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Version: 3.15.0

Face Estimation

In this section you will learn how to integrate Emotion and Gender estimators to your C++ project.

Emotion Estimation (C++)​

Requirements​

  • Windows x86 64-bit or Linux x86 64-bit system.
  • Installed OMNI package windows_x86_64 or linux_x86_64 (see Getting Started).

1. Creating an Emotion Estimator​

1.1. To create an Emotion Estimator, follow steps 1-3 described in Creating a Processing Block and specify the values:

  • "EMOTION_ESTIMATOR" for the "unit_type" key;
  • path to Emotion Estimator model file for the "model_path" key.
configCtx["unit_type"] = "EMOTION_ESTIMATOR";

// default path to Emotion Estimator model file is "share/faceanalysis/emotion.enc" in the package's root directory
configCtx["model_path"] = "share/faceanalysis/emotion.enc";

1.2. Create an Emotion Estimator Processing block:

pbio::ProcessingBlock emotionEstimator = service->createProcessingBlock(configCtx);

2. Emotion Estimation​

2.1. Create a Context container ioData for input-output data using the createContext() method: 

auto ioData = service->createContext();

2.2. Create a Context container imgCtx with RGB-image following the steps described on Creating a Context container with RGB-image.

2.3. Put input image to the input-output data container:

ioData["image"] = imgCtx;

2.4. Call the emotionEstimator and pass the context with source image ioData:

emotionsEstimator(ioData);

The result of calling emotionEstimator() will be appended to ioData container. The format of the output data is presented as a list of objects with the "objects" key. This list object has the "class" key with the "face" value.

/*
{
 "objects": [{ "id": {"type": "long", "minimum": 0}
               "class": "face",
               "emotions" : [
                   "emotion":  {
                     "enum": ["ANGRY", "DISGUSTED", "SCARED", "HAPPY", "NEUTRAL", "SAD", "SURPRISED"]
                    }
                   "confidence": {"type": "double", "minimum": 0, "maximum": 1}
                   ]
              }]
}
*/

3. GPU Acceleration​

Emotion Estimator can be used with GPU acceleration (CUDA). For more information, please follow this link.

Gender Estimation (C++)​

Requirements​

  • Windows x86 64-bit or Linux x86 64-bit system.
  • Installed Face SDK package windows_x86_64 or linux_x86_64 (see Getting Started).

1. Creating a Gender Estimator​

1.1. To create a Gender Estimator, follow steps 1-3 described in Creating a Processing Block and specify the values:

  • "GENDER_ESTIMATOR" for the "unit_type" key;
  • path to Gender Estimator model file for the "model_path" key.
configCtx["unit_type"] = "GENDER_ESTIMATOR";

// default path to Gender Estimator model file is "share/faceanalysis/gender.enc" in the Face SDK's root directory
configCtx["model_path"] = "share/faceanalysis/gender.enc";

1.2. Create a Gender Estimator Processing block:

pbio::ProcessingBlock genderEstimator = service->createProcessingBlock(configCtx);

2. Gender Estimation​

2.1. Create a Context container ioData for input-output data using the createContext() method: 

auto ioData = service->createContext();

2.2. Create a Context container imgCtx with RGB-image following the steps described on Creating a Context container with RGB-image.

2.3. Put input image to the input-output data container:

ioData["image"] = imgCtx;

2.4. Call the genderEstimator and pass the context ioData with source image:

genderEstimator(ioData);

The result of calling genderEstimator() will be appended to ioData container.

/*
{
 "objects": [{ "id": {"type": "long", "minimum": 0}
               "class": "face",
               "gender":  {
                 "enum": ["FEMALE", "MALE"]
                }
              }]
}
*/

3. GPU Acceleration​

Gender Estimator can be used with GPU acceleration (CUDA). For more information, please follow this link.