detect_img.cpp

#include <iostream>
#include <fstream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>

using namespace std;
using namespace cv;
using namespace dnn;


int main(int, char**) {
    std::vector<std::string> class_names;
    ifstream ifs(string("../../../input/object_detection_classes_coco.txt").c_str());
    string line;
    while (getline(ifs, line))
    {
        class_names.push_back(line);
    }  

    // load the neural network model
    auto model = readNet("../../../input/frozen_inference_graph.pb", 
                        "../../../input/ssd_mobilenet_v2_coco_2018_03_29.pbtxt.txt", 
                        "TensorFlow");

    // read the image from disk
    Mat image = imread("../../../input/image_2.jpg");
    int image_height = image.cols;
    int image_width = image.rows;
    //create blob from image
    Mat blob = blobFromImage(image, 1.0, Size(300, 300), Scalar(127.5, 127.5, 127.5), 
                            true, false);
    //create blob from image
    model.setInput(blob);
    //forward pass through the model to carry out the detection
    Mat output = model.forward();

    Mat detectionMat(output.size[2], output.size[3], CV_32F, output.ptr<float>());

    for (int i = 0; i < detectionMat.rows; i++){
        int class_id = detectionMat.at<float>(i, 1);
        float confidence = detectionMat.at<float>(i, 2);
        
        // Check if the detection is of good quality
        if (confidence > 0.4){
            int box_x = static_cast<int>(detectionMat.at<float>(i, 3) * image.cols);
            int box_y = static_cast<int>(detectionMat.at<float>(i, 4) * image.rows);
            int box_width = static_cast<int>(detectionMat.at<float>(i, 5) * image.cols - box_x);
            int box_height = static_cast<int>(detectionMat.at<float>(i, 6) * image.rows - box_y);
            rectangle(image, Point(box_x, box_y), Point(box_x+box_width, box_y+box_height), Scalar(255,255,255), 2);
            putText(image, class_names[class_id-1].c_str(), Point(box_x, box_y-5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,255,255), 1);
        }
    }    

    imshow("image", image);
    imwrite("../../../outputs/image_result.jpg", image);
    waitKey(0);
    destroyAllWindows();

}