DnnOpenVinoDetector C++ lib. Objects detection based on Intel OpenVino API
DnnOpenVinoDetector C++ library version 1.0.1 is designed for automatic detection of objects on videos through the utilization of neural networks.
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Downloads
Programmer’s manual: DOWNLOAD
Demo application to test library on your video and with your models (test model and video included): DOWNLOAD
Overview
DnnOpenVinoDetector C++ library version 1.0.1 is designed for automatic detection of objects on videos through the utilization of neural networks. The library is implemented in C++ (C++17 standard) and exclusively relies only on one third-party library, which is Intel's OpenVINO™ runtime. The library supports various neural network models, if only model is supported by OpenVINO and has standard one-batch output (eg. yolov5). NN model can be specified in detector parameters, library inherits its interface from the ObjectDetector class, offering flexible and customizable parameters. The library provides simple interface.
Simple interface
class DnnOpenVinoDetector : public ObjectDetector
{
public:
/// Get string of current library version.
static std::string getVersion();
/// Init object detector.
bool initObjectDetector(ObjectDetectorParams& params) override;
/// Set object detector param.
bool setParam(ObjectDetectorParam id, float value) override;
/// Get object detector param value.
float getParam(ObjectDetectorParam id) override;
/// Get object detector params structure.
void getParams(ObjectDetectorParams& params) override;
/// Get list of objects.
std::vector<Object> getObjects() override;
/// Execute command.
bool executeCommand(ObjectDetectorCommand id) override;
/// Perform detection.
bool detect(cr::video::Frame& frame) override;
/// Set detection mask.
bool setMask(cr::video::Frame mask) override;
/// Decode command and execute command.
bool decodeAndExecuteCommand(uint8_t* data, int size) override;
}
Simple example
#include <opencv2/opencv.hpp>
#include "DnnOpenVinoDetector.h"
int main(void)
{
// Open video file "test.mp4".
cv::VideoCapture videoSource;
if (!videoSource.open("test.mp4"))
return -1;
// Create and init detector.
cr::detector::DnnOpenVinoDetector detector;
cr::detector::ObjectDetectorParams params;
params.initString = "./yolov7s.onnx;640;640";
params.maxObjectHeight = 96;
params.maxObjectWidth = 96;
params.minObjectHeight = 4;
params.minObjectHeight = 4;
params.type = 1;
detector.initObjectDetector(params);
// Create frames.
cv::Mat frameBgrOpenCv;
// Main loop.
while (true) {
// Capture next video frame.
videoSource >> frameBgrOpenCv;
if (frameBgrOpenCv.empty()) {
// If we have video file we can set initial position to replay.
detector.executeCommand(cr::detector::ObjectDetectorCommand::RESET);
videoSource.set(cv::CAP_PROP_POS_FRAMES, 0);
continue;
}
// Copy frame data from OpenCv frame to Frame.
cr::video::Frame frameBgr;
frameBgr.fourcc = cr::video::Fourcc::BGR24;
frameBgr.width = frameBgrOpenCv.size().width;
frameBgr.height = frameBgrOpenCv.size().height;
frameBgr.size = frameBgr.width * frameBgr.height * 3;
frameBgr.data = frameBgrOpenCv.data;
// Detect objects.
detector.detect(frameBgr);
// Get list of objects.
std::vector<cr::detector::Object> objects = detector.getObjects();
// Draw detected objects.
for (int n = 0; n < objects.size(); ++n) {
rectangle(frameBgrOpenCv, cv::Rect(objects[n].x, objects[n].y,
objects[n].width, objects[n].height),
cv::Scalar(0, 0, 255), 1);
putText(frameBgrOpenCv, std::to_string(objects[n].p),
cv::Point(objects[n].x, objects[n].y),
1, 1, cv::Scalar(0, 0, 255));
}
// Show video.
cv::imshow("VIDEO", frameBgrOpenCv);
// Wait ESC.
if (cv::waitKey(1) == 27)
return -1;
}
return 1;
}
