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using namespace std; | |
using namespace cv; | |
int main(int argc, char **argv) | |
{ | |
std::string projectBasePath = "/home/user/ultralytics"; // Set your ultralytics base path | |
bool runOnGPU = true; | |
// | |
// Pass in either: | |
// | |
// "yolov8s.onnx" or "yolov5s.onnx" | |
// | |
// To run Inference with yolov8/yolov5 (ONNX) | |
// | |
// Note that in this example the classes are hard-coded and 'classes.txt' is a place holder. | |
Inference inf(projectBasePath + "/yolov8s.onnx", cv::Size(640, 480), "classes.txt", runOnGPU); | |
std::vector<std::string> imageNames; | |
imageNames.push_back(projectBasePath + "/ultralytics/assets/bus.jpg"); | |
imageNames.push_back(projectBasePath + "/ultralytics/assets/zidane.jpg"); | |
for (int i = 0; i < imageNames.size(); ++i) | |
{ | |
cv::Mat frame = cv::imread(imageNames[i]); | |
// Inference starts here... | |
std::vector<Detection> output = inf.runInference(frame); | |
int detections = output.size(); | |
std::cout << "Number of detections:" << detections << std::endl; | |
for (int i = 0; i < detections; ++i) | |
{ | |
Detection detection = output[i]; | |
cv::Rect box = detection.box; | |
cv::Scalar color = detection.color; | |
// Detection box | |
cv::rectangle(frame, box, color, 2); | |
// Detection box text | |
std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4); | |
cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0); | |
cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20); | |
cv::rectangle(frame, textBox, color, cv::FILLED); | |
cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0); | |
} | |
// Inference ends here... | |
// This is only for preview purposes | |
float scale = 0.8; | |
cv::resize(frame, frame, cv::Size(frame.cols*scale, frame.rows*scale)); | |
cv::imshow("Inference", frame); | |
cv::waitKey(-1); | |
} | |
} | |