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#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;
const size_t width = 300;
const size_t height = 300;
Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
{
Mat mean;
const int meanValues[3] = {104, 117, 123};
vector<Mat> meanChannels;
for(size_t i = 0; i < 3; i++)
{
Mat channel(imageHeight, imageWidth, CV_32F, Scalar(meanValues[i]));
meanChannels.push_back(channel);
}
cv::merge(meanChannels, mean);
return mean;
}
Mat preprocess(const Mat& frame)
{
Mat preprocessed;
frame.convertTo(preprocessed, CV_32FC3);
resize(preprocessed, preprocessed, Size(width, height)); //SSD accepts 300x300 RGB-images
Mat mean = getMean(width, height);
cv::subtract(preprocessed, mean, preprocessed);
return preprocessed;
}
const char* about = "This sample uses Single-Shot Detector "
"(https://arxiv.org/abs/1512.02325)"
"to detect objects on image\n"; // TODO: link
const char* params
= "{ help | false | print usage }"
"{ proto | | model configuration }"
"{ model | | model weights }"
"{ image | | image for detection }"
"{ min_confidence | 0.5 | 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;
}
cv::dnn::initModule(); //Required if OpenCV is built as static libs
String modelConfiguration = parser.get<string>("proto");
String modelBinary = parser.get<string>("model");
//! [Create the importer of Caffe model]
Ptr<dnn::Importer> importer;
// Import Caffe SSD model
try
{
importer = dnn::createCaffeImporter(modelConfiguration, modelBinary);
}
catch (const cv::Exception &err) //Importer can throw errors, we will catch them
{
cerr << err.msg << endl;
}
//! [Create the importer of Caffe model]
if (!importer)
{
cerr << "Can't load network by using the following files: " << endl;
cerr << "prototxt: " << modelConfiguration << endl;
cerr << "caffemodel: " << modelBinary << endl;
cerr << "Models can be downloaded here:" << endl;
cerr << "https://github.com/weiliu89/caffe/tree/ssd#models" << endl;
exit(-1);
}
//! [Initialize network]
dnn::Net net;
importer->populateNet(net);
importer.release(); //We don't need importer anymore
//! [Initialize network]
cv::Mat frame = cv::imread(parser.get<string>("image"), -1);
//! [Prepare blob]
Mat preprocessedFrame = preprocess(frame);
dnn::Blob inputBlob = dnn::Blob::fromImages(preprocessedFrame); //Convert Mat to dnn::Blob image
//! [Prepare blob]
//! [Set input blob]
net.setBlob(".data", inputBlob); //set the network input
//! [Set input blob]
//! [Make forward pass]
net.forward(); //compute output
//! [Make forward pass]
//! [Gather output]
dnn::Blob detection = net.getBlob("detection_out");
Mat detectionMat(detection.rows(), detection.cols(), CV_32F, detection.ptrf());
float confidenceThreshold = parser.get<float>("min_confidence");
for(int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);
if(confidence > confidenceThreshold)
{
size_t objectClass = detectionMat.at<float>(i, 1);
float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
float xRightTop = detectionMat.at<float>(i, 5) * frame.cols;
float yRightTop = detectionMat.at<float>(i, 6) * frame.rows;
std::cout << "Class: " << objectClass << std::endl;
std::cout << "Confidence: " << confidence << std::endl;
std::cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << std::endl;
Rect object(xLeftBottom, yLeftBottom,
xRightTop - xLeftBottom,
yRightTop - yLeftBottom);
rectangle(frame, object, Scalar(0, 255, 0));
}
}
imshow("detections", frame);
waitKey();
return 0;
} // main
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