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#include <opencv2/dnn.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <algorithm>
#include <cstdlib>
using namespace std;
const size_t network_width = 416;
const size_t network_height = 416;
const char* about = "This sample uses You only look once (YOLO)-Detector "
"(https://arxiv.org/abs/1612.08242)"
"to detect objects on image\n"; // TODO: link
const char* params
= "{ help | false | print usage }"
"{ cfg | | model configuration }"
"{ model | | model weights }"
"{ image | | image for detection }"
"{ min_confidence | 0.24 | min confidence }";
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
std::cout << about << std::endl;
parser.printMessage();
return 0;
}
String modelConfiguration = parser.get<string>("cfg");
String modelBinary = parser.get<string>("model");
//! [Initialize network]
dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
//! [Initialize network]
if (net.empty())
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "cfg-file: " << modelConfiguration << endl;
cerr << "weights-file: " << modelBinary << endl;
cerr << "Models can be downloaded here:" << endl;
cerr << "https://pjreddie.com/darknet/yolo/" << endl;
exit(-1);
}
cv::Mat frame = cv::imread(parser.get<string>("image"));
//! [Resizing without keeping aspect ratio]
cv::Mat resized;
cv::resize(frame, resized, cv::Size(network_width, network_height));
//! [Resizing without keeping aspect ratio]
//! [Prepare blob]
Mat inputBlob = blobFromImage(resized, 1 / 255.F); //Convert Mat to batch of images
//! [Prepare blob]
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
//! [Make forward pass]
cv::Mat detectionMat = net.forward("detection_out"); //compute output
//! [Make forward pass]
float confidenceThreshold = parser.get<float>("min_confidence");
for (int i = 0; i < detectionMat.rows; i++)
{
const int probability_index = 5;
const int probability_size = detectionMat.cols - probability_index;
float *prob_array_ptr = &detectionMat.at<float>(i, probability_index);
size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
float confidence = detectionMat.at<float>(i, (int)objectClass + probability_index);
if (confidence > confidenceThreshold)
{
float x = detectionMat.at<float>(i, 0);
float y = detectionMat.at<float>(i, 1);
float width = detectionMat.at<float>(i, 2);
float height = detectionMat.at<float>(i, 3);
float xLeftBottom = (x - width / 2) * frame.cols;
float yLeftBottom = (y - height / 2) * frame.rows;
float xRightTop = (x + width / 2) * frame.cols;
float yRightTop = (y + height / 2) * frame.rows;
std::cout << "Class: " << objectClass << std::endl;
std::cout << "Confidence: " << confidence << std::endl;
std::cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << std::endl;
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
rectangle(frame, object, Scalar(0, 255, 0));
}
}
imshow("detections", frame);
waitKey();
return 0;
} // main