| OpenCV 3.4.1 Open Source Computer Vision

YOLO DNNs

## Introduction

In this text you will learn how to use opencv_dnn module using yolo_object_detection (Sample of using OpenCV dnn module in real time with device capture, video and image).

We will demonstrate results of this example on the following picture.

Picture example

## Examples

VIDEO DEMO:

## Source Code

The latest version of sample source code can be downloaded here.

// 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/dnn.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <fstream>
#include <iostream>
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}"
"{ out | | path to output video file}"
"{ fps | 3 | frame per second }"
"{ style | box | box or line style draw }"
"{ 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");
dnn::Net net = readNetFromDarknet(modelConfiguration, modelBinary);
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;
VideoWriter writer;
int codec = CV_FOURCC('M', 'J', 'P', 'G');
double fps = parser.get<float>("fps");
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;
}
}
if(!parser.get<String>("out").empty())
{
writer.open(parser.get<String>("out"), codec, fps, Size((int)cap.get(CAP_PROP_FRAME_WIDTH),(int)cap.get(CAP_PROP_FRAME_HEIGHT)), 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);
}
String object_roi_style = parser.get<String>("style");
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);
Mat inputBlob = blobFromImage(frame, 1 / 255.F, Size(416, 416), Scalar(), true, false); //Convert Mat to batch of images
net.setInput(inputBlob, "data"); //set the network input
Mat detectionMat = net.forward("detection_out"); //compute output
vector<double> layersTimings;
double tick_freq = getTickFrequency();
double time_ms = net.getPerfProfile(layersTimings) / tick_freq * 1000;
putText(frame, format("FPS: %.2f ; time: %.2f ms", 1000.f / time_ms, time_ms),
Point(20, 20), 0, 0.5, Scalar(0, 0, 255));
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_center = detectionMat.at<float>(i, 0) * frame.cols;
float y_center = detectionMat.at<float>(i, 1) * frame.rows;
float width = detectionMat.at<float>(i, 2) * frame.cols;
float height = detectionMat.at<float>(i, 3) * frame.rows;
Point p1(cvRound(x_center - width / 2), cvRound(y_center - height / 2));
Point p2(cvRound(x_center + width / 2), cvRound(y_center + height / 2));
Rect object(p1, p2);
Scalar object_roi_color(0, 255, 0);
if (object_roi_style == "box")
{
rectangle(frame, object, object_roi_color);
}
else
{
Point p_center(cvRound(x_center), cvRound(y_center));
line(frame, object.tl(), p_center, object_roi_color, 1);
}
String className = objectClass < classNamesVec.size() ? classNamesVec[objectClass] : cv::format("unknown(%d)", objectClass);
String label = format("%s: %.2f", className.c_str(), confidence);
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(p1, Size(labelSize.width, labelSize.height + baseLine)),
object_roi_color, FILLED);
putText(frame, label, p1 + Point(0, labelSize.height),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
}
}
if(writer.isOpened())
{
writer.write(frame);
}
imshow("YOLO: Detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
} // main

## How to compile in command line with pkg-config

# g++ `pkg-config --cflags opencv` `pkg-config --libs opencv` yolo_object_detection.cpp -o yolo_object_detection

Execute in webcam:

$ yolo_object_detection -camera_device=0 -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names

Execute with image:

$ yolo_object_detection -source=[PATH-IMAGE] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names

Execute in video file:

$ yolo_object_detection -source=[PATH-TO-VIDEO] -cfg=[PATH-TO-DARKNET]/cfg/yolo.cfg -model=[PATH-TO-DARKNET]/yolo.weights -class_names=[PATH-TO-DARKNET]/data/coco.names

Questions and suggestions email to: Alessandro de Oliveira Faria cabel.nosp@m.o@op.nosp@m.ensus.nosp@m.e.or.nosp@m.g or OpenCV Team.