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Version: 3.19.2

Face, Body and Object Detection

In this section you will learn how to integrate Face Detector, Human Body Detector and Object Detector to your C++ or Python project.

Face Detection (С++/Python)

Types of Detection Blocks for the Key "unit_type"

  • FACE_DETECTOR
  • HUMAN_BODY_DETECTOR
  • OBJECT_DETECTOR

1. Create a Face Detector

1.1 To create a Face Detector, follow steps 1-3 described in Creating a Processing Block and specify the value of the block you are interested in for the "unit_type" key :

configCtx["unit_type"] = "FACE_DETECTOR";

// optional, default value is "ssyv"
configCtx["modification"] = "ssyv";

// optional, default value is 1l
configCtx["version"] = 1l;

// optional, default value is 0.5
configCtx["confidence_threshold"] = 0.5;

// optional, default value is 0.5
configCtx["iou_threshold"] = 0.5;

1.2 Create a Face Detector processing block:

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

2. Face Detection

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.

// putting an image into the container
auto imgCtx = ioData["image"];
pbio::context_utils::putImage(imgCtx, input_rawimg);

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

ioData["image"] = imgCtx;

2.4 Call the faceDetector and pass the Context-container ioData that contains an image:

blockDetector(ioData);

The blockDetector() method will add the result of processing samples (images) to the ioData container.

The output format is a list of objects, accessible by the "objects" key.

Each list object has a "class" key with a value corresponding to the class of the detected object.

The "bbox" (bounding box) key contains an array of 4 double numbers {x1, y1, x2, y2} which are the relative coordinates of the source image.

The "confidence" key contains a double number in the range [0,1].

/*
{
"objects": [{ "id": {"type": "long", "minimum": 0},
"class": "face",
"confidence": {"double", "minimum": 0, "maximum": 1},
"bbox": [x1, y2, x2, y2] }]
}
*/