Body and Object detection

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

Body Detection (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 a Human Body Detector

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

• "HUMAN_BODY_DETECTOR" for the "unit_type" key;
• path to Human Body Detector model file for the "model_path" key.

configCtx["unit_type"] = "HUMAN_BODY_DETECTOR";

// default path to Human Body Detector model file is "share/bodydetectors/body.enc" in the package's root directory
configCtx["model_path"] = "share/bodydetectors/body.enc";

// 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 Human Body Detector Processing block:

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

2. Body 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.

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

ioData["image"] = imgCtx;

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

bodyDetector(ioData);

The result of calling bodyDetector() will be appended to ioData container. The format of the output data is presented as a list of objects with the "objects" key. Each list object has the "class" key with the "body" value. The "bbox" (bounding box) key contains an array of four numbers of type double {x1, y1, x2, y2} that are relative coordinates of source image. The "score" key contains a double number in a range of [0,1].

/*
{
 "objects": [{ "id": {"type": "long", "minimum": 0},
               "class": "body",
               "score": <score_value>,
               "bbox": [x1, y2, x2, y2] }]
}
*/

Follow iteration expression to get the result of bodies detection and draw it on a source image:

if (ioData.contains("objects"))
    for(const auto& obj : ioData["objects"])
    {
        if(obj["class"].getString().compare("body"))
            continue;
        const auto& rectCtx = obj.at("bbox");
        cv::Rect rect(cv::Point{rectCtx[0].getDouble()*input_image.cols, rectCtx[1].getDouble()*input_image.rows},
                      cv::Point{rectCtx[2].getDouble()*input_image.cols, rectCtx[3].getDouble()*input_image.rows});
        cv::rectangle(image, rect, {0, 255, 0}, 1);
    }

cv::imshow("img", image);
cv::waitKey();

3. GPU Acceleration

Human Body Detector can be used with GPU acceleration (CUDA). For more information, please follow this link.

Object Detection (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 Object Detector

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

• "OBJECT_DETECTOR" for the "unit_type" key;
• path to Object Detector model file for the "model_path" key.

configCtx["unit_type"] = "OBJECT_DETECTOR";

// default path to Object Detector model file is "share/objectdetectors/det-object.enc" in the <ShortProductName />'s root directory
configCtx["model_path"] = "share/objectdetectors/det-object.enc";

// 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 an Object Detector:

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

2. Object 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.

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

ioData["image"] = imgCtx;

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

objectDetector(ioData);

The result of calling objectDetector() will be appended to ioData container. The format of the output data is presented as a list of objects with the "objects" key. Each list object has the "class" key with the value according to detected object. The "bbox" (bounding box) key contains an array of four numbers of type double {x1, y1, x2, y2} that are relative coordinates of source image. The "score" key contains a double number in a range of [0,1].

/*
{
 "objects": [{ "id": {"type": "long", "minimum": 0},
               "class": <class_of_detected_object>,
               "score": <score_value>,
               "bbox": [x1, y2, x2, y2] }]
}
*/

Follow iteration expression to get the result of object detection and draw it on a source image:

if (ioData.contains("objects"))
    for(const auto& obj : ioData["objects"])
    {
        std::string class_name = obj.at("class").getString();
        const auto& rectCtx = obj.at("bbox");
        cv::Rect rect(cv::Point{static_cast<int>(rectCtx[0].getDouble()*image.size[1]), static_cast<int>(rectCtx[1].getDouble()*image.size[0])},
                      cv::Point{static_cast<int>(rectCtx[2].getDouble()*image.size[1]), static_cast<int>(rectCtx[3].getDouble()*image.size[0])});
        cv::rectangle(image, rect, {0, 255, 0}, 1);
    }

cv::imshow("img", image);
cv::waitKey();

3. GPU Acceleration

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