text: /***
   * This is a sample program to demonstrate the use of the model_loader.h files
   ***/
  
  #include "./saved_model_loader.h"
  #include <opencv2/core.hpp>
  #include <opencv2/imgcodecs.hpp>
  #include <opencv2/highgui.hpp>
  #include <opencv2/imgproc.hpp>
  
  #define THRESHOLD 0.8
  
  
  int main(int argc, char* argv[]){
  	if (argc != 4){
  		std::cout << "Error! Usage: <path/to_saved_model> <path/to_input/image.jpg> <path/to/output/image.jpg>" << std::endl;
  		return 1;
  	}
  
  	// Make a Prediction instance
  	Prediction out_pred;
  	out_pred.boxes = std::unique_ptr<std::vector<std::vector<float>>>(new std::vector<std::vector<float>>());
  	out_pred.scores = std::unique_ptr<std::vector<float>>(new std::vector<float>());
  	out_pred.labels = std::unique_ptr<std::vector<int>>(new std::vector<int>());
  
  	const string model_path = argv[1]; 
  	const string test_image_file  = argv[2];
  	const string test_prediction_image = argv[3];
  
  	// Load the saved_model
  	ModelLoader model(model_path);
  
  	//Predict on the input image
  	model.predict(test_image_file, out_pred);
  
  	using namespace cv;
  	Mat img = imread(test_image_file, IMREAD_COLOR);
  
  	Size size = img.size();
  	int height = size.height;
  	int width = size.width;
  
  	auto boxes = (*out_pred.boxes);
  	auto scores = (*out_pred.scores);
  
  	for (int i=0; i < boxes.size(); i++){
  	    auto box = boxes[i];
  	    auto score = scores[i];
  	    if (score < THRESHOLD){
  	        continue;
  	    }
  		int ymin = (int) (box[0] * height);
  		int xmin = (int) (box[1] * width);
  		int h = (int) (box[2] * height) - ymin;
  		int w = (int) (box[3] * width) - xmin;
  		Rect rect = Rect(xmin, ymin, w, h);
  		rectangle(img, rect, cv::Scalar(0, 0, 255), 2);
  	}
  
  	if (img.empty()){
  		std::cout <<" Failed to read image" << std::endl;
  	}
  
  	imwrite(test_prediction_image, img);
  }
  