#include #include #include #include #include #include #include #include #include static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def) { for (int i = 1; i + 1 < argc; ++i) if (key == argv[i]) return argv[i + 1]; return def; } int main(int argc, char** argv) { std::string model = argVal(argc, argv, "--model", "tensorflow_inception_graph_2026jul.onnx"); std::string image = argVal(argc, argv, "--image", "example_outputs/input_image.png"); std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png"); std::string labels = argVal(argc, argv, "--labels", ""); cv::Mat img = cv::imread(image); if (img.empty()) { std::cerr << "could not read image: " << image << std::endl; return 1; } cv::Mat rgb; cv::cvtColor(img, rgb, cv::COLOR_BGR2RGB); cv::resize(rgb, rgb, cv::Size(224, 224)); rgb.convertTo(rgb, CV_32F); if (!rgb.isContinuous()) rgb = rgb.clone(); int blobShape[] = {1, 224, 224, 3}; cv::Mat blob(4, blobShape, CV_32F, rgb.data); cv::dnn::Net net = cv::dnn::readNetFromONNX(model); net.setInput(blob); cv::Mat scoresMat = net.forward(); float* scores = (float*)scoresMat.data; int n = (int)scoresMat.total(); int top = (int)(std::max_element(scores, scores + n) - scores); float conf = scores[top]; std::string label = std::to_string(top); if (!labels.empty()) { std::ifstream f(labels); std::vector names; std::string line; while (std::getline(f, line)) names.push_back(line); if (top < (int)names.size()) label = names[top]; } std::cout << "class " << top << " " << label << " confidence " << conf << std::endl; cv::Mat out = img.clone(); cv::putText(out, cv::format("%s (%.2f)", label.c_str(), conf), cv::Point(10, 30), cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0, 255, 0), 2); cv::imwrite(output, out); std::cout << "wrote " << output << std::endl; return 0; }