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opencvでyolov2を動かすためのサンプルコード(opencvのmasterから消されたので置いておく)
// Brief Sample of using OpenCV dnn module in real time with device capture, video and image.
// VIDEO DEMO: https://www.youtube.com/watch?v=NHtRlndE2cg
#include<opencv2\core\ocl.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <iostream>
#include <algorithm>
#include <cstdlib>
#include<Windows.h>
using namespace std;
using namespace cv;
using namespace cv::dnn;
static const char* about =
"This sample uses You only look once (YOLO)-Detector (https://arxiv.org/abs/1612.08242) to detect objects on camera/video/image.\n"
"Models can be downloaded here: https://pjreddie.com/darknet/yolo/\n"
"Default network is 416x416.\n"
"Class names can be downloaded here: https://github.com/pjreddie/darknet/tree/master/data\n";
static const char* params =
"{ help | false | print usage }"
"{ cfg | | model configuration }"
"{ model | | model weights }"
"{ camera_device | 0 | camera device number}"
"{ source | | video or image for detection}"
"{ min_confidence | 0.24 | min confidence }"
"{ class_names | | File with class names, [PATH-TO-DARKNET]/data/coco.names }";
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
cout << about << endl;
parser.printMessage();
return 0;
}
String modelConfiguration = parser.get<String>("cfg");
String modelBinary = parser.get<String>("model");
if (ocl::haveOpenCL())
{
std::cout << "opencl can use" << std::endl;
ocl::setUseOpenCL(true);
}
else
{
std::cout << "opencl cannot use" << std::endl;
}
//! [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);
}
VideoCapture cap;
if (parser.get<String>("source").empty())
{
int cameraDevice = parser.get<int>("camera_device");
cap = VideoCapture(cameraDevice);
if (!cap.isOpened())
{
cout << "Couldn't find camera: " << cameraDevice << endl;
return -1;
}
}
else
{
cap.open(parser.get<String>("source"));
if (!cap.isOpened())
{
cout << "Couldn't open image or video: " << parser.get<String>("video") << endl;
return -1;
}
}
vector<string> classNamesVec;
ifstream classNamesFile(parser.get<String>("class_names").c_str());
if (classNamesFile.is_open())
{
string className = "";
while (std::getline(classNamesFile, className))
classNamesVec.push_back(className);
}
for (;;)
{
Mat frame;
cap >> frame; // get a new frame from camera/video or read image
if (frame.empty())
{
waitKey();
break;
}
if (frame.channels() == 4)
cvtColor(frame, frame, COLOR_BGRA2BGR);
//! [Prepare blob]
Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false); //Convert Mat to batch of images
//! [Prepare blob]
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
Mat detectionMat = net.forward("detection_out"); //compute output
vector<double> layersTimings;
double freq = getTickFrequency() / 1000;
double time = net.getPerfProfile(layersTimings) / freq;
ostringstream ss;
ss << "FPS: " << 1000 / time << " ; time: " << time << " ms";
putText(frame, ss.str(), Point(20, 20), 0, 0.5, Scalar(0, 0, 255));
std::cout << time << std::endl;
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 = 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);
int xLeftBottom = static_cast<int>((x - width / 2) * frame.cols);
int yLeftBottom = static_cast<int>((y - height / 2) * frame.rows);
int xRightTop = static_cast<int>((x + width / 2) * frame.cols);
int yRightTop = static_cast<int>((y + height / 2) * frame.rows);
Rect object(xLeftBottom, yLeftBottom,
xRightTop - xLeftBottom,
yRightTop - yLeftBottom);
rectangle(frame, object, Scalar(0, 255, 0));
if (objectClass < classNamesVec.size())
{
ss.str("");
ss << confidence;
String conf(ss.str());
String label = String(classNamesVec[objectClass]) + ": " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom + labelSize.height),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 0));
}
else
{
cout << "Class: " << objectClass << endl;
cout << "Confidence: " << confidence << endl;
cout << " " << xLeftBottom
<< " " << yLeftBottom
<< " " << xRightTop
<< " " << yRightTop << endl;
}
}
}
imshow("YOLO: Detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
} // main
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