feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/dms_yolo/code/examples/YOLO11-Triton-CPP/main.cpp

@@ -0,0 +1,76 @@
+// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#include "inference.hpp"
+#include <iostream>
+#include <vector>
+#include <string>
+#include <sstream>
+#include <iomanip>
+#include <cstdint>
+#include <chrono>
+
+#define MODEL_INPUT_IMAGE_WIDTH 640
+#define MODEL_INPUT_IMAGE_HEIGHT 640
+#define NETWORK_THRESHOLD 0.50
+#define IMAGE_CHANNEL 3
+
+double get_time_since_epoch_millis()
+{
+    using namespace std::chrono;
+    auto now = system_clock::now();
+    auto duration = now.time_since_epoch();
+    return duration_cast<microseconds>(duration).count() / 1000.0;
+}
+
+int main(int argc, char *argv[])
+{
+    std::string triton_address= "localhost:8001";
+    std::string model_name= "yolo11";
+    std::string model_version= "1";
+	std::string image_path = "test.jpg";
+	std::string output_path = "output.jpg";
+	std::vector<std::string> object_class_list = {"class1", "class2"};
+
+    std::vector<uint16_t> triton_request_data;
+    triton_request_data.resize(IMAGE_CHANNEL*MODEL_INPUT_IMAGE_WIDTH*MODEL_INPUT_IMAGE_HEIGHT);
+    std::vector<struct detection_struct> detections;
+
+    std::shared_ptr<TritonCommunication> triton_communication = std::make_shared<TritonCommunication>(triton_address, model_name, model_version, IMAGE_CHANNEL, MODEL_INPUT_IMAGE_WIDTH, MODEL_INPUT_IMAGE_HEIGHT,object_class_list.size());
+
+    cv::Mat frame = cv::imread(image_path);
+    if (frame.empty())
+    {
+        std::cerr << "Image couldn't read: " << image_path << std::endl;
+        return -1;
+    }
+
+    int image_width = frame.cols;
+    int image_height = frame.rows;
+
+    double preprocess_time = get_time_since_epoch_millis();
+	Image::preprocess(&frame, triton_request_data, MODEL_INPUT_IMAGE_WIDTH, MODEL_INPUT_IMAGE_HEIGHT);
+    std::cout << "Preprocess time : " << (get_time_since_epoch_millis() - preprocess_time)<< " millisecond."<< std::endl;
+
+    double infer_time = get_time_since_epoch_millis();
+    triton_communication->infer(triton_request_data.data());
+    std::cout << "Triton Server execute time : " << (get_time_since_epoch_millis() - infer_time) << " millisecond." << std::endl;
+
+    getDetectionsFromTritonRawData(triton_communication->output_raw_data, detections, object_class_list, NETWORK_THRESHOLD, image_width, image_height);
+
+    for (int i = 0; i < detections.size(); i++)
+    {
+        std::ostringstream oss;
+        oss << detections[i].name << " "
+        << std::fixed << std::setprecision(2)
+        << detections[i].confidence_score;
+
+        cv::rectangle(frame, detections[i].bbox, cv::Scalar(255, 0, 0), 2);
+        cv::putText(frame, oss.str(), cv::Point((detections[i].bbox.x), (detections[i].bbox.y - 5)), cv::FONT_HERSHEY_DUPLEX, ((frame.cols / 640.0f) * 0.35), cv::Scalar(0, 0, 0), (int)(frame.cols / 640.0f) + 1);
+        cv::putText(frame, oss.str(), cv::Point((detections[i].bbox.x), (detections[i].bbox.y - 5)), cv::FONT_HERSHEY_DUPLEX, ((frame.cols / 640.0f) * 0.35), cv::Scalar(0xFF, 0xFF, 0xFF), (int)(frame.cols / 640.0f));
+    }
+
+    cv::imwrite(output_path, frame);
+    std::cout << "Result image saved!"<< std::endl;
+
+    return 0;
+}