized": "iVBORw0KGgoAAAANSUhE....quWKAAC"
        },
       {
         "bbox": {
           "left": 205,
           "top": 91,
           "right": 336,
           "bottom": 223
         },
         "detection_score": -0.00041600747,
         "normalized": "iVBORw0KGgoAAAANSUhEUgAA....AByquWKAACAAElEQVR4nKy96XYbybIdnF"
       }
     ]
   }
 ]
}

Extract Facens

By default, findface-facenapi detects faces in images and sends them to findface-nnapi for a facen extraction. Then findface-facenapi saves the obtained facen to MongoDB and Tarantool databases. You can use Extraction API as a better alternative to findface-nnapi in this pipeline.

The main advantage of Extraction API in contrast with findface-nnapi is its built-in ability to clone into multiple instances and automatically balance the traffic across them, while for findface-nnapi, load balancing has to be manually set up via NginX.

To extract facens via Extraction API, do the following:

  1. Open the findface-facenapi.ini configuration file:

    sudo vi /etc/findface-facenapi.ini
    
  2. Uncomment and edit the extractor parameter in the following way:

    extractor = 'extraction-api'
    

    Warning

    The findface-facenapi.ini content must be correct Python code.

  3. Uncomment and/or edit extraction_api_url to align with your network specification:

    extraction_api_url = 'http://localhost:18666'
    
  4. Start Extraction API and enable its autostart.

    sudo service findface-extraction-api start && sudo systemctl enable findface-extraction-api
    
  5. Restart findface-facenapi.

    sudo service findface-facenapi restart
    
  6. Stop findface-nnapi and disable its autostart.

    sudo service findface-nnapi stop && sudo systemctl disable findface-nnapi
    
  7. Check the services status. The command will return the services description, status (should be Active), path and running time.

    sudo service 'findface*' status