main.cpp

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//
//  main.cpp
//  AI Watch A1
//
//  Created by Denny Caruso on 06/07/22.
//
 
// License: Apache 2.0. See LICENSE file in root directory.
// Copyright(c) 2022. All Rights Reserved.
 
#include "FacadeSingleton.hpp"
 
 
 
const int expected_argc = 5;
const char * expectedUsageMessage = "Usage: sudo ./AI_Watch_A1.bin <path/openpose folder/> <./path/openpose.bin> "
                                    "<path/images folder/> <path/JSON output folder/>";
 
 
 
int main (int argc, char ** argv) {
    rs2::pipeline pipelineStream;
    // Scaling factor for distance frame and topic name for kafka connection.
    float scale;
    const char * destinationKafkaTopic = "topic1";
    // Optimal resolution for D435: 848x480.
    unsigned int user_nFrame = 50, resX = 848, resY = 480;
    const unsigned short int framesToSkip = 5;
    const float skeletonThreshold = 0.05;
    struct rs2_intrinsics color_intrin;
    Room room = CVPR_Lab();
    
    // Declaring a pointer to a FacadeSingleton object (it will be helpful to easily interact with all submodules) and starting D435 camera.
    FacadeSingleton * myUtility = FacadeSingleton::getInstance(expected_argc, expectedUsageMessage);
    myUtility->startEnvironment(pipelineStream, color_intrin, & scale, resX, resY, destinationKafkaTopic, room);
 
    /*
        An infinite loop that captures the frames seen from the camera, gives them to OpenPose, gets the output, and builds skeletons.
        After that, we create a properly formatted output JSON file and send it via Kafka for further elaboration.
    */
    while (true) {
        myUtility->getVideoFrames(user_nFrame, pipelineStream, scale, framesToSkip);
        myUtility->getVideoBodyKeyPoints(& argc, & argv);
        myUtility->showSkeletons(user_nFrame, skeletonThreshold);
        myUtility->sendData(user_nFrame);
        myUtility->cleanBuildFolder();
    }
    return EXIT_SUCCESS;
}