#include <vector>
// You must check the num_classes, conf_ths, nms_ths
struct Detection {
cv::Rect2f box;
float score;
int class_idx;
};
std::vector<Detection> nms(const std::vector<Detection>& detections, float nms_ths) {
std::vector<int> indices(detections.size());
iota(indices.begin(), indices.end(), 0);
std::sort(indices.begin(), indices.end(), [&](int i1, int i2) {
return detections[i1].score > detections[i2].score;
});
std::vector<Detection> picked;
while (!indices.empty()) {
int current_index = indices.front();
const auto& current_det = detections[current_index];
picked.push_back(current_det);
indices.erase(indices.begin());
auto it = indices.begin();
while (it != indices.end()) {
const auto& det = detections[*it];
float intersectionArea = (current_det.box & det.box).area();
float unionArea = current_det.box.area() + det.box.area() - intersectionArea;
float iou = intersectionArea / unionArea;
if (iou > nms_ths) {
it = indices.erase(it); // Remove this index as it has high overlap
}
else {
++it;
}
}
}
return picked;
}
// std::vector<int> nms(const std::vector<Detection>& detections, float nms_ths) {
// std::vector<int> indices;
// // Your NMS implementation goes here
// return indices;
// }
Ort::Session* get_model(Ort::Env* env, const wchar_t* path) {
//set any option (e.g. GPU ID)
Ort::SessionOptions session_options;
session_options.AppendExecutionProvider_CUDA(0);
return new Ort::Session(*env, path, session_options);
}
float* inf_post(Ort::Session* model, std::vector<unsigned char>* inputs_value) {
int num_classes = 10;
float conf_ths = 0.5
float nms_ths = 0.45
std::vector<int64_t> shape = { 1, 800, 1440, 3 };
std::vector<const char*> names_input = { "images" };
std::vector<const char*> names_output = { "outputs" };
// long long* shape=new long long[4]{1,800,1440,3};
// const char** names_input=new const char* [1]{"images"};
// const char** names_output=new const char* [1]{"outputs"};
Ort::Value input_tensor = Ort::Value::CreateTensor<unsigned char>(
Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault), inputs_value->data(), inputs_value->size(), shape, 4);
Ort::Value output_tensor = model->Run(Ort::RunOptions(nullptr), names_input.data(), &input_tensor, 1, names_output.data(), 1)[0];
float* outputs = output_tensor.GetTensorMutableData<float>();
std::vector<int64_t> output_shape = output_tensor.GetTensorTypeAndShapeInfo().GetShape();
int num_detections = static_cast<int>(output_shape[1]);
// float* outputs=model->Run(Ort::RunOptions(nullptr),names_input,&input_tensor,1,names_output,1)[0].GetTensorMutableData<float>();
// num_detections * [cx, cy, w, h, conf, class1_score, ..., classN_score]
std::vector<Detection> detections;
for (int i = 0; i < num_detections; ++i) {
float* base = outputs + i * (5 + num_classes);
float cx = base[0];
float cy = base[1];
float w = base[2];
float h = base[3];
float obj_score = base[4];
float max_cscore = -1.0;
int max_class = -1;
for (int j = 0; j < num_classes; ++j) {
float class_score = base[5 + j];
if (class_score > max_cscore) {
max_cscore = class_score;
max_class = j;
}
if (obj_score * max_cscore > conf_ths) {
float x = cx - w / 2;
float y = cy - h / 2;
float x2 = cx + w / 2;
float y2 = cy + h / 2;
detections.push_back({ cv::Rect2f(x, y, x2 - x, y2 - y), obj_score * max_cscore, max_class });
}
}
}
auto selected_dets = nms(detections, nms_ths);
return [0,0];
}
int main(void) {
std::vector<unsigned char>* inputs_value = new std::vector<unsigned char>(3 * 800 * 1440, 255);
float* outputs;
Ort::Env env;
Ort::Session* model = get_model(&env, L"./model.onnx");
while (true) {
outputs = inf_post(model, inputs_value);
std::cout << outputs[0] << std::endl;
}
}
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