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#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <iostream>
#include <cstdlib>
#include <opencv2/core_detect.hpp>
using namespace cv;
using namespace std;
using namespace cv::dnn;
using namespace cv::dnn_objdetect;
int main(int argc, char **argv)
{
if (argc < 4)
{
std::cerr << "Usage " << argv[0] << ": "
<< "<model-definition-file> "
<< "<model-weights-file> "
<< "<test-image> "
<< "<threshold>(optional)\n";
return -1;
}
std::string model_prototxt = argv[1];
std::string model_binary = argv[2];
std::string test_input_image = argv[3];
double threshold = 0.7;
if (argc == 5)
{
threshold = atof(argv[4]);
if (threshold > 1.0 || threshold < 0.0)
{
std::cerr << "Threshold should belong to [0, 1]\n";
return -1;
}
}
// Load the network
std::cout << "Loading the network...\n";
Net net = dnn::readNetFromCaffe(model_prototxt, model_binary);
if (net.empty())
{
std::cerr << "Couldn't load the model !\n";
return -2;
}
else
{
std::cout << "Done loading the network !\n\n";
}
// Load the test image
Mat img = cv::imread(test_input_image);
Mat original_img(img);
if (img.empty())
{
std::cerr << "Couldn't load image: " << test_input_image << "\n";
return -3;
}
cv::namedWindow("Initial Image", WINDOW_AUTOSIZE);
cv::imshow("Initial Image", img);
cv::resize(img, img, cv::Size(416, 416));
Mat img_copy(img);
img.convertTo(img, CV_32FC3);
Mat input_blob = blobFromImage(img, 1.0, Size(), cv::Scalar(104, 117, 123), false);
// Set the input blob
// Set the output layers
std::cout << "Getting the output of all the three blobs...\n";
std::vector<Mat> outblobs(3);
std::vector<cv::String> out_layers;
out_layers.push_back("slice");
out_layers.push_back("softmax");
out_layers.push_back("sigmoid");
// Bbox delta blob
std::vector<Mat> temp_blob;
net.setInput(input_blob);
cv::TickMeter t;
t.start();
net.forward(temp_blob, out_layers[0]);
t.stop();
outblobs[0] = temp_blob[2];
// class_scores blob
net.setInput(input_blob);
t.start();
outblobs[1] = net.forward(out_layers[1]);
t.stop();
// conf_scores blob
net.setInput(input_blob);
t.start();
outblobs[2] = net.forward(out_layers[2]);
t.stop();
// Check that the blobs are valid
for (size_t i = 0; i < outblobs.size(); ++i)
{
if (outblobs[i].empty())
{
std::cerr << "Blob: " << i << " is empty !\n";
}
}
int delta_bbox_size[3] = {23, 23, 36};
Mat delta_bbox(3, delta_bbox_size, CV_32F, outblobs[0].ptr<float>());
int class_scores_size[2] = {4761, 20};
Mat class_scores(2, class_scores_size, CV_32F, outblobs[1].ptr<float>());
int conf_scores_size[3] = {23, 23, 9};
Mat conf_scores(3, conf_scores_size, CV_32F, outblobs[2].ptr<float>());
InferBbox inf(delta_bbox, class_scores, conf_scores);
inf.filter(threshold);
double average_time = t.getTimeSec() / t.getCounter();
std::cout << "\nTotal objects detected: " << inf.detections.size()
<< " in " << average_time << " seconds\n";
std::cout << "------\n";
float x_ratio = (float)original_img.cols / img_copy.cols;
float y_ratio = (float)original_img.rows / img_copy.rows;
for (size_t i = 0; i < inf.detections.size(); ++i)
{
int xmin = inf.detections[i].xmin;
int ymin = inf.detections[i].ymin;
int xmax = inf.detections[i].xmax;
int ymax = inf.detections[i].ymax;
cv::String class_name = inf.detections[i].label_name;
std::cout << "Class: " << class_name << "\n"
<< "Probability: " << inf.detections[i].class_prob << "\n"
<< "Co-ordinates: " << inf.detections[i].xmin << " "
<< inf.detections[i].ymin << " "
<< inf.detections[i].xmax << " "
<< inf.detections[i].ymax << "\n";
std::cout << "------\n";
// Draw the corresponding bounding box(s)
cv::rectangle(original_img, cv::Point((int)(xmin * x_ratio), (int)(ymin * y_ratio)),
cv::Point((int)(xmax * x_ratio), (int)(ymax * y_ratio)), cv::Scalar(255, 0, 0), 2);
cv::putText(original_img, class_name, cv::Point((int)(xmin * x_ratio), (int)(ymin * y_ratio)),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(255, 0, 0), 1);
}
try
{
cv::namedWindow("Final Detections", WINDOW_AUTOSIZE);
cv::imshow("Final Detections", original_img);
cv::imwrite("image.png", original_img);
cv::waitKey(0);
}
catch (const char* msg)
{
std::cerr << msg << "\n";
return -4;
}
return 0;
}
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