| #include <opencv2/dnn.hpp> |
| #include <opencv2/imgproc.hpp> |
| #include <opencv2/imgcodecs.hpp> |
| #include <fstream> |
| #include <iostream> |
| #include <string> |
| #include <vector> |
|
|
| using namespace cv; |
|
|
| 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", ""); |
|
|
| Mat img = imread(image); |
| if (img.empty()) |
| { |
| std::cerr << "could not read image: " << image << std::endl; |
| return 1; |
| } |
|
|
| Mat rgb; |
| cvtColor(img, rgb, COLOR_BGR2RGB); |
| resize(rgb, rgb, Size(224, 224)); |
| rgb.convertTo(rgb, CV_32F); |
|
|
| int dims[] = {1, 224, 224, 3}; |
| Mat blob(4, dims, CV_32F, rgb.data); |
|
|
| dnn::Net net = dnn::readNet(model); |
| net.setInput(blob); |
| Mat scores = net.forward().reshape(1, 1); |
|
|
| Point classId; |
| double conf; |
| minMaxLoc(scores, 0, &conf, 0, &classId); |
|
|
| std::string label = std::to_string(classId.x); |
| if (!labels.empty()) |
| { |
| std::ifstream f(labels); |
| std::vector<std::string> names; |
| std::string line; |
| while (std::getline(f, line)) names.push_back(line); |
| if (classId.x < (int)names.size()) label = names[classId.x]; |
| } |
|
|
| std::cout << "class " << classId.x << " " << label << " confidence " << conf << std::endl; |
|
|
| Mat out = img.clone(); |
| putText(out, format("%s (%.2f)", label.c_str(), conf), Point(10, 30), |
| FONT_HERSHEY_SIMPLEX, 1.0, Scalar(0, 255, 0), 2); |
| imwrite(output, out); |
| std::cout << "wrote " << output << std::endl; |
| return 0; |
| } |
|
|
