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onnx models (#12)
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#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <algorithm>
#include <array>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
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<std::string> 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;
}